Proceedings - Corona Range and Livestock Research

Transcription

PROCEEDINGS OF THE
4th Grazing Livestock
Nutrition Conference
July 9-10, 2010
Estes Park Conference Center
Estes Park, Colorado USA
Organized by: W 1012 (Formerly WERA 110)
“Improving Ruminant Use of Forages in Sustainable
Production Systems for the Western United States”
Untitled-1
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6/5/2003, 2:42 PM
4th GRAZING LIVESTOCK NUTRITION CONFERENCE
July 9-10, 2010
Estes Park Conference Center, Estes Park, CO
Organized by: W 1012 (Formerly WERA – 110)
“Improving Ruminant Use Of Forages in Sustainable Production Systems for the Western United States” SCHEDULE
THURSDAY, JULY 8, 2010
6:00 – 8:00 p.m.
Registration and Poster Set-Up
FRIDAY, JULY 9, 2010
8:00 – 8:30 a.m.
Registration
Opening Remarks - Richard Waterman
8:30 – 8:40 a.m.
Welcome and Introductions
Moderator - Mark Petersen
8:40 – 8:45 a.m.
History
8:45 – 9:30 a.m.
Issues in Grazing Livestock Nutrition
Dr. Tim DelCurto - Oregon State University, Union, OR
Dr. Ken Olson - South Dakota State University, Rapid City, SD
SESSION I: RUMINAL MICROBIAL ECOLOGY AS INFLUENCED
BY THE NUTRITIONAL ENVIRONMENT OF GRAZING
RUMINANTS
Moderator - Doug Tolleson
9:30 – 10:15 a.m.
Emerging Methods in Rumen
Microbiology
Dr. Bryan White - University of Illinois, Urbana, IL
10:15 – 10:45 a.m.
BREAK
10:45 – 11:30 a.m.
Application of Rumen Microbiological
Techniques to the Grazing Ruminant
Dr. Shanna Ivey - New Mexico State University, Las Cruces, NM
11:30 a.m. – 1:00 p.m.
LUNCH
SESSION II: PERSPECTIVES ON INTAKE IN GRAZING STUDIES
AND IN PRACTICE
KEYNOTE SPEAKER
Moderator - Jim Sprinkle
1:00 – 2:00 p.m.
Assessment of Intake for Grazing
Ruminants
Dr. Hugh Dove - CSIRO Plant Industry, Canberra, Australia
2:00 – 2:15 p.m.
Geospatial Methods and Data
Analysis for Assessing Distribution
of Grazing Livestock
Dr. Dean Anderson - USDA-ARS, Las Cruces, NM
SESSION III: IMPACT OF NUTRITIENT BALANCE ON
BIOLOGICAL EFFICIENCY AND LIFETIME PRODUCTIVITY OF
GRAZING LIVESTOCK
Moderator - Jim Carpenter
2:45 – 3:30 p.m.
Metabolic Signals of the Beef Cow in
Negative Energy Balance
Dr. Richard Waterman - USDA –ARS Miles City, MT
Dr. Ron Butler - Cornell University, Ithaca, NY
3:30 – 3:45 p.m.
BREAK
3:45 – 4:30 p.m.
Maternal Plane of Nutrition Impacts
on Newborn/Offspring
Dr. Joel Caton - North Dakota State University, Fargo, ND
Dr. Bret Hess - University of Wyoming, Laramie, WY
4:30 – 5:15 p.m.
Excess Nutrients Leading to
Imbalance, High-Quality Forages
Dr. Monty Kerley - University of Missouri, Columbia, MO
5:15 – 5:30 p.m.
Wrap-up and announcements
5:30 – 7:00 p.m.
Social and poster session
(Presenters attending posters)
SATURDAY, JULY 10, 2010
8:00 – 8:15 a.m.
Meet and Greet
8:15 – 8:30 a.m.
Welcome and announcements
SESSION IV: APPLICATION OF REQUIREMENT SYSTEMS FOR
GRAZING LIVESTOCK; ASSESSING NRC AND CSIRO SYSTEMS
FOR GRAZING LIVESTOCK
Moderator - Sergio A. Soto-Navarro
8:30 – 9:30 a.m.
Application of Nutrient Requirement
Schemes to Grazing Animals
Dr. Stuart McLennan - Queensland Primary Industries and
Fisheries Animal Research Institute, Moorooka Q, Australia
Dr. Hugh Dove - CSIRO Plant Industry, Canberra, Australia
Prof. Dennis Poppi - University of Queensland, Gatton, Australia
SESSION V: SUPPLEMENTATION STRATEGIES TO ACHIEVE
BIOLOGICAL AND ECONOMIC EFFICIENCY
Moderator - Eric Scholljegerdes
9:30 – 10:15 a.m.
Strategic Supplementation to Correct
for Nutrient Imbalances
Dr. Greg Lardy - North Dakota State University, Fargo, ND
Dr. Rachel Endecott - Montana State University, Miles City, MT
10:15 – 10:45 a.m.
BREAK
10:45 – 11:30 a.m.
Impact of Nutritional Decisions on Net
Returns in an Era of Increasing Costs
for Feedstuffs
Dr. Allen Torell - New Mexico State University, Las Cruces, NM
Dr. Neil R. Rimbey – University of Idaho, Caldwell, ID
SESSION VI : A SUMMARY OF ADVANCES AND EMERGING
PRACTICES IN GRAZING LIVESTOCK NUTRITION
Moderator - Richard Waterman
11:30 – 12:30 p.m.
Challenges to Predicting Productivity
of Grazing Ruminants - Where to
Now?
Dr. Mark Petersen - USDA-ARS Miles City, MT
12:30 – 12:45 p.m.
Closing Remarks
Recognition:
-Sponsors
-Editorial committee
-Organizing committee
12:45 – 1:00 p.m.
Remove Posters
Foreword
Welcome to the Fourth Grazing Livestock Nutrition Conference. The first call-to-order occurred in July 1987 at
Jackson Hole, Wyoming. That meeting was dedicated to Drs. Lorin Harris and C. Wayne Cook who inspired the
science of Range Livestock Nutrition in the Western United States. Therefore, we would like to dedicate this fourth
conference to members of the original planning committee, Dr. D. C. Clanton, Professor Emeritus, University of
Nebraska, Dr. J. E. Wallace, Professor Emeritus, New Mexico State University, and Dr. F. Hinds, Professor
Emeritus, University of Wyoming. Much of the discussions at this conference are outcomes of their research. We
are indebted to them for their foresight and example.
The inspiration for the Grazing Livestock Nutrition Conference is a desire to facilitate a forum where researchers,
practitioners and students meet, explore advances, and learn the science of grazing animal nutrition. Estes Park is a
perfect location that is appealing, with a beautiful outdoor venue, to encourage participants coming together for
formal and informal discussions. Much of the conference agenda is related to semi-arid and arid extensive
rangelands, although topics of importance to improved pasture settings are included. Another important
consideration in the planning of this meeting includes the realization that livestock grazing occurs around the globe
primarily in extensive grazing scenarios; this recognition has prompted an international aspect to this meeting.
Highly respected international researchers have been key congress speakers presenting challenging theories and
intriguing experimental results. Their contribution in the past and this year is highly valued.
The Livestock Grazing Nutrition Conference occurs infrequently with 13 years passing since the adjournment of the
third conference. There have been many technological advances during that period. The conference papers,
abstracts, and discussion reflect some of these developments. This 4th meeting was planned and organized by
members of the Multistate Research Project, W1012. We hope the meeting and proceedings are useful.
Sincerely,
Planning Committee
Sponsors
Western Section American Society of Animal Science
USDA – NIFA*
Alpharma Animal Health
Cargill
Kahne Animal Health
Agricultural Experiment Stations (Arizona, Colorado, Montana, and Wyoming)
Conference Organizing Committee
Richard C. Waterman, USDA – ARS Fort Keogh LARRL, Chairmen
Bret W. Hess, University of Wyoming
Jack C. Whittier, Colorado State University
Mark K. Petersen, USDA – ARS Fort Keogh LARRL
Cover Designers
Jack C. Whittier, Colorado State University
Richard C. Waterman, USDA – ARS Fort Keogh LARRL
Cover photo provided by Colorado State University Photographic Services
CITATION FORMAT†:
Author(s). 2010. Paper title. Pages __–__ in Proc. 4th Grazing Livestock Nutrition Conference. B. W. Hess, T.
DelCurto, J.G.P. Bowman, and R.C. Waterman eds. West. Sect. Am. Soc. Anim. Sci., Champaign, IL.
*This project was supported by Agriculture and Food Research Initiative grant no. 2010-65206-20719 from the
USDA National Institute of Food and Agriculture.
†
The editorial committee wishes to express their sincere gratitude to those individuals who donated their time and
effort to provide professional reviews of manuscripts and abstracts published in this proceeding.
Grazing Livestock Nutrition Conference
Table of Contents
ISSUES IN GRAZING LIVESTOCK NUTRITION
T. DelCurto and K. C. Olson ..................................................................................................................... 1
EMERGING METHODS IN RUMEN MICROBIOLOGY
J. M. Brulc, C. J. Yeoman, K. E. Nelson, and B. A. White ...................................................................... 10
PRACTICAL APPLICATION OF MODERN RUMEN MICROBIOLOGICAL TECHNIQUES TO
GRAZING RUMINANTS
S. L. Lodge-Ivey ....................................................................................................................................... 24
KEYNOTE: ASSESSMENT OF INTAKE AND DIET COMPOSITION OF GRAZING LIVESTOCK
H. Dove ..................................................................................................................................................... 31
GEOSPATIAL METHODS AND DATA ANALYSIS FOR ASSESSING DISTRIBUTION OF
GRAZING LIVESTOCK
D. M. Anderson......................................................................................................................................... 57
METABOLIC SIGNALS OF THE BEEF COW IN NEGATIVE ENERGY BALANCE
R. C. Waterman and W. R. Butler ............................................................................................................ 93
MATERNAL PLANE OF NUTRITION: IMPACTS ON FETAL OUTCOMES AND POSTNATAL
OFFSPRING RESPONSES
J. S. Caton and B. W. Hess ..................................................................................................................... 104
POTENTIAL FOR NUTRITIONAL IMBALANCE IN HIGH-QUALITY FORAGES
M. Kerley ................................................................................................................................................ 125
APPLICATION OF NUTRIENT REQUIREMENT SCHEMES TO GRAZING ANIMALS
H. Dove, S. R. McLennan, and D. P. Poppi ............................................................................................ 133
STRATEGIC SUPPLEMENTATION TO CORRECT FOR NUTRIENT IMBALANCES
G. P. Lardy and R. L. Endecott ............................................................................................................... 152
ECONOMICALLY EFFICIENT SUPPLEMENTAL FEEDING AND THE IMPACT OF
NUTRITIONAL DECISIONS ON NET RANCH RETURNS
L. Allen Torell and Neil R. Rimbey........................................................................................................ 170
CHALLENGES TO PREDICTING PRODUCTIVITY OF GRAZING RUMINANTS:
WHERE TO NOW?
M. K. Petersen, J. T. Mulliniks, A. J. Roberts, and R. C. Waterman ...................................................... 180
Abstracts
1 EFFECTS OF RESTRICTING TIME AT PASTURE ON FEEDING STATION BEHAVIOR OF
CATTLE DURING THE FIRST GRAZING SESSION OF THE DAY. P. Gregorini, K. McLeod,
C. Clark, C. Glassey, A. Romera, and J. Jago ........................................................................................................ 191
2 SPATIAL AND TEMPORAL FREE-RANGING COW BEHAVIOUR PRE AND POST-WEANING.
D. M. Anderson, C. Winters, M. Doniec, C. Detweiler, D. Rus, and B. Nolen ...................................................... 192
3 EFFECT OF SUPPLEMENT ENERGY LEVEL ON FATTY ACID PROFILES AND MEAT QUALITY
OF STEERS FINISHED ON WINTER ANNUAL PASTURE. H. O. Patino and F. S. Medeiros .................. 193
4 USE OF N-ALKANES AND LONG CHAIN ALCOHOLS TO ESTIMATE FORAGE INTAKE AND
DIET COMPOSITION OF CATTLE GRAZING TWO FORAGE SPECIES.
H. T. Boland and G. Scaglia ................................................................................................................................... 194
5 PROTEIN AND CARBOHYDRATE DEGRADATION CHARACTERISTICS AND RATIOS OF
ANTHOCYANIDIN-ACCUMULATING LC-ALFALFA AND ALFALFA SELECTED FOR A LOW
INITIAL RATE OF DEGRADATION IN GRAZING CATTLE. A.. Jonker, M. Gruber, Y. Wang,
and P. Yu ................................................................................................................................................................ 195
6 GROWING AND FATTENING CATTLE ON THE NORTHERN GREAT PLAINS WITH
RUMINALLY-PROTECTED FLAXSEED. S. L. Kronberg, E. J. Murphy, R. J. Maddock3,
and E. J. Scholljegerdes .......................................................................................................................................... 196
7 GRASS INTAKE OF GRAZING DAIRY COWS USING THE N-ALKANE TECHNIQUE.
A. van den Pol-van Dasselaar and A. Hensen ........................................................................................................ 197
8
GRAZING IN EUROPE. A. van den Pol-van Dasselaar .............................................................................. 198
9 PATHWAY FOR THE ELIMINATION OF MELAMINE IN LACTATING DAIRY COWS. J. Shen,
J. Wang, H. Wei, D. Bu, and P. Sun ....................................................................................................................... 199
10 COMPARE THE DIGESTIBILITY OF RUMEN UNDEGRADABLE PROTEIN OF CORN DDGS
AND DDG IN INTESTINE OF CHINESE HOLSTEIN DAIRY COWS USING MOBILE NYLON BAG
TECHNIQUE. Z. H. Yan, J. Q. Wang, D. P. Bu, H. Y. Wei, L. Y. Zhou, and P. Sun ........................................ 200
11 EFFECT OF SITE AND SOURCE OF LYSINE SUPPLEMENTATION ON NITROGEN
METABOLISM IN BEEF CATTLE. Y. Q. Guo, Y. D. Zhang, J. Q. Wang, D. P. Bu, K. L. Liu,
and T. Hu ................................................................................................................................................................ 201
12 SUPPLEMENTATION WITH PROTEIN AND VARIOUS FEED ADDITIVES IMPROVES IN SITU
DEGRADATION CHARACTERISTICS OF BERMUDAGRASS AND BUFFELGRASS IN STEERS
CONSUMING A LOW-QUALITY FORAGE DIET. K. C. McCuistion, B. D. Lambert, T. A. Wickersham,
R. O. Dittmar, L. Wiley, and L. Dobson................................................................................................................. 202
13 CASE STUDY: MOLASSES AS THE PRIMARY ENERGY SUPPLEMENT ON AN ORGANIC
GRAZING DAIRY FARM. K. Hoffman, L. E. Chase, and K. J. Soder ............................................................. 203
14 POTENTIAL OF LEGUMES AS SUBSTITUTES FOR NITROGEN FERTILIZER IN SUMMER
STOCKER GRAZING SYSTEMS. R. R. Reuter, J. T. Biermacher, J. K. Rogers, T. J. Butler, M. K. Kering,
J. R. Blanton, and J. A. Guretzky ........................................................................................................................... 204
15 EFFECTS OF CONVENTIONAL AND GRASS FED SYSTEM ON CARCASS TRAITS AND
PERFORMANCE OF ANGUS STEERS. G. Cruz, G. Acetoze, and H. Rossow.............................................. 205
16 ASPECTS OF CARCASS COMPOSITION AND ORGAN WEIGHTS OF ANGUS STEERS FINISHED
ON GRASS AND HIGH GRAIN DIETS. G. Acetoze, G. D. Cruz, and H. A. Rossow .................................... 206
17 PERFORMANCE AND EFFICIENCY ON PASTURE OF CATTLE WITH DIVERGENT
PHENOTYPES FOR RESIDUAL FEED INTAKE. T. D. A. Forbes, A. D. Aguiar, L. O. Tedeschi,
F. M. Rouquette, Jr., G. E. Carstens, and R. D. Randel .......................................................................................... 207
18 EFFECTS OF SAND SAGEBRUSH CONTROL ON STOCKER CATTLE PERFORMANCE
ON A SOUTHERN MIXED PRAIRIE COMMUNITY. S. A. Gunter, T. L. Springer, E. Thacker,
and R. L. Gillen ...................................................................................................................................................... 208
19 PASTURE MANAGEMENT EFFECTS ON NONPOINT SOURCE POLLUTION OF
MIDWESTERN PASTURES. D. A. Bear, J. R. Russell, and D. G. Morrical .................................................... 209
20 SUPPLEMENTATION OF RUMINALLY UNDEGRADABLE PROTEIN TO MAINTAIN
ESSENTIAL AMINO ACID SUPPLY DURING NUTRIENT RESTRICTION ALTERS CIRCULATING
ESSENTIAL AMINO ACIDS OF BEEF COWS IN EARLY TO MID-GESTATION. A. M. Meyer,
J. S. Caton, M. Du, and B. W. Hess........................................................................................................................ 210
21 EFFECT OF FORAGE TYPE ON PERFORMANCE OF WEANLING BEEF STEERS. G. Scaglia,
B. Corl, W. S. Swecker, Jr., and A. Lillie ............................................................................................................... 211
22 ESTIMATES OF DRY MATTER INTAKE OF BEEF STEERS GRAZING HIGH QUALITY
PASTURES USING ALKANES. G. Scaglia and H. Boland .............................................................................. 212
23 EFFECT OF ESTABLISHMENT METHOD OF WHEAT PASTURE AND FALL STOCKING RATE
ON PERFORMANCE OF GROWING STEERS. P. Beck, M. Morgan, T. Hess, D. Hubbell, M. Anders,
and B. Watkins ....................................................................................................................................................... 213
24 GROWTH PERFORMANCE EFFECTS OF MINERAL VS. SALT SUPPLEMENTATION ON
STOCKER CATTLE GRAZING SPRING-SUMMER PASTURES OVER TWO CONSECUTIVE YEARS.
P. J. Guiroy, S. E. Showers, and B. McMurry ........................................................................................................ 214
25 USE OF DRIED DISTILLERS GRAINS AS A SUPPLEMENTAL FEEDSTUFF FOR GROWING
CATTLE ON WHEAT PASTURE. E. D. Sharman, P. A. Lancaster, G. W. Horn, and J. T. Edwards ............. 215
26 A GENETIC MARKER FOR RESISTANCE TO FESCUE TOXICOSIS IN BEEF CATTLE.
C. J. Kojima, J. C. Waller, D. E. Spiers, R. L. Kallenbach, B. T. Campbell, T. A. Cooper, and J. K. Bryant ....... 216
27 PERFORMANCE OF CATTLE GRAZING SWITCHGRASS OR COMBINATIONS OF BIG
BLUESTEM AND INDIANGRASS DURING THE SUMMER IN THE MID-SOUTH. W. M. Backus,
J. C. Waller, P. D. Keyser, G. E. Bates, C. A. Harper, F. N. Schrick, and B. T. Campbell .................................... 217
28 BOTANICAL COMPOSITION, NUTRITIONAL VALUE, VOLUNTARY FEED INTAKE
ESTIMATION AND BEHAVIORAL HABITS OF CATTLE GRAZING ON RANGE PASTURES.
D. Rodriguez-Tenorio, R. Gutierrez-Luna, R. D. Valdez-Cepeda, F. G. Echavarria, M. A. Salas, J. I. Aguilera-Soto,
M. A. Lopez-Carlos, C. F. Arechiga, and J. M. Silva-Ramos ................................................................................ 218
Author Index ......................................................................................................................................... 219
Proceedings, Grazing Livestock Nutrition Conference
July 9 and 10, 2010
T. DelCurtoŧ and K. C. Olson*
ŧ
Eastern Oregon Agricultural Research Center, Oregon State University, Union 97883;
*West River Agricultural Research and Extension Center, South Dakota State University, Rapid City 57702
difficulty in maintaining esophageal fistulated cattle,
increased scrutiny from Institutional Animal Use and Care
Committees, and in turn, the relative ease of maintaining
rumen cannulated cattle.
One of the most fundamental equations for a grazing
animal nutritionist is Forage Intake = Fecal Output /(1 –
diet digestibility). This equation is simple; however, the
components (fecal output and digestibility) required to
make it biologically meaningful have plagued grazing
livestock nutritionist for the past 60 years. The ability to
estimate fecal output has obvious problems. The most direct
technique is to use fecal bags and collect the feces either
daily or twice daily depending on diet and size of the
animal. This technique may work well for studies
conducted with animals in confinement and studies with
animals grazing small pastures or paddocks, but this
approach has limited application to animals in extensive
free-ranging environments. An alternative approach is to
use markers, a topic reviewed in previous grazing
conferences (Cochran et al., 1987; Pond et al., 1987).
However, many of the marker techniques have limitations
with animals in extensive free-ranging environments.
Likewise, digestibility estimates can be challenging and the
best technique often depends on the research needs and
experimental conditions. Whenever the research questions
involve the need for “free-ranging” herbivory, the
techniques used to assess fecal output and (or) digestibility
become more difficult.
Nutritionists working with grazing animals have
historically focused on chemical composition of diets
because of the direct relationship to predicting and
explaining animal performance. Often, little attention was
devoted to botanical composition of diets. However, a
growing number of researchers are interested in techniques
to determine botanical composition of diets. In fact, grazing
behavior research is a growing area of emphasis and is
reflected in an increased number of papers with reference to
botanical composition of diets. Holechek et al. (1982) have
provided a review of techniques to estimate botanical
composition of diet. Both bite count (Wickstrom et al.,
1984; Canon et al., 1987) and microhistological techniques
(Sparks and Malechek, 1968; Holechek et al., 1982) have
useful applications with grazing animal research. With
increasing demands to demonstrate that domestic livestock
production is compatible with biodiversity of native
vegetation and wildlife, it would seem logical that diet
composition research will continue as long as this research
is clearly connected to available vegetation. This research,
in turn, will be used to develop preference indices that can
INTRODUCTION
Grazing livestock nutrition is an area of academic study
that is challenging, deals with dynamic ecosystems, and,
currently seems to function across academic disciplines.
Although the US Land Grant system has demonstrated 150
years of success in promoting agriculture in the U.S. and
the World, the study of grazing livestock nutrition has never
received the focus and (or) funding that this discipline
deserves. When evaluating cow/calf production and the
relative time spent in grazing scenarios versus feeding
conserved forages, most of a beef cow’s productive life is
spent grazing improved pastures, native meadows, and
rangelands. Most beef cattle managers recognize that at
least half of a cow’s productive life is spent in free-range
grazing scenarios and most low cost production systems
have their mature cows grazing greater than 75% of the
year. Escalating prices of fossil fuel coupled with increased
costs of cereal grains and other feed commodities makes
extending the grazing period and reducing cow costs ever
more important. Finally, the future of ruminant livestock
production may be a direct result of the ability to convert
forages and other high-fiber food by-products to usable
high-quality protein sources for human diets. Simply put,
grazing livestock nutrition is going to be paramount to the
success of the ruminant livestock industry in the future.
HISTORY OF GRAZING LIVESTOCK NUTRITION
Clanton and Raleigh (1987) discussed the “history” of
grazing livestock nutrition in the proceedings of the first
Grazing Livestock Nutrition Conference. Although
numerous studies are evident over the past 100 years, most
of the literature specific to grazing livestock has been
published in the last 60 years. The earliest research
appeared to focus on the quality of the available forage
(e.g., Cook and Harris, 1950; Forbes and Garrigus, 1950).
The ability to measure quality of consumed diets did not
become viable until the development of esophageal (Torell,
1954) and ruminal (Lesperance et al., 1960) fistula and
cannula sampling techniques. Both approaches have
advantages and disadvantages, and it seems the use of
rumen cannulated cattle has become more popular among
researchers (Olson, 1991). This is likely due to the
1
Corresponding author: [email protected]
1
ideas among attendees before and after sessions. To
promote attendance, they wanted to hold it immediately
preceding the National American Society of Animal
Science (ASAS) Meeting in a desirable, resort location near
the ASAS site. Thus, the first Grazing Livestock Nutrition
Conference (GLNC) was held in 1987 in Jackson,
Wyoming, immediately before National ASAS hosted by
Utah State University in Logan. The goal of the resort
location was to provide an enjoyable atmosphere where
attendees could interact in a relaxed setting, further
promoting collegiality and communication. Another key
element was the publication of proceedings that would
provide state-of-the-art documentation on topics related to
grazing livestock nutrition. The 1st GLNC was a great
success. The proceedings have been an invaluable asset and
have been heavily cited. From there, plans developed to
hold recurring GLNCs every four years, always
immediately before National ASAS when it was located in
the Western Section of ASAS (back in those days, the
national meeting rotated among the 4 sections in a regular
fashion). Thus, the 2nd GLNC was held in 1991 in
Steamboat Springs, Colorado before National ASAS was in
Laramie, Wyoming, and the 3rd GLNC was held in 1996
near Custer, South Dakota before National ASAS was in
Rapid City. An additional key element added in the 2 nd and
3rd Conferences was inclusion of International keynote
speakers. This was intended to augment the original key
element of fostering communication and collegiality to an
international level.
Themes of the 1st GLNC included: “Factors Influencing
Energy Requirements of Livestock”, “Techniques for
Estimating Nutrient Intake and Utilization”, “Factors
Influencing Digesta Flow”, and “Supplementation
Practices”. Those themes were (and still are) core to grazing
livestock nutrition, i.e., they included core elements of
ruminant nutrition and grazing management presented in
the context of the grazing animal. The 1st GLNC provided
the opportunity to gather and present state-of-the-art
information from experts in each of these themes. For
example, the section on “Factors Influencing Energy
Requirements of Livestock” included 6 papers (Ferrell and
Jenkins, 1987; Havstad and Doornbos, 1987; Morris and
Sanchez, 1987; Adams, 1987; Robertshaw, 1987; Young,
1987) that covered major topics related to increases in
energy requirements as a result of the grazing environment.
A particularly meaningful theme was “Techniques for
Estimating Nutrient Intake and Utilization”. The mid- to
late-1980’s was a period of intense technique development
in ruminant nutrition that was of particular interest to
grazing livestock nutritionists. The papers in this section
(Cochran et al., 1987; Pond et al., 1987; Goetsch and
Owens, 1987) played key roles in establishing the state of
development at the time of the conference, and stimulated
further development beyond that point.
The 2nd and 3rd GLNC built upon the solid foundation
that the 1st GLNC provided. Some themes of these
subsequent conferences were similar and built upon the 1st,
while other themes were novel. For example, energetic
requirements in the context of the grazing environment can
be found in all three conference proceedings (i.e.,
previously cited papers in the 1st GLNC, Crooker et al.,
and will be used to model the long term effects of
herbivores on plant diversity and successional trends.
Some of the most interesting past research with grazing
animals involved supplementation research in free-ranging
conditions (Clanton and Raleigh, 1987). These projects
demonstrated that we could design supplementation
strategies that optimized the use of diverse native or
improved rangelands with beef cattle and sheep production.
These studies were well received by beef cattle managers
and are still relevant to current production systems.
Nutritionists should always be cognizant that the best
indicator of diet quality is actual animal performance.
Estimates of intake and chemical composition of diet are
only tools to predict animal performance under varying
range conditions and management strategies. It is our
opinion that recent research has lost sight of the fact that the
best data is actual performance data (weight change, body
condition change, reproductive efficiency, etc.) conducted
in “real world” management systems. Perhaps the
publication demands and economic limitations of the
modern Land Grant University have hindered our
willingness to invest time and resources into more applied,
performance oriented research.
One of the more significant developments in grazing
livestock nutrition research in North America relates to the
establishment of regional projects and (or) coordinating
committees. These efforts are designed to foster multiuniversity and multi-agency collaborations that more
effectively address regional issues and needs. Range
livestock nutrition was first covered by the establishment of
the W-34 project in 1955 (Clanton and Raleigh, 1987).
Over the past 55 years, this project has oscillated from
regional project to coordinating committee status and
currently is a multi-state research project, W-1012. This
group has produced numerous projects and publications that
addressed techniques to estimate intake, diet quality, and
diet selection, as well as research that relate to range
livestock management.
Clearly, the grazing livestock nutrition discipline is still
relatively new and has numerous areas that could be
improved upon. For individuals that have dedicated a
substantial portion of their lives to this discipline, you likely
understand the great rewards and frustrations in working
with grazing animal nutrition and management. For
younger people possibly interested in grazing animal
nutrition research, there are certainly opportunities to make
substantial contributions to this discipline and, in the
process, you may also provide a huge service to the range
livestock industry.
HISTORY OF THE GRAZING LIVESTOCK
NUTRITION CONFERENCE
In the early to mid 1980s, a group of grazing livestock
nutritionists from throughout the western U.S. recognized
the opportunity and need for a conference to discuss the
state of the art and future opportunities in grazing livestock
nutrition. That group envisioned several key elements for
the meeting. They wanted it to last 2 to 3 days, first so that
in-depth treatment could be provided for important topics,
but also to promote communication and cross-fostering of
2
grazing setting (e.g., Brock and Owensby, 2000).
Opportunities for rapid development will continue, and the
evaluation of grazing livestock use of their environment and
the environment’s influence on patterns of nutrient harvest
by livestock will be important in future development of our
knowledge in grazing livestock nutrition.
1991; Frisch and Vercoe, 1991; Hennessey, 1996; Caton
and Dhuyvetter, 1996). A wide variety of presentations
about supplementation practices are included in all three
proceedings. In fact, the 3rd GLNC was entirely focused on
supplementation. By following the progression through the
three proceedings, a reader can follow the evolution of
thinking on specific topics over that 9-yr time span.
Particularly with the recurring supplementation theme, the
development of knowledge of utilization of supplemental
protein, energy, and minerals is evident. For example,
presentations about protein supplementation progress from
being primarily N and CP based by Petersen (1987) and
Owens et al. (1991) to an MP basis in 1996 (Paterson et al.,
1996). This new emphasis in 1996 coincided with the
release of NRC (1996), which converted calculation of beef
cattle protein requirements from a CP to an MP basis. In
turn, influence of this knowledge on applied aspects such as
development of supplementation practices is evident,
particularly in the 3rd GLNC proceedings (e.g., Torell and
Torell, 1996). Another development that is evident over the
span of the 3 proceedings is the increased knowledge of
grazing behavior and appreciation for its influence on
nutrient intake by grazing ruminants (i.e., Dougherty, 1991;
Provenza, 1991; Ruyle and Rice, 1996; Rittenhouse and
Bailey, 1996).
After the 3rd GLNC, no further conferences have been
convened. Members of WERA-110, a regional
education/extension and research committee (recently
reapproved as a multi-state research project, W1012)
comprised of several scientists focused on grazing livestock
nutrition, recognized the need to re-establish the GLNC.
The GLNC had fulfilled an important role in the past, and
the committee envisioned that role continuing. Thus,
planning began in 2007 to convene this 4 th GLNC in 2010.
The intent has been to reinstitute the key elements of past
GLNC, including the multi-day event held in a resort
setting near to and immediately before commencement of
the annual meeting of ASAS. Additionally, the
International speaker element has also been retained. The
intent is to rekindle the mission of the prior GLNC to
address and discuss the current state and future of the
discipline in a setting that will foster collegiality and
communication.
Rumen Microbial Genomics
The various “omics” fields have been active in animal
science, but have often been considered too basic for the
applied nature of grazing livestock nutrition as a discipline.
However, the ability to link the benchtop nature of these
molecular disciplines with the applied disciplines is rapidly
advancing. This provides an exciting opportunity,
particularly in the realm of rapidly expanding the ability to
evaluate rumen microbiology, and it is especially exciting
because of the opportunity to bring this ability into the field
setting. Much like some of the papers in the 1st GLNC
prompted rapid development of better grazing nutrition
techniques; the inclusion of this theme in the 4 th GLNC will
hopefully foster linkage between these fields of study and a
similar rapid development of techniques and new
knowledge.
New Knowledge about Metabolism from Molecular to
Organism Levels
Since the last GLNC, knowledge of animal metabolism
and techniques to evaluate it have continued to develop.
These developments range from whole organisms to tissues,
cells, and molecules. The integration of techniques across
this gradient has vastly improved our ability to understand
the metabolic mechanisms that drive the empirical
observations of the past. There is a need to revisit themes of
past GLNC and consider those relationships with metabolic
drivers of processes and responses in grazing livestock.
ISSUES AND CHALLENGES IN GRAZING
LIVESTOCK NUTRITION SINCE 1996
Grazing Intake Techniques
EVOLUTION OF GRAZING LIVESTOCK
NUTRITION SINCE 1996
Geospatial Methodologies
Range science and other natural resource disciplines
have made rapid advancement in recent years using
geospatial tools such as graphic information systems (GIS)
and global positioning systems (GPS) to evaluate spatial
relationships across landscapes of rangelands. The ability to
evaluate relationships among plant communities, soils,
geologic features, and physiographic features has been
enlightening (Johnson et al., 2000; Boyce et al., 2002).
Further development with use of GPS to track animal 1.
locations (both domestic livestock and wildlife) in relation
to these landscape features has further added to the value of
geospatial techniques in considering livestock in the
3
Unfortunately, techniques to estimate grazing animal
intake have not improved over the past 15 years, and in
many respects, have lost ground from a practical standpoint.
One of the most versatile external markers, chromic oxide,
has been listed as a potential carcinogen and, as a result, the
purchase and use of this marker has become limited.
Chromic oxide was a marker that was easily added to
supplements and (or) rations and was easily detected with
atomic absorption using an acetylene head. In addition, the
manufacture of sustained release boluses (chromic oxide
and alkane; Captec Ltd., Auckland, New Zealand) has been
discontinued. The loss of manufacture of sustained release
boluses is presumably due to one or more of the following
factors:
1. Chromic oxide being listed as a potential carcinogen;
2. The inability of researchers to measure alkanes in a
repeatable manner; and
and quality (Putman and DelCurto, 2007). Successful beef
producers are not the ones who wean the heaviest calves, or
obtain 95% conception, or who provide the most optimal
winter nutrition. Instead, successful producers are those
who demonstrate economic viability and sustainability
(Figure 1) while maintaining ecological and social values.
Western US rangelands have historically been
managed to accommodate livestock production. However,
Congress has altered the framework that governs federal
land management with the passage of the Multiple Use Act
(1968), National Environmental Policy Act (1969), Clean
Water Act (1972), and the Threatened and Endangered
Species Act (1973). The continued use of public and private
rangelands depends on our ability to develop sustainable
systems that maintain or enhance biological diversity of
forages, riparian function, and wildlife across the western
US. Grazing livestock nutrition and management must
develop systems for economic viability that also maintain
biological diversity (vegetation and wildlife) and the
traditions and integrity of the industry. Research that is
grounded in economic and ecologic sustainability should be
encouraged and supported. Recent reviews evaluating the
management of livestock distribution and applied
management strategies for optimal distribution on arid
rangelands provide relevant background for this discussion
(Bailey, 2005; DelCurto et al., 2005).
One of our biggest challenges may be in defining better
approaches to measure or interpret cow efficiency. While
academia and other support industries are putting greater
efforts into “cow efficiency,” we still struggle with the
concept and, to date, refuse to accept that the ideal cow for
one nutritional environment may not be ideally suited for
another environment. Likewise, we need to provide a
framework for our beef industry to make genetic selection
decisions that optimizes production for specific nutritional
environments. We still have a chronic problem with
producers who choose genetics that exceed their
environmental potential and subsequent reproductive
efficiency or failure effectively limits their desired genetic
improvement. Obviously, we have work to do before these
goals and aspirations will be fully realized.
3. The low demand for sustained release boluses in respect
to overhead and production cost.
Additionally, the interest and time devoted to
developing new techniques or refining existing techniques
seems to be declining. This may be driven by an inability to
find external funding for research technique development
and (or) the decreased number of researchers devoted to
this area of research.
Inadequacy of NRC (1996)
For many range livestock nutritionists, the 1996 Beef
NRC has limited application to applied cow/calf nutrition.
First, for grazing beef cattle in extensive scenarios where
the cattle are adapted to limited nutritional environments,
meeting the animal’s CP and energy requirements is the
first priority. Balancing protein in terms of metabolizable
protein, rumen undegradable protein (RUP), and rumen
degradable protein (RDP) is beyond the needs of the
average range livestock manager. Additionally, most
grazing livestock nutrition issues deal with supplementation
strategies that optimize the use of dormant, low-quality
forage. In these scenarios, the intake formulas used in the
1996 Beef NRC do not accurately predict the forage intake
response to protein supplementation. This may be due to the
fact that microbial efficiency is a key component to these
formulas and is based primarily on digestion studies
conducted in the mid-west with low-quality warm season
forages. Recently, Bohnert et al. (2007) demonstrated that
the intake response to supplemental protein differed among
warm season and cool season forage. This observation is
presumably due to the differing cell wall structure despite
the fact that both forages were similar in CP and NDF
values. Likewise, research with frequency of protein
supplementation and the success of less frequent strategies
(Huston et al., 1999; Bohnert et al., 2002) suggest that
protein fractions (RUP vs. RDP) are not necessarily
important to mature cow nutrition in an extensive rangeland
production environment.
CHALLENGES TO GRAZING LIVESTOCK
INDUSTRY
FUNDING CHALLENGES FOR LAND GRANT
UNIVERSITY RESEARCH
Historically, grazing animal nutrition has focused on
optimizing beef cattle performance with maximal use of
rangeland forages. Beef production is optimized in that the
goal usually involves minimizing supplemental inputs and
harvested forages while maximizing the use of native and
(or) introduced vegetation by the grazing animal. We have
all heard the comments that we need to think of the grazing
ruminant as the “harvester” of the available forage. The
“ideal” or most efficient cow (or ewe) is the one that works
best in the specific rangeland environment and requires the
least amount of external nutritional input but still produces
a viable, wholesome food product. Obviously, this creates
difficulty in defining “what is the ideal animal” because the
answer is specific to the nutritional environment that is
provided by the rangeland environment and the
management preference. Furthermore, the beef industry in
the western U.S. is highly dependent upon variable arid
environments and the subsequent effects on forage supply
An unfortunate legacy since the last GLNC has been
continual erosion of financial support of public universities
in general, and the Land Grant University system
throughout the U.S. has not been immune from this loss.
This means there are fewer scientists using far fewer dollars
to study the nutrition of grazing livestock now than there
were before and during the era of the previous GLNC. This
makes one pause about the sustainability of the field of
study as well as the need and opportunity for further GLNC
after the 4th. Grazing livestock nutritionists will need to
recognize and embrace opportunities and challenges for the
discipline to not only survive, but thrive. Novel approaches
to garnering external funding and new partnerships with
other disciplines will play a role in the future of grazing
livestock nutrition as a viable discipline.
4
GOALS OF THE 4TH GRAZING LIVESTOCK
NUTRITION CONFERENCE
Cook, C. W. and L. E. Harris. 1950. The nutritive value of
range forage as affected by vegetation type, site, and
state of maturity. Utah Agr. Exp. Sta. Tech. Bull. 344.
Crooker, B. A., P. T. Anderson, and R. D. Goodrich. 1991.
Maintenance energy requirements and energetics of
tissue deposition and mobilization in cattle. Pages 1-12
in Proc. 2nd Grazing Livestock Nutrition Conference.
Oklahoma Agr. Exp. Stn. Pub. MP-133.
DelCurto, T., M. Porath, C. T. Parsons, and J. A. Morrison.
2005. Management strategies for sustainable beef cattle
grazing on forested rangelands in the Pacific northwest.
Invited synthesis paper. Rangeland Ecology Manage.
58:119–127.
Dougherty, C. T. 1991. Influence of ingestive behavior on
nutrient intake of grazing livestock. Pages 74-82 in
Proc. 2nd Grazing Livestock Nutrition Conference.
Oklahoma Agr. Exp. Stn. Pub. MP-133.
Ferrell, C., and T. G. Jenkins. 1987. Influence of biological
types on energy requirements. Pages 1-7 in Proc.
Grazing Livestock Nutrition Conference, Jackson, WY.
Univ. Wyoming, Laramie.
Forbes, R. M. and W. P. Garrigus. 1950. Some relationships
between chemical composition, nutritive value, and
intake of forages grazed by steers and wethers. J. Anim.
Sci. 9:354-362.
Frisch, J. E. and J. E. Vercoe. 1991. Factors affecting the
utilization of nutrients by grazing beef cattle in Northern
Australia. Pages 198-212 in Proc. 2nd Grazing Livestock
Nutrition Conference. Oklahoma Agr. Exp. Stn. Pub.
MP-133.
Goetsch, A. L. and F. N. Owens. 1987. Impact of forage
characteristics on microbial fermentation and ruminant
performance. Pages 55-66 in Proc. Grazing Livestock
Nutrition Conference, Jackson, WY. Univ. Wyoming,
Laramie.
Havstad, K. M. and D. E. Doornbos. 1987. Effect of
biological type on grazing behavior and energy intake.
Pages 9-15 in Proc. Grazing Livestock Nutrition
Conference, Jackson, WY. Univ. Wyoming, Laramie.
Hennessey, D. W. 1996. Appropriate supplementation
strategies for enhancing production of grazing cattle in
different environments. Proc. 3rd Grazing Livestock
Nutrition Conference. M. B. Judkins and F. T.
McCollum III (eds). Proc. West. Sec. Amer. Soc. Anim.
Sci. 47(Suppl. 1):1-18.
Holechek, J. L., M. Vavra, and R. D. Pieper. 1982.
Botanical composition determination of range herbivore
diets: A review. J. Range Manage. 42:248-251.
Huston, J. E., H. Lippke, T. D. A. Forbes, J. W. Holloway,
and R. V. Machen. 1999. Effects of supplemental
feeding interval on adult cows in western Texas. J.
Anim. Sci. 77:3057–3067.
Johnson, B. K., J. W. Kern, M. J. Wisdom, S. L. Findholt,
and J. G. Kie. 2000. Resource selection and spatial
separation of mule deer and elk during spring. J. Wildl.
Manage. 62:685-697.
Lesperance, A. L., V. R. Bohman, and D. W. Marble. 1960.
Development of techniques for evaluating grazed
forage. J. Dairy Sci. 43:682-689.
The first three conferences were tremendous successes
that stimulated interest and continued collaboration for the
discipline of grazing livestock nutrition. Simply put, it has
been 14 years since the last “nutrition conference.” It is our
sincere hope and intention that we will reinvigorate this
conference and provide motivation and information to
move our profession into the future.
The organizing committee for the 4th Grazing Livestock
Nutrition Conference has assembled an esteemed group of
speakers to address the topics at hand. The charge to these
speakers for their topic is to (1) provide historical
perspective, (2) present the state-of-the-art knowledge, and
(3) discuss future research needs and opportunities.
LITERATURE CITED
Adams, D. C. 1987. Influences of winter weather on range
livestock. Pages 23-29 in Proc. Grazing Livestock
Laramie.
Bailey, D. W. 2005. Identification and Creation of optimum
habitat conditions for Livestock. Invited synthesis
paper. Rangeland Ecology Manage. 58:109–118.
Bohnert, D. W., C. S. Schauer, and T. DelCurto. 2002.
Influence of rumen protein degradability and
supplementation frequency on performance and nitrogen
use in ruminants consuming low-quality forage: Cow
performance and efficiency of nitrogen use in wethers. J.
Anim. Sci. 80:1629-1637.
Bohnert, D. W., T. DelCurto, A. A. Clark, M. L. Merrill, S.
J. Falck, and D. L. Harmon. 2007. Protein
supplementation of ruminants consuming low-quality
cool or warm-season forage: Differences in intake and
digestibility. Proc. West. Sec. Am. Soc. Anim. Sci.
58:217-220.
Boyce, M. S., P. R. Vernier, S. E. Nielsen, and R. K. A.
Schmiegelow. 2002. Evaluating resource selection
functions. Ecological Modeling 157:281-300.
Brock, B. L. and C. E. Owensby. 2000. Predictive models
for grazing distribution: A GIS approach J. Range
Manage. 53:39-46.
Canon, S. K., P. J. Urness, and N. V. DeBule. 1987.
Foraging behavior and dietary nutrition of elk in burned
aspen forest. J. Range Manage. 40:433-438.
Caton, J. S., and D. V. Dhuyvetter. 1996. Manipulation of
maintenance requirements with supplementation. Proc.
3rd Grazing Livestock Nutrition Conference. M. B.
Judkins and F. T. McCollum III (eds). Proc. West. Sec.
Amer. Soc. Anim. Sci. 47(Suppl. 1):72-82.
Clanton, D. C. and R. J. Raleigh. 1987. Forty years of
grazing livestock nutrition research. Pages 151-156 in
Proc. Grazing Livestock Nutrition Conference, Jackson,
WY. Univ. Wyoming, Laramie.
Cochran, R. C., E. S. Vanzant, K. A. Jacques, M. L.
Galyean, D. C. Adams, and J. D. Wallace. 1987. Internal
markers. Pages 39-48 in Proc. Grazing Livestock
Laramie.
5
Morris, J. G. and M. D. Sanchez. 1987. Energy expenditure
of grazing ruminants. Pages 17-22 in Proc. Grazing
Livestock Nutrition Conference, Jackson, WY. Univ.
Wyoming, Laramie.
NRC. 1996. Nutrient Requirements of Beef Cattle. 7 th rev.
ed. Natl. Acad. Press, Washington, DC.
Olson, K. C. 1991. Diet sample collection by esophageal
fistula and rumen evacuation techniques. J. Range
Manage. 44:515-519.
Owens, F. N., J. Garza, and P. Dubeski. 1991. Advances in
amino acid and N nutrition in grazing ruminants. Pages
109-137 in Proc. 2nd Grazing Livestock Nutrition
Conference. Oklahoma Agr. Exp. Stn. Pub. MP-133.
Paterson, J., R. Cochran, and T. Klopfenstein. 1996.
Degradable and undegradable protein responses of cattle
consuming forage-based diets. Proc. 3rd Grazing
Livestock Nutrition Conference. M. B. Judkins and F. T.
Sci. 47(Suppl. 1):94-103
Petersen, M. K. 1987. Nitrogen supplementation of grazing
livestock. Pages 115-121 in Proc. Grazing Livestock
Laramie.
Pond, K. R., J. C. Burns, and D. S. Fisher. 1987. External
markers - Use and methodology in grazing studies.
Pages 49-53 in Proc. Grazing Livestock Nutrition
Conference, Jackson, WY. Univ. Wyoming, Laramie.
Provenza, F. D. 1991. Behavior and nutrition are
complementary endeavors. Pages 157-169 in Proc. 2nd
Grazing Livestock Nutrition Conference. Oklahoma
Agr. Exp. Stn. Pub. MP-133.
Putman, D. H. and T. DelCurto. 2007. Forage systems for
Arid Zones. Pages 323-339 In: R. F. Barnes, C. J.
Nelson, K. J. Moore, and M. Collins (eds). Forages,
Volume II. The Science of Grassland Agriculture 6th
Edition. Iowa State Press.
Rittenhouse, L. R. and D. W. Bailey. 1996. Spatial and
temporal distribution of nutrients: Adaptive significance
to free-grazing herbivores. Proc. 3rd Grazing Livestock
Sci. 47(Suppl. 1):51-61.
Robertshaw, D. 1987. Heat stress. Pages 31-35 in Proc.
Grazing Livestock Nutrition Conference, Jackson, WY.
Univ. Wyoming, Laramie.
Ruyle, G. B. and R. W. Rice. 1996. Aspects of forage
availability and short-term intake influencing range
livestock production. Proc. 3rd Grazing Livestock
Sci. 47(Suppl. 1):40-50.
Sparks, D. R. and J. C. Malechek. 1968. Estimating
percentage dry weight in diets using a microscopic
technique. J Range Manage. 21:264-265.
Torell, D. T. 1954. An esophageal fistula for animal
nutrition studies. J. Anim. Sci. 13:878-884.
Torell, L. A. and R. C. Torell. 1996. Evaluating economics
of supplementation practices. Proc. 3rd Grazing
Livestock Nutrition Conference. M. B. Judkins and F. T.
Sci. 47(Suppl. 1):62-71.
Wickstrom, M. L., C. T. Robbins, T. A. Hanley, D. E.
Spalinger, and S. M. Parish. 1984. Food intakes and
foraging energetics of elk and mule deer. J. Wildl.
Manage. 48:1285-1301.
Young, B.A. 1987. Thermal factors influencing energy
requirements of livestock. Page 37 in Proc. Grazing
Livestock Nutrition Conference, Jackson, WY. Univ.
Wyoming, Laramie.
6
Figure 1. Grazing Animal Nutrition needs to focus on research applications that provide balanced economic return,
ecological integrity of rangelands, and social acceptability to beef cattle producers and land managers.
7
T. DelCurto and K. C. Olson
Notes
July 9 and 10, 2010
J. M. Brulcŧ, C. J. Yeomanŧ, K. E. Nelson*, and B. A. White1ŧ
ŧ
Department of Animal Sciences and the Institute for Genomic Biology, University of Illinois, Urbana 61801;
*
The J. Craig Venter Institute, Rockville, MD 20850
Estimates are that the rumen protozoa are present at a
concentration of 1 x 106 per mL of rumen content. Rumen
protozoa have been shown to impact the pH of the rumen
(Veira, 1986) and may be responsible for up to 30% of
ruminal fiber degradation (Demeyer, 1981). Rumen
protozoal species have also been implicated in the removal
and metabolism of toxic compounds that might present in
the rumen (Veira, 1986). Obligately anaerobic fungi most
closely related to the Chytridiomycete fungi of the order
Spizellomycetales (Bowman et al., 1992) have been found
in the rumen (zoospore population densities of 10 3 to 105
per mL divided into 4 genera). The anaerobic rumen fungi
exhibit two basic forms: “polycentric” species in which
nuclei are located throughout the hyphal mass, and
“monocentric” species with a concentration of nuclear
material in a zoosporangium (Brookman et al., 2000). The
rumen anaerobic fungi are probably best studied in relation
to their plant cell wall degrading enzymes or glycoside
hydrolases (GH), which include an assortment of
cellulases, xylanases, mannanases, glucosidases, and other
glucanases. Recent studies have shown there is a strong
similarity between the GH of rumen fungi and bacteria,
suggesting that many of the GH of anaerobic fungi have
been acquired by horizontal gene transfer from coresident
bacteria (Garcia-Vallve et al., 2000). The rumen anaerobic
fungi also produce several forms of esterases, which release
esterified acetic and hydroxycinnamic acids from plant cell
walls (Borneman et al., 1992; Fillingham et al., 1999).
Archaea are estimated to comprise approximately 0.3 to 3%
of the biomass in the rumen. To date only strictly anaerobic
methanogens have been identified (Lin et al., 1997, Ziemer
et al., 2000). Cultivation-based approaches have revealed
just seven genera that include Methanobacterium,
Methanobrevibacter, Methanomicrobium, Methanoculleus
and Methanosarcina spp. although a greater diversity is
expected (Janssen and Kirs, 2008).
ABSTRACT: Advances in genomics and next-generation
sequencing technologies have spawned the field of
metagenomics. Metagenomics enables DNA sequencing of
all the microbial species (the microbiome) that are present
in an environment using culture-independent approaches.
Initial studies have revealed an unanticipated level of
microbial diversity in soils and the oceans. These same
levels of complexity are now being revealed in studies of
the microbiomes from human gastrointestinal tracts (GIT),
and the GIT of production animals, including poultry and
cattle. Continuing cost-reductions associated with
sequencing using these next-generation technologies makes
it possible to query these microbiomes at an unprecedented
depth of sequence coverage. Although the ruminant GIT
microbiome has been examined using culture-based and
16S rRNA phylogenetic analyses, metagenomics represents
an emerging method for advancing the field.
Metagenomics, coupled with the generation of reference
genome sequences of the predominant rumen microbes,
holds great promise for increasing our understanding of the
diversity of species present in the rumen of both dairy and
beef production animals, and how these species can be
manipulated to improve feed efficiency and reduce the
associated environmental impacts, including mitigation of
green house gases.
Key words: metagenomics, rumen, genomics, DNA
sequencing, microbiome
INTRODUCTION
The rumen microbial community, or microbiome, is
characterized by its high population density, broad diversity
and complexity of interactions. The rumen microbiome
resides in what can be considered a fermentation vat that
maintains a near constant environment with respect to pH,
nutrient availability, and temperature. This microbiome has
evolved the ability to degrade complex plant polymers and
provide the animal (host) with VFA for growth. Additional
products of this fermentation are H2, CO2, and CH4.
Although bacteria are numerically predominant (10 11 cells
per mL), the rumen also contains microbial representatives
from the archaea, protozoa, and fungi (Hespell et al., 1997).
1
EMERGING PARADIGMS FOR STUDYING
GASTROINTESTINAL TRACT MICROBIOMES
It is estimated that the GIT microbiome of mammals
contains approximately 1014 total bacteria and is composed
of between 500 to 1000 species, although few of these have
been characterized (Turnbaugh et al., 2007; Ley et al.,
2008a,b). Most of these microbes have not been grown in
culture, their identities remain unknown, and it is unclear
which, if any, are involved in growth promotion or
enhancement of host physiology. Furthermore, each
bacterial species can be present at vastly different
10
fundamentally important that these challenges are
overcome as microbial genome projects move forward.
A list of microbial genomes that have been or are
being sequenced from the rumen is shown in Table 1.
Among the first of the rumen microbial genomes to be
sequenced was the 2.3 Mbp genome of Mannheimia
succiniciproducens
(Hong
et
al.,
2004).
M.
succiniciproducens MBEL55E is a non-motile, non-sporeforming, mesophilic and capnophilic gram-negative
organism with a rod morphology (Lee et al., 2002). This
bacterium efficiently fixes CO2 and produces large
quantities of succinic acid (Lee et al., 2002). Actinobacillus
succinogenes 130Z from the bovine rumen is also
capnophilic, producing large amounts of succinate. A.
succinogenes is also osmotolerant, and uses a range of
sugars (Guettler et al., 1999), its 2,319,663 bp genome is
available at the Joint Genome Institute website:
(http://www.ncbi.nlm.nih.gov/sites/entrez?db=genome&cm
d=Retrieve&dopt=Overview&list_uids=21212;
http://genome.jgi-psf.org/actsu/actsu.home.html).
Butyrivibrio proteoclasticus B316, formerly designated
Clostridium proteoclasticum (Moon et al., 2008) is a
proteolytic, hemicellulose-degrading bacterium that has
been shown to play a significant role in the
biohydrogenation of fatty acids (Wallace et al., 2006). B.
proteoclasticus was isolated from the rumen contents of
cattle grazing fresh forage in New Zealand (Attwood et al.,
1996). It is has being sequenced to completion by
AgResearch, New Zealand and is due for release this year.
The completed Wolinella succinogenes genome sequence
and its analysis was released in 2003 (Baar et al., 2003).
Despite a close phylogenetic relationship to the GIT
pathogens Helicobacter pylori and Campylobacter jejuni, it
has not been shown to cause any diseases and is as such
considered to be non-pathogenic to the host (Baar et al.,
2003). The rumen fungi act synergistically with rumen
bacteria and protozoa in the degradation of plant material.
Nicholson et al. (2005) initiated genome sequencing and
analyses of the rumen fungus Orpinomyces. Along with
Piromyces (another rumen fungi), genome sequencing has
proved difficult for rumen fungi due to their extraordinarily
high proportion of AT residues relative to GC (~80 to 85 %
of the total DNA and > 95 % in certain loci). Renewed
attempts to capture the genomes of both Orpinomyces and
Piromyces using next-generation sequencing methods are
in progress through collaborations between the Joint
Genome Institute (JGI) and the Pacific Northwest National
Laboratory.
The North American Consortium for Genomics of
Fibrolytic Ruminal Bacteria (NACGFRB) was created in
2000 with support from USDA Initiative for Future
Agriculture and Food Systems. Initial whole genome
sequencing projects have been completed by the
consortium for a number of physiologically important
isolates from the rumen. The consortium has enhanced our
genetic understanding of closely related rumen isolates
through several suppressive subtractive hybridization
(SSH) analyses (Galbraith et al., 2004, Qi et al., 2008;
Morrison et al., 2009). The consortium has produced a
closed genomes for Fibrobacter succinogenes strain S85
(Morrison et al., unpublished data), Prevotella ruminicola
concentrations. For example, microbes such as the
Bacterioides sp., are present at much higher concentrations
(~1010 to 1012 per gram of feces) compared with many other
microbes such as Escherichia coli (~104 to 105 per gram of
feces). Because of the proportional differences in biomass
and therefore overall metabolic contributions to the GIT, it
has been long believed that the more highly represented
bacteria (core microbiome) are more substantial
contributors to nutrient utilization, animal health, and are of
greater importance to food safety (Turnbaugh et al., 2007,
2009). Evolutionary relationships between the host and
environment,
including
microbiomes,
drive
the
composition of core (high abundance) and variable (low
abundance) microbiomes. Core microbiomes are
hypothesized to be composed of a high density, but
relatively low diversity of microbial taxa, whereas the
variable microbiome is composed of low density, but
highly diverse microbial taxa (Turnbaugh et al., 2007,
2009).
This has lead to the NIH-funded Human Microbiome
Project (HMP; http://nihroadmap.nih.gov/hmp/) whose
primary goals are to determine if there is a core human
microbiome (Turnbaugh et al., 2007). In addition to this,
the aims are to understand the changes in the human
microbiome that can be correlated with human health, and
to develop new technological and bioinformatics tools to
support these goals. Initial sequencing objectives for this
program are in place, and include the development of a
microbial genome reference set comprising at least 600 to
1,000 genomes. Additionally, 16S rDNA phylogenetic
analyses followed by metagenomic sequencing are being
used to characterize the microbial communities from the
different body sites of a pool of 250 individuals. It is this
emerging paradigm for analyzing microbiomes that is
becoming the model for studying other GIT microbiomes
like the rumen. Therefore, any future large-scale
comparative analyses of microbial community structure and
functional metabolic potential of the rumen microbiome
will rely on, and be greatly advanced by the recent
technological advances and powerful genomic and
metagenomic approaches outlined in the HMP. To fully
explore the microbial complexity and environmental impact
of such a diverse biome, a robust experimental approach
would utilize a combination of sequencing technologies; 1)
generation of reference microbial genome sequences; 2)
16S rDNA phylogenetic analysis; and 3) metagenomic
sequencing of select samples.
REFERENCE GENOMES FROM THE RUMEN
MICROBIOME
Phylogenetic and metabolic sequence information still
relies heavily on reliable curation of complete genomes in
the databases. Currently, the National Center for
Biotechnology Information (NCBI) has listed 1,094
complete microbial genomes and 2,595 microbial genomes
in progress. Sequencing technologies are becoming more
affordable and with the increasing read-lengths from
pyrosequencing, storage of such massive data sets and
ensuring data curation is already becoming an issue. It is
11
approaches oriented at specific subpopulations have
focused on targeting specific phyla and gene families.
A SSU rDNA survey specifically oriented toward the
bovine rumen sequenced 84 full-length clones generated
from the rumen contents of Holstein cows (Tajima et al.,
1999). Comparing these sequences to the ribosomal
database project (RDP) resulted in 56% of the generated
sequences showing less than 90% similarity to known
species, highlighting an untouched diverse population yet
to be sampled. Tajima et al. (2000) also found a shift in the
overall rumen bacterial communities coincident with a shift
from a primarily hay-based diet to a grain diet. In total, 150
full-length SSU rDNA sequences were generated in this
study, revealing that in both diets, low G+C gram-positive
bacteria were dominant, representing 90.2% and 72.4% of
the 16S rDNA population, respectively. In the hay diet
some low G+C gram-positive bacterial sequences clustered
with known cellulose-degraders Ruminococcus flavefaciens
and Ruminococcus albus.
Surveys using SSU rDNA have emphasized the vastly
and diverse microbial component that has yet to be cultured
(Whitford et al., 1998; Kocherginskaya et al., 2001; Koike
et al., 2003; Edwards et al., 2004). A comparative analysis
of all published rumen libraries found that only 11% of the
sequences found within these studies possessed similarity
to cultured members (Edwards et al., 2004). However, in
phylogenetic analysis, it is apparent that the rumen
community appears to be dominated by low G+C grampositive bacteria and the Cytophaga-FlavobacteriumBacteriodes (CFB) group. In one of the most
comprehensive SSU rDNA surveys to date, Yu et al. (2006)
created a ribosomal sequence tag library (a SSU rDNA
library composed of the concatemers of hypervariable
region 1) increasing the amount of phylotypes sampled and
sequence information retrieved (Yu et al., 2006). The 190
clones sequenced resulted in 1055 ribosomal sequence tags
(RSTs) and 236 unique phylotypes with 95% sequence
identity. Rarefaction analysis estimated the ruminal
diversity to comprise no greater than 353 different
phylotypes at 95% similarity, similar to previous estimates
(341 OTUs; Edwards et al., 2004) indicating less than 25%
coverage of bacterial diversity. An additional analysis of
the rumen archaeal population presence by SSU rDNA
libraries resulted in the generation of 44 clones (Tajima et
al., 2001b). Although rumen archaea appear to be
dominated by Methanobrevibacter and Methanosarcina
species, the SSU rDNA analysis resulted in sequences that
formed a novel cluster of archaea that did not associate
with these methanogens (Janssen and Kirs, 2008).
To specifically quantify overall populations of
microbes or specific subgroups, competitive or real-time
PCR has been employed. Competitive PCR enumerated the
three major cellulolytic bacteria in the rumen (Fibrobacter
succinogenes,
Ruminococcus
flavefaciens
and
Ruminococcus albus) finding that F. succinogenes is
present in greater concentrations (107 cells per mL) than
Ruminococcus species (104-6) cells per mL (Koike and
Kobayashi, 2001). Real-time PCR has also been used to
monitor not only fluctuations in bacterial cellulolytic
groups over time, but also fungal populations (Denman and
McSweeney, 2006).
strain 23 (Purushe et al., unpublished data) and draft (7-8x
coverage) for R. albus strain 8 and Prevotella bryantii
strain B14. The consortium also developed the FibRumBa
database (http://www.jcvi.org/rumenomics/) as a resource
designed for individuals with an interest in the microbial
ecology of the rumen. The FibRumBa database provides an
near-complete list of rumen microbial genome projects at
the J. Craig Venter Institute (JCVI) that are currently
underway or completed (Morrison et al., 2009). The site
keeps a running list of the rumen genomes that have been
completed to date by the JCVI and welcomes suggestions
for improvement from the rumen community (Morrison et
al., 2009).
Most recently, the genome of R. flavefaciens FD-1 has
been sequenced to 29x-coverage using both Sanger and 454
pyrosequencing and the draft sequence was assembled into
119 contigs providing 4,576,399 bp of unique sequence.
The genome has a GC content of 45%, and is predicted to
encode at least 4,339 ORFs with an average gene length of
918 bp. R. flavefaciens contains a cellulosome with at least
225 dockerin-containing ORFs, including previously
characterized cohesin-containing scaffoldin molecules
(Zhang and Flint, 1992; Flint et al., 1993; Aurilia et al.,
2000; Jindou et al., 2006; Rincon et al., 2005). These
dockerin-containing ORFs encode a variety of catalytic
modules including glycoside hydrolases, polysaccharide
lyases, and carbohydrate esterases. The genomic evidence
indicates that R. flavefaciens FD-1 has the largest known
number of fiber-degrading enzymes likely to be arranged in
a cellulosome architecture (Berg Miller et al., 2009).
PHYLOGENETIC STUDIES OF THE RUMEN
MICROBIOME
Traditionally, culture-based methods have been used to
numerically and phenotypically classify organisms in the
rumen microbiome. However, since such a low portion of
the microbial component can be cultivated, this approach
imposes severe limitations on studying complex microbial
ecosystems (Hugenholtz and Pace, 1996). Moreover, the
lack of a clear definition of a microbial species in addition
to the role horizontal gene transfer plays in microbial
biomes, renders these traditional taxonomic classifications
no longer applicable (Hugenholtz and Pace, 1996;
Hugenholtz et al., 1998). These difficulties have led
scientists to conceptually define species based on a more
robust biological organization using small subunit (SSU)
rRNA sequence-based phylogenetic tree classification
proposed by Carl Woese (Woese et al., 1990; Pace, 1997).
Since the discovery and classification of the third domain
of life (Fox et al., 1977; Woese and Fox, 1977), the SSU
rDNA gene has not only been a useful tool for phylogenetic
analysis but has also provided a framework for evaluating
microbial communities without the need for prior
cultivation and is further applied with more recent
sequence-based technological advances. Using this
technology, studies have provided significant insight into a
variety of microbial ecosystems (Amann et al., 1995; Pace,
1997; Denman and McSweeney, 2006). Approaches
employed in analyzing the overall rumen community
structure have mainly used SSU rDNA surveys, while
12
In a diet-directed approach, real time-PCR was used to
quantify 13 species of rumen of bacteria during the
transition from a hay to grain diet (Tajima et al., 2001a).
Those authors found F. succinogenes dominates in hay
samples but was 57-fold reduced when the diet was
switched to grain. Additionally, R. flavefaciens decreased
10-fold across diet transitions. In contrast, Streptococcus
bovis, Selenomonas ruminantium, Prevotella ruminicola,
and Prevotella bryantii increased in number by 7 to 375fold during the same diet transition. They also found
differential amplification with some rumen bacteria
suggesting that although some species predominate, they
may not be found or accurately enumerated in SSU rDNA
surveys.
Although the use of oligonucleotide probes to observe
bacteria populations is not a new technique (Stahl et al.,
1988), taxa specific probes can offer a range of specificity
across varying taxonomic levels. This targeted approach
was taken in the generation of genus specific Fibrobacter
SSU rRNA oligonucleotide probes (Lin et al., 1994; Lin
and Stahl, 1995). Also, relative abundance measurements
obtained from Ruminococci-specific probes were compared
to numerical estimates using more traditional enumeration
techniques (i.e., direct microscopic counts) for validation
(Krause et al., 1999). In a more broad representation of all
domains of life in ruminant animals, Lin et al. (1997)
designed oligonucleotide probes to reveal that bacteria
represented anywhere from 60% to 90%, Eukarya, 3% to
30% and Archaeal 0.5% to 3% of the rRNA populations
(Lin et al., 1997).
Targeted phyla approaches have utilized fluorescence
in situ hybridization (FISH) to observe cellulolytic bacteria.
Shinkai et al. (2007) found F. succinogenes adherent to hay
stems while R. flavefaciens was found adherent to the
surfaces of leaves, thereby indicating species specificity or
preference in location adherence (Shinkai and Kobayashi,
2007). Since F. succinogenes is found in greater numbers
in the rumen and possesses the ability to solubilize
crystalline cellulose, a more recalcitrant form of cellulose,
it is hypothesized that this organism makes a greater
contribution to fiber degradation. However, highlighted in
these studies is an unprecedented amount of rumen
microbial diversity often missed due to probe specificity.
meaning a full plate run may yield 0.5 Gb of high quality
sequence. Thus, this technology has been adapted to allow
deep SSU rRNA hypervariable tag sequencing (Dethlefsen
et al., 2008; Huse et al., 2008) for use in analyses of
vertebrate GIT systems. One reason is this approach yields
very high information content (see for example, Neufeld et
al., 2004; Kysela et al., 2005; Neufeld and Mohn, 2005;
Sogin et al., 2006; Roesch et al., 2007). Pyrosequencing
targeting hypervariable regions of the 16S rDNA allows for
deeper surveys. This approach was recently validated for
the human vertebrate GIT in studies of both lean and obese
twins (Turnbaugh et al., 2009) and patients undergoing
antibiotic treatment (Dethlefsen et al., 2008; Huse et al.,
2008).
METAGENOMIC APPROACHES
Natural environments are composed of a genomically
diverse set of organisms. Often these organisms,
particularly those that are underrepresented, are missed in
culture despite their potential to supply significant and (or)
unique metabolic contributions in these complex
environments (Ley et al., 2006; Sogin et al., 2006;
Turnbaugh et al., 2006). Microbiologists began using
culture-independent methods, metagenomics, to circumvent
the low proportion (often less than 1%) of cultivatable
members of a microbiome (Handelsman, 2004; Schloss and
Handelsman, 2008). Metagenomics allows genomic access
to the entire population of microorganisms and allows for
independent analysis of these microbes in conjunction with
their natural habitat.
Traditional metagenomic analyses generally began
with total extracted genomic DNA of that community. The
DNA can be restriction digested, ligated into a vector and
propagated in a host, often E. coli. For a sequence-driven
analysis, clones can be chosen at random and subsequently
sequenced. For functional-driven analyses, the clones can
be screened for phylogenetic markers, enzymatic activity,
or antibody binding. Heterologous gene expression then
allows for physiological identification of small molecules
or proteins. More recently, sequenced-based metagenomic
approaches have strayed from cloning techniques, which
introduce their own levels of bias, to a more random
sequencing strategy, pyrosequencing (Ronaghi et al., 1996,
1998; Margulies et al., 2005). Regardless of the approach
taken, metagenomics appears to be a very useful tool to
answer some of the basic questions that have persisted
since the 19th century: who’s there, how many of them are
there, and what are they doing? More specifically,
microbiologists and microbial ecologists want to go beyond
single organism genomics and delve into a more
encompassing view that not only includes the types and
numbers of these organisms present, but also their function
and (or) influence as a community in the environment.
The early world of metagenomics began with
traditional Sanger sequencing. As microscopic enumeration
and colony counts were compared to the resulting numbers
of microbes cultured, it became apparent that there was a
large majority of organisms that were overlooked in these
traditional studies (Handelsman, 2004; Schloss and
Handelsman, 2007). A demand for genomic tools arose that
DEEP SMALL SUBUNIT rRNA HYPERVARIABLE
TAG SEQUENCING
New techniques are constantly being developed to
further elucidate the diversity of microbial communities.
Pyrosequencing is a promising new technology developed
by 454 Life Sciences (http://www.454.com/; Hyman, 1988;
Ronaghi et al., 1996, 1998; Margulies et al., 2005). The 454
technology presents a major advantage over traditional
sequencing technologies by eliminating the need for
cloning vectors, which are known to be biased in terms of
the impaired ability to clone certain DNA fragments
(http://www.454.com/). This sequencing technology also
reads through secondary structures readily, and has a
capacity to produce very large amounts of sequence.
Current estimates are that this instrument produces about
1.25 million sequence reads, with read lengths of ~ 400 bp,
13
combined results lend EGT data to serve as a “fingerprint”
for each microbiome.
The use of Sanger sequencing poses some issues with
metagenomic analyses. Often, the ability to generate large
amounts of sequence information was limited by the cost of
sequencing ($8,000 to $10,000 per Mbp; Goldberg et al.,
2006) and the need for cloning introduces a bias, resulting
in the more abundant members of the community present
more often in the number of reads compared with less
abundant members. Additionally, cloning impedes the
analysis of viral metagenomes since expression of
virulence genes often kill the host (Edwards and Rohwer,
2005). Next generation sequencing technologies, like
pyrosequencing, remove the need for cloning. This and the
Solexa system are the landmark technologies that have
completely changed the face of metagenomics. Using the
Roche Titanium system, one can generate up to 1,250,000
reads per run, with >400 bp/read (0.5 Gbases of total
sequence) for less than $10,000. While the Illumina GAII
system produces shorter reads (<100 bp), it can result in
over 50 GB/run. This system was recently applied to
human GIT metagenomics by the MetaHIT group with very
promising results (Qin et al., 2010). Its replacement, the
HiSeq2000 will generate upwards of 100 bp fragments with
up to to 200 Gb in a single run for less than $10,000.
Edwards et al. (2006) were the first to apply random
sample pyrosequencing to total environmental DNA
collected from a subterranean environment, the Soudan
Mine. Following a whole genome PCR amplification step,
phylogeny was analyzed via SSU rDNA distributions and
coding sequences were categorized into functional
subsystems. A SSU rDNA clone library was generated for
comparative analysis against the more traditional Sanger
sequencing method. They generated approximately 350,000
sequences (average read length ~100 bp) for two samples
that were in close proximity (100 m apart) to one another
(Edwards et al., 2006). From these sequences, a high
congruency was found between SSU rDNA sequences
obtained from pyrosequencing and the taxonomic profile
observed the SSU clone library. Additionally, the metabolic
potential of these communities was determined via
comparative analysis in the SEED database (Overbeek et
al., 2005). The SEED database uses an approach based on
subsystems, a set of functional roles often coordinating
groups of genes found in a metabolic pathways or cell
structure components. These subsystems are curated from
complete genomes by an expert in that particular field. The
subsystem analysis indicated that these two distinct
communities possessed extremely different metabolisms.
For example, oxidized and reduced samples were greatly
overrepresented in corresponding subsystems (RodriguezBrito et al., 2006) and these metabolisms correlated with
sampled environmental factors enabling the formation of a
working microbiome model on how these microbial
communities coexist.
Similar to the Sanger sequencing study of Tringe et al.
(2005), a functional metabolic meta-analysis of multiple
metagenome data sets (over 14 million sequences from 45
microbiomes and 42 viromes), that differ significantly in
biome origin, revealed differential representation of
metabolisms based on environment (Dinsdale et al., 2008).
would allow for a more accurate picture of the phylogenetic
distribution of the microbial diversity present. Moving
beyond SSU rDNA surveys, Sanger sequencing of large
insert libraries, traditionally using single organism
genomics, was applied to total community DNA (Rondon
et al., 2000). In this study, heterologous gene expression
using low copy bacterial artificial chromosomes (BAC) to
sequence community DNA from a soil microbiome
(Rondon et al., 2000) resulted in more than 1 gigabase pairs
of DNA between two libraries. In addition to successfully
finding sequences from a diverse array of taxa, they were
able to screen the clones for functional diversity resulting
in novel gene discovery. This enabled a tied study of
genetics to functional expression for each of the selected
clones.
Another landmark study, the acid mine drainage
(AMD) study demonstrated the ability to reconstruct
genomes from a simple microbial community (Tyson et al.,
2004). A biofilm community was found growing atop a
water surface composed of H2SO4 and FeOH3, which the
microbes had produced in response to an anthropogenic
disturbance caused by mining. These microbes performing
the reaction subsequently lowered the pH of the
environment, limiting the number of organisms that coinhabit that location. This study generated a total of 76.2
megabase pairs (~100,000 reads) with an average insert
size of 3.2 kb. The 384 SSU rDNA clones end-sequenced
resulted in 3 bacterial lineages and 3 archaeal lineages.
From about 85% of the shotgun reads assembled into
scaffolds greater than 2kb, Tyson et al. (2004) discovered
almost complete genomes for an iron-oxidizing bacteria
and an acidophilic archaea, thereby proving that multiple
genomes could be reconstructed from a simple community
using metagenomic shotgun sequencing methods.
Additionally, with the ability to recreate and compare
contigs, Tyson et al. (2004) found evidence of horizontal
gene transfer, determined potential proteins that indicated
the physiological source of organismal chemical product
generation, and found an underrepresented taxa group that
may be the keystone for AMD community survival.
More complex communities, such as the Sargasso Sea
(Venter et al., 2004), the human metagenome (Gill et al.,
2006; Turnbaugh et al., 2006, 2007; Kurokawa et al., 2007)
and the termite hindgut (Warnecke et al., 2007), have been
subjected to a shotgun sequencing approach. Although full
microbial genomes are rarely reconstructed from these type
of sequencing projects (generally less than 1% of reads
overlap), much can be elucidated about their physiological
contribution (Tringe et al., 2005). Tringe et al. (2005)
performed a comparative analysis of multiple metagenomes
using “predicted genes on small DNA fragments,” or
environmental gene tags (EGT), to observe if gene sets for
specialized functions would vary in number and type
according to the environment sampled. Results indicated
that each microbiome possesses gene sets similar to another
microbiome if the metabolic demands are similar. By the
same token, unique gene sets, such as bacteriorhodopsin
found in Sargasso Sea samples and not in the soil
metagenome, are restricted to select environments in which
their gene products would be most utilized. These
14
pyrosequence data that had been derived from the fiberadherent and pooled liquid populations from 3 animals that
had been fed the same diet. The samples had been obtained
from 3 fistulated 5-yr old Angus Simmental Cross steers
that had been maintained at the Illinois State University.
Three liters of rumen contents were collected 1 h after
feeding, and samples divided into Liquid and Fiber
adherent. After DNA extraction, and quality control checks,
the 4 total samples were sequenced using an early version
of the 454 Life Sciences Genome Sequencer, GS20 (454
Life Sciences). Data that were obtained post sequencing
were analyzed with the SEED annotation engine
(http://metagenomics.nmpdr.org). For the assignment of
carbohydrase enzymes, BLAST-based approaches against
the CAZy database (www.cazy.org) were used. In parallel,
16S rRNA gene libraries were constructed with the
universal
prokaryotic
primers
8FPL
(AGTTTGATCCTGGCTCAG)
and
1492RPL
(GGYTACCTTGTTACGACTT). Matches were identified
with BLASTN and rarefaction analysis was performed with
DOTUR
(http://www.plantpath.wisc.edu/fac/joh/dotur.html). These
16S rRNA gene sequences were compared against those
present in the metagenomic libraries with Nonmetric
Multidimensional Scaling (NM-MDS).
The results of the rarefaction analysis that was
conducted in this study suggested that between 161 and 259
OTUs (97% identity level) could be found in each of the 4
libraries for which full length 16S rRNA clones were
constructed. This is in agreement with the earlier estimates
of 177 OTUs (Edwards et al., 2004b). When all 16S rDNA
libraries were compared, 510 unique OTUs were observed.
It appeared that the most populous organism (64% of the
sequences in 59 OTUs) was present in all the animals,
contrasting with 10% of the sequences in 273 OTUs that
were present in only 1 animal (Brulc et al., 2009).
The microbial diversity in these 4 samples was
correlated with the bacterial EGTs in the liquid and solid
fractions of the samples that were analyzed (Brulc et al.,
2009). The bacterial EGTs in these samples accounted for
up to 95% of total EGTs. The bulk of these belonged to
Bacteriodetes,
Firmicutes,
and
Proteobacteria.
Ruminococcus was rare in these sample sets. Archaeal
species EGTs accounted for 2.3% of the total EGTs with
other estimates of archaeal populations in the rumen. Only
a few Eukaryotes were identified (1.3%) and no fungal
rRNA gene sequences were identified. Viral EGTs were
rare (0.1%). The microbiome samples from all animals had
the presence of gene sequences to metabolize plant cellwall carbohydrates components including cellulose and
xylan. A number of similarities and differences between
this dataset and the previously documented termite and
chicken GIT studies were presented in this study. For
example, a wide variety of GH catalytic modules were
observed in the rumen samples, but the termite hindgut
contains more modules that would be associated with the
degradation of cellulose and xylan, and as the authors
propose, this is likely because of differences in diets being
consumed by the different species (Warnecke et al., 2007).
The termite gut microbiome also had more genes involved
in N fixation (Warnecke et al., 2007).
Compared to the Soudan Mine study, where two
communities that were proximately close had distinctly
different metabolisms, Dinsdale et al. (2008) found
characteristic metabolisms in metagenomes that were
similar environmentally. Although all metagenomes were
functionally diverse (number of metabolic processes
represented), the evenness or relative abundance of a
particular metabolism depended on the biome origin
indicating that the frequency of a gene encoding a
particular metabolic function reflects its relative importance
in an environment.
RUMEN METAGENOMICS
Limited varied studies have been conducted on the
rumen metagenome, the most detailed of which today are
those that have been conducted in our laboratory. In the
earliest of these so-called metagenomic studies of
ruminants, Ferrer et al. (2005) described “Novel hydrolase
diversity retrieved from a metagenome library of bovine
rumen microflora”. For this study, the rumen samples were
derived from one New Zealand dairy cow and the
metagenomic expression library created in a bacteriophage
lambda ZAP phagemid vector (Stratagene). Inserts were
screened for esterase, cellulose, and amylase-like activities.
The 22 clones with hydrolytic capabilities (Ferrer et al.,
2005) included 12 esterases, 9 endo-beta-1,4-glucanases,
and 1 cyclodextrinase. Eight of these were being observed
for the first time, and highlight the potential of the rumen
for holding novel enzymatic and biochemical activities. In
a subsequent study, this same rumen metagenomic library
was used to identify RL5, a gene coding for a novel
polyphenol oxidase whose recombinant protein revealed an
ability to work on a range of substrates including 2,6dimethoxyphenol, tetramethylbenzidine, and phenol red
(Beloqui et al., 2006). The signficance of this “laccase” was
the lack of similarity to other previously described laccases
and wide substrate affinity. Most recently, this group
described a novel amylase from the same rumen
metagenomic library. The enzyme had activity towards
maltooligosaccharides,
cyclomaltoheptaose,
cyclomaltohexaose, cyclomaltooctaose, soluble starch,
amylose, pullulan and amylopectin, and it was further
characterized as being a cyclomaltodextrinase (Ferrer et al.,
2007).
Liu et al., (2009) described the isolation and
characterization of lipases from a rumen metagenomic
library. The rumen library was constructed from Holstein
cows. One of the lipases showed highest similarity to a
carboxylesterase from Thermosinus carboxydivorans Nor1.
Both recombinant lipases were thermally unstable and had
high activity to laurate, palmitate, and stearate (Liu et al.,
2009). Studies with rumen samples from buffalo have also
revealed novel genes that encode acidic cellulases (Duan et
al., 2009). Here, the authors created metagenomic libraries
and screened for functionalities to identify 61 independent
clones with cellulase activity.
We published the largest metagenomic gene set from
ruminant species to date (Brulc et al., 2009). For this
study, a combination of sequencing, assembly, phylotype,
and subsystems-based analysis was used to study
15
in the rumen. The conversion of plant biomass to products
of nutritional value is carried out by a consortium of
microbes. The complex chemical processes involved in this
process are not carried out by any one species. The
differences in microbial abundance, and therefore
metabolic contributions to the digestive tract, may indicate
that highly represented bacteria are more significant
contributors to nutrient utilization, animal health and wellbeing, and food safety; however, less abundant organisms
are no less important because they may make significant
and (or) unique metabolic contributions in these complex
environments. A better understanding of the metabolic
potential in the rumen microbial community and how we
might utilize this potential is arising from metagenomics
and offers possibilities for the discovery of novel metabolic
pathways and enzymatic functions that could have
profound implications for animal production. Moreover,
rumen metagenomics will provide strategies that have
significant environmental benefits, such as reduced fecal
waste and a decrease in ruminant-derived methane
production. Metagenomic-mediated information has the
profound potential to increase feed efficiency, thereby
reducing the amount of feed per animal and increasing the
resulting products of commercial interest. As we continue
to use metagenomic approaches to investigate the rumen,
we will learn more about the previously uncultured species
that reside there, their contributions to the microbiome and
their potential to improve the overall ruminant farming
process. By doing so, metagenomic approaches will
contribute productively to animal growth and nutrient
utilization and foster a positive image for the agricultural
sector with respect to the environment.
SUBTRACTIVE HYBRIDIZATION
Another approach to identify functional genomic
differences between rumen microbial communities,
regardless of phylogenetic origin, is the use of suppressive
subtractive hybridization (SSH, also referred to as
suppression subtractive hybridization). Suppressive
subtractive hybridization enables the identification of
unique microbial DNA sequences present in one
metagenomic sample relative to another, by subtracting the
ones that are common to both, and amplifying the unique
sequences. The technique was originally developed to
amplify rare mRNA transcripts for full representation in
mRNA libraries from eukaryotic tissues (Bonaldo et al.,
1996). Suppressive subtractive hybridization introduced the
use of specially designed adaptors. When these adaptors are
ligated to themselves or to small DNA fragments then the
secondary structure (a “panhandle”-like structure)
introduced by their sequence content effectively inhibits the
PCR amplification in the final preparation. This efficiently
decreases the detection of background fragments and
allows the identification of larger, differential DNA of
sufficient “informational” length. The amended version of
SSH was demonstrated to compare the genomes of two
strains of Helicobacter pylori (Akopyants et al., 1998).
We applied the SSH technique to a mixed microbial
community to compare the metagenomes of microbiome
from rumen samples (Galbraith et al., 2004). Our SSH
study was conducted with samples of total genomic DNA
obtained from rumen fluid samples of 2 steers fed hay.
Tester-specific SSH fragments were found in 95 of 96
randomly selected clones. This metagenomic approach was
able to expose an unexpectedly large difference in Archaeal
community structure between the rumen microbial
populations of 2 steers fed identical diets and housed
together. This work was conducted using very small Sanger
DNA sequence sets, and we anticipate that when applied to
deep 454 pyrosequencing samples, that unique metabolisms
and taxonomies will be revealed.
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SUMMARY
The advent of next generation cost-effective highthroughput sequencing is allowing the application of
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dissimilar domains linked by an asparagine/glutaminerich sequence. Mol. Microbiol. 6:1013-1023.
Ziemer, C. J., R. Sharp, M. D. Stern, M. A. Cotta, T. R.
Whitehead, and D. A. Stahl. 2000. Comparison of
microbial populations in model and natural rumens
using 16S ribosomal RNA-targeted probes. Environ.
Schloss, P. D. and J. Handelsman. 2008. A statistical
toolbox for metagenomics: assessing functional
diversity
in
microbial
communities.
BMC
Bioinformatics 9:34.
Shinkai, T. and Y. Kobayashi. 2007. Localization of
ruminal cellulolytic bacteria on plant fibrous materials
as determined by fluorescence in situ hybridization and
real-time PCR. Appl. Environ. Microbiol. 73:16461652.
Sogin, M. L., H. G. Morrison, J. A. Huber, D. M. Welch, S.
M. Huse, P. R. Neal, J. M. Arrieta, and G. J. Herndl.
2006. Microbial diversity in the deep sea and the
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(USA) 103:12115-12120.
Stahl, D. A., B. Flesher, H. R. Mansfield, and L.
Montgomery. 1988. Use of phylogenetically based
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Tajima, K., R. I. Aminov, T. Nagamine, H. Matsui, M.
Nakamura, and Y. Benno. 2001a. Diet-dependent shifts
in the bacterial population of the rumen revealed with
real-time PCR. Appl. Environ. Microbiol. 67:27662774.
Tajima, K., R. I. Aminov, T. Nagamine, K. Ogata, M.
Nakamura, H. Matsui, and Y. Benno. 1999. Rumen
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Tajima, K., S. Arai, K. Ogata, T. Nagamine, H. Matsui, M.
Nakamura, R. I. Aminov, and Y. Benno. 2000. Rumen
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Tajima, K., T. Nagamine, H. Matsui, M. Nakamura, and R.
Aminov. 2001b. Phylogenetic analysis of archaeal 16S
rRNA libraries from the rumen suggests the existence
of a novel group of archaea not associated with known
methanogens. FEMS Microbiol. Ecol. 200:67-72.
Tringe, S. G., C. von Mering, A. Kobayashi, A. A.
Salamov, K. Chen, H. W. Chang, M. Podar, J. M. Short,
E. J. Mathur, J. C. Detter, P. Bork, P. Hugenholtz, and
E. M. Rubin. 2005. Comparative metagenomics of
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Turnbaugh, P. J., M. Hamady, T. Yatsunenko, B. L.
Cantarel, A. Duncan, R. E. Ley, M. L. Sogin, W. J.
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Henrissat, A. C. Heath, R. Knight, and J. I. Gordon.
2009. A core gut microbiome in obese and lean twins.
Nature 457:480-484.
Turnbaugh, P. J., R. E. Ley, M. Hamady, C. M. FraserLiggett, R. Knight, and J. I. Gordon. 2007. The human
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Turnbaugh, P. J., R. E. Ley, M. A. Mahowald, V. Magrini,
E. R. Mardis, and J. I. Gordon. 2006. An obesityassociated gut microbiome with increased capacity for
energy harvest. Nature 444:1027-1031.
Tyson, G. W., J. Chapman, P. Hugenholtz, E. E. Allen, R.
J. Ram, P. M. Richardson, V. V. Solovyev, E. M.
Rubin, D. S. Rokhsar, and J. F. Banfield. 2004.
Community structure and metabolism through
reconstruction of microbial genomes from the
environment. Nature 428:37-43.
20
21
Incomplete
Closed
Bovine
Bovine
Bovine
Elephant
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Mannheimia succiniproducens
Methanobrevibacter ruminantium M1
Orpinomyces OUS1
Piromyces E2
Prevotella ruminicola 23
Prevotella bryantii B14
Ruminococcus albus 8
Ruminococcus flavefaciens FD-1
Selenomonas ruminantium subsp. lactilytica
TAM642
Wolinella succinogenes DSM1740
Draft (>10x)
Draft (8x)
Closed
In progress
In progress
Closed
Closed
Closed
2.11
n/a
4.4
4.2
3.8
3.62
n/a
n/a
2.9
2.31
3.84
4.14
Genome
size (Mb)
2.32
48.5
n/a
45
n/a
39
48
~15-20
~15-20
33
42.5
48
40
% GC
content
46
Joint Genome Institute, California, USA
2
AgResearch Limited, Palmerston North, New Zealand
3
The North American Consortium for Genomics of Fibrolytic Rumen Bacteria
4
Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
5
Pastoral Greenhouse Gas Research Consortium, Wellington, New Zealand
6
Pacific Northwest National Laboratory, Washington, USA
7
National Institute of Technology and Evaluation, Tokyo, Japan
8
MaxPlanck Institute, Tübingen, Germany
1
Draft (29x)
Bovine
Fibrobacter succinogenes S85
Closed
Bovine
Butyrivibrio proteoclasticus B316
Closed
Status
Bovine
Origin
Actinobacillus succinogenes 130Z
Species
Table 1. Rumen microbial genome sequencing initiatives
5
MPI
BX571656
E
n/a
NITE7
8
ACOK00000000G
NACGFRB3
FibRumBa
F
3
NACGFRB
FibRumBaF
NACGFRB3
n/a
FibRumBaF
1
n/a
CP001719
F
NACGFRB3
PNWNL / JGI
6
PNWNL6 / JGI1
AgResearch / PGGRC
2
AE016827G
KAIST4
n/a
FibRumBaF
NACGFRB3
AgResearch
CP000746G
JGI1
2
Available
Sequencing institute
Baar et al. (2003)
n/a
Berg Miller et al. (2009)
n/a
Purushe et al. (unpublished data)
Purushe et al. (unpublished data)
n/a
n/a
Leahy et al. (2010)
Hong et al. (2004)
Morrison et al. (unpublished data)
Kelly et al. (unpublished data)
n/a
Reference
J. M. Brulc, C. J. Yeoman, K. E. Nelson, and B. A. White
Notes
July 9 and 10, 2010
PRACTICAL APPLICATION OF MODERN RUMEN MICROBIOLOGICAL TECHNIQUES
TO GRAZING RUMINANTS
S. L. Lodge-Ivey1
Department of Animal and Range Science, New Mexico State University, Las Cruces 88003
(4 yr average = approximately $205/cow; Miller et al.,
2001). Therefore, increasing efficiency of forage utilization
by grazing ruminants will reduce feed costs and augment
sustainable rangeland agriculture production systems
because ruminants are uniquely capable of converting
rangeland forage into consumable products.
Domestication of herbivores dates back as far as
10,000 years ago, and herbivores still provide products that
are useful in our daily lives. Cattle, sheep, and goats have
the ability to convert forages into high value protein
products for human use and thereby convert lands
unsuitable for crop production to usable lands. Over the
past 50 years, ruminant animal production on grazing lands
has become more efficient due to improvements in genetic
selection, as well as increased knowledge of feed utilization
and land management. Improvements in production of
grazing ruminants resulting directly from manipulations of
the rumen microbial ecology have yet to come to fruition.
Supplementation of protein and energy has proven to be a
useful tool in increasing performance by meeting nutrient
deficiencies inherent in the forage base. The increased
production efficiency due to supplementation is related to
changes in VFA profile and ruminal NH3-N production,
which likely result in changes of the microbial population
dynamics. Although the application of modern tools such as
pyrosequencing, metagenomics, and the many possible
combinations of next-generation sequencing technologies
have allowed for an increased understanding of microbial
diversity and detection of novel genes involved in nutrient
processing (Brulc et al., 2010), direct linkages between
shifts in microbial ecology that can be detected with
sophisticated molecular biology techniques and animal
performance are limited. The objective of this paper is to
share ideas germane to the application of modern
techniques used to study rumen microbial ecology of
grazing ruminants, including techniques for sampling and
selection of appropriate modern methodology for a given
research question.
ABSTRACT: Domestication of herbivore species dates
back as far as 10,000 years ago, and herbivores today still
provide products that are useful in our daily lives.
Production of ruminants has changed over the past 50 years
due to improvements in genetic selection and technical
advancements in feeding and land management practices.
Most ruminant animal production systems around the world
use fibrous feeds as the bulk portion of ruminant diets. High
levels of forage in ruminant diets results in less efficient
production compared with feeding diets with greater levels
of concentrate. Further characterization of the rumen
microbial communities may eventually lead to enhanced
production efficiency of grazing ruminants. Conventional
culture-based
methods
of
enumerating
rumen
microorganism are being replaced by culture independent
methods that rely on analysis of nucleic acids extracted
from ruminal samples. Sampling location within the rumen,
liquid vs. particulate fraction, and sample handling are all
areas of consideration when collecting ruminal samples for
analysis with DNA- and RNA-based techniques. This paper
is intended to provide suggestions for methods to collect
rumen samples from grazing ruminants and selection of
molecular techniques to answer questions about rumen
microbial communities relevant to grazing ruminant
production systems.
INTRODUCTION
The majority of ruminant animal production systems
around the world incorporate fibrous feeds into diets. The
US has approximately 1.8 billion acres of land deemed
suitable for food and fiber production and 74% of this area
is classified as forest, range, or pasture land (FAO, 2009).
Approximately 50% of the land area in North America is
rangeland suitable for grazing (Rinehart, 2008). Almost half
of the global carbon fixed annually by photosynthesis is
incorporated into plant cell walls making rangeland forage
the most renewable carbon source on the planet (Krause et
al., 2003). Rangelands and grasslands have the capability of
producing millions of tons of energy annually (Rinehart,
2008). Rate of gain and feed efficiency are less when
ruminant animals are fed high levels of forage vs. being fed
high levels of cereal grains. Additionally, supplemental
feed costs are the greatest factor influencing profit of
commercial beef cows operations, accounting for over 63%
of the variation in total annual cow costs
RUMEN SAMPLING
Location
Obtaining quality data from the application modern
techniques used to study rumen microbial ecology is
dependent upon collecting a representative sample of
ruminal contents. The composition of the rumen microflora
is known to be responsive to changes in diet, antibiotic
usage, and age and health of host animal, and varies with
1
24
season, geographical location and feeding regimen
(Kocherginskaya et al., 2001). Ruminal samples can be
obtained via ruminal cannula, oral lavage, or at slaughter.
Choice of ruminal sampling method and frequency of
sampling grazing ruminants are complicated by the fact that
experimental animals are not housed in pens and may be
grazing in pastures encompassing very large areas. Potential
impact that sample collection could have on animal
foraging behavior, pasture utilization, loss of animal value,
and limitation of experimental animals due to rumen
cannula implantation should also be considered during the
study design phase. Sample handling after collection is
another consideration due to the potential remoteness of
pasture locations and animal location within the pasture
coupled with the need to obtain intact high quality DNA,
and in some cases, RNA.
Sampling location within the rumen has been observed
to influence ruminal pH, VFA, and NH3-N (Leedle et al.,
1982); however, few publications have compared sampling
location using molecular biology techniques. Li et al.
(2009) used ruminally cannulated dairy cows fed a lactation
diet and collected ruminal fluid from 5 locations in the
rumen at 3 time points. Ruminal fluid was strained and
analyzed for VFA, pH, NH3-N, and microbial diversity
using PCR-denaturing gradient electrophoresis (DGGE).
Those authors reported that detectable bacterial structure
was highly conserved among different locations in the
rumen and, while the quantity of individual bacterial
species may change over time, the changes were not
correlated with pH, VFA, or NH3-N.
The observations by Li et al. (2009) are in contrast to
Leedle et al. (1982), who showed that total bacteria counts
and fermentation acids increased after feeding. This
represents a fundamental difference between culture studies
and some DGGE studies. Leedle et al. (1982) used different
carbohydrate medium and replicate plating techniques to
assess diurnal variation in direct and viable cell counts in
cattle fed high-forage or high-concentrate. Li et al. (2009)
quantified total bacteria using DGGE, which relied on DNA
extracted from ruminal fluid and a PCR primer set for
amplification of the V2-V3 region of the16S rDNA gene.
The similarity analysis relied on quantifying total number
of bands in each sample and compared the resulting
patterns to the other samples. The total number of bands is
related to the number of dominant phenotypes (Muyzer et
al., 1993) and this type of bacterial quantification method
does not take into account the density of each bacterial
species.
Density of bacterial species can be quantified by
analyzing DGGE data for band intensity or quantitative
real-time PCR (qRT-PCR). Real-time PCR was used by Li
et al. (2009) to determine bacterial density of 8 species of
ruminal bacteria and confirmed that the quantity of
individual bacterial species may change over time;
however, this was not correlated with ruminal pH, VFA, or
NH3-N. These data were consistent with those of Tajima et
al. (2001), who pooled samples from a number of locations
within the rumen of dry dairy cows and used qRT-PCR to
quantify 13 bacterial species of rumen bacteria during
abrupt dietary transitions from hay to a grain diet. Tajima et
al. (2001) observed that cellulolytic bacteria decreased 10-
fold during diet transition and amylolytic bacteria increased
7 to 375-fold during the same diet transition.
Collection samples from ruminants via oral lavage to
characterize rumen microbial ecology has been used in a
number of studies. Oral lavage allows for sampling of a
greater number of animals including wildlife and grazing
ruminants with the added advantage that the value of the
animals is not lost due to the surgical implantation of a
ruminal cannula. The drawback is the inability to sample
the rumen from a variety of locations. However, LodgeIvey et al. (2009) reported that collecting ruminal samples
by oral lavage vs. rumen cannula did not affect DGGE
profiles, VFA, or NH3-N.
Collecting ruminal samples at slaughter is commonly
used to study the gut contents of wildlife and feedlot
animals. Samples collected at slaughter have been used to
characterize the microbial diversity of the rumen
microbiome of Norwegian reindeer (Sundset et al., 2009),
two geographically separated sub-species of reindeer
(Sundset et al., 2007), and feedlot steers (Guan et al., 2008).
Ruminal contents should be collected immediately after
slaughter (Sundset et al., 2007, 2009; Guan et al., 2008; Li
et al., 2009), particularly if pH, VFA, and NH3-N are to be
analyzed because these values could be inflated as a result
of reduced transport of metabolites out of the rumen when
the animal is slaughtered.
Particulate vs. Liquid Fraction
The question of which fraction of the rumen material
should be sampled is related to sampling location, but is
further refined to address sampling the rumen liquor, fiber
particulates, or whole rumen fluid fractions. Results from
electron microscopy indicate that there are a large number
of bacteria, protozoa, and fungi attached to undigested feed
particles in the rumen. Forsberg and Lam (1977) estimated
that 75% of microbial ATP was associated with fiber
particulate fraction. Likewise, Craig et al. (1987) observed
that 70 to 80% of microbial organic matter in whole rumen
contents was associated with the particulate phase and that
particulate-associated microbial organic matter was greatest
within 1 h after feeding.
Animal Variation
Animal variation may be a contributor to bacterial
diversity noted when using molecular biology techniques.
Microbial communities present in the rumen are very
complex and individual animals fed similar diets and
housed together may have differences in their rumen
microbial community (Kocherginskaya et al., 2001;
Regensbogenova et al., 2004). Regensbogenova et al.
(2004) observed animal-to-animal variation when
quantifying ruminal ciliates using DGGE, but no difference
was observed when samples were collected from single
sheep at differing time points. Individual animal seemed to
impact DGGE grouping rather than species or sampling
method (Lodge-Ivey et al., 2009). Likewise, Li et al. (2009)
noted low similarity of detectable bacterial diversity among
rumen digesta samples collected from different animals fed
the same diet. However, diet seems to affect the magnitude
25
structure from genomic DNA-based analysis does not take
into account the viability or metabolic state of the
community members. In order to describe metabolic
activity of a microbial community, RNA-based methods are
more suitable (Kang et al., 2009).
The production of rRNA produced by microbial cells is
directly correlated with the growth activity of bacteria
(Wagner, 1994). Ribonucleic acid is much more susceptible
to attack by nuclease than DNA and enzymatic degradation
of RNA is probably the greatest source of loss encountered
with RNA isolation procedures. In addition to the
precautions outlined for DNA isolation, samples for RNA
extraction should also be collected into to containers treated
with diethylpyrocarbonate (DEPC) and all reagents and
glassware to be used during RNA isolation should be
treated with a 0.1% DEPC solution followed by
autoclaving. Also, all samples to be used for RNA isolation
should be frozen and stored at -80o C. Kang et al. (2009)
evaluated 4 methods to extract RNA for ruminal and fecal
samples for efficiency and purity of RNA. It was found that
treating samples with a dissociation solution followed by
RNA extraction with Trizol after bead beating produced a
higher quality and quantity of RNA when compared with
similar methods using phenol/chloroform.
of animal-to-animal variation detected among rumen
microbial communities. Kocherginskaya et al. (2001) found
that steers fed a 70% concentrate diet varied more from one
another than cattle consuming 70% medium-quality alfalfa
hay diet. The above observations also could be related the
limited number of animals (typically 2 to 4 per treatment)
sampled for rumen microbial community characterization.
Thus, it is important to recognize that it is often difficult to
separate animal variation from experimental variation due
to the small number of animals typically used to
characterize rumen microbial ecology.
Sampling of few animals is common and is most likely
related to cost of sample analysis (e.g., refer to Brulc et al.,
2010). As molecular techniques for studying rumen
microbial ecology evolve, sampling of just a handful of
animals will not be adequate to answer questions related to
the interaction of the rumen microbial population and
animal performance. Fortunately, technologies such as
pyrosequencing are now available which provide highthroughput capacity and continuous sequence reads from
250-500 bp in a cost effective manner (McSweeney et al.,
2009). Greater cost savings is also achieved with
phylogenetic based studies where researchers can take
advantage of “barcoding” individual samples by
incorporating unique base pair tags into the PCR primers
for each sample then pooling the samples before
sequencing. This allows for greater statistical power in
experimental design and data interpretation where
previously individual samples were pooled before
sequencing. Kunin et al. (2008) explained that even though
next generation sequencing allows for metagenomic
analysis of complex microbiomes leading to phylogenetic
descriptions of the system, it is still difficult to assign
functional roles of individual bacteria within a metagenome
due to small read lengths and the complexity of the
community.
APPLICATION OF TECHNIQUES
Areas of interest for researchers studying grazing
animals may include influence of season (diet quality),
nutrient supplementation, impact of ingestion of toxic
plants, surveying rumen microbial ecology of efficient vs.
inefficient grazing ruminants, and determination of
microbial ecology of wildlife. Nucleic acid-based
technologies can now be employed to examine microbial
community structure primarily through the use of small
subunit (SSU) rRNA gene analysis including 16S and 18S
rRNA and functioning of complex microbial ecosystems
can be described by combined analysis of multiple genomes
(metagenomics) and message from expressed gene (mRNA)
in the rumen.
Before choosing a molecular technique for studying the
rumen microbial ecology of grazing ruminants, the
researcher must decide if the goal is to quantify, indentify,
or monitor microbial communities and (or) gene function
because this will determine the techniques or combination
of techniques that should be utilized. Also, common
ruminant nutrition indicator analyses, such as ruminal pH,
VFA, and NH3-N, and animal production responses should
be included in microbial ecology experiments. Because
most nucleic acid-based techniques rely on bulk-extracted
nucleic acids, the assumption is that recovered 16S rRNA
sequences relate to an in vivo active member of the
microbial community. That may not always be the case and
shifts in microbial communities may be interesting, and
certainly within detection sensitivity of many current
techniques, but until there is linkage between microbial
communities and animal performance, some of these data
will not contribute to enhancing production efficiency of
grazing ruminants. An example of this was summarized by
McSweeney et al. (2009) where Proteobacteria had
commonly been believed to make up less than 2% of the
Sampling Handling after Collection
The steps following sample collection are crucial to
obtaining high-quality DNA or RNA. Enzymes responsible
for the degradation of RNA and DNA are ubiquitous in
most environments as well as in microbial cells and may be
responsible for the significant loss of nucleic acids during
purification. Standard precautions such as wearing gloves
while collecting samples, immediate chilling of ruminal
samples by placing samples on ice, dry ice, or in liquid N
and autoclaving of reagents will be adequate to preserve
DNA for later extraction. Samples for DNA extraction can
either be extracted immediately or frozen at -20oC for later
processing. Isolation of nucleic acids from ruminal fluid is
not a trivial matter; therefore, extraction of community
DNA representing the complete diversity of rumen
microbial communities is required. Methods that increase
DNA yield will in theory translate to better representation
of community DNA. Yu and Morrison (2004) reported a
repeated bead-beating plus column extraction method that
is commonly used by most laboratories today. Generally,
DNA from rumen digesta can be used as a template for
PCR amplification of 16S rRNA genes and subsequent
community analysis. However, inferred community
26
uncultured bacteria, thereby making isolation more
efficient.
A number of fibrolytic bacteria in the rumen are
uncultured. For example, 77% of the fiber-associated
bacteria community members are uncultured (Koike et al.,
2003). Koike et al. (2010) focused on two groups of
uncultured fiber-associated bacterial groups, unculutured
group 2 and 3 (U2 and U3, respectively). Those authors
used qRT-PCR and fluorescent in situ hybridization (FISH)
to characterize members of U2 and U3. To enrich for
members of U2, rice straw or cellulose was incubated in the
rumen in nylon bags. After 5 days, samples incubated in the
rumen were inoculated into enriching medium described by
Bryant and Burkey (1953). The enrichment was then
serially diluted and transferred to agar containing rice
straw. Isolated colonies were picked and characterized
using PCR. Koike et al. (2010) were able to isolate two
strains of bacteria that seem to be important to
hemicellulose degradation in the rumen. This research is an
example of how nucleic acid-based techniques can be
combined with traditional culturing methods to attain a
greater understanding of the rumen microbial community.
These techniques may also be useful to isolate bacteria
involved in toxic plant degradation in the rumen.
Similar techniques used to study bacterial communities
in the rumen can also be utilized to study protozoal and
fungal populations. Techniques such as qRT-PCR and
DGGE have been used to monitor the abundance and
diversity of several genera and species of rumen protozoa
(see McSweeney et al., 2006 for extensive review).
Anaerobic fungi in the rumen also can be studied by qRTPCR (Edwards et al., 2008); however, qRT-PCR does not
discriminate between the fungal genera and is therefore not
useful to characterize diversity. Edwards et al. (2008)
proposed that rumen fungal community characterization is
better studied using the technique of automated ribosomal
intergenic spacer region (ARISA). Furthermore, study of
temporal interrelations between diets and different members
of the rumen fungi population was possible with ARISA
(Edwards et al., 2008).
microbial community based on rDNA analysis. Analysis
based on RNA showed that Proteobacteria represented
28% of the clones in a 16S rDNA library. Members of
Proteobacteria group include a variety of pathogenic
bacteria (Escherichia, Salmonella, and Vibrio) and many
bacteria involved in N fixation. The influence of the nowrecognized Proteobacteria group on rumen metabolism is
not known.
The rumen microbiome represents a huge resource with
great potential. Given the current political climate and
public perception in regards to genetically modified
organisms searches for natural microorganisms that are
capable of toxin degradation and efficient fiber degradation
are of utmost importance. Guan et al. (2008) reported that
steers with efficient residual feed intake had similar DGGE
clusters when compared with inefficient steers. Efficient
steers also had nearly twice the concentration of total VFA
in their ruminal contents. The next step with this research
would be to apply metagenomic sequencing or RNA
analysis to determine active metabolic genes and greater
microbial community detail.
Emphasis also should be placed on studying microbial
communities not just individual isolates. Degradation of
pyrolizidine alkaloids common in tansy ragwort (Senecio
jacobaea) was found to be performed by a consortium of at
least 6 microbes isolated from the rumen of sheep (LodgeIvey et al., 2005). Individual isolates of the pyrolizidine
alkaloid degrading consortium were not able to degrade the
pyrolizidine alkaloids found in tansy ragwort. However,
there is evidence in the literature that shows individual
bacteria can be responsible for detoxification of toxic
plants. Synergistes jonesii is able to detoxify
dihydroxypyridine, a toxin found in leucaena (Allison et al.,
1992). More recently, a bacterium related to Synergistes
jonesii was isolated from the cattle rumen that degraded
monofluoroacetate
(McSweeney
et
al.,
2009).
Monofluoroacetate is a plant toxicant found in 40 species of
plants around the world and is responsible for poisoning
thousands of livestock (McCosker, 1989). Mining genes of
unique rumen bacteria should provide information into their
nutritional and physiological requirements making them
good candidates for management programs to optimize
their detoxifying capacity in the rumen. This may become
more important as interest increases in using ruminants to
control and minimize invasion of toxic or opportunistic
plants that alter native communities as an alternative to
costly management methods, such as fire, herbicides, or
mechanical clearing.
Monitoring and isolating of uncultured bacterial strains
from the rumen may be of interest to researcher dealing
with grazing ruminants. Scenarios of interest may include
observed tolerance to toxic plants, greater production
efficiencies or other unique characteristics in populations of
domesticated or wild ruminants. One of the main
drawbacks to employing nucleic acid-based techniques is
that if a bacterium or consortium of bacteria of interest is
identified, the bacteria are not isolated using culture
independent techniques. However, all is not lost because by
classifying bacteria into phylogenetic groups may provide
insight into nutritional and physiological requirements of
CONCLUSIONS
The future of rumen microbiology research is dependent
upon the application of molecular research technologies.
There is a need to apply modern technologies to improve
production efficiency of grazing ruminants. It is important,
however, not to become so caught up in the business of
surveying rumen microbial communities that the real
purpose of the research is forgotten. Focusing attention on
how application of modern approaches in rumen
microbiology can lead to lowering production input
requirements and (or) increasing output of ruminant animal
products will augment sustainability of production systems
that rely on rangeland resources.
LITERATURE CITED
Allison, M. J., W. R. Mayberry, C. S. McSweeney, and D. A.
Stahl. 1992. Synergistes jonesii, gen. Nov., sp.. nov.: A
27
Li, M., G. G. Penner, E. Hernanadez-Sanabria, M. Oba, and
L. L. Guan. 2009. Effects of sampling location and time,
and host animal on assessment of bacterial diversity and
fermentation parameters in the bovine rumen. J. Appl.
Microbiol. 107:1924-1934.
Lodge-Ivey. S. L., J. Browne-Silva, and M. B. Horvath.
2009. Technical note: Bacterial diversity in rumen fluid
samples collected via oral lavage or rumen cannula. J.
Anim. Sci. 87: 2333-2337.
Lodge-Ivey, S. L., M. S. Rappe, W. H. Johnston, R. E.
Bohlken, and A. M. Craig. 2005. Molecular analysis of
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28
PRACTICAL APPLICATION OF MODERN RUMEN MICROBIOLOGICAL TECHNIQUES
TO GRAZING RUMINANTS
S. L. Lodge-Ivey
Notes
July 9 and 10, 2010
KEYNOTE: ASSESSMENT OF INTAKE AND DIET COMPOSITION OF
GRAZING LIVESTOCK
H. Dove1
CSIRO Sustainable Agriculture National Research Flagship, G.P.O. Box 1600, Canberra, A.C.T. 2601, Australia
(or at least a sample of the ‘consumed diet’) together with
an estimate of total intake. In other words, what do grazing
livestock eat and how much? Unfortunately, measuring
these under field conditions has always been difficult,
mainly due to shortcomings in the available methods of
estimation. Additionally, a number of methods can
themselves disturb normal grazing behavior and thus the
very things they are attempting to measure. This can be a
major issue in rangeland environments.
Techniques for estimating diet composition and intake
have been extensively reviewed (Langlands, 1987; Dove
and Mayes, 1996; Mayes and Dove, 2000; Dove and
Mayes, 2005). Most were developed with ruminant
livestock grazing relatively simple swards and their
application to animals in heterogeneous environments
introduces further problems. As a generalization,
measurements can be based either on the plant biomass or
on animal-based measurements. The former, being based on
estimates of plant biomass before and after grazing, are
difficult to apply under continuous grazing and have the
added disadvantage of not providing estimates of intake for
individual animals. Sward-based measurements will thus
not be discussed further. Rather, this paper will concentrate
on selected animal-based techniques, especially markerbased techniques, used to estimate diet composition and
intake and will highlight some of the problems associated
with their use. The use of the words ‘diet composition’ is
deliberate because all these methods measure the outcome
(diet composition) rather than the process (diet selection),
though they may shed some light on the latter.
ABSTRACT: The use of many methods, but particularly
methods based on plant-wax markers, for estimating forage
intake, diet composition, and supplement intake in grazingbrowsing livestock is reviewed and discussed. The saturated
hydrocarbons (alkanes) of plant waxes are shown to be well
validated as markers for estimating intake. Alkane-based
estimates of intake have the advantage of accommodating
individual differences in digestibility and those arising from
supplement-forage interactions. Alkanes have also been
well validated for estimating diet composition and hence
the inputs required to estimate the intake of component
species. Further work is still needed on the factors that
influence fecal alkane recovery. It is probable that the
alkane method can discriminate fewer species in the diet of
grazing livestock than they encounter in complex plant
communities. A range of approaches for coping with this
problem is discussed, such as grouping species in diet
composition estimates, combining plant-wax methods, as
well as other techniques and the use of other plant-wax
markers as diet composition markers. Recent data show that
within the other components of plant wax, long-chain
alcohols and long-chain fatty acids are very useful
additional diet composition markers, which should allow
discrimination of a greater number of species in the diet.
Supplementary feeds fed to animals can be considered
another ‘species’ in the diet and the estimation of
supplement intake using plant hydrocarbons, as a special
case of the estimation of diet composition, is discussed.
Finally, in situations when supplement intakes are known,
alkane-based estimates of the supplement proportion in the
diet can be used to estimate the intake of all dietary
components, without the need to dose animals with
synthetic alkanes.
ESTIMATION OF INTAKE
Under field conditions, the ultimate goal would be to
estimate total forage intake and within that, to obtain some
estimate whole-diet digestibility. This would allow
comparison of nutrient supply with published nutrient
requirements (Dove et al., 2010). In an ecological sense, it
would also be useful to have an estimate of the intake of
forage components (plant species or plant parts) to establish
which components of the plant biomass are under the
greatest grazing pressure. Additionally, if grazing livestock
are also offered supplements, an estimate of supplement
intake would be invaluable for defining nutrient supply and
quantifying the interaction between supplement and
herbage intake.
Over the last 60 years, the most successful and widely
used methods for estimating intake by grazing livestock
have taken advantage of the relationship between intake (I),
Key words: intake, diet composition, ruminants, alkanes,
plant wax
INTRODUCTION
Grazing livestock usually consume a diet that differs in
terms of plant species, plant parts, and nutrient content from
the average of the available plant biomass. It follows that
their diet cannot be well quantified using plant-based
measurements alone. An accurate estimate of the nutrient
intake by grazing animals is more readily made if one has
an estimate of both the botanical composition of their diet
1
31
the digestibility of the whole diet (D) and the resultant fecal
output (F), that is:
(1)
I = F/(1-D)
With this general approach, note that intake is estimated
from separate estimates of fecal output and digestibility of
the consumed diet. There are errors attendant to the
estimation of both F and D, but the latter measurement is
the greater cause for concern (see below and also
Langlands, 1987; Dove and Mayes, 1996; Mayes and Dove,
2000).
when diet digestibility is high because the denominator of
the equation is so small (Langlands, 1987; Dove and
Mayes, 1996; Mayes and Dove, 2000).
Esophageal fistulated animals have often been used to
obtain samples ‘representing’ the diet of the test animals,
although there are increasing concerns about the
preparation, maintenance and use of such animals. In vitro
estimates of the digestibility of these samples can then be
conducted (Tilley and Terry, 1963; Jones and Hayward,
1975). These techniques must be calibrated against in vivo
estimates of digestibility. The possible problems associated
with this approach have been discussed in detail elsewhere
(Langlands, 1987; Dove et al., 2000; Mayes and Dove,
2000).
A major issue is the assumption that the samples
obtained by fistulated animals represent the diet of intact,
test animals; there is no absolute way to test this assumption
(Mayes and Dove, 2000). There are doubtless occasions
when the assumption is valid (Dove et al., 2000), but this
assumption is frequently questioned because esophageal
extrusa samples are usually collected over a time-span of
minutes, whereas the test animals may be grazing the area
for days or weeks. Moreover, the diet selected by the
esophageal-fistulated animals may differ from that of the
test animals because the former animals are surgicallyprepared, are of different sex or physiological state, or are
handled and managed differently from the test animals.
Raats et al. (1996) suggested that the possible problems
associated with the short collection period used to obtain
esophageal extrusa could be overcome by the use of a
cannula fitted with a remote-control valve, which in
foraging fistulated goats permitted the collection of a diet
sample over the course of a whole day, without interrupting
normal grazing behavior.
A further problem is that the single in vitro estimate of
digestibility is applied to all the test animals, despite the
strong likelihood that digestibility of a given diet differs
between individual animals (Dove et al., 2000) and that
these animals may differ markedly in sex, stage of growth,
reproductive status, intake level, and even species from the
animals used in the original in vitro-in vivo calibration. As
will be discussed below, one of the major advantages of
estimates of intake based on plant-wax components (e.g.,
alkanes) is that, like all estimates based on internal markers,
differences in digestibility between individuals can be
accommodated more adequately, especially if intake is
estimated following an estimate of diet composition in
individuals.
An added complication is that grazing animals are often
fed supplements when pasture quantity or quality is
inadequate. The in vitro methods cannot accommodate
possible interactions between forage and supplement during
digestion; for example, the possible reduction in the
digestibility of roughages when animals are also consuming
starchy components such as cereal grain (Dixon and
Stockdale, 1999).
It is important to realize that these are potential rather
than inevitable sources of error in estimating intake and that
accurate data of great scientific and practical importance
have been obtained using the Cr-in vitro procedure. Of the
problems listed, obtaining a representative sample of the
Estimating Fecal Output
Under field conditions, total fecal collection is difficult,
laborious, and can disturb normal foraging behavior. Fecal
output is therefore more commonly estimated from the
dilution in feces of a so-called ‘external’ marker,
administered orally. A wide range of external markers has
been used for the estimation of fecal output (see Table 2 in
Mayes and Dove, 2000) but to date, none has proved ideal.
Of these, Cr2O3 has been the most widely used and on many
(though not all) occasions has performed satisfactorily as a
fecal output marker (see examples in Langlands, 1987;
Mayes and Dove, 2000). It has been administered orally in
gelatin capsules, paper pellets, or by dosing with an intraruminal, controlled-release device (CRD; Luginbuhl et al.,
1994). Once fecal Cr concentrations have equilibrated, fecal
output is computed from Cr dilution in feces. Possible
errors in the use of Cr2O3 for fecal output estimation have
been reviewed by Langlands (1987), Parker et al. (1990),
Dove and Mayes (1991) and Dove et al. (2000). The use of
a CRD to administer Cr helped to overcome some of the
problems associated with cyclic changes in fecal Cr
concentration arising from once- or twice-daily dosing.
However, concerns about possible carcinogenic effects of
Cr2O3 have resulted in a major reduction in its use and the
Cr-CRD is no longer commercially available. Even if a
perfect fecal output marker were available, allowing fecal
output to be estimated with little error, a greater cause for
concern in the estimation of intake using equation (1) is
with the estimate of digestibility.
Estimating Whole-Diet Digestibility
A major difficulty in obtaining an accurate estimate of
whole-diet digestibility is that of obtaining a sample
representative of the diet actually consumed by the grazing
animal. Even if this is obtained, errors in the actual estimate
of digestibility of that sample can seriously reduce the
accuracy of the estimate of intake derived using equation
(1). Since fecal output is the numerator of this equation,
errors of ± 5% in the estimate of fecal output will result in
equivalent errors in the estimate of intake (Figure 1).
However, because the digestibility term is in the
denominator, for a diet of 75% digestibility, errors of ± 5
percentage units in the digestibility estimate will result in
errors between -16.7% and +25% in the estimate of intake.
If diet digestibility is 85% as in some lush pastures and
vegetative crops, the error range encompasses -25% to
+50%. Errors in digestibility are thus particularly serious
32
consumed diet is probably the greatest cause for concern
because it is the one over which the researcher has least
control. A possible advantage of the use of plant alkanes as
intake markers is that they can provide a check of the
assumption that fistulated and test animals are consuming
the same thing (see below and Dove et al., 2000).
To avoid some of the problems associated with in vitro
estimates of digestibility, an alternative approach is to use
indigestible diet components as indicators or ‘internal
markers’ of digestibility. In individual animals, the increase
in marker concentration between diet and feces provides an
estimate of diet indigestibility or the proportion of
consumed diet that is excreted in feces, calculated as
(marker concentration in feed)/(marker concentration in
feces). Digestibility is then calculated as (1 –
indigestibility). An advantage of the internal marker
approach is that it can be applied to animals consuming a
mixture of forage and supplement and will reflect the
interactions occurring between these components during
digestion. This approach requires that the intake and
internal marker concentration of the supplement be known.
Mayes and Dove (2000) reviewed the range of dietary
components that have been used as internal markers (see
their Table 3). The approach has had variable success. In
particular, many of the proposed markers are not
chemically discrete compounds with specific assays (e.g.,
‘chromogen’, potentially indigestible cellulose). This leads
to uncertainty about whether the marker assayed in feces is
chemically identical to that assayed in the feed and has
probably contributed to the variable results used with these
empirical internal markers.
The unsaturated aliphatic hydrocarbons (alkenes) are
relatively common in the floral parts of plants and also in
the leaves of a number of tree and shrub species. Most of
the alkenes are odd-chain monoenes, with chain lengths
ranging from 23 to 33 carbon atoms. Alkenes have potential
as diet composition markers (see below), despite their
relatively low fecal recoveries. This aspect is discussed in
more detail below in relation to the estimation of diet
composition.
Wax Esters and Free Fatty Acids and Alcohols
In most plants, wax esters of saturated, unbranched
LCFA and LCOH are the main component of plant wax,
with chain lengths in the range C32 to C64. Although both
LCOH and, more recently, LCFA have been considered as
diet composition markers (Ali et al., 2004; Ali et al.,
2005a,b; Fraser et al., 2006; Dove and Charmley, 2008; Lin
et al., 2008; Ferreira et al., 2009a), these assessments have
been based upon their total concentrations (free plus
esterified) because the analytical methods involve a
saponification step that cleaves the wax esters (Dove and
Mayes, 2006). As yet, neither the intact wax esters nor the
free LCFA or LCOH of plant wax have been evaluated as
markers in their own right.
The LCOH of plant wax are predominantly straightchain, saturated compounds with even-numbered chain
lengths up to C34 (Table 1), though in some gymnosperms,
LCOH such as 10-C29-alcohol are major components
(Table 1) and can function as diet composition markers. As
a rule, 1-C30-alcohol is found in much greater
concentrations in legumes than grasses (Table 1), and thus
can be a useful marker for distinguishing between them. A
further useful feature of the LCOH is that in some major
pasture grass species with very low alkane concentrations
(e.g., Phalaris aquatica), there are abundant quantities of
LCOH.
The LCFA of plant wax are also mainly straight-chain,
saturated compounds with even-numbered chain lengths up
to C34 (Table 2). In nutritional studies, LCFA with chain
lengths C20 to C34 have been used because they have high
fecal recoveries (Grace and Body, 1981; Ali et al., 2004;
Ferreira et al., 2009a) and because, unlike some shorter
LCFA, they derive exclusively from plant material. The
wide variation in LCFA and LCOH patterns in different
plants (Tables 1, 2) combined with their relatively high
fecal recoveries makes LCFA and LCOH potentially useful
diet composition markers (see below). There is also
potential to use them as intake markers.
USING PLANT CUTICULAR WAX MARKERS IN
STUDIES OF THE INTAKE OF HERBIVORES
The Nature of Plant Cuticular Wax
The cuticular wax on the external surface of plants is a
complex mixture of aliphatic lipid compounds, the chemical
composition of which differs greatly among different plant
species and, to a lesser extent, between different parts of the
plant (Dove et al., 1996, 1999; Piasentier et al., 2000; Smith
et al., 2001; Bugalho et al., 2004; Ali et al., 2005a,b; Lin et
al., 2008). The composition of plant waxes has been
comprehensively reviewed elsewhere (Dove and Mayes,
1991, 2005, 2006) and this paper will only discuss the main
plant wax compounds that have been used in or offer
potential for studies of herbivore nutrition. Principally, this
means aliphatic hydrocarbons, long-chain alcohols (LCOH)
and long-chain fatty acids (LCFA).
Using n-alkanes to Estimate Intake
Hydrocarbons
The saturated hydrocarbons were initially suggested as
digestibility markers (Mayes and Lamb, 1984), but are not
ideal for this purpose because they have fecal recoveries of
less than 100%. The conceptual leap that allowed their use
as intake markers was the realization by Mayes et al. (1986)
that if animals are dosed orally with a synthetic even-chain
alkane of similar recovery to a plant alkane of adjacent
carbon-chain length, then the fecal recovery of the markers
is no longer an issue. In essence, the plant alkane is
Aliphatic hydrocarbons are present in the waxes of most
higher plants, though only rarely as the main component; nalkanes are the most common hydrocarbons. They are
present as mixtures with chain lengths ranging from 21 to
37 carbon atoms, with over 90% by weight of the n-alkanes
being those with odd-numbers of carbon atoms; C29, C31
and C33 alkanes predominate in most forage or browse
species (Table 1).
33
LCOH coupled with either C32 or C36 alkane as the dosed
marker.
The alkane method for estimating intake offers a
number of advantages over other techniques:
1. Provided that the sample of vegetation analyzed for
alkanes is representative of the actual diet consumed, the
method gives intake estimates that can be regarded as
‘individual’, to the extent that they accommodate the
digestibility of the diet in individual animals. Intake
estimates are even more truly ‘individual’ if the
composition of the diet consumed by individual animals can
also be computed using alkanes (see below). Regardless of
whether alkane dose rates from an intra-ruminal alkane
CRD are measured directly in a sub-group of animals, or
assumed from the manufacturer’s specifications, note that
the application of a single alkane dose rate to all animals
(e.g., when using an alkane CRD) renders the intake
estimates less independent and thus less individual.
2. Dosing with an even-chain alkane such as C32 alkane
allows the estimation of intake, but does not itself allow an
estimate of whole-diet digestibility. If the animals are also
dosed with an external marker, fecal output could be
estimated and from this, whole-diet digestibility. The
marker Cr2O3 could be used but hexatriacontane (C36
alkane) can be used instead (Charmley and Dove, 2007;
Dove and Charmley, 2008), which is the reason for its
inclusion in the alkane CRD. This permits the estimation of
fecal output without having to conduct a separate analysis
for fecal Cr.
3. The method can be extended to the simultaneous
estimation of diet composition so that the intake of
individual plant species, plant parts or both can be
estimated (see below).
4. It can be used with animals that are also receiving
supplementary feeds, provided the alkane concentrations of
the supplement and supplement intake are known. Methods
for estimating supplement intake are discussed below.
5. The plant, supplement, and fecal marker alkane
concentrations are determined at the same time by the same
analytical procedure, which reduces analytical error and
bias. Because it is the ratio of the concentrations of alkanes
in feces which is used, it is in fact not even necessary to
obtain absolute fecal concentrations.
6. If the assumption of equal fecal recoveries of the alkane
pair is contravened, in comparative terms, the error in
estimated intake that arises is much less than that associated
with an equivalent error in the digestibility estimate in
equation (1) and more comparable with that associated with
errors in fecal output (Figure 1). It can be calculated from
equation (2) that if the fecal recoveries of the alkane pair
used to estimate intake do differ, each 1% difference in
recovery will result in a 1.1% error in the estimate of
intake, as shown in Figure 1. The error in intake would thus
be expected to be:
closely related to the differences in alkane fecal
recoveries;
much less than that associated with errors in the
digestibility estimate, and;
comparable with the intake errors arising from errors in
the fecal output estimate.
functioning as an internal marker to provide an estimate of
digestibility, while the dosed alkane functions as the
external, fecal output marker. However, in practice and in
contrast with equation (1) above, digestibility and fecal
output are not calculated separately but rather, for a given
alkane pair (plant odd-chain alkane i and dosed even-chain
alkane j), intake is calculated directly from the following
equation, which can be derived from equation (1) (Dove
and Mayes, 1991):
(2)
Dose ratej
Intake =
Fecal contentj * Recoveryi
* Herbage conenti - Herbage contentj
Fecal contenti * Recoveryj
There are several key points to note about equation (2):
1. It is ultimately the ratio of the fecal alkane
concentrations that is important, not their absolute
concentrations. This is of particular relevance to the issue of
possible diurnal variation arising from alkane dosing, in
that there can be diurnal variation in the concentration of
alkanei or of alkanej, and this diurnal variation could be a
problem if either alkane concentration was used on its own.
However, as Dove et al. (2002) demonstrated, there can be
diurnal variation in alkane concentrations but no diurnal
variation in the ratio of these concentrations, because their
diurnal variation is similar. This renders the alkane method
less susceptible to temporal changes in alkane
concentrations.
2. If the fecal recoveries of the plant alkane (recoveryi) and
the dosed alkane (recoveryj) are equal, they cancel out in
equation (2). As a result, errors associated with incomplete
fecal recovery also cancel out and an unbiased estimate of
intake will be obtained. This is in contrast to methods based
on the separate use of internal and external markers to
estimate digestibility and fecal output separately, where
errors arising from the incomplete recoveries of each
marker are additive.
3. It does not matter if the herbage also contains the dosed
alkane; this is allowed for explicitly in the denominator of
equation (2).
The work of Mayes et al. (1986) and many subsequent
studies (Dove and Oliván, 1998; Charmley and Dove, 2007;
Oliván et al., 2007; Elwert et al., 2008) demonstrated that,
in ruminant livestock, the fecal recovery of alkanes
increases in curvilinear fashion with carbon-chain length
and approaches complete recovery for alkanes C33 and
above (see discussion below under ESTIMATION OF
DIET COMPOSITION). As a result, adjacent alkanes
such as C32 and either C31 or C33 would be expected to
have incomplete but similar fecal recoveries. This means
that intake can be estimated accurately using, for example,
dosed C32 alkane and either C31 or C33 alkane from the
plant.
It is useful to note in passing that equation (2) does NOT
require that each of the two markers be alkanes. Intake
could equally well be computed using a plant LCOH or
LCFA, together with a dosed alkane, provided the
assumption of similar fecal recovery of dosed and natural
marker was met. Dove and Charmley (2008), for example,
achieved satisfactory estimates of intake using natural C28
34
to establish the exact release rate under the specific
conditions of the experiment (Ferreira et al., 2004). These
can be obtained either by monitoring CRD release in rumen
fistulated animals (Dove et al., 2002; Ferreira et al., 2004;
Oliveira et al., 2008) or by taking rectal grab samples
beyond the pay-out period of the CRD, to establish the time
when alkane release ceases (Charmley and Dove, 2007).
Despite occasional problems with their use (Charmley et
al., 2003), the alkane CRD greatly reduced the labor
requirement for estimating intake under field conditions.
Unfortunately, a commercial decision was made in early
2008 to stop producing the devices so that users of the
alkane technique now have to employ one of the other
dosing procedures described above or to devise another
means of administering the markers. One alternative
approach is to incorporate the markers into a supplement;
this approach is described in more detail below.
Oliván et al. (2007) and Charmley and Dove (2007) tested
this proposition by estimating intake in housed animals
from a range of alkane pairs, some of which differed
markedly in fecal recovery. Their recalculated data are
shown in Figure 2. Errors in estimated intake were linearly
related to the observed differences in fecal recovery of the
alkanes used in the estimate by an equation that did not
differ significantly from that expected on theoretical
grounds from equation (2).
Indoor validation studies have shown that the alkane
procedure provides reliable estimates of measured intake in
a range of animal species (Table 3) and across a wide range
of forage types. By definition, validation under field
conditions is not possible, because ‘actual’ intakes are
unknown and because alternative techniques for estimating
intake may be no better than and possibly inferior to the
alkane method with which they are being compared. In
grazing ewes, Dove et al. (2000) found that there was no
simple relationship between estimates of herbage intake
based on the alkane or Cr-in vitro methods. The relationship
between the two methods for estimating herbage intake was
affected both by the physiological state of the animals (late
pregnancy v. early lactation v. mid-lactation) and by the
level of intake. The major cause of the discrepancy between
the intakes estimated by the two different methods was that
in vitro estimates of digestibility did not adequately
represent in vivo digestibility. Their results indicate that in
contrast to the Cr-in vitro procedure, the alkane procedure
accommodated the real differences in digestibility between
animals.
A major consideration in using the alkane method is that
the procedures used for dosing of the even-chain alkane do
not generate cyclic temporal variation in the fecal alkane
ratio used in the intake calculation. Even-chain alkanes
have now been administered to sheep or goats using a wide
range of carrier matrices, including shredded paper (Mayes
et al., 1986; Valiente et al., 2003; Giraldez et al., 2004;
Oliván et al., 2007), cellulose powder in gelatin capsules
(Dove et al., 2000; Giraldez et al., 2004), paper bungs or
filter tips (Lewis et al., 2003; Giraldez et al., 2004), xanthan
gum suspension (Marais et al., 1996; Mann and Stewart
2003), or by spraying onto or mixing into dietary
components (Unal and Galsworthy, 1999; Elwert and Dove,
2005; Charmley and Dove 2007). The advantages and
disadvantages of most of these approaches have been
discussed by Dove and Mayes (2006). An intra-ruminal
alkane CRD was also developed, tested (Molle et al., 1998;
Dove et al., 2002; Ferreira et al., 2004), and commercially
released. In addition to containing C32 alkane for the
estimation of intake, the commercial alkane CRD also
contained C36 alkane as a fecal output marker, which also
allowed for an estimate of in vivo digestibility of the diet
consumed by the grazing animal (Decandia et al., 2000).
These devices greatly reduced labor inputs by delivering
alkane at a known rate over a period of 20 d after insertion
into the rumen; fecal sampling commenced 5 to 7 d after
insertion. They have been shown to give accurate estimates
of intake in sheep and cattle (see Table 3 and Molle et al.,
1998; Dove et al., 2002; Ferreira et al., 2004).
Under grazing conditions, the alkane CRD proved to be
a convenient mode of alkane delivery, but care was needed
ESTIMATION OF DIET COMPOSITION
Animal-based methods for estimating the species
composition of the diet have included: microhistological
examination of plant fragments (usually cuticle) in
esophageal extrusa, a gut compartment, or in feces; and the
estimation of carbon-isotope ratios in such samples or in
wool or hair, and; the use of plant-wax components as
markers. The merits of these various methods have been
reviewed extensively (Holechek et al., 1982; Dove and
Mayes, 1996; Mayes and Dove, 2000) and in the present
paper, the first two approaches will be discussed only
briefly.
Using Esophageal-Fistulated Animals
In domestic herbivores at least, esophageal-fistulated
animals have been used to estimate the composition of the
diet in terms of plant species (Salt et al., 1994; Dove et al.
2000), plant parts (Dove et al., 1999) or both (Dove et al.,
2003). Several authors have presented data to indicate that
fistulated and test animals have selected similar diets (Dove
et al., 2000), but this cannot always be assumed to be the
case. Before the use of alkanes as diet composition markers,
there was no way to test this assumption. However, the use
of alkanes to estimate diet composition now permits the
argument that if fistulated and test animals are consuming
the same diet, then their fecal alkane profiles should be
similar, or there should be similarity between the alkane
profile of esophageal extrusa collected by the fistulated
animals and the feces of the test animals (after correction to
100% fecal alkane recovery, see below). Dove et al. (2000)
adopted the latter approach to investigate whether
esophageal samples collected by castrate male sheep could
be assumed to reflect the diet of grazing ewes and
demonstrated very close correspondence between the
alkane profiles of esophageal samples from fistulated
animals and feces samples from either pregnant or lactating
ewes. This suggests that the ewes were selecting similar
diets. Similarities in the alkane-based estimates of diet
composition of fistulated and test animals were also
observed by Dove et al. (1999) in sheep grazing senescing
pasture.
35
combined with the 13C procedure (García et al., 1999) or
with behavioral studies based on active transponder systems
(Swain et al., 2008).
Microhistological Procedures
The microhistological approach is based upon the
identification of plant tissue fragments in prepared samples
of esophageal extrusa, stomach contents, or feces
(Holechek et al., 1982). Diet composition is then estimated
in terms of the proportion of these fragments coming from
each plant species. In order to relate diet composition and
intake, calibrations will usually be needed to convert these
proportions to an estimate of diet composition on a DM
basis.
The method has been used extensively with wild
animals, often using stomach contents from slaughtered
animals (Holechek et al., 1982). Fecal sampling is more
generally applicable and allows repeat sampling and larger
numbers of animals to be used, but also has major
disadvantages, principally the effect of differential
digestion of fragments arising from the different plant
species. There is also the problem that a large proportion of
fragments can remain unidentifiable, which reduces the
quantitative reliability of the method. Nevertheless, the
approach can be very useful for establishing the presence or
absence of a particular plant species in the diet and can thus
complement other methods, such as the use of plant wax
markers by ensuring that particular species are included in
the estimate of diet composition.
Using Plant Wax Markers for Diet Composition
Because plant species differ in their cuticular wax
profiles of alkanes, LCOH, and LCFA, in theory, any or all
of these compounds could be used to estimate diet
composition. In practice, the alkanes have been more
widely used as diet composition markers (Mayes and Dove,
2000) because they are easier to analyze, but there is
increasing use of the LCOH (Bugalho et al., 2004; Dove
and Charmley, 2008).
A major advantage of using plant wax markers is that
diet composition is estimated in the actual test animals so
that the resultant alkane concentrations in their consumed
diet can be calculated and used as inputs in equation (2) to
estimate herbage intake (Mayes et al., 1986). This obviates
the need to use esophageal-fistulated animals to obtain a
sample of ‘consumed diet’, and on scientific, labor, and
animal welfare grounds, this must be seen as a major
advantage of the alkane approach. However, it also means
that if intake is estimated using the diet composition
estimate to provide the ‘herbage’ alkane concentrations, the
accuracy of the intake estimate is itself a function of the
accuracy of the diet composition estimate. Care must thus
be exercised in estimating diet composition, or the estimate
of herbage intake may also be incorrect.
Stable Isotope Discrimination and Spectroscopic
Procedures
Tissue from plants that exhibit the C3 or C4 pathways of
photosynthesis contains different ratios of the 13C and 12C
isotopes of carbon. This difference (δ13C) can be used to
estimate the proportion of C3 and C4 herbage in the diet
based on esophageal samples (Coates et al., 1987) or fecal
samples (Jones et al., 1979). This is especially useful in
tropical grazing systems in which grass species make up the
main source of C4 plants whereas legumes, browse, and
forbs provide most of the C3 plants. Conversely, the method
is of limited use in colder environments, where there are
few C4 plants. When fecal samples are used, the effect of
differential digestion remains an issue and the method will
tend to underestimate the intake of plants of high
digestibility because less of their carbon remains in feces.
Moreover, the fecal carbon isotope ratio can be perturbed
by fecal endogenous carbon. The method cannot resolve the
diet to the species level, but is still useful in providing an
estimate of legume content of the diet, a key variable in
many management systems.
Spectroscopic procedures, such as laser-induced
fluorescence spectroscopy (Anderson et al., 1996) and
particularly fecal near infra-red reflectance spectroscopy
(F.NIRS; Kronberg and Grove, 2000; Garnsworthy and
Unal, 2004; Keli et al., 2008; Dixon and Coates, 2009) also
show promise for estimating diet composition. It seems
unlikely that the latter technique could be used to quantify a
suite of alkanes that differ only by single methyl groups
(S.W. Coleman, personal communication). However, there
seems to be great scope to combine alkane and
spectroscopic approaches for estimating diet composition
(Keli et al. 2008). Similarly, alkane-based methods can be
Using n-alkanes as Diet Composition Markers
There are marked differences in the alkane profiles of
plants, both in forage and in browse species (Table 1).
These differences have been used to estimate the species
composition of esophageal extrusa (Salt et al., 1994; Dove
et al., 2000) and have been validated and used as markers
for estimating the diet composition of sheep (Mayes et al.,
1994; Dove and Mayes, 1996; Kelman et al., 2003; Lewis
et al., 2003; Valiente et al., 2003; Lin et al., 2007), cattle
(Hameleers and Mayes, 1998; Ferreira et al., 2007a), goats
(Merchant, 1996; Brosh et al., 2003), and equines (Ferreira
et al., 2007a; Smith et al., 2007).
The use of alkanes (or any other plant wax marker) to
estimate the composition of a mixture relies on the principle
that the alkane profile in the mixture (be it esophageal
extrusa, digesta, or feces) must arise from some
combination of the alkane profiles in the components that
might contribute to the mixture of plant species and (or)
plant parts. If the number of marker alkanes at least equals
the number of diet components, then in theory at least, diet
composition could be calculated using simultaneous
equations. However, this approach rapidly becomes
computationally difficult with mixtures of 4 or more dietary
components. Perhaps more importantly, if there are more
alkanes available as markers than there are dietary
components, the use of simultaneous equations can involve
prior and possibly arbitrary choice of which alkanes to use
in the calculations. Moreover, the information provided by
those markers that are not included is lost. In such
situations, least-squares optimization methods are more
36
satisfactory, in which the best estimate of diet composition
is sought using all the data. A number of similar algorithms
have been described and used (e.g. Mayes et al., 1994;
Dove and Moore, 1995; Newman et al., 1995; Martins et
al., 2002; Fraser et al., 2006) and there have been a number
of comparisons of these algorithms. For example,
Hameleers and Mayes (1998) compared 3 different leastsquares procedures for estimating diet composition using
alkanes (Mayes et al., 1994; Dove and Moore, 1995;
Newman et al., 1995) and found that they gave almost
identical answers. The mathematical procedures available
to estimate diet composition are thus satisfactory. It is more
important to pay proper attention to fecal recovery
corrections and to the choice of which alkanes to use in the
calculations.
Basically, all these algorithms attempt to minimize the
squared deviations between the observed alkane
concentrations in feces (or other mixtures), appropriately
corrected for incomplete alkane recovery, and the
concentration profile arising from the estimate of diet
composition. Thus, for a simple example of 3 dietary
components:
(3)
Σ[Actual – Estimated]2alk:1…n
fecal alkane recoveries usually show a curvilinear increase
with alkane chain length (Mayes et al., 1986; Dove and
Oliván, 1998; Moshtagi-Nia and Wittenberg, 2002;
Charmley and Dove, 2007; Oliván et al., 2007; Ferreira et
al., 2009b). As can be seen from the data in Figure 3(a),
published recoveries show a quite consistent pattern, both
within and between the major ruminant livestock species
(sheep, cattle, and goats). Brosh et al. (2003) reported a
linear increase in odd-chain recovery in goats, but when
considered in relation to other published recovery data
(Figure 3a) it is clear that the general patterns of recovery
are similar. There are fewer estimates of fecal alkane
recovery in non-ruminant livestock such as equines, but as
shown in Figure 3(b), equine fecal alkane recoveries tend to
be higher than in ruminants (especially at shorter carbonchain lengths) and much less affected by chain length. As
discussed below in relation to alkenes, it may thus be
possible to estimate diet selection in equines without fecal
recovery corrections because the relative recovery of the
different alkanes is so similar. It would be useful to
investigate this point further.
In correcting fecal alkane concentrations for incomplete
recovery, a further assumption made is that the fecal
recovery of a given alkane is the same regardless of plant
species. Data in support of this assumption remain
equivocal. Elwert and Dove (2005), Charmley and Dove
(2007), Lin et al. (2007) and Elwert et al. (2008) reported
small but significant effects of diet composition on fecal
alkane recovery, whereas Ferreira et al. (2007a) noted that
as diet digestibility declined, fecal alkane recovery
increased. By contrast, other studies have reported that
neither diet composition (Brosh et al., 2003; Elwert et al.,
2004; Elwert et al., 2006) nor diet digestibility (Elwert et
al., 2004) affected fecal alkane recovery. Important as it is,
the effect of diet composition or digestibility on fecal
alkane recovery thus remains unresolved and would be a
fruitful area of further research.
The alkane recovery data required to correct fecal
concentrations can be obtained from metabolism cage
studies with animals fed similar diets. In this case, indoor
validation trials have shown that the accuracy of estimated
diet composition is best if recovery data for individual
animals within a dietary treatment are used, declines when
treatment mean recovery data are used, and declines further
when recovery data are computed as ‘grand means’ across
dietary treatments (Brosh et al., 2003; Elwert et al., 2004;
Charmley and Dove, 2007; Ferreira et al., 2007a). As an
alternative approach, grazing animals can be dosed with a
mix of synthetic alkanes of different chain length (e.g.,
C24, C28, C32, and C36). The relative recoveries of other
alkanes can then be calculated by interpolation (Dove and
Mayes, 1996). The relative consistency of the recovery
estimates presented in Figure 3(a) suggests that it might be
possible to use literature values, though this should be done
with caution.
In order to provide some quantification of the impact of
recovery corrections on estimated diet composition, it is
useful to estimate diet composition with and without fecal
recovery corrections. Furthermore, an important point to
note is that that recovery values can be computed either as
absolute recoveries (proportion of known alkane intake
or:
Σ[Fi – (xAi +yBi + zCi)]2alk:1…n
where, for n alkanes:
Fi = actual fecal concentration of alkane i
x, y, and z = respective amounts of dietary components A,
B, and C
Ai, Bi, and Ci = respective concentrations of i in A, B, and
C.
The amounts of dietary components A, B, and C can be
converted into dietary proportions of these components
from expressions such as:
(4)
Dietary proportion of component A = x/(x+y+z)
As dimensional analysis can demonstrate, x, y, and z in
equation (3) can be considered as the amounts of dietary
components A, B, and C which, together, will result in 1 kg
of feces of the observed concentration profile. It follows
that this information can be used to obtain an estimate of
whole-diet digestibility, given by:
(5)
Whole-diet digestibility = ((x+y+z)-1)/(x+y+z)
This is not only useful information in its own right, but also
serves as a check on whether the least-squares diet
composition estimate is ‘realistic’.
Fecal Recovery Corrections for Estimating Diet
Composition
When alkanes are used to estimate herbage intake, fecal
alkane concentrations do not require correction for
incomplete fecal recovery. By contrast, before alkanes (or
any other plant wax marker) can be used as diet
composition markers, their fecal concentrations must be
corrected for incomplete recovery. In ruminant livestock,
37
obtained if certain alkanes are not used in the calculation
(Dove et al., 1999; Lewis et al., 2003; Lin et al., 2007).
Theoretically, these would be the alkanes that contribute
substantially to the discrepancy between observed and
expected concentrations, but little to the actual
discrimination of diet components; the issue is how to
identify these. This is an issue of current concern and one
which requires resolution if plant wax components are to
gain wider acceptance as a method for estimating diet
composition.
Multivariate statistical procedures, such as Canonical
Variates Analysis (discriminant analysis) or Principal
Components Analysis, can be used to establish whether
plant species can be distinguished and which alkanes are
best related to the ability to discriminate between diet
components (Dove et al., 1999; Piasentier et al., 2000;
Bugalho et al., 2004; Charmley and Dove 2007; Lin et al.,
2008). It is thus a useful exercise to use such statistical
procedures to explore whether it is mathematically likely
that the diet components can be discriminated. Conversely,
it is important to note that that the demonstration that they
can be distinguished in theory does not mean that they will
be in practice due to such problems as a given ‘mix’ of
some dietary components mimicking the marker profile of
another.
More dietary components than markers. A more usual
situation, especially in species-diverse plant communities
such as rangelands, is that there are more plant species
available for possible consumption than there are alkanes to
discriminate them. Under these circumstances, several
general approaches could be attempted in order to obtain a
diet composition estimate:
1. Combine the discriminatory power of alkanes and other
methods for estimating diet composition. For example, Salt
et al. (1994) combined microhistological and alkane
procedures to estimate the diet composition of sheep
grazing mixed grassland-heathland. Similarly, stable
isotope procedures (Jones et al., 1979; Anderson et al.,
1996) or F.NIRS (Keli et al., 2008) could be used in
combination with alkanes to increase discrimination. In
such approaches, one technique could allow a reduction in
the number of plant pools to be discriminated by
establishing, for example, that particular plant species are
not consumed. In this regard, data obtained by close
observation of grazing behavior should not be dismissed.
2. Decrease the number of dietary pools to be discriminated
by grouping species in the diet on some logical basis or, at
the extreme, leaving out species altogether. The latter
approach may be valid if there are behavioral or other data
indicating the species is never consumed, but it should be
used with caution. The grouping of species is another area
in which multivariate statistical procedures can be used
(Bugalho et al., 2002, 2004; Ferreira et al., 2007b) to
indicate that, a priori, it will be difficult to separate some of
the available species because they are too similar in their
marker profiles. This provides an objective justification for
grouping those species. Alternatively, species can be
grouped on a ‘functional’ basis such as herbage vs. browse
species (Bugalho et al., 2002) or on taxonomic grounds
(e.g., grasses vs. clovers vs. Lotus spp.; Kelman et al.,
2003). Note that selection by different animals between
excreted in feces), or as relative recoveries (the recoveries
of a suite of alkanes relative to an assumed value, usually 1,
for the longest alkane). For an estimate of diet composition
only, relative recoveries are sufficient. This means that
under field conditions, animals can be given a known daily
dose of a mix of alkanes, including a long-chain alkane
(e.g., C36) with high recovery, and the fecal concentrations
of dosed alkanes used to get the relative recoveries of other
alkanes by interpolation. However, in order to obtain an
unbiased estimate of whole-diet digestibility with leastsquares minimization calculations, absolute recovery values
must be used.
On theoretical grounds, the maximum number of
components that can be discriminated in the diet is limited
to the number of available alkanes (C21 to C37), thus
making it theoretically possible to distinguish more than 15
dietary components. However, in practice fewer dietary
components can be discriminated, for two reasons:
1. The odd-chain alkanes usually make up 90% or more of
total alkane content and the concentrations of even-chain
alkanes are usually much lower (often <10 to 30 mg/kg
OM) and may have larger analytical errors, so that they are
usually (but not always) less useful as markers.
2. It is likely that the reliability of the diet composition
estimate will decline as the number of components
increases because there is an increased likelihood that
different combinations of components could result in the
same fecal alkane patterns. For example, in certain
combinations, the alkane profile of a mix of diet
components A and B may be indistinguishable from that of
component C.
Despite the above concerns about factors influencing
fecal alkane recovery or how this can be estimated and
applied, the results of a number of validation studies
indicate that accurate estimates of diet composition can be
obtained using this approach. The data in Table 4 are
regression relationships between known and estimated diet
compositions either published in or recalculated from the
results of these studies. In 13 of the 15 examples shown, the
relationship between known and estimated diet composition
does not differ from the line of equality.
Which Alkanes to Use in Diet Composition Calculations?
As a generalization, two situations can be considered,
the first involving more markers (alkanes) than diet
components and the second involving more diet
components than there are alkanes to distinguish them.
More markers than diet component. In some grazing
situations involving simple associations in a renovated
pasture planted to improved forage species (e.g., grassclover pastures) there might be more markers (alkanes)
available than there are possible species in the diet. As
indicated above, the least-squares procedures employed can
accommodate information about all available alkanes, but
this may not be a sensible approach because it is possible
that some alkanes may discriminate much better than others
between components. Indeed, other alkanes may prove to
be negatively correlated with the capacity to distinguish
between dietary components (Dove et al., 1999). In such
cases, more reliable estimates of diet composition may be
38
to emphasize the point made above that ultimately it is the
relative recoveries of the markers, which are important for
estimating diet composition, not the absolute recoveries. In
this case, the relative recoveries of alkenes C29 to C33
were so similar that recovery correction did not alter
estimated diet composition.
Long-chain alcohols. The LCOH are now proving to be
useful diet composition markers. Large between-species
differences in LCOH concentrations exist (Table 1) and
account for 60 to 90% of the variance in LCOH
concentrations between plant species (Bugalho et al., 2004;
Ali et al., 2005b). In the dicotyledons, concentrations of
C30 alcohol are generally much greater than in the
monocotyledons, which is likely to be a very useful feature
when using alcohol concentrations in diet composition
estimates. While primary alcohols predominated in most of
the plants analyzed, the secondary alcohol, 10-nonacosanol,
could be a potentially important diet composition marker in
studies involving gymnosperms.
Implicit in the use of LCOH or LCFA profiles together
with alkane profiles is the assumption that the first two
groups of compounds contribute extra discriminatory
information. This assumption needs to be checked because
if there are high correlations between the concentration
profiles of LCOH, LCFA, and alkanes, it may well be that
the LCOH or LCFA provide little extra information. This
can be assessed by comparing multivariate statistical
analyses of, for example, alkane and LCOH profiles for the
same species, using techniques such as Orthogonal
Procrustes Rotation. In their estimations of diet composition
of housed sheep, Dove and Charmley (2008) used this
approach to demonstrate that the information supplied by
the LCOH was additional to that supplied by alkanes, a
finding confirmed by Lin et al. (2008). As a result,
estimates of diet composition based on both markers are
better than those based on alkanes alone (Bugalho et al.,
2004; Dove and Charmley 2008).
The fecal recoveries of dietary LCOH have now been
determined in several studies and show a progressive
increase with chain length similar to that observed with nalkanes (Figure 4a). In these studies, recoveries have ranged
from about 0.6 for C22-OH to about 0.9 for C30-OH and by
contrast with alkanes, tend to show a linear increase in fecal
recovery with increasing carbon-chain length.
The LCOH have now been successfully used in a
number of recent estimates of diet composition of livestock
(Bugalho et al., 2004; Ali et al., 2005; Fraser et al., 2006;
Dove and Charmley, 2008; Lin et al., 2008).
Long-chain fatty acids. Procedures for LCFA analysis,
as an extension of the procedure used for alkanes and
LCOH, have now been published (Ali et al., 2004; Dove
and Mayes, 2006). Fecal recoveries of individual LCFA, as
with other cuticular wax components, increase with
increasing chain length (Figure 4b) in sheep (Ali et al.,
2004) and in goats (Ferreira et al., 2009a). The widespread
incidence of LCFA in plant cuticular wax, the betweenspecies differences in LCFA profile, and the fecal recovery
data to date suggest that these compounds could also be
used as diet composition markers. Ali et al. (2005) reported
concentration data for the alkanes, LCOH, and LCFA in a
range of plant species. The best estimates of diet
plant species within such groupings may influence the
accuracy of the diet composition estimate. Bugalho et al.
(2002) found that after grouping the herbage species into a
single component, simulated selection within the grouping
only had a minor effect on the estimates of diet
composition. However, data of Ferreira et al. (2007b)
suggest that this may have been a case-specific outcome
and dietary grouping based on multivariate statistical
analysis should be used with care. If species are grouped for
the purposes of estimating diet composition, it would be
sensible to carry out some assessment of the impact of
possible selection within the groups.
3. A more recent development is the approach in which
diet composition is estimated from ‘possible infinite
bounded combinations of plants species’, the upper and
lower limits of which are determined by linear
programming (Barcia et al., 2007). Those authors suggest
that this approach can be applied when the number of
markers is lower than the number of species, thus avoiding
the need for grouping. Though promising, the approach
requires further testing.
4. An obvious approach is to increase the number of
‘discriminators’ used to estimate diet composition; this
could be achieved either by combining the use of alkanes
(or other plant wax markers) with other methods for
estimating diet composition (as described above) or by
recruiting further groups of plant wax markers.
Using Other Plant Wax Markers to Estimate Diet
Composition
The use of other plant wax markers to estimate diet
composition has been an area of recent activity following
the development of reliable analytical procedures for both
the LCOH and LCFA (Ali et al., 2004; Dove and Mayes,
2006). At present, the additional components of plant wax
that might be considered as diet composition markers are
the alkenes (Dove and Oliván, 2006), LCOH (Kelman et
al., 2003; Ali et al., 2005b; Fraser et al., 2006; Dove and
Charmley, 2008; Lin et al., 2008) and LCFA (Ali et al.,
2004; 2005b; Lin et al., 2008; Ferreira et al., 2009a). These
components also have different concentrations in the
different plant species and plant parts and can be obtained
together with alkanes as part of the same analytical
procedure (Ali et al., 2004; Dove and Mayes, 2006).
Alkenes. The unsaturated hydrocarbons or alkenes tend
to be predominant in the floral parts of plants and have also
been found in a number of browse species; they are
extracted from plant material and feces in the same fraction
as n-alkanes. There are only a few estimates of fecal
recoveries of plant alkenes, which in sheep are much lower
(25 to 40%) than the recoveries of alkanes of equivalent
chain length (Dove and Oliván, 2006; Elwert et al., 2004;
Elwert and Dove, 2005) and show little variation with
carbon-chain length, especially from C29 to C33 (Figure
3(c)). Paradoxically, this means that despite their low fecal
recoveries, alkenes C29 to C33 gave excellent estimates of
diet composition (Dove and Oliván, 2006), which did not
differ from those estimated using alkanes (see below).
Moreover, there was no advantage in correcting fecal
alkene concentrations for incomplete recovery. This serves
39
In the approaches discussed to this point, to obtain an
estimate of total intake and thus of supplement intake, it
was necessary to dose the animals with a source of evenchain alkanes (e.g., the alkane CRD). However, recent
studies indicate that estimates of forage intake could be
obtained without having to separately dose the animals with
alkanes.
composition were obtained with a combination of all three
marker classes (Ali et al., 2005b), indicating that each class
of marker provided different discriminatory information.
ESTIMATION OF SUPPLEMENT INTAKE
In many grazing systems, there are times of the year
when the supply or quality of herbage is inadequate for the
production target and animals are offered supplementary
feed. Interpretation of animal responses and the interaction
between herbage and supplement intakes would be much
easier if individual intakes of supplement were known.
Supplement intake by individual animals has been
estimated using markers that are incorporated into and
measured in the body water pool of the animal (e.g.,
tritiated water or lithium salts), or methods involving fecal
markers (e.g., chromic oxide, ytterbium salts). The features
of these methods have been discussed in detail by Mayes
and Dove (2000) and will not be considered further. The
present discussion will be restricted to the possible use of
plant wax markers for estimating supplement intake, which
is in essence a special case of the estimation of diet
composition.
Using Supplement Intake to Estimate Forage Intake
In the above examples, plant wax marker profiles were
used to estimate diet composition, which then allowed the
calculation of total intake and thus supplement intake in
dosed animals. However, there are a number of situations in
which the intake of supplement by individual animals either
is known or could be known with relatively little trouble;
for example, cows receiving known amounts of supplement
while in the dairy parlor. If these supplements either contain
or can be labeled with alkanes, the proportion of
supplement in the diet can be estimated as above. However,
because actual supplement intake is also known, total and
thus herbage intake can also be computed. For example, if
supplement is estimated to comprise 10% of the diet of a
sheep and known supplement intake is 150 g/day, then total
intake must be 1500 g/day and forage intake 1350 g/day.
This approach provides a means of estimating the intake of
pasture or its component species, without having to dose
animals with alkanes, which in turn, may avoid undue stress
or perturbations of normal grazing behavior. Moreover, it
may provide an alternative approach to estimating herbage
intake, now that the alkane CRD is no longer commercially
available.
The procedure for estimating intakes in this way is as
follows:
1. Estimate diet composition as described above, using the
alkane profiles of diet components and of feces (recoverycorrected). In a two-component diet (forage and
supplement), if the known supplement intake is Is, and the
proportion of supplement in the diet is ps, then the total
intake It is given by:
(6)
It = Is/ps
Intake of the forage component of the diet (I f) is then given
by (It – Is).
2. This method for two-component diets (forage +
supplement) can be restated in more general terms to allow
the estimation of the intake of all the forage components of
the diet in situations where the animals are consuming
known amounts of supplement and unknown amounts of a
number of forage species. By rearrangement of the above
terms, it can be shown that If can be estimated directly as
(7)
If = Is*(pf/ps)
where pf is the proportion of forage in the diet. The
advantage of the approach embodied in equation (7) is its
equal applicability to multi-component diets (supplement,
forage species 1, forage species 2…forage species n). A
further and major advantage is that the intake of the various
forage species and (or) plant parts is obtained without the
separate dosing of the animals. In effect, all of the alkanes
in the supplement are used as the dose. The use of
Using Plant Wax Alkanes
If the supplement has its own alkane profile or can be
labeled with a distinctive profile (Dove and Oliván, 1998,
Elwert and Dove 2005; Charmley and Dove, 2007), then
the intake of supplement can be estimated by treating it as if
it were one of the ‘species’ in the diet. Diet composition is
first estimated as described above; this provides an estimate
of the proportion of supplement in the diet. If the animals
are also dosed with even-chain alkanes (e.g., C32), this
allows the estimation of total intake using equation (2)
above. Diet composition then allows intake to be
partitioned into its components, including supplement, thus
providing an estimate of supplement intake. A further
advantage of this approach is that it can provide an estimate
of whole-diet digestibility, which accommodates, in
individual animals, any interactions of supplement and
herbage during digestion.
If the supplement is itself based on roughage (e.g., hay,
silage, or pelletted herbage) then the natural alkanes of the
supplement can be employed without further manipulation.
However, in some cases (e.g., legume grains, coarse grains,
or oilseed meals), the levels of alkane may be insufficient to
permit the estimation of diet composition. Under these
circumstances, supplements can be labelled with an external
source of hydrocarbons, such as beeswax (Dove and
Oliván, 1998; Elwert and Dove, 2005). Validation studies
(Dove and Oliván, 1998; Figure 5) have shown that this
provides accurate estimates of supplement intake.
Moreover, as described above, supplement intake estimates
were equally good when based on the unsaturated
hydrocarbons (alkenes; Dove and Oliván, 2006).
It is worth re-emphasizing that if the diet composition
estimate is in error, so will be the estimates of supplement
and total intake. This means that care must be taken in
estimating the diet composition itself.
40
supplement intake to estimate the intake of the forage
components of the diet was validated for 2-component diets
(Elwert and Dove, 2005; Figure 6a) and for multicomponent diets (Charmley and Dove, 2007; Figure 6b).
Regardless of whether individual, treatment mean or grand
mean fecal recovery data were used to estimate diet
composition, the resultant estimates of intake using this
‘supplement dosing’ approach were close to actual intake
(Table 5) and to those estimated from alkane-CRD dosing
using either the C32/C33 or C36/C33 alkane pairs
(Charmley and Dove 2007; Dove and Charmley, 2008).
Ali, H. A. M., R. W. Mayes, B. L. Hector, and E. R.
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markers in studies of the botanical composition of the
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Charmley, E., D. R. Ouellet, D. M. Veira, R. Michaud, J. L.
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CONCLUSIONS
In a variety of species of grazing livestock, the alkane
procedure for estimating forage intake is now well
validated. Under field conditions, the procedure has been
compared with previous techniques such as the Cr-in vitro
procedure. Although no simple relationship between these
approaches should be expected, the alkane-based estimates
are likely to be more accurate because the natural alkane
functions essentially as an internal marker and thus
accommodates differences in forage digestibility between
individuals and those arising from interactions between
supplement and forage. A further (and major) advantage of
the use of plant wax markers is that they also allow an
estimate of diet composition and thus the partitioning of
total intake into its component plant species. Estimates of
diet composition require correction for incomplete fecal
marker recovery and more work is required to quantify the
relative effects of animal species and particularly plant
species on the recovery of a given alkane. If estimates of
whole-diet digestibility are wanted, actual fecal alkane
recoveries must be used, but for an estimate simply of diet
composition, relative recoveries (i.e., the recovery of the
alkanes relative to each other) will suffice. When used
alone, alkanes can discriminate between fewer species than
is often encountered by grazing livestock utilizing complex
plant communities. To address this, the LCOH and LCFA
of plant wax can now also be used as diet composition
markers. This, plus the use of multivariate statistical
procedures to guide the grouping of species within the
possible diet, should extend substantially the number of
species that can be discriminated in the diet. In addition,
other compounds in plant wax, as yet unevaluated as
markers, could further extend diet composition estimates.
Supplement intake can also be estimated using wax
markers, and there is considerable scope for exploiting
known supplement intakes as a means of estimating the
intake of other diet components, thus avoiding the need to
separately dose the test animals with synthetic alkanes.
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Valiente, O. L., P. Delgado, A. de Vega, and J. A. Guada.
2003. Validation of the n-alkane technique to estimate
intake, digestibility, and diet composition in sheep
consuming mixed grain:roughage diets. Aust. J. Agric.
Res. 54:693-702.
44
45
13
18
15
2
13
9
4
Calluna vulgaris
Fagus sylvatica
Lotus pedunculatus
Picea sitchensis
Pinus sylvestris
Trifolium repens
Trifolium subterraneum
0
24
10
0
27
32
Cynodon dactylon
Festuca arundinacea
Lolium perenne
Pennisetum clandestinum
Phalaris aquatica
Setaria anceps
82
17
7
33
42
11
89
15
16
35
28
3
151
361
63
6
C27
62
21
12
77
129
30
180
116
250
108
21
8
212
13
160
456
C29
74
16
79
103
216
66
243
60
74
124
1
4
55
3
636
136
C31
25
7
195
84
59
91
137
34
10
15
10
2
37
3
458
5
C33
Alkane carbon-chain length
5
2
204
11
2
58
32
4
-
4
4
0
1
0
14
0
C35
28
19
30
104
27
31
64
13
193
18
81
85
28
191
363
23
1-C24
20
3726
9
2628
639
25
56
85
408
143
58
18
2463
89
167
142
1-C26
-
23
-
10
10
-
-
17
0
0
1853
2065
0
7
14
196
10-C29
-
45
-
446
101
-
187
4052
281
61
13
14
1327
3
260
25
1-C28
LCOH carbon-chain length
Adapted from data in Bugalho et al. (2004), Ali et al. (2005a), and Dove and Mayes (2005).
12
Chloris gayana
1
6
Bromus carthaticus
Monocotyledons
2
C25
Brassica oleracea
Dicotyledons
Species
24
472
0
627
58
0
149
84
4709
1297
114
20
1285
39
298
29
1-C30
Table 1. Concentrations (mg/kg DM) of the major (odd-chain) n-alkanes and the major long-chain alcohols (LCOH; even-chain
except for 10-C29-OL) in a selection of dicotyledons and monocotyledons1
Table 2. Concentrations (mg/kg DM) of the major (even-chain) long-chain fatty acids (LCFA) in a
selection of dicotyledons and monocotyledons1
LCFA carbon-chain length
C20
C22
C24
C26
C28
C30
C32
Acer pseudoplanatus
49
2
293
203
421
278
23
Brassica oleracea
36
29
41
13
16
20
3
Dicotyledons
2
Calluna vulgaris
NR
700
556
516
572
445
449
Fagus sylvatica
94
336
111
71
292
16
7
Picea sitchensis
502
657
430
60
48
110
31
Pinus sylvestris
570
1069
159
21
32
63
36
Trifolium repens
NR
613
716
608
792
139
41
Heliotropium sp.
267
483
275
151
99
63
36
Stylosanthes guianensis
232
357
540
363
352
398
398
Lolium perenne 1
99
190
174
156
159
88
34
Lolium perenne 2
NR
252
257
187
204
171
74
Cenchrus ciliaris
236
287
331
279
249
235
285
Chloris gayana
241
258
312
223
146
225
521
Hordeum vulgare
283
466
461
270
369
299
170
Monocotyledons
1
Re-tabulated from the data in Ali et al. (2005a, 2005b) and Ferreira et al. (2009a).
NR = not reported.
2
46
Table 3. Comparison of measured herbage intakes of herbivores with those estimated using dosed and herbage alkanes
Known intake
Source
Animals/conditions
Mean discrepancy ± SE1
Sheep
Mayes et al. (1986)
Dove and Oliván (1998)
Dove et al. (2002)
Sibbald et al. (2000)
Lewis et al. (2003)
Valiente et al. (2003)
36 kg lambs/fresh herbage
(g of DM/d)
579
0 ± 4.7 (0%)
30 kg sheep/chaff+sunflower meal
720
-6 ± 6.4 (0.8%)
Adult sheep/pre-frozen herbage
Adult sheep (Group 1)/pelletted grass
meal
Lambs (0.3, 0.45 of mature weight)/
forage diets
57 kg ewes/straw+barley grain
914
0 ± 15.5 (0%)
2040
-39 ± 27.4 (1.9%)
2016
-49 ± 28.3 (2.4%)
748
-46 ± 24.0 (6.1%)
Goats
Giraldez et al. (2004)
(g of DM/d)
43 kg goats/fresh herbage, pulse dose of
alkane
Cattle
808
18 (-2.1%)
(kg of DM/d)
8.1
7.9
22.5
-0.1 ± 0.04 (-1.4%)
-0.2 ± 0.05 (-1.0%)
0
Unal and Galsworthy
(1999)
Holstein-Friesian cows (±
lactating)/hay+rolled barley
Berry et al. (2000)
611 kg Brown Swiss
cows/forage:concentrate mix
12.7
0.03 (0.2%)
Mann and Stewart (2003)
Yearling bulls/fresh kikuyu
6.3
0.1 (1.1%)
457-635 kg cows/meadow hay
6.7
-0.3 ± 0.17 (-3.9%)
233 kg Tuli steers/poor-quality hay
3.2
-0.2 (-6.3%)
380 kg Nelore steers/TMR
5.0
0.1 (2.0%)
490 kg geldings/ryegrass or kikuyu
153 kg donkey/poor-quality hay
149-223 kg donkey/hay+barley straw
228-496 kg horse/haylage
(kg of DM/d)
7.2
2.3
3.0
5.4
0.3 (4.2%)
-0.1 (-2.7%)
0.4 (13.0%)
0.5 (9.1%)
Ferreira et al. (2004)
Smith et al. (2007)
Oliveira et al. (2008)
Equids
Stevens et al. (2002)
Smith et al. (2007)
Cervids
(kg of DM/day)
295 kg pregnant wapiti hinds/alfalfa
Gedir and Hudson (2000)
9.2
-0.2 (-2.2%)
cubes
1
Discrepancy = (known intake – estimated intake), with SE of mean discrepancy where possible. Values are g of DM/d for
sheep and goats, kg of DM/d for other species. Values in parenthesis are the percentage over- or under-estimates of known
intake.
47
Table 4. Regression1 relationships between known diet composition (X) and that estimated from the
alkane profiles of feces and of diet components (Y) for a range of livestock species
Species
Reference
Slope
Intercept
R2
Significance
Cattle
Sheep
Goats
Horses
Ferreira et al. (2007a)
0.959
0.014
0.998
NSD Y = X
Ferreira et al. (2009b)
0.972
0.006
0.996
NSD Y = X
Lewis et al. (2003)
0.990
0.023
0.950
NSD Y = X
Valiente et al. (2003)
0.995
0.236
0.990
NSD Y = X2
1.006
-0.025
0.990
P < 0.053
Elwert et al. (2004)*
1.003
-0.002
0.946
NSD Y = X
Elwert and Dove (2005)*
0.995
0.002
0.998
NSD Y=X
Fraser et al. (2006)
0.907
0.009
0.978
NSD Y = X4
Charmley and Dove (2007)
1.001
-1.986
0.812
NSD Y = X
0.891
0.020
0.887
NSD Y = X
Brosh et al. (2003)
0.998
0.000
0.985
NSD Y = X
0.983
0.006
0.999
P < 0.055
0.857
0.025
0.918
NSD Y = X
0.925
0.025
0.987
Slope P < 0.055
0.923
0.013
0.964
NSD Y = X
1
Regressions shown are either as published in the references shown (*) or are calculated from data
therein; for multi-component diets, all components were included in the regression. Unless otherwise
stated, fecal recovery corrections were based on treatment mean values.
2
Values for proportion of grain in diet.
3
Recalculated values for proportion of alfalfa in diet.
4
Values are for best solution (M1), without fecal correction because recoveries were complete.
5
Intercept not significantly different from zero and regression constrained through origin not significantly
different from Y = X.
48
Table 5. Comparison of actual organic matter (OM) intakes (g/d) by sheep with those estimated using the C32/C33 or
C36/C33 alkane pairs following dosing with intra-ruminal alkane CRD, or those estimated from diet composition and
the known intake of an alkane/LCOH-labeled supplement
Intake method
Daily OM intake
Difference cf. actual (%)
Actual intake
674.1
-
C32/C33
702.9
+4.3
C36/C33
699.2
+3.7
Alkane/individual1
679.4
+0.8
Alkane/treatment mean
670.8
-0.5
Alkane/grand mean
683.9
+1.5
(LCOH+alkane)/individual
675.0
+0.1
(LCOH+alkane)/treatment mean
698.8
+3.7
(LCOH+alkane)/grand mean
671.3
-0.4
From alkane CRD
From labeled supplement
1
Markers used to estimate diet composition/data used in fecal recovery correction. Values recalculated from Charmley
and Dove (2007) and Dove and Charmley (2008).
49
Resultant error in intake estimate (%)
30
20
10
0
-10
-20
-5
-4
-3
-2
-1
0
1
2
3
4
5
Error (%) in fecal output or digestibility estimate;
difference (%) in alkane recovery
Figure 1. Comparative effect on the error of estimated intake of errors of ± 5% in the estimate of fecal output (■), ± 5% in the
estimate of a digestibility of 75% (□), or differences in fecal recovery of ± 5 percentage units in fecal recovery of the alkanes
used to estimate intake (Δ).
60
Y = 1.28X - 0.83
Error in intake estimate (%)
r2 = 0.986, P<0.001
40
20
0
-40
-20
0
20
40
60
-20
-40
Difference in recovery of alkane pair (% )
Figure 2. Effect of the difference in recovery between the 2 alkanes used to estimate intake on the resultant error in estimated
intake. The solid line is the fitted regression, which does not differ significantly from the theoretical relationship (dotted line).
Recalculated from Charmley and Dove (2007) and Oliván et al. (2007).
50
1.2
(a) Alkane recovery in ruminant species
recalculated from the following:
(a)
Fecal recovery
1
Sheep: Dove and Oliván (1998; ■); Elwert
et al. (2004; □); Elwert and Dove (2005;
▲); and Ferreira et al. (2009b; Δ).
0.8
Cattle: Smith et al. (2007; ●) and Ferreira et
al. (2009b; ○).
0.6
Goats: Brosh et al. (2003; ♦ odd-chain
alkanes) and Ferreira et al. (2009b; ◊).
0.4
0.2
0
24
25
26
27
28
29
30
31
32
33
34
35
36
1.2
(b) Alkane recovery in equines recalculated
from the following:
(b)
Fecal recovery
1
Horses: Ordakowski et al. (2001; ■ means
across diets); Stevens et al. (2002; □); and
Ferreira et al. (2009b; Δ)
0.8
Donkeys: Smith et al. (2007; ▲)
0.6
0.4
0.2
0
24
25
26
27
28
29
30
31
32
33
34
35
36
1.2
(c) Alkene recovery data (sheep only)
adapted from Elwert et al. (2004; ■), Elwert
and Dove (2005; □), and Dove and Oliván
(2006; ▲).
(c)
Fecal recovery
1
0.8
0.6
0.4
0.2
0
24
25
26
27
28
29
30
31
32
33
34
35
36
Alkane or alkene carbon-chain length
Figure 3. Effect of carbon-chain length on fecal recovery of alkanes and
alkenes of equivalent chain length.
51
1
(a)
Faecal recovery
0.8
0.6
0.4
0.2
0
20
22
24
26
28
30
32
LCOH carbon-chain length
1
(b)
Fecal recovery
0.8
0.6
0.4
0.2
0
20
22
24
26
28
30
32
34
LCFA carbon-chain length
Figure 4. Influence of carbon-chain length on fecal recovery of LCOH (a) recalculated from the data of Ali et al., (2004; ■)
and Dove and Charmley (2008; □) and LCFA (b) recalculated from the data of Ali et al. (2004; ■) and Ferreira et al. (2009a;
□).
52
500
Estimated supplement intake (g DM/d)
y = 1.0066x + 9.0859
R2 = 0.9905
400
300
200
100
0
0
100
200
300
400
500
Known supplement intake (g DM/d)
Figure 5. Relationship between known supplement intakes and those estimated using beeswax as an alkane marker, and
natural and dosed alkanes to estimate total intake (adapted from Dove and Oliván, 1998). The fitted regression shown did not
differ from Y = X. Supplement intakes estimated using alkenes (unsaturated hydrocarbons; Dove and Oliván, 2006) were
related to known intakes by the following expression, which also did not differ significantly from Y = X: [Y = 1.057 * X 0.010 (r2 = 0.995)].
53
Estimated intake (g DM/day)
750
(a)
650
550
450
350
Estimated intake (g OM/day)
350
450
550
650
750
150
300
450
600
600
(b)
450
300
150
0
0
Known roughage intake (g OM/day)
Figure 6. Comparison of known forage intakes and the intake of (a) a single forage (Elwert and Dove, 2005) or (b) multiple
forages (Charmley and Dove, 2007) calculated using the estimated proportions of forage and supplement in the diet, together
with known supplement intake. In neither case, the fitted regression did not differ from the line of equality (solid lines
shown).
54
KEYNOTE: ASSESSMENT OF INTAKE AND DIET COMPOSITION OF GRAZING LIVESTOCK
H. Dove
Notes
July 9 and 10, 2010
GEOSPATIAL METHODS AND DATA ANALYSIS FOR ASSESSING DISTRIBUTION OF
GRAZING LIVESTOCK
D. M. Anderson1
2
USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003
Key words: foraging, geographic information system,
global positioning system, landscape utilization, range
animal ecology
ABSTRACT: Free-ranging livestock research must begin
with a well conceived problem statement and employ
appropriate data acquisition tools and analytical techniques
to accomplish the research objective. These requirements
are especially critical in addressing animal distribution.
Tools and statistics used to describe the plant-livestock
interface must consider that foraging is highly place
dependent, foraging is not a random process and livestock
foraging mainly occurs in groups. Since animal movement
implies consecutive locations, each the result of a host of
interactions between the animal and its surroundings,
classical agronomic statistics may not provide the optimum
analytical tools to address spatially related phenomena. By
far the greatest amount of research on quantitative tools to
understand the use of space by animals has come from the
wildlife community and more recently from ecologists
exploring the distribution of both animal and plant species
over large geographic areas. However, interest in geospatial
data analysis methods is also found among animal-range
scientists, primarily because new electronic technologies
make it possible to accumulate data from free-ranging
animals in seconds or less. Therefore, the rate at which data
can now be collected produces inordinate amounts of
information that must be correctly analyzed. Consecutive
animal locations on a landscape are not statistically
independent; therefore, autocorrelation is no longer a
theoretical but real concern and must be addressed in
animal distribution analyses. This paper presents an
overview of research that affects landscape use together
with possible geospatial tools that can be used when
evaluating free-ranging animal distributions. Geospatial
methodology is not trivial nor is it a mature field, but rather
is evolving; therefore, the range-animal scientist should
work as a team member with other disciplines including,
but not limited, to statisticians, modelers, computer
scientists, cognitive scientists, physicists, sociologists, and
possibly geographers when attempting to understand animal
distribution. Literature referenced in this paper is
comprehensive in breadth but not exhaustive in detail and
thus serves as a beginning point for those wanting to know
what has already been researched concerning livestock
distribution on landscapes.
INTRODUCTION
Patterns of behavior that result in the distribution of
free-ranging animals over extensive landscapes are poorly
understood (Vavra and Ganskop, 1998). Vavra and
Ganskop (1998) stated that the greatest frustration in the
study of foraging behavior has been the lack of a unifying
theory capable of explaining the selective process of
foragers and their distribution. Sumpter (2006) suggested
that because there are many underlying principles involving
collective animal behavior it is unlikely “a single
formalism” can describe them all. Distribution is a
complex issue to address biologically and mathematically
because an animal‟s use of space (its distribution) is related
to both internal (biological rhythms and drives) as well as
external (environmental) factors (Calenge et al., 2009).
Understanding animal production is not in itself adequate to
comprehend the complexities of foraging (Morley, 1981).
Furthermore, since free-ranging animals are not static either
spatially or temporally, the complexity of understanding
distribution is no trivial matter.
It is unlikely free-ranging animals will use the whole of
the space available to them on their own unless stocking is
intensive (Arnold and Dudzinski, 1978). Arnold and
Dudzinski (1978) further suggested flocks and herds that
split into more than a single group may disperse further
over the landscape, although research addressing this
concept is lacking. Under the direction of a herder or range
rider (Skovlin, 1957) livestock are directed to the most
palatable forage (Turner et al., 2005). Herding shows great
promise as a means to improve animal distribution (Bailey,
2004) and with new innovations such as virtual fencing,
“electronic herding” may become practical in the 21 st
century (Anderson, 2007). Herding tends to provide
foraging animals with a higher quality diet than if animals
are left to their own behaviors. Hunt et al. (2007) suggests
that management techniques that rely on forcing cattle to
use certain areas, such as fencing or developing
appropriately located points for drinking water may be
more effective than passive approaches that allow cattle
considerable freedom to choose where in a paddock they
graze. Neither feral (Lazo, 1994) nor domestic cattle
(Schmidt, 1969) use paddocks uniformly. Casual
observation reveals both under- and overuse areas occur
within the same paddock (Coffey, 2009), indicating neither
1
Corresponding author: [email protected].
Mention of a trade name does not constitute a guarantee,
endorsement, or warranty of the product by the USDA-ARS
over other products not mentioned.
2
57
distribution, an approach normally outside the training of
most animal ecologists.
In designing foraging experiments, it is necessary to
consider biological, practical, and statistical implications
(Bransby, 1989). Furthermore, software development is
never trivial (de Smith et al., 2007). Therefore, it should be
immediately obvious that geospatial data analysis of groups
must be considered from the standpoint of the biologist,
cognitive scientist, psychologist, physicist, mathematician,
computer scientist, as well as the geographer and
sociologist (Schellinck, 2008; Calenge et al., 2009).
Assembling teams that communicate and work together
effectively to address free-ranging animal behavior is a
challenge. Just as early work on animal behavior was not
conducted by scientists, trained as ethologists (Squires,
1975), many biologists of today are attempting to model
and understand the plant-animal interface without a
complete mathematical understanding of the models.
Conversely, some modelers lack a complete biological
understanding of what they are attempting to model
(Loewer, 1989).
There are other reasons plant-animal interface research
is challenging. First, no standardized protocol in
experimental grazing studies has been advocated (Amer.
Soc. Agron. et al., 1952) making comparisons among
studies challenging. Second, the importance of proper
grazing management is often ignored by development and
conservation communities (Turner et al., 2005). Third,
although livestock husbandry is one of the oldest industries,
little from the fields of monitoring and processing used by
other industries has been adopted because the factors
required for biological monitoring are inherently variable
and unpredictable (Frost et al., 1997). Forth, often if freeranging animal behavior is considered important, it is
assumed the individuals that make up a herd differ little in
behavioral characteristics and hence will respond similarly
to management practices and actions (Hunt et al., 2007).
Fifth, although animal behavior is now recognized as
pivotal in the foraging process and the use of space
(Hancock, 1953; Stricklin and Kautz-Scanavy, 1984; Senft
et al., 1987; Coughenour, 1991; Stuth, 1991; Bailey et al.,
1996), history bears out this has not always been the case.
Animal behavior has only been recognized as a full-fledged
part of animal science since the mid-1950‟s (Curtis and
Houpt, 1983). Early foraging research either overlooked or
chose not to address the complexities associated with
animal behavior. Burns et al. (1989) recommended that the
following 4 measurements be made in all grazing
experiments: 1) estimate herbage mass; 2) estimate green
leaf; 3) estimate diet quality; and 4) determine herbage
density, but nothing was mentioned concerning animal
behavior. Sixth, guidelines on how to conduct grazing
studies frequently do not address how to document animal
behavior in foraging studies (Henderson, 1959; Davies,
1961; Morley, 1981; Cook and Stubbendieck, 1986).
wild animals (Seaton, 1909) nor livestock use landscapes
uniformly. This fact has been substantiated by monitoring
consecutive 24-h periods of free-ranging cows (Anderson,
2007) as well as by investigating fecal deposition patterns
(Tate et al., 2003). One of the largest risks in attempting to
address large scale phenomena such as animal dispersion is
that it may not be possible to identify processes in smallscale studies that are crucial on a larger scale (Marsh and
Jones, 1988). However, Shiyomi and Tsuiki (1999) were
able to find free-ranging cattle in small herds to be
aggregated and not randomly distributed.
This paper presents selected literature of past research
directed at understanding the multifaceted phenomenon
called free-ranging animal distribution. Because of the fast
moving changes in the software industry and the continued
development of new tools and analytical techniques this
manuscript should only serve to focus further research on
what questions have yet to be answered and aim future
research towards increasing our understanding of freeranging livestock distribution and how to manipulate
livestock distribution to foster ecologically based
management.
BACKGROUND
Foraging is a complex process (Reid, 1951) and remains
a major issue facing rangeland managers (Bailey et al.,
1996). Animals distribute themselves based on multi-level
responses to their perceived environment; however, the
effect of social interactions on spatial choices has largely
been ignored. Although most livestock species are
gregarious, it is not known if spatial decisions are made
collectively or if only 1 or more animals make these
decisions (Bailey and Rittenhouse, 1989; Bailey, 1995).
Huber et al. (2008) observed that purebred animals (2
groups with 50 Ankole cattle) had closer nearest-neighbor
distances and engaged in more non-agonistic social
interactions than crossbred cows (2 similar groups of 50
Holstein x Ankole cattle). An animal‟s preference for
heterogeneously distributed resources results in the
heterogeneous usage of space, which is a joint function of
preference and accessibility (Matthiopoulos, 2003).
Although wildlife sciences have led in the development of
tools and analytical techniques to analyze free-ranging
animals, we remain relatively ignorant about an animal‟s
use of space even with the many technological advances in
the field of radiotelemetry (Marzluff et al., 2001).
Horne et al. (2008) suggested foraging is complex
because the use of space by animals is a multivariate
process and multivariate statistics require a solid
understanding of matrix algebra. Few grazing researchers
are fully trained in experimental design and data analysis
(Hart and Hoveland, 1989). Moorcroft and Lewis (2006)
noted that animal ecologists have often used purely
descriptive statistics to describe animal movements rather
than using models that may require a more rigorous
understanding of mathematics, including differential
equations. For example, Paraan and Esquerra (2006) have
derived exact expressions involving the Poisson distribution
for the first 4 moments of displacement within a
THE IMPORTANCE OF BEHAVIOR (EXAMPLES)
Most US grazing experiments have used beef steers
(Burns and Standaert, 1985); yet, the results from such
studies are often applied directly to the management of
58
female animals. Anderson and Holechek (1983) found
heifer diets had 4% greater forb content than steer diets,
which was the likely explanation for heifers having a 3%
greater (P < 0.05) dietary CP content than steers (14%). If
nutritional data from 1 class of animal should not be
extrapolated directly to another animal class when making
nutritional management decisions, then same should be true
for applying behavioral data to management. Socialization
among animals also has been largely ignored, yet this
phenomenon has been shown to affect animal distribution.
Bond et al. (1967) suggested that interspecific associations
could produce an important variable in grazing studies. The
observation that spatial distribution of mixed species groups
can be a positive in management prompted an entirely new
research direction involving mixed-species stocking and
flerds (Anderson et al., 1988). Another often overlooked
variable is animal numbers. A simplistic example will serve
to illustrate the point. If 20 cows are stocked on an area for
2 days, it equates to 40 animal-unit-days (AUD) of use, an
animal unit consisting of a cow-calf pair with the cow
weighing approximately 454 kg (Havstad et al., 2004).
However, we could arrive at the exact same number of
AUD if the same area were stocked with 2 cows for 20
days, although the set of biotic and abiotic factors affecting
the plants, animals and their subsequent use of the area
(distribution) would be entirely different. The “group”
behavior of 20 cows is intuitively different than that of 2
cows, but there is currently a dearth of research on the
effect of group size and its affect on distribution.
Replication remains a concern and a point frustration
when analyzing plant-animal interface studies (Bonham,
1986). Replication is essential and should be done where
appropriate to elucidate treatment differences. However,
similar to landscape ecology in which there is a conflict
between the need to replicate and the need to study
processes (Hargrove and Pickering, 1992), some behavior
studies seem to defy classical replication. Anderson et al.
(1987) designed a study involving socialization of sheep to
heifers in pen confinement. Those authors found that 1
particular randomly assigned heifer was physically abusive
to the sheep, and as a result, the sheep did not become
socialized to the heifers. Despite repeated attempts made to
replicate the trial, neither the intra- nor interspecies
responses documented in the initial “group” were ever
again observed. This outcome suggests that simply
randomly assigning a mix of animals may produce a group
dynamic that thwarts socialization of the group, and
highlights the complexity involved in attempting to conduct
free-ranging animal behavior research. Although all the
classic factors (age, gender, past handling history, and
numbers) were controlled by Anderson et al. (1987), there
remained a “psychological aspect” that was not possible to
replicate. The take home lesson was one must be concerned
with much more than simply numbers to understand animal
groups. Mirabet et al. (2007) developed an animal grouping
model for fish that accounted for numbers of individuals as
well as those influential neighbors that impact the grouping,
which too should be considered when attempting to model
cattle groupings.
WHAT CONSTITUTES A GROUP?
An ethology dictionary by Immelmann and Beer (1989)
did not provide a numerical definition for “group.” Gerard
et al. (2002) suggested group fusion and fission is dynamic
with group size and is often suboptimal. Furthermore,
cooperative and non-cooperative behaviors apparently
change with group size (Chase, 1980) and may be related to
predator avoidance strategies (Fortin et al., 2009). Haydon
et al. (2008) found in a herd of 100 elk studied over 4 years
that the spatial and social context an individual experiences
influences the amount of time spent in different movement
states, suggesting that social influences impact spatial
distribution. Spacing and social behavior also have been
investigated using animats (Stricklin et al., 1995). Many
behavior models that address animal groupings consider
predation to play a large part in this phenomenon (Reluga
and Viscido, 2005). Although this has a theoretical and
historical interest, the selection pressures of domestication
most likely have reduced the importance of this behavioral
trait in explaining group formation in domestic livestock.
Today, livestock groupings are almost totally under man‟s
control, which alters natural tendencies. The motion of
individuals in a group is assumed to be the combined result
of both density-independent as well as density-dependent
decisions (Gueron et al., 1996) and may even be related to
physiological variables, such as the size of the neocortex as
in primates (Dunbar, 1992). Furthermore, the resource
dispersion hypothesis (RDH) asserts that group living may
be less costly where resources are heterogeneous in space
or time (Johnson et al., 2002). Thus, the aggregation of
individuals into groups is a complex phenomenon and
rather than providing an explicit definition for animal
groups we frequently rely on our familiarity with common
animal aggregations (i.e., a flock of sheep or a herd of
cows) instead of addressing the mechanisms specific
species use to aggregate (Schellinck, 2008). The first
theoretical description of aggregate behavior may have
been given by Parr (1927) from observation of chub
mackerel (Scombrus coligs). Schellinck (2008) suggested
the movement of groups involves the use of an animal‟s
sensory and perceptual abilities to successfully aggregate
because there are no other apparent aggregation
mechanisms available to animals; thus, movement follows a
loop of information beginning with perception that leads to
cognition followed by action and then returning back to
perception. Warburton and Lazarus (1991) used computer
simulation to evaluate attraction-repulsion distance
functions to evaluate inter-individual social cohesion in
animal groups.
An optimal animal size group for livestock that remains
cohesive under all situations has yet to be determined
(Stricklin and Mench, 1987) and probably does not exist,
yet cattle herds are composed of social subgroups that
remain somewhat stable (Sowell et al., 2000). Bransby
(1989) suggested paddocks be stocked with at least 3 to 5
animals throughout a grazing period and that a completely
random design with rectangular paddocks oriented in the
direction of known or expected variation be used to
minimize experimental error. He also noted that an
experiment with less than 8 paddocks and 4 error degrees of
59
including electrocardiogram (ECG), electroencephalogram
(EEG), and respiratory signals from unrestrained animals is
now possible (Lowe et al., 2007). Although video
techniques have also been used with livestock (Sherwin,
1994), video is not the tool of choice when attempting to
document foraging studies. However, video techniques may
be useful at locations where an entire group may
congregate, such as at drinking water locations or where
mineral is provided. It is worth noting that a camera should
be placed on a tower for two reasons. First, the electronic
equipment is removed from the direct path of animals that
could damage the equipment. Second, it positions the
equipment at a height that increases the field of view and
the amount of information from the surroundings that may
be useful in interpreting the data of individuals. If video is
used in conjunction with other electronic data acquisition
techniques, a better understanding of group behavior may
result, especially if not all the animals within the group are
monitored or instrumented with individual devices.
The attachment of devices to monitor distance traveled
may have originated with pedometers (Anderson and
Urquhart, 1986). However, the question of “how is the
observer influencing the observation” must always be asked
when devices are attached to animals. In some cases, there
may be a negative effect on behavior because the mass of
the device may alter an animal‟s foraging range (Wilson et
al., 1986). Technology in the form of telemetry removes
observer influence, but there remains a dearth of research
addressing how present-day instrumentation affects
“normal behaviors” (Ropert-Coudert and Wilson, 2005).
Collars that self-adjust to changes in growth of an animal
have been designed for wildlife species, including
pronghorn fawns (Keister et al., 1988). Generally, behavior
altercation is normally not observed with domestic
livestock because of the small mass of the device relative to
the animal‟s mass and method of attachment (usually
collars or neck-body saddles) does not seem to have
negative long-term effects on behavior. Animals soon (few
hours to a few days) accept attached devices without
producing any unusual visual behavior. With the advent of
microelectronics began the focused study of foraging
strategies among free-ranging animals (Gordon, 1995). The
attachment of sensory and recording systems to animals has
been termed “bio-logging” (Naito, 2004). Modern devices
record animal data and are capable of recording data about
the external environment through which the animal is
moving (Boehlert et al., 2001). The smallest bio-loggers
available presently have been developed for avian research
(Ropert-Coudert and Wilson, 2005). von Hünerbein and
Rüter (2001) described a GPS flight recorder for homing
pigeons weighing less than 50 g.
Most livestock researchers focused on animal
distribution use GPS technology. This navigation and
precise-positioning tool was developed by the United States
Department of Defense in 1973 for locating objects
(Herring, 1996). Other techniques, including the use of very
high frequency (VHF) collars or tags for locating animals
predates GPS (Le Munyan et al., 1959; Rempel et al.,
1995). It should be mentioned that many of the tools and
techniques used today with free-ranging domestic animals
had their roots in the wildlife sciences. To learn more about
freedom (df) is hard to justify regardless of design. On
California rangelands, Harris et al. (2007) found the optimal
animal number to be 3 to 6 cows. Hernandez et al. (1999)
found 98% of feral cattle on the Chihuahuan Desert in
Mexico resided in groups of 1 to 20 while domestic cattle
were never found in groups of less than 30 and 90% of the
time the cows were in groups greater than 50. Lazo (1994)
found for feral cattle in Spain, the number of mature cows
in each of 4 herds was between 13 and 32 during autumn.
With the advent of global positioning system (GPS),
researchers must avoid drawing conclusions from data
coming from too few animals, not only from a statistical
stand point but more importantly, because of missing
individual behaviors that ultimately affect group behaviors
(Hunt et al., 2007). Girard et al. (2006) found that for
determining moose habitat, it was more important to sample
more animals than to increase the number of locations
sampled for each animal. Because of the inherent variability
among cattle, 5 animals per treatment are probably not
adequate when investigating beef cattle behaviors (BishopHurley et al., 2007). Between 10 and 20 animals per
treatment seems more realistic, but this will depend upon
the behaviors being investigated. If possible, a preliminary
trial should be conducted to investigate sample size prior to
conducting a larger study. Group size affects social
strategies in farm animals (Estevez et al., 2007) and
cognitive mechanisms in both wild and domesticated
animal species (Croney and Newberry, 2007).
Unfortunately, most studies on group size have focused on
penned (Christman and Leone, 2007) rather than freeranging animal groupings. A question yet to be answered is
how does manipulating free-ranging group size while
maintaining a constant stocking density affect behavior?
EQUIPMENT
One of the first decisions a researcher must make after
the objective of the research project has been established is
the equipment to be used to collect the data. There are a
number of sensing systems available, each suited to answer
specific questions under particular circumstances (Table 1).
Foraging animals may be observed directly or measured
with mechanical or electronic devices (Hart and Hoveland,
1989). The earliest information on time spent foraging was
obtained through visual observation over a period of days
(Gary et al., 1970). Regardless of the equipment used to
locate animals, including the human eye, there will be error
in determining the animal‟s exact location (Springer, 1979).
Vibracorders, devices developed to monitor the
operating times of logging trucks, may have been the first
commercially available device adapted to free-ranging
livestock providing a time stamped signature of the
temporal aspects of foraging (Allden, 1962; Stobbs, 1970).
Canaway et al. (1955) developed a recorder that nonsubjectively recorded walking, lying, standing, grazing and
cudding. Young (1966) developed equipment for recording
jaw movement patterns, and O‟Shea (1969) developed an
animal mounted device for recording jaw movements and
elapsed time. Devices have also been built for monitoring
physiological parameters for use in extensive animal
monitoring (Puers et al., 1993). Transmission of data,
60
equipment was bulky and required manual processing. By
combining electronic data acquisition using systems (i.e.,
GPS) with direct observation, daily behavior itineraries
have been accurately determined (Schlecht et al., 2006).
Using GPS fixes between 1 Hz (Anderson, 2007) and 4 Hz
(Swain et al., 2008) to evaluate foraging locations appears
promising. The optimal time interval between recordings
remains an issue in the geostatistical analysis of GPS
trajectory data (Hengl et al., 2008). Remple and Rodgers
(1997), using differential correction, were able to decrease
GPS location error from 80 to 4 m (P < 0.0001) and the
range of 25-75th percentile location error from 74.3 to 5.0
m. Location error in radiotelemetry data lowers the power
of statistical tests (Findholt et al., 2002). Therefore,
Zimmerman and Powell (1995) advocated for researchers to
use statistics derived from the linear distances between
actual and estimated locations of test transmitters to
estimate location error.
Precision agriculture (PA) has focused on understanding
agronomic spatial variability in soils and crops (Stafford,
2000); however, the sensors used on arable crops are
different from those necessary to discriminate plant species
on grassland (Schellberg et al., 2008). For example, texture
features and spectral characteristics of plant species may be
sufficient for accurate and fast identification of forbs,
whereas plant shape analysis may be more important on
arable landscapes. Determining gap spacing may be crucial
in grasslands. Lock et al. (2004) successfully calculated size
and shape of artificially created gaps in turf grass.
Landscape composition may have a larger impact on
foraging (utilization) patterns than grazing management
(Senft, 1989). The use of high resolution spectral sensors
and photography has a long history (Deering et al., 1975;
Flynn et al., 2008). Furthermore, this approach appears to
offer economic (Pickup et al., 1994) as well as biotic
(Starks, et al., 2004) and abiotic (Jackson, 1986) advantages
for landscape monitoring involving land degradation
(Graetz, 1987). Patch dynamics appear to be the most
recent focus to understanding ecological processes (Ludwig
et al., 2000; Butler, 2002), yet their role in foraging
herbivores is less well understood because herbivores tend
to exploit food resources that are generally continuous
(Searle and Shepley, 2008). Swain et al. (2008) suggested
that high duration GPS fix rates could be used to derive
speed histograms, and if used in conjunction with low fix
rate data, may establish the probability of an animal visiting
a given patch within a landscape.
Introducing technology into the equation for
determining animal distribution adds additional concerns
even though technology offers many opportunities (Cox,
2002). One of the challenges when using GPS data may be
to accurately differentiate between resting and foraging
because foraging has a very low forward trajectory due to
the inherent location error of current GPS devices (Schlecht
et al., 2004). Selecting a high GPS fix rate and carefully
selecting data sets with low Position Dilution of Precision
(PDOP) values may greatly enhance the accuracy of
activity assessment. Graves and Waller (2006) suggested
that an analysis of the pattern of missing fixes should
always be part of any trajectory analysis because the
radiotelemetry and its use in wildlife science, the text by
Amlaner and Macdonald (1980) is considered seminal.
Very high frequency radio tags were first deployed in
wildlife studies during the late 1950‟s (Kenward, 2001a)
and satellite tracking of animals began in the early 1970‟s
(Kenward, 1987) when location accuracies were in the 5 km
range (Gillespie, 2001). One possible reason equipment
developed for wildlife was not simultaneously used with
domestic livestock during its early history was that early
wildlife equipment was adequate for monitoring very
“coarse-grained” movements over very large areas, but the
technology had limitations for accurately monitoring
movements in relatively small areas, i.e., paddocks or
finer-scale temporal scales (Keating et al., 1991).
Accuracies of 5 m true position are now common under
open sky conditions with consumer-grade GPS receivers
(Wing et al., 2005), and with more sophisticated receivers,
0.01 m accuracy is possible (Bajaj et al., 2002). Hyperbolic
telemetry can improve the accuracy of VHF data to within a
few meters (Rodgers, 2001), but differential correction can
do the same for GPS data (Hurn, 1993; Rempel and
Rodgers, 1997). Moreover, Kenward (2001b) pointed out
that GPS receivers remain relatively expensive. A recent
effort to develop a small-animal satellite tracking system,
named International Cooperation for Animal Research
Using Space (ICARUS) will employ radio technology on a
near-earth orbit satellite for tracking animals (Wikelski et
al., 2007).
Using GPS does not come without challenges. Canopy
cover can attenuate GPS signal strength when tracking
wildlife (Dussault et al., 2000; DeCesare et al., 2005).
Antenna position with respect to the sky can have a
significant impact on number of GPS fixes that are missed
(D‟Eon and Delparte, 2005; Heard et al., 2008). However,
Hiroaki and Takaaki (2007) found that in Brazil even if
animals wore GPS equipment with their antennas pointed
downward the units still received radio wave signals
without significant problems. They attributed this to their
sampling position being near the equator. Bolstad et al.
(2005) found that a forest canopy produced significant
differences in the mean positional error of GPS receivers
whereas no statistically significant differences were
observed under open locations. Furthermore, GPS data
coming from different devices may not necessarily be
identical, which can lead to challenges when comparing
data among studies. Buerkert and Schlecht (2009) found
that 3 unnamed commercial GPS devices programmed to
capture fixes every 15 sec produced a similar (P > 0.05)
number of position fixes when operating in open terrain but
when the terrain was obstructed, the 3 devices produced a
different (P < 0.001) number of fixes. This was attributed to
factory settings of the GPS receivers, which evidently
placed specific weights on signal reliability and
trigonometric properties. The earliest studies to track
wildlife (Cochran et al., 1965) and free-ranging cattle
(Petrusevics and Davisson, 1975) required researchers to
build their own equipment. Techniques have become more
sophisticated and may include the use of stable isotopes to
track animal migration (Hobson and Wassenaar, 2008).
Canaway et al. (1955) may have been the pioneers in
adapting telemetry to foraging animals even though their
61
frequency of data acquisition can affect the path the animal
traverses (Schwager et al., 2007).
environment.
Furthermore,
animals
respond
to
environmental heterogeneity at different scales and in
different ways (Johnson et al. 1992). Other obvious factors
affecting animal movement are nutrients (this includes
water), forage quantity and quality, as well as weather
(Ehrenreich and Bjugstad, 1966) and other abiotic factors as
nebulous as slope and aspect (Turner et al., 2001). These
factors are all integrated through the physiological and
mental well being of the kind or mix of animals under
consideration. Foraging decisions are a balance between
motivation to eat and satiation (Baumont et al., 2000) and
they usually do not take place in isolation but in groups that
vary with season and forage condition (Dudzinski et al,
1982). Furthermore, Wolff (1993) suggested mammal
dispersal distances may not be completely explained by
resource competition. Over a 3 year study involving 29 Bos
indicus cows, certain individuals were found to form
preference partners and these preference partners were also
observed among calves (Reinhardt and Reinhardt, 1981). In
Montana foothill rangeland, cows tended to have preference
partners when the herd size was less than 30, but when the
herd size was greater than 30 there was no evidence that
cows had preference partners (Pollak et al. 2008). As a
result of these associations, foraging animals are
responsible for creating spatial patterns of sward
heterogeneity that change throughout utilization (Rook et
al., 2004).
Feeding rate may be a valuable tool for assessing the
degree to which an animal is constrained by its social
environment (Nielsen, 1999). In general, the time spent
foraging will lengthen in response to a decline in available
herbage (Hodgson, 1933) and animals foraging on a
heterogeneous resource will usually select a diet higher in
quality than the standing-crop can offer (Baumont et al.,
2000).
Tribe (1980) stated that the main challenge in future
grazing management research will be to improve the
efficiency of animal production from pastures.
Accompanying this is the need for statistical designs that
encompass the animal and its behavior (Manly et al., 1993).
Rutter (2007) stated that in order to understand the spatial
dynamics of foraging behavior, we need to combine
information on animal position, foraging behavior and
vegetation maps.
ANIMAL DISTRIBUTION
If stocking rate is the first variable to get correct when
managing free-ranging animals (Walker, 1995), surely
obtaining proper forage utilization by managing animal
distribution must be the second most important principle of
grazing management. What drives animal distribution and
how to manage it continue to be major research foci (Bailey
et al., 1996; DelCurto et al., 2005; Anderson, 2007). An
early review by Brown and Orians (1970) investigated
proximate causes, ecological consequences, and adaptive
significance of spacing patterns in mobile animals.
Foraging distribution is heterogeneous, especially in large
paddocks on extensively managed arid, semiarid, and
mountainous rangelands (Coughenour, 1991; Bailey et al.,
1996). Wildlife biologists have had the ability to track and
map free-ranging animal locations and movements for
decades even though the early data were relatively crude,
inaccurate, and imprecise (Sampson and Delgiudice, 2006).
The rich history of tracking wildlife has been documented
by Kenward (2001a). More recently, remote sensing has
been used for estimating terrestrial animal distribution and
diversity (Leyequien et al., 2007). Tracking animals is
mainly a research endeavor at the present time (Schellberg
et al., 2008). Determining what affects distribution is not a
trivial pursuit. Between 1926 and 2009, at least 68 factors
were identified that affected animal distribution (Table 2)
either alone or in combination and this list is far from
complete. Macdonald and Johnson (2001) believed animal
dispersal was one of the least understood aspects of animal
ecology in terms of conservation biology. Furthermore,
much of what is understood about mammalian dispersal
comes from the study of small mammals (Stenseth and
Lidicker, 1992); however, dispersal behavior among large
mammals may not be identical (Sinclair, 1992).
Furthermore, many approaches used to address free-ranging
livestock dispersal were borrowed from wildlife research,
and while principles involved in wildlife dispersal may be
applicable to livestock, dispersal within fenced paddocks
requires much discernment when making such linkages.
Early studies identifying factors affecting animal
distribution patterns, especially foraging, were primarily
descriptive (Senft et al., 1983) and involved following
animals and periodically recording locations on pasture
maps (Dean and Rice, 1974). This approach was followed
by using light aircraft to determine cattle distributions
(Low, 1978; Low et al., 1981). Such tracking usually took
place during early mornings, because behavioral studies
indicated that cattle were most likely to be in the vegetation
community in which they would spend most of their time
foraging. It was also assumed animals would take the
shortest paths from their grazing areas to water (Pickup and
Chewings, 1988). More recently, Fleming and Tracey
(2008) indicated that counting as well as recording animal
behaviors from aircraft was fraught with errors.
Animal movements are the result of complex
interactions between an individual animal and its internal
(Loeb, 1918) as well as its external (Forester et al., 2007)
COMBINING GEOGRAPHIC INFORMATION
SYSTEM AND GLOBAL POSITIONING
SYSTEM INFORMATION
It is impossible to find a single succinct and clear
definition of what constitutes a Geographic Information
System (GIS). Geographic information system is a
technological field that began in the late 1960‟s and early
1970‟s that integrates hardware and software for capturing,
managing, analyzing, and displaying geographically
referenced (spatial) data that can be referenced to locations
on the earth. Wong and Lee (2005) suggested beginners in
GIS may wish to consult online training courses such as
those sponsored by ESRI (http://www.esri.com/). Those
authors suggested their book called Statistical Analysis with
ArcView GIS (Lee and Wong, 2001) was the first serious
62
accuracy, yet neither data model is intrinsically superior
and persons who have a grasp of both tools are equipped to
develop the most efficient and accurate analysis (Price,
2006). In general, the raster approach is better suited for
depicting areas with inexact boundaries and can quantify
spatial variability within polygons but requires a large
amounts of disk storage; this is in contrast to the vector
approach that accurately represents points and linear
features having exact boundaries but can only apply an
attribute uniformly to an entire polygon and requires a large
amount of memory to perform spatial analyses of the data
stored in this format (Johnson, 1990). Pickup and Chewings
(1988), using band 5 Landsat imagery in the visible red part
of the spectrum (0.6-0.7 µm), spatially rectified if
necessary, were successful in determining the reduction in
percentage of vegetative cover as a result of foraging.
Processes that regulate foraging strategies are neither linear
nor density dependent across all scales. Extrapolation of
known plant-animal interactions across scales is
questionable; for example, how do measures of animal
productivity on small trials relate to production outcomes in
large paddocks (Roshier and Nicol, 1998). What works to
improve distribution at one scale should not be assumed to
improve distribution at all scales; thus, a combination of
techniques effective at different scales seems appropriate
(Hunt et al., 2007). Pickup and Chewings (1988) pointed
out that scale is important when different data are recorded
at different scales. They used Landsat pixels giving a
resolution of 57 x 79 m for mapping vegetation while their
cattle distribution data were only accurate to about 0.25 km;
hence, there was little point in modeling at the Landsat
pixel scale given the spatial resolution of the cattle data.
Had the Landsat data been aggregated into larger pixels by
re-sampling and smoothing, a significant loss of
information would have occurred that would have adversely
affected the accuracy of the vegetation classification system
they were using. Karnieli et al. (2008) found that a pixel
size of 171 m was small enough to monitor the effects of
livestock foraging gradients in concentric circles out from
drinking water for 6 km. It is beyond the scope of this paper
to discuss the complexity of pixels and their optimum size
except to say that geospatial research must be designed
such that field observations match what the satellite “sees”
(Cracknell, 1998; Weber, 2006). Semivariogram analysis of
California chaparral indicated that 6 m pixels would be
optimum for retaining most spatial variation of shrubs
(Rahman et al. 2003), however no one pixel size fits all PA
management requirements.
attempt to integrate spatial analytical and statistical
functions with GIS. Textbooks on the basic mathematics
and statistics relevant to GIS (Bluman, 2003; Allan, 2004;
Dale, 2005; Longley et al., 2005; de Smith, 2006; de Smith
et al., 2007) as well as many advanced texts (Haining,
2003) are also available. Methods have been proposed to
assess error associated with data in presence-absence
matrices (Murguia and Villasenor, 2000). Roloff et al.
(2009) points out that most people using GIS data did not
collect the GIS data and therefore may not be aware of the
data‟s specific limitations. Geographic information system
datasets or themes include both spatial and attribute data
(Wong and Lee, 2005). deSmith et al. (2007) pointed out
that using one GIS package on a given dataset can rarely be
exactly matched to results produced using any other
package or through hand-crafted coding, yet one product is
not necessarily better or more complete than another.
Combining GPS and GIS can provide a useful tool for
quantifying foraging distribution of sheep and goats
(Kawamura et al., 2005) as well as cattle (Barbari et al.,
2006; Brosh et al, 2010). Together GPS and GIS provide a
powerful toolset for studying free-ranging animal
distribution. Commercial products rarely provide access to
source code or full details of the algorithms employed even
though this information would aid greatly in understanding
reproducibility and further development (de Smith et al.,
2007). In addition to the software packages listed in Table
3, de Smith et al. (2007) list 46 GIS software packages,
some of which are free. Trade magazines such as
Geocommunity (http://www.geocomm.com/) provide ad
hoc reviews of GIS software, especially new releases.
Choosing the “best” tool is up to the user based on the
specific questions to be addressed, and at present there are
no standardized tests for the quality, speed, and accuracy of
GIS procedures (de Smith et al., 2007).
Johnson (1990) indicated GIS programs store and
manipulate data as either rasters (cells or pixels) or as
vectors (points, lines or polygons). Vector and raster refer
to two different kinds of mapping methods (Wade et al.,
2003). Geographic information system products should
support at least 2D mapping (display and output) of raster
(grid-based, which is widely used in environmental sciences
and for remote sensing) and (or) vector (point, line, or
polygon) data with a minimum of basic map manipulation
facilities (de Smith et al., 2007). When derivative
measurements are used to combine raster and vector data, a
conversion of all the data to either one or the other is
required for processing (Wade et al., 2003). Although
locational resolution for raster-based systems can be
improved by reducing pixel size data, volume will increase
(Shaw and Atkinson, 1990). Ludwig et al. (2004) found that
a 30 to 100 m pixel size from Landsat imagery is adequate
to assess the condition of rangelands in Australia along
foraging gradients extending from drinking water locations.
Conversion of vector data into a raster format may create
the modifiable areal unit problem (MAUP; Dark and Bram,
2007) that involves scale and zonation effects that can have
a definite impact relative to the analysis of spatially explicit
data usually present in various types of large-scale spatial
data analysis. It has been assumed that raster calculations
were faster whereas vector methods provided higher
PRACTICES INFLUENCING
GEOSPATIAL ANALYSES
The location of resources such as shade and a feeding
site can have a major impact on the spatial distribution of
cattle (Dolev et al., 2008). Although changing mineral
location will cause free-ranging cattle to move, Ganskopp
(2001) concluded moving water was more effective than
moving salt because the impact of the new salt location
waned after about 2 days and the cattle began drifting back
towards their previous location. Barnes et al. (2008)
suggested that distribution can be improved by switching
63
incorrect conclusions regarding whether relationships
between variables or factors are real (Wong and Lee, 2005).
For example, the positions of individual cows in a herd are
not independent observations but are auto-correlated
because animals tend to move together, precluding the use
of cows as replicates in parametric statistical tests (Harris et
al.,
2002).
However,
Bailey
(2010,
personal
communication) suggested that if a herd of ≥ 30 cow‟s
break into several small groups and the individuals that
compose each of the smaller groups change daily then it
may be possible to consider each member in the group as an
experimental unit. However, it is more likely that animals
form groupings with companions rather than strangers that
are stable over time to meet their social needs (Brunson and
Burritt, 2009). The positions of sequentially collected
animal locations may be serially correlated (Swihart and
Slade, 1985) and as such, use of these locations as the
sample size would constitute pseudo replication (Hulbert
1984). Cliff and Ord (1981) noted there are simple indices
to evaluate the level of spatial autocorrelation in statistical
analysis. Graphical methods for presentation of errors and
correlations in errors in spatial data are not fully developed,
yet a modeling procedure framework for handling variables
recorded at different resolutions has been recommended
(Buckland and Elston, 1993). Christman and Lewis (2005)
highlighted the importance of knowledge of the spatial
arrangement of the group before applying a particular
statistic because different test statistics may behave quite
differently under the same conditions. There are no
universal one-size-fits-all rules that apply everywhere and
at every scale; therefore, data from different scales or
spatial resolution levels do not yield the same statistical
results (Wong and Lee, 2005). Morales and Ellner (2002)
suggested that the main challenge for scaling up animal
movement patterns resides in the complexities of individual
behavior rather than in the spatial structure of the
landscape.
Classical parametric statistics not only describe how
values are distributed (descriptive) but are used to describe
or compare a population (inferential) (Wong and Lee,
2005). With classical statistics 1) samples are drawn from
populations in which the attribute would form a bell-shaped
curve, 2) the population variance is homogeneous within
the bounds of random variation, and 3) measurements to be
analyzed are continuous with equal intervals (Lehner,
1979). Statistical regression fits these assumptions and has
probably been the most common tool used in animal
science for population data analysis (Jones and Langemeier,
2009). Jones and Langemeier (2009) noted that data
acquisition under less tightly controlled experimentation, as
with free-ranging animal studies, will most likely lead to
severe violations of the underlying assumptions of the
classical linear regression model. Senft et al. (1983) were
the first to use regression as a means of predicting patterns
of cattle behavior, and observed nonlinear mathematical
relationships among behavior and pasture characteristics.
Their approach lacked both the derivation and model data
to incorporate effects of herd structure and social
interactions and their impact on spatial behavior, especially
large herds. Yet this approach had the important property of
boundlessness (i.e., not being site-specific). Subsequent
from extensive to intensive stocking; however, this level of
intensification requires fencing which is costly to purchase
and time consuming to construct. Cattle may not
necessarily choose to forage in areas with the greatest
quantity of standing crop but rather may prefer areas
dominated by annual grasses and forbs with lower biomass
(Hunt et al., 2007). James et al. (1999) found that uniform
heavy foraging across the whole landscape could be
detrimental to some species native to the landscape. Senft et
al. (1983) found that cattle used as little as 30% of a 125 ha
pasture and approximately 38 ha received over 60% of the
foraging. If this concept of less than non-uniform use of an
area is universally true, then stocking rate needs to be
calculated based on the areas actually utilized and not the
entire pasture. A first attempt at refining stocking rate
calculations may be to base it on the area of the landscape
on which animals actually traveled and did their foraging
(Anderson, 2007; Table 1) rather than assume the entire
area will be used uniformly. If this approach were
considered to refine the accuracy in calculating stocking
rate, one may find predictive relationship between the
actual area used and the total area available.
Drinking water developments have been advocated on
arid and semi-arid landscapes to promote more uniform
spatial foraging; however, based on the research of Li and
Wang (2006) it seemed that animals distribute themselves
out from the center of a resource point in concentric circles.
Water points tend to be a focus point for cattle in paddocks
9 to 57 km2 (Hunt et al., 2007). Stafford-Smith (1988)
suggested water points were seminal in developing foraging
models to address cattle and sheep distribution on arid
landscapes; however, piosphere (Washington-Allen et al.,
2004) models must take into account that utilization of
vegetation near water is different for wildlife than for
livestock (Thrash and Derry, 1999).
DATA ANALYSIS
Statistical tools involve techniques for collecting,
analyzing, and drawing conclusions from data (Snedecor
and Cochran, 1967), and although spatial statistics are
strongly based on classical statistics, the data are also
spatially referenced (Wong and Lee, 2005). Spatial
autocorrelation is essentially the nature of geography and
consequently will almost always be present in spatial data,
in contrast to data analyzed with classical statistical
techniques that assume observations and associated data
lack spatial dependency or autocorrelation (Wong and Lee,
2005). There is now general acceptance that individual
locations in spatial data lack statistical independence
(Kenward, 2001b). Kernoham et al. (2001) emphasized the
importance of sample size and autocorrelation of data in
estimating home ranges and animal movements. Griffith
(1987) offered a detailed analysis of the impact of spatial
autocorrelation. The preferred method to quantify
autocorrelation is to determine the Moran coefficient (MC),
and although spatial autocorrelation is well understood with
linear models, it is not well understood with generalized
linear mixed models (Griffith, 2009). Overall,
autocorrelation does not affect the accuracy of estimating
parameters in inferential statistics; however, it may cause
64
Miller 1965), it most likely is more complex. Fractal
geometry, although not widely used to characterize and
model animal movement paths, has been used to analyze
spatial location data using Lévy flight modeling and
generalized entropy (Hagen et al., 2001). Fractal analyses
may only work well in situations where the fractal
dimension of a movement path is scale-independent and
where animal movements are being analyzed (Turchin,
1996).
Geographic objects can be abstractly represented as
lines, polygons, or points, and if the data are points, they
may contain cartographic generalizations or location
inaccuracy; therefore, scale is critical for analyzing point
patterns (Wong and Lee, 2005). Boots and Getis (1988)
discussed detecting spatial patterns in point distributions.
The description of any spatial relationship between
individual points requires the application of a special type
of spatial statistics called centrographic measures
(Kellerman, 1981). In geospatial analysis, if a location or
the extent of a frame changes or if objects within a frame
are repositioned, the results will also change (de Smith et
al., 2007). Palancioğlu (2003) described these “movement
signatures” resulting in movement patterns of objects using
fuzzy and neuron-fuzzy approaches where uncertainties
exist in the movement of the objects. Swarming is another
approach that has been used in geospatial reasoning and is a
method to explore possible paths through a landscape
(Parunak et al., 2006).
Central tendency measures in the spatial context will be
the mean center used to define the centroid of a polygon
assuming that all geographic features have the same
weights or frequencies (Wise, 2002; Wong and Lee, 2005).
The concept of average in classical statistics can be
extended to the concept of geographic center, a measure of
spatial central tendency, yet, there is no absolutely correct
way to find the center of a spatial distribution (Wong and
Lee, 2005). Standard distance is the spatial analogy of
standard deviation in classical statistics and is expressed in
distance units, which is a function of the coordinate system
or projection adopted (Wong and Lee, 2005). In North
America, the median center is more often defined as the
center of minimum travel from all points (Wong and Lee,
2005). In its simplest form, systematic sampling selects
regularly spaced locations to ensure complete coverage of
the entire study and can be combined with random
sampling such that the geographical space is divided
systematically but sampling is random within each
partitioned region (Wong and Lee, 2005). Chang et al.
(2008) developed a spatio-temporal approach that identifies
abnormal spatio-temporal clustering patterns.
Geospatial or spatial analysis can be defined as the use
of analytical methods that are robust and capable of
operating over a range of spatial and temporal scales to
describe geographic based datasets using spatial software
(de Smith et al., 2007). Spatial analysis and spatial
statistics are more than a half century old (Wong and Lee,
2005). Furthermore, spatial analysis is a rapidly changing
field and increasingly, GIS packages are including
analytical tools as standard built-in facilities or as optional
toolsets, add-ins, or analysts (de Smith et al., 2007). Spatial
data sets are becoming more prevalent and quantitative
research by Senft et al. (1985) demonstrated yearling
heifers used forage quality and quantity to select
community preference using ad hoc regression models.
However, regression models do not imply causal
relationships between the variables being examined (Wong
and Lee, 2005). Regression approaches have been used with
points as well as site or grid square data; however, it may
be difficult to reduce the effects of spatial correlation using
this approach (Buckland and Elston, 1993). Krysl et al.
(1989) noted that Latin square designs are infrequently used
in range studies because of the potential row × column
interactions that make treatment comparisons valid but tend
to be conservative due to potential interactions. Partial least
squares (Stone and Brooks, 1990) have been used and may
be appropriate for behavior data because it allows modeling
in situations where the number of explanatory variables is
greater than the number of observations. Multivariate
techniques often perform poorly or are not applicable to
animal distribution data, especially categorical data such as
that used to describe vegetation types (Stockwell and
Noble, 1992).
Covariance analysis can be used when land replication
is not possible if the study has 3 or more stocking rates or
grazing pressures (Riewe, 1961). Animal-to-animal
variation is usually the greatest source of variation in
grazing trials; hence, properly applied covariance analysis
may be useful for identifying and controlling some sources
of animal variation in grazing trials (Petersen and Lucas,
1960). When production per animal or production per unit
area is the response variable of interest, the experimental
unit is the paddock and the animals that forage on it
(Bransby, 1989). Wang and Li (2005) have used stochastic
process and differential equations to discuss the spatial and
temporal patterns of foraging. Analysis of covariance
allows you to readily compare if the relationship between
grazing use and factors, such as distance to water, differ
between treatments (see Bailey et al., 2006 and Bailey et
al., 2008).
Geospatial techniques (Renolen, 1999) are currently
being used to track humans (Buliung and Remmel 2008),
wildlife (Millspaugh and Marzluff, 2001), and domestic
animals (Wade et al., 1998). Geospatial analyses are robust,
and capable of operating over a range of spatial and
temporal scales (de Smith et al., 2007). Spatial data are
unique in that observations describing geographic
phenomena or events have spatial dependency embedded in
them as described in the First Law of Geography (Sui,
2004). At the center of all spatial analysis is the concept of
place that requires a coordinate system to give a rigorous
and precise definition of place or location (Guptill, 2001;
Wong and Lee, 2005). Furthermore, geospatial data require
visualization, so analyses must include the creation and
manipulation of images on at least a 2D coordinate system,
to produce maps, diagrams, charts, or more likely, surface
analysis that involves 3D views of terrestrial activities and
their associated tabular datasets (de Smith et al., 2007).
Iliffe (1999) provided information on map projections.
Because free-ranging livestock can move in 3D space,
Renolen (1999) referred to them as “spatio-temporal”
objects. Although cattle distribution has been treated as a
transport theory or a first passage time problem (Cox and
65
external non-random environmental factors (Williams and
Gillard, 1971). In rangeland conditions, patch or mosaic
foraging may be even more pronounced than in mono- or
di-cultures where dung fouling and location of water and
minerals may inhibit uniform foraging and patch formation,
and in turn, creates difficulty in assessing the forage
availability (Coleman et al., 1989). Hill (2004) assembled a
comprehensive assessment of remote sensing methods and
applications for grasslands.
methods to accurately understand the phenomena are under
investigation (Unwin, 2009). The text books by Wong and
Lee (2005) and de Smith et al. (2007) provide basic
statistical concepts along with ready-to-use tools and
programs in spatial analysis and statistics that are integrated
with highly accessible GIS packages. Table 4 lists 46
different mathematical approaches that have been used to
evaluate animal distribution and its various components.
Austin (2007) suggested that the combination of ecological
knowledge and statistical skill is more important than the
precise statistical method used to evaluate species
distribution, especially for conservation and climate change
management.
Most software packages have been developed for
commercial use rather than research, making it difficult to
use “canned” programs to manage data and develop new
statistical methods for evaluating animal trajectories
(Calenge, 2006; Calenge et al. 2009). Fotheringham et al.
(2002) addressed the geographically weighted animal
distribution results based on animal movement that consist
of several parts, including animal trajectories that have
recently been evaluated by Calenge et al. (2009). Tortuosity
of an animal‟s trajectory may provide information on the
animal‟s foraging strategies (Benhamou, 2004). The
tortuosity of an animal‟s trajectory tends to reflect the
intensity of the animal‟s search for food and for this reason
has been studied in great detail (Calenge, 2009).
An animal‟s use of its habitat is determined by its
trajectory (Aebischer et al., 1993), and the number of
animals used to obtain accurate data is critical. Aebischer
et al. (1993) suggested 6 radio-tagged animals constitute an
absolute minimum number when evaluating habitat use and
this number should definitely be above 10, and preferably
above 30, if attempting to describe a population since
individual behavior varies. Thus, pooling data across
animals is justifiable only if they do not differ.
Wong and Lee (2005) indicated that the first effort to
integrate spatial analysis and statistical functions with GIS
began in 1999. Geospatial analysis is concerned with where
something happens and makes use of geographic
information that links features and phenomena on the
Earth‟s surface to their locations (de Smith et al., 2007).
Geospatial analysis reflects the melding of the broad field
of spatial analysis (Cressie, 1991) with the latest generation
of Geographic Information System (GIS; Longley et al.,
2005) and related software (de Smith et al., 2007). An
interactive text version on geospatial analysis is available
on the web (www.spatialanalysisonline.com).
When certain a phenomenon occurs, it may be due to a
random process or a systematic process, and if part of a
systematic process, the numeric or spatial patterns will be
of interest (Wong and Lee, 2005). Procedures have been
developed, based on random processes, to allow us to
determine if a sample is a reliable representative of the
population (Wong and Lee, 2005). Griffith and Amrhein
(1991, pp 215) presented a sampling scheme that was
designed to accommodate the sampling of observations in
the geographic space referred to as spatial sampling (Wong
and Lee, 2005).
Pattern analysis can be complementary to statistical
analysis of grazing experiments, especially if influenced by
MODELS
Models provide a logical framework in which to
evaluate factors that impact a process. Modeling real world
phenomena in space and time is a non-trivial task (Renolen,
1999). Basically all wildlife habitat evaluation models have
attempted to evaluate the carrying capacity of the wildlife
habitat for a particular wildlife species (Kushwaha and Roy,
2002). Van Dyne et al. (1980) listed 75 models that involve
large herbivore foraging; only 1 was built to handle feeding
behavior. There is no standard or current best practice when
modeling a species environmental niche or geographical
distribution, whether plant or animal Austin (2007). Certain
models, such as the Hierarchical Object Orientated
Foraging Simulator (HOOFS) have been used for
experimentation with individual-oriented, spatially-explicit
herbivore vegetation systems (Beecham and Farnsworth,
1998).
Recent models have been developed that will detect
areas of over- as well as underutilization using Landsat-TM
imagery (Röder et al., 2007). Although Wiegand et al.
(2008) provided a historical development in ecological
theory to describe grazing models that stimulated and
mirrored the conceptual changes and advances in the
understanding of grazing systems, no model will ever allow
one to take into account the entire complexity of animal
behavior, and no universal recipe is available to analyze
animals‟ trajectories (Calenge et al., 2009).
Models are being developed using GIS data that
integrate cattle nutritional requirements with satellite databased standing crop data to estimate foraging capacity on
small (1 to 20 ha) paddocks (Phillips et al., 2009); however,
animal behavior may not be simultaneously considered in
these models. Furthermore, not all models appear to predict
the distribution of foraging animals. Kennedy and Gray
(1993) found the Ideal Free Distribution (IFD) model
consistently under matched the distribution of resources
with the distribution of organisms, suggesting that to
accurately predict the foraging distribution of animals,
much more information is required than just the relative
distribution of resources. Marion et al. (2005) developed a
model involving the interaction of the sense of sight and
smell that demonstrated the importance of individual
animal behaviors. In this model, sight attracted an animal to
a particular location based on standing crop height and
subsequently smell determined forage consumption based
on the presence or absence of fecal contamination.
Movement models have been constructed in an attempt to
describe animal movement (Farnsworth and Beecham,
1999; Codling et al., 2008). In general, models that involve
information garnered from field observations may provide a
66
grazing that uses a simple general formula for predicting
sustainable levels of grazing if local site productivity is
known; however, it lacked the ability to evaluate the effects
of trampling and uneven grazing distribution.
Regression modeling rarely examines correlation of
variables in terms of a process (Austin, 2007). The
Geographically Weighted Regression (GWR) model was
developed to accommodate non-stationary parameters in a
regression framework (Fotheringham et al., 2002).
Techniques that address stationary parameters in spatial
regressions have been addressed by Griffith (1988; 2003).
Although random walk models suggest randomness, these
models can be made very complex and nonrandom, being
limited only by the capacity of computers to simulate and
the capacity of the human brain to assimilate the results
(Turchin, 1996).
Using and combining more than a single method to
describe foraging of free-ranging animals appears useful.
Guo et al. (2009) developed a hierarchical modeling
methodology that successfully combined Hidden Markov
Models (HMM) and long-term prediction to describe the
behavior among 6 cows foraging on 7 ha of tame pasture in
Queensland, Australia. Doug Johnson at Oregon State
University and a team have developed a computerized
multifactor decision making tool that incorporates
landscape parameters to determine the suitability of each
site on the landscape in terms of animals usage (KRESS,
2006). More recently, Horne et al. (2008) developed a
model that simultaneously couples home range behavior
and resource selection analysis into a single model that
links statistical and process-based movement approaches to
home range modeling. Although patch utilization models
appear to work with grazing herbivores, mounting evidence
suggests applying patch optimization models to browsers
may be incorrect (Searle and Shipley, 2008).
richer set of biological detail than models that attempt to
describe individual space-use patterns without this
information (Don and Rennolls, 1983).
For those that deal with livestock herd models, the
question always arises concerning the degree of agreement
between the model and the actual system being modeled
(Sørensen, 1990). Most models implicitly assume
homogenous or, at most, very simple heterogeneous
environments in which no interaction between individuals
is assumed (Codling et al., 2008). This is simply not the
case for domestic livestock confronted with complicated
environments that affect movement behavior (Vuilleumier
and Metzger, 2006). Furthermore, with gregarious (i.e.,
herd or flocking) domestic animal species, interactions
between and among individuals exist and these interactions
have an important effect on overall animal dispersal
(Couzin et al., 2005). Models have been developed to
examine not only group behaviors (Stillman, 2008) but also
behavior between animal pairs (Rands et al., 2008). Okubo
(1980) modeled the spread of animals as a diffusion process
using the suite of geostatistical methods known as kriging
(Matheron, 1965). Kriging was the name given by Prof.
Georges Matheron in honor of the South African mining
engineer Danie Krig (Krige, 1951) who developed an
optimal spatial regression geospatial interpolation technique
for making spatial predictions at unobserved locations using
point data from observed locations (Bailey and Gatrell,
1995; Stein, 1999). Cressie and Kornak (2002) derived a
trend-estimation and kriging methodology for geostatistics
in the presence of location error. Kriging has been used to
describe animal distributions because of its ability to make
predictions of processes in space while taking into account
spatially correlated observations. However, kriging has the
disadvantage that it is designed for point data rather than
site or grid data. Cokriging refers to a technique used with
spatially sampled curves, such as the dives of elephant seals
(Nerini et al., 2010).
Because many factors affect distribution, it should be
recognized that as factors included in a model increase the
complexity of the model, a concomitant reduction in model
reliability occurs (Wang and Li, 2005). Horne and Garton
(2006) suggested that using multiple models may be more
appropriate than using a single model. Horne et al. (2008)
suggested that fitting more than a single model to data is
appropriate in order to reflect all the different assumptions
and (or) hypotheses concerning the individual‟s use of
space, and models should vary in complexity to guard
against over- or under-fitting models. Hybrid models exist
that utilize not only a motion function but also movement
patterns useful in forecasting an objects future location
(Jeung et al., 2008). Farnsworth and Beecham (1999)
proposed a mathematical model that combines features of
cognitive and non-cognitive navigation and unites foraging
behaviors at different scales in order to model a wide range
of behaviors.
Regarding use of space (home-range), Trail and Bigalke
(2006) used a GIS-based model, using presence–only data
collected with a GPS receiver, to predict habitat suitability
for large grazing ungulates. They found that greenness of
grass was the main determinant of habitat utilization. Read
et al. (2002) described a simple process-based model for
HOME RANGE
The term home range was coined by Burt (1943), and
although it is usually associated with wildlife, the term can
also be used with livestock. Furthermore, home range is
defined without reference to the presence or absence of
particular types of behavior (Brown and Orians, 1970). It
refers to the area in which an individual conducts its normal
activities of food gathering, mating, and caring for young.
Burt (1943) makes a distinction between home range and
territory in that territory is an area an animal will protect by
fighting or display of aggressive gestures towards others of
its own kind. In 1988, Reiss wrote “we still do not know the
precise reasons why animals have the home range sizes
they do.” Roath and Krueger (1982) suggested cattle home
ranges persist from year to year. Using both GPS and GIS
data, the “kernel home-range” method has been used to
evaluate cattle grazing (Barbari et al., 2006). Home ranges
(areas, rarely found in convenient geometric designs) are
not static over time and the home ranges of different
individuals may and do overlap and these areas tend to vary
with sex, age, animal density, and season (Burt, 1943).
Loehle (1990) noted that fractals may be an appropriate
way to investigate home range since within a home range
an animal will use some areas repeatedly while other areas
67
involves gathering data focused on animal distribution and
the second involves appropriately analyzing it.
Unfortunately, the lack of theory and the lack of data
appear to be mutually reinforcing (Grünbaum, 1998).
Global positioning system technology currently seems
to be the preferred tool of choice to study the spatial and
temporal patterns of landscape use by free-ranging animals.
Fully understanding the geographical linkage of GPS
animal location data within rangeland landscapes will
require range animal ecologists to work as members of
research teams composed of a number of different
disciplines, including statisticians and modelers trained in
geospatial mathematics. Based on the literature perused for
this paper, there does not seem to be a single protocol that
combines GPS and GIS into a “single best” package for
evaluating all situations involving animal distribution. The
wildlife profession has led in much of the foundational
research on animal distribution. The range-animal livestock
ecologist must adopt or modify existing approaches using
discernment while developing new tools, techniques, and
protocols where those in wildlife either do not exist or are
inappropriate. Banhazi and Black (2009) suggested that the
greatest technological progress in precision livestock
farming will come from developing new technological tools
rather than fine-tuning existing methods. Furthermore, the
most exciting application of a new technology may not
come from its intended application but when it is applied
outside its original field (Rogers, 2001), and here is where
assembling teams with the required experience and
expertise can provide their greatest benefit. Current
precision agriculture methodologies are focused on arable
land rather than grassland (Schellberg et al., 2008). This
will change as sensor technology advances concurrently
with our understanding of how free-ranging animals use
space and how best to manage the plant-animal interface.
One broad area of research that is needed immediately
involves determining which animals and how many should
be assembled to form stable spatial and temporal freeranging groups designed to accomplish a specific goal for a
particular landscape and configuration of paddock(s) and
associated working facilities. The future will encounter
larger and larger volumes of data that must be processed,
summarized, and analyzed thus forming ever growing
linkages of animal data with GIS data from landscapes,
producing increasingly complex geospatial models
(Kenward, 2001b). As this occurs, research specifically
addressing optimum geospatial protocols for applying
statistical techniques to evaluate free-ranging livestock
distribution seems to be a fruitful area to parallel biological
investigations into free-ranging animal behavior.
are never used and all this kind of detail is lost if a line is
simply drawn around the points where the animal has been
seen. Might fractals be used to calculate more accurate
stocking rates?
Both parametric and nonparametric methods can be used
to investigate home-range data, but nonparametric
approaches have the great advantage of flexibility (Worton,
1989). Worton (1987) has described and compared various
home range models and considers the kernel method and
Fourier Transform method to provide good flexibility.
Autocorrelation should also be considered in radio-tracking
data especially those used for home range estimation (Otis
and White, 1999).
Kernel-density estimation (KDE) is one of the most
widely used home range estimators in ecology (Hemson et
al., 2005); however, results of Hemson et al. (2005)
indicated that it may not be the most reliable method for
estimating home range. Kernel-based estimates from small
samples are poor for identifying fine structure and will
over-estimate the home range size (Seaman and Powell,
1996). The disadvantage of kernel analysis lies in its failure
to recognize the asymmetry of animal movement patterns,
and “canned” programs for kernel utilization distribution
(UD) estimation do not account for this asymmetry
(Amstrup et al., 2004). Kernel methods consist of placing a
kernel (a probability density) over each observation point in
the sample and if kernel-based estimates are derived from
small samples, the results will overestimate home range
size (Seaman et al., 1999). Therefore, Seaman et al. (1999)
recommended that home range studies using kernel
estimators should have representative samples from over 30
locations and preferably more than 50. Because of the
importance of sample size, it was strongly recommended
that authors report sample sizes in addition to the exact
home range estimation method used.
Comparing home range size estimates among
researchers can lead to inaccurate conclusions (GalleraniLawson and Rodgers, 1997). Those authors found that
using the same data to calculate home-range sizes with 5
commonly used home-range programs to determine
minimum convex polygon, harmonic mean, and kernel
estimators at 3 levels of resolution (95%, 75%, and 50% of
locations) produced different results. They further
suggested research manuscripts include in their materials
and methods section the home range estimator software
used, the reason it was selected, the user-selected options,
and input values of required parameters.
CONCLUSIONS
Monitoring free-ranging animals to accurately quantify
and subsequently understand distribution is not a trivial
task. Asking the proper researchable questions in the proper
sequence, using the correct equipment to collect data, and
finally analyzing data using the most appropriate geospatial
statistical tools are the essential steps required to reach
accurate conclusions. Although the list of factors currently
known to affect animal distribution is lengthy, most
scientists would agree the list is incomplete and more
research is warranted (Bailey, 2005). The type of research
needed can be divided into two broad areas. The first
SPECIAL CONSIDERATIONS
The literature reviewed in this paper has revealed
several points that bear consideration when conducting
research into animal distribution. First, there remains a need
to focus on understanding how abiotic as well as internal
and external biotic factors drive group fission and fusion
and the subsequent leadership within groups. Second,
experimental designs should attempt to embrace how the
animal perceives its world rather than how the researcher
68
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perceives the world. Third, outliers normally considered a
nemesis to most scientists attempting to compare treatment
means may provide more useful information to
understanding free-ranging livestock behavior than does the
mean. If this is true, new and innovative statistical tools will
need to be developed to analyze behavioral data. Forth,
because behavior among free-ranging individuals can be
quite variable, behavioral studies should be conducted using
larger animal numbers than considered minimal for
classical animal science nutrition research. Fifth, because
size and composition of animal groupings affect behaviors,
applying research results should be done conservatively
unless conditions are “identical”. Sixth, statistics should
only serve as a suggestion for the presence or absence of
behaviors that may have cause and effect relationships.
Seventh, livestock behavior has been influenced by man‟s
selection pressures; therefore, extrapolating wildlife
behaviors to domestic animals should only be done with
discernment. Eight, how best to collect and analyze freeranging animal behavioral data aimed at understanding
animal distribution is not a trivial undertaking; therefore,
developing workshops focused on bringing together the
various disciplines required to address the most pressing
questions concerning free-ranging animal behavior seems
appropriate. Workshop topics should address the following
questions. What part of animal behavior studies can and
should be replicated? What experimental designs should be
used to examine the spatial and temporal formation of
groups? What geospatial tools provide the most accurate
results for predicting distribution behaviors? What part of
free-ranging animal behavior studies should be standardized
to make results more universally applicable?
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Wong, D. W., and J. Lee. 2005. Statistical analysis of
geographic information with ArcView GIS® and
ArcGIS®. John Wiley and Sons, Inc. Hoboken, NJ.
Workman, J. P. and J. F. Hooper. 1968. Preliminary
economic evaluation of cattle distribution practices on
mountain rangelands. J. Range Manage. 21:301-304.
Worton, B. J. 1987. A review of models of home range for
animal movement. Ecol. Model. 38:277-298.
Worton, B. J. 1989. Kernel methods for estimating the
utilization distribution in home-range studies. Ecol.
70:164-168.
Wu, H-i., B. L. Li., T. A. Springer, and W. H. Neill. 2000.
Modeling animal movement as a persistent random walk
in two dimensions: Expected magnitude of net
displacement. Ecol. Model. 132:115-124.
Young, B. A. 1966. A simple method for recording of jaw
movement patterns. J. Inst. Anim. Tech. 17:20-21.
Zar, J. H. 1974. Biostatistical analysis. Prentice Hall, Inc.,
Englewood Cliffs, NJ.
81
Zimmerman, J. W. and R. A. Powell. 1995. Radiotelemetry
error: Location error method compared with error
polygons and confidence ellipses. Can. J. Zool. 73:11231133.
Zuo, A. and M. S. Miller-Goodman. 2003. An index for
description of landscape use by cattle. J. Range Manage.
56:146-151.
82
83
Distance traveled
Temporal foraging
Distance traveled
Location and movement
Group domestic animal activity
Remote photography; animal borne
video
Foraging
Information available to animal
trackers
Activities
4
5
6
7
8
Foraging location
Activity and inactivity
Food choices
Animal location
Video face detection
Animal location
Bite placement
Animal identification
Activity and inactivity
Bite counter collars
Telemetry system
Steps during foraging
Lying, standing, and walking
Tracking
13
14
15
16
17
18
19
20
21
22
23
24
25
26
12
11
10
9
Walking
3
Videorecording
Accelerometer; IceTag® (IceRobotics, Edinburgh, UK)
UNETracker
GPS/GIS/electronic compass
Developed by National Agricultural Research Center, Hokkaido
Pitch-roll sensor
Radio frequency identification (RFID)
Video/acoustic
Global positioning system (GPS)
Gregorini et al. (2009)
Darr and Epperson (2009); Trénrl et al. (2009)
Trotter (2009)
Gilsdorf et al. (2008)
Hiroaki and Takaaki (2007)
Umstatter et al. (2006)
Roberts (2006)
Griffiths et al. (2006)
Clark et al. (2006)
Burghardt and Ćalić (2006)
Ropert-Coudert and Wilson (2005)
“Smart dust”
Kanade-Lucas-Tomasi method
Beringer et al. (2004)
Müller and Schrader (2003)
Porath et al. (2002); Agreil and Meuret (2004)
Harris et al. (2002)
Blake et al. (2002)
Pedersen and Pedersen (1995)
Cutler and Swann (1999); Robert et al. (2009); Moll
et al. (2009)
Gibb et al. (1999)
Kao et al. (1995)
Anderson and Urquhart (1986)
Allden (1962); Stobbs (1970); Lathrop et al. (1988)
Dwyer (1961)
Animal mounted wireless video camera
Actiwatch®
Observation
Night-vision binoculars
Field computer
Passive infrared detectors
A review paper covering various types of photography; video plus
other sensors
Jaw movement sensor
Touch panel position sensor (TPPS)
Pedometers
Vibracorders
Following animals in a vehicle
Series of switches; accelerometers
Canaway et al. (1955); Robert et al. (2009)
Castle et al. (1950); Moreau et al. (2009)
Grazing behavior
2
Infrared equipment; tri-axial accelerometer
Cory (1927)
Citation(s)
Table 1. Equipment for monitoring animal movement and (or) foraging
No.
What is recorded
Sensing system
1
Distance traveled
Following animals on foot
84
Water
Mineral
Stocking rate and season
“Family groupings”
Supplementation
Shade
Mowing
Direction of wind
Herding
Nitrogen fertilization
Time of day
Distribution of experimentally
altered animals may differ
between collection and noncollection periods
2
3
4
5
6
7
8
9
10
11
12
Size of paddock
Class of animal
Plant community composition
Burning
Herd leadership
External parasites
Animal groupings and fences
Herbicides
Supplement timing
Tree overstory
Rotating animals to a new
paddock
Standing crop
Topographic features
15
16
17
18
19
20
21
22
23
24
27
26
25
Barometric pressure and wind
speed
14
13
Salt
1
Items such as brush, cliffs, and ravines affect distribution
Previous year‟s dead standing crop affects current year use.
Movement of cows to a new paddock increased mean daily travel 30%.
Horn flies increased cattle travel.
Sterile bulls spent more time closer to fences and corners than steers when heifers were
present in an adjoining paddock.
Cows preferred foraging on plots treated with 20% tebuthiuron pellets compared to
untreated plots regardless of grass species in the plots.
Steer supplemented in the AM tended (P < 0.2) to graze longer than steers supplemented
in the PM while steers receiving no supplement traveled less (P < 0.02) than either AM
or PM supplemented steers.
Cattle prefer foraging under open canopied forest compared to a dense canopy.
Certain cows may initiate movement and direction affecting the entire herd.
Yearling cattle can travel further than pairs.
Forage consumption of yearling cattle increased as grass production increased and forest
density decreased.
Cattle preferred burned to clipped or control treatments of Agropyron spicatum.
Time spent foraging and ruminating increased in response to either a rising or falling
pressure but these changes were short lived while distance travel was inversely
correlated to mean wind velocity.
Subdividing large paddocks into smaller units improves distribution.
Esophageally fistulated cattle may forage closer to fences and corrals during sampling
compared with periods of non-sampling.
Cattle foraged during the night and loafed around drinking water during the day.
Animals are drawn to green forage.
Optimizing spatial and temporal decisions based on experience.
Artificial shade can alter cattle dispersion.
Cattle preferred mowed brush treatments, especially in the first year following the
treatment.
Cattle and horses graze with the wind; sheep and goats graze into the wind.
Supplemented cows walked less than non-supplemented cows.
Strategic placement of salt.
Cattle under heavy stocking compared with light stocking exerted more “vigorous”
foraging with total length of daylight foraging increasing from August to October.
Certain animals prefer to remain in the presence of known peers.
Forage utilization decreases as distance from drinking water increases.
Will aid in the distribution of free-ranging cattle.
Table 2. Various attributes that have been shown to affect animal distribution with selected references
No.
Attribute
Observation
Selected references
Senft et al. (1987)
Norton and Johnson (1986)
Anderson and Urquhart (1986)
Mitchell and Rodgers (1985)
Adams (1985)
Scifres et al. (1983)
Hinch et al. (1982)
Harvey and Lauchbaugh (1982)
Reinhardt and Reinhardt (1981)
Willms et al. (1980)
Clary et al. (1978)
Arnold and Dudzinski (1978)
Sato et al. (1976); Hunt et al. (2007)
Malechek and Smith (1976)
Dean and Rice (1974)
Workman and Hooper (1968); East (2003)
Hooper et al. (1969); Smith and Lang (1958);
Samuel et al. (1980)
Schmidt (1969)
Hafez (1968); Squires (1978)
Powell and Box (1966)
McIlvain and Shoop (1965)
Box et al. (1965)
Skovlin (1957)
Peterson and Woolfolk (1955)
Williams (1954)
Chapline and Talbot (1926); Martin and Ward
(1973)
Valentine (1947); Franklin et al. (2009)
85
Memory
Herd size
Drought
Animal memory
Attention of the livestock
manager
Slope
Supplement and walking
Size of animal groupings
Fencing
Body size
Patch size and condition
Interspecific socialization
Early life experiences
Historical foraging patterns
Selection
Trails
Visual cues
Animal experience
Supplement placement
Diurnal patterns
Shape of paddock
Size of preferred patch
Domestic stock may affect
wildlife distribution
Fire
Integration of several factors
simultaneously
Kind of animal
29
30
31
32
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
54
53
52
51
34
33
Rotation schedules
28
Cattle travel further from water than goats, which travel further than sheep while feral
pigs are more reliant on access to water than livestock and rabbits are not dependent on
surface water.
Fires may promote more uniform grazing following a burn.
Season of use, supplement placement, drinking water development, and herding
managed simultaneously can positively affect distribution.
Mule deer and elk avoided areas used by cattle.
Yearling steers could associate visual cues with feed quality to forage more efficiently
Animals unfamiliar with an area widen their searching leading to a more uniform
distribution during foraging compared with experienced animals.
Improved utilization from 0 to 600 m from supplement location during late summer,
autumn, and winter.
As daylight decreases, night time foraging tends to increase.
Sheep tend to utilize herbage more efficiently in square rather than rectangular pastures;
similar data does not exist for cattle.
Heifers and ewes preferred larger patches over smaller patches of ryegrass.
Current foraging programs seldom consider the previous years utilization
Steers apparently use different processes when selection between compared to selecting
within feeding stations.
Cattle prefer to use trails.
Small and medium sized cows were more selective in their diets compared with males.
Steers selected short and tall patches of Agrostis/Festuca and Lolium grasslands over
stemmy patches but preferred short patches.
Bonding of small ruminants (sheep and goats) to cattle will alter the spatial distribution
of the small ruminants in free-ranging conditions.
Cattle tend to return to areas in which they were raised as calves.
Creating smaller paddocks forces more use of the entire area.
Cows remember locations and previously selected diets.
With the implementation of grazing systems more attention to all aspects of management
may increase, including those affecting distribution.
Cattle avoid slopes greater than 7%.
Steers supplemented with cotton seed meal (CSM) did not spend more time walking
than unsupplemented steers grazing wheatgrass pasture.
Bison calves followed dams more when in small groups than in larger groups
Cattle forage more evenly when rotation schedules were timed to plant growth not fixed
calendar dates.
Past foraging locations may be remembered by cattle up to 8 h.
Behavior in a herd of 3 cattle is likely to be atypical when herd size varied between 3
and 24 animals.
Cattle tend to disperse.
Table 2 (Continued). Various attributes that have been shown to affect animal distribution with selected references
No.
Attribute
Observation
Pringle and Landsberg (2004)
Bailey (2004)
Archibald and Bond (2004)
Stewart et al. (2002)
Dumont et al. (2002)
Sevi et al. (2001); Laca (2009)
Linnane et al. (2001)
Bailey et al. (2001)
Ksiksi and Laca (2000)
Howery et al., 2000
Ganskopp et al. (2000)
WallisDeVries et al. (1999)
Vavra and Ganskopp (1998)
Howery et.al. (1998)
Anderson (1998)
WallisDeVries and Daleboudt (1994)
Lazo and Soriguer (1993)
Hart et al. (1993)
Green (1992)
Barton et al. (1992)
Pinchak et al. (1991)
Hart et al. (1991)
Bailey and Rittenhouse (1989)
Pick and Chewings (1988)
Hacker et al. (1988)
Bailey (1988)
Anderson (1988)
Selected references
86
Breed of animal
Intensive vs. extensive
management
Conspecific attraction
Man made structures
Visual cues
Coat color
Socioeconomic factors
57
60
61
62
63
Cow age (experience)
Use of new management
technologies
68
Conspecifics
67
66
65
64
59
Seasonal travel and breed
differences
Season
Cow phenology
56
58
Standing crop composition
55
Cattle tend to group up when attempting to cross a virtual fence line.
Cattle used roads as paths of least resistance.
Heifers grazed more high-quality patches compared to low quality patches when visual
cues were placed in those patches.
Holstein Friesian dairy cows with predominantly black coats used shade less than cows
with predominately white or black and white coats.
Government regulations may prevent producers from implementing ecologically optimal
management strategies.
Cows decreased daily walking even as herbage quality declined between spring and
summer; Baladi cows walked longer daily distances than Beefmaster x Simford crosses.
Heifer foraging time increased between June and September.
Naïve calves showed a higher foraging activity on their first day on pasture when in the
presence of experienced conspecifics compared to naïve calves left on their own.
Cows greater than 3 years-of-age preferred areas of less canopy closure; young cows
selected lower elevation and steeper slopes compared with the oldest cows.
Cattle prefer to forage on standing crop that has previously been grazed (green)
compared to foraging in areas with both green and cured material, suggesting utilization
patterns are self-sustaining.
Cows that were heavier and taller traveled further (horizontally) from water than did
lighter cows.
Spanish (Corriente) cattle travel further than Angus and Herefords.
Cattle managed under intensive management foraged more evenly over the landscape
compared to cattle managed under extensive management.
An unexplored potential mechanism possibly affecting home range behaviors.
Table 2 (Continued). Various attributes that have been shown to affect animal distribution with selected references
No.
Attribute
Observation
Wark et al. (2009)
Walburger et al. (2009)
Hessle (2009)
Hejcmanová et al. (2009)
Aharoni et al. (2009)
Wiegand et al. (2008)
Tucker et al. (2008)
Renken et al. (2008)
Cooper et al. (2008)
Börger et. al. (2008)
Barnes et al. (2008)
Sheehy (2007)
VanWagoner et al. (2006)
Ganskopp and Bohnert (2006)
Selected references
87
Tracking Analyst
BIOTAS
Excel
HOME RANGE*
LOAS
LOTE
Lotus 1-2-3
MacComp
MARK
MATLAB
NCSS
QuattroPro
RANGES 8
5
6
7
8
9
10
11
12
13
14
15
16
17
Operating
system
D
W
W
M
W
D
W
W
W
W
D
W
A
M
M
D
D
F
M
x
x
x
x
x
x
x
x
x
x
x
x
Data formatting
and error
checking
Autocorrelation
x
x
x
x
x
x
x
x
Travel
path
Animal
interactions
x
Kernel
F/A
F/A
F
F/A
F/A
A
X
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
X
X
X
x
x
x
x
X
x
Harmonic
mean
Bivariate
normal
Fourier
Minimum
Convex
Polygon
x
x
x
x
http://www.anatrack.com/
http://www.esri.com/software/arcview/index.html
http://www.absc.usgs.gov/glba/gistools/
http://www.esri.com/software/arcgis/extensions/spatia
lanalyst/index.html
http://www.esri.com/software/arcgis/extensions/tracki
nganalyst/index.html
http://www.ecostats.com/software/biotas/biotas.htm
http://www.microsoft.com/office/excel/default.htm
http://www.cnrhome.uidaho.edu/default.aspx?pid=730
83
http://www.ecostats.com/software/loas/loas.htm
http://www.ecostats.com/software/lote/lote.htm
http://www.lotus.com/home.nsf/tabs/lotus123
http://detritus.inhs.uiuc.edu/wes/habitat.html
http://www.cnr.colostate.edu/~gwhite/mark/mark.htm
http://www.mathworks.com
http://www.ncss.com
http://www.corel.com
http://detritus.inhs.uiuc.edu/wes/home_range.html
Source as of November 18, 2009
Adapted from Larson (2001) KEY: HOME RANGE for use in ARC/INFO, which is a product of ESRI, Redlands, CA USA.
*Operating System: A = indicates that some sample code is available to be compiler for the designed operating system, D = DOS, F = Fortran, M = Macintosh, W = Microsoft Windows
Travel Path: includes animated graphics and analyses of angles, distances, and rates of animal movement between successive telemetry locations.
Animal Interactions: includes analyses of site fidelity and static and dynamic interactions among marked individuals.
Kernel: indicators the program calculates for (F)ixed, (A)dative, or both (F/A) kernels, when known.
1
Spatial Analyst
Antelope
Calhome
Dixon
Homer
HomeRange*
Kernelhr
McPaal
Home Ranger
Ulysses
Wildtrak
ArcView and
extensions
Animal Movements
4
3
2
1
No.
Cluster
Home range estimators
Grid cell
Table 3. Software useful for monitoring animal movements with information available over the web 1
Digitized
polygon
88
19
18
Animal-habitat
relationships
Distribution during foraging
and utilization
Animal movement in twodimensions
Utilization of space
16
17
Home range
15
11
12
13
14
10
9
8
7
Vegetation types and
distance from water
Utilization distribution
(UD)
Movement patterns
Spatial and temporal
utilization on foothill range
Response of animals to
their environment
Habitat use
Cattle home range size
Spatial distribution
Distribution
Animal movement
5
6
Orientation and movement
Grazing + travel, resting,
and bedding patterns
Real-time duration and
latency measures
Animal activity area
4
3
2
1
None specified
None specified
30
None specified
None specified
None specified
1200
2,430
None specified
None specified
4,597
248
None specified
170,000
None specified
None specified
None specified
11-125
None specified
Minimum convex polygons (MCP),
fixed-kernels and clusters
Model of persistent 2D random walks
Tobit analysis; Proc Genmod
Genetic algorithm for rule set production
(GARP)
Log-ratio analysis of compositions
CALHOME
Multivariate general linear modeling
Convection-diffusion approach
Kernel Home Range program
(KERNELHR)
Nonparametric model in which the
Thiessen model delineated areas of
intensive use more effectively than
Kernel estimators
Chi-square analyses
Transition matrix and cluster analysis
Kernel methods
Convection-diffusion equation involving
the inverse Gaussian density function
Biased random walk movement model
Multifactorial sets of angular data
Kruskal-Wallis & Mann-Whitney
statistical analysis
Stepwise multiple regression
Harmonic mean of an aerial distribution
Table 4. Mathematical approaches to evaluate animal distribution and its associated factors
Area
No.
Parameter(s) Investigated
Analytical Tool
(ha, range)
Wu et al. (2000)
Girard et al. (2002)
Mohr (1947);
Worton (1989); and
Kenward (1987)
Brock and Owensby (2000)
Casaer et al. (1999)
Seaman et al. (1998)
Aebischer et al. (1993)
Kie and Boroski (1996)
Landsberg and Stol (1996)
Pickup and Bastin (1997)
Stockwell and Noble (1992)
Pinchak et al. (1991)
Worton (1989)
Pickup and Chewings (1988)
Marsh and Jones (1988)
Underwood and Chapman
(1985)
Hendrie and Bennett (1984)
Senft et al. (1983)
Dixon and Chapman
(1980)
Manuscript Citation
Wu et al. (2000)
Tobin (1958); SAS (1993)
Boots (1980)
Worton (1989)
Stockwell and Noble
(1992)
Aitchison (1986)
Kie et al. (1996)
SYSTAT (1992)
Pickup (1994)
Zar (1974)
Lehner (1979)
Worton (1989)
Kareiva and Shigesada
(1983; correlated
random walk)
Moore and Clarke (1983);
Folks and Chhikara (1978)
Mardia (1972)
Hendrie and Bennett
(1984)
None provided
Neft (1966)
Analytical Tool Citation
89
39
38
37
35
36
34
33
32
31
30
29
28
27
25
26
24
23
Group interactions
Distribution
Movement based on
object‟s trajectory
Temporal and spatial
location of cattle
Activity vs. inactivity
Distance traveled
Landscape use
Distribution between two
resource points
Animal spatial and
temporal groupings
Grazing, traveling, and
resting
Tracking distribution
changes
Time spent foraging and
distance traveled
Animal dispersal
Resting, grazing, and
walking
Distances between
individuals
Foraging paths
Foraging
Detection of animals
22
21
Feces pattern
Distribution Evenness
Index (DEI)
20
No.
466
None specified
329 & 258
466
250-1530
4,168
900 - 57,000
75.2
None specified
28-859
810
2,350
0.15 - 0.35
2,000
120,000190,000
0.05
None specified
None specified
3.3
150.5
Area
(ha, range)
Jeung et al. (2008)
Ljung (1999)
System identification with
least-squares fitting
Littell et al. (1996)
Arabie et al. (1996)
Estevez and Tatem (2007)
Kiker et al. (2006)
Rogers and Carr (2002);
Worton (1989)
Weber et al. (2001)
Li and Wang (1998a,b)
Breiman et al. (1984) and
SAS (2002)
Schauer et al. (2005)
www.baylor.edu/grass
Long et al. (2005)
Milne (1992)
Kenward (1987)
None specified
SAS (2000)
Pinheiro and Bates (2000)
Shannon and Weaver
(1949)
Ramsey and Harrison
(2004)
Hybrid Prediction Model
PROC MIXED
Home Range extension for ArcView;
kernel analysis
Integration of a habitat suitability index
(HSI) into ACRU2000
K-means classification algorithm
Spatial Analyst in ArcMap9.1
Association software, ASSOC1
Repeated statement with the Mixed
procedure of SAS
Multiple regression, discrimination
analyses and classification and
regression trees together with the JMP
statistical analysis program
(Release 5.01a)
Stochastic process and
differential equation
GRASS software
Meta-analysis
Fractal analysis
Grid cell method
Simple and multiple regression
Discriminate analysis
Distance sampling
Shannon-Wiener Index
Linear mixed effects model
Analytical Tool
Table 4 (Continued). Mathematical approaches to evaluate animal distribution and its associated factors
Schwager et al. (2008)
Jeung et al. (2008)
Schwager et al. (2007)
Tomkins and O‟Reagain (2007)
Pandey (2007)
Hunt et al. (2007)
Harris et al. (2007)
Wang and Li (2005)
Ungar et al. (2005)
Schauer et al. (2005)
Schacht et al. (2005)
Long et al. (2005)
Garcia et al. (2005)
Kawamura et al. (2005)
Shiyomi (2004)
Schlecht et al. (2004)
Ramsey and Harrison (2004)
Tate et al. (2003)
Zuo and Miller-Goodman
(2003)
Manuscript Citation
90
Table 4 (Continued). Mathematical approaches to evaluate animal distribution and its associated factors
Area
No.
Analytical Tool
(ha, range)
Calenge (2006);
Object class “ltraj” developed for
40
Trajectory analysis
None specified
R Development Core
R software
Team (2006)
41
Foraging / walking
829-864
Forward stepwise regression
Ungar et al. (2005)
Herd movement using
Hidden Markov models and long-term
Bharucha-Reid (1960);
42
individuals as the central
7
trajectory prediction
Murphy (1998)
unit of measure
Observer Kalman filter identification
Jung (1994; OKID);
Pitch angle of neck (animal
43
None specified
(OKID) and multiple-model adaptive
Ferreira and Waldmann
activity and inactivity)
estimation (MMAE)
(2007)
Log-transformed/residual
REML; Genstat 8.2, VSN
44
Distance traveled
93-117
maximum likelihood
International, Hertz, UK
45
Animal associations
None specified
SOCPROG
Whitehead (2009)
Whitehead (2009)
Tomkins et al. (2009)
Nadimi and Søgaard (2009)
Guo et al. (2009)
Ganskopp and Bohnert (2009)
Calenge et al. (2009)
Manuscript Citation
GEOSPATIAL METHODS AND DATA ANALYSIS FOR ASSESSING DISTRIBUTION
OF GRAZING LIVESTOCK
D. M. Anderson
Notes
July 9 and 10, 2010
METABOLIC SIGNALS OF THE BEEF COW IN NEGATIVE ENERGY BALANCE1
R. C. Waterman2ŧ and W. R. Butler*
ŧ
USDA - ARS, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301;
*
Department of Animal Science, Cornell University, Ithaca, NY 14853
grasses (Heitschmidt et al., 1995, 1999; Grings et al., 2005).
As summer progresses, temperatures increase while
precipitation decreases, followed by decreases in forage
quality that continue through fall and winter, which
negatively impacts livestock production (Adams and Short,
1988). Waterman et al. (2006) demonstrated that varying
concentrations of glucogenic precursors fed to postpartum
range cows consuming supplements containing glucogenic
AA with or without Ca-propionate influenced nutrient
partitioning. Supplements with greater concentrations of
glucogenic precursors minimized maternal tissue
catabolism and allowed partitioning of dietary nutrients
toward maternal tissue. As a result, rates of glucose
sequestration into maternal tissues were modified. If
nutritional stresses that render maternal tissues less
responsive to actions of insulin can be identified, then
metabolically targeted supplementation protocols may be
designed to minimize this effect. Seasonal alterations in
glucose metabolism (i.e., oxidative metabolism), as
influenced by tissue responsiveness to insulin in beef cows
grazing rangelands in the Northern Great Plains, occur in
relation to forage quality. Altered glucose metabolism may
ultimately affect physiological performance and
reproductive function in range beef cows (Waterman et al.,
2007).
INTRODUCTION
Extensive beef cattle enterprises endure difficulties
trying to maintain beef cow production that is both
sustainable and profitable. Furthermore, beef cow
production on arid and semiarid rangelands is directly
influenced by environmental conditions (i.e., forage
quantity and quality available for consumption). To achieve
optimal levels of production, harvested and purchased
feedstuffs are often fed to range cows to supplement
periods of nutritional stress. The cost of delivering these
feedstuffs to range cows is one of the largest input costs a
producer will encounter. Moreover, the range beef cow still
must perform by being reproductively and biologically
efficient within these changing environments. Under these
demanding circumstances, beef cows experience similar
metabolic dysfunctions as observed in high producing dairy
cows following parturition; however, seasonal changes in
forage quality play a larger role in beef cow production and
can induce metabolic dysfunctions throughout the
production cycle year at times besides around parturition.
Excellent reviews that discuss the nutritional controls on
reproduction are available for the beef cow (Keisler and
Lucy, 1996; Hess et al., 2005) and the dairy cow (Bauman
and Currie, 1980; Butler, 2000; van Knegsel et al., 2005;
Chagas et al., 2007; van Knegsel et al., 2007). The purpose
of this proceedings paper will be to expand the topic of
negative energy balance that occurs around parturition and
(or) early lactation to include the entire production cycle of
the beef cow.
DERIVED METABOLIC SIGNALS AND TARGETED
TISSUES OF BEEF COWS IN NEGATIVE
ENERGY BALANCE
In order to better understand the intertwined web of
metabolic signals, it is essential to review the tissues
involved and the metabolic signals they elicit. Therefore,
the initial portion of this proceeding is designed to describe
specific tissues involved in producing metabolic signals and
the targeted tissues of those signals when beef cows
encounter negative energy balance.
NUTRITIONAL ENVIRONMENT INFLUENCING
BEEF COWS IN NEGATIVE ENERGY BALANCE
In extensive western livestock production systems,
timing and amount of precipitation are crucial in spring
through early summer for establishment of cool-season
Rumen
1
USDA, Agricultural Research Service, Northern Plains
Area, is an equal opportunity/affirmative action employer,
and all agency services are available without
discrimination. Mention of any trade name or proprietary
product does not constitute a guarantee or warranty of the
product by USDA, Montana Agric. Exp. Stn., or the authors
and does not imply its approval to the exclusion of other
products that also may be suitable.
2
Ruminants have a unique synergistic relationship with
microflora that inhabits the reticlo-rumen. This relationship
enables cows to consume forages high in fiber (cellulose
and hemicellulose) that are fermented by the ruminal
microflora to produce VFA. The ruminal microflora then
uses dietary N and fermentable energy sources to
manufacture microbial cells (i.e., microbial protein), which
in turn, can be utilized by the host ruminant.
93
receptors in the anterior pituitary and hypothalamus via
peripheral circulation (Tschop et al., 2000). In ruminants,
ghrelin concentrations increase before scheduled meals
(Sugino et al., 2002) or in response to fasting (Wertz-Lutz
et al., 2006), and are less variable when diets are consumed
ad libitum (Sugino et al., 2002). Upon feeding, ghrelin
concentrations are suppressed (Wertz-Lutz et al., 2006).
Other gut peptides that may play pivotal roles during
negative energy balance include gastrin, cholecystokinin
(CCK), glucose-dependent insulinotropic polypeptide
(GIP), and glucagon-like peptide 1 (GLP-1). The exact role
that these gut peptides play in ruminant metabolism is
unclear. However, Relling and Reynolds (2007) speculated
that these peptides may play a role in “fine-tuning” nutrient
metabolism through their anabolic effects on adipose or
mammary gland metabolism. Concentrations of GIP and
GLP-1 were greater in lactating sheep than in non-lactating
sheep (Faulkner and Martin, 1997). Insulin concentrations
were lesser in lactating ewes than in non-lactating ewes,
indicating a lack of association between GIP, GLP-1, and
insulin. Gastrin appears to stimulate pancreatic release of
insulin and glucagon as dietary nutrients reach the small
intestine (Bloom et al., 1975; Brockman, 1978).
The primary VFA produced by the ruminal microflora
include acetate, butyrate, and propionate. Beef cows
confined to open rangelands are reliant upon environmental
conditions and often consume senescent forages leading to
periods of inadequate nutrition (Hawkins et al., 2000).
During these conditions, beef cows experience high ruminal
acetate production, but propionate production is often
considered to be inadequate. Propionate is important as the
principle glucogenic precursor in ruminants (Bergman,
1973; Forbes, 1982; Bell and Bauman, 1997) and when
lacking, other glucogenic substrates (i.e., glucogenic AA,
glycerol, and lactate) derived from maternal catabolism
(i.e., loss of body reserves) are required for
gluconeogenesis. Acetate is primarily absorbed through the
rumen epithelium virtually unchanged with very little
conversion to ketones. However, ruminal butyrate is largely
converted to ketones (β-hydroxybutyric acid, acetoacetate,
and acetone) by the rumen epithelium. Ruminal propionate
also passes primarily unchanged through the ruminal
epithelium with only small amounts being converted to
lactic acid (Fahey and Berger, 1987). When cellular energy
from glucose is compromised, the secondary energy source
becomes ketones.
Volatile fatty acids absorbed through the ruminal
epithelium enter the peripheral blood supply and are
predominately taken up by the liver. Ketones formed from
ruminal VFA across the rumen epithelium enter the
peripheral blood supply and can be used in oxidative
metabolism by heart, kidney, skeletal muscle, and the
lactating mammary gland. However, ketone usage by the
ruminant brain may be minimal (Pell and Bergman, 1983).
Another important metabolic signal originating in the
rumen includes various forms of N. Ruminant animals have
the unique ability to conserve N or optimize N balance in
the rumen. Excess ruminal N is transported across the
ruminal epithelium as ammonia by simple diffusion of the
non-ionized lipid soluble form [NH3; (McDonald, 1948)] or
NH4+ via potassium channels (Abdoun et al., 2006).
Ammonia absorbed into the mesenteric and portal blood
supply is removed by the liver and converted to urea and
glutamine (Parker et al., 1995). During inadequate
nutritional conditions, N intake deficiencies reduce ruminal
microbial cell synthesis, DMI, and energy availability, thus
resulting in diminished fermentation (Hawkins et al., 1999).
Fundamental Principles: Metabolic signals derived
from the gastrointestinal tract include ghrelin, gastrin,
CCK, GIP, and GLP-1. The metabolic roles of these
signals are influenced by nutrients presented to the
stomach and small intestine.
Liver
As stated by Forbes (1982), “The liver is at the
crossroads of metabolism”. The liver is responsible for
detoxification (i.e., ammonia to urea), production of cellular
fuel (i.e., glucose) via gluconeogenesis, synthesis of
ketones, production of insulin-like growth factors (IGFs;
which are polypeptides with conserved sequences and
actions similar to insulin), and storage of glycogen. An in
depth review of N metabolism by the liver is beyond the
scope of this proceeding; readers are directed to the review
of Reynolds (1992) for more detail. As Reynolds (1992)
pointed out, a substantial amount of dietary N is absorbed
via the rumen and transported to the liver as ammonia.
The role of the liver is to detoxify ammonia primarily by
converting it to urea, which is then released into peripheral
circulation to be recycled back into the rumen via saliva or
direct absorption across the ruminal wall via a
concentration gradient. Excess urea not recycled back to the
rumen is excreted to the environment through urine.
Another role of N in the liver is utilization of peripheral N
in the form of glucogenic AA for glucose synthesis (i.e.,
gluconeogenesis). Although this is an inefficient use of N, it
is essential, especially for ruminants exhibiting negative
energy balance and when ruminal fermentation is unable to
provide an adequate supply of propionate.
Propionate is the principle glucogenic precursor in
ruminants (Bergman, 1973; Forbes, 1982; Bell and
Bauman, 1997) and when inadequate, other glucogenic
substrates (i.e., glucogenic AA, glycerol, and lactate) are
from the rumen include ammonia (processed in the liver
to urea and important in N recycling back to the rumen)
and the VFA, acetate and butyrate (primarily converted
to ketones, β-hydroxybutyric acid, acetoacetate and
acetone), and propionate (primarily processed in the
liver and important for proper TCA cycle function and
gluconeogenesis).
Gastrointestinal Tract
Ghrelin is a 28-amino acid peptide secreted primarily by
abomasal and ruminal cells (Hayashida et al., 2001;
Bradford and Allen, 2008) that stimulates appetite and
secretion of growth hormone (GH). Ghrelin acts on
94
utilized, resulting in the catabolism of maternal tissues (loss
of body weight) in an attempt to satisfy the energy
requirement of a range beef cow. Also, metabolizable
protein (e.g., ruminally undegradable protein and microbial
protein) absorbed from the small intestine may be
inadequate to properly support gluconeogenesis. Thus,
concurrent with inadequate gluconeogenesis, the
tricarboxylic acid cycle becomes unable to regenerate
oxaloacetate (OAA), which is a key intermediate required
for gluconeogenesis (Salway, 1994) and acetate metabolism
in the liver. This results in the utilization of acetate in futile
cycles, which increases heat production and decreases
energetic efficiency while redirecting acetate carbon
towards the synthesis of ketones. As stated previously, this
alteration in metabolism is exaggerated by a decrease in
DMI, which reduces the supply of incoming ruminal
acetate, and a reduction in the efficiency of energy
metabolism as reflected by increases in serum ketones,
which are primarily in the form of β-hydroxybutyrate
(Kaneko, 1989).
During negative energy balance in cattle, insulin-like
growth factor 1 (IGF-1) becomes uncoupled from GH
stimulation, and as a result, the liver produces less IGF-1
(Lucy et al., 2009). This uncoupling between GH and IGF-1
contributes to delayed return to ovulatory cycles and
prolonged anestrous. Another role of IGF-1 is the insulinlike anabolic actions it initiates in other tissues.
Muscle
In times of negative energy balance, muscle responds to
endocrine systems by catabolizing protein rich tissues
(Chagas et al., 2007). As stated above, glucogenic AA are
important precursors for gluconeogenesis; however, a
balance must be maintained to avoid excessive
gluconeogenesis at the expense of peripheral protein by
sparing glucose utilization (Brockman, 1978). During
periods of low energy availability, muscle produces
myostatin, a member of the transforming growth factor-β
superfamily (Lee, 2004). Myostatin promotes mobilization
of muscle proteins as free AA, making them available for
use by other tissues (Zimmers et al., 2002). Increased
myostatin in the blood stream seems to down regulate the
insulin-dependent glucose transporter, Glut 4, thus
increasing insulin resistance by muscle and adipose tissue
(Antony et al., 2007).
from muscle include AA and myostatin. These signals
are derived when muscle catabolism occurs.
Adipose Tissue
Adipose tissue provides a crucial role during
undernutrition of ruminants (Chilliard et al., 2000). When
ruminants experience undernutrition, glucagon exerts its
effects on adipose tissue by stimulating hormone-sensitive
lipase, thereby activating lipolysis and converting
triglycerides into glycerol and NEFA, which are released
into the peripheral circulation to be used for energy by other
tissues. Non-esterified fatty acids are metabolized by the
liver to contribute acetyl CoA to the TCA cycle, but also
result in the formation of ketones when OAA cannot be
efficiently regenerated. Chilliard et al., (2000) indicates that
apart from adipose tissue serving as an energy storage site,
it acts as an endocrine gland secreting autocrine, paracrine,
and endocrine factors.
Leptin secretion by adipose tissue is markedly reduced
during undernutrition in ruminants, resulting in
neuroendocrine inhibition of the satiety control center to
encourage DMI. Conversely, this neuroendocrine stimuli
may also have implications that extend anestrous via
inhibition of the reproductive axis (Friedman and Halaas,
1998; Heiman et al., 1999).
from the liver include urea, glucose, ketones, and IGF1. These signals are influenced by the peripheral
concentrations of circulating precursors.
Pancreas
The pancreas has an important role in animal
metabolism; however, for purposes of this proceeding we
will focus on the roles of insulin and glucagon. The
pancreas also has other important exocrine roles and the
authors direct the reader to the review by Croom et al.,
(1992) for more details. Insulin and glucagon play
important roles in regulating intermediary metabolism.
Insulin (hypoglycemic hormone) promotes glucose uptake
from blood by peripheral tissues (Bowen, 1964; West and
Passey, 1967), including muscle (Jarrett et al., 1974) and
adipose tissue (Khachadurian et al., 1966), and promotes
storage of other metabolites in peripheral tissues
(Brockman, 1978). Ruminants are different than nonruminants such that gluconeogenesis and lipogenesis occur
simultaneously via substrates derived from ruminal
fermentation (VFA; propionate and acetate, respectively).
Glucagon (hyperglycemic hormone) promotes both
gluconeogenesis and lipolysis to maintain glucose
homeostasis (Brockman, 1978).
from the adipose tissue include NEFA and leptin. These
signals are important in energy status.
Uterus and Ovary
Ovarian and normal estrous cycle activities are
suppressed during undernutrition or when cows are
experiencing negative energy balance. Furthermore,
negative feedback to the hypothalamus via estradiol extends
the anestrous period in cows experiencing negative energy
balance (Short et al., 1990; Keisler and Lucy, 1996) and
compounds the inhibitory effects of suckling by the calf.
from the pancreas include insulin and glucagon;
primary role is the regulation of glucose in the
peripheral circulation.
Muscle
95
produces catecholamines (i.e., epinephrine) which are also
responsive to ACTH. Epinephrine elicits a response at the
liver to increase gluconeogenesis, muscle to mobilize
protein, and adipose to mobilize triglycerides and fatty
acids.
Obviously, an extended period of anestrous and delayed
ovulation reduces the reproductive capacity of range beef
cows during the breeding period.
Fundamental Principles: Metabolic signals perceived
by other tissues can be exacerbated by ovarian estradiol.
Estradiol signaling is essential to achieve post-partum
reproductive competency.
from the adrenal gland include cortisol and epinephrine.
These signals are important in energetic efficiency.
Brain (Hypothalamus and Pituitary)
INTERTWINED WEB OF METABOLIC SIGNALS
FOR BEEF COWS IN NEGATIVE ENERGY
BALANCE
The satiety control center located in the ventromedial
hypothalamic nucleus is responsible for modulating signals
of hunger (orexigenic) and satiety (anorexigenic)
originating from numerous peripheral tissues throughout the
ruminant (Roche et al., 2008). The authors refer readers to
the review by Roche et al., (2008) for greater detail on the
neuroendocrine and physiological regulation of intake in
ruminants.
Neuropeptide Y is a 36-amino acid peptide synthesized
in both the central nervous system and the peripheral nerves
(Hulshof et al., 1994). Animals that are nutritionally
compromised have elevated levels of NPY indicating that
one role of NPY is to stimulate feed intake (McShane et al.,
1993; Prasad et al., 1993; Thomas et al., 1999).
Additionally, the preoptic area (POA) of the
hypothalamus is responsive to neuronal signals (NPY and
opiates), reproductive hormones (E2 and P4), as well as
nutritional metabolites (glucose, acetate, ketones, NEFA,
and excitatory AA). The medial basal hypothalamus (MBH)
is responsive to signals from leptin, insulin, and IGF-1.
Neuroendocrine responses from the POA and MBH
regulate anterior pituitary secretion of follicle stimulating
hormone and luteinizing hormone (FSH and LH,
respectively) that mediate and control ovarian follicular
development necessary for ovulation. Lastly, during
parturition when the fetus is presented to the cervix, signals
are sent to the hypothalamus that then signals the posterior
pituitary to release oxytocin. Oxytocin influences the
mammary gland to initiate milk let down. This action
prompts mammary tissue to sequester glucose, which
creates a nutrient sink that exacerbates the requirement for
glucose, especially when gluconeogenesis is compromised
due to the lack of glucogenic precursors.
Parturition and (or) Early Lactation
The metabolic conditions occurring in range beef cows
that ultimately lead to negative energy balance are in
principle the same as those observed for high producing
dairy cows at parturition and through early lactation. The
exception is that high producing dairy cows have been
genetically selected for high milk production, which
directly sets up a cascade of events leading to negative
energy balance (and possibly ketosis) just before parturition
that may continue through early lactation. Range beef cows
are less likely to exhibit negative energy balance or ketosis
directly from the additional demands for lactation, but
rather are driven into negative energy balance by the
inability to consume enough dietary energy to support
requirements for both maintenance and lactation, not to
mention the requirements for growth in young developing
beef cows. The dairy cow generally consumes a total mixed
ration that is formulated to meet her requirements; however,
the extreme demand for milk production exceeds the
biological capacity for oxidative metabolism, which in turn,
creates potential for the occurrence of negative energy
balance. The most crucial time for dairy cows is the two
weeks preceding parturition and the first few weeks of
lactation. For range beef cows, this change can also
originate before parturition, but negative energy balance is
reached much more gradually.
The endocrine changes surrounding parturition and onset
of lactation have metabolic effects in multiple tissues. An
increased demand for glucose requires an increase in
glucogenic precursors; however, DMI is sometimes
suppressed in late gestation leading to a reduction in
fermentable substrate to support gluconeogenesis (i.e.,
decreased propionate). Liver glycogen stores are most
likely depleted due to the dam’s increased energy
requirements leading up to and during parturition.
Therefore, glucogenic precursors must come from
catabolism of maternal tissues. Initially, stress hormones
(i.e., cortisol and epinephrine) target adipose tissue and
stimulate lipolysis, which releases triglycerides and NEFA
into the peripheral circulation. These hormones also target
muscle tissue to initiate protein catabolism releasing AA
into the blood stream. Another role for epinephrine is to
target the liver and stimulate gluconeogenesis.
The catabolism of muscle mobilizes glucogenic AA to
the liver, but this supply may not be sufficient to regenerate
from the brain (hypothalamus and pituitary) include
GnRH, GHRH, CRF, ACTH, LH, FSH, GH, oxytocin,
and NPY. These signals are essential for energetic and
reproductive competency.
Adrenal Gland
The adrenal gland produces glucocorticoids (i.e.,
cortisol) in the adrenal cortex in response to pituitary
release of adrenocorticotropic hormone (ACTH). Cortisol
elicits a response at the liver to increase gluconeogenesis,
muscle to mobilize protein, adipose to mobilize
triglycerides and fatty acids, and consequently directly
inhibits glucose transport into cells. The adrenal medulla
96
parturition. As a result, range beef cows may experience
periods of short and extended negative energy balance.
Undernutrition may occur during early gestation and
potentially cause early embryonic mortality. Therefore, the
initial cause that exacerbates maternal catabolism of tissues
is the cow’s inability to obtain sufficient nutrients from
their diet to meet requirements for maintenance, growth,
and gestation. Once a pregnancy is established, the cow will
usually provide sufficient nutrients to maintain that
pregnancy at the expense of maintenance and growth. The
beef cow may be able to reestablish her maintenance
requirements by decreasing activity and DMI. Range beef
cows can rebound quickly in the short term; however,
extended periods of undernutrition can greatly impact the
beef cow’s oxidative metabolism leading to undesirable
performance. If the period of negative energy balance
occurs in the last third of gestation, there is a greater
propensity for dystocia and increased negative energy
balance at parturition on into lactation. The impact of
negative energy balance on in utero development of the
fetus is not well understood and deserves further study. A
body of new information is coming forthwith suggesting
that nutrient deficiencies in utero may compromise
production traits of the developing fetus. A literature review
by Du et al. (2009) demonstrated that nutrient deficiencies
at key times during gestation can alter not only the number
of muscle fibers but their growth as well. The metabolic
signals associated with negative energy balance are very
similar at any time throughout the production cycle, with
the main differences being the removal of signals produced
or directed to reproductive organs once pregnancy is
established.
As range beef cows begin to experience negative energy
balance, stress hormones from the adrenal gland are
secreted along with pancreatic release of glucagon in an
effort to maintain homeorhesis. These events can cascade,
and if sustained for a lengthy period of time, the cow
experiences severe negative energy balance. This is again
caused by the lack of OAA regeneration in the TCA cycle
due to insufficient supply of glucogenic precursors. The
condition is manifested by the inability of insulin to elicit
an effect on tissues and allow the translocation of the Glut 4
transporter to the cell membrane for glucose and other
essential nutrient uptake into tissue cells. Dietary nutrients
must be provided in sufficient quantities to repartition
nutrients toward maternal tissue while meeting the needs of
the animal for maintenance, gestation, and growth in
younger cows.
Harrelson et al., (2009) compared feeding a traditional
cottonseed meal supplement [36% CP (35% ruminally
undegradable protein)] at 454 g/d and prorated to be
delivered 3 d/wk compared to a biologically potent small
package supplement [33% CP (60% ruminally
undegradable protein)] offered daily at 113 g/d. Cows
receiving the small package supplement experienced rapid
glucose clearance from blood, indicating faster uptake of
glucose by tissues. This would also stipulate that ketones
and NEFA concentrations in the peripheral blood supply
were reduced with the small package supplement, a result
of faster glucose clearance rates and improved oxidative
metabolism (i.e., regeneration of OAA).
OAA in the TCA cycle fast enough to utilize the excess
acetyl-CoA supplied to the liver by NEFA from adipose
tissue. Thus, excess acetyl-CoA is either metabolized in
futile cycles or converted to ketones (β-hydroxybutyrate).
Meanwhile, GH and IGF-1 become uncoupled as IGF-1
synthesis is reduced in the liver. The catabolism of muscle
produces myostatin, which acts on muscle tissue and
adipose tissue disrupting the translocation of Glut 4 to the
cell membrane (Antony et al., 2007), resulting in tissues
being less responsive to the actions of insulin. To make
things worse, tissues become even more unresponsive to
insulin when concentrations of ketones and NEFA are in
abundance in the peripheral circulation as they too disrupt
the translocation of Glut 4 to the cell membrane (Dresner et
al., 1999; Schmitz-Peiffer et al., 1999; Tardif et al., 2001).
Elevated concentrations of cortisol are also responsible for
direct inhibition of glucose transport into cells.
As a result, the cascade of events discussed above render
it difficult to formulate diets for range beef cows in
negative energy balance, and prolonged periods of negative
energy balance can deplete maternal energy stores resulting
in diminished production (milk, calf gain, resumption of
estrus, and delayed conception). In order to regain
homeorhesis [i.e., coordination of metabolism in various
tissues to support a given physiological state (Bauman and
Currie, 1980)], dietary nutrients must be sufficient to
redirect nutrient partitioning back toward maternal tissue.
Figure 1 illustrates the metabolic signals that are produced
and the tissues they affect. As evident by the figure, one can
see the complexity of the intertwined signals involved in
regulating energy balance in the cow.
Mulliniks et al., (2009) summarized 7-years of work
conducted at the Corona Range and Livestock Research
Center, Corona, New Mexico. These studies evaluated
postpartum supplementation strategies in young beef cows
with various concentrations of glucogenic precursors [i.e.,
Ca-propionate and ruminally undegradable protein
(containing glucogenic AA)]. Range supplements with
greater concentrations of glucogenic precursors decreased
days to first estrus and improved pregnancy rates in 2- and
3-yr-old cows. This was accomplished by repartitioning
nutrients away from lactation and toward maternal tissues.
Furthermore, the addition of Ca-propionate in range
supplements fed to cows improved pregnancy rates. The
higher cost of adding Ca-propionate to supplements was
offset by increasing subsequent year calf crop, which also
increased ranch revenue compared with feeding other
supplements.
Seasonal Changes during the Production Cycle
It is rare for dairy cows to experience negative energy
balance at times other than just prior to parturition and early
in lactation. This is a result of total mixed rations and the
quality of feedstuffs used in diets to meet the requirements
of dairy cows. On the other hand, range beef cows are
extremely dependent upon environmental conditions and
the response of rangeland forages to these changing
conditions. It is not improbable for range beef cows to
experience 2 or 3 periods of weight loss throughout a
production cycle in addition to that which occurs around
97
strategically implementing supplementation protocols that
target periods of nutritional stress. Identification of these
periods of time throughout a production cycle will
encourage better utilization of low quality forages, improve
nutrient utilization, and lend to achieving production goals
for livestock grazing western rangelands. These
supplementation strategies may include separation of cattle
based on their nutrient status. Nutritionally stressed cows
may be eligible to receive supplements ranging from a
small packaged, biologically potent, supplement
incorporated in a loose mineral mix or delivery of up to 0.9
kg/d of a 40% protein supplement (Petersen, 2006). The
key is early intervention, which may result in
supplementing livestock during times of the year that
traditionally may not have been done or no intervention at
all. Understanding the consequences to livestock
experiencing a state of negative energy balance are the keys
to creating more efficient strategies.
FUTURE STRATEGIES
Range beef cows are reliant upon the quality and
quantity, which are controlled by environmental conditions,
of rangeland forages to maintain positive energy balance.
Range beef cows in arid and semi-arid environments may
only have adequate supplies of nutrients to maintain
positive energy balance 40% of the time. Thus, it is
essential that strategic supplementation regimes be used in a
manner that is economically and biologically attainable.
Researchers at Fort Keogh, Livestock and Range Research
Laboratory are striving to identify the onset of negative
energy balance in beef cows and use biologically potent
strategic supplementation to correct this energy imbalance.
Glucose tolerance tests are routinely used to measure
glucose half-life, glucose area under the curve, and insulin
area under the curve as a measure of the onset or severity of
negative energy balance. However, these tests are time
consuming and the analytic procedures are conducted long
after the time when changes could be implemented. We are
now evaluating the efficacy of using chute side measures
(i.e., as ketone meters), which may allow beef cows to be
sorted out of a chute within 10 sec of placing a single drop
of blood on the meter’s ketone test strip. This may become
a viable tool for producers to make management decisions
that allow different feedstuffs to be fed to groups of cows
experiencing the same metabolic condition. This may be an
approach that strategically allows producers to manage their
cows better and be more economically stringent with their
supplemental feed.
LITERATURE CITED
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Bell, A. W. and D. E. Bauman. 1997. Adaptations of
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SUMMARY
In extensive, range forage-based production systems,
beef cows are expected to adapt to vast array of changing
environmental conditions. These changing environmental
conditions often lead to unfavorable metabolic states that
limit animal performance and subsequently impact
production efficiency. Thus, range livestock producers and
managers must strive to identify livestock best fitted to
produce in their unique environment and management
scheme. Unfortunately, none of us have a “Crystal Ball” to
tell us when forage quality will diminish in a given year,
but we can rely on some historical data to help make
management decisions. For the Northern Great Plains at
Fort Keogh, 75 to 90% of annual range forge production
occurs by July 1, and it is at the 90% forage production
level in areas where cool season grasses (C3) are dominate
(Heitschmidt and Vermeire, 2005). So why is this
important? If an assessment of forage production is made in
July a producer can adjust stocking rates to ensure that
livestock grazing rangelands have ample opportunity to
consume as much forage as they desire (forge intake
therefore is not a limiting factor). This leaves the question
to quality and whether range livestock can consume
sufficient nutrients to achieve a desired production goal.
This paper presented the metabolic consequences to range
livestock when they are unable to consume sufficient
nutrients to attain positive or neutral energy balance.
Research underway at Fort Keogh and previously reported
from New Mexico State University has focused on
98
Chagas, L. M., J. J. Bass, D. Blache, C. R. Burke, J. K.
Kay, D. R. Lindsay, M. C. Lucy, G. B. Martin, S.
Meier, F. M. Rhodes, J. R. Roche, W. W. Thatcher, and
R. Webb. 2007. Invited review: New perspectives on
the roles of nutrition and metabolic priorities in the
subfertility of high-producing dairy cows. J. Dairy Sci.
90:4022-4032.
Chilliard, Y., A. Ferlay, Y. Faulconnier, M. Bonnet, J.
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101
Figure 1. Metabolic signals (releasing and target tissues) that are influenced by energy balance of beef cows (POA = preoptic area of the hypothalamus, MBH = medial
basal hypothalamus, AP = anterior pituitary, and PP = posterior pituitary).
METABOLIC SIGNALS OF THE BEEF COW IN NEGATIVE ENERGY BALANCE1
R. C. Waterman and W. R. Butler
Notes
July 9 and 10, 2010
MATERNAL PLANE OF NUTRITION: IMPACTS ON FETAL OUTCOMES AND
POSTNATAL OFFSPRING RESPONSES1,2
J. S. Caton3ŧ and B. W. Hess*
ŧ
Center for Nutrition and Pregnancy, Animal Sciences Department, North Dakota State University, Fargo 58108
*Department of Animal Science, University of Wyoming, Laramie 82071
ABSTRACT: Objectives of this review are to summarize
the current information regarding effects of maternal
nutrition on fetal outcomes and postnatal offspring
performance and provide a platform to assist in the
formation of additional hypotheses regarding relevant
aspects of developmental programming in ruminant
livestock species. Inadequacies in maternal nutrition have
been implicated in developmental programming and
resulting pre and postnatal changes that affect long-term
offspring halth and performance. Developmental
programming is the concept that perturbations during
critical developmental periods may have long-term impacts
on offspring outcomes. Although this concept not new, both
early epidemiological studies and subsequent follow up
work with animal models has increased interest among
many biological scientists. Within extensive ruminant
livestock production systems, periods of nutrient
inadequacy are common and present production challenges.
Decreased growth rate and suboptimal carcasses cost
feedlot producers millions of dollars annually. In addition,
fetal growth restriction resulting from perturbed maternal
nutrition has been associated with negative impacts upon
developing tissues, metabolism, growth efficiency, and
body composition. The degree to which inadequacies in
maternal nutrition impacts efficiencies and profitability of
ruminant livestock production systems remains to be fully
determined.
INTRODUCTION
Maternal nutritional plane (Barker, 2004; Wu et al.,
2006) has been implicated in developmental programming
and resulting pre and postnatal changes that affect longterm offspring health and performance. Developmental
programming is the concept that perturbations during
critical prenatal or postnatal developmental stages can have
lasting impacts on growth and adult function. Maternal
nutritional status (Wallace, 1948; Wallace et al., 1999;
Godfrey and Barker, 2000; Wu et al., 2006) is a major
factor implicated in development and function of the fetal
organ systems. Effective nutritional (Bull and Carroll,
1937; Barcroft, 1946; Morrison, 1949; Maynard and Loosli,
1956; Crampton and Lloyd, 1959; NRC, 1970)
management during gestation has long been recognized as
an important component of sound livestock production
practices. Fetal intrauterine growth restriction (IUGR) is an
issue in relevant livestock species in certain circumstances
(Wu et al., 2006) and can be precipitated by numerous
forms of perturbed maternal nutrition. Recent observations
and reviews in the area of developmental programming
(Barker et al., 1992; Godfrey and Barker, 2000; Armitage et
al., 2004; Barker, 2004; Wu et al., 2006) have elevated the
concept of maternal onset of adult disease and lifelong
performance to the forefront of investigation in numerous
national and international laboratories. In addition,
underlying concepts of developmental programming are
driving the development of research programs in multiple
laboratories and shaping thought in funding arenas in both
biomedical and animal agriculture. This review will focus
on the impacts of maternal nutritional plane on resulting
fetal and postnatal outcomes in relevant livestock species.
Emphasis will also be placed on growth, development,
metabolic, and performance outcomes, timing of nutritional
perturbations, potential long-term production consequences,
and areas for future research.
Key words: developmental programming, fetal growth,
maternal nutrition, offspring performance, ruminants
1
Mention of trade names or commercial products in this
publication is solely for the purpose of providing specific
information and does not imply recommendation or
endorsement by the authors.
2
The authors would like to thank the numerous
collaborators that have contributed to the data and concepts
contained within this manuscript. Appreciation is also
expressed to the organizing committee of the Grazing
Nutrition Conference. This project was partially supported
by the National Research Initiative Competitive Grants no.
2003-35206-12814,
2005-35206-15281,
2006-5561816914, and 2009-35206-05253 from the USDA
Cooperative State Research, Education, and Extension
Service.
3
MATERNAL NUTRITIONAL PLANE AND
DEVELOPMENTAL PROGRAMMING
Developmental Programming
Developmental programming, also termed “Fetal
Programming,”
“The
Barker
Hypothesis,”
or,
“developmental origins of health and disease,” is a major
concept underpinning research in many biomedical and
agricultural laboratories (Hanson and Gluckman, 2005; Wu
104
confer an immediate survival advantage to the offspring
assuming the experienced prenatal environment is
indicative of the ensuing postnatal environment. An
example would be in the case of maternal nutrient
restriction resulting in developmental changes that
predispose the offspring for better survival in a nutrient
limiting environment. However, if the postnatal
environment does not match the prenatal environment the
offspring may have adverse consequences that resemble
aspects of metabolic syndrome later in life.
Aspects of developmental programming have been
described by both the thrifty phenotype hypothesis and by
predictive adaptive responses. In addition severe
perturbations during development can have dramatic
impacts on developmental and offspring outcomes.
Certainly, severe malnutrition has both immediate and long
term consequences to developing offspring, as would
infectious agents or toxins. These adverse affects during
development from sever circumstances are more
pronounced and often not viewed as adaptive responses, but
more as consequences of extreme perturbations (Hanson
and Gluckman, 2005). However, the importance of such
examples should not be overlooked in providing insight into
mechanism driving more modest responses to less
compromised intrauterine environments.
Clearly, maternal nutritional status is one of the factors
implicated in programming, nutrient partitioning, and
ultimately growth, development, and function of the major
fetal organ systems (Wallace, 1948; Wallace et al., 1999;
Godfrey and Barker, 2000; Wu et al., 2006; Caton et al.,
2007). The prenatal growth trajectory is sensitive to direct
and indirect effects of maternal dietary intake from the
earliest stages of embryonic life when the nutrient
requirements for conceptus growth are negligible (Robinson
et al., 1999). This is especially relevant because pre-term
delivery and fetal growth restriction are associated with
greater risk of neonatal mortality and morbidity. Growth
restricted neonates are not only at risk of immediate
postnatal complications, but may also exhibit poor growth
and development, with significant consequences later in life
(Barker et al., 1993; Godfrey and Barker, 2000; Barker,
2004; Wu, 2006). Because of accumulating data and
potential ramifications, the concept that a maternal
stimulus, including plane of nutrition may result in
developmental programming responses that impact
neonatal, growth and development, and adult responses is
gaining traction in animal agriculture.
et al., 2006). Developmental programming is the concept
that perturbations during critical developmental periods
may have long-term “programmed” impacts on offspring
outcomes. While not new, this concept has piqued interest
among biological scientists by both early human
epidemiological studies (Barker et al., 1989; Barker, 1994,
2004) and subsequent follow up work with animal models.
Barker and his colleagues evaluated human birth record
data, and related maternal nutrient restriction and infant
weight and physical characteristics at birth to health status
in later life. They found standard mortality ratios in men
and in women decline as birth weight increased. They
further indicated that low birth weight infants tend to have
higher mortality ratios from coronary heart disease. Based
upon these data, they suggest that maternal nutrition
restriction during the first half of pregnancy can lead to
smaller for gestational age infants at birth resulting in
increased likelihood of health problems, including obesity,
diabetes, and cardiovascular disease, that can be
experienced later in life. Later work has suggested that
similar responses could also be observed in the absence of
reduced birth weight.
Building upon these concepts (Hales and Barker, 1992)
proposed the “Thrifty Phenotype Hypothesis” which was
originally put forward to explain the relationship between
low birth weight and type-2 diabetes. The thrifty phenotype
hypothesis indicates that offspring born from nutrient
restricted or malnourished intrauterine environments will
have a competitive advantage if they are born into postnatal
environments that are also nutrient limiting. Later, Hales
and Barker (2001) expanded this hypothesis to explain fetal
origins of metabolic syndrome (sometimes called syndrome
X). In humans, metabolic syndrome is defined as
possessing three of the following five conditions: high
blood pressure, elevated abdominal adiposity, increased
serum triglycerides, decreased serum HDL cholesterol, and
fasting hyperglycemia (Wilson and Grundy, 2003 a,b).
Others have suggested additional conditions for inclusion
into metabolic syndrome (Armitage et al., 2004) including
endothelial dysfunction, which some (Pinkney et al., 1997;
Bonora et al., 2003) have suggested as a potential
underlying cause. Naturally, these issues are of paramount
concern in human biomedical areas, particularly in light of
increasing obesity and metabolic syndrome in populations
of developed countries. From a livestock production
perspective, both internal and external fat are economically
important and related to carcass grading and reproductive
efficiency. Proper vascular, and by association, endothelial
functions also are critical for nutrient exchange in key
nutrient transferring tissues (intestine, placenta, kidney, and
mammary gland) that support livestock production and
health.
Responses to environmental perturbations including
nutrient restriction and maternal stress have been termed
“predictive adaptive responses” (Gluckman and Hanson,
2004a,b; Hanson and Gluckman, 2005). This concept
differs from the thrifty phenotype hypothesis discussed
above in that predictive adaptive responses confer a benefit
later in life (Hanson and Gluckman, 2005; Martin-Gronert
and Ozanne, 2006). The thrifty phenotype hypothesis
attempts to explain intrauterine developmental changes that
Practical Relevance in Livestock
Within livestock production systems, there is real
potential for ruminants to undergo periods of undernutrition
(extensive grazing or high milk output) or overnutrition
(overfeeding) during gestation. Decreased growth rate and
suboptimal carcasses cost feedlot producers millions of
dollars annually (Smith et al., 1995; Gardner et al., 1998).
Fetal growth restriction and maternal undernutrition are
implicated in negative impacts upon growth efficiency and
body composition (Greenwood et al., 1998, 2000; Wu et al.,
2006; Caton et al., 2007; Larson et al., 2009). Maternal
nutrient restriction can significantly alter composition of
105
historical concept is that during pregnancy nutrient
partitioning favors the conceptus at the expense of the dam
(Barcroft, 1946). However, exposure of pregnant sheep to
severe maternal undernutrition at all stages of pregnancy,
and particularly during late gestation, reduces fetal growth
(Mellor, 1983; Robinson, 1983; Vincent et al., 1985; Parr et
al., 1986).
Nutrient Restriction. Nutrient restriction is broadly
defined as any series of events that reduce fetal and (or)
perinatal nutrient supply during critical windows of
development. Nutrient restriction can result from altered
maternal nutrient supply, placental insufficiency, deranged
metabolism and regulation, physiological extremes, and
environmental conditions. From a practical standpoint,
maternal nutrient supply and environmental conditions
leading to stress responses are the most likely observed
causes of nutrient restriction in ruminant livestock.
However, the incidence of multiple births in some breeds
can also contribute to physiological extremes and result in
reduced nutrient supply to developing fetuses. We will
focus primarily on maternal dietary-induced nutrient
restriction in this review.
Due to the pattern of placental growth in relation to fetal
growth during gestation (Redmer et al., 2004), it is
important to realize that the effects of nutrient restriction
during pregnancy may depend on the timing, level, and (or)
length of nutrient restriction. In an excellent review, Luther
et al., (2005) indicated that maternal nutrient restriction in
sheep during and the through mid pregnancy could reduce
placenta size and function, while having minimal impacts
on fetal body weight near term. Additionally, Reed et al.
(2007) and Swanson et al. (2008) demonstrated that
maternal nutrient restriction during the last two thirds of
pregnancy in sheep reduces birth weights. Sletmoen-Olson
et al. (2000) indicated that both low and high levels of
metabolizable protein supplementation to mature beef cows
reduce birth weights relative to controls fed at projected
requirement. In contrast, protein supplementation of cows
during the last trimester of pregnancy has been reported to
have little effect on birth weights (Martin et al., 2007;
Larson et al., 2009). In addition, a moderate level of total
nutrient restriction during the second (Lake et al., 2005) and
last two thirds of gestation (H. Freetly, personal
communication) in mature beef cows has not impacted birth
weights. From the available data it appears that birth
weights in sheep are more susceptible to maternal nutrient
restriction than beef cattle.
Nutrient Excess. Increasing plane of nutrition to help
beef cows achieve a body condition score of 6 (9-point
scale) at parturition did not affect calf birth weights (Lake
et al., 2005), but others (Spitzer et al., 1995 and Stalker et
al., 2006) reported that increasing body condition before
parturition can increase calf birth weight. Maternal
overnutrition during gestation can also have adverse effects.
It has been shown that overnourishing the singleton-bearing
adolescent ewe throughout gestation results in rapid
maternal growth, and most particularly of maternal adipose
tissue, at the expense of the nutrient requirements of the
gravid uterus (Wallace et al., 1996, 1999, 2001). In this
paradigm, rapid maternal growth results in placental growth
restriction, premature delivery of low-birth weight lambs
offspring growth in the absence of birth weight differences
(Gardner et al., 2005; Ford et al., 2007). Permanent changes
in postnatal metabolism induced by maternal nutritional
perturbations may present significant challenge to livestock
producers because nutritional management decisions are
often based on, for example, average body weight of a
given group of animals, which may result in undernutrition
of animals of below average body weight or overnutrition
of animals heavier than average. Therefore, information
regarding factors contributing to animal variation, such as
developmental programming, has potential to improve
efficiency and profitability of rearing programs.
Investigating maternal nutritional effects on offspring
growth is very economically relevant to agricultural
producers. Weaning weight is an important factor affecting
profitability for producers who sell their calves at weaning
(Reed et al., 2006). Fetal undernutrition frequently occurs
in animal agriculture, leading to reduced fetal growth (Wu
et al., 2006). Nutritional requirements of spring-calving
beef cows grazing dormant range during late gestation
typically exceed the nutrient value of the grazed forage
(NRC, 1996; Johnson et al., 1998; Cline et al., 2009: Lardy
and Caton, 2010). Gestating ewes grazing rangeland
pastures often experience prolonged bouts of undernutrition
(Thomas and Kott, 1995) and the nutrient uptake of grazing
ewes in the western United States is often less than 50% of
NRC recommendations (NRC, 1985). Maternal nutrition
pre- and postpartum has been shown to affect calf growth
through weaning (Perry et al., 1991; Beaty et al., 1994;
Spitzer et al., 1995; Stalker et al., 2006), with greater
weaning weights produced from cows on a greater plane of
nutrition.
In most of the previously mentioned studies offspring
were allowed to nurse dams until weaning. The true effect
of maternal nutrition on offspring is difficult to decipher
when offspring are left nursing their dams until weaning,
since prepartum nutrition and postpartum nutrient supply
(lactation), both contribute to postnatal growth and
performance. Some investigators (Greenwood et al., 1998,
2000; Caton et al., 2007; Swanson et al., 2008; Neville et
al., 2010; Meyer et al., 2010b) have raised offspring
independent of postnatal maternal influences, which should
eliminate the confounding effects of prenatal and postnatal
nutritional supply on offspring performance.
Maternal Nutrition
Maternal nutrient supply in terms of total nutrient intake
or specific nutrient availably can vary widely in practical
conditions and can be driven by dietary quantity and (or)
quality issues, perturbations of intake, and environmental
conditions. Maternal nutritional status is one of the factors
implicated in programming postnatal nutrient partitioning
and ultimately growth and development, including that of
muscle and adipose tissue (Wallace, 1948; Godfrey and
Barker, 2000; Armitage et al., 2005; Wu et al., 2006; Taylor
and Poston, 2007). Growth restricted infants are not only at
risk of immediate postnatal complications, but also may be
„programmed‟ to exhibit poor growth and productivity and
also to develop significant diseases later in life (Barker et
al., 1993; Godfrey and Barker, 2000; Wu et al., 2006). The
106
compared with moderately nourished ewes of equivalent
age. Swanson et al. (2008) also demonstrated that birth
weights are decreased by 9.2% when adolescent ewes are
fed 140% of energy requirements from d 40 of pregnancy
until parturition, which indicates that moderate
overnutrition during the last two-thirds of pregnancy can
cause moderate fetal growth restriction. Although placental
and fetal growth restriction is generally seen only in
adolescent, overnourished ewes, gestation length, and
colostrum yield are negatively affected in adult ewes
overnourished throughout pregnancy (Wallace et al., 2005).
Thus, the health and growth of offspring from adult animals
may also be altered by maternal overnutrition.
Animal Models and Mechanisms of Developmental
Programming
Livestock animal models have and will continue to
provide significant impacts in critical areas of nutritional
and biomedical research. In fact, our current understanding
of nutritional principles and applications has been greatly
impacted by both discovery and application-based research
with livestock (Reynolds et al., 2009; 2010b). Briefly, the
broad nutritional areas of energetics (Nichols and Reeds,
1991; Johnson, 2007), proteins (Mathewes, 1991; Baracos,
2004; Bergen, 2007), carbohydrates and lipids (Bergen and
Mersmann, 2005; Nafikov and Beitz, 2007), vitamins
(McDowell, 2000), minerals (O‟Dell and Sunde, 1997;
Underwood and Suttle, 1999; Sandstead and Klevay, 2000;
McDowell, 2003), and growth and body composition
(Mitchell, 2007) have been largely impacted by research in
livestock species. Likewise, in the case of most known
macro and micronutrients, contributions from livestock
animal models to our current understanding are extensive.
From a historical perspective, nutritional research has
progressed as complementary research efforts in
agriculturally relevant species, rodents, and humans, with
breakthroughs in each fostering complimentary work in the
other species (McCollum, 1957; Carpenter, 2003a,b,c,d;
Stahl et al., 2008). Consequently, early work in the area of
energetics and micronutrients were instrumental in
development of the disciplines of biochemistry and
subsequently molecular biology (Nichols and Reeds, 1991).
Use of appropriate animal models, especially those with
agricultural relevance, is and will continue to be critical in
nutritional and biomedical research (Baker, 2008; Stahl et
al., 2008). Emerging functional examples of continued
importance of livestock animal models to a broader
understanding of nutrition and biology include, but are not
limited to porcine models of obesity and metabolic
syndrome (Spurlock and Gabler, 2008), bovine models of
fat synthesis regulation (Bauman et al., 2008), and ovine
models of developmental programming and pregnancy
outcomes (Wallace et al., 2005; Symonds et al., 2007;
Barry and Anthony, 2008; Reynolds et al., 2010a).
FETAL GROWTH AND CRITICAL WINDOWS:
OPPORTUNITIES FOR
Nutrient Supply during Fetal Development
The developmental programming concept makes
biological sense considering an individual passes more
milestones in development in utero than postnatal. The first
half of gestation is the time when maximal placental
growth, development, and vascularization are occurring.
Progressively, as gestation advances blood flow increases to
the gravid uterus, and more specifically, nutrient and
oxygen exchange at the maternal:fetal interface is vital for
optimal fetal growth and development. The ruminant
placenta appears unable to extract additional amounts of
nutrients per unit of maternal blood in response to fetal
demand; therefore, increasing the number of blood vessels
at the site of the maternal:fetal exchange is absolutely
necessary to optimize blood flow and thus nutrient transfer.
Patterns of Fetal Growth in Ruminant Livestock
Most growth of the bovine (and other ruminant) fetus
occurs during the final third of gestation (Ferrell et al.,
1976; Prior and Laster, 1979). Fetal nutrient uptake
becomes quantatively important as a contributor to maternal
nutrient requirements mid-gestation (Ferrell et al., 1983).
Thus, severe nutritional restriction for at least the last half
to one-third of pregnancy is usually required to reduce
bovine fetal growth. Although placental characteristics may
be altered by nutrition during early and mid pregnancy
without significantly affecting fetal size (Rasby et al., 1990)
or birth weight (Perry et al., 1999; 2002), development and
growth of vital organs precedes development of bone,
muscle, and fat (Figure 1). Hence, the mass of the relatively
late maturing carcass tissues are generally considered more
susceptible to the effects of maternal nutrition during later
pregnancy when the impacts on fetal growth are likely to be
greatest. More subtle effects on organ and tissue
development due to nutrition during early pregnancy may
occur, with potential for long-term consequences for health
(Wu et al., 2006).
Critical Developmental Windows
The first of 10 principles of developmental
programming outlined by Nathanielsz (2006) is that there
are critical periods of vulnerability to suboptimal conditions
during development. Periods of vulnerability or critical
windows differ for specific tissues and organ systems
dependent upon developmental stages and timing. During
these critical windows, specific challenges like maternal
nutritional imbalances can result in changes that have both
short and long-term consequences. These include the
periconceptual period, placental development and
organogensis, rapid fetal growth, and the perinatal period
(Figure 2; Fowden et al., 2006; Nathanielsz, 2006;
Symonds et al., 2007). In several mammalian species,
including livestock, evidence exists that perturbations
during these critical windows can alter development and
function. Nutrient restriction during the periconceptional
period results in impaired blastocyst formation (Borowczyk
107
which maintained body weight and body condition through
an 8 week pre-superovulation period, an overfed group that
increased adiposity, and a restricted group that lost body
weight. The overfed group was fed for ad libitum feed
consumption and the restricted group was fed at 60% of
controls. After 8 weeks ewes were superovulated and
oocytes collected. There was no difference in the number of
characterized healthy oocytes in control, overfed, and
restricted ewes. However, both the overfed and the
restricted ewes had reduced successful fertilizations,
morulas, and blastocysts, indicating that maternal nutrition
(both inadequate and excess) before mating can have
profound impacts on oocyte quality and fertilization rates.
et al., 2006), shortened gestation, and abnormal function of
the hypothalamic-pituitary axis (Kumarassamy et al., 2005)
in sheep. Likewise, insults during various critical stages
(Figure 2) can also have measurable and lasting effects on
particular tissues or organs undergoing rapid growth and
(or) differentiation (see below).
PERTURBATIONS OF FETAL DEVELOPMENT
AND OFFSPRING RESPONSES IN
RUMINANT LIVESTOCK
Periconceptional Nutrition and Oocyte Quality
Nutritional status is a major factor influencing an
animal‟s ability to reproduce (Robinson, 1990; Webb et al.,
1999; O‟Callaghan et al., 2000; Hess et al., 2005). Nutrition
has a significant impact on numerous reproductive
functions including hormone production, fertilization, and
early embryonic development (Boland et al., 2001;
Armstrong et al., 2003; Boland and Lonergan, 2005).
Changing nutrient supply during the peri-conceptual period
can have profound impacts on reproduction. Nutritional
status has been correlated with embryo survival and is a key
factor influencing efficiency in assisted reproductive
technologies (Armstrong et al., 2003; Webb et al., 2004).
Conflicting results have been reported for the effects of low
or high energy diets on oocyte quality and early embryonic
development in ruminants (Kendrick et al., 1999; Boland et
al., 2001; Papadopoulos et al., 2001). For example, sheep
fed low energy diets have embryos with decreased cleavage
rates compared with high-energy diets (Papadopoulos et al.,
2001). In contrast, a higher proportion of ova from ewes fed
low-energy diets were considered viable compared with
those from ewes fed high-energy diets (McEvoy et al.,
1995). Others (Bloomfield et al., 2003; Kumarasamy et al.,
2005) have reported that approximately half of ewes
experiencing nutrient restriction during the periconceptual
period have marked reductions in gestation length. For
cows, positive (Nolan et al., 1998; Kendrick et al., 1999;
Boland et al., 2001), negative (Yaakub et al., 1999;
Armstrong et al., 2001) or no effects (Tripp et al., 2000) of
plane of nutrition (high vs. low energy diets) on oocyte
quality, fertilization rate, and early embryonic development
have been reported.
Recent studies (Borowczyk et al., 2006; Grazul-Bilska
et al., 2006) have investigated the effects of maternal plane
of nutrition before superovulation and in vitro fertilization
on oocyte quality and embryonic development. Borowczyk
et al. (2006) compared control ewes fed a maintenance diet
to an underfed group that was fed at 60% of controls. After
8 weeks ewes were superovulated, oocytes collected and
evaluated, in vitro fertilization rates of oocytes determined,
and viability to morula and blastocysts states of embryos
determined. There were no differences in the number of
healthy oocytes collected from control and restricted ewes.
However, at fertilization, restricted ewes produced oocytes
that fertilized more poorly. In addition, morulas and
blastocysts were less in the underfed group.
In a follow-up study, Grazul-Bilska et al. (2006) added
an additional over fed treatment. In the second study, which
was also with ewes, these researchers had a control group,
Placental Development
A primary role of the placenta is to provide for
physiological interface, including nutrient and waste
exchange between the fetal and maternal systems (Meschia,
1983; Reynolds and Redmer, 1995; Reynolds et al., 2010a).
Therefore, adequate placental circulation is imperative to
successful pregnancy and is exemplified by observed close
relationships among fetal weight, placental size, and uterine
and umbilical blood flows during normal pregnancies
(Reynolds et al., 2005a, 2005b, 2006; 2010a). In the ewe,
cotyledonary growth is exponential during the first 10 to 11
wk of pregnancy, with significant slowing until term
(Stegeman, 1974: Ferrell et al., 1976). In the cow, the
cotyledonary growth progressively increases throughout
gestation (Reynolds et al., 1990; Vonnahme et al., 2007).
Uterine and umbilical blood flows, which represent the
circulation to maternal and fetal portions of the placenta,
respectively (Ramsey, 1982; Mossmann, 1987), increase
exponentially throughout gestation, essentially keeping
pace with fetal growth (Reynolds and Redmer, 1995;
Magness, 1998). For example, in sheep the absolute rate of
uterine blood flow increases by approximately 3-fold
throughout the last half of pregnancy (Meschia, 1983).
Over a similar interval of gestation, uterine blood flow in
cows increase by 4.5-fold (Reynolds, 1986).
As summarized in Table 1, in sheep studied during late
gestation, uterine or umbilical blood flows, or both, are
reduced in every model of compromised pregnancy in
which they have been evaluated. These models of
compromised pregnancy include overfed adolescents,
underfed adolescent and adult dams, as well as
environmental heat-stress, hypoxic stress, and multiple
fetuses.
In addition, although vascular development of the
placenta also is decreased in several of the models of
compromised pregnancy, it is increased in others (Table 1).
Interestingly, the models in which placental vascularity is
increased show no effect on fetal size (high dietary Se or
hypoxic stress) or were associated with long-term genetic
selection (Romanov vs. Columbia genotype), suggesting an
adaptive placental response that preserves the fetal nutrient
supply.
108
low-energy diets had fewer fast glycolytic fibers than the
offspring of sows fed a high-energy diet during the first 50
d of gestation. These results correspond with results of
Schantz et al. (1983), where energy-deficient animals had
less fast glycolytic fibers but retained slow oxidative fiber
numbers.
Muscle fiber type and size not only affect prenatal and
postnatal growth, they also can have an impact on endproduct quality. Percentage of slow muscle fibers present
can affect meat color (Monin and Ouali, 1992) and speed of
tenderization that occurs postmortem (Ouali, 1990). Fiber
diameter has an impact on meat tenderness (Maltin et al.,
1997). Altering fiber type will also impact glycogen storage
capabilities, which has an impact on water-holding capacity
(Fernandez and Thornberg, 1991). These product quality
traits have a large impact on the acceptability and consumer
demand of animal food products.
Fetal Organ Development and Postnatal Function
Nutrient restriction to the developing conceptus,
regardless of the reason (maternal nutrient restriction,
environmental conditions, carunclectomy or other
experimental models), often results in impaired fetal
organogenesis and (or) development. The degree of
compromised internal organ growth is usually more severe
with increasing extremes of nutrient restriction. Timing of
nutrient deprivation can also result in differential effects of
fetal organ systems, with restriction during early to mid
pregnancy, a critical time for fetal organ growth and
development (Figure 1), being more likely to compromise
function when compared with moderate levels of restriction
during late pregnancy. Various organ systems may respond
differently to specific timing and severity of nutrient
restriction because of differing growth trajectories and
maturation time points. In fact, recent data indicate that
both low and high planes of maternal nutrition can impact
growth of numerous fetal organs (Reed et al., 2007; Carlson
et al., 2009; Caton et al., 2009).
Our laboratories we have a particular interest in
intestinal development, growth, and function. Intestinal
tissues are important to livestock production because of
their role in nutrient uptake, immunocompetence, and their
disproportional use of energy (and other nutrient resources)
in relation to their contribution to overall body mass. A
detailed discussion of fetal and offspring intestinal tissue
response to perturbations during gestation are beyond the
scope of this effort; readers are referred to Reed et al.
(2007), Carlson et al. (2009), Caton et al. (2009), Neville et
al. (2010), Meyer et al. (2010a), and Zhu et al. (2009).
Adipose Tissue Development and Postnatal Accumulation
In sheep and cattle, adipose tissue development begins
during gestation, but the vast majority of adipose tissue
growth occurs postnatally (Mersmann and Smith, 2005).
Perirenal adipose tissue represents the primary adipose
tissue depot at birth in sheep (Muhlhausler et al., 2002).
The perirenal adipose depot undergoes rapid and
considerable expansion in the first month of life, during
which time the adipose tissue transitions from having a
thermoregulatory function to one of energy storage (Clarke
et al., 1997). Adipose tissue development during the
neonatal period is accompanied by a well-characterized
pattern of gene expression as adipose tissue expresses the
proteins required for the synthesis of triacylglycerol from
glucose and fatty acids (Mersmann and Smith, 2005). Early
postnatal growth of adipose tissue is driven by hyperplasia
(proliferation) of undifferentiated preadipocytes and
subsequent hypertrophy of adipocytes, characterized by the
accumulation of triacylglycerol. In adults, adipose tissue
growth is driven primarily by hypertrophy of the existing
adipocytes, but preadipocyte hyperplasia continues at a low
rate for replacement of adipocytes that undergo apoptosis
(Mersmann and Smith, 2005). However, marked increases
in preadipocyte hyperplasia can occur as a result of
excessive caloric intake relative to nutrient requirements
(Miller et al., 1984; Shillabeer and Lau, 1994). Clearly, an
important concept regarding adipose tissue growth is that
animals and humans retain the ability to recruit
preadipocytes throughout life, indicating that growth of
adipose tissue is essentially unlimited (Hausman et al.,
2001). As the lamb grows and approaches maturity, visceral
adipose (perirenal and omental tissues) represents
approximately one-third of the total adipose tissue mass,
with the remainder comprising subcutaneous and
intramuscular fat (Moloney et al., 2002). Accumulation of
visceral adipose tissue represents a significant inefficiency
in meat animal production, considering that much of the
visceral fat is discarded at slaughter. While regulation of
lipogenesis has depot-specific characteristics as well,
relatively few studies have addressed the biological
mechanisms by which maternal nutrition alters adipose
tissue growth and development in offspring. Additionally,
Muscle Development and Product Quality
Other more traditionally thought of as “production
oriented” tissues like muscle and adipose also appear
responsive to programming effects in utero. Maternal
nutritional status is one of the factors impacting nutrient
partitioning and ultimately growth and development of fetal
skeletal muscle (Wallace, 1948; Wallace et al., 1999;
Godfrey and Barker, 2000; Rehfeldt at al., 2004; Strickland
et al., 2004; Rehfeldt and Kuhn, 2006). Skeletal muscle has
a lower priority in nutrient partitioning compared with the
brain and heart during development, rendering it potentially
more vulnerable to nutrient perturbed nutrient supply
(Bauman et al., 1982; Close & Pettigrew, 1990). The fetal
period is crucial for skeletal muscle development because
no net increase in the number of muscle fibers occurs after
birth (Glore & Layman, 1983; Greenwood et al., 2000;
Nissen et al., 2003). Numerous studies carried out in a
range of mammalian species have shown that maternal
undernutrition during gestation can significantly reduce the
number of both muscle fibers and nuclei in the offspring
(Bedi et al, 1982; Ward and Strickland, 1991). Zhu et al.
(2004) observed that nutrient restriction from early to midgestation resulted in a reduction in the number of fetal
skeletal muscle fibers, which might be related to a downregulation of mammalian target of rapamycin (mTOR)
signaling. Bee (2004) also observed differences in fiber
type in semitendinosus muscle where progeny from the
109
treatment, maternal nutritional plane during gestation
continued to affect lamb growth through d 19 of age (Meyer
et al., 2010b). Specifically, lambs born to ewes restricted to
60% of control intake weighed less than controls at multiple
time points up to necropsy at 19 d. Also, lamb ADG
decreased in lambs from restricted ewes compared with
lambs from control and over fed ewes from birth to d 19. In
agreement with the current study, Neville et al., (2010;
Table 2) recently reported that lambs born to restricted
ewes weighed less at birth than lambs born to control or
over-fed dams with some differences persisting to both
weaning and 180 d of age. Consistent with these
observations, effects of maternal nutrition during gestation
on postnatal growth has been reported in cattle (Stalker et
al., 2006; Martin et al., 2007; Larson et al., 2009).
Growth, digestibility, and N retention were studied in
offspring from dams receiving different levels of nutrition
and Se during gestation (Neville et al., 2010). As in the
work of Meyer et al. (2010b) mentioned above, lambs in
these studies were removed from ewes at birth and raised
independently. Neville et al. (2010) reported that male
offspring from ewes not supplemented with Se had greater
ADG then those receiving high Se during gestation.
Conversely, male offspring from over fed ewes not
supplemented with Se had lower ADG compared with those
receiving high Se during gestation. No differences in DMI
resulted in the similar responses in Gain:Feed as were
found in ADG. Those authors also reported reductions in
diet digestibility in growing lambs from ewes fed high Se
during gestation when compared with those born to ewes
fed adequate Se. No differences were observed in N
retention. It was speculated that lambs from high Se dams
either had increased passage rates or decreased digestive
efficiencies. Others (Martin et al., 2007) found greater
residual feed intake in female calves from dams that
received a protein supplement during late gestation and an
early lactation hay diet. Additionally, Stalker et al. (2006)
and Larson et al. (2009) observed no differences in ADG,
DMI, or Gain:Feed in steer calves during the finishing
period when dams received protein supplements during late
gestation.
little data are available that addresses the long-term
consequences of altered adipose tissue development on
body composition and carcass value.
The role of maternal nutrition in programming postnatal
body composition and growth trajectory may involve
permanent alterations in adipocyte morphology, cellularity,
and (or metabolism (Taylor and Poston, 2007). In an
evaluation of the effects of maternal protein restriction,
Guan et al. (2005) revealed marked changes in the
characteristics of visceral adipose tissue in the offspring,
such as upregulation of genes associated with preadipocyte
proliferation, adipocyte differentiation, lipogenesis, and
angiogenesis, and a concomitant downregulation of antiangiogenic factors. These data in rodents suggest that, in
meat animals, maternal nutrition may alter the trajectory of
visceral adipose tissue growth of offspring and prove
detrimental to the value of the carcass at slaughter.
In sheep, the effects of maternal undernutrition on birth
weight and fetal adipose tissue mass have been inconsistent
and depend on the timing, level, and (or) length of dietary
restriction (Bispham et al., 2003; Symonds et al., 2004). A
more consistent observation in lambs from undernourished
ewes is that the characteristics of fetal adipose tissue are
altered. Other studies have documented increased total
visceral (Gardner et al., 2005) and perirenal (Ford et al.,
2007) adiposity of the offspring by maternal undernutrition,
which was accompanied by insulin resistance compared
with offspring from adequately-nourished ewes (Gardner et
al., 2005; Gnanalingham et al., 2005; Ford et al., 2007;
Caton et al., 2007).
Ford et al. (2007) reported that expanded perirenal fat
mass in offspring from nutrient-restricted ewes is associated
with a reduction in semitendinosus and longissimus muscle
mass as a proportion of hot carcass weight. These data,
coupled with relative insulin resistance in the offspring
from nutrient-restricted dams (Gardner et al., 2005; Ford et
al., 2007), indicate that visceral adipose tissue may
contribute to abnormal nutrient metabolism.
Postnatal Performance Measurements
Growth (an increase in the number and size of cells or
in mass of tissue) and development (changes in the
structure and function of cells or tissues) of the calf are
complex biological events. There are numerous growth and
development pathways that may influence productive
characteristics of ruminants. Growth and development of
the bovine fetus, which can be influenced drastically by
restricted nutrition during the latter half of gestation (Dunn,
1980) has documented consequences on survival (Holland
and Odde, 1992). Likewise, pre-weaning plane of nutrition
or level of maternal milk production effects on growth to
market weights of cattle are well characterized (Berge,
1991). However, investigations on effects of various
treatments or management practices might also need to be
made in early- to mid-gestation (Anthony et al., 1986).
Long-term consequences of more specific nutritional
influences during various stages of fetal and neonatal calf
development remain to be determined.
When lambs were separated from their dams and raised
on an identical management system regardless of maternal
DEVELOPMENTAL PROGRAMMING OF
OFFSPRING BORN INTO CONVENTIONAL
GRAZING LIVESTOCK PRODUCTION SYSTEMS
Many extensive beef cattle production systems are
designed for cattle to receive the majority of their nutrients
from grazed forages. Forage quality is often poor,
particularly in dry and winter seasons, and may be
inadequate to support optimal nutrition for growth,
pregnancy, and lactation without provision of supplemental
nutrients. Supplementation of nutrients may not occur for
cows during early through mid-gestation because nutrients
required to support the growing and developing fetus
appear minimal, especially when compared with later stage
of gestation and early to mid-lactation. Bovine fetal
undernutrition is quite likely to occur during late
pregnancy, particularly if cows do not receive supplemental
energy and protein. Low precalving plane of nutrition of the
cow has been associated with low birth weight of the calf
110
Although fetal weight tended to be reduced, and
caruncular (maternal placental) and cotyledonary (fetal
placental) weights from nutrient restricted cows were less
than control cows on day 125 of gestation, there was no
effect of diet on fetal and caruncular weight by day 250
(Zhu et al., 2007). Even after realimentation, cotyledonary
weight remained less on day 250 in nutrient restricted cows
compared with control cows. Realimentation after ~90 d of
nutrient restriction seemed to be the stimulus for altering
placental vascularity and development as well as placental
function (Vonnahme et al., 2007), resulting in similar fetal
weights between nutrient restricted and control cows on day
250 of gestation.
It may be misleading to interpret a lack of differences in
fetal weights and visceral organ mass of fetuses (Molle et
al., 2004), as a result of cow plane of nutrition (Figure 3), to
suggest that the offspring will exhibit normal physiological
functions later in life. As Figure 4 depicts, both glomerular
number and glomeruli proportion of kidney tissue
decreased
in
fetuses
gestated
by
nutrient
restricted/realimented cows (Ford et al., 2005). Other
researchers (Gilbert et al., 2005) working at the center with
lambs born to ewes that were nutrient restricted in early to
mid-gestation demonstrated that a 20% reduction in
glomerulus number was associated with a 17 mmHg
increase in mean arterial pressure (r =–0.85; P <0.01; y =–
5.82x +416). In a companion study with sheep, Vonnahme
et al. (2003) reported that both the right and left ventricle of
the fetal heart were enlarged if ewes were nutrient restricted
during the early to mid-gestation period.
At weaning, steer calves from the aforementioned study
were placed into a feedlot for 168 days, and pulmonary
arterial pressure measurements were taken within a week
before steers were slaughtered. Unlike the sheep studies,
steers born to nutrient restricted cows did not exhibit an
increase in blood pressure (Han et al., 2008). In retrospect,
data for steers may be confounded because the bull used for
the experiment sired calves which exhibit unusually high
pulmonary arterial pressures. Nevertheless, when steers
with high pulmonary arterial pressures were compared with
steers gestated by nutrient restricted/realimented cow it was
noted that 8 common genes were differentially expressed in
right ventricles of the heart.
Male calves born to cows receiving 68% of their energy
requirements from day 31 through 125 of gestation
followed by realimentation to achieve similar body
condition scores by 250 days of gestation converted feed
more efficiently than their control counterparts (Underwood
et al., 2008). Greater gain efficiency by steers born to
nutrient restricted dams could be related to gain of skeletal
muscle because the proportion of lean in 9th through 11th rib
section was greater for these animals compared with control
steers. Indices of glucose and lipid metabolism did not
differ between control steers and steers born to nutrient
restricted cows. Although it has not been substantiated, it is
interesting to speculate that greater protein gain was related
to less protein turnover (Du et al., 2004) and not enhanced
protein synthesis (Du et al., 2005). Additional research
must be conducted before definitive conclusions can be
drawn because preliminary results from recent experiments
in our lab would not suggest that feedlot performance and
(Bellows et al., 1971), and calf birth weight was increased
in heifers and cows in response to dietary supplementation
with protein and energy concentrates during late gestation
(Clanton and Zimmerman, 1970; Bellows and Short, 1978).
Consequences of Poor Plane of Nutrition Early to MidGestation
Nutritional inadequacy is likely to occur in beef cattle
production systems in which cows are managed to produce
a calf annually. In these systems, with an average gestation
length of 285 days, a cow must conceive within 80 days
postpartum to maintain a yearly calving interval. In
lactating heifers and cows beginning at the second or
greater parity, milk output generally peaks at about 2
months postpartum, but feed intake usually lags behind,
resulting in negative energy balance during early to midlactation (Bauman and Currie, 1980). The low plane of
nutrition will be more evident if cows are managed to calf
during the dry or winter seasons when poor forage quality is
more probable. For example, the average weight loss by the
University of Wyoming spring-calving cowherd was 11.8
kg while grazing native range during mid-summer. Only
54% of the cows were losing weight, and average weight
lost by those cows in negative energy balance was 31.7 kg.
The latter average weight loss is comparable to average
weight loss experienced by a fall-calving cowherd grazing
native range during the autumn (30.8 kg).
With the aforementioned loss of body weight during
early to mid-gestation in mind, researchers at the University
of Wyoming Center for the Study of Fetal Programming
conducted a study in which Angus x Gelbvieh rotationally
crossed cows were blocked by body weight and allocated to
either a control or nutrient restricted diet from day 31 to
120 of gestation. The control diet consisted of native grass
hay (12.1% CP; 70.7% in vitro OM disappearance
[IVOMD]) fortified with vitamins and minerals fed at NRC
(1996) recommendations for a non-lactating, mature cow to
gain 0.72 kg/day during the initial 120 day of gestation. The
nutrient-restricted diet consisted of feeding one-half of the
control diet‟s minerals and vitamins and millet straw (9.9%
CP; 54.5% IVOMD) to provide 68.1% NE m and 86.7% of
metabolizable protein requirements during the first 120 day
of gestation (NRC, 1996). At day 125 of gestation, a subset
of cows was harvested for tissue collection, and the
remaining were blocked by treatment, body weight, and
body condition score, and allocated to 16 pens. Control
cows remained on the control diet, and nutrient restricted
cows were provided a realimentation diet consisting of the
nutrient restricted hay and the control diet‟s vitamins and
minerals, plus a supplement of 79.6% cracked corn, 6.1%
soybean meal, 5.3% sunflower meal, 4.2% cane molasses,
2.6% safflower seed meal, and 1.6% dried skim milk
(13.2% CP; 77.6% IVOMD) to provide 2.15 Mcal more
NEm/day than the control diet to help nutrient restricted
cows attain a body condition score similar to the control
cows by day 250 of gestation (Figure 3). Another subset of
cows was harvested for tissue collection at day 250 of
gestation, and the remaining cows were then co-mingled
and fed to meet NRC (1996) requirements for late gestation
beef cows.
111
from birth to weaning then backgrounded to the same
feedlot entry weight. Thus, limiting compensatory growth
by nutritional restriction up to weaning resulted in smaller
cattle and carcasses at an equivalent age end points. Effects
of early-postpartum maternal nutrition on growth, feed
intake, and growth efficiency of steers (Stalker et al., 2006)
and heifers (Martin et al., 2007) in the feedlot soon after
weaning were not evident. Literature reviewed by Berge
(1991) also demonstrated that feed conversion efficiency
was not necessarily affected by plane of nutrition before
weaning.
As discussed earlier, an assessment of the relationship
between fetal development and subsequent postnatal
performance requires consideration of the consequences of
nutrition during pregnancy on subsequent maternal
performance when offspring rely on their dams for the
majority of their nutrients. This concept can be illustrated
by evaluation of an ongoing study being conducted at the
University of Wyoming (Price et al., 2007). On day 45 of
gestation, 36 of most uniform cows (12 triparous and 24
diparous) were selected to be individually fed native grass
hay plus 1 of 3 supplements from day 45 through 185 of
gestation. The control diet consisted of native grass hay
plus a soybean meal-based supplement formulated for
pregnant replacement heifers (590 kg mature BW) to
achieve 0.43 kg/day of BW gain (NRC, 2000), which we
estimated would be comparable to daily gain of 0.51
kg/day of BW gain for non-lactating cows pregnant with
their second and third calf. The other dietary treatments
were 70% of NEm provided by control, and 70% of NE m
provided by control plus a ruminally undegraded protein
supplement (6.8% porcine blood meal, 24.5% hydrolyzed
feather meal, and 68.7% menhaden fish meal; DM basis;
Scholljegerdes et al., 2005a) designed to provide duodenal
essential amino acid flow equal to that of cattle fed control.
Basal flows of total essential amino acids were predicted
for each treatment using the equation reported by
Scholljegerdes et al. (2004; total essential amino acid flow
to the small intestine, g/d = [0.055 × g of OM intake] +
1.546). The quantity of protein supplement delivered was
adjusted for more extensive ruminal degradation of the
supplement in cattle fed restricted amounts of forage
(Scholljegerdes et al., 2005b). Using actual mineral content
of each dietary ingredient, the control supplement was
fortified to ensure that cows fed control would consume the
same amount of mineral per unit body weight as cows fed
the protein supplement. Feed offered daily was adjusted for
biweekly changes in body weight and increased NEm
requirements as gestation proceeded.
Control cows had greater body weight than nutrient
restricted cows from day 73 through 185 of gestation. Body
weight of cows fed the protein supplement was intermediate
until day 115 of gestation; these cows had significantly
greater body weight than nutrient restricted cows thereafter,
but body weight of cows fed supplemental protein did not
differ from control cows throughout the experiment.
Change in body weight for nutrient restricted cows was not
statistically significant indicating that the nutrient restricted
cows maintained body weight. Cows were fed to achieve
similar body weight within 4 weeks of parturition, and calf
birth weights were not affected by gestational dietary
subsequent carcass merits are reduced if steers were
gestated by cows gaining 17 kg less during mid-gestation.
Consequences of Poor Nutrition during the Latter Stages
of Gestation
Offspring born with low birth weights are less viable
and adjust less rapidly to the extrauterine environment
(Cundiff et al., 1986). Furthermore, low birth weight is
associated with high neonatal morbidity and mortality rates.
In reviewing 13 datasets in which researchers placed cows
on a low plane of nutrition during late gestation, Dunn
(1980) noted that the incidence of neonatal calf mortality
increased 5 percentage units if cows were underfed. More
recent data summarized by Wu et al. (2006) showed that
preweaning deaths of heifer calves born alive in the United
States was 10.5% with 70% of the deaths occurring within
the first 7 days after birth. Incidence of neonatal mortalities
may be linked to less thriftiness and reduced immune
transfer for calves born to cows that were undernourished
during gestation (Odde, 1988).
If the low birth weight calf survives the early neonatal
period, it is possible that it will grow slower than calves of
normal birth weight at all stages of postnatal growth. Calves
born 35% lighter due to severely restricted maternal
nutrition from day 80 to 90 of pregnancy to parturition
remained smaller at any given postnatal age compared with
well-grown or better nourished counterparts (Greenwood
and Cafe, 2007). However, it is difficult to ascertain
whether or not this response represents permanent stunting
or simply delayed of attainment of mature size. When
differences in birth weight were less severe, however, postweaning growth was not significantly affected by birth
weight (Cafe et al., 2006a). Nonetheless, influences of
nutrition during mid and late pregnancy on calf weaning
weight have been demonstrated, irrespective of effects on
fetal growth (Cafe et al., 2006b; Stalker et al., 2006).
Although effects of variable nutrition during mid and (or)
late pregnancy on weight at birth seem to be overcome by
adequate nutrition within 56 days postpartum (Freetly et al.,
2000), a cursory review of several datasets would suggest
that, while not statistically significant in each experiment,
absolute weight of cattle at the time of harvest is
consistently less if calves are born lighter or are lighter at
weaning (Freetly et al., 2000; Banta et al., 2006; Stalker et
al., 2006).
Postnatal Nutrition
The major nutritional factors affecting pre-weaning calf
growth and composition at weaning are the lactational
performance of the dam and the quality and availability of
nutrients from pasture and (or) supplementation before and
after parturition. In summarizing data of Greenwood et al.
(2005; 2006), Greenwood and Cafe (2007) illustrated that a
difference in weaning weight of 73 kg resulted in a 40 kg
difference in live weight and 24 kg in carcass weight at 30
months of age. The low weaning weight cattle grew more
rapidly during backgrounding and at a similar rate in the
feedlot. Similarly, Cafe et al. (2006a) reported some
compensatory growth occurred in steers restricted in growth
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treatment. Despite common postpartum nutritional
management, cows fed supplemental protein during early to
mid-gestation produced 1.7 kg/d less milk at peak lactation
than cows from the other two gestational dietary treatments.
As a result, calves suckling cows fed supplemental protein
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Although the long-term consequences of a lesser growth
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their lower plane of nutrition imposed during the first 6
months of life.
SUMMARY AND CONCLUSIONS
Accumulating data is providing traction for relevance
of developmental programming concepts in livestock.
Maternal nutrition during gestation is a major determinant
to fetal growth. Improper nutrition during gestation creates
situations during critical windows of development where
offspring may be predisposed to long-term complications.
Inadequate nutrition during early to mid-gestation alters
development of a variety of fetal tissues, which may affect
subsequent health but effects on postnatal growth
performance and carcass characteristics seem to be
equivocal. A low plane of nutrition during mid to late
gestation may reduce the ruminant offspring‟s absolute
body weight at birth through slaughter, but again, effects on
postnatal growth performance and carcass characteristics
are less affected if gestation nutrition restriction is not
severe. Improper nutrition during the perinatal period will
increase offspring mortality. A low plane of nutrition
during lactation may reduce absolute body weight
throughout the growing period because calves experiencing
lower planes of nutrition during the first 6 months of life
are less apt to compensate later in life. Additional research
efforts in these areas are needed to quantify degree of
impact of maternal nutritional perturbations during
gestation on both short and long-term metabolism, health,
production, product quality, and economics in ruminant
livestock species.
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Table 1. Changes in fetal and placental weights, uterine and umbilical blood flows, and placental vascularity in various
models of compromised pregnancy in sheep 1
Day of
gestation2
Model
Overfed adolescent
High dietary Se
130
130-144
135
130
133-135
140
↓2028%
↓17%
↓12%
↓11%
↓43%
↓42%
↓30%
135
NSE
130-134
Underfed adolescent
Underfed adult
Adolescent vs. Adult
Genotype
Heat-stressed adult
Multiple pregnancy
Fetal
weight
Placental
weight
Uterine
blood
flow
Umbilical
blood
flow
Vascularity3
↓45%
↓36%
↓37%
↓31% (total capillary vol.)
NSE
--↓29%
↓47%
↓51%
↓37%
---↓17-32%
------↓26%
↓23%
---NSE
------↓60%
----
↓24%
----
----
↓20% (cap. area density, CAR)
↓14% (cap. area density, CAR)
---↑36%
---↓30% (total cap. vol., COT)
↑20% (cap. number density,
COT)
↑(cap. area density, CAR &
COT)
4
Hypoxic (hypobaric)
140
NSE
---↓35%
---stress
1
Table adapted from Reynolds et al. (2006).
2
Length of gestation = approximately 145 d.
3
cap. = capillary; CAR = caruncle (maternal placenta); COT = cotyledon (fetal placenta/villus).
4
NSE = no significant effect.
Table 2. Influence of maternal dietary selenium and nutritional intake during pregnancy on offspring body weight from birth
through weaning at 57 d of age1
Selenium
treatment2
ASe
HSe
Nutrition
treatment3
CON
HIGH
P-values4
Nut
Se*Nut
Item
SEM
RES
SEM
Se
BW, kg
Birth
Males
4.49
4.06
0.25
3.95a
4.78b
4.10a
0.29
0.18
0.06
0.96
a
b
Females
4.11
4.43
0.12
3.85
4.50
4.45b
0.15
0.05
0.002
0.71
Combined
4.25
4.37
0.10
4.02a
4.67b
4.25a
0.12
0.37
0.0007
0.66
Weaning, 57 d
Males
19.52
19.45
0.95
18.10
20.35
20.01
1.27
0.96
0.28
0.75
Females
19.60
19.72
0.64
18.85
20.18
19.96
0.82
0.89
0.32
0.25
Combined
19.61
19.55
0.49
18.57a
20.33b
19.84ab
0.67
0.94
0.06
0.40
180 d
Males
53.04
52.61
1.43
52.22ab 55.75a
50.51b
1.91
0.83
0.08
0.98
Females
49.41
51.47
1.73
50.59
50.80
49.94
2.15
0.35
0.95
0.85
Combined
51.23
51.98
1.11
51.31
53.27
50.24
1.48
0.61
0.26
0.80
1
Adapted from Neville et al. (2010).
2
Selenium treatments were daily intake of organically bound Se, adequate Se (ASe; 9.5 µg/kg BW) vs. high Se (HSe; 81.8
µg/kg BW).
3
Nutritional treatments were RES (fed at 60% of CON), CON (control; 100% requirements for gestating ewe lambs), and
HIGH (fed at 140% of CON).
4
Probability values for effects of selenium (Se), nutrition (Nut), and the interaction.
ab
Means within a row having differing superscripts differ (P < 0.10).
120
Figure 1. Timeline of bovine fetal growth and development (courtesy of Dr. Kim Vonnahme).
Periconceptional
Implantation and Placental Growth
Organogenesis
Rapid Fetal Growth
Perinatal
Postnatal
Conception
Mid-Gestation
Birth
Figure 2. Critical windows of developmental programming (adapted from Fowden et al., 2006).
121
660
Weight (kg)
640
620
600
580
560
540
520
500
30
60
90
120
150
180
210
240
Day of Gestation
Figure 3. Observed change in cow body weights in response to plane of nutrition from day 31 through 240 of gestation.
Glomeruli per gram tissue
3500000
50000
3000000
*
2500000
*
2000000
40000
1500000
30000
1000000
500000
0
Glomeruli/gram tissue
Absolute glomerular number
Total glomerular numbers
20000
C
NR
Figure 4. Glomerular counts of kidneys collected from 250-day fetus of control (C) or nutrient restricted cows (NR).
122
MATERNAL PLANE OF NUTRITION: IMPACTS ON FETAL OUTCOMES AND POSTNATAL
OFFSPRING RESPONSES
J. S. Caton and B. W. Hess
Notes
July 9 and 10, 2010
M. Kerley1
Division of Animal Sciences, University of Missouri, Columbia 65211
adoption by the industry.
Change in metabolizable energy (ME) density of forages
as plant growth proceeds has been well characterized.
Management procedures (grazing management, legume
interseeding, annuals, etc) have been developed to extend
availability of high-quality forage to grazing cattle.
Substantial periods of the growing season produce forage
with ME density capable of supporting gains of growing
calves in excess of 1 kg/d. Post-ruminal supply of AA
(microbial and forage origins), however, are not sufficient
to support energy-allowable gain and create an imbalance
between energy and protein. We have concluded from our
research that arginine, histidine, lysine, methionine, and
threonine are potentially limiting, with the most limiting
being methionine, in high-quality forage diets. Equally
important is adequate degradable protein in forages
containing less than 6% crude protein. This paper will focus
on impact of an imbalanced absorbable AA to energy ratio
on cow and weaned calf performance when fed a forage
diet.
INTRODUCTION
Increased price of feed grains has provided a
renaissance of interest in using forage for beef production
post-weaning. Interest in maximizing use of forage for the
cow herd has intensified as well due to economic pressures
placed on beef producers. We find ourselves asking the
question all businesses are asking, how we can do more
with less? There are two topics emphasized in this paper:
(1) how will selection for efficiency change cattle intake
and (2) how nutrient imbalances can limit performance of
forage-fed cattle.
General agreement exists that forage intake is driven by
nutrient demand. While several nutrient imbalances can
influence intake, the two most readily at our control and
likely to be limiting are energy and protein. A substantial
body of research supports the hypothesis that intake is
regulated by energy and AA requirement and supply.
Rodent models have shown that satiety was controlled
either when caloric or limiting AA consumption satisfied
growth requirement. Extrapolating to cattle grazing forage
or fed hay, it was hypothesized that intake and (or)
performance would be determined by capability of the
forage to satisfy nutrient requirements of the animal.
Repeated observations of 1.4-fold difference in intake
among populations of cattle with no difference in rate of
gain led to the hypothesis that intake differences relate to
differences in metabolic efficiency among individuals. This
hypothesis was supported by research demonstrating equal
correlative differences in mitochondrial respiration rates
among individuals and also by the degree of heritability for
efficiency. The importance of these findings is that feed
efficiency can be theoretically improved 40% within beef
populations via selection. Residual feed intake (RFI)
populations are identified as efficient (1 standard deviation
negative from the mean), inefficient (1 standard deviation
positive from the mean), or average. Forage intake by
nonlactating and lactating cows were reduced by 20 and
11%, respectively, when efficient cows were compared
with inefficient cows (Meyer et al., 2008). As genetic
selection skew cows in a herd toward greater efficiency,
available forage should increase. This in turn should
increase potential productivity of the animal,
simultaneously creating greater potential for correcting
nutritional imbalances. It is my view that economic value
of feed efficiency technology will be pull-through for
IMPACT OF ANIMAL EFFICIENCY SELECTION
ON INTAKE
Residual feed intake was introduced as a measure of
efficiency to prevent confounding effects of growth rate
that occurs with feed to gain ratio measurements. Residual
feed intake is moderately heritable and has been
biologically linked to mitochondrial metabolism. It is
calculated as the difference between intake and expected
intake. Expected intake is calculated by regressing intake of
a population against their ADG and metabolic mid-weight
(it is preferred to also include backfat into the regression).
Independent variable regression coefficients are then used
to calculate expected intake.
Our interest was to measure the influence of selecting
for efficiency (negative RFI) in a cow herd on forage
intake. Fall calving Hereford cows were RFI phenotyped
and then grouped as –RFI, average RFI, or +RFI. We
contrasted –RFI against +RFI groups. Each group was
assigned to 1 of 2 replicates based on BW and BCS. Cows
grazed tall fescue (not infected with endophyte) pastures
May to August, 2006 and February to April, 2007 (as pairs
in 2007). Summer 2006 paddocks were grazed
continuously. Forage availability was maintained in excess
by using weekly electronic rising plate meter (RPM;
FarmWorks, Feilding, New Zealand) readings to compute
total RPM units per paddock. Rising plate meter units
served as estimates of forage DM on offer, as each unit
represented approximately the same DM yield on a given
sampling date. Total RPM units per paddock were
Key words: metabolic efficiency, metabolizable energy,
rumen undegraded protein, intake
1
125
computed to compare forage yield between paddocks in
each replicate. This measure was corrected for paddock size
using the equation: RPMtotal = RPMavg * Areapaddock.
Paddocks were then adjusted in size to keep total RPM
units within 10 per paddock for replicates from each pasture
(block). Cows used during winter 2007 grazed on
stockpiled tall fescue. Paddocks were strip-grazed using
electric poly-tape and movable step-end posts, and a new
strip was allocated every 3.5 d. Initial strip allocations were
determined using a set residual and assuming that cows
would consume 1.2% of their BW in NDF per day
(Mertens, 1987). After this, strip size was allocated based
upon the residual forage left. The goal of strip allocation
was to not limit intake, while also keeping utilization
similar between paddocks. Strip size was calculated based
on the number of days expected for grazing each allocation
when a 3.5 d moving schedule could not be kept. New
grazing strips were provided as needed so that forage
availability was not limiting for more than 12 h. Forage
quality and availability were limiting at trial initiation for
the winter grazing period; therefore, in order to maintain
body condition of cows, cow-calf pairs were supplemented
daily with 3.31 kg of pelleted soyhulls (9.6% CP, DM
basis) regardless of RFI rank. Cows had adequate bunk
space and ad libitum access to water and mineral
supplement for both grazing periods.
Intake data, calculated using RPM data, are presented in
Table 1. When cows were gestating and not lactating, –RFI
cows consumed 80% as much forage as +RFI cows, and
when lactating, –RFI cows consumed 89% as much forage
as +RFI cows. We currently believe intake differences
between lactation stages is due to greater energy intake by
+RFI cows being used for milk production whereas –RFI
cows must increase intake to meet milk production needs.
The result is that efficiency differences between RFI groups
narrows. We measured similar results in RFI-phenotyped
fall-calving first-calf heifers. Efficient (–RFI) and
inefficient (+RFI) animals were contrasted through the
winter while they were grazing stockpiled tall fescue. Cow
BW at weaning and calf weaning weight were not different,
but forage intake by efficient cows was 87% of intake by
inefficient cows (Table 2). These differences agreed with
other research (Nkrumah et al., 2004, 2006; Kolath et al.,
2006; Castro Bulle et al., 2007) and reduction in intake
between groups when lactating compared to non-lactating
agreed with mice data (Hughes and Pitchford, 2004).
Selection for efficiency can reduce DMI an average of 16%
without any notable change in condition or BW of the cow.
As generations selecting for efficiency are stacked, it is
logical to conclude that intake reduction will be even
greater.
Progeny generated from matings of animals with known
efficiency phenotypes will likewise differ in intake. Table 3
presents data of offspring from dams with known RFI
phenotypes. Dams were phenotyped as heifers and then
bred to bulls of known RFI phenotype. Dams were grouped
one standard deviation above (+RFI; inefficient) and below
(–RFI; efficient) mean RFI. Intake was reduced 7.4% and
feed conversion was improved 10.9% when progeny from
efficient dams were compared with progeny of inefficient
dams. Sire selection can have similar impact on progeny
performance. Two sires were contrasted in Table 4. Sires
were mated to cows such that cow RFI was evenly
distributed across sires. Although this only contrasts 2 sires,
we have measured similar response across a wider array of
sire groups. In this example, progeny from sire B consumed
11.7% less DM and had an improved feed conversion
efficiency of 19.4%.
Economic impact of selecting for efficiency is
substantial enough to pull through industry adoption of this
technology. The outcome expected would be more efficient
use of forage resources.
IMPACT OF ENERGY AND AMINO ACID
IMBALANCE ON PERFORMANCE
Forage digestion by cattle yields short chain fatty acids
and microbial protein from ruminal fermentation. Protein
not fermented also passes to the small intestine. Amino acid
profile of microbial protein is relatively constant across
various diets. Protein present in forages is primarily
photosynthetic protein, so likewise, is relatively constant
across plants in being highly degraded in the rumen and in
AA composition. The ME yield from forage is a function of
digestible cell wall content as has been demonstrated by
numerous experiments relating animal performance to
forage digestibility. Because microbial protein production is
a function of fermentable energy and microbial AA
composition is constant, microbial AA flow post-ruminally
should be relative and constant to forage ME. High rumen
degradability and similarity of AA profile across plants
results in consistency of absorbable AA profile available to
the animal when fed a range of forage diets. What would be
expected is that total mass of AA would change as forage
protein content and availability changed, but AA profile
would remain constant.
The hypothesis that AA to ME remains constant is
important because it allows us to identify an imbalance of
AA to energy that occurs across forage diets. Assuming
that rumen-degradable protein (RDP) is not limiting
microbial growth in the rumen, AA supply is typically
limiting maximum growth potential on forage diets. Figure
1 contrasts the energy (ME) allowable gain and absorbable
AA allowable gain for backgrounding calves fed orchard
grass hay (2.35 Mcal ME and 16% CP). Allowable gains
are calculated as potential gain supported by the ME
consumed or by the most limiting AA calculated as AA
supplied by microbial protein and undegraded forage
protein (70% RDP). This hypothetical forage would contain
enough energy to support 0.9 kg/d gain with DMI at 2.25%
of BW but only yield post-ruminal methionine (most
limiting AA) flow capable of supporting 0.3 kg/d gain. The
point made is that imbalance between energy and AA
supply exists for cattle fed forage diets. Richardson and
Hatfield (1978) reported methionine was first-limiting when
post-ruminal protein supply was predominantly of
microbial origin. Storm and Orskov (1984) concluded that
microbial protein was limiting in methionine, lysine,
histidine, and arginine. Greenwood and Titgemeyer (2000)
likewise measured methionine to be first-limiting in a
structural carbohydrate-based diet. A strong body of
research exist demonstrating response to post-ruminal AA
126
the level of protein and AA that are limiting. Correcting
imbalances in absorbable AA to energy ratio for high
quality forage diets provide enough benefit that they are
generally profitable. Application spans from backgrounding
calves to young cows.
supply in cattle fed forage-based diets, indicative of an
imbalance between absorbable AA supply and ME
availability.
Imbalance of AA and ME will be most pronounced in
cattle capable of growth (backgrounding calves, developing
heifers, pregnant heifers, or young cows). Forcherio et al.
(1995) evaluated this concept using lactating two-year old
cows grazing spring tall fescue pastures. Each cow was
supplemented with 1 kg/d of corn mixed with soybean meal
or bloodmeal to provide 54 or 250 g of rumen-undegraded
protein (RUP), respectively. Supplementing RUP improved
cow ADG, reduced milk production, and had no effect on
calf ADG (Table 5). Concluded from this study was that
RUP supplementation improved gain by partitioning tissue
accretion to the animal and away from milk production.
When energy was supplemented in absence of UIP, milk
production increased and BW loss occurred. It is our belief
that correcting imbalance of AA to energy ratio in forage
diets can be beneficial to young cows that are biologically
committed to competing functions of body tissue accretion
and milk synthesis.
We have measured beneficial responses in developing
heifers to methionine supplementation. Heifers were fed
supplements containing 0, 7.5, or 15 g of MFP (rumen
stable methionine) daily for 85 d (Table 6). The higher level
of methionine supplementation increased ADG and
reproductive tract score of heifers. We concluded that
heifers fed supplemental methionine were more
physiologically mature due to more rapid growth during
development. Rumen escape of MFP is reported to be 40%.
Our nutritional model predicted 6 to 7 grams of additional
methionine were needed to maximize ADG on the forage
diet fed to these heifers. Prediction of additional methionine
required to improve gain and improvement in gain from
supplying 6 to 7 grams additional methionine were in
agreement.
Rodriquez et al. (2009) fed stocker steers supplement
containing either corn gluten meal or Alimet (rumen stable
methionine) to supply sulfur AA post-ruminally. Supplying
methionine individually increased gain of pastured cattle
similar to providing RUP as corn gluten meal (Table 7).
Gain response was also level-dependent for both protein
sources. We also provided supplemental RUP in the form of
bloodmeal to calves grazing alfalfa pasture. Although
alfalfa protein was high, supplemental RUP improved gain
(Table 8).
Data can be interpreted supporting the conclusion that
growing cattle consuming forage diets have an imbalance of
absorbable AA supply relative to energy availability. This
imbalance is created by discrepancy in microbial AA
profile relative to animal AA requirement. It is my present
belief that gain improvements occurring from increased
supply of AA post-ruminally are due to increased lean
tissue accretion at the expense of lipid accretion. When
energy supply is greater than absorbable AA supply, energy
is deposited as lipid. However, when absorbable AA supply
is increased to match energy supply, lean tissue will be
accreted. The greater water content of lean tissue results in
measurement of improved daily gain. This view is
corroborated by response to supplemental RUP having a
point of diminishing return. There is general agreement on
CONCLUSIONS
Technology now being adapted can move beef cows to
consuming 20% less forage while maintaining growth and
milk production. While it is unknown how efficient animals
adjust nutrient requirements or if they even do, we need to
consider how potential for reduced intake will affect intake
of protein and minerals. It has been demonstrated that
imbalance exists between amino acid and energy in cattle
fed forage diets. Will this be exacerbated by more efficient
cattle that consume less feed? Likewise, as we use and
develop growth promotants for forage-fed cattle, the impact
of amino acid-energy imbalance needs to be considered.
Can a calf grazing high-quality pasture maximally respond
to a growth implant if inadequate amino acids are supplied
to support lean growth? Production economics likely will
continue to select a heavier calf for feedlot production,
giving greater profit potential for backgrounding calves. As
methods are developed to select for more efficient cattle,
forage intake should decrease. Greater value of
backgrounding and change in intake potential by cattle
emphasizes the importance of correcting nutritional
imbalances in cattle fed forage.
LITERATURE CITED
Castro Bulle, F. C. P., P. V. Paulino, A. C. Sanches, and R.
D. Sainz. 2007. Growth, carcass quality, and protein and
energy metabolism in beef cattle with different growth
potentials and residual feed intakes. J. Anim. Sci. 85:
928-936.
Forcherio, J. C., G. E. Catlett, J. A. Paterson, M. S. Kerley,
and M. R. Ellersieck. 1995. Supplemental protein and
energy for beef cows consuming endophyte-infected tall
fescue. J. Anim. Sci. 73:3427-3436.
Greenwood, R. H. and E. C. Titgemeyer. 2000. Limiting
amino acids for growing Holstein steer limit-fed
soybean hull-based diets. J. Anim. Sci. 78:1997-2004.
Hughes, T. E. and W. S. Pitchford. 2004. How does
pregnancy and lactation affect efficiency of female mice
divergently selected for post-weaning net feed intake?
Aust. J. Exp. Agric. 44:501-506.
Mertens, D. R. 1987. Predicting intake and digestibility
using mathematical models of ruminal function. J.
Anim. Sci. 64:1548-1558.
Meyer, A. M., M. S. Kerley, and R. L. Kallenbach. 2008.
The effect of residual feed intake classification on
forage intake by grazing beef cows. J. Anim. Sci. 86:
2670-2679.
Nkrumah, J. D., J. A. Basarb, M. A. Price, E. K. Okine, A.
Ammoura, S. Guercio, C. Hansen, C. Li, B. Benkel, B.
Murdoch, and S. S. Moore. 2004. Different measures of
energetic efficiency and their phenotypic relationships
127
Richardson, C. R. and E. E. Hatfield. 1978. The limiting
amino acids in growing cattle. J. Anim. Sci. 46:740-745.
Storm, E. and E. R. Orskov. 1984. The nutritive value of
rumen micro-organisms in ruminants. 4. The limiting
amino acids of microbial protein in growing sheep
determined by a new approach. Br. J. Nutr. 52:613-620.
with growth, feed intake, and ultrasound and carcass
merit in hybrid cattle. J. Anim. Sci. 82:2451-2459.
Nkrumah, J. D., E. K. Okine, G. W. Mathison, K. Schmid,
C. Li, J. A. Basarab, M. A. Price, Z. Wang and S. S.
Moore. 2006. Relationships of feedlot feed efficiency,
performance, and feeding behavior with metabolic rate,
methane production, and energy partioning in beef
cattle. J. Anim. Sci. 84: 145-153.
Table 1. Forage intake by fall-calving cows of different residual feed intake (RFI) phenotype (one-third most and least
efficient1)
–RFI
RFI, kg/d
-4.37
Initial BW, kg
591
Initial BCS
5.3
Nonlactating
DMI, kg/d
12.4
Lactating (Pairs)
DMI,kg/d
12.5
1
Efficient (–RFI) and inefficient (+RFI) cows.
+RFI
5.04
565
5.3
% Difference
15.6
79.5
14.1
88.6
Table 2. Forage intake, average cow body weight, and calf weaning weights contrasted between efficient (–RFI) and
inefficient (+RFI) cows
Forage intake, kg/d
Calf weaning weight, kg
Cow BW, kg
a,b
Means with unlike superscripts differ (P < 0.10)
–RFI
14.1b
260
616
+ RFI
16.2a
273
620
Table 3. Effect of dam phenotypic selection for residual feed intake (RFI) on progeny performance
RFI group1
Dam RFI
Calf ADG, kg/d Calf intake, kg/d
Calf FCR2
Calf RFI
- RFI
-1.73
1.75
9.55
5.46
-0.41
µ RFI
0.14
1.76
10.02
5.69
0.07
+ RIF
1.77
1.68
10.31
6.13
0.29
-RFI vs. +RFI, %
+ 4.2
-7.4
-10.9
1
Dams were grouped one standard deviation above (+RFI; inefficient) and below (-RFI; efficient) mean RFI.
2
Feed conversion ratio.
Table 4. Effect of sire phenotype selection for residual feed intake (RFI1) on progeny performance
Calf ADG, kg/d
Sire A (+RFI)
1.72
Sire B (-RFI)
1.88
% Change
+ 9.3
1
Efficient (–RFI) and inefficient (+RFI) sire.
2
Feed conversion ratio.
Calf intake, kg
10.79
9.53
-11.7
128
Calf FCR2
6.28
5.06
-19.4
Calf RFI
0.73
-0.60
Table 5. Effect of rumen undegradable protein (RUP) supplementation on two-year-old cow performance
RUP1
Tall fescue2
+
E+
Ea
b
Cow ADG, kg/d
0.1
-0.10
-0.2
0.2
Calf ADG, kg/d
0.9
0.9
0.9
1.0
Milk intake, kg/d
7.3
9.8
8.2
9.6
1
Ad libitum access to tall fescue plus 1.07 kg/d of supplement consisting of cracked corn with soybean meal (-) or blood meal
(+) to provide 54 or 250 g of RUP, respectively.
2
Free-choice mineral provided ad libitum to endophyte infected (E+) or free (E-) tall fescue pasture.
a,b
Means with unlike superscripts differ (P < 0.05).
Table 6. Effect of methionine (MFP) supplementation on reproductive development of heifers
MFP, g/d
7.5
369
0
374
Initial BW, kg
ADG, kg/g
day 0 to 30
0.8b
day 0 to 85
0.8
Pelvic area, cm2
184
1
Reproductive tract score
4.2b
1
1 = infantile, 5 = mature.
a,b
Means with unlike superscripts differ (P < 0.05).
15
374
1.0a,b
0.8
186
4.2b
1.2a
0.9
183
4.5a
Table 7. Effect of rumen undegradable protein (RUP) and rumen-protected methionine (Alimet) on growth performance of
stocker calves fed Bermuda grass hay
Control
TSAA1 from CGM, g/d
2
4
6
Intake, kg/d
Hay
4.2
4.6
Supplement
2.0
2.0
ADG, kg/d
0.5
0.6
1
Total sulfur amino acids from corn gluten meal.
4.9
2.0
0.7
4.7
2.0
0.7
2
4
5.1
2.1
0.6
5.0
2.1
0.6
Alimet, g/d
6
5.2
2.1
0.7
8
5.0
1.9
0.6
Table 8. Effect of rumen undegradable protein (RUP) supplementation on daily gain of calves rotationally grazing alfalfa
pastures
Control
Energy1
100g BM2
200g BM3
ADG, kg/d
0.6
0.7
0.8
0.8
1
Heifers were supplemented with ground corn at 0.3% of BW.
2,3
Heifers were supplemented with ground corn at 0.3% of BW and 100 or 200 g of blood meal.
129
1.6
1.4
ADG, kg/d
1.2
1
Energy
0.8
Amino Acid
0.6
0.4
0.2
0
0
1
2
3
4
DMI, % BW
Figure 1. Comparison of gain potential based upon 2.35 Mcal ME and 16% CP orchard grass hay at increasing intake (% of
BW).
130
M. Kerley
Notes
Proceeding, Grazing Livestock Nutrition Conference
July 9 and 10, 2010
H. Dove*1, S. R. McLennan†, and D. P. Poppi‡
*
†
CSIRO Sustainable Agriculture National Research Flagship, G.P.O. Box 1600, Canberra, A.C.T. 2601, Australia
Queensland Primary Industries and Fisheries, Animal Research Institute, Yeerongpilly, Qld 4105, Australia; and
‡
Schools of Animal Studies and Veterinary Science, University of Queensland, Gatton, Qld 4343, Australia
ABSTRACT: The ability to apply nutrient requirement
schemes to grazing animals can be related to the purpose of
the application, the extent to which the requirement scheme
truly represents animal requirements, the nature of input
data required about the plants and the animals, and the
nutrient supply that results from the diet composition and
intake of the animals. These issues are discussed in relation
to the published energy and protein requirements within the
American (NRC, 2007) and Australian (CSIRO, 2007)
requirement schemes. Data are presented to indicate that
with respect to both energy and protein, the differences
between schemes are not great, especially when differences
in assumptions of the schemes are taken into account. An
understanding of these assumptions is thus critical to the
field application of published requirements. With respect to
protein requirements, it is suggested that further work is
needed to incorporate N recycling into published
requirement schemes. In housed animals, live weight gains
are satisfactorily predicted by both schemes, but it is
suggested that the greatest impediment to the field
application of nutrient requirements is the difficulty of
obtaining data on nutrient supply (i.e., data on the diet
composition and intake of grazing livestock). The
Australian requirement system, as captured in the decision
support tool GrazFeed, incorporates an algorithm to predict
actual intakes based on pasture and animal data. Data are
presented to indicate that with temperate pastures, this
approach allows accurate prediction of live weight gains of
sheep and cattle under field conditions, but it is less
successful under tropical and rangeland conditions, for
which the intake algorithm is not designed. Under tropical
conditions, the application emphasis may shift from the
prediction of animal performance to the prediction of intake
from observed animal performance, as a means of fodder
budgeting; ongoing work in this area is discussed. Finally,
it is suggested that the field application of nutrient
requirement schemes, particularly by producers, will be
constrained as long as this involves the manipulation of
complex equations and tabulated data. Such application
could be accelerated if it were supported by software
packages that could be used by producers or their advisors
and that could assist the complex calculations and allow
them to be conducted for temperate, rangeland and tropical
grazing conditions.
INTRODUCTION
Most of the world‟s domestic ruminants obtain their
daily requirements of nutrients from improved pasture,
natural pasture, or browse. Any attempt to apply published
nutrient requirements under grazing conditions therefore
must also consider the amount and quality of the forage
resource because as this resource responds to regional
climate, seasonal weather conditions, and the plant species
present, and these factors influence the amount and quality
of the diet selected by the animals. Nutritional management
of livestock thus implies an understanding of nutritional
supply from plants which, by contrast with housed animals,
is difficult to define and ever-changing in terms of both
quantity and quality. This means that a key aspect of the
application of nutrient requirement schemes becomes a
consideration of the accuracy of the published nutrient
requirements relative to the accuracy with which nutrient
supply can be quantified.
Research and discussion on the nutrient requirements of
ruminants has continued for more than a century. As
Corbett and Freer (2003) emphasized, in an historical
context, this has been driven in part by the need in cold
climates to house livestock for some of the year, which
generates a need for data about diet quality and animal
requirements to permit diet formulation for a desired level
of animal performance. However, the main issue for the
nutritional management of grazing animals is mostly the
reverse, of predicting likely performance from a forage
intake usually poorly quantified (Corbett and Freer, 2003;
Dove, 2009).
The nutrient requirements of grazing ruminants have
been reviewed and tabulated recently by CSIRO (2007) and
also in a series of National Research Council publications
(NRC, 2000; 2001; 2007). These publications themselves
draw on earlier feeding standards (e.g., NRC, 1981; 1985;
INRA, 1989; SCA, 1990; AFRC, 1993; Tamminga et al.,
1994). All provide information on the daily requirements of
livestock for energy, protein, minerals, vitamins, and water
for the purpose of maintenance and the additional
requirements for live weight gain, pregnancy, lactation, and
fiber production. In this paper, we will restrict our
discussion to the requirements for and the supply of energy
and protein in grazing ruminants. We do not present a
detailed comparison of the various nutrient requirement
systems; rather, our emphasis will be on the application of
requirements under field conditions. Nevertheless,
differences exist between the published requirement
Key words: nutrient supply, diet composition, intake,
digestibility, protein content, nitrogen recycling
1
133
accuracy of the output decreases across this continuum (i.e.,
scientists may be seeking a highly quantitative, accurate
answer, whereas livestock managers may be content with
„an indication‟). The level of accuracy also interacts with
the reason for the application. For example, the prediction
of animal performance under field conditions implies that
as part of the application of requirements, diet composition
and intake are being estimated or predicted to compare with
requirements and thereby predict animal performance.
Prediction in this manner demands a greater complexity of
input and accuracy of output than, for example, backcalculating from observed animal performance and
published nutrient requirements to get an indication of
forage intake for fodder budgeting purposes (e.g., Baker,
1982; 2004; and see below).
A related issue to our question is the ease with which
the various requirement systems allow application under
field conditions. For example, if the information about
requirements is only available as complex equations or as
tabulated data (e.g., NRC, 2007), for some users (especially
producers) application under field conditions may be more
difficult than if the requirement system is supported by a
user-friendly, computer-based package that encapsulates the
system for field use. An example of the latter is the use of
the GrazFeed and GrassGro software packages (Freer et al.
1997; Moore et al., 1997) to support field application of
both the SCA (1990) and CSIRO (2007) systems.
systems, some of which are discussed in more detail below,
but we will argue that these are not the major constraint in
applying feeding standards to the grazing animal. More
usually, the constraint to the field application of published
requirements has been the quantification of the interaction
between the animal and the available forage in the
determination of the animal‟s dietary intake and the
nutritive value of its diet. This constraint led Alderman
(1978) to suggest that in the grazing situation, it was too
difficult to use published nutrient requirements and that
“…emphasis on quantitative nutrition has to be replaced
by…expected yield of forages, optimization of stocking
density and measurement of animal production as an
estimate of feed intake.” An emphasis on forage yield and
stocking density in grazing management is appropriate, but
as Corbett and Freer (2003) discussed, the abandonment of
quantitative nutrition and the use of animal production to
infer intake severely constrain the application of feeding
standards to predict livestock performance under field
conditions, although the latter may be useful for fodderbudgeting purposes (see below).
Conceptually, the application of nutrient requirement
systems to grazing livestock can be addressed in terms of
several key questions:
1. Who is doing the applying and for what purpose? The
reason for applying the system and how this is done will
determine, in part, the level of accuracy that is acceptable.
2. How well do the various systems estimate key
requirements? Are the published requirements sufficiently
quantitative and accurate that they represent the
transactions in digestion and metabolism and thus serve as a
„target‟ against which to balance nutrient supply in the
field?
3. What plant and animal inputs are required to apply the
systems under grazing conditions; is it possible to obtain
this information easily, and are the techniques for defining
the nutritive value (NV) of the dietary components
sufficiently accurate to allow NV to be related to nutrient
requirements?
4. What nutrient supply results from grazing the forage
resource? In short, how can we estimate diet composition
and intake, and will the system accommodate the provision
of extra nutrients as supplementary feed?
Points 2, 3, and 4 clearly indicate that, under field
conditions, the issues are those of determining whether the
requirement systems accurately define the performance of
the animals from known nutrient intake, and whether the
necessary inputs can be described in terms of the intake and
diet quality with sufficient accuracy to quantify this nutrient
intake.
HOW WELL DO THE DIFFERENT SYSTEMS
ESTIMATE REQUIREMENTS FOR THE
MAJOR NUTRIENTS?
After more than a century of effort to define the nutrient
requirements of livestock, our understanding of ruminant
digestion and metabolism is sufficiently advanced that most
of the published requirement systems are probably accurate
enough to permit field application. This is not to say that
further refinements are not possible, particularly in relation
to the validity of some of the assumptions underpinning the
estimation of requirements. As often as not, differences in
assumptions explain the major differences between the
various schemes; this can be illustrated by a consideration
of recently derived energy and protein requirements.
Energy Requirements
Modern systems for expressing the energy requirements
of livestock usually do so either in terms of metabolizable
energy (ME; SCA, 1990; AFRC, 1993; CSIRO, 2007), net
energy (NE; INRA, 1989), or both (NRC, 1985; NRC,
2007). The efficiency of conversion of ME to NE varies
with the purpose for which ME is used and is greatest for
maintenance. Moreover, to varying degrees in the different
requirement schemes, the efficiencies of conversion of ME
to NE for maintenance (km), growth (kg), and lactation (kl)
are themselves regarded as functions of dietary quality,
conveniently described by M/D (MJ ME/kg DM). It follows
that it is not possible to attribute a single NE value to a
feed. By contrast, the ME content of a feed can be well
predicted from other, simpler measures of information such
as its DM or OM digestibility.
WHO IS DOING THE APPLYING AND
FOR WHAT PURPOSE?
Although who and what questions in the section title
may seem simple, it is fundamental to the extent that it
determines, in part, the level of accuracy that is acceptable
to the user. Conceivably, nutrient requirement systems
could be applied under field conditions by scientists, by
agricultural advisors, and in some cases, by livestock
producers themselves. It is likely that the necessary
134
weight. For example, in beef cattle, NRC (2000) used
changes in BCS as a means of practically assessing the
energy requirements of mature cows. For field application,
this might be more practical than a system based on
measuring changes in live weight. Despite such issues, as a
generalization, the current accuracy of published energy
requirements seems sufficient to allow their application in
the nutritional management of grazing livestock, at least in
temperate regions.
There are differences in the published estimates of
energy requirements between the different systems. For
example, the ME requirement of a 30-kg lamb growing at
200 g/d is about 16% greater in the Australian system
(CSIRO, 2007) compared with AFRC (1993). This is due,
in part, to the greater maintenance requirement within the
former estimate. By contrast, the ME requirements of
housed mature, dry (non-lactating) ewes maintaining
weight are very similar between the Australian (CSIRO,
2007) and USA (NRC, 2007) systems (Figure 1). For
housed ewes consuming equivalent diets, the difference in
estimated ME requirements for maintenance is only 2 to
3%, which would be unlikely to be of practical significance.
The requirements for grazing ewes using CSIRO (2007) are
17 to 18% greater than those for housed ewes estimated
using NRC (2007), reflecting the 15% increment for
grazing embedded in the former estimate.
A similar comparison for lactating ewes with a single
lamb in early lactation highlights the need for an
understanding of the assumptions underpinning requirement
estimates (Figure 2). In this case, for equivalent conditions,
the total ME requirements estimated from CSIRO (2007)
are on average about 10% greater than those tabulated in
NRC (2007). Investigation of the source of this difference
in estimates shows that it is related to two assumptions used
in the CSIRO (2007) calculations that are not in the NRC
(2007) estimates. The first relates to the respective
assumptions concerning kl; in NRC (2007) this is assumed
to be constant (0.644), whereas in CSIRO (2007) it is a
function of dietary M/D and for the dataset in Table 15.1 of
NRC (2007) varies from 0.56 to 0.60. Recalculation of
NRC (2007) requirements using a variable kl increases
estimated requirements and brings them within 5% of those
of CSIRO (2007).
The second assumption relates to the maintenance
component of the total requirement, which in NRC (2007)
is not related to the level of production. By contrast, the
CSIRO (2007) estimate of maintenance is, in part, a
function of the level of production in the ewe [0.10 * (ME
for milk + ME for live weight change)]. Further
recalculation of the NRC (2007) estimates by incorporating
an effect of level of production on maintenance gives
estimated requirements that are statistically similar to those
of CSIRO (2007; Figure 3). This example emphasizes the
point that although „differences‟ in estimated requirements
might be „arithmetically real‟, they are often not large and
are often explicable in terms of the assumptions underlying
the different estimates. In turn, this indicates the need to
appreciate the assumptions involved.
With continued research, and as the validity of these
various assumptions is investigated further, refinements to
published ME requirements of livestock can be expected.
This is particularly the case in pregnancy and (or) lactation,
during which it is very likely that patterns of true maternal
weight loss can be masked by changes in tissue hydration
(see CSIRO, 2007). These changes mean that the ME
supplied by weight loss during lactation, or the amount of
extra grazing or supplement required to compensate for the
weight loss, will be underestimated. To an extent, the issues
arising from changing tissue hydration can be overcome by
monitoring body condition score (BCS) rather than live
Protein Requirements
Under grazing conditions, the protein content of green
feed may often be sufficient to meet the protein requirement
of grazing livestock, although grazing, high-producing
dairy cows may have a need for extra protein. In animals
grazing low-quality roughages, such as dry summer pasture,
crop residues, or dead herbage in the dry tropics, the need
for protein becomes a major issue. A system for defining
protein requirements is thus needed to identify and quantify
the need for protein supplements under these circumstances.
Such a system must account for:
the needs of the rumen microflora for soluble N
(degradable protein intake; RDP) and for fermentable
energy, to optimize fiber digestion and the production of
microbial protein;
the need with some productive purposes for an
increased supply of dietary AA post-ruminally (undegraded
dietary protein; RUP);
and feedbacks between tissue AA availability,
efficiency of utilization of absorbed AA, and feed intake.
Current systems for expressing protein requirements in
general accommodate these needs, although to different
extents. Ultimately, the usefulness of any requirement
system also has to be a balance between the precision with
which it describes rumen digestive processes and the
difficulty of obtaining the input data required to describe
these processes. This is particularly relevant in estimating
protein requirements because of the difficulty of defining
the degradability of forage protein. For the grazing animal,
the lower precision with which the manager can define the
nutrient supply is usually a greater constraint than the
accuracy of requirements.
Protein requirements were expressed in terms of
„absorbed protein‟ by NRC (1985) and of „metabolizable
protein‟ (MP) by AFRC (1993) and by NRC (2007),
although the latter also tabulates requirements in CP terms
for different levels of degradable intake protein. The SCA
(1990) publication expressed requirements as „apparently
digested protein leaving the stomach (ADPLS),‟ whereas
the French system (INRA, 1989) used „protein truly
digested in the small intestine‟ (PDI). The recent Australian
system (CSIRO, 2007) moved from ADPLS to „truly
digested protein leaving the stomach‟ (DPLS) by estimating
total protein requirements at the tissue level and converting
from this to DPLS using an assumed efficiency of
conversion of DPLS of 0.7 for all purposes except wool
growth, for which efficiency of conversion of DPLS to
wool protein was assumed to be 0.6. The DPLS
requirements for a growing lamb presented in CSIRO
135
(2007; see their Table 2.5) are less than would be estimated
from AFRC (1993). However, for live weight gains of 100
to 250 g/d in a 4-month old, 30-kg lamb (maturity 0.6), the
DPLS requirements of 68 to 88 g/d that can be calculated in
CSIRO (2007) are similar to the MP requirements of 74 to
89 g/d tabulated in NRC (2007, see their Table 15.2).
In making such comparisons between estimated
requirements, it is important to appreciate how the
magnitude of any differences between requirement systems
compares with the likely effects of plant species or stage of
growth of the herbage consumed. Yu et al. (2003)
compared the Dutch DVE/OEB system (Tamminga et al.,
1994; DVE = truly absorbed protein in the small intestine;
OEB = degraded protein balance) with the NRC (2001)
system, for predicting the supply of protein to dairy cows
from alfalfa and timothy cultivars at various stages of
growth. The „degraded protein balance‟ (OEB) compares
the potential microbial protein synthesis from RDP
consumption vs. that from the energy supplied by rumen
fermentation. Positive values indicate potential N loss from
the rumen, whereas negative values imply a possible N
shortage in the rumen; an optimum OEB should thus be
close to zero (Yu et al., 2003). These calculations do not
take into account recycling of N to the rumen, which can be
substantial. When the predicted DVE supply from the
various feeds (g/kg DM) was compared, the slope of the
line relating NRC (2001) estimates and the DVE/OEB
system did not differ from 1, although there was a
significant difference in intercepts, indicating a positive
bias of 4 to 10 g DVE/kg DM in the NRC (2001) estimates
(Figure 4). However, note that the effect of pasture species
(timothy vs. alfalfa) or stage of growth on absorbed protein
supply was much greater than the difference between the
systems. This trend is even more marked in RDP balances
predicted by the two systems, which were not different
statistically (Figure 5). By contrast, there were marked
differences between pasture species and within the species
differences, between stages of growth. There were also
differences between timothy cultivars, but not between
alfalfa cultivars. These data serve as a reminder that a
proper evaluation of the forage resource and its NV may be
of greater quantitative significance than any concern about
possible differences between requirement systems. Nitrogen
recycling to the rumen is not accounted for in any system,
and in itself, is not of any importance within high-N,
temperate pasture systems. However, in the desire to
decrease N excretion and its environmental consequences,
this issue is being revisited. It would be an advantage to
enhance N recycling to the rumen, especially with highproducing dairy cows, so as to decrease the calculated N (or
CP) requirement (Reynolds and Kristensen, 2008). This
affects the calculation of N or RDP balance across the
rumen and the requirements for quantity and type of
supplement to be used. The issue is even more critical with
low-N content forages. For example, in tropical northern
Australia, for a speargrass (Heteropogon contortus) pasture
of 50% OM digestibility, 40 g CP/kg DM, and a microbial
protein yield of 130 g MCP/kg DOM (or 9 g MCP/MJ
fermentable ME), the RDP deficit is 27.5 g RDP/kg grass.
A 200-kg weaned steer might eat up to 20 g/kg BW/d or 4
kg/d of pasture, resulting in a requirement to supplement
with 55.7 g/d of a urea-ammonium sulphate mix. However,
if recycling of N of 10.9 g N/d (Dijkstra et al., 1992) is
assumed then the required supplement intake is only 27.9
g/d of a urea-ammonium sulphate mix. Interestingly, from
practical field supplement experiments with weaned cattle,
the current recommended intake of urea-ammonium
sulphate mix to optimize the live weight gain response in
northern Australia is 30 g/d (Winks et al., 1970).
To be useful in livestock nutritional management, MP
or DPLS requirements are converted to daily requirements
for CP (g/kg DM), as in both CSIRO (2007) and NRC
(2007). However, this can introduce a further difficulty in
comparing the various protein requirement schemes
because of different assumptions about the effective
degradation of the dietary protein (Edg). The Edg is the
protein degradability (dg) that will occur at a given rumen
outflow rate and is the „link‟ between dietary CP content
and MP/DPLS. The Edg is difficult to measure in vivo and
more frequently is derived from dg, measured as the
disappearance of N from a feed suspended in polyester or
nylon bags in the rumen for fixed times. It is crucial that the
estimate of dg makes allowance for microbial colonization
of the sample during rumen incubation, especially for lowquality forages, and the conversion from dg to Edg clearly
depends on the assumptions made about the likely outflow
rate from the rumen (see CSIRO, 2007 for more detailed
discussion).
As with energy requirements, continuing refinements of
protein requirements can also be expected. For example,
requirements may be increased to allow for the interaction
between increased protein supply and the capacity of
animals to deal with gastrointestinal parasites in both the
short and long term (Datta et al., 1999) and especially in
late pregnancy (Robinson, 2002).
Despite the difficulties in estimating Edg and the need
to resolve issues, such as nutrient-parasite interactions, the
above discussion suggests that inaccuracies or differences
between feeding systems in estimated protein requirements
are unlikely to be the main constraint to the application of
nutrient requirement data under field conditions.
Prediction of Animal Performance
Given that, after allowance for differences in underlying
assumptions, there is a general similarity between the
predictions of different requirement systems, the next
crucial question is how predicted requirements relate to
observed performance of animals given diets of known
intake and nutritive value. We investigated this question by
using data from a series of our own pen-feeding
experiments (McLennan, 1997; 2004; Bolam, 1998;
Marsetyo, 2003) where young, growing Bos indicus
crossbred beef steers (n = 240; ca. 12 to 15 months; range
156 to 243 kg live weight) were restricted to pens and fed
diets based on generally low-quality tropical forages with a
wide range of supplement types and intakes of varying
energy and protein content. We compared measured live
weight gains (range -0.1 to 1.2 kg/d) with those predicted
using either the SCA (1990) equations or the Cornell Net
Carbohydrate and Protein System (CNCPS; Fox et al.
2004). The two systems are quite different in their approach
136
to apportioning energy utilization; SCA (1990) and its
successor CSIRO (2007), use the ME system, whereas the
CNCPS is aligned to the NE system and uses either a
simple theoretical model to estimate a value for TDN
content (Level 1 approach) of the diet or a mechanistic
approach based on pool sizes of carbohydrate and protein
fractions and their digestion and passage rates (Level 2
approach). Because various diet descriptors required to run
CNCPS level 2 were not available, surrogate values were
taken from feed sources of similar chemical composition in
the tropical feed library included in the software. Both SCA
and CNCPS require an estimate of the mature size of the
animal and in both cases, we assumed values suitable for
the type of animal used in these trials. Both systems predict
live weight gain based on an estimate of energy retention,
as determined by various attributes mainly associated with
the diet and animal descriptions. With the CNCPS both an
ME-allowable and an MP-allowable gain are predicted, and
in our simulations, the lesser of these was used as the
predicted gain. The SCA equations represented an MEallowable gain, as the effects of MP supply were already
accounted for in determining ad libitum intake by the steers.
The regressions for these model simulations were as
follows:
sacrifice in the precision of the predictions. With SCA, the
intercept of the regression line was not different from zero
(P > 0.05), indicating good prediction at low growth rate,
for instance when forages were provided in the absence of
supplement. However, the slope was greater than 1 (P <
0.05), showing a trend for increased under-prediction as
growth rate increased in response to increased supplement
intake. An overall assessment would be that both systems
provided a reasonable prediction of growth rate and thus
apparently also of the use of energy for maintenance and
growth.
The prediction of growth rate, as shown above, entails a
number of key steps in the requirement systems, including a
description of the animal relative to a mature “finished”
animal, and determinations of the M/D of the diet, the
maintenance requirements of the animal (ME m), and the
efficiencies of conversion of ME to NE for various
productive purposes (i.e., km and kg, and of the conversion
rate from energy retained to live weight gain). Given the
small differences in prediction of growth rate by the SCA
and CNCPS systems above, it might be tempting to think
that the calculated values for the various components of
energy use are also similar for the different systems.
However, taking one of these elements, ME m, and applying
the appropriate equations in each system, it becomes
obvious that there are marked differences between systems
in the approach taken, assumptions made, and eventual
calculated values. With the SCA system, the maintenance
requirement increases with increasing energy intake
because the equation (Equation 1.22) to calculate it includes
a multiple of ME intake (MEI) to account for the higher
maintenance requirements of productive animals. The
equation, as applicable to a Bos indicus steer is as follows:
CNCPS (Level 2): Observed LWG = 0.123 + 0.947
Predicted LWG, (R2 = 0.71; residual standard deviation
(rsd) = 0.159; standard error of prediction (sep) = 0.185;
bias = 17.3%);
SCA: Observed LWG = -0.031 + 1.171 Predicted LWG,
(R2 = 0.79; rsd = 0.141; sep = 0.153; bias = 9.6%).
Based on these simulations, errors in the application of
these systems would arise from two main sources: the
general slight under-prediction of growth rate by both of the
above equations and the not insignificant variation in the
relationships between predicted and observed live weight
gains (i.e., R2 = 0.71 or 0.79 with rsd values of between
0.14 and 0.16 kg/d and sep values from 0.130 to 0.185
kg/d). Such variability is not unexpected with biological
data of this nature and the extent to which it can be
tolerated depends on the purpose for which the predictions
are being made, as alluded to earlier. With the CNCPS, the
slope of the trend line was not different from 1 (P > 0.05),
but the intercept was greater than zero (P < 0.05), indicating
a constant under-prediction of growth rate (approximately
0.08 kg/d) over the full range. When the CNCPS Level 1
approach was taken, the predictions were comparable with
CNCPS Level 2:
Observed LWG = 0.148 + 0.894 Predicted LWG, (R2 =
0.77; rsd = 0.147; sep = 0.179; bias = 22.3%).
There was a similar slight under-prediction of
performance at low growth rates using Level 1, with the
intercept differing from zero (P < 0.05), but a tendency for
over-prediction as growth rate increased, although the slope
of the trend line was not different from 1 (P > 0.05). From a
practical point of view this is an important finding, as it
suggests that for workers in the field with limited
information about the composition of the diet of grazing
animals, or with limited experience in the use of CNCPS,
the simpler Level 1 approach can be used without major
MEm = 0.26W0.75exp(-0.03A)/km + 0.09 MEI, where A is
age in years.
By contrast, there is no adjustment for energy intake in the
determination of maintenance requirements in the CNCPS
system. In this case, NE for maintenance (NEm) is
calculated as a function of metabolic body size with
adjustments for breed, physiological state, activity, urea
excretion, acclimatization, and heat or cold stress. In our
simulations, the main variable between treatments was the
weight of the steers, and when NEm was expressed on a BW
basis, it was relatively constant as energy intake increased.
However, computed MEm on a live-weight basis decreased
with increasing energy intake; accordingly km increased.
The regression equations for changes in estimated ME m
with increasing energy intake are as follows.
SCA: MEm = 476.4 + 0.049 MEI, (R2 = 0.52; rsd = 6.91)
CNCPS: MEm =514.2 – 0.032 MEI, (R2 = 0.06; rsd =
18.36), where both MEm and MEI are expressed as kJ/kg
W0.75.d-1.
The values for MEI were determined using the observed
intakes by the cattle but the separate calculations of the
energy density of the diet (MJ/kg DM) for each system.
The low R2 value for CNCPS can be attributed to outliers,
which were associated with animals with high intakes of
137
development and use of fecal near-infra-red reflectance
spectroscopy (F.NIRS; Lyons and Stuth, 1992; Coleman
and Henry, 2002; Dixon and Coates, 2005; 2009) for
estimating the quality of the diet selected by grazing
animals. The F.NIRS approach relates dietary attributes,
such as crude protein content and DM digestibility (DMD),
to the NIR spectra of the feces collected from the animals.
This allows rapid, frequent, and relatively inexpensive
determination of diet quality that can be used as an input for
the requirement systems to estimate herbage intake and NV,
which ultimately can be used for prediction of animal
performance.
In improved temperate pastures, once the digestibility of
herbage falls below about 70%, there is a marked and
increasing reduction in herbage intake. The effect on live
weight gain in young animals will be proportionately more
marked because the efficiencies of utilization of ME for
both maintenance and gain also decreased as digestibility
decreases (see CSIRO, 2007).
Dietary ME content is difficult to measure directly and
is usually obtained by indirect calculation and expressed as
M/D = MJ ME/kg DM. This is a key „driver‟ of the
estimation of requirements in both the CSIRO (2007) and
NRC (2007) systems, and the prediction of intake in the
former. As a result, the output from the application of the
system under field conditions is sensitive to the M/D value
used, and considerable research effort has gone into M/D
prediction from DM digestibility (DMD). For example,
CSIRO (2007) uses the following equations to calculate ME
content of roughage or concentrate feeds in terms of M/D,
from DMD (%):
cottonseed meal (up to 2%W/d), where maintenance
requirements were increased to account for the ME cost of
urea excretion.
Similar differences between systems could be shown for
the other elements of energy use, but in combination, the
predictions of animal performance may be quite similar as
shown above. This fact highlights the importance of using
the system in totality rather than selecting different
elements from different systems in isolation.
WHAT PLANT AND ANIMAL INPUTS ARE
REQUIRED TO APPLY NUTRIENT REQUIREMENT
SYSTEMS UNDER GRAZING CONDITIONS?
The above question actually comprises two parts. First,
can we adequately describe the livestock themselves?
Second, can we define the NV of the diet available to or
consumed by the grazing livestock? The first question is far
easier to answer than the second. As discussed in more
detail below and as emphasized by Dove (2009),
quantifying what grazing livestock eat, and how much,
remains the major constraint to applying nutrient
requirement data under field conditions (esp. rangeland
conditions).
In describing the livestock themselves, their breed, sex,
physiological state, and current weight are of obvious
importance, although experience in Australia with the field
application of nutrient requirements demonstrated that
current weight was not enough. Some allowance was
required for differences in the mature size of different sheep
and cattle breeds, otherwise the same nutrient requirement
or herbage intake would be predicted for a large, lean
animal and a small, fat animal of the same current weight.
SCA (1990) and subsequently CSIRO (2007) addressed this
issue by scaling many of their functions to a „standard
reference weight‟ (SRW), defined as the weight of the
mature female in the middle of the condition score range,
for each breed of sheep and cattle. This approach has
simplified these systems by allowing generalized equations
for sheep and cattle, in which only the coefficients change
with species. It has become a cornerstone concept of these
systems, but because it influences so many equations in the
systems, it is also most important to use an appropriate
SRW when applying the systems. In general, defining SRW
has been more difficult for users than defining current
weight.
The main drivers of herbage intake (and thus nutrient
supply) are the amount of herbage and its NV; the two most
important NV measurements required are the digestibility
and CP content. Digestibility is not routinely measured in
vivo, but rather estimated indirectly using techniques such
as an in vitro procedure.
Obtaining an accurate estimate of diet quality is
problematical under grazing conditions due to the difficulty
of mimicking diet selection by the animal by sampling of
the pasture on offer. Grazing animals invariably select a
higher-quality diet than the average of or that sampled from
the pasture, and the quality of this diet is constantly
changing with changing pasture conditions. One of the
encouraging developments in this area in the southern USA
and northern Australia over the last decade has been the
Roughage M/D = 0.172 DMD – 1.707;
Concentrate M/D = 0.134 DMD + 0.235 EE + 1.23.
The prediction of M/D for concentrates is markedly
improved if ether extract (EE, %) is included in the
equation, but its inclusion for roughages adds very little to
the precision because of the low EE levels in roughage
(usually well under 5%; see CSIRO, 2007). For a more
complete discussion of the factors influencing predicted
M/D of feeds, the reader is referred to CSIRO (2007).
One of the difficulties often confronting users of the
requirement systems is that of knowing which equation to
use to calculate M/D. Even small differences in the
calculated value can have marked effects on predicted
animal performance by virtue of the fact that it is the major
determinant of the efficiencies ME use and thereby also
contributes to the calculation of maintenance requirements.
Similarly, Edg can also be calculated from the in vitro
DMD (%) of the plant material. For example, the following
equations (Equations 2.13 and 2.14 of CSIRO 2007, with
rumen fractional outflow rate = 0.02), provide estimates of
Edg corrected for microbial colonization of the sample, for
temperate and tropical forages, respectively:
Edg = 0.1852 + 0.01 IVDMD;
Edg = 0.408 + 0.0072 IVDMD.
In general, the NV of a known ruminant diet can thus be
described in terms that allow a comparison with published
138
The provision of pasture with high legume content is a
potent way for the manager to ensure greater nutrient
intakes in livestock, because they will consume 15 to 20%
more legume forage than grass forage of the same
digestibility (Freer and Jones 1984). The effect on live
weight gain is proportionately greater because the products
of legume digestion are also used with greater efficiency for
growth. Nonetheless, the evidence that grazing sheep
actively select for legume is equivocal. This aspect is
discussed in detail by NRC (2007, see Chapter 3). Although
there is some evidence of selection for white clover, this
does not seem to be the case with subterranean clover in
Australia. As a result, in its intake prediction algorithms,
the CSIRO (2007) system incorporates effects of herbage
mass or herbage digestibility, but does not explicitly model
selection for legume. If pasture legume content is increased,
predicted intake will increase (Freer and Jones, 1984), but
the intake will contain the same legume content as the
sward.
The relative importance of herbage supply and sward
legume content in managing intake depends on how much
herbage is present. At low herbage masses (e.g., 0.5 to 1.0 t
DM/ha in a sheep system), an increase in sward legume
content will usually result in a smaller increase in live
weight gain than an increase in herbage mass, which is the
greater constraint to intake. In short, emphasis on sward
legume content when very little herbage is available is
misplaced management; it is more important to increase the
quantity of herbage available.
However, as the amount of herbage increases further
(e.g., to 1.5 to 2.0 t DM/ha and beyond in sheep systems), at
a given legume content, the effect of herbage mass on live
weight gain diminishes markedly, as animals rapidly
approach their potential intakes (Freer et al., 1997). By
contrast, an increase in legume content results in continued
increases in live weight gain because the animals can eat
more of the legume (Freer and Jones, 1984). Hence, as
herbage supply increases, it becomes increasingly important
to shift the managerial emphasis to increasing sward
legume content. In practice, herbage mass and legume
content are not entirely separate and the manager would
hope to provide more herbage containing more legume.
In an attempt to increase the practical application of
their published requirements in grazing livestock, a number
of nutrient requirement systems have provided functions for
estimating the potential intake of grazing animals; the
various approaches are discussed in more detail in NRC
(2007, see Chapter 3). However, a major criticism of many
systems is that they make no allowance for differences in
the mature size of different livestock breeds; predicted
intakes would thus be the same intake for a large, lean
animal and a small, fat animal of the same current weight.
As noted previously, SCA (1990) and subsequently CSIRO
(2007) addressed this issue by scaling potential intake
functions to SRW, an approach later adopted in part by
NRC (2007) in developing functions for potential intake.
For example, if Z is the ratio of the current weight of an
immature animal to its SRW (both in average condition
score), potential intake (I, % of mature weight) of an allgrass diet of non-limiting digestibility is given by:
I = 6.8Z – 4Z2
requirements (see CSIRO 2007, NRC 2007 for discussion).
However, the quantification of what the grazing animal
actually consumes is a more complex problem. Although
there are general relationships between intake and various
characteristics of the forage on offer, it remains difficult to
specify the diet of the grazing animal with the same
accuracy as current feeding systems define its nutrient
requirements.
ESTIMATING NUTRIENT SUPPLY IN
THE GRAZING ANIMAL
There can be little doubt that the major constraint to the
application of published nutrient requirements to grazing
animals is estimating the quantity and the NV of the diet
actually consumed. The problems associated with these
estimates, and the various approaches to such
measurements are discussed by CSIRO (2007), NRC (2007)
and Dove (2010). No matter how accurate systems for
estimating nutrient requirements might become, it remains
difficult to compare them with the „nutrient supply‟ side of
the balance because diet composition and forage intake are
difficult to quantify and continually changing as pasture
conditions change.
Herbage intake, the key „driver‟ of nutrient supply and
for improved pastures, is itself highly responsive to: the
amount of pasture present; its digestibility, as affected by
season and by species content; and its legume content.
These same drivers of intake also apply in native (i.e.,
unsown) pastures, but as the herbage association tends
towards rangeland, the species composition of the plant
biomass and its spatial distribution become increasingly
important (see Hobbs, 1999 for discussion).
As described above, studies with temperate pastures
have shown that as the digestibility of the herbage on offer
decreases, so too will herbage intake, especially below a
digestibility of 70%. It follows that in the nutritional
management of livestock grazing temperate pastures, there
is little point in worrying about the accuracy of published
nutrient requirements if the forage resource is of low
digestibility. Managing the pasture to increase forage
digestibility and thus intake should be the greater concern,
although maintaining RDP supply is also a concern when
forage digestibility is low. Under rangeland or tropical
conditions, the situation is rather different. The DM or OM
digestibility of the plant biomass is frequently less than
70% and the management of protein supplementation is
also a major concern when the plant biomass is of low
digestibility and protein content.
Herbage mass is also a key driver of intake and live
weight gain. For example, in a improved pasture of 75%
digestibility, there will be little increase in herbage intake
by sheep once the amount of herbage exceeds 2 t DM/ha
because animals have reached their potential intakes (Freer
et al., 1997). Herbage intake in sheep begins to decrease at
herbage masses below about 1.2 t DM/ha and declines
markedly below 1 t DM/ha. It can be calculated (Freer et
al., 1997) that at a herbage mass of 0.5 t DM/ha, predicted
intake is only 67% (and predicted live weight gain only
52%) of that at 2 t DM/ha.
139
systems. Observed live weight gains agreed closely with
predicted live weight gains (Figure 7) suggesting that both
the requirement predictions and the intake algorithms
within the package had acceptable accuracy. For housed
animals, application of the CSIRO (2007) requirements can
be achieved by setting pasture mass to zero, but is also
supported by the spreadsheet programs „ME_Required‟ and
„CP_Required‟, which are available at the above website.
In grazing systems based on rangeland or on tropical
pastures, a major challenge that remains is to devise similar
algorithms to those described above to allow the routine
prediction of intake for given conditions of the plant
biomass on offer. However, in many such situations, the
information of more value to field workers may not be the
prediction of intake and thus growth rate of the animals but
a determination of their intake from known growth rate.
This could be of great value for fodder budgeting purposes
or to set appropriate stocking rates; for example, at the end
of the growing season in tropical grazing systems when the
available pasture usually represents the available reserves
for the ensuing dry season period. If the equations
describing the utilization of energy by animals are sound
and their application results in relatively accurate prediction
of growth rate from known intake and diet composition, or
for growth rates in the field (Figures 6 and 7), then it
follows that this process can be reversed and intake could
be estimated by back-calculation from live weight change
and an estimate of the energy density of the diet (M/D). In
practice, the live weight change used in the system could be
either a measured live weight change or alternatively a
prediction based on previous experience in the same area
(historical value). This approach to intake prediction has
been suggested before, (e.g., MAFF, 1975; Baker, 1982;
2004; Minson and McDonald, 1987). What has usually
been lacking is the validation of the predictions under
practical feeding conditions. With some indication of the
validity of the energy utilization equations in SCA (1990),
as described earlier, we have developed our own
spreadsheet intake calculator (QuikIntake; McLennan and
Poppi, unpublished data), which is currently being
validated. The main inputs required for an unsupplemented,
confined (no grazing) animal include a description of the
animal in terms of its sex, age, current live weight, SRW
and its live weight gain (either measured or forecast), and
an estimate of the M/D of the diet, which, under grazing
conditions, could be calculated from the DMD derived from
F.NIRS analysis. The ME intake required to achieve the
live weight gain can then be determined and thereby also
DMI of the forage by dividing by M/D.
Early results indicate some problems with intake
prediction are emerging with low-quality tropical forages in
the variability of the relationship between predicted and
observed DMI for a measured live weight change, which
relate to the variability around the equations for energy use
(see earlier). For example, for weaned steers consuming a
hay of Rhodes grass (Chloris gayana) the predicted
compared with observed intake for the measured live
weight change was 1.50 vs. 1.67 %W/d, whereas for
speargrass it was 1.84 vs. 1.15 %W/d. Thus, although there
is confidence in the nature of the equations, there is
sufficient variability in predicted intakes to be of concern in
A young ewe weighing 0.85 of its mature weight of 80 kg
thus has a potential intake of 2.31 kg DM/d. Note that this
equation allows for the slight decrease in intake in animals
with Z > 0.85 (see NRC, 2007). Functions are also available
to adjust potential intakes for physiological state (e.g.,
lactation) and for diet quality (see CSIRO, 2007 and NRC,
2007 for further details).
Only the Australian nutrient requirement system (SCA,
1990; CSIRO, 2007) has attempted the next step of moving
from potential intake to an estimate of the actual intake as
constrained by prevailing pasture conditions. It does so not
by tabulating potential or actual intakes as in, for example,
(NRC, 2007) but by capturing the estimated nutrient
requirements within decision-support tools that also feature
algorithms for predicting herbage intake for given animal
and pasture conditions (GrazFeed - Freer et al., 1997;
GrassGro – Moore et al., 1997; Freer et al., 2009 and see
www.pi.csiro.au/grazplan). The algorithms are based on
published relationships between herbage intake and
variables, such as herbage mass, herbage height,
digestibility, legume content, and in pastures of low
digestibility, herbage N content as it drives RDP supply.
The average digestibility of herbage, an input provided by
the user, is allocated by the algorithm into conceptual
„digestibility pools‟ within the herbage, from which the
animal progressively selects until its intake limit is reached.
It should be noted that in rangeland situations or with
tropical pastures, the algorithms may not predict intake
accurately because they are derived from relationships
established with temperate pastures (Freer et al., 1997;
Freer et al., 2009). Nevertheless, the incorporation of the
intake-prediction algorithm has had 3 major consequences:
1. It has provided the „technical support‟ and the
mathematical power to permit the extensive use of the SCA
(1990) or CSIRO (2007) nutrient requirements under
normal grazing conditions, including use by graziers
themselves.
2. It allows for interactions between pasture and
supplement intakes and thus has proved very useful in
predicting the need for and the response to supplementary
feeds.
3. Indirectly, it has resulted in a population of users who
have become trained in the skills of assessing pasture
quantity and quality and also animal condition. For
example, the use of the GrazFeed package in south eastern
Australia increased markedly once formal training in these
assessments was offered by Departments of Agriculture.
The ability of the CSIRO (2007) requirement system,
coupled with the intake-prediction algorithm in the above
packages, to predict accurately the live weight gain of
animals grazing temperate grasslands can be assessed from
Figures 6 and 7. In the former, the live weight gains of
lambs grazing low-quality summer pasture with or without
supplements of 2:1 oat grain:decorticated sunflower meal
(Freer et al., 1985; Freer et al., 1988) are compared with
live weight gains predicted from the GrazFeed package.
The relationship between observed and predicted gains did
not differ from the line of equality.
Cohen et al. (2003) used the GrassGro package to make
a similar comparison for steers grazing mixed grass-alfalfa
pasture in western Canada under a range of management
140
3. Further amendments to existing nutrient requirement
estimates will undoubtedly be required to deal with, for
example: the interaction in pregnancy between possible
increases in tissue hydration with simultaneous maternal
live weight loss; the interaction between gastrointestinal
parasites and nutrient requirements, especially for protein;
the effects of pregnancy and (or) lactation on digesta
kinetics and especially possible increases in the efficiency
of microbial protein production in late pregnancy; and the
possible need for extra dietary protein to support mammary
development in late pregnancy.
general application. Currently, the application of
QuikIntake based on the standard equations seems to
generally over-estimate intake for a live weight change or
loss around maintenance for low-CP forages. The cause of
this variability is still being explored, but is most likely
related to different efficiencies of MCP production and
hence MP supply, possibly as a function of variable N
recycling under these conditions.
Extension of this process of intake prediction to the
grazing situation presents further challenges. In effect,
allowance can be made for the additional energy costs of
grazing and foraging of the grazing animal, as well as any
cost for temperature regulation under extreme conditions. A
simplified calculation of the energy expended in eating and
walking has been provided by Freer et al. (2009). However,
where there already appear to be inaccuracies associated
with energy use for pen-fed animals, adding the extra
dimension of predicting energy use for grazing activity is
likely to further reduce the precision of intake prediction.
When Lardy et al. (2004) applied the NRC beef cattle
requirement model (NRC, 1996) to cows grazing rangeland
and sub-irrigated pastures in Nebraska, they found that at
times predicted energy deficits were excessive and
“biologically unreasonable”. Prediction errors were likely
to have originated from low predictions of the energy
density of the diet selected and the resulting low predicted
DMI. This reinforces the point that models predicting
grazing animal performance need first to provide a
relatively accurate estimate of voluntary intake by the
animals before then predicting the use of that ingested
energy for production. The multiplication of errors for both
aspects, as suggested by our results and those of Lardy et al.
(2004), could provide wide discrepancies in predicted vs.
observed performance.
CONCLUDING REMARKS
Current approaches to evaluating the NV of feeds or the
nutrient requirements of sheep are sufficiently accurate for
livestock managers to use them as the basis for matching
nutrient requirement and supply in grazing systems.
Quantifying nutrient supply is a much greater challenge; in
the future, more use could be made of computer-based
decision support tools to address this problem.
In attempting to match nutrient supply and requirement
in grazing animals, there is a temptation to do so on an
individual animal basis because this is how requirements
are usually presented. However, in many grazing systems,
profit is driven by animal production/ha, not
production/animal. From the system-wide standpoint of
production efficiency, it is thus often more profitable to
schedule key events (e.g., lambing or calving) or increase
stocking rates (animals/ha) to the point that for some of the
year at least, the intake of individual animals is constrained.
For example, lambing well before the spring flush of
pasture growth may place pregnant or lactating ewes under
nutritional stress, but this approach ensures that their lambs
are weaned onto good-quality pasture. Similarly, over dry
summers, adult animals may be deliberately managed such
that they lose weight to minimize the costs of
supplementary feeding, a major discretionary cost for
producers. Part of the manager‟s role is to decide whether
these intentional short-term imbalances between nutrient
supply and nutrient requirement can be tolerated or whether
they need to be offset with supplementary feed.
Increasingly, a component of this assessment will have to
be the perceived welfare needs of individual animals or
groups of animals. However, as a general approach it is
unlikely that profit from a livestock enterprise will be
maximized if the balance between nutrient supply and
nutrient requirement is pursued at the individual-animal
level.
FUTURE ISSUES AND CONCERNS
The application of nutrient requirement systems under
field conditions has raised a number of concerns about
issues that might limit future such applications unless they
are resolved.
1. For as long as the user of nutrient requirement schemes
is required to work with only complex published equations,
plus tabulated data that cover only a subset of the possible
field situations, the application of such schemes under field
conditions will be constrained. Therefore, with many
systems, more support needs to be provided to users to
facilitate such applications. The extent of uptake and use of
the GrazFeed and GrassGro packages, which encapsulate
the SCA (1990) and now the CSIRO (2007) systems,
indicates that this support is seen by users as a major issue.
2. Possible application of nutrient requirement schemes in
tropical or rangeland environments, with plant associations
that are very diverse nutritionally and spatially, is a major
concern and should be a focus of further research. The use
of F.NIRS approaches to estimate the species composition
and (or) the NV of the consumed diet, and possibly its
intake, may provide a promising approach to resolving this.
Further research is needed to provide the relationships that
can be used to build intake-prediction algorithms for these
environments.
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ME requirement (MJ/day)
20
17.5
15
12.5
10
7.5
5
30
50
70
90
110
130
150
Ewe mature weight (kg)
Figure 1. Comparison of the ME requirements for weight maintenance in housed mature dry ewes of mature weight 40 to 140
kg. NRC (2007) data (■) from their Table 15.1; CSIRO (2007) data (Δ) estimated using the same input data as NRC (2007).
Maintenance requirements for grazing ewes (▲) calculated from CSIRO (2007) only.
143
ME requirement (MJ/day)
30
25
20
15
10
30
50
70
90
110
130
150
Ewe mature weight (kg)
Figure 2. Comparison of the ME requirements for milk production in housed, mature ewes of mature weight 40 to 140 kg,
with a single lamb. NRC (2007) estimates (■) and data for milk production and ewe live weight change were taken directly
from their Table 15.1; CSIRO (2007) data (▲) were estimated using the same input data as NRC (2007). The remaining two
requirement curves are: □ NRC (2007) estimates with efficiency of use of ME for milk production (k l) altered from a
constant, as in NRC (2007), to a function of diet M/D as in CSIRO (2007); (o) NRC (2007) estimates with this adjustment
and also with maintenance component increased to include a function of the level of production, as in CSIRO (2007).
144
ME requirement (MJ/day; adjusted NRC 2007)
30
25
20
15
10
10
15
20
25
30
ME requirement (MJ/day; CSIRO 2007)
Figure 3. Comparison of the ME requirement of a housed, lactating ewe with a single lamb, estimated as in Figure 2 from
CSIRO (2007) or from NRC (2007) with adjustment for an effect of dietary M/D on k l and for an effect of level of production
on maintenance ME requirement. The linear relationship through these data points (■) does not differ from the line of
equality (solid line).
145
Truly absorbed protein (g/kg DM, NRC
system)
100
80
60
40
20
0
0
20
40
60
80
100
Truly absorbed protein (g/kg DM, Dutch
system)
Figure 4. Comparison of the truly digested and absorbed protein supply from the small intestine (g/kg DM) of dairy cows,
predicted from the NRC (2001) system and the Dutch DVE/OEB system (Tamminga et al., 1994) for different pasture
species and stages of growth. Constructed from the tabulated data of Yu et al. (2003): ■ Pioneer alfalfa; □ Beaver alfalfa; ▲
Climax timothy; and Δ Joliette timothy. Within a pasture species and cultivar, each symbol represents a stage of growth. The
solid line is y = x. The broken line is the fitted regression in which the slope does not differ from 1, but the intercept (12.9 ±
3.61 g/kg DM) differs significantly from zero (P < 0.01).
146
Degraded protein balance (g/kg DM, NRC
system)
80
60
40
20
0
-60
-40
-20
0
20
40
60
80
-20
-40
-60
Degraded protein balance (g/kg DM, Dutch
system)
Figure 5. Comparison of the degraded protein balance across the rumen (g/kg DM) of dairy cows, predicted from the NRC
(2001) system and the Dutch DVE/OEB system (Tamminga et al., 1994) for different pasture species and stages of growth.
Constructed from the tabulated data of Yu et al. (2003): ■ Pioneer alfalfa; □ Beaver alfalfa; ▲ Climax timothy; and Δ Joliette
timothy. Within a pasture species and cultivar, each symbol represents a stage of growth. The solid line is the fitted
regression y = 1.04x + 1.48 (R2 = 0.993; P < 0.001), which does not differ from y = x.
147
Observed live weight gain (g/day)
120
80
40
0
-80
-40
0
40
80
120
-40
-80
Predicted live weight gain (g/day)
Figure 6. Comparison of the observed live weight gain of lambs grazing poor-quality summer pasture (with or without
supplements of 2:1 oat grain:sunflower meal) with the gains predicted using the GrazFeed decision-support package (Freer et
al., 1997). Data are re-calculated from Freer et al. (1985; □) and Freer et al. (1988; ■). The broken line is the fitted regression
(Observed = 0.908*Predicted + 7.626; R2 =0.918, P < 0.001; residual standard deviation = 19.1), which does not differ from
the line of equality (solid line).
148
Observed live weight gain (kg/day)
1.5
1.2
0.9
0.6
0.6
0.9
1.2
1.5
Predicted live weight gain (kg/day)
Figure 7. Comparison of the observed live weight gains of crossbred steers grazing mixed grass:alfalfa pastures in western
Canada, with those predicted using the GrassGro decision-support package, which incorporated the SCA (1990) nutrient
requirements plus an intake predictor. The broken line is fitted regression (Observed = 1.054*Predicted – 0.068; R2 = 0.992,
P < 0.001, residual standard deviation = 0.025), which does not differ from the line of equality (solid line). Recalculated from
the data in Cohen et al. (2003).
149
H. Dove, S. R. McLennan, and D. P. Poppi
Notes
July 9 and 10, 2010
G. P. Lardy1ŧ and R. L. Endecott*
ŧ
*
Department of Animal Sciences, North Dakota State University, Fargo 58108; and
Department of Animal and Range Sciences, Montana State University, Miles City 59301
presented in Table 1. The NRC (1996) includes an
interactive computer model, which allows the user to model
cow size as a continuous variable and estimate nutrient
requirements for an unlimited number of cow sizes. Larger
cows require more total nutrients. In addition, cows with
greater genetic potential for milk production also require
more nutrients.
Nutrient requirements for both cows were lowest during
the middle trimester of gestation, following weaning
(cessation of lactation). Requirements for protein and
energy gradually increased throughout gestation, as a
function of fetal growth. Approximately 70% of fetal
growth occurs during the last third of pregnancy (NRC,
1996). This results in an increased nutrient requirement
(esp., energy and protein) during the last trimester of
pregnancy to support the rapidity of fetal growth during this
time period.
Lactation requires increased nutrient intake on the part
of the lactating female. Protein and energy requirements
during lactation are a function of level of milk production.
Milk production typically peaks approximately 6 to 8 weeks
following parturition and then declines gradually for the
remainder of the lactation period. Genetic potential for milk
production varies with breed type and varies widely within
breeds as well, depending on selection pressure for milk
production. Subsequently, levels of milk production vary
accordingly. Large quantities of high-quality forage are
required to maintain high levels of milk production. In
situations where milk production is relatively high but
forage nutrients are relatively low, the animal will typically
lose weight and may fail to conceive in a timely manner.
Table 2 shows the nutrient requirements for pregnant
beef heifers. It is important to remember that these heifers
have requirements for maintenance, growth, and pregnancy.
In many cases, the requirement for growth is often
overlooked, to the detriment of the nutritional status of the
heifer.
Nutrient requirements of growing animals, whether they
are grazing or fed in confinement situations, are defined by
physiological potential of the animal for growth and
nutrients supplied by the forage. Of particular interest from
the standpoint of animal nutrition and livestock
performance is forage digestibility and protein content
because nutrient supply generally limits productivity.
Obviously, increases in digestibility and protein content
will support greater levels of growth. For growing steers,
energy and protein requirements increase with increased
average daily weight gain (Table 3).
INTRODUCTION
Cow-calf producers in many areas of the world have
access to dormant grasses during fall and winter months
when lack of snow or ice cover permits grazing. Stockpiled
forages (native range) represent a potentially low-cost
forage resource for these producers because it requires no
haying or feeding operation to deliver it to cattle (Adams et
al., 1996). Cow-calf production systems that rely heavily on
purchased or harvested feed inputs are also heavily reliant
on fossil fuel inputs. As a consequence, these systems may
be less sustainable in the long run (Heitschmidt et al.,
1996). In most cases, accurate strategic supplementation is
necessary to make any extensive dormant grazing program
successful. Rumen-degraded protein (RDP), which is
protein available to the rumen microbes, is the first limiting
nutrient in dormant native range (Hollingsworth-Jenkins,
1996). Strategic supplementation will improve forage
utilization and performance of cattle consuming dormant
grasses. This manuscript will focus largely on strategic
protein supplementation of grazing and forage-fed cattle.
NUTRIENT REQUIREMENTS OF CATTLE
Many texts have devoted considerable attention to
describing nutrition of cattle. This paprer will simply
summarize basic requirements. Readers are referred to NRC
(1996) for a much more in depth review into nutrient
requirements. The NRC (1996) Beef Cattle Requirements
Model represents a significant change in the expression of
protein requirements. The model acknowledges the protein
requirements of ruminal microorganisms are different from
host animal requirements. Rumen-degraded protein is the
fraction of the total protein that is the primary source of N
for the ruminal microorganisms. Metabolizable protein
(MP) is the sum of the digestible bacterial protein and
rumen-undegraded protein (RUP) present in a feedstuff.
Nutrient requirements of cattle are impacted by many
different factors, but the primary drivers to consider in
commercial ranching operations, include cow size, stage of
production (e.g. maintenance, gestation, lactation), weather
(heat or cold stress), and terrain conditions (e.g. level
terrain vs. hilly or mountainous terrain). Cow size and
stage of production influence all types of nutrient
requirements whereas terrain and weather mainly affect
energy requirements.
For the purposes of simplicity, only 2 cow sizes are
1
152
between supplements were similar if cows were provided
prairie hay in addition to grazing native range. No
advantage was noted with feeding either biuret in a dry
supplement or urea in a liquid supplement compared with
feeding urea in a dry supplement. These studies were
conducted with gestating cows which calved during the
trials, which makes interpretation difficult, because protein
requirements change dramatically from gestation to
lactation. Urea and biuret supply degradable protein needed
by the ruminal microorganisms, but do not supply
metabolizable protein required by the animal.
The use of 15% urea:85% corn, 100% soybean meal,
10% corn:40% soybean meal:50% urea, or 14% corn:36%
blood meal:50% urea as supplements for low-quality native
forage was investigated by Petersen et al. (1985). They
found OM digestibility, bacterial N flow to the abomasum,
feed N flow to the abomasum, and microbial efficiency
were not different among these supplements. In a
companion N balance study (Peterson et al., 1985), no
supplement, soybean meal, soybean meal and urea, and
urea and blood meal were fed with low-quality native
forage. Supplementation increased digestibility of DM and
NDF, and N retention was greater for steers fed soybean
meal or blood meal-urea. They concluded that a blend of
blood meal and urea was an effective supplement for cattle
consuming low quality native grass.
Köster et al. (2002) conducted four trials to evaluate the
effect of increasing level of urea in protein supplements on
performance of prepartum cows grazing dormant native
range or cannulated steers fed low-quality dormant native
range. Forage intake and digestibility were not affected by
urea level when supplements were fed to cannulated steers.
At the higher levels of urea inclusion, cows lost more
weight and body condition prepartum than those fed no or
lower levels of urea. Pregnancy rate was generally
unaffected by supplemental urea level; however, there was
a tendency for it to be lower in one study at the 40% (of
supplemental RDP) level. They concluded that urea could
replace from 20 to 40% of the supplemental RDP in 30%
CP supplements without significantly altering either
supplement palatability or cow performance. It should be
noted that some supplement refusal was noted when cows
were fed supplements containing 60% of the supplemental
DIP from urea. These problems may be overcome with
additional research, but should be a practical consideration
when formulating supplements.
Farmer et al. (2004) investigated the interaction between
urea level and supplementation frequency. They noted
increased supplement refusal when greater levels of urea
were included in the supplement and when supplements
were fed on alternate days. Additionally, increasing level of
urea in the supplement resulted in increased BW loss in
cows grazing dormant tallgrass prairie. Those researchers
also noted a tendency for lower pregnancy rates at higher
inclusion rates of urea in one of the three prepartum cow
trials. Supplements used in Köster et al. (2002) and Farmer
et al. (2004) were provided at a rate of 2.16 and 1.64 kg/d
of DM, respectively. It is unclear whether similar results
would be observed if supplements were delivered in a
smaller package size with less carrier.
PROTEIN SUPPLEMENTATION
Non-Protein Nitrogen Supplements
If a source of ammonia is all that is necessary to
improve digestion and intake of low-quality forages, nonprotein nitrogen (NPN) should provide adequate ammonia
levels for this purpose. Clanton (1978) summarized several
experiments conducted on native sandhills range using
NPN in protein supplements and found that NPN was not as
effective as all natural protein supplements as a source of
protein for growing calves wintered on native range.
Possibly the MP requirement of these calves was great
enough that they responded to the additional escape protein.
Calves supplemented with biuret had similar rates of gain
compared with calves supplemented with urea (Clanton,
1978), indicating that biuret provided no additional benefit
over and above urea.
Currier et al. (2004) compared daily and alternate-day
urea and biuret supplementation of low-quality (4% CP)
grass straw to no supplementation for wethers and
prepartum cows. Performance was improved when animals
received supplement, but neither source of NPN nor
frequency of supplementation of NPN impacted
digestibility, N balance, or N retention by wethers, or
weight and body condition change of prepartum cows. The
study did not include a positive control (natural protein
supplement), which leaves one to speculate whether
performance would have been further improved if a natural
protein source were included for comparison.
When urea replaced soybean meal in supplements
offered to cannulated steers fed low-quality range forage,
digestion of DM and OM decreased, but synthesis of
microbial protein was relatively unchanged (Kropp et al.,
1977). In this study, soybean meal was replaced
isocalorically and isonitrogenously with urea and ground
sorghum. It is possible that the sorghum in the supplements
influenced DM and OM digestibility due to negative
associative effects of starch digestion on fiber utilization
(Mertens and Loften, 1980; Chase and Hibberd, 1987).
Ground sorghum levels ranged from 9.5 to 19.6% of the
diet and DMI averaged 4.6 kg/d. Starch content of the
sorghum and starch intake was not reported.
Mlay et al. (2003) supplemented dairy heifers
consuming low-quality hay with either urea or soybean
cake at two levels. At each level, the urea and soybean cake
were isonitrogenous, and urea was added via rumen
cannulae. Nitrogen supplementation, regardless of source,
linearly increased forage intake and digestibility, which led
those authors to conclude that soybean cake and urea were
equivalent sources of supplemental N. Soybean cake was
suggested to be slightly superior to urea due to its slower
degradation in the rumen, which probably better matched
the degradation rate of the low-quality hay.
The effectiveness of biuret or urea in dry or liquid
supplements compared with all natural protein supplements
for cows grazing dormant winter range was investigated by
Rush and Totusek (1976). They found that cows fed the all
natural protein supplement had less weight loss than
isonitrogenous blends of molasses and urea when cows
were foraging on native range only. However, weight losses
153
Other workers have not reported increased intake when
protein supplements were offered to cattle consuming lowquality forages (Krysl et al., 1989; Hollingsworth-Jenkins et
al., 1996; Reed et al., 2004). In studies that report no effect
on forage intake in response to protein supplementation,
forage CP levels are generally greater than in studies that do
report a response. This should be intuitive, however, it is
difficult to generalize when responses may or may not be
observed, simply based on forage CP level. Basal forage
intake likely contributes to some of the variation in the
responses observed in these studies. For example, in the
case of Hollingsworth-Jenkins (1996), intake by control
cows was approaching 2.0% of BW, while intake of control
animals in studies that have reported responses are
sometimes considerably lower, such as in the work of
DelCurto (1990b) in which intake averaged 0.49% of BW
and DelCurto et al. (1990a) in which intake averaged 0.87%
of BW. Clearly, more research is needed to provide
ranchers with the information necessary to make more
strategic decisions regarding supplementation.
Responses in digestibility to protein supplements are
also variable. Some workers have reported increased
digestibility when protein supplements were fed (Fleck et
al., 1988; Sunvold et al., 1991; Horney et al., 1996),
whereas others (Kartchner, 1980; Petersen et al., 1985;
Köster et al. 1996) reported no differences in digestibility
with supplementation. Villalobos et al (1997b) reported
increases in digestibility with protein supplementation when
measured in metabolism studies, but differences in
digestibility were not detected with grazing cows fed
similar supplements (Villalobos et al., 1997a). Some of
these differences are attributable to methods used to
determine digestibility in the various studies (Galyean et al.,
1986; Cochran et al., 1987).
Unfortunately, there is a great deal of variation in
response to feeding supplemental protein in grazing or
forage feeding situations. Factors which affect this variation
include environmental influences such as temperature and
snow cover, forage quality, and physiological status of the
animal.
Additional research which focuses on
understanding mechanisms which elicit responses in intake
and digestibility are needed in order to allow for improved
decision making regarding protein supplementation.
Provision of microbial growth factors. The effect
mediated by natural protein supplements may be due, in
part, to the provision of branched-chain VFA through the
fermentation of leucine, isoleucine, and valine. These VFA
are either required or highly stimulatory to cellulolytic
bacteria (Mackie and White, 1990). No differences in
intake, digestibility, or microbial CP production were
observed in steers given supplemental branched-chain
VFA, indicating that the supply of branched-chain VFA
were not limiting or had no effect on microbial
fermentation with the diets tested (McCollum et al., 1987).
Natural protein supplements may also supply limiting
AA. Blends of urea and DL-methionine, urea and
ammonium sulfate, or soybean meal as supplements for
ruminally cannulated crossbred beef cows fed a 75:25 blend
of grass hay and barley straw were investigated by Clark
and Petersen (1988). In situ rate of fermentation was
increased with the methionine treatment. Similar treatments
Urea and other forms of NPN may be used to provide a
portion of the supplemental RDP for cattle grazing dormant
native range or offered other low-protein forages. However,
they do not seem to be adequate as the sole source of
supplemental RDP for cattle consuming low-quality forage
diets. The stimulatory effect of natural protein, peptides,
and (or) AA in vitro indicate that rumen microbes require
natural protein and (or) AA for optimum growth. The
results of supplementation trials comparing NPN to natural
protein supplements indicate that urea alone is not an
effective N supplement for cattle grazing dormant native
range unless a portion of the N is supplied by protein or
peptides.
Effects of Feeding Natural Protein Supplements
Several broad categories of effects have been reported
when supplementing low-quality forages with natural
protein-based supplements. These include increases in
forage intake, increased digestibility, and increased rate of
passage. Variation in responses is common and not all
effects are observed in each trial.
Most responses to protein supplements were observed
when forage CP levels were below 7% CP (Paterson et al.,
1994). In most studies, protein supplements were based on
oilseed meals, which contain both RDP and RUP; thus,
ascertaining the biological mechanism responsible for
eliciting responses difficult. Additionally, protein
deficiency has not always been demonstrated. Because of
differential responses between urea-based supplements and
natural protein, a factor other than rumen ammonia
provided by natural protein supplements may be
responsible for the effects observed when natural protein
supplements are fed. These factors could include: 1)
provision of N in a form other than ammonia (i.e., AA or
peptides), which are possibly stimulatory toward the rumen
microbial population; 2) provision of some other growth
factor required by the ruminal bacteria, which could include
branched-chain VFA; 3) provision of RUP, which helps to
meet the MP requirements of the animal; or 4) stimulation
of ruminal kinetics, allowing increased flow of nonnitrogenous and N-containing compounds (Petersen, 1987).
A review of literature pertaining to supplementation
programs for ruminants grazing dormant native range
indicates that responses to supplemental protein have been
highly variable and vary from year to year. This fact alone
argues for more complete data regarding seasonal effects on
nutrient content, protein degrability, and microbial
efficiency. Factors contributing to or influencing this
variation include weather conditions, which includes
temperature and snow cover (Kartchner, 1980), animal
factors such as cow age (Adams et al., 1986), and
differences in forage quality from year to year (Soder et al.,
1995; Kartchner, 1980).
Protein supplementation of forages low in CP increased
intake (Hennessy et al., 1983; McCollum and Galyean,
1985; Gilbery et al., 2006). Mathis et al (1999) fed
increasing levels of soybean meal to beef steers consuming
5.3% CP native grass hay. Forage OM intake increased
until supplemental soybean meal intake reached 0.16% of
BW.
154
detected in DM intake, possibly because the basal diet was
8.75% CP.
Supplements varying in ruminal degradability (25 or
50% RUP as a % of CP) were fed to 2 groups of lactating
spring calving cows (Dhuyvetter et al., 1992). Cows were
split into groups based on calving date. Early calving cows
grazed native range and were fed 5.4 kg of medium-quality
grass hay (10.5% CP); late calving cows grazed native
range only. Late calving cows had similar weight losses,
regardless of which supplement was fed. Early calving
cows fed the 50% RUP supplement lost 39 kg less weight
than cows fed the 25% RUP supplement. Dhuyvetter et al.
(1992) concluded that the late calving cows did not respond
to RUP in the same manner as the early calving cows
because RDP may have been limiting.
Response to RUP was dependent on inclusion level in
the diet with lactating ewes (Loerch et al., 1985). When
compared to a diet supplemented with soybean meal, blood
meal fed at 3.3% of diet DM increased milk production
from 2.5 to 3.2 kg/d. In a second study, when the blood
meal was included at 6.8% of the diet, feed intake, and milk
production tended to be lower as compared with the diet
supplemented with soybean meal. The authors attributed
these responses to the poor palatability of the diet
containing high levels of blood meal. Dry matter intake was
3.0 kg when soybean meal was used as the supplement and
2.7 kg when 6.8% blood meal was included in the diet.
Milk production by Hereford cows increased with
increasing level [0 to 21 g supplement/kg body weight 0.75]
of protein supplement was offered with fed a low-quality
tropical grass hay (2.7% CP; Lee et al., 1985). Lee et al.
(1985) used a protein supplement made up of a blend of
cottonseed meal, fishmeal, meat meal, and mineral mix at
75.5%, 9.45%, 9.45%, and 5.6% of the supplement DM,
respectively. The authors stated that 53% of the N in the
supplement remained after a 15-h in situ incubation. Nonsupplemented cows lost 2.56 kg/d whereas cows fed the
highest level of supplementation gained 0.15 kg/d. Growth
rates of calves that nursed cows receiving higher levels of
supplement were also higher.
Sletmoen-Olson et al. (2000) fed lactating beef cows
increasing levels of RUP. Control cows (nonsupplemented) had greater forage intake than supplemented
cows. No differences between supplemented cows were
noted. No differences in total intake (forage plus
supplement) were noted. Supplemented cows had greater
BW during lactation than non-supplemented controls, but
no differences existed between supplemented groups.
Encinias et al. (2005) offered no supplement, an energy
control, RDP supplement, or a supplement containing both
RDP and RUP to lactating beef cows consuming moderatequality forage (9.6% CP). No differences in cow
performance, milk production, or calf weight gain were
noted in the study, indicating that MP supply from the
forage and microbial protein was adequate.
Waterman et al. (2006) supplemented young postpartum
beef cows grazing dormant native range with one of three
36% CP supplements: 1) RDP (cottonseed meal-based), 2)
50:50 RDP:RUP supplement (cottonseed meal and feather
meal), and 3) 50:50 RDP:RUP supplement plus 100 g/d
propionate salt. Cows supplemented with RUP had
were imposed on weaned heifer calves, but no differences
were found between the soybean meal and urea-methionine
supplements.
The use of a blend of sugar beet pulp and DLmethionine or soybean meal as a supplement for gestating
beef cows grazing native range was investigated by
Lodman et al. (1990). The methionine treatment did not
support the same level of performance as the soybean meal
treatment. Momont et al. (1993), in a similar trial,
compared soybean meal to a blend of urea, corn, and
methionine for cows grazing dormant winter range. They
also found that the urea, corn, and methionine treatment did
not support the same level of cow performance as the
soybean meal treatment.
Feeding to meet the metabolizable protein needs.
Patterson et al. (2003) evaluated the effects of feeding
supplements balanced to meet the CP needs or the MP
requirements of primiparous beef heifers. The research
involved 2,120 primiparous heifers maintained in a
commercial ranching environment in the Nebraska
Sandhills. Heifers supplemented to meet their MP
requirements had similar BW and BCS at the end of the
feeding periods. However, heifers fed to meet their MP
requirements were heavier at the time of pregnancy
diagnosis as two-year olds. Pregnancy rates were also
greater (91 vs. 86%) for heifers fed to meet their MP
requirements compared with heifers fed to meet their CP
needs.
Supplementation of Lactating Ruminants
Lactating cows grazing smooth bromegrass pastures and
fed increasing levels of supplemental RUP had increased
milk production and increased calf weight gain (Blasi et al.,
1991). No response was noted when cows grazed big
bluestem, however. In situ analysis of forage samples
suggested that RUP content of smooth bromegrass was less
than that of big bluestem.
The effect of time of initiation of protein
supplementation on spring-calving cow performance was
examined by Sowell et al. (1992). Protein supplementation
began either pre-calving or post-calving. Cows that were
supplemented pre-calving had less spring weight loss and
greater prebreeding BCS than cows that did not receive
supplement until after calving. However, no differences
were noted in reproductive performance.
Ovenell et al. (1991) reported no interactions between
lactational status and supplement type (soybean meal,
wheat middlings, or corn-soybean meal) for hay intake,
digestibility, or particulate passage rate. Differences in
intake were detected, 2.1% and 1.9% of BW, respectively
between lactating and pregnant cows. No differences were
observed in DM digestibility or particulate passage rate.
Hunter and Magner (1988) supplemented lactating
primiparous cows with formaldehyde-treated casein. They
found no differences in milk production during the first 8
weeks of lactation; however, during the second half of
lactation, supplemented heifers produced less milk than
unsupplemented heifers. Longer periods of anestrus were
noted for unsupplemented heifers. No differences were
155
change, or cow BCS change, most often the economics of
the situation justify alternate day or twice weekly feeding of
protein supplements.
increased milk production yet returned to estrus sooner after
calving compared with cows that received the RDP
supplement. Overall pregnancy rates were similar for all
cows. Cows fed the propionate salt-containing supplement
also produced more milk and returned to estrus quicker than
cows fed RDP. Cows fed the propionate salt also had the
shortest glucose half-life after a glucose tolerance test,
suggesting that nutrient partitioning can be influenced by
supplement composition.
The most consistent response from supplementing
natural protein to the lactating beef cow was increased milk
production. However, this is not observed in all studies and
may be related to supply of essential AA in the
supplemental source of protein. In many cases, calf gain
increased due to increased milk production when lactating
cows were supplemented with natural protein.
Interaction Between Supplemental Degradable Protein
and Energy
Level of supplemental protein fed when feeding cereal
grain supplements also seems to interact with forage
quality. Bodine et al. (2000) fed supplemental corn at 0 or
0.75% of BW to beef steers consuming low-quality (6%
CP) grass hay. When supplemental RDP was provided, the
negative associative effects of corn feeding were less
apparent. Additional research at Oklahoma State University
investigated the relationship between supplemental RDP
and energy supplementation for cattle grazing dormant
forages (Bodine and Purvis, 2003). Those researchers noted
improved performance in growing steers grazing dormant
tallgrass prairie when the supply of RDP was balanced with
total dietary TDN supply. Baumann et al. (2004) reported
interactions between source of energy supplement (corn vs.
soyhulls) and provision of a RDP supplement when cattle
were fed a low-quality forage diet.
Supplementation Frequency
Protein supplements do not need to be provided on a
daily basis. Many reports in the literature indicate only
small changes in forage utilization, cow weight change, or
cow BCS change when protein supplements are provided as
often as daily or as infrequently as weekly (Kartchner and
Adams, 1982; Beaty et al., 1994; Farmer et al., 2001).
Farmer et al. (2001) supplemented 2, 3, 5, or 7 times per
week and found that forage use was improved by
supplementing more frequently, but that impacts on animal
performance were likely minimal. Beaty et al. (1994)
concluded that while digestion characteristics were favored
slightly by daily supplementation, supplementing 3 times
per week decreased labor with only minimal consequences
on cow performance. Huston et al. (1999) supplemented
cows 0, 1, 3, or 7 days per week and found that
supplemented cows lost less BW and body condition than
non-supplemented cows, and less variability in supplement
intake was observed in cows fed once or 3 times per week
compared with those fed daily. Those authors attributed this
to the aggressive competition during the short consumption
period for the daily-supplemented animals. Decreased
variation in forage intake and live BW change were also
noted in the cows fed less frequently. Huston et al. (1999)
concluded that protein entering the cow’s system in large
amounts does not require immediate use to have lingering
value. Krehbiel et al. (1998) investigated the effects of
soybean meal supplementation frequency on portal and
hepatic flux of nutrients in ewes fed 7.5% CP bromegrass
hay. Soybean meal was supplemented daily or every third
day to provide 80 g CP daily and a non-supplemented
control was included in the treatment design. They noted
that net flux of nutrients was generally unchanged by
frequency of supplementation. However, urea removal by
the portal drained viscera was greater when supplement was
provided every third day, which may have implications for
the N efficiencies of the ruminal microorganisms.
Schauer et al. (2005) investigated the effect of
supplementation frequency (daily or every six days) on
performance, grazing behavior, and variation in supplement
intake. No differences in performance, grazing behavior, or
variation in supplement intake were noted. Even when
small differences existed in forage utilization, cow weight
FAT SUPPLEMENTATION OF
GRAZING RUMINANTS
Fat supplementation of ruminants has been investigated
as a means to increase diet energy density, to modify and
improve reproductive function, or to alter fatty acid
composition of end products. Results of fat supplementation
have been variable. Two comprehensive reviews of fat
supplementation that readers are encouraged to reference
are Funston (2004) and Hess et al. (2008).
Feeding fat can increase diet energy density, and
limiting supplemental fat to less than 2% of diet DM will
decrease the potential negative associative effects for cows
consuming forages (Hess et al., 2008). Positive
reproductive responses to fat supplementation are probably
due to changes in fatty acid status rather than diet energy
density (Funston, 2004, Hess et al., 2008). Supplementing
replacement heifers for 60 to 90 days before breeding has
positive effects, but supplementing fat postpartum did not
Fat
impact pregnancy rate (Hess et al., 2008).
supplementation of the gestating dam can improve fatty
acid status of the neonate, which can improve survivability
in harsh environments (Hess et al., 2008).
STRATEGIC SUPPLEMENTATION TO
MANIPULATE GRAZING DISTRIBUTION
In many areas of the western U.S., grazing distribution
has become an increasingly important concern, especially in
environmentally sensitive areas (e.g., riparian areas). The
use of supplementation as a means to manipulate livestock
movements has been investigated in a series of studies
conducted by scientists in Montana and Oregon. Bailey et
al. (2008) compared the use of salt or low-moisture
molasses blocks as a means to improve pasture utilization
in areas that were historically underutilized. Low-moisture
molasses blocks were more useful in implementing this
156
conditions, such as amount and timing of precipitation, date
of frost at the beginning and end of the growing season, and
other conditions that affect plant growth. Table 4 gives the
seasonal changes in nutritive value of native range in
southwestern North Dakota. Crude protein and digestibility
of native range forage decreased as the growing season
advances. In some production systems, this may make
supplementation necessary for a portion of the grazing
season.
Table 5 illustrates the seasonal changes in nutritive
value of subirrigated meadow in the Nebraska Sandhills.
Table 6 gives the seasonal changes in nutritive value of
native range in the Nebraska Sandhills. The nutrient
analysis from these forage samples clearly underly the
importance of understanding the type of forage and nutrient
characteristics of each plant mix throughout the grazing
season. The subirrigated meadow is predominantly cool
season species whereas the upland range is predominantly
warm season. Dominant grass species on the native upland
range sites were as follows: little bluestem (Schizachyrium
scoparium [Michx.] Nash), prairie sandreed (Calamovilfa
longifolia [Hook.] Scribn.), sand bluestem (Andropogon
gerardii var. paucipilus [Nash] Fern.), switchgrass
(Panicum virgatum L.), sand lovegrass (Eragrostis
trichodes [Nutt.] Wood), indiangrass (Sorghastrum nutans
[L.] Nash), and blue grama (Bouteloua gracilis [Willd. Ex
H.B.K.] Lag. Ex Griffiths). Common forbs and shrubs
include western ragweed (Ambrosia psilostachya DC.) and
leadplant (Amorpha canescens [Nutt.] Pursh). Dominant
vegetation on the subirrigated meadows consisted of
smooth bromegrass (Bromus inermis Leyss), redtop
(Agrostis gigantea Roth.), timothy (Phleum pratense L.),
slender wheatgrass (Elymus trachycaulus [Link] Gould ex
Shinners), quackgrass (Elytrigia repens [L.] Nevski.),
Kentucky bluegrass (Poa pratensis L.), prairie cordgrass
(Spartina pectinata Link), and several species of sedges
(Carex) and rushes (Juncus). Less-abundant grass species
were big bluestem (Andropogon gerardii var. gerardii
Vitman), indiangrass (Sorghastrum nutans [L.] Nash), and
switchgrass (Panicum virgatum L.). Abundant legumes
included red clover (Trifolium pretense L.).
Differential responses to protein supplementation with
cool and warm season forages have also been noted. Work
by Bohnert et al. (2007) with bluegrass straw (C3; 5.7%
CP) and tall-grass prairie hay (C4; 6.3% CP) indicated
larger increases in intake with the C4 (47% increase)
compared with the C3 (7% increase) when protein
supplement was provided. Additionally, DM digestibility
responded similarly with digestibility increasing 12% with
C4 and 9% with C3 forage. These results indicate the need
for additional research to investigate the plant and animal
mechanisms involved in the response.
Additional data regarding seasonal changes in RDP
content of native forage under various growing conditions
is necessary to better understand the need for
supplementation (Lardy et al., 2004). Cost-effective
decisions regarding protein supplementation of grazing
livestock ultimately requires knowledge of both the
animal’s nutrient requirements as well as seasonal changes
in forage nutrients.
strategy than was salt. When low-moisture molasses blocks
were used, cows grazed higher elevations and further
horizontally from water than when salt was used. Bailey
and Jensen (2008) compared the use of range cake (cubes)
and low-moisture molasses blocks as a means to improve
grazing distribution in Montana foothills grazing areas.
Cows fed the low-moisture blocks were observed to graze
steeper areas than the cows fed range cake. Those scientists
also reported differences in forage utilization when the 2
supplement types were compared. Forage utilization
decreased as slope increased to a greater degree when range
cake was fed than when low-moisture molasses block was
fed. Cows spent more time within 100 m of sites with a
low-moisture molasses block than sites where range cubes
were offered. Ganskopp (2001) investigated the use of
manipulation of sites where cattle accessed water and salt
as a means to improve grazing distribution. This work
moved water sources and salt locations either together or
separately to investigate which was more effective in
achieving improved pasture utilization. Results indicated
cattle were more often found near water sites than sites
where salt was offered. Centers of activity moved further
when water locations were changed than when salt
locations were changed. When salt locations were changed,
cattle used the new sites for approximately 2 d and then
began to move back to more familiar parts of the pasture.
SUPPLEMENT TYPE CONSIDERATIONS
Bowman and Sowell (1997) reviewed the effects of
supplement type and noted the following observations. The
percentage of non-eaters (animals not consuming
supplement) was similar for blocks and dry supplements
(approximately 15%), but slightly greater for liquid
supplements (23%). In studies that directly compared
hand-fed supplements and self-fed supplements, the
percentage of non-eaters was greater for the self-fed
supplements (19%) than the hand-fed supplements (5%).
The coefficient of variation for individual supplement
consumption averaged 79% for blocks, 41% for dry, and
60% for liquid supplements. In some formulation processes,
chemical hardeners are used as a means to control intake of
block supplements. As one would expect, as block hardness
increases block intake decreases. However, the coefficient
of variation in supplement intake increases as block
hardness increases (Zhu et al., 1991). Livestock producers
should take supplement type into consideration as they
develop strategic supplementation programs, realizing that
each has inherent advantages and disadvantages. What
works well for one ranch may not work well for another
due to a variety of factors, including animal behavior, ranch
resources, management ability, and resource utilization
goals.
SEASONAL CHANGES IN NUTRIENT QUALITY
OF NATIVE RANGE
Numerous research stations have investigated seasonal
changes in nutrient quality of native range over the growing
season. Year-to-year variation in forage quality occurs.
Much of this variation is related to environmental
157
performance. Protein supplements may be used as a
strategic means of enhancing forage utilization in areas with
rough terrain. This is a particularly useful tool in
environmentally sensitive rangeland areas. The role of
supplementation in developmental programming is just
beginning to be explored. Early results indicate potential to
manipulate carcass quality and progeny reproductive
success with strategic supplementation. Strategic
supplementation will improve beef cattle performance and
allow utilization of dormant native range with minimal cost.
A review on strategic supplementation would not be
complete without some discussion of developmental
programming. Developmental programming is a term
coined to describe the response that a fetus has to an inutero insult or stress (Barker, 2004; Wu et al., 2006;
Reynolds et al., 2010). In relation to this manuscript, it is
also used to describe the potential effects of strategic
supplementation during gestation (or lack thereof) and
subsequent effects on offspring growth, development, and
productivity. In some cases, these effects are observed well
after supplementation ends (Funston et al., 2010).
Perhaps the best evidence for this phenomenon in beef
cattle is evidenced by the work of Martin et al. (2007). In
this work, a 2 x 2 factorial was used to investigate the
effects of late gestation protein supplementation and early
lactation nutrition strategy. No effects were noted due to
early lactation nutrition. However, dams supplemented with
protein during the last one-third of pregnancy gave birth to
heifer calves that subsequently had increased pregnancy
rate and increased percentages calving in the first 21 d of
the calving season compared with heifers from nonsupplemented dams. Additional work from the same
laboratory indicates that fewer heifers from nonsupplemented dams attained puberty before the first
breeding season compared with heifers from supplemented
cows in a subsequent study (Funston et al., 2008).
Supplementation also increased the percentage of carcasses
grading USDA Choice when those progeny were finished.
Additional research is necessary to determine the key
times during gestation when protein supplementation may
allow these responses to manifest themselves and to fully
understand the mechanisms behind them. This work should
involve both an understanding of the underlying biology at
work, but also encompass a systems approach that allows
producers insight into appropriate, strategic, and
management decisions regarding supplementation to
improve profitability.
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Hawkins, G. B. Donart, and M. K. Petersen. 2006.
Effects of supplements that contain increasing amounts
of metabolizable protein with or without Ca-propionate
salt on postpartum interval and nutrient partitioning in
young beef cows. J. Anim. Sci. 84:433-446.
Wu, G., F. W. Bazer, J. M. Wallace, and T. E. Spencer.
2006. BOARD-INVITED REVIEW: Intrauterine
growth retardation: Implications for the animal sciences.
J. Anim. Sci. 84:2316-2337.
Zhu, X., C. W. Deyoe, K. C. Behnke, and P. A. Seib. 1991.
Poured feed blocks using distillery by-products as
supplements for ruminants. J. Sci. Food Agric. 54:535.
161
162
1
10.9
DMI, kg
0.30
0.20
Ca, % DM
P, % DM
0.29
0.19
Ca, % DM
P, % DM
0.21
0.31
903
10.69
9.1
12.6
1.34
59.9
0.21
0.32
848
11.18
9.1
11.4
1.36
60.9
2
8.2
11.5
1.30
58.6
3
0.19
0.29
856
9.92
8.2
12.9
1.25
57.6
0.19
0.30
801
10.38
Table adapted from NRC (1996); MP = metabolizable protein.
1
824
10.10
MP, g/d
CP, % DM
7.6
12.2
DMI, kg
Milk, kg/day
1.30
58.7
NEm, Mcal/kg
TDN, % DM
545-kg mature weight, 9.1 kg peak milk production
769
10.54
MP, g/d
CP, % DM
7.6
1.32
NEm, Mcal/kg
Milk, kg/day
59.6
TDN, % DM
454-kg mature weight, 9.1 kg peak milk production
Item
Table 1. Nutrient requirements of mature beef cows1
0.18
0.26
772
9.25
6.6
12.5
1.21
56.2
0.18
0.27
717
9.65
6.5
11.1
1.23
57.0
4
0.17
0.24
688
8.54
4.9
12.0
1.17
54.7
0.17
0.24
633
8.86
4.9
10.7
1.19
55.4
5
0.15
0.22
620
7.92
3.5
11.7
1.12
53.4
0.15
0.22
564
8.17
3.6
10.3
1.14
54.0
6
0.12
0.15
442
5.99
0.0
11.0
0.81
44.9
0.11
0.15
386
5.98
0.0
9.6
0.81
44.9
7
Month since calving
0.12
0.15
454
6.18
0.0
11.0
0.84
45.8
0.11
0.15
397
6.16
0.0
9.5
0.84
45.7
8
0.12
0.15
476
6.50
0.0
10.9
0.90
47.1
0.11
0.15
416
6.47
0.0
9.5
0.88
47.0
9
0.16
0.26
513
7.00
0.0
10.9
0.97
49.3
0.15
0.24
449
6.95
0.0
9.5
0.97
49.1
10
0.16
0.25
574
7.73
0.0
11.0
1.08
52.3
0.15
0.24
502
7.66
0.0
9.5
1.08
52.0
11
0.16
0.25
670
8.78
0.0
11.2
1.21
56.2
0.15
0.24
587
8.67
0.0
9.7
1.19
55.7
12
163
50.2
0.46
7.8
7.16
374
0.22
0.17
50.5
0.46
9.0
7.19
432
0.23
0.18
50.5
0.46
8.8
7.21
422
0.23
0.18
2
50.1
0.46
7.6
7.18
365
0.22
0.17
1
1
454-kg mature weight
TDN, % DM
NEm, Mcal/kg
DMI, kg
CP, % DM
MP, g/d
Ca, % DM
P, % DM
545-kg mature weight
TDN, % DM
NEm, Mcal/kg
DMI, kg
CP, % DM
MP, g/d
Ca, % DM
P, % DM
Item
Table 2. Nutrient requirements of pregnant replacement heifers1
50.7
0.46
9.2
7.18
443
0.22
0.18
50.4
0.46
8.1
7.16
384
0.22
0.17
3
50.9
0.48
9.5
7.22
458
0.22
0.17
50.7
0.46
8.3
7.21
397
0.21
0.17
51.4
0.51
9.8
7.31
476
0.22
0.17
51.3
0.48
8.5
7.32
413
0.21
0.17
Month since conception
4
5
52.3
0.53
10.1
7.52
501
0.21
0.17
52.3
0.53
8.8
7.56
436
0.20
0.16
6
53.8
0.57
10.5
7.89
539
0.31
0.23
54.0
0.57
9.1
7.99
472
0.32
0.23
7
56.2
0.66
10.8
8.53
598
0.31
0.22
56.8
0.66
9.4
8.74
527
0.31
0.23
8
59.9
0.77
11.1
9.62
688
0.30
0.22
61.3
0.81
9.68
10.20
610
0.31
0.22
9
164
10.0
10.6
10.3
0.91
1.38
9.8
1.38
0.33
10.0
0.91
9.3
1.38
9.5
9.5
0.91
0.33
9.0
8.7
1.38
0.33
9.0
0.91
8.2
1.38
8.5
8.4
0.91
0.33
DMI,
kg/d
8.0
ADG,
kg/d
0.33
70
60
50
70
60
50
70
60
50
70
60
50
70
60
TDN,
% DM
50
1
409
382
354
327
BW,
kg
300
1.67
1.34
0.99
1.67
1.34
0.99
1.67
1.34
0.99
1.67
1.34
0.99
1.67
1.34
NEm,
Mcal/kg
0.99
1.06
0.77
0.44
1.06
0.77
0.44
1.06
0.77
0.44
1.06
0.77
0.44
1.06
0.77
NEg,
Mcal/kg
0.44
Table 3. Nutrient requirements for growing steers and heifers (mature BW = 545 kg)1
10.2
8.4
6.6
10.8
8.8
6.8
11.4
9.2
6.9
12.2
9.7
7.1
13.0
10.2
CP,
% DM
7.3
708
594
443
711
589
430
713
584
416
702
570
399
682
550
MP,
g/d
379
0.37
0.28
0.19
0.39
0.30
0.20
0.42
0.32
0.20
0.45
0.34
0.21
0.49
0.36
Ca,
% DM
0.22
0.19
0.16
0.12
0.20
0.16
0.13
0.21
0.17
0.13
0.23
0.18
0.13
0.24
0.19
P,
% DM
0.13
Table 4. Effect of advancing season on nutrient composition of native range in southwestern North Dakota1
Mid-June
Late July
Early
September
Early
October
MidNovember
MidDecember
NDF, %
59.5
51.0
58.7
59.6
67.9
72.1
ADF, %
35.7
34.8
40.3
38.5
40.9
41.8
CP, %
13.6
14.9
10.2
9.7
6.6
6.2
IVOMD, %
68.1
60.5
55.6
54.3
57.3
53.3
Item
1
Adapted from Johnson et al. (1998); IVOMD = in vitro OM disappearance.
165
166
Primary
Primary
APR
MAY
2
1
1
3
2
3
3
1
1
1
1
# OBS
8.1 ± 0.31
9.1
14.9
15.6 ± 1.73
17.2 ± 3.58
12.4 ± 2.74
17.3 ± 4.58
17.6
29.4
16.6
12.1
CP, %
0.47 ± 0.01
0.48
0.68
0.67 ± 0.07
0.63 ± 0.18
0.67 ± 0.09
0.75 ± 0.13
1.09
0.92
0.91
0.76
NDIN, %
0.19 ± 0.02
0.23
0.14
0.14 ± 0.03
0.08 ± 0.03
0.13 ± 0.04
0.13 ± 0.01
0.17
0.03
0.22
0.12
ADIN, %
1.0 ± 0.06
1.2
1.3
1.8 ± 0.25
2.3 ± 0.78
2.0 ± 0.20
2.3 ± 0.41
3.1
4.2
1.2
1.4
RUP, %
7.0 ± 0.25
7.9
13.6
13.9 ± 1.48
14.8 ± 2.81
10.4 ± 2.56
15.0 ± 4.28
14.5
25.3
15.4
10.7
RDP, %
83.1 ± 0.65
78.6
63.8
70.52 ± 5.01
63.2 ± 11.56
72.0 ± 4.69
68.6 ± 11.99
73.0
49.3
66.8
77.2
NDF, %
55.8 ± 0.98
53.8
44.1
43.7 ± 4.61
44.4 ± 4.29
41.7 ± 4.65
39.2 ± 6.17
42.3
27.3
42.4
50.2
ADF, %
54.2 ± 1.94
47.5
67.7
63.4 ± 4.18
64.4 ± 4.39
66.0 ± 5.28
70.8 ± 5.49
68.3
68.3
60.2
53.4
IVOMD, %
Type: Regrowth = growth following July haying; Primary = growth before July haying.; # OBS: number of sampling dates analyzed for a given month, each
observation represents 4 to 7 diets collected by esophageal fistulated cows or ruminally cannulated steers; ADIN = acid detergent insoluble nitrogen; RUP =
rumen-undegraded protein; RDP = rumen-degraded protein; IVOMD = in vitro organic matter disappearance.
2
Standard deviations listed are for averages of diets collected over 1992 and 1994 within each month, not for laboratory analysis within a particular sample
collection.
1
DEC
Regrowth
Regrowth
NOV
2
Regrowth
Regrowth
Regrowth
Primary
OCT
2
2
SEPT
AUG
JUL
2
Primary
Primary
MAR
JUN
Regrowth
JAN
2
Type
Date
Table 5. Means and standard deviations of laboratory analysis of meadow diet (OM basis) samples collected in 1992 and 1994 at Gudmundsen Sandhills
Laboratory, Whitman, Nebraska1
167
2
2
3
4
3
2
1
2
MAR
APR
JUN
JUL
AUG
SEPT
NOV
DEC
6.5 ± 0.60
5.9
7.4 ± 0.34
11.3 ± 2.52
12.3 ± 1.50
13.8 ± 2.53
11.4 ± 1.86
6.0 ± 0.24
6.3
CP, %
0.39 ± 0.08
0.37
0.51 ± 0.06
0.79 ± 0.11
0.90 ± 0.06
0.85 ± 0.15
0.79 ± 0.05
0.48 ± 0.10
0.45
NDIN, %
0.13 ± 0.06
0.27
0.12 ± 0.02
0.16 ± 0.02
0.14 ± 0.01
0.12 ± 0.02
0.11 ± 0.01
0.09 ± 0.05
0.15
ADIN, %
1.2 ± 0.20
0.7
1.1 ± 0.23
1.8 ± 0.43
2.2 ± 0.30
2.5 ± 0.15
1.2 ± 0.11
1.0 ± 0.02
0.8
RUP, %
5.4 ± 0.40
5.2
6.4 ± 0.58
9.5 ± 2.26
10.1 ± 1.31
11.3 ± 2.38
10.2 ± 1.75
5.0 ± 0.27
5.5
RDP, %
86.0 ± 0.96
84.4
79.7 ± 1.30
77.9 ± 4.42
79.8 ± 3.62
72.4 ± 2.70
77.5 ± 5.29
82.5 ± 0.89
83.6
NDF, %
54.5 ± 0.40
56.1
48.8 ± 1.41
46.4 ± 4.28
43.6 ± 4.26
40.6 ± 2.52
43.2 ± 6.17
53.3 ± 0.24
52.5
ADF, %
53.9 ± 5.51
48.3
60.7 ± 1.21
63.7 ± 3.62
67.5 ± 2.4
67.6 ± 2.57
67.6 ± 9.30
54.8 ± 0.69
58.0
IVOMD, %
# OBS: number of sampling dates analyzed for a given month, each observation represents 4 to 7 diets collected by esophageal fistulated cows or ruminally
cannulated steers; NDIN = neutral detergent insoluble nitrogen; ADIN = acid detergent insoluble nitrogen; RUP = rumen-undegraded protein; RDP = rumendegraded protein; IVOMD = in vitro organic matter disappearance.
2
Standard deviations listed are for averages of diets collected over 1992 and 1994 within each month, not for laboratory analysis within a particular sample
collection.
1
1
# OBS
JAN
Sample date
Table 6. Means and standard deviations of laboratory analysis of upland range diet (OM basis) samples collected in 1992 and 1994 at Gudmundsen Sandhills
Laboratory, Whitman, Nebraska1,2
G. P. Lardy and R. L. Endecott
Notes
July 9 and 10, 2010
ECONOMICALLY EFFICIENT SUPPLEMENTAL FEEDING AND THE IMPACT OF
NUTRITIONAL DECISIONS ON NET RANCH RETURNS
L. Allen Torell1* and Neil R. Rimbey†
*Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces
88005; and †Department of Agricultural Economics and Rural Sociology, University of Idaho, Caldwell Research and
Extension Center, Caldwell 83605
ABSTRACT: The difference between biological efficiency
and economic efficiency as it relates to supplementation of
beef cattle is discussed in this paper. We review well
known economic principles and models used to determine
the economically optimal supplemental feeding rate for a
given cost-price situation. What is known about the
economics of feeding supplements for various reasons are
explored, including supplemental feeding for improved
growth and gain; to maintain and improve reproductive
efficiency; to more efficiently harvest rangeland forage and
move cattle from sensitive areas by improving livestock
distribution; and the importance of supplemental and
replacement forages for drought management. The
literature review shows that the economics of supplemental
feeding is highly variable because beef prices and costs
continually change and the output response to supplements
is variable and generally poorly quantified. Maintaining
high rates of reproductive efficiency is of key economic
importance for cow-calf producers and supplementing to
maintain the health and condition of the cow herd has
significant economic potential, whereas the economics of
supplemental feeding to add a few pounds of sale weight is
many times not profitable.
The economics of supplementation has been shown to
be variable and dependent on livestock and rangeland
conditions. There is no one prescription or rule that can be
followed for making sound economic nutritional decisions.
On this basis, we review the basic economic principles and
models required to assess economic impacts of
supplemental feeding practices and compare the differences
between economic and biological efficiency. We review the
basic production economic concepts required to assess the
economics of supplementation and highlight what is known
about the economics of supplemental feeding. Because
supplemental feeds have a major role during drought
periods we particularly explore the economic impacts of
nutritional decisions on ranch returns during dry periods.
ECONOMICALLY EFFICIENCT INPUT USE
As detailed in any production economic text book (e.g.,
Workman, 1986), there is a relationship between the
amounts of output that can be obtained by different
amounts of input use. Numerous shapes of what is called
the production function are possible, but the “law of
diminishing returns” must eventually hold. By this well
known principle, if an input is added while holding other
inputs constant, the addition of one more unit of the
variable input means output must eventually increase at a
decreasing rate and diminishing marginal returns will
prevail. For supplemental feeding with a goal of
maximizing profit, this means we cannot afford to produce
the biggest calf or have the highest possible calf crop.
Unless the input is free, the economically optimum level of
input use will always be less than that which would give
peak biological efficiency, which occurs at the maximum of
the production function. The economically efficient or
profit maximizing point will occur where the slope of the
production function is equal to a linear line that has a slope
equal to the ratio of input (supplement) price (Px) divided
by output (beef) price (Py). This economically efficient
point would occur at X* in Figure 1, Panel A.
A different presentation of the economic concepts
involved in profit maximization is more common.
Recognizing that comparing added value to added cost is
the key, Figure 1, panel B shows the standard graphical
economic model for optimal input use. It starts with a curve
defining the slope of the production function given in
Figure 1, what is called marginal physical product (MPP).
The MPP curve defines how much more output would be
Key words: beef cattle, economic efficiency, feed
supplements, rangelands
INTRODUCTION
Supplementation has been used to improve the growth
and rate of gain of stocker cattle and replacement heifers,
maintain and improve body condition of cows grazing lowquality forage, improve intake and digestibility of lowquality forage, provide macro- and trace minerals that are
deficient in forages, and provide additional nutrients and
forage alternatives in periods of drought. Another reason
for supplementation that has not been widely explored is
the use of supplements to modify grazing distribution
patterns on rangelands, and encourage cattle to spend less
time in riparian and sensitive areas. Each reason for
supplementation can enhance the potential profitability of
livestock production. Furthermore, when livestock grazing
is tied to sound management that improves rangeland
conditions, society may benefit beyond economic returns to
livestock producers.
1
170
replicates. These long-term, multi-rate studies are expensive
and rarely conducted over the multiple years and conditions
required to estimate the production function that forms the
basis of economic evaluations of profit-maximizing input
use. Unknown production responses and linkages are the
lacking element for evaluating the economics of nutritional
decisions. We are left with a partial budget assessment of
whether the added economic value of livestock gain and
production obtained by feeding at the levels used in
selected research trials was enough to justify the added
costs.
In addition to partial budgeting, in situations where it is
established that protein, energy, or some mineral is
deficient in the diet, and that a supplement will be fed,
calculating the least-cost method of providing for the
dietary shortfall may be the best available economic
assessment alternative. Evaluating the relative merits of
feedstuffs means more than comparing price tags. The
$/unit of the limiting nutrient is the important consideration.
The price of each available feedstuff potentially used as a
supplement is divided by the total pounds of limiting
nutrient in a ton of feed, adjusted for moisture content
(Torell et al., 1993). Numerous computer programs have
been developed to assist in evaluating least cost
supplementation and feeding strategies. For example,
University of Nevada scientists developed a spreadsheet
program to evaluate feeding alfalfa hay to beef cattle
(http://www.unce.unr.edu/publications/other/AlfalfaForBee
fCows.xls).
obtained for each successive increase in input use.
Multiplying MPP by Py defines the added economic value,
or value of the marginal product (VMP). In a similar way,
APP is defined to be the average physical product (APP =
Y/X) at each level of X, and when multiplied times Py the
average value product (AVP) curve is defined. For a price
taking business that can buy as much of the input as desired
at the going market rate, marginal factor cost (MFC = Px) is
a horizontal line at the specified cost level. Yet, for
supplemental feeding decisions, the cost line may not
remain fixed at this particular point because wide-spread
drought would increase supplement demand, raising input
price, and this would increase costs for all producers.
The profit maximizing condition is where VMP = MFC
(Figure 2, Panel B). The shaded area in the graph defines
the profit that would be made, and profit is maximized at
X*. At levels of input use below this point additional profit
is possible because VMP > MFC. Beyond X* the added cost
of the input exceeds the added value.
Diminishing marginal returns begins at the top of the
VMP curve and this guarantees that a profit maximizing
point exists. The part of VMP that lies below the AVP
curve defines the producers demand curve for the input.
Rearranging the profit-maiximizing condition, Py × MPP =
Px to MPP = shows the relationship between Panel A and
Panel B.
As input prices increase, less input will optimally be
used because the slope of the price line increases (Panel A).
Or using Panel B, if drought increased supplemental feed
costs, the MFC would shift upwards and thus decrease the
amount of supplements optimally used. As output price
increases, the economic optimum will move closer to peak
possible production. A new technology (e.g., a better
supplement or forage of improved quality) will potentially
shift the production function up and alter the economically
efficient level of input use. Value of the marginal product
and AVP would shift upwards as well. Economic efficiency
is elusive. It varies depending on prices, costs, and
production conditions.
The simple economic model defining optimal input use
has numerous underlying assumptions, and the well known
“marginal principles” apply. It is defined for a price-taking
business in both the input and output market. A key
assumption is that input use during the current year has no
impact on production in future years. The shape and
location of the production function will vary for different
forage and weather conditions, for different animal
conditions, and potentially for different breeds and grazing
strategies.
Defining economically optimal supplementation rates
depends on and is limited by estimation of the key
biological relationships defined by the production function.
One of the biggest limitations in estimating this key
relationship is differences in study objectives. The animal
scientist applies 2 or 3 alternative supplementation rates and
attempts to measure whether there was a significant
difference in observed production between treatments. By
comparison, estimating the production function requires
numerous feeding rates over a wide range, perhaps with less
THE ECONOMICS OF SUPPLEMENTAL FEEDING
Supplementation for Growth and Gain
There are numerous examples of partial budget
assessments applied to supplemental feeding practices and
these studies generally show a great deal of variability in
gain response to supplementation with both energy and
protein (Wallace, 1987). Important considerations for
predicting performance responses to supplements include
forage availability, digestibility, animal growth stage and
production requirements, limiting nutrients, and CP content
of available forages (DelCurto et al., 2000). Response
variability implies a great deal of uncertainty for livestock
producers as to whether supplemental feeding for added
gain will be a worthwhile economic endeavor. As further
noted by DelCurto et al. (2000) and Wallace (1987),
alternate-day, once-weekly, and daily feeding of protein
supplements have been shown to yield similar production
responses, thus labor costs can be reduced by less frequent
supplementation. DelCurto et al. (2000) further notes that
studies show less variation in supplementation response
when greater quantities are fed less frequently, presumably
because of less competition for the available supplement.
Because calf growth rates interact seasonally with
environmental and forage conditions, decisions about
optimal sale dates and sale weights become important
(Lambert, 1989; Tronstad and Teegerstrom, 2003).
VanTassell et al. (1987) found average calf weights in June
and at weaning were related to a combination of variables,
171
Using the desirable moderate 5 to 6 BCS score rating as
a comparable base, Wikse et al. (1995) found thin cows
with BCS of 4 had 74% less economic value when
pregnancy rates, weaning weights, and sale price
differences were considered. This resulted in a net loss in
revenue of $64/cow once reconditioning (increasing body
condition) costs were subtracted. Cows sliding to a BCS of
3 had production of only 48% that of BCS 5 and 6 cows,
and this meant a net loss of $124/cow.
The poor productivity of thin beef cows makes the
economics of reconditioning thin cows extremely favorable.
Wikse (1995) estimated the benefit to cost ratio for
reconditioning thin pregnant cows was over 2:1 ($1 dollar
spent reconditioning returns over $2 in economic value).
DelCurto et al. (2000) further suggested that because BW
change and body condition can be manipulated more easily
during pregnancy than after calving, a general strategy for
supplemental feeding beef cows is to target a BCS at
calving of at least 5 and avoid significant weight loss
between calving and the beginning of the breeding season.
Starving through a drought is clearly not an economic
option given the significant reductions in production from
thin cows. The marginal principles of Figure 1 apply.
Research data (Richards et al., 1986; Selk et al., 1988;
Wikse et al., 1995) indicated the non-quantified production
relationship between BCS and calf crop increases very
steeply when cows are at a low nutrition level but then
flattens near peak levels. A maximum calf crop is not the
most economical (Torell et al., 1982), but economic losses
from allowing cows to lose substantial weight and body
condition are substantial.
including calf age, weather conditions, level of supplements
fed the cow herd during the winter, crossbreeding and
stocking rates. Tronstad and Teegerstrom (2003) found
supplemental feeding compensated for the nearly 0 rate of
gain for calves reaching 7 to 12.5 months of age. Those
authors also found calves supplemented and carried over for
May sale were generally the most profitable in central
Arizona, where the climate is dependent upon winter rains
for cool season grass growth. For the southeast Arizona
region, where summer monsoon rains largely determine the
production of warm season grasses, lighter nonsupplemented calves also sold in May were most profitable.
On the New Mexico State University Corona Range
Livestock Research Center (CRLRC) in central New
Mexico, where little gain is anticipated over the winter
period, sale of weaned calves in the fall was found to be
most profitable (Murugan, 2007). Supplementation costs
and seasonal sale price differences are obviously an
important factor in decisions about supplemental feeding
for enhanced gain and calf retention. Some years
supplementing and carrying over calves would be the best
strategy, whereas in other years, the cost-price situation will
not justify the expense.
Supplementing the Cow Herd
Evaluating the economics of supplementing the cow
herd to improve reproductive success means an expansion
of the simple economic model detailed earlier. No longer
can it be assumed that supplementation decisions during the
current period have no impact on production rates in the
future. In fact, supplementing the cow herd is done to
improve calf crop and production in the future. There are
now numerous interactions of economic importance ranging
from the body condition of cows before and after calving,
cow death rates, conception rates, calving intervals,
weaning weights, cull cow weights, and feedlot
performance of calves born to supplemented versus nonsupplemented cows. Waldner et al. (2009) evaluated the
pattern of losses of cows and replacement heifers in western
Canada and concluded that factors related to cow nutrition
accounted for 25% of deaths. Stalker et al. (2007) found
cow supplementation strategies and weaning date interacted
with offspring in the feedlot and impacted feedlot returns
well after calves had left the ranch. Including these
interactions in an economic model is a challenge that has
never been done, but clearly a dynamic model is
appropriate.
The importance of adequate pre-calving BCS and BW to
obtain acceptable pregnancy rates has been well
documented (Richards et al., 1986; Selk et al., 1988; Wikse
et al., 1995). As noted by Torell and Bruce (1998), a cow
that cycles and rebreeds 83 days post-calving will deliver a
calf near the same date the following year. Postpartum
intervals have been shown to exceed this desired interval
when BCS is below the moderate (5 to 6) level, on a 9-point
scale. Conception percentage is also reduced from desired
rates above 90% to the 70% range for thin cows (Houghton
et al., 1990), and thin cows wean lighter calves with
reduced survival (Wikse, 1995).
Supplementing to Improve Rangeland Conditions
Some have argued that government feed programs
should be eliminated because feed assistance programs
encourage livestock producers to buy supplements and keep
livestock during dry periods with stocking levels in excess
of sustainable amounts for dry-period forage conditions
(Holechek and Hess, 1995). However, potential also exists
to improve range conditions using supplements and other
livestock management tools designed to improve grazing
distribution. Herding, fencing, strategic supplementation,
altered seasons of grazing use, development of off-stream
water, and early weaning are management options
considered by Tanaka et al. (2007) in an economic study
about improving grazing distribution. The economic
assessment was based on cattle distribution research
conducted on the Eastern Oregon Agricultural Research
Center Hall Ranch. Based on the grazing trials, projections
were made about how weight gain, calf crop, and costs and
revenues would change by implementing each alternative
management strategy. Resources and costs for a typical 300
cow ranch in northeastern Oregon were used to develop a
multi-period linear programming model for the analysis.
The production linkages and assumptions about expected
production changes from alternative management strategies
were the obvious weak link in this economic research, as is
usually the case.
172
can support about 683 AUY (16.7 ha/AUY) when forage
conditions are near average. In the economic study a
conservative stocking rate set at 18.5 ha/AUY (90% of
average grazing capacity for the CRLRC) was compared to
flexible grazing use where animal numbers were adjusted to
match forage conditions. Allowed adjustments to drought
included maintaining a flexible yearling enterprise along
with a base cow herd, and adjusting annual forage use with
yearlings; purchasing replacement forage; and reducing
animal numbers. The supplementation practices that
Holechek and Hess (1995) criticized, where a protein or
energy supplement is fed while animals remain on
rangeland and continue to remove forage, was not
considered in the economic study. Instead, replacement
forage was assumed to be outside leased forage or relatively
low-quality hay fed to confined animals.
A profit-maximizing, multi-period linear programming
(LP) model was used to evaluate how alternative drought
management strategies affected net discounted ranch
returns computed over a 40-year planning period. Onehundred different beef price scenarios were evaluated in the
Monte-Carlo study. Beef prices were different for each year
of the analysis (4000 total simulations or iterations with the
40 year planning period), but simulated prices recognized
the linkage in price between animal classes and typical
price cycles.
Annual forage production was considered to be variable
in the model based on the finding that herbaceous
production on the CRLRC is normally distributed with an
average of 656 kg/ha and a standard deviation of 200.
“Proper grazing” was defined using guidelines detailed by
Bement (1969) and this required that 336 kg/ha of
herbaceous plant material remained at the end of the
grazing season during a normal or above-average
production year. The required residual of herbage was
reduced to 224 kg/ha during drought years, which were
defined to occur when production was more than 1 standard
deviation below the mean (< 451 kg/ha). During dry years,
an 11% reduction in sale weights was included and next
year’s calf crop was reduced by 5% based on research by
Bement (1969), Hart et al., (1988b) and Wikse et al. (1995).
As noted by Bement (1969), during periods of drought a
livestock producer may decide to leave animals in a pasture
even though less than 200 kg/ha of herbaceous production
remains. Based on the thin-cow research of Wikse et al.
(1995) and others, further reductions in animal performance
would be expected as would impacts to future rangeland
productivity. The LP model considered the stocking rate
restriction to be a maximum amount, penalizing livestock
production rates during dry years, but allowing downward
herd flexibility if economically optimal. Overgrazing
(removing herbaceous production to below 224 kg/ha) was
not allowed and animal forage requirements had to be met
in all years by either reducing numbers or purchasing
alternative feeds. The profit-maximizing model determined
the optimal (profit maximizing) choice between selling
cows and buying supplemental feed to offset forage
shortfalls. It did not determine an optimal level of
supplemental feed to use given the complexities and lack of
data defining key production relationships.
The
benefit-to-cost
assessment
of
strategic
supplementation assumed that low-moisture blocks would
be fed per animal at a rate of 0.32 kg/d for 3 months in the
late fall (Oct. through Dec.). The blocks would be placed in
previously underutilized areas, and by utilizing these areas,
the rangeland grazing period would be extended by a
month, reducing hay feeding costs. Grazing costs for the
supplement were assumed to increase by $0.23/d or by
$6.83/AUM. The extended grazing period would mean 95
tons less hay would be required for the 300 cow ranch. This
would mean a $14.59/cow benefit and a benefit to cost ratio
of 1.8:1 for the strategic supplementation practice. An
important conclusion drawn from the grazing distribution
economic assessment was that the most valuable forage is
not in June when animals are gaining at peak levels.
Instead, the most valuable forage is in the winter or early
spring when the forage is dry and low-quality, but few feed
alternatives exist. When an expensive purchased feed is the
next best alternative, strategies that improve the distribution
of animals and more fully utilize less expensive grazing
resources can be very profitable.
Drought Management
Livestock producers have generally endured drought by
reducing livestock numbers, leasing forage, temporarily
grazing rangelands beyond their capacities, and (or)
increasing supplemental feeding. The need for feed
supplements and the purchase of alternative forages and hay
interacts with the level of stocking; the higher the stocking
rate, the higher the likelihood that alternative forages will
have to be purchased, or cows sold, when dry conditions
occur. Unfortunately, proper grazing practices are not
always followed and dry years are when cows become thin
with reduced production and lengthened calving intervals.
Commonly recommended strategies for dealing with
drought and variable forage conditions include maintaining
a conservative stocking rate, maintaining grazing flexibility
by having yearlings or stocker animals as one of multiple
enterprises on the ranch, and leaving a significant amount
of herbaceous production at the end of the grazing season
(Hart and Carpenter, 2005). In addition to questions about
the base level of stocking that should be applied when
annual forage conditions are variable, herd mix is another
important consideration as different animal classes offer
different degrees of flexibility. A common recommendation
has been that ranges should be stocked with a basic
breeding herd that is not detrimental to the range during
drought years, and any excess forage produced in average
or above-average years should be utilized by purchased
stocker animals. Hart and Carpenter (2005) recommended
that in areas prone to drought, breeding herds should
constitute no more than 50% to 70% of the total carrying
capacity of the ranch during average production years. The
rest of the herd should be carry-over yearlings or purchased
stocker animals.
An economic evaluation of recommended drought
management strategies was recently completed using the
production rates, costs and characteristics of the CRLRC in
central New Mexico (Murugan, 2007). The 11,381-ha ranch
173
or cows were sold (or both) to adjust in these dry years
depended on the production alternatives considered to be
available, and beef prices. Attempting to feed at least part
of the cow herd through a drought was economically
optimal in the majority of situations. When only the cowcalf alternative was included, outside forage would be
purchased at $50/AUM in 75% of the dry years, and as an
average this forage would maintain 225 AUY. Herd
reductions would also occur with the average size of the
cow herd optimally reduced by 71 AUY.
In the 25% of drought years when it was not profitable
to replace forage, the cow herd was reduced to an average
of 336 AUY, which is 55% of the conservative capacity
defined in the analysis. Beef prices were about 3% below
average during dry years when herd reductions occurred,
but the major consideration was that many of these years
were those when drought persisted over several consecutive
years. This confirms the obvious. If a prolonged drought
occurs, the best strategy is to sell cows.
If a flexible yearling enterprise was included, forage
leasing was reduced to 22% of the years. The optimal
adjustment in this case would be to gradually reduce cow
numbers and switch to more flexible yearling production as
dry conditions forced cow herd reductions. Yearlings were
in the optimal mix, not necessarily because they were more
profitable but because in a dynamic situation, they provide
a relatively low-cost method to adjust to variable forage
conditions.
The biggest economic limitation of a conservative
stocking strategy is that much forage remains unused during
favorable years, and that forage only has economic value to
the profit maximizing rancher when it is harvested. As
noted by Holechek et al. (2004), implementing a flexible
stocking strategy that keeps a relatively constant ratio
between forage and livestock is the desire of many
rangeland managers, but anticipating and accurately
predicting annual forage availability is an impossibility.
This limits the practical adoption of flexible stocking
strategies. Yet, flexible grazing strategies have significant
economic potential to increase ranch profits if accurate
projections of forage conditions can be made. Taking
advantage of favorable forage production years increased
average net annual returns to $69,520 compared to $55,126
with a conservative maximum stocking rate imposed (a
26% increase). Even in the absence of accurate forage and
weather forecasts, grazing management can be vastly
improved by better incorporating any existing
understanding of climate variability into stocking decisions.
It is clearly not profitable to starve through a drought
(Wikse et al., 1995). Maintaining grazing flexibility and
adjusting to forage conditions are the logical economic
strategies to follow.
It was recognized in the study that few opportunities
may exist to purchase outside forage on an as-needed basis
during drought, because drought and forage shortages are
generally widespread. Two different price situations for
leased forage ($20 and $50/AUM) were considered. The
lower price reflects a normal full-price forage lease where
local forage can be leased with full care provided for
displaced animals, or when economically optimal for herd
expansion. The $20/AUM price was used during normal or
favorable precipitation conditions. The $50/AUM rate
reflects the situation where displaced cattle would be
shipped a considerable distance or fed relatively lowquality roughage during drought.
With variable beef prices and forage conditions,
negative net annual returns occurred 22% of the time in the
simulation. These poor economic years were largely during
short-forage years. When herbaceous production was one
standard deviation below the mean (< 451 kg/ha) average
estimated net returns (the residual amount left to pay a
return to capital investment and to provide a return for
management and risk) were -$68,742 compared with
+$75,248 during other years. Reduced economic returns
carried over for several years with reduced herd sizes and
the cost of rebuilding the herd.
A key finding of the economic study was the economic
importance of following standard drought management
recommendations of keeping stocking flexible (Hart and
Carpenter, 2005). Herd expansion must largely occur with
yearlings to be profitable; increasing and decreasing cow
numbers to match forage conditions is too expensive
relative to the potential short-term gain, and this is true
even when forage conditions are known with certainty.
Compared to a cow-calf ranch, adding flexible yearling
enterprises increased net ranch returns by 26% with
conservative stocking and by up to 66% if an accurate
weather forecast made a flexible grazing strategy possible.
Without annual variation in forage production, about 84%
of available forage would be optimally allocated to cowcalf production. As forage variability increased to levels
typically observed on western rangelands, a 50:50 forage
allocation between cow-calf and yearling enterprises was
found to be optimal. Annual forage variability was an
important economic factor. Net annual returns increased
from $63,076 to $101,006, a 60% increase, when the
economic model was defined without annual forage
variability.
Leased forage was an economically important strategy
for adjusting to drought and for herd expansion during
favorable beef price years. With no upper stocking rate
restriction imposed, and with both cow-calf and yearling
enterprises on the ranch, outside forage was leased in 44%
of the years. When a conservative maximum stocking rate
set at 90% of capacity was imposed, with cow-calf
production only, forage was leased in one-third of the years
depending on prices and forage conditions.
Of the 4,000 beef price and forage situations considered
in the economic analysis, 559 years (14%) were defined to
be drought years, based on randomly simulated rainfall and
resulting herbaceous production falling 1 standard deviation
below the mean level. Whether expensive forage was leased
IMPROVING ECONOMIC
NUTRITIONAL DECISIONS
For the most part, livestock producers must purchase
supplemental feeds and make nutritional decisions based on
little more than the assurance of the feed salesman that the
right economic decision is being made. Beef prices can
174
Hart, R. H., J. W. Waggoner, Jr., T. G. Dunn, C. C.
Kaltenbach, and L. D. Adams. 1988b. Optimal
stocking rate for cow-calf enterprises on native
range and complementary improved pastures. J.
Holechek, J., R. D. Pieper, and C. H. Herbel. 2004. Range
Management: Principles and Practices. Prentice
Hall.Upper Saddle River, NJ.
Holechek, J. L. and K. Hess, Jr. 1995. Government policy
influences on rangeland conditions in the united
states: A case example. Environ. Monit. Assess.
37:179-187.
Houghton, P. L., R. P. Lemenager, L. A. Horstman, K. S.
Hendrix, and G. E. Moss. 1990. Effects of body
composition, pre- and postpartum energy level and
early weaning on reproductive performance of
beef cows and preweaning calf gain. J. Anim. Sci.
68:1438-1446.
Lambert, D. K. 1989. Calf retention and production
decisions over time. West. J. Agric. Econ. 14:9-19.
Manley, W. A., R. H. Hart, M. J. Samuel, M. A. Smith, J.
W. Waggoner, Jr., and J. T. Manley. 1997.
Vegetation, cattle, and economic responses to
grazing strategies and pressures. J. Range Manage.
50:638-646.
Murugan, S. 2007. Profit maximizing livestock production
and marketing strategies to manage climate
variability. Thesis. New Mexico State Univ. Las
Cruces, NM.
Richards, M. W., J. C. Spitzer, and M. B. Warner. 1986.
Effect of varying levels of postpartum nutrition
and body condition at calving on subsequent
reproductive performance in beef cattle. J. Anim.
Sci. 62:300-306.
Selk, G. E., R. P. Wettemann, K. S. Lusby, J. W. Oltjen, S.
L. Mobley, R. J. Rasby, and J. C. Garmendia.
1988. Relationships among weight change, body
condition and reproductive performance of range
Stalker, L. A., L. A. Ciminski, D. C. Adams, T. J.
Klopfenstein, and R. T. Clark. 2007. Effects of
weaning
date
and
prepartum
protein
supplementation on cow performance and calf
growth. Rangeland Ecol. Manage. 60:578-587.
Tanaka, J. A., N. R. Rimbey, L. A. Torell, D. T. Taylor, D.
Bailey, T. DelCurto, K. Walburger, and B.
Welling. 2007. Grazing distribution: The quest for
the silver bullet. Rangelands 29:38-46.
Torell, L. A., C. F. Speth, and C. T. K. Ching. 1982. Effect
of calf crop on net income of a nevada range cattle
operation. J. Range Manage. 35:519-521.
Torell, R., J. Balliette, and L. J. Krysl. 1993. Pricing protein
and energy supplements. Beef Cattle Handbook
BCH-5451.
Available
at:
http://catalog2.nmsu.edu:2048/login?url=http://sea
rch.ebscohost.com/login.aspx?direct=true&db=agr
&AN=IND20448659&site=ehost-live&scope=site.
Torell, R. and B. Bruce. 1998. Backing up calving dates
(reducing postpartum interval of beef cows). Univ.
of Nevada - Reno Fact Sheet 98-19.
generally be reasonably estimated. The cost of feedstuffs is
known when purchased. Thus, economic variables are not
the key factors limiting the economic decision. Instead, it is
unknown response and production relationships (Figure 1)
that primarily limits sound nutritional decisions. Long-term
studies that evaluate animal responses to grazing systems,
stocking rates, and alternative levels of supplementation,
like those conducted by Hart et al. (1988a) and Manley et
al. (1997), are now rarely funded. The emphasis of
agricultural research and extension programs has changed
greatly as we have evolved to a more urban and
environmentally aware population. Colleges of Agriculture
and traditional range and animal science departments have
added the terms “environmental”, “ecology”, and
“conservation” to their names. Funding priorities have
changed and the new priorities are not the long term grazing
studies needed to quantify key production relationships
required for economic evaluation of nutrition-based grazing
alternatives.
Within the current research environment, the key to
improving the ability of grazing and feeding trial research
to contribute to improved economic decisions is to
remember that more and varied rates of study treatments are
important. Furthermore, when the top input rates of the
study begin to cause output to increase at a decreasing rate,
it does not mean that the maximum relevant rate has been
reached. Instead, it means the beginning of the economic
relevant production stage has been reached. Measurement
over a number of years with varied conditions is important.
Partial budgeting of a limited number of feeding rates will
likely remain the key analysis tool given research budgets
and priorities.
Several key points about the economics of supplemental
feeding practices are clear from the view of a couple of
economists. First, the marginal principles very much apply
to nutritional decisions. Just because some new feed
supplement or additive increases weaning weights or calf
crop does not mean it is economically justified. One must
consider the added cost and added value of adopting the
practice. Further, there is an economically optimal rate of
the production input to use and it will be less than that
which would yield the biggest and most output.
LITERATURE CITED
Bement, R. E. 1969. A stocking-rate guide for beef
production on blue-grama range. J. Range
Manage. 22:83-86.
DelCurto, T., B. W. Hess, J. E. Huston, and K. C. Olson.
2000. Optimum supplementation strategies for
beef cattle consuming low-quality roughages in the
western united states. J. Anim. Sci. 77:1-16.
Hart, C. R. and B. B. Carpenter. 2005. Stocking rate and
grazing management. Texas Agric. Ext. Serv. E64.
Hart, R. H., M. J. Samuel, P. S. Test, and M. A. Smith.
1988a. Cattle, vegetation, and economic responses
to grazing systems and grazing pressure. J. Range
Manage. 41:282-286.
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Tronstad, R. and T. Teegerstrom. 2003. Economics of sale
weight, herd size, supplementation, and seasonal
factors. J. Range Manage. 56:425-431.
VanTassell, L. W., R. K. Heitschmidt, and J. R. Conner.
1987. Modeling variation in range calf growth
under conditions of environmental uncertainty. J.
Waldner, C. L., E. G. Clark, L. Rosengren, and R. I.
Kennedy. 2009. A field study of culling and
mortality in beef cows from western canada. Can.
Vet. J. 50:491-499.
Wallace, J. D. 1987. Supplemental feeding options to
improve livestock efficiency on rangelands. Pages
92-100 In: Achieving Efficient Use of Rangeland
Resources. R. S. White and R. E. Short (ed) The
Fort Keogh Res. Symp. Sept., 1987. Miles City,
MT. Montana State Univ. Agric. Exp. Sta.
Bozeman, MT.
Wikse, S. E. 1995. Use of performance ratios to calculate
the economic impact of thin cows in beef cattle
herds. N. Amer. Vet. Conf. Proc. 9:507-508.
Wikse, S. E., D. B. Herd, R. W. Field, P. S. Holland, J. M.
McGrann, J. A. Thompson, C. White, and R.
Angerstein. 1995. Use of performance ratios to
calculate the economic impact of thin cows in a
beef cattle herd. J. Amer. Vet. Med. Assoc.
207:1292-1297.
Workman, J. P. 1986. Range Economics. McMillan
Publishing Co. New York, NY.
176
Panel A
Panel B
Added Value and Cost ($)
60
VMP
50
40
AVP
30
Profit
MFC
20
10
0
0.000
5.000
10.000
15.000
20.000X*
25.000
30.000
Level of Input use (X)
Figure 1. Graphical presentation of the traditional economic model of optimal input use.
177
ECONOMICALLY EFFICIENT SUPPLEMENTAL FEEDING AND THE IMPACT OF NUTRITIONAL
DECISIONS ON NET RANCH RETURNS
L. Allen Torell and Neil R. Rimbey
Notes
July 9 and 10, 2010
WHERE TO NOW?1
M. K. Petersen2ŧ, J. T. Mulliniks*, A. J. Roberts ŧ, and R. C. Watermanŧ
ŧ
USDA - ARS, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301;
Department of Animal Science and Range Sciences, New Mexico State University, Las Cruces 88003
*
ABSTRACT: The Fourth Grazing Livestock Nutrition
Conference was convened at Estes Park July 9 and 10.
There were over 28 poster presentations and 12 conference
papers presented. The papers were organized in 6 topical
sessions ranging from microbiology to supplementation.
The first session covered the potential for rumen
microbiological studies in grazing animals. Then the
keynote address focused on methods to assess intake of
grazed vegetation by the use of alkane as an internal and
external marker. Followed was a discussion of the
methodology and types of analysis for assessment of
grazing animal behavior, which is considered a key
component of livestock distribution. There was a very
informative session covering the metabolic and practical
consequences of weight loss, undernutrition during
pregnancy, and nutrient excess associated with high-quality
forage diets. Integration of the previous session into
discussion of prediction models and their value to predict
animal productivity in Australia and US followed. The
conference finished with a summary of the state of the art in
regards to supplementation strategies and the implications
of nutritional decisions on net returns. The conference
provided current information and inspired a number of new
research ideas by the participants.
advances to keep pace with the need for new information,
the authors suggested the importance of grazing livestock
nutrition is greater than before and requires more emphasis.
To keep the rate of advances there needs to be a rebirth of
research endeavors and conservative funding of the past is
no longer appropriate. In another context, the authors
effectively pointed out an ongoing dilemma in accurately
assessing intake and described successes in both research
and practical arenas in the elucidation of supplementation
effectiveness and management. Records of these advances
can be found in conference proceedings of the first, second,
and third meetings. Authors Tim DelCurto and Ken Olson
linked research success to production and economic
application as the ultimate evaluation of research outcomes.
They pointed out the contradiction with Journals preferring
mechanistic results when the setting for grazing livestock
nutrition research is the field environment. Their statement
implies that a number of important applied studies have not
been published.
RUMEN MICROBIAL ECOLOGY AS INFLUENCED
BY THE NUTRITIONAL ENVIRONMENT OF
GRAZING RUMINANTS
Presentations on methods and procedures to manipulate
microbial populations in grazing animals organized by
Bryan White (Brulc et al., 2010) and Shanna Ivey (LodgeIvey, 2010), illustrated prospective impacts this area of
research may have on grazing livestock nutrition. Riddled
with a number of challenges, this area is largely
unexplored. One of the major success stories highlighted
was introduction of bacteria into Australian goats that are
capable of degrading 3-190 hydroxy-4 (1H)-pyridone
(DHP). The Australian animals were then able to use
Leucaena leucocephala, which contained the toxicant
mimosine that degrades to a toxic goitrogen. Other
promising developments were presented and evaluated. The
future is wide open for researchers in the study of rumen
microbiology in free ranging ruminants since the field is
young. For example, a basic evaluation of the microflora
nutrient needs in relation to nutrients consumed by the host
is not well defined for native vegetation diets. Knowledge
of microflora nutrient limitations under grazing conditions
will potentiate the ability to augment nutrient supply.
INTRODUCTION
This ambitious conference, arranged jointly by Western
Multistate Research Project W-1012, the University of
Wyoming, and Colorado State University brought together
a number of experts in grazing livestock nutrition. The
conference was comprised of presentations from domestic
and internationally renowned researchers. It began with an
excellent overview of Issues in Grazing Livestock Nutrition,
which framed the conference agenda. The authors
(DelCurto and Olson, 2010) first described hallmarks in
progress attained by past and current researchers. For future
1
USDA, Agricultural Research Service, Northern Plains
Area, is an equal opportunity/affirmative action employer,
and all agency services are available without
discrimination. Mention of any trade name or proprietary
product does not constitute a guarantee or warranty of the
product by USDA, Montana Agric. Exp. Stn., or the authors
and does not imply its approval to the exclusion of other
products that also may be suitable.
2
180
interferes with cross-location comparisons, outlook in
interpretation, and publication of results. A sidebar
comment by Anderson (2010) suggested the most effective
method of grazing management to best utilize high-quality
vegetation was with a herder. The application of technology
(e.g., a virtual fence) to move (herd) stock to areas desired
without the expense for labor should be commercially
available soon. Both Anderson (2010) and DelCurto and
Olson (2010) pointed out the importance of understanding
free-ranging animal behavior and the deficit in this
knowledge is slowing advancement of understanding
animal distribution and grazing animal nutrition. Anderson
(2010) also shared postulates that can be used to structure
and stratify behavioral data for interpretation and analysis.
He suggested that single points of observations (i.e., 1
animal) could be misleading at best, because it ignores the
gregarious nature of domestic livestock and their
interdependence. This suggestion was driven home by
encouraging experiments with (at a minimum) typical
naturally sized social groups. Another challenge across
locations is to integrate dissimilarities among instruments,
methodologies, and interpretations. Mechanisms guiding
animal distribution (imbedded within nutrient location
richness) are the least understood characteristic of grazing
animal nutrition that may be accounting for a large portion
of nutrient consumption. He continued by suggesting
foraging decisions are the balance between the motivation
to eat and satiation (which do not occur in isolation). We
have more to discover before we can successfully integrate
animal landscape use as part of a prediction tool for nutrient
capture by grazing animals.
PERSPECTIVES ON INTAKE IN GRAZING
STUDIES AND IN PRACTICE
The next section of the conference focused on
assessment of the use of landscapes by the grazing
ruminant. The keynote talk was presented by Hugh Dove.
Dr. Dove addressed the research application of alkane
assisted simultaneous digestibility and fecal output
measurements (adoption and evaluation of the alkane
technique is an objective of W1012). First, Dove (2010)
described the historical slow progress in perfecting
methodology for measurement of grazed vegetation
consumption. He went on to explain the thought process
behind the alkane technology and the possibility that if the
assumptions were true then this technology would be a
breakthrough for our discipline. This methodology has been
slow in adoption by researchers in the western US. With the
loss of the slow release bolus, intake assessment is less
convenient. This is reflected by fewer intake measurements
in recent literature, making those reports less complete and
resulting in an unfortunate gap in knowledge. The
promising aspects according to Dove (2010), suggest that
the technique does not require quantitative evaluation of
alkanes in grazing animal diets as long as the ratio of plant
(diet) derived alkane in the feces and that of a dosed similar
chain length alkane can be determined. Support for this
important assumption of the technique is dependent on
nearly equal total tract digestibilities of 2 similar length
alkanes. Therefore, the fecal ratio should be equal to the
feed ratio. This assumption is a central tenet to move
towards implementation of the method, especially for
workers conducting livestock intake studies on extensive
native rangelands. Another benefit of this dual marker
system is that only 1 laboratory analysis needs to be
conducted not 2 separate analyses for each component of
the duo-markers. It is clear that the challenge of the alkane
technique is assessment of its capability and robustness in
specific experimental settings. Dove (2010) pointed out a
number of appealing attributes, such as: the technique
adjusts to each animals digestive character; the external
marker is required for determination of both digestibility
and fecal output; only 1 laboratory analysis is conducted;
measurement can be conducted with animal fed
supplements; relative concentrations are important (not
quantitative evaluation); and accuracy is less susceptible to
variation caused by changes in physiological state. If
researchers achieve accurate results by applying this
technique, then we will have overcome a major obstacle to
predicting productivity of grazing ruminants.
The second paper in this session discussed measurement
peripheral to intake assessments such as grazing behaviors.
Dean Anderson explained how new geospatial technologies
pioneered first by the military and adapted by wildlife
researchers are being used to understand landscape use by
domestic ruminants. The data recording potential of
instruments can collect a datum point every second, which
leads to precise differentiation of location, rate of
movement, and potential identification of specific
behaviors. Not having a universal model to analyze and
interpret all location data is perhaps realistic but
problematic. However, the lack of continuity probably
IMPACT OF NUTRIENT BALANCE ON
BIOLOGICAL EFFICIENCY AND LIFETIME
PRODUCTIVITY OF GRAZING LIVESTOCK
Adaptive metabolic changes occurring in beef cattle
when the nutritional or external environment becomes
extreme was the focus of one of the afternoon sessions. The
first example began with the paper by Richard Waterman
and Ron Butler, who contrasted metabolic dysfunction of
the high producing dairy cow with the postpartum range
cow. These scenarios are similar metabolically but they
have different causations. Dairy cows are prolific milk
producer fed a very high-quality diet whereas range beef
cows produce much less milk and consume low-quality
diets. The net effect of each circumstance is that nutrient
intake is insufficient to meet nutrient demands for milk
production regardless of cow type, which results in BW
loss. The dairy literature possesses a deep body of work
revealing the mechanisms at the root of metabolic
dysfunction. Few range cow studies have the stepwise
methodology characteristic of the efforts in dairy cow
research. As an area of investigation, the phenomenon of
weight loss and its efficiency lacks definition although it is
a predictable event with known negative consequences.
Waterman and Butler (2010) made the case that an
inadequate supply of glucose maybe one of the causes of
metabolic dysfunction. Speculation on the cause of a low
supply of glucose included low propionate derived from
ruminal fermentation and an increased demand for glucose
181
environments, they make the point that excess nutrients are
also a stressor. A key point was that a changing nutrient
supply during the peri-conceptual period can have profound
impacts on reproduction. Implication of nutritional status in
embryo survival is a key factor influencing efficiency of
assisted reproductive management. Evaluation of nutritional
status of an individual is a broad term encompassing the
sum of the current trend in nutrient intake and (or) use as
well as the product of historically accumulated impacts
occurring from the embryonic stage to a current point in
time. The authors lead the reader to make a critical
observation, that the nutritional history of females used in
experimental evaluations has been neglected as a source of
variability that seems to have overwhelming implications
on cow responses within a study or when comparing across
studies from different laboratories. This conclusion is
derived from experimental results showing gene expression
differences driving a number of functions, such as
adipocyte growth and differentiation, lipogenesis,
angiogenesis, and anti-angiogenic operations. They suggest
that experimental data show low nutrition during the
perinatal period will increase offspring mortality.
Additionally, a lower plane of nutrition during lactation
may reduce absolute BW of the neonate and throughout the
growing period because calves experiencing lower planes
of nutrition during the first 6 months of life are less apt to
compensate later in life. Overall, the consequences of
undernutrition as described are variable depending on the
severity, timing, and the pre and post nutrient intake in
relation to an undernutrition event. In some scenarios, the
reader could construct a case where undernutrition may
improve efficiency later or severe undernutrition has
profound negative implications. As a consideration, we are
challenged to predict short- and long-term influences of
undernutrition as impacted by the neonate’s birth weight.
This relationship gives an opportunity to exert a little
management since there is a degree of control over birth
weight via sire selection. Some of the effects described by
the authors may be ameliorated by intentional selection of
lower birth weight sires to reduce physiological state
induced stress. Then the question becomes, Will the
impacts of metabolic adaption be significantly modulated
by a 5, 10, or 15% decrease in birth weight? It is probable
that supply of specific metabolic intermediates is playing a
primary role in pregnancy and nutritional status interactions
affecting the trends toward weight loss, maintenance, or
gain. Lastly, the lifetime consequences of maternal
nutritional status on progeny as a neonate and as an adult
may have strong influences on traits of economic
importance.
For the final presentation in the afternoon session
pertaining to nutrient balance on biological efficiency,
Monty Kerley (Kerley, 2010) asked the question “Do
ruminants experience imbalances when they consume high
quality forages? His talk started with a reflection on the
finding that there is considerable variation in 2 associated
measures of animal responses to their diet (i.e., intake and
gain). He pointed out that cattle with the same rate of gain
can have a 40% difference in the quantity of feed
consumed. It was proposed that intake differences relate to
divergence in metabolic efficiency among individuals and
to support milk production resulting in supply exhaustion.
This scenario is associated with body tissue mobilization
and weight loss. Mobilization of adipose for utilization in
oxidative metabolism and protein tissues degraded to AA
with a significant portion of carbon skeleton diversion to
gluconeogenesis, while other AA are utilized in oxidative
metabolism. Description of metabolic consequences of
weight loss, involvement of 6 organs in this process,
metabolic foot prints highlighting metabolic adjustments,
and general interpretations were presented. A description of
changes (or signals) and analytical measurements to verify
the severity of nutrient deficiency were suggested. The case
was made that knowledge of weight loss and practices to
ameliorate weight loss are crude at best. Methods to
increase glucose supply in range supplements and
application of a hand-held, chute-side ketone meter for
rapid assessment of glucose status were discussed.
Adaptive changes by a cow to weight loss may alter
(hopefully improve) her ability to cope with the external
and nutritional environment. These metabolic modifications
have an impact on energy metabolism and expenditure and
most likely carcass composition and nutrient priority
utilization. Proportional fate of absorbed AA for protein
tissue anabolism, gluconeogenesis, oxidative metabolism,
fetal demand, or milk production is poorly understood
during weight loss. The pathway a fraction of metabolic
protein follows while cows undergo metabolic adjustments
most likely has a profound influence on efficiency and
response to dietary nutrients. Alteration of this phenomenon
probably is modified by nutritional history, such as: In the
recent past, was a cow fat or thin? Is she increasing or
decreasing in condition? Has she been grazing range her
entire life or was she developed and raised in a feedlot?
Alternatively, was the cow developed in a region with 50%
greater or lesser precipitation than where she is today? The
paucity of knowledge on the impact of these factors
interferes with our ability to develop the most efficient
nutritional management schemes.
Presentation and discussion of the importance of nutrient
balance and nutrient sufficiency in the pregnant ewe and
cow by Joel Caton, who with Bret Hess, examined
implications of dam nutritional status on her progeny’s
lifelong vigor. Emphasis was placed on progeny growth,
development, and performance outcomes as modified by
timing of nutritional perturbations and potential long-term
production effects. Discussion started with known
conditions that are outcomes of alternative metabolic
patterns (i.e., the thrifty phenotype and predictive adaptive
responses) that promote immediate well being. The
consequences of these metabolic patterns may include
insulin resistance, altered shape of growth curve, and body
composition alterations at maturity. From their ideas, it is
conceivable to propose that slight to moderate maternal
nutrient restriction occurs in a high proportion of western
range cows. As suggested by Waterman and Bulter (2010),
and reiterated by Caton and Hess (2010), there is evidence
to support the theory that maternal nutrient supply,
physiological demand, and environmental conditions
(energy demand) lead to stress responses modulated or
amplified by nutrient restriction. Although the major
portion of discussion concerned inadequate nutritional
182
the case was made that more efficient animals will be more
widespread in the future as a result of preferences for bulls
with known lower feed intake will be selected. This practice
will most likely affect caloric requirements and
simultaneously impact the proportional need and demand
for other nutrients. In case of grazing high-quality forages,
post-ruminal supply of AA (microbial and feed origin) may
not be sufficient to support energy-allowable gain creating a
disparity between energy and protein. In fact, he reported
that arginine, histidine, leucine, lysine, methionine, and
threonine are limiting, with the most limiting being
methionine in high-quality forage diets. Therefore, he
focused on potential imbalance of absorbable AA to
available energy on cow and weaned calf performance
consuming a forage diet. He discussed the results of cow
selection based on relative feed intake and identified cows
that maintained the similar body condition and weight while
consuming significantly different amounts of feed. He
noted that their daughters followed the same trends with
lesser magnitudes in intake differences. Because the AA
composition of microbial protein is nearly constant, it is
easy to predict sufficiency of protein composition in
comparison to available energy. From those predictions, it
was determined that a grazed forage intake with sufficient
calories to support 0.9 kg/d would be limited by methionine
supply equivalent to 0.3 kg/d. He went on to contrast how
supplementation with rumen-undegraded protein (RUP) to
young lactating cows reduced milk production and
improved BW gain while supplementation with additional
energy increased milk production and increased BW loss.
He concluded that this tool could be effect for managing the
challenges young cows face in nutrient allocation between
growth and milk production. Conclusion of the discussion
occurred with results of studies demonstrating a partial
response to the above-described response is partially due to
the supply of methionine. Kerley (2010) suggested that 7 g
of methionine per day has potential to improve heifer
growth and development. The take home message of the
presentation was voluntary intake of beef cattle in the future
will most likely be less while maintaining at least the same
productivity (i.e., high efficiency). Those foreshadowed
changes are in proportions and absolute nutrient density of
diets for the future. It is very possible that the scenario
described above has already occurred in isolated cases
where a strict selection criterion has been followed in the
development of a cowherd in various environments. The
utilization of RUP to partition nutrient use in young cows is
a powerful tool to reduce nutritional stress in young
cows. In practice, intake responses have not been
intentionally identified but efficiency has been indirectly
selected for where conservative feeding practices have been
exercised with strict reproductive standards imposed on the
heifers and cows. In these environments, only cows that are
the most efficient will be productive in a nutrient
conservative environment.
APPLICATION OF REQUIREMENT SYSTEMS FOR
GRAZING LIVESTOCK: ASSESSING NRC AND
CSIRO SYSTEMS FOR GRAZING LIVESTOCK
The second day started with a talk on assessing nutrient
requirement schemes for grazing animals. Stuart McLennan
and colleagues (Dove et al., 2010) showed the strengths and
limitations of how the NRC and CSIRO models can be used
to understand the role grazed vegetation plays in animal
growth and maintenance. At the outset, those authors
demonstrate the difficulty we have is characterizing nutrient
intake at a variety of levels and referred back to the
presentation addressing alkanes for intake determination.
Making the point that no matter how good a prediction
models may be, it will not be effective if the predicted
nutrient intake is inappropriate. Therefore, a perspective
proposed by the authors examined the use of models to
predict animal performance or predict forage adequacy.
With that in mind, they then make the generalization that
current accuracy of published energy requirements seems
sufficient for application to nutritional management of
grazing livestock (in temperate environments). A weakness
of systems is the present inability to account for N
recycling, especially in dormant forage settings. They also
pointed out that protein demand sometimes interacts with
issues, such as parasites, but they felt these relations are not
so great that they render the application of the models
ineffectual under field conditions. The authors pointed out
different approaches accounting for the dynamics of
maintenance requirements by the CSIRO and Cornell
systems. The Australian system accounts for maintenance
energy modifications with changes in intake, whereas the
Cornell model adjusts with metabolic size. Scaling of
maintenance requirements within the CSIRO system occurs
in relation to a standard reference weight. An example of a
gap in the requirements includes the utilization of body
tissue (weight loss) during pregnancy that is masked by
increases in hydration. Finally, the discussion leads to the
influences of management goals in how we use prediction
models. Key points included available biomass (kg/ha),
perspective of productivity per animal in comparison to
productivity per ha, acceptability of nutrient imbalances, or
reduced nutrient intake and subsequent suboptimal
performance.
SUPPLEMENTATION STRATEGIES TO ACHIEVE
BIOLOGICAL AND ECONOMIC EFFICIENCY
The second morning session focused on review of
supplementation as a nutritional management tool. The
authors, Greg Lardy and Rachel Endecott (Lardy and
Endecott, 2010) suggested that the recent rise in feedstuff
prices has created a heightened awareness of
supplementation and feeding in general as a major cost of
grazing animal production. They illustrated the dynamic
nature of the supplementation decision process by
integrating changing forage quality and shifting nutrient
requirements of the female while considering the needs of
both the ruminal microflora and the host animal. This
complexity is compounded by interactions of physical and
chemical form nutrients are supplied in a supplement. They
183
also presented circumstances where supplement used to
improve grazing distribution had value due to the
improvement in the duration of grazing in a pasture. Lastly,
the authors discussed the value of flexibility for dealing
with changing conditions using a base cowherd augmented
by disposable yearling or stocker cattle. They summarized
by encouraging researchers to conduct multiyear
experiments in varied conditions and to use partial
budgeting as a method to evaluate.
pointed out that form of carbohydrate, source of N and
potential impact of AA composition, and microbial nutrient
and (or) co-factors needs can be important considerations
affecting efficiency. Animal responses in general are highly
predictable but in some specific situations predictability is
much lower. The largest source of variability influencing
responses is associated with the inconsistency of the year
(precipitation, snow accumulation, duration of high and low
temperatures, forage production, etc.). Responses due to
nutritional management are specifically unpredictable each
year. Lardy and Endecott (2010) suggested milk production
would increase with an increased supply of metabolizable
protein (MP). They pointed out that supplementation to
satisfy MP needs will tend to increase pregnancy rate. The
apparent contradiction in the effectiveness of infrequent
protein supplementation was noted as was delivery cost
saving associated with that management scheme. Another
role protein provision can play is to reduce the negative
associative effect on digestibility when non-structural
carbohydrates are fed and other interactions occurring with
carbohydrate sources. The authors finished their discussion
on nutrients/feedstuffs with a summary of reviews of
literature on research with addition of fat to supplements.
They suggested that fat is not generally an effective feed
source to increase dietary energy intake. However in some
cases, fat may have beneficial effects on reproduction in
heifers or cows when fed before calving. They closed their
presentation by suggesting that more thorough and better
characterization of seasonal changes in degradability of
forage CP, as well as forage intake is needed for successful
and cost effective supplementation programs.
The final talk of the meeting by Torell and Rimbey
(2010) proposed integration of the previous topics into an
economic analysis. With this purpose, those authors set out
to clarify the economic goals of domestic animal grazing
and nutrition. For the range beef cow, maintaining high
rates of reproductive efficiency is a key economic driver.
Supplementation to augment economic success can occur
by improving health or condition of brood cows, but to add
a few kg to sale weight via supplementation is not likely to
give a feasible return. There are an infinite number of
outcomes when evaluating the amount of output in
comparison to different amounts of input. This idea was
framed by the comment that added value (due to
supplementation) should be compared to added cost. The
predictability of the net effect is confounded by the elusive
nature of economic efficiency, which varies by price and
production conditions. Publications of experiments to
evaluate manipulation of cow metabolic efficiency driven
by inputs using an economic outcome as the response
criteria are rare. Few long-term, multi-rate studies and
multiyear studies are conducted. However, multiple year
studies are required to estimate the production function that
forms the basis of economic evaluation. There is a need for
long-term studies to evaluate practices that can suggest
alternatives for future production practices. This concept is
illustrated by the depth in the body of work used to assign
economic values to cows with various BCS. At a BCS of 4,
the author’s estimated 74% less economic value compared
to a cow with a BCS of 5. In this context, 1 dollar spent in
reconditioning returns 2 dollars in economic value. They
EMERGING CONCEPTS IN GRAZING LIVESTOCK
NUTRITION – WHERE TO NOW?
The various grazing environments described in the
presentations ranged geographically from Missouri to North
Dakota to New Mexico to Oregon to Australia. The
common thread among the studies described in these papers
is a grazing ruminant harvesting plant materials, but the
environments are different. Despite obvious variability in
locations where grazing cattle are managed, the paper by
Dove et al. (2010) comparing prediction models was quite
promising. In some conditions, the models predict animal
responses well. In other presentations, we were made aware
of situations that can lead to temporary or long-term
changes in animals’ responses to environment and nutrition;
these situations may not fit the models as well.
Although research has been conducted under this
assumption, expecting linear response in a physiological
system reflects lack of knowledge that responses to
supplemental nutrients are linear. We know that responses
to limiting nutrients can be curvilinear, as described by the
economic diminishing returns curve discussed by Torell
and Rimbey (2010). In fact, the classical methodology for
determination of nutritional requirements uses a dose
response evaluation. When the response ceases with
incremental increases in dose then that point is determined
to be the requirement.
At the Corona Range and Livestock Research Center in
New Mexico, cows have been developed and maintained
over the past 10+ years using a conservative nutritional
management scheme. This site would be considered a
piñon-juniper rangeland with blue grama as the
predominate grass. Normal yearly precipitation is 330 mm.
Replacement yearling heifers achieve 50 to 55% of their
mature weight by breeding. Over the last 8 years,
pregnancy rate in heifers has averaged 80% in a 45-d or less
breeding season (Table 1). With this type of heifer
development where 20% of heifers are culled for
reproductive failure, it is assumed that the fittest heifers
become pregnant (Hawkins et al., 2000). Subsequent
pregnancy rates for these females has averaged 92% as 2year-old cows after their first calf with a 60-d breeding
season, 90% as 3-year-old cows, and 95% as 5-year-old and
older cows. In most years, all cows receive less than 100 kg
of a 36% CP supplement per year with the remainder of
their consumed nutrients supplied by grazed native range
vegetation (Mulliniks et al., 2009).
The above described management scheme inspired us to
begin investigating methods to achieve optimal biological
and economic efficiency. We set out with a series of studies
to inspect the lower amounts of protein supplement needed
184
to elicit highly efficient optimal responses. To assess
efficiency, we compared our animal responses to the
predicted gain from the NRC Beef Cattle Requirements.
The first scenario to be presented will be cows 5 years
old and older, grazing dormant range in the winter (grazable
forage was not limiting; Table 2). The cows were bred to
begin calving the third week in February. Their initial BCS
in October at weaning were 5.0 + 0.1. Over the ensuing 4year period, duration of supplementation was varied
depending upon conditions of the year. Duration of
supplementation ranged from 25 to 90 d (average 61 d).
Cows received 1 of 3 supplement treatments: 3.2 kg per
week of a 36% CP cottonseed meal-based supplement fed
in equal amounts on Monday, Wednesday, and Friday
(CSM), 1.6 kg per week of a 65% CP (or higher) animal
protein-based supplement (either blood and feather meal or
fish meal) that was self-fed with a mineral mix (SMP), or a
negative control where cows received the CSM at 0.3 kg
per week as prescribed at the discretion of the manager
depending upon weather conditions (NC). The 3
supplement regimes supplied 72, 49 or 45% of the CP
requirement (for CSM, SMP, and NC, respectively). Actual
vs. predicted weight change for the 61-d period was -0.2 vs.
-54; 1.8 vs. -62.8; and -12.6 vs. -64.7 kg/d for CSM, SMP,
and NC, respectively (Sawyer et al., 2010). There are 2
thoughts suggested from these cow responses. The
efficiency at which CP was utilized varied greatly between
the CSM and the SMP supplement. The response to
supplement is confounded, however, by the delivery
method since CSM was hand-fed 3 days per week and the
SMP supplement was self-fed and always available. The 2
supplements varied in ruminal degradability. The second
observation is that the actual and predicted responses were
not in good agreement. The explanation for this lack of
agreement could be due to the inaccuracy of the values we
used in the model or our cows were different than the cows
whose data were used to develop the model. We propose
that our cows are highly adapted to their environment and
are therefore more efficient (Heilbronn et al., 2006). The
first indication that these cows have the potential to be more
efficient is related to their amount of activity. Cibils (2009,
personal communication) described distance traveled by
cows will be in excess of 16 km/d even when water is
within 0.5 km. When reviewing literature investigating the
responses to exertion, a few key responses stand out. Heart
rate declines, oxygen consumption declines, cellular
mitochondria increases, and insulin sensitive increases, all
which allow for greater energetic efficiency in spite of
increased expenditure.
A second possible explanation is a potential reduction in
organ mass compared with traditionally managed animals.
Moderate energy restriction has been shown to decrease
organ mass and maintenance energy requirements (Baldwin
et al., 2006; Tovar-Luna et al., 2007). If maintenance
energy requirements tend to be lower, then a greater portion
of energy intake would be available for productive
functions.
Another potential explanation for improved efficiency
may involve modification of gene expression. It is probable
that maternal nutrient supply during gestation, as described
by Caton and Hess (2010), may have an influence on the
progeny. An example of an occurrence of altered gene
expression in a production scale experiment initiated at Fort
Keogh Range and Livestock Research Laboratory in the
autumn of 2001 (Roberts et al., 2009). At initiation of the
study, heifers were developed in the winter with a typical
nutritional management scheme or feed 20% less. All
heifers were treated the same after the wintering period.
Daughters of these cows have also been factorialized across
the same 2 winter treatments. Heifers fed the traditional
nutritional management scheme have a higher pregnancy
rate and greater BW than contemporaries fed 20% less.
Other difference have become apparent (Figure 1) after 5
calf crops from daughters who themselves were fed either
traditional or conservative and born from dams fed either
traditional or conservative. Cows raised by conservatively
fed dams and who themselves were conservatively fed have
the greatest productivity and longevity while conservativefed cows from conventional-fed dams had lowest longevity.
This difference in longevity due to difference in treatment
of dam provides evidence that gene expression is altered in
utero. It is possible that this changed expression is
maintained throughout their life and may potentially be
expressed in the daughters’ progeny.
Other improvements in efficiency may occur through
fine tuning our understanding and management of nutrient
and (or) metabolic limitations. Both Lardy and Endecott
(2010) and Kerley (2010) described positive changes
occurring when postpartum cows were supplied with
supplemental MP. Metabolizable protein could be utilized
to satisfy MP requirements or go towards supporting
gluconeogenesis, as suggested by Waterman and Butler
(2010). Kerley (2010) suggested the potential for responses
to specific AA, especially methionine. Waterman et al.
(2006) reported improved N retention with increasing
methionine up to 15 g/d for cows fed typical low-quality
roughage diets. Relatively small amounts of AA will lend
themselves to smaller, more biologically potent
supplements by weight. Smaller supplement should have
lower storage, delivery, and feeding costs.
A method to determine nutritional adequacy is to
monitor and evaluate behavior of grazing livestock. During
the conference, we were exposed to improvements in digital
technologies that will allow for assessment of precise
utilization of which regions of a pasture are used to better
model quality of diet intake. These same technologies may
be used for a number of grazing behaviors.
SUMMARY
The speakers and poster presenters provided the
participants with an invigorating conference. Future work
with the implementation of the alkane technique in the US
is sure to be natural outcome from the information received.
The ability to link intake measurement with digital tracking
and assessment of behavior, especially in the elucidation of
grazing distribution, should lead to major advances in the
field.
There has been a clear progress in our knowledge
concerning metabolic dysfunctions relating to lactation and
pregnancy. The impact on progeny has better definition.
185
The implication of management on gene expression is a
fascinating area that is rich to pursue in the future.
The importance of grazing livestock nutrition was easy
to realize during the conference. It has an enviable position
where it is a field utilizing the most current breakthroughs
in science while using those findings for practical
applications to improve the lives of producers and
consumers and enhances the sustainability of grasslands.
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animals. Pages 133–149 in Proc. 4th Grazing Livestock
Hawkins, D. E., M. K. Petersen, M. G. Thomas, J. E.
Sawyer, and R. C. Waterman. 2000. Can beef heifers
and young postpartum cows be physiologically and
nutritionally manipulated to optimize reproductive
efficiency? J. Anim. Sci. 77:1-10.
Heilbronn, L. K., L. de Jonge, M. I. Frisard, J. P. DeLany,
E. Larson-Meyer, J. Rood, T. Nguyen, C. K. Martin, J.
Volaufova, M. M. Most, F. L. Greenway, S. R. Smith,
W. A. Deutsch, D. A. Williamson, and E. Ravussin.
2006. Effect of 6-month calorie restriction on
186
Table 1. Four years of yearling heifer development on the ranch (supplemented with one of two protein
supplements varying in ruminal degradability) vs. drylot
Measurement
RUP
Development1
RDP
Drylot
SEM
P-value
Heifer BW, kg
Oct (weaning wt)
224
223
224
3
0.97
Jan/Feb
256
255
255
3
0.98
April/May
276
276
315
4
< 0.01
Sept/Oct (palpation wt)
393
402
403
5
0.24
ADG, kg/d
Jan to May
0.26
0.27
0.69
0.02
< 0.01
May to Sept
0.80
0.85
0.61
0.01
< 0.01
Jan to Sept
0.58
0.62
0.64
0.01
< 0.01
Heifer hip height, cm
Jan/Feb
116.38
116.64
116.13
0.53
0.80
April/May
120.80
120.88
118.49
0.46
< 0.01
Sept/Oct
123.80
124.76
124.82
0.56
0.32
Heifer BW:HH ratio, kg/cm
Jan/Feb
2.20
2.19
2.19
0.02
0.94
April/May
2.28
2.28
2.65
0.01
< 0.01
Sept/Oct
3.16
3.20
3.21
0.02
0.14
Hip height change, cm
Jan to May
4.42
4.27
2.34
0.30
< 0.01
May to Sept
2.97
3.89
6.32
0.36
< 0.01
Jan to Sept
7.39
8.13
8.66
0.38
< 0.01
Reproductive performance
Pregnancy rate, %
94
88
84
-0.10
Ratio
60/64
56/64
53/63
-1
On-ranch supplements varied in ruminal degradability; RUP = rumen-undegraded protein and RDP =
rumen-degraded protein.
Table 2. Body weight and body condition responses of gestating cows to different supplementation
strategies1
Item
CSM
SMP
NC
SE2
P-value
Body Weight Responses
Weaned BW3, kg
488
492
490
17
0.98
Initial BW4, kg
522
521
534
14
0.78
Final BW, kg
522
523
521
14
0.99
Predicted BW change kg
-54.3
-62.8
-64.7
Observed BW change5, kg
-0.2a
1.8a
-12.6b
3.9
0.06
a
a
Observed BW change, %
0.1
0.5
-2.2b
0.8
0.09
Body Condition Responses
Initial BCS
5.0
4.9
5.0
0.1
0.49
Final BCS
4.9a
4.9a
4.6b
0.1
0.12
BCS change
-0.1a
-0.1a
-0.4b
0.1
0.10
Total feed consumed, kg
38.2
13.7
2.6
--1
Cows received 3.2 kg per week of a 36% CP cottonseed meal-based supplement fed in equal amounts
on Monday, Wednesday, and Friday (CSM), 1.6 kg per week of a 65% CP (or higher) animal proteinbased supplement (either blood and feather meal or fish meal) that was self-fed with a mineral mix
(SMP), or the CSM at 0.3 kg per week as prescribed at the discretion of the manager depending upon
weather conditions (NC).
2
n = 6.
3
Cow BW at weaning in October.
4
Cow BW at initiation of supplementation period.
5
Cow BW change (final BW – initial BW).
a,b
Means without a common superscript differ (P < 0.10).
187
100%
90%
80%
70%
60%
50%
40%
30%
1
2
C Cdam
3
C Rdam
R Cdam
4
5
R Rdam
Figure 1. Percent retention of cows over five years from the cow longevity study of Roberts et al. (2009): CCdam =
conventional fed daughter and dam; CRdam= conventional fed daughter and restricted dam; RCdam = restricted fed daughter
and conventional dam; and RRdam= restricted fed daughter and restricted dam.
188
WHERE TO NOW?
M. K. Petersen, J. T. Mulliniks, A. J. Roberts, and R. C. Waterman
Notes
Abstracts — Grazing Livestock Nutrition Conference
Friday, July 9, 2010
POSTER PRESENTATIONS
1 EFFECTS OF RESTRICTING TIME AT PASTURE ON FEEDING STATION BEHAVIOR OF CATTLE
DURING THE FIRST GRAZING SESSION OF THE DAY. P Gregorini*, K McLeod, C Clark, C Glassey, A Romera, and J
Jago, DairyNZ, Hamilton, Waikato, New Zealand.
Information about short-term behavioral adaptation of cattle to time restrictions at pasture is lacking. The feeding station is the
area of pasture an animal can reach at each eating step (ES). This work explored feeding station behavior of cattle during the first
grazing session of the day (0800–1200 h) in response to daily restrictions in time at pasture. Six groups of eight Holstein-Friesian
cows (470 ± 47 kg, 35 ± 9 days in milk) were strip-grazed on a perennial ryegrass (Lolium perenne L.) pasture for either 4 h after
each milking (2  4), 8 h between milkings (1  8), or the whole 24 h period excluding milking times (control). Herbage
allowance was 0.07 kg herbage DM/kg live weight/d for all treatments. Mean pre-grazing herbage mass was 3,191 kg DM/ha.
The new daily strip of pasture was allocated at 0800 h (after morning milking). Six focal cows per replicate (group) were
equipped with Icetags® 3D motion sensors (Ice Robotics, Ltd.) and GPS collars (Data Carter Ltd.) to measure the total distance
walked while eating, the number of ES (a.k.a. feeding station) per min (ESR), and calculate the eating step length (ESL, m).
Eating behavior was defined as cows with heads down and engaged in acquiring herbage into the mouth. The ESL was calculated
by dividing the length of a straight line by the number of steps taken on it. This line was determined by two GPS data points
while cows were eating. Bite rate (BR, bites per min) and eating time (ET, min) were determined by six trained observers,
randomly assigned to each group and focal cows. Bite rate and ESR were determined at four fixed times during the grazing
session for all cows over three consecutive weeks. Eating time was determined every 2 min by scan-sampling, also during the
grazing session for all cows over the same three consecutive weeks. The area of feeding stations was calculated as the product of
ESL and cows neck length. Bites per ES (BES), velocity of walking while eating (VWE, m per min), total ES (TES), total
distance walked while eating (DWE, m), and area explored while eating (AE = area of feeding station  TES) were calculated
from measurements. Treatment effect was analysed using mixed models , including group of cow as a random effect. Group of
cows was the experimental unit. GenStat 11.1 was used for statistical analysis. Results are presented in Table 1. Restricting time
at pasture leads to differential food deprivation and consequently different stimuli to eat at the time to face the pasture. The
results of this experiment demonstrated that dairy cows reacted to such stimuli mainly by changing locomotion behavior (step
length, velocity of walking while eating and distance walked while eating) and eating time, which led to considerable changes in
the net area explored while eating (AE). At a constant area allocation per cow (e.g. 100 m2 in the present experiment) and
assuming that bite depth is proportional to the sward surface height, the evaluation of feeding stations dynamics permits a general
estimation of herbage depletion either in mass or proportion or the sward surface height. This study of foraging behavior
contributes to the understanding of the adjustment of eating and moving behavior of cattle to hunger.
Feeding station behavior of dairy cows during the first grazing session of the day as affected by restricting time at
pasture, either 4 h after each milking (2 × 4), 8 h between milkings (1 × 8) or none (Control).
Treatments
18 24
Control SED
P value
BR (bites/min)
52.1
54.9
46.5
0.54
0.001
ESR (ES/min)
3.3
3.4
2.9
0.32
0.757
BES (bites/eating step) 13.9
14.9
15.0
1.47
0.328
ESL (m)
0.99
0.73
0.82
0.09
0.034
VWE (m/min)
3.3
2.5
2.7
0.26
0.041
ET (min)
195.6 164.4
141.4
5.04
0.004
TES
646.1 544.9
401.2
39.68 0.019
DWE (m)
644.2 389.3
334.3
31.96 0.004
AE (m2)
516.9 311.4
267.4
25.57 0.004
Key words: cattle, pasture restriction, foraging behavior
191
2 SPATIAL AND TEMPORAL FREE-RANGING COW BEHAVIOUR PRE AND POST-WEANING.
D.M. Anderson*1, C. Winters1, M. Doniec2, C. Detweiler2, D. Rus2, and B. Nolen1, 1USDA-ARS Jornada
Experimental Range, Las Cruces, NM, USA, 2Massachusetts Institute of Technology, Cambridge, MA, USA.
Global positioning system (GPS) technology can be used to study free-ranging cow behaviors. GPS equipment was
deployed on each of ten cows ranging in age between 3 and 15 years to compare and contrast mean ± standard errors
for pre- and post-weaning travel (m·h-1) in two similar ( 433 ha) arid rangeland paddocks. Data were collected at a
rate of one position fix per second between 12 March and 8 April 2009 for a total of 8.7 million raw GPS fixes
across both paddocks. In addition, all 10 instrumented cows, 5 per paddock, were observed for a total of 20 h and
their behaviors were recorded on a minute by minute basis during daylight observations among 13 d. The
observational data were used to characterize rate of travel (m·min-1) into walking, foraging and stationary behaviors.
Initially the observed data were merged with the GPS data. Next the merged data were examined to classify the GPS
data by path speed (m·s-1). The path speed associated with "stationary" was due to the inherent variability within the
uncorrected GPS data used in this study. Intervals of 30 s, 60 s, 120 s and 180 s were evaluated to determine the
optimum path speed sampling period to discriminate among walking, foraging and stationary behaviors. Regardless
of interval chosen, three distinct peaks were observed. Within the ranges evaluated longer sampling intervals (180 s)
appeared to show better peak definition especially for those peaks associated with foraging (0.10 m·s-1) and
"stationary" (0.03 m·s-1) behaviors. Shorter sampling periods (30 s) produced smother curves having less distinct
peaks, probably as a result of more samples. The fastest movement had a peak at 1.00 m·s-1 and corresponded to
periods when the cows were observed to be traveling between points on the landscape. A 1.0 m·s-1 rate of travel is
roughly 2.2 miles per hour (mph), a speed used to manually gather these cows. Subsequent analyses were performed
using 60 s sampling intervals as a compromise for well defined peaks and troughs, smoothness of the curve, and
relatively high frequency counts at the high speed end of the distribution.
Overall, mean cow travel increased post-weaning over 5 d in Paddock 10B from 1,428 ± 92.6 m·h-1 to 1,955 ± 143.4
m·h-1 and over 8 d post-weaning in Paddock 14A from 1,166 ± 105.8 m·h-1 to 1,509 ± 92.0 m·h-1. Mean travel in the
two paddocks during foraging both pre- and post-weaning varied within a day but not in an identical manner even
though the two paddocks were similar in size and topography. Among four 6 h time intervals foraging travel
decreased following weaning between 6 am and noon and between 6 pm and midnight in both paddocks while travel
associated with foraging increased between noon and 6 pm in both padocks. Foraging travel between midnight and 6
am decreased in Paddock 10 following weaning (64 ± 5.6 m·h-1 vs 42 ± 4.4 m·h-1) but increased in Paddock 14A (24
± 6.6 m·h-1 vs 60 ± 7.3 m·h-1) during this same time interval following weaning. Differences in foraging travel
between midnight and 6 am in the two paddocks may have resulted from several factors including: the number of
non-instrumented cows being different in the two paddocks, different weaning dates 13 d apart, and a different
spatial distribution of cows within each paddock. Furthermore, GPS data were lost possibly due to battery failure.
Only one 24 h period of data were lost from one cow in Paddock 10 B (post-weaning) compared to loss of GPS data
from two and three of the five cows for 24 h pre- and post-weaning, respectively, in Paddock 14A. Further data
analysis evaluating tortuosity of travel (a metric that relates path speed or the summation of distances between each
of 60 positions within a minute to displacement speed the distance between only the first position and the 60th
position within a minute) appears promising to further characterize free-ranging cow travel.
Key words: range animal ecology, beef cow travel, GPS tracking
192
3 EFFECT OF SUPPLEMENT ENERGY LEVEL ON FATTY ACID PROFILES AND MEAT QUALITY
OF STEERS FINISHED ON WINTER ANNUAL PASTURE. Harold Ospina Patino* and Fabio Schuler
Medeiros, Universidade Federal de Rio Grande do Sul, Porto Alegre, RS, Brazil.
Twenty four Aberdeen Angus Charolais steers were used in a completely randomized design to evaluate the effect
of supplemental energy levels on performance, meat quality and fatty acid profile of intramuscular fat. The
experimental treatments were levels of feeding (0, 0.4, 0.8 and 1.2 kg DM/100 kg BW/day) of a corn based
supplement in a winter pasture of annual ryegrass (Lolium multiflorum L.) and oats (Avena strigosa Schreb)
managed for warrant higher forage allowance (15 kg DM/100 kg BW/day). Animals were supplemented daily
(14:00 – 16:00 h) in individual pens and slaughtered when they achieved 4.5 mm of fat cover on the rump point
(ultrasound evaluation). No differences were observed in fat deposition, measured in live animals with ultrasound at
the end of the performance period, and in live weight gain, which had average values of 3.9 mm and 1.54 kg/day,
respectively (P>.05). Increasing levels of energy supplementation did not influence tenderness (3.36 kg/cm2,
marbling (5.63 points), pH (5.64), lipid concentration (9.38%), Color a (15.25), Color b (12.84) in the Longissimus
dorsi samples (P>.05). Sample lightness (L) were linearly increased with increasing levels of supplementation on
pasture (P=.05). Supplementation level linearly decreased n-3 FA ( P<.001) and linearly increased n-6:n-3
ratio(P=.0011). Concentration of CLA decreased with increased supplementation level (P<.001). Fattening animals
on winter pastures using increasing levels of energy supplementation does not result in differences in meat quality
but results in changes in fatty acids profile of intramuscular fat.
Key words: winter pasture, fatty acid profile, energy supplementation
193
4 USE OF N-ALKANES AND LONG CHAIN ALCOHOLS TO ESTIMATE FORAGE INTAKE AND
DIET COMPOSITION OF CATTLE GRAZING TWO FORAGE SPECIES. H.T. Boland*1 and G. Scaglia2,
1
Prairie Research Unit, Mississippi State University, Prairie, MS, USA, 2Iberia Research Station, Louisiana State
University, Agricultural Center, Jeanerette, LA, USA.
Estimation of dry matter intake (DMI) and diet composition of livestock is a valuable area of study due to its impact
on the health, nutritional status, and productivity of animals. It also helps to increase our knowledge of how foraging
behavior can impact biodiversity of plant communities. The use of long chain saturated hydrocarbons (n-alkanes)
has been shown to be a non-invasive and accurate method to estimate DMI of grazing livestock. In recent years,
other components of the cuticular wax of plants, such as long chain alcohols (LCOH) have been explored for use in
the estimation of diet composition of animals grazing mixed diets. This technique is possible because the patterns of
n-alkane and LCOH concentrations in forage species differ, particularly between grasses and legumes. When
animals must search for a preferred forage within a mixed sward, the animal's ability to eat what it wants when it
wants becomes constrained. Within this constraint, diet preference of an animal cannot be evaluated. However, this
constraint can be minimized when forages are offered as monocultures in adjacent areas. The objectives of the
present study were: 1) to determine the DMI of beef steers grazing adjacent monocultures of tall fescue [Lolium
arundinaceum (Schreb.) Darbysh. = Festuca arundinacea Schreb.] and alfalfa (Medicago sativa L. ssp. sativa)
across two years using naturally occurring and dosed n-alkanes, and 2) to determine diet composition using nalkanes and LCOH. Twenty-four Angus-crossbred steers (12 steers yr-1, Yr 1 initial BW = 392 ± 8 kg, Yr 2 initial
BW = 323 ± 9 kg; 16 months old) were used. Each steer was dosed with an intra-ruminal n-alkane (C32 and C36)
controlled release fecal marker capsule. After 7 days, forage and fecal samples were collected twice daily for the
next 7 days. Purified n-alkane and LCOH extracts from those samples were quantified by gas chromatography. Diet
composition was then estimated using a non-negative least squares procedure ('EatWhat' software program). Data
were also analyzed using SAS Proc MIXED (ver. 9.1; Cary, NC). The model included yr, forage type, and their
interaction for analysis of marker profiles and nutritive value. The model inclued yr, day, and their interaction for
the analysis of diet composition and DMI. The repeated measure was day within yr. The n-alkane profiles of tall
fescue and alfalfa were similar to those previously reported and there were effects of yr (P  0.07) for the majority
of chain lengths within forage species. Higher concentrations of n-alkanes in Yr 2 may have been due to high air
temperatures and drought conditions in that year. The LCOH profile of tall fescue was also similar to previously
reported data; however information on the LCOH profile of alfalfa had not been reported at the time of this analysis.
There were effects of yr (P  0.09) on LCOH profile of forages however the pattern of effect (whether an increase or
decrease in concentration) varied between forage species and chain lengths. The use of LCOH added additional
characterization of the forages, but in this case diet composition estimates were not different (P  0.22) than when
estimated using four different n-alkanes. Diet composition estimation indicated that steers consumed similar (P =
0.13) diets of 79 and 70% alfalfa in Yr 1 and Yr 2, respectively. Dry matter intake differed (P = 0.002) between
years with values being lower (4.5 kg d-1, 1.4% BW) in Yr 2 than Yr 1 (9.4 kg d-1, 2.3% BW) which may be
explained by differences in weather conditions. Results suggest that if n-alkane profiles of the forages being grazed
are different, the additional analysis needed to determine LCOH concentrations may not be necessary. However, in
pastures with greater plant species diversity, the use of LCOH or other additional plant-wax markers would most
likely be required to estimate diet composition.
Key words: n-alkanes, long chain alcohols, diet composition
194
5 PROTEIN AND CARBOHYDRATE DEGRADATION CHARACTERISTICS AND RATIOS OF
ANTHOCYANIDIN-ACCUMULATING LC-ALFALFA AND ALFALFA SELECTED FOR A LOW
INITIAL RATE OF DEGRADATION IN GRAZING CATTLE. A Jonker*1,2, M Gruber2, Y Wang3, and P Yu1,
1
Department of Animal and Poultry Science, University of Saskatchewan, Saskatoon, SK, Canada, 2Saskatoon
Research Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada, 3Lethbridge Research Centre,
Agriculture and Agri-Food Canada, Lethbridge, AB, Canada.
Alfalfa suffers from the disadvantage of having an excessive rapid protein (CP) degradation which leads to poor
protein efficiency and bloat in grazing cattle. Accumulation of monomeric and polymeric anthocyanidins in alfalfa
forage or increased alfalfa plant cell wall content have been found to reduce protein degradation in the rumen. The
objective of this study was to determine CP and carbohydrates (CHO) degradation and synchronization in the rumen
of newly developed anthocyanidin-accumulating T1Lc-alfalfa populations and an alfalfa variety called AC
Grazeland that was selected for a low initial rate of degradation (LIRD). Three winter hardy alfalfa varieties Beaver,
Rambler and Rangelander were crossed with three T0 Lc-alfalfa genotypes to generate progenies T1BeavLc1,
T1RambLc3 and T1RangLc4, respectively. Purple T1Lc-progeny from each of these three populations were
compared with LIRD-alfalfa, which was previously found to reduce the incidence of bloat in grazing cattle. Alfalfa
samples were collected at a vegetative pre-bud stage during the growing season of 2008 (AAFC, Saskatoon, SK,
Canada). An in situ trial was performed in duplicate runs and results inserted into an exponential model to determine
ruminal degradation characteristics and synchronization. The results were analyzed in a completely randomized
design using Proc Mixed of SAS. The T1Lc-alfalfa populations had an average anthocyanidin concentration of
163.4 µg/g DM while LIRD-alfalfa did not accumulate anthocyanidin. Undegradable CHO and CHO residues at 36
and 72 h of ruminal incubation were lower (P<0.05) in T1Lc-alfalfa compared with LIRD-alfalfa. Protein residue at
36 and 72 h of ruminal incubation was lower (P<0.05) in T1Lc-alfalfa compared with LIRD-alfalfa. Total rumen
degradable protein tended to be higher (P<0.10) in T1Lc-alfalfa compared with LIRD-alfalfa (225.6 vs. 213.8 g/kg
DM). Total rumen degradable N to CHO ratio was lower (P<0.05) in T1RambLc3 compared with T1BeavLc1 which
had and lower (P<0.05) N to CHO ratio compared with T1RangLc4 (110.2 vs. 115.2 vs. 118.8 g/kg) while LIRDalfalfa was not (P>0.05) different from T1BeavLc1 and T1RambLc3. Hourly rumen degradable N to CHO ratio was
similar (P>0.05) between the four populations with mean values of 197.9, 110.3, 87.3 and 59.9 g/kg after 1, 2, 6 and
12 h of incubation, respectively. In conclusion, T1Lc-alfalfa accumulated anthocyanidin and had similar degradation
profiles and ratios compared to LIRD-alfalfa which was earlier found to be bloat reduced in grazing cattle. Thus, a
further increase in monomeric and polymeric anthocyanidins accumulation in alfalfa is required in order to develop
an alfalfa variety with superior nutritional and bloat preventing characteristics compared to currently available
varieties.
Key words: anthocyanidin-accumulating alfalfa, in situ ruminal degradation characteristics, grazing cattle
195
6 GROWING AND FATTENING CATTLE ON THE NORTHERN GREAT PLAINS WITH
RUMINALLY-PROTECTED FLAXSEED. S. L. Kronberg*1, E. J. Murphy2, R. J. Maddock3, and E. J.
Scholljegerdes1, 1USDA-ARS, Northern Great Plains Research Laboratory, Mandan, North Dakota, USA,
2
Departments of Pharmacology, Physiology and Therapeutics, and Chemistry, University of North Dakota, Grand
Forks, North Dakota, USA, 3Department of Animal Sciences, North Dakota State University, Fargo, North Dakota,
USA.
Feeding ruminally-protected flaxseed may help minimize time in a feedlot and improve profitability and
sustainability of beef production and healthfulness of beef. Twelve yearling steers grazed together on prairie
grassland from June to early August then on August 16th were weighed (452.8 ± 15.5 kg) and randomly divided into
two groups: l) received ground flaxseed (908 g/steer/d; FLAX; n = 6); 2) received protected flaxseed at the same rate
(PFLAX; n = 6). Steers were individually fed the treatments. Steers grazed together on Proso millet from August
16th to September 26th, then on irrigated smooth brome-dominated pasture until October 10th. From October 11th
to November 14th they were penned and each morning individually fed their treatments along with rolled barley
(6.36 kg/steer/d for final 27 d of trial) then given free-choice access to prairie grass hay (all steers together) for
remainder of day. Steers were weighed at end of the grazing period and at end of the penned period. Growth rates
and carcasses were evaluated. Samples of LM muscle were collected, muscle fat was separated into neutral and
polar lipids, and fatty acid (FA) profiles determined with GLC. There was no difference between the treatment
groups for ADG while grazing (P = 0.86; 1.38 kg/d), nor while penned (P = 0.46; 1.76 kg/d). No differences
between groups were observed for HCW (P = 0.37; 322.8 kg), USDA yield grade (P = 0.25; 2.4), fat thickness over
12th rib (P = 0.77; 7.4 mm), or marbling score (P = 0.42; 394). However, 67% of PFLAX steers graded USDA High
Select or greater quality grade; whereas, only 33% of FLAX steers did this. No differences (P  0.41) were observed
between groups for levels (mole %) of 18:3n-3, 20:5n-3, 22:5n-3 or total n-3 FA in the polar lipid fraction of LM
muscle, but levels of 16:0 and total saturated FA were higher (P  0.02) in the neutral lipid fraction of LM muscle of
FLAX, while levels of total unsaturated FA and monounsaturated FA were higher (P  0.01) in the neutral lipid
fraction of LM muscle of PFLAX. Feeding treated flaxseed to yearling steers did not increase their growth rate or
levels of n-3 FA in LM muscle, but appeared to improve USDA quality grade and decreased levels of total saturated
FA while increasing levels of total unsaturated FA and monounsaturated FA in the neutral lipid fraction of LM
muscle.
Key words: fatty acids, Omega-3 fatty acids, beef
196
7 GRASS INTAKE OF GRAZING DAIRY COWS USING THE N-ALKANE TECHNIQUE. A. van den
Pol-van Dasselaar*1 and A. Hensen2, 1Wageningen UR Livestock Research, Lelystad, the Netherlands, 2Energy
research Centre of the Netherlands, Petten, the Netherlands.
Grass intake of grazing dairy cows can be estimated using the n-alkane technique. N-alkanes are saturated, aliphatic
hydrocarbons with lengths varying from 21 to 37 carbon atoms. They are part of the cuticular wax of plant leaves.
When synthetic alkanes are added to the diet in a fixed dose, the herbage intake in the field can be estimated using
the ratio between natural and synthetic alkanes in the faeces of grazing animals.
Two experiments were carried out. A first experiment was carried out to investigate the effectiveness of the n-alkane
technique for rations of grass, maize silage and concentrates. The n-alkane technique has shown to deliver reliable
estimates of dry matter (DM) intake during grazing, but no information was available on the recovery of diets of
grass supplemented with maize silage. In the Netherlands, rations of grass and maize silage are common. A second
experiment was carried out to study the relationship between grass intake, as estimated by the n-alkane technique,
and methane emissions of dairy cows in a grazing situation with supplemental feeding.
The first experiment was carried out indoors with four productive dairy cows. Two rations were compared: a ration
of fresh grass and concentrates and a ration of fresh grass, maize silage and concentrates. The concentrates were
enriched with the synthetic alkanes C32 and C36. Feeds and manure were analysed as to the relevant alkanes. Maize
silage showed much lower n-alkane levels than grass (3 to 15 times lower). Individual alkane recoveries of C25, C27,
C29, C31, C32, C33, C35 and C36 were estimated by total faecal collection. The results showed that the ration may affect
recoveries of n-alkanes. The recoveries of the shorter alkanes (especially C25 and C27) decreased significantly by
supplementation of maize silage (P<0.1). Recoveries of alkanes like C32 and C33 were not significantly affected by
the inclusion of maize silage in the ration. For a reliable application of the n-alkane technique, the recovery of the
individual alkanes is crucial. Using the shorter alkanes will not give reliable estimates for intake of rations of grass
and maize silage.
The second experiment was carried out in the field. We used C32 and C33 to measure herbage intake. There were
three measurement periods in two consecutive years. In the first year of the experiment, five productive dairy cows
were used. In the second year, six productive dairy cows were used. The animals stayed indoors during night and
grazed during the day. Their milk production was on average 25 kg milk per day. Indoors they were fed a ration of 5
kg DM maize silage per day and 3 kg DM concentrates per day. The concentrates were enriched with a C32 alkane
marker. Grass intake during grazing was estimated using the n-alkane technique using the daily dose of C32 alkanes
from the concentrates, the concentration of natural occurring C33 alkanes in the grazed grass and the ration C32:C33 in
the faeces. Furthermore, methane emissions of the grazing dairy cows were measured using the sulphur hexafluoride
(SF6) tracer technique. Grass intake per animal varied between 8 and 13 kg DM per day. Grass intake was positively
related to methane emissions. The base-line of methane emissions differed between the measurement periods. On
average, however, per kg additional DM intake an extra 35 g CH4 per animal per day was emitted (r2=0.6-0.7). The
animals with a high DM intake generally also produced more milk. This led to less methane emitted per kg milk for
animals with a high DM intake compared to animals with a low DM intake. Other research confirms that high
productive dairy cows emit less methane per kg milk than low productive dairy cows.
We conclude that using the n-alkane technique with a combination of C32 and C33 is suitable to provide reliable
estimates of fresh grass intake in situations with supplemental feeding. Using this n-alkane technique, we found a
positive response between DM intake and methane emissions of grazing dairy cows.
Key words: grass intake, n-alkane technique, grazing
197
8
GRAZING IN EUROPE. A. van den Pol-van Dasselaar*, Wageningen UR Livestock Research, Lelystad, the Netherland.
Throughout Europe, forage is the main feed for dairy cattle. Grass is fed either fresh - predominantly through grazing - or in a
preserved form as silage or hay. Long-term data of grazing in Europe are hardly available.
The objective of this study was to provide insight in the state-of-the-art with respect to grazing in Europe. In 2010, a survey
among scientists of several countries in Europe was carried out. Leading scientists related to the Working Group Grazing of the
European Grassland Federation were asked to provide data on grazing. In most countries, statistical data on this subject were not
available. In those cases, the scientists were asked for their expert judgement. The survey indicated that zero-grazing becomes
more and more popular. There is a large variation in percentage of grazing dairy cows, both between and within countries, e.g.:
Finland, 80% grazing, slow decrease, Sweden, 100% grazing, defined by law, Denmark, 35-45% grazing, strong decrease, the
Netherlands grazing, 79%, decrease, Belgium, 90% grazing, decrease, Luxembourg, 85% grazing, strong decrease, NW Spain,
15% grazing, slow increase, Slovenia, 25% grazing, stable or lower, Greece, 15% grazing, slow increase.
Grazing systems used differ between countries. The survey revealed that rotational grazing is practised the most often. When
grazing is practised, cows graze mainly during the day. During the night, cows are indoors and get supplemental feeding. The
number of hours grazed per year and per day is decreasing. In some countries (e.g. the Netherlands, Denmark, Belgium) a new
development in grazing is the mobile automatic milking system, which is able to milk the animals in the field. This system is
currently being tested in research projects.
In Northern Europe, grazing is practised more often than in Southern Europe. However, also in Northern Europe the percentage
grazing is decreasing rapidly. Is this a matter of concern? The grazing system affects various aspects like grassland productivity,
animal welfare, environment, economy, labour and even society. Grazing has advantages and disadvantages (see Table). The
importance attached to the various effects of grazing is very personal. Restricted grazing scores well on the whole.
We expect the trend of less grazing in Europe to continue. There are economical, practical and personal motives behind this. In
the end, the personal preferences of the farmer determine the grazing system used. The decline in the popularity of grazing is
supported by current trends in livestock farming in Europe. Average herd sizes increased during the last years and the number of
automatic milking systems increased. Grazing of large herds is difficult to manage. And even though grazing in combination with
an automatic milking system is possible, it is experienced as difficult. The average milk production per cow increased and
farmers with high yielding cattle like to control rations. Again, control of rations is more difficult in grazing situations. In
countries where grass growth is delayed in summer, another reason for less grazing may be the uncertainty whether grass growth
suffices for the demand of grazing cows. Finally, reasons for zero-grazing may be better grassland utilisation, the need to reduce
mineral losses and ease of labour. Especially the latter is an important factor for many farmers. In most countries, there are hardly
any tools available to support farmers in grazing.
In conclusion, a survey among scientists in Europe showed that the number of grazing dairy cows is rapidly decreasing. Simple
and easy-to-use grazing systems and practical management tools have to be developed to support farmers in grazing.
The effect of grazing (unrestricted grazing, restricted grazing, zero-grazing) on various aspects. The score ranges from - to ++, with ++ signifying that the system concerned scores positive for the point in question, e.g. high health, low losses.
Unrestricted Restricted Zero
Grass yield and grass use
-
+
+
Balanced diet
-
+/-
++
Natural behaviour
++
++
+
Animal health
++
+
+/-
Nitrate leaching, N2O emission -
+
++
N losses
-
+
++
P losses
-
+/-
+
Ammonia volatilisation
++
+
+/-
Energy use, CH4 emission
+
-
--
Fatty acid composition of milk ++
+
+/-
Labour/hours work per year
++
+
+
Economics
+
+
-
Image of dairy farming
++
+
-
Key words: grazing, Europe, grazing systems
198
9 PATHWAY FOR THE ELIMINATION OF MELAMINE IN LACTATING DAIRY COWS. Junshi
Shen, Jiaqi Wang*, Hongyang Wei, Dengpan Bu, and Peng Sun, State Key Laboratory of Animal Nutrition, Institute
of Animal Science, Chinese Academy of Agricultural Sciences, Institute of Animal Science, Chinese Academy of
Agricultural Sciences, Beijing, China.
Melamine (MEL) might be degraded into cyanuric acid (CYA) and some other analogues by the rumen
microorganism. The objective of this study was to investigate the pathway for the elimination of MEL in dairy cows.
Four late-lactating dairy cows (Body weight = 564 ± 17 kg, Days in milk = 265 ± 14d, Milk yield = 13.2 ± 2.16kg)
fitted with ruminal cannulas were dosed with MEL (purity  99.5%) at 800 mg/d per cow, divided into two equal
daily doses. The trial lasted for 20 d. The first 13 d was preliminary period, followed by a 7-d sample collecting
period, during which feed intake and total output of feces, urine, and milk were measured from d 14 to 16. Blood
samples were collected at 0 (pre-dose), 4, and 8 h after morning feeding on d 17 and 18, and rumen fluid were
collected at 0 (pre-dose) 1, 2, 4, and 8 h after morning feeding on d 19 and 20. Plasma MEL or CYA concentration
was analyzed using the GLM procedures of SAS system. The percentage of MEL excreted through milk, urine, or
feces was calculated as the ratio between the mean output of MEL in milk, urine, or feces and the MEL administered
per day, separately for each cow. Method of Liquid chromatography and tandem mass spectrometry (LC-MS/MS)
was utilized to determine MEL and CYA simultaneously. Before the trial started, no MEL or CYA was detected in
samples of feed, milk, plasma, urine and feces. MEL concentration in rumen fluid (Y, mg/L) decreased
exponentially after the morning feeding (X, h) (Y = 3.85591e -X/9.25674 + 1.35924, R 2 = 0.99), but no CYA was
detected. Plasma MEL concentration was relatively stable at the three different sampling times (P > 0.05). The
percentage of MEL excreted through milk, urine and feces were 0.48 ± 0.06, 10.98 ± 3.88 and 44.07 ± 10.79%,
respectively. Therefore, 44.47 ± 7.98% of ingested MEL was degraded in the rumen. This speculation was
conformed by the fact that CYA was also detected in plasma, urine and feces. Whether ammelide and ammeline
were excreted commonly with MEL needs further study. The results of the present work implied that the metabolism
of MEL in dairy cows was different from monogastric animals. High percentage of MEL may be degraded gradually
by rumen microorganism to produce its analogues (CYA). The digestive tract and kidney are two main pathways for
the excretion of MEL through feces and urine.
Key words: melamine, cyanuric acid, dairy cow
199
10 COMPARE THE DIGESTIBILITY OF RUMEN UNDEGRADABLE PROTEIN OF CORN DDGS
AND DDG IN INTESTINE OF CHINESE HOLSTEIN DAIRY COWS USING MOBILE NYLON BAG
TECHNIQUE. Z.H. Yan, J.Q. Wang*, D.P. Bu, H.Y. Wei, L.Y. Zhou, and P. Sun, State Key Laboratory of Animal
Nutrition, Institute of Animal Science, Chinese Academy of Agriculture Sciences., Beijing, China.
As the bioethanol industry continues to expand, Corn dried distillers grains with solubles (DDGS) and corn dried
distillers grains (DDG) become increasing available as animal feed. Three DDGS samples and three DDG samples
were collected from ethanol plants in Middle Eastern China. Dry matter and CP disappearance in rumen and small
intestine were measured in four Chinese dairy cows (638±61kg) in dry period using a mobile nylon bag technique.
Samples of each DDG and DDGS were incubated for 16 h in the rumen; the residues from these bags were
transferred to mobile bags. All bags were placed in a 0.1 N HCl solution containing pepsin (1 g/L; Sigma P7000;
Sigma-Aldrich, St Louis, MO, USA), and incubated for 1 h at 39°C to mimic abomasal digestion, and then used for
determination of intestinal digestibility in situ. Ruminal degradability and intestine digestibility of DM and CP were
analyzed using a randomized complete block design with the MIXED procedure of SAS (SAS Institute, 2001).
Ruminal degradability of DM and CP for DDGS and DDG did not differed after 16 h ruminal incubation (DM, 42.2
and 49.9% for DDG and DDGS, P=0.12; CP, 31.0 and 33.6% for DDGS and DDG, P=0.59). The ruminal
degradation of DM and CP for DDG or DDGS differed between plants (P <0.0001). Ruminal degradation of DM
and CP were greater for DDGS1 and DDGS3 compared with DDGS2. Intestine digestibility of DM was greater for
DDGS compared with DDG (P=0.001). The intestine digestibility of CP was almost the same for DDG and DDGS
(P =0.99). And intestine digestibility of DM and CP for DDG or DDGS differed significantly between plants. Result
of this study showed that ruminal degradation differed larger for DDG samples, whereas intestinal digestibility
changed more for DDGS. In concern with the nutritional characteristic of DDGS or DDG differed largely in various
plants, it would be effective to develop a quick and easy test technique to determine the feed value of the
DDGS or DDG that could be used by the farmer.
Table 1 Ingredient and nutrient composition of the diets fed to cows during the in situ experiment
Ingredients
% DM Nutrient composition
Alfalfa hay
16
NEL 1 (Mcal/kg DM) 1.57
Chinese wildrye
14
CP, %
17.89
Corn silage
20
NDF, %
36.77
Corn ground
23
ADF, %
22.13
Wheat bran
6
EE, %
3.1
Soybean meal
10.5
Ca, %
0.84
Cotton seed meal
4
P, %
0.48
Rapeseed meal
4
Calcium carbonate
1.2
Calcium phosphate, dibasic 0.3
NaCl
Premix
0.5
2
0.5
1
NEL was estimated from the feeding standards of dairy cattle, China NongYe HangYe Biaozhun/Tuijian-34 (China
NY/T34, 2004). 2 Premix contained vitamin A ＞700,000 UI/kg, vitamin D3 ＞120,000UI, vitamin E＞2100mg, Fe
1750mg/kg, Cu 1600mg/kg, Zn 10000mg/kg, Mn 3500mg/kg, Se 42mg/kg, I 84mg/kg, and Co 42mg/kg
Key words: DDGS(DDG), ruminal degradation, intestinal digestibility
200
11 EFFECT OF SITE AND SOURCE OF LYSINE SUPPLEMENTATION ON NITROGEN
METABOLISM IN BEEF CATTLE. Y.Q. Guo, Y.D. Zhang, J.Q. Wang*, D.P. Bu, K.L. Liu, and T. Hu, State
Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agriculture Sciences., Beijing,
China.
Four Limousin-cross cows (BW=582±42 kg) fitted with both duodenal and ruminant cannulas were used to
investigate the effect of ruminal supplementation of Lysine (15 g/d, RL) or rumen protected Lysine (26 g/d,
protected by polyoxylate IV and hydrogenated vegetable tallow containing of Lysine 15g, RPL) and duodenum
infusion of Lysine (15 g/d, DL) on N utilization and plasma amino acids in a 44 Latin Square design with each
period lasting 7 d. The group of basal diets served as the control. Blood samples were collected on d 7, and ruminal
fluid and feces, urine were collected for 3 d from d 4 to d 6 per 7 d period. All statistical analyses were performed
using PROC GLM of SAS. The results showed that supplementation with Lysine did not affect N intake (P>0.05),
regardless of supplementation site or source. Higher ruminal NH3-N concentration (6.54 mg/dl vs. 5.19 mg/dl,
P=0.11), plasma urea N concentration (7.16 mg/l vs. 5.36 mg/l, P=0.02), urinary N excretion (6.51 g/d vs. 6.36 g/d,
P=0.64), N retention efficiency (33.41 vs 23.66, P=0.42, g/d as well as percentage of absorbed N) and less fecal N
excretion (38.34 g/d vs. 42.40 g/d, P=0.18) were observed when compared with the control. RPL and DL treatments
increased plasma Ala, Pro, Tyr, Leu, Phe, Arg and essential amino acids concentrations (P<0.05). The
concentrations of Lysine were improved in RPL (11.18 ug/ml) and DL (16.97 ug/ml) vs. RL (8.00 ug/ml) and
control (7.31 ug/ml, P=0.0001). The ratio of RPL Lysine concentration to DL Lysine concentration was 66.1% in
plasma. The results suggest that supplementation of Lysine could improve N utilization efficiency to some extent
and rumen addition of protected Lysine is an effective way to supply Lysine for Limousin-cross cows.
Key words: lysine, nitrogen metabolism, beef cattle
201
12 SUPPLEMENTATION WITH PROTEIN AND VARIOUS FEED ADDITIVES IMPROVES IN SITU
DEGRADATION CHARACTERISTICS OF BERMUDAGRASS AND BUFFELGRASS IN STEERS
CONSUMING A LOW-QUALITY FORAGE DIET. K.C. McCuistion*1, B.D. Lambert2, T.A. Wickersham3,
R.O. Dittmar3, L. Wiley1, and L. Dobson3, 1Texas A&M University-Kingsville, Kingsville, TX, U.S., 2Tarleton State
University and Texas AgriLife Research, Stephenville, TX, U.S., 3Texas A&M University, College Station, TX, U.S.
Ruminant animals face periods of nutrient restriction due to changes in forage quality and quantity often the result of
environmental factors including drought. If nutrient inputs through forage quality and intake do not meet animal
requirements, optimal production will not be achieved. Supplementation is a risk management tool that can be used
to overcome nutrient deficiencies. Supplementation also provides a vehicle for delivery of compounds which alter
metabolism and/or improve digestion. The objective of this study was to determine the rate and extent of in situ
disappearance of two forages, Bermudagrass (Cynodon dactylon (L.) Pers.) and Buffelgrass (Pennisetum ciliare (L.)
Link), when incubated ruminally in steers fed a basal diet of low-quality hay. Ten ruminally cannulated Angus steers
(311 ± 28 kg) were used in an incomplete Latin square design. Each steer was randomly assigned to one of five
treatments with two steers per treatment and two treatment periods (n=4). Treatments were: 1) control (C; no
supplement); 2) protein supplement (CSM; cottonseed meal at 2g/kg BW); 3) protein supplement (2g/kg BW CSM)
plus calcium propionate (200 g per d; P); 4) protein supplement (2g/kg BW CSM) and sodium formate (2.5 g per d;
SF); and 5) protein supplement (2g/kg BW CSM) and monensin (200 mg per d; M). Supplements were provided
once daily to individual steers. In order to simulate nutritionally stressed conditions, low-quality old world bluestem
hay (2.7% CP, 82.4% NDF, and 68.0% ADF) was ground to pass through a 75mm  75mm screen and provided ad
libitum. Steers had free access to water and a trace mineral salt. Each period lasted 14 d which consisted of a 10 d
adaptation to treatment followed by the in situ incubation. Both Bermudagrass (12.8 %CP, 66.5% NDF, and 45.0%
ADF) and Buffelgrass (5.7% CP, 73.8% NDF, and 60.3% ADF) were incubated in situ for 0, 2, 4, 8, 16, 24, 48, and
72 h to determine rate and extent of DM disappearance. There was no interaction between treatment and forage
variety (P  0.24). In situ disappearance components included the following fractions: a (soluble fraction), b
(potentially degradable insoluble fraction), k (hourly fractional disappearance of b) and L (discrete time lag before
disappearance of b began). Bermudagrass had a larger a fraction and a faster rate of disappearance for b than
Buffelgrass (P < 0.01; Table 1), as would be expected given its higher nutritive value. When total DM disappearance
was calculated for each treatment, the C had the lowest amount of a fraction whereas SF had the highest, with CSM,
P, and M being intermediate (P = 0.01). Fractional rate of disappearance followed the same trend (P < 0.01),
indicating that all supplements and feed additives had a greater rate of forage disappearance than the C. Results
indicate that protein supplementation, in addition to certain feed additives, can improve DM disappearance of lowand moderate-quality forage when provided to cattle consuming a basal diet of low-quality forage.
Table 1. Rumen in situ DM disappearance characteristics for forage variety and treatment.
a (%)
b (%)
k (/h)
L (h)
Bermudagrass 34.74x
35.74x
0.033x
3.67x
23.58y
51.17y
0.024y
7.78y
18.31a
54.38b
0.013a
7.40
0.028
b
4.06
0.036
b,c
7.36
Forage
Buffelgrass
Treatment
C
26.03
a,b
P
35.23
b,c
M
30.14b,c 41.98a
CSM
SF
36.06
c
46.64
a,b
37.54
a
36.74
a
0.027b
5.89
c
3.91
0.038
1
C = Control; CSM = cottonseed meal protein supplement; P = calcium propionate + CSM; M = monensin + CSM;
SF = sodium formate + CSM. x,yMeans not bearing a common superscript letter differ by (P < 0.05) for forage
variety. a,b,cMeans not bearing a common superscript letter differ by (P < 0.05) for treatment.
Key words: feed additives, low-quality forage, in situ
202
13 CASE STUDY: MOLASSES AS THE PRIMARY ENERGY SUPPLEMENT ON AN ORGANIC
GRAZING DAIRY FARM. K. Hoffman1, L.E. Chase2, and K.J. Soder*3, 1USDA-NRCS, Norwich, NY USA,
2
Cornell University, Ithaca, NY USA, 3USDA-ARS, University Park, PA USA.
Due to increasing organic grain costs, organic dairy farmers are looking for less expensive ingredients. Molasses
seems to be a less expensive source of supplemental energy. Organic dairy farmers inquire about molasses as an
alternative based on farmer testimonials. Research has been conducted on molasses as feed for many decades, much
of it published in the 1950's to 1970's. Today's knowledge of dairy nutrition is much greater, so results of that
research may not be directly applicable. Also, little research was conducted with dairy cows fed a high rate of
molasses as the sole energy supplement, or with dairy cows grazing cool-season pastures in the northeastern USA,
and limited, at best, research is expected in the next few years, but farmers are seeking answers now. Anecdotal
results have been mixed, with some farms reporting success, while others reporting failure. The objective of this
case study was to quantify nutrient intake, milk production and animal performance on an organic dairy farm in
central New York that is currently feeding molasses. Forage quality, nutritional data, and other management
techniques (i.e. pasture management) were collected monthly during monthly farm visits during the 2008 grazing
season. Data was transferred to Microsoft Excel files and summarized. Pasture and supplements samples were
collected during each farm visit and analyzed for protein, fiber, energy, and mineral content. The Cornell Net
Carbohydrate and Protein System (CNCPS) model was used to analyze the diet on a monthly basis. The CNCPS has
the ability to predict milk production based upon descriptive parameters of the animals, environment, activity level,
and the quality of the diet. In this study, it was used to compare actual performance to predicted performance. Body
condition score (BCS) averaged slightly over 2.5 in May (40-60 days in milk). At the lowest point in August, BCS
averaged 2.1 and increased to 2.71 by November. Peak milk production occurred in May at 24 kg per cow, and then
gradually declined through the rest of the study period. Production in November was 11 kg per cow, which is a
persistency rate decline over the 6 month period of 12% per month. However, the rate of decrease in persistency
from late May to early July was greater at 25% per month, likely due to the lower pasture quality during this time.
Also, breeding began in early June, which may have increased their energy requirements slightly. Pregnancy rate
was 95% using natural service, indicating that the cows were in an adequate energy status. Milk components were
very consistent throughout the grazing season, ranging from 4.01% to 5.53%. Milk protein and other solids were
also consistent, with milk protein averaging 3.4% and trending up towards the end of the study as milk production
declined. Further, by November the cows were being fed a fair amount of dry hay and baleage, which would help to
increase the milk fat percentage. The milk urea nitrogen levels were relatively high (16.2 mg/dl average) throughout
the grazing season, as would be expected on a high protein pasture diet with low levels of non-fiber carbohydrates.
Compared with simulated results from the CNCPS, the cows were not as efficient at utilizing the pasture protein as
the model expected. These results raise these questions: 1) does molasses provides a higher level of energy
compared to corn?; and 2) are the model values for molasses used to predict rumen dynamics accurate? The cost of
organic starch sources in comparison to the cost of organic molasses needs to be evaluated both individually and in
combinations of various feeding rates, as interest in the use of molasses is motivated by higher organic grain costs.
This study was continued through the 2009 grazing season, as the farm decided to feed a slightly higher grain rate
due to some cows beginning their lactation in low body condition, the potential for better milk production, and
changes in the feed to milk price ratio. The information gathered will be used to develop recommendations for
successful use of molasses on organic dairies to be disseminated to the industry.
Key words: dairy, molasses, supplementation
203
14 POTENTIAL OF LEGUMES AS SUBSTITUTES FOR NITROGEN FERTILIZER IN SUMMER
STOCKER GRAZING SYSTEMS. R. R. Reuter*1, J. T. Biermacher1, J. K. Rogers1, T. J. Butler1, M. K. Kering1,
J. R. Blanton1, and J. A. Guretzky2, 1The Samuel Roberts Noble Foundation, Inc., Ardmore, OK, USA, 2The
University of Nebraska-Lincoln, Lincoln, NE, USA.
Considerable interest exists among producers concerning the biological and economic impacts of replacing synthetic
nitrogen fertilizer with legumes in bermudagrass pastures in the southern Great Plains. The objective of this
experiment was to compare stocker cattle performance and net returns of two alternative legume systems in
bermudagrass with the conventional practice of applying fertilizer. Therefore, 9, 1.4-ha bermudagrass (Cynodon
dactylon) paddocks were randomly assigned to one of three management systems in a completely random design in
2007. The first management system was nitrogen fertilizer (BG/N; 112 kg/ha actual nitrogen). The second system
was bermudagrass interseeded with a perennial legume [BG/A; alfalfa (Medicago sativa)]. The third system was
bermudagrass interseeded annually with an annual legume mix [BG/C; hairy vetch (Vicia villosa), crimson clover
(Trifolium incarnatum), and arrowleaf clover (Trifolium vesiculosum)]. Legumes were drilled into the bermudagrass
sod in the fall. Alfalfa did not establish successfully the first year, and was replanted in the fall of 2008. These
paddocks were grazed for two years (2008 and 2009) with 238-kg steers stocked at 2.9 steers / ha during the spring
and summer as long as forage availability exceeded 1121 kg/ha. Steers were shrunk in drylot and weighed every 28
d during grazing. Data were analyzed as a mixed model CRD with 3 replications in SAS. The model included a
fixed effect of management system and a random effect of year for all dependent variables. Least squares means of
management systems were separated by least significant difference in the presence of a significant F-test. Average
grazing days were greater for BG/N than for BG/C and BG/A (110, 67, and 76 d, respectively, P < 0.05). Forage
availability (measured by hand clipping every 14 d during grazing) was also greater for BG/N as compared to BG/C
and BG/A (2374, 1745, and 1520 kg/ha respectively, P < 0.05). Forage nutritive value was determined by NIRS
analysis of 14-d, hand-clipped samples. Forage IVDMD percentage was greatest for BG/C and least for BG/N, with
BG/A intermediate (73.7, 69.4, and 71.9, P < 0.05). Further, BG/C CP percentage was greater than either BG/N or
BG/A (16.9, 14.6, and 14.4, P < 0.05). Interestingly, these improvements in forage nutritive value did not translate
into greater steer ADG (0.80, 0.82, and 0.76 kg/d for BG/N, BG/C, and BG/A systems, respectively; P > 0.05).
However, body weight gain / ha favored (P < 0.05) the BG/N system by an average of 74 kg/ha over the legume
systems. Total production cost for the BG/N, BG/C and BG/A systems averaged $188, $215, and $114/ha,
respectively (seed cost in the BG/A system was prorated across an expected 5 year life of the alfalfa stand).
Recurring annual seed costs coupled with relatively low steer gain / ha caused net return for the BG/C system to be
negative in both years. During this study, actual nitrogen fertilizer cost for the BG/N system was $0.99 / kg of actual
nitrogen. The nitrogen cost that would cause the BG/A system to breakeven with the BG/N system was $1.35 / kg.
In 2008, the price of nitrogen fertilizer approached $2.00 / kg in some regions of the southern Plains. Therefore, in
situations of very high nitrogen fertilizer prices, a perennial legume may be economically competitive with synthetic
fertilizer for use in grazed bermudagrass pastures in the southern Great Plains. Total production cost and gain / ha
(largely a function of grazing days) were the primary variables that influenced net return. Legume effects on animal
performance were of relatively minor importance.
Key words: bermudagrass, legume, fertilizer
204
15 EFFECTS OF CONVENTIONAL AND GRASS FED SYSTEM ON CARCASS TRAITS AND
PERFORMANCE OF ANGUS STEERS. G Cruz*, G Acetoze, and H Rossow, Univerisity of California, Davis,
CA USA.
Although the US market for grass finish beef is small, the demand has increased. Beef producers have been working
on different management, nutrition and breeding strategies to increase the sustainability of this system. The
objectives of our study were to quantify differences in daily gain and carcass characteristics for Angus steers fed a
high concentrate diet or only forage to identify steers that would perform well in a grass finish system. Thirty
animals were randomly selected from a local ranch. Fourteen were fed a corn based finishing diet (10.0% CP, 20%
NDF) at the University of California, Davis feedlot and the remaining stayed on a white clover and rye grass (15.8
%CP and 49.8% NDF) pasture. Animals were weighed and ultrasound once a month. For both feedlot and grass
finish groups, cattle were fed until they reached similar quality grade (Se+, Ch-) at time of slaughter using
ultrasound. Differences in measures of performance, ultrasound and carcass merit among steers in different dietary
treatments were determined using the linear procedure in R Programming, and included the fixed effect of dietary
treatments. Mean initial body weight of the two groups were not statistically different with 414.5 kg and 409.7kg
(P=0.8) for feedlot and grass-finish, respectively. As expected, animals at the feedlot had higher average daily gain
(1.35 vs. 0.73 kg/d; P<0.001), rib-eye area gain per day (0.08 vs. 0.03 cm2/d; P<0.05), back fat gain per day (0.004
vs. 0.001 cm/d; P<0.001) and rump fat gain per day (0.005 vs. 0.002 cm/d; P<0.001).The average days on feed for
the feedlot animals were significantly lower than pasture animals (167 vs. 286 days; P<0.001). When comparing
carcass characteristics, feedlot animals had a higher hot carcass weight (404 vs. 352 kg; P<0.001), dressing
percentage (63 vs. 57%; P<0.001), Yield Grade (3.5 vs. 2.9; P<0.001) and Quality Grade (Ch vs. Se+; P<0.001).
Feedlot steers also had a higher back fat (1.19 vs. 0.89 cm; P<0.001) and rump fat (1.39 vs. 1.06 cm; P<0.001), yet
rib eye areas were similar (75.5 vs. 76.3 cm2; P=0.8) which explains the better Quality Grade results. Overall,
feedlot steers gained more, were larger and had more fat cover over a shorter period of time than the forage finished
steers. However, grass finish steers were able to have large rib eye areas and finish on forage if given enough time
and quality forage. Therefore criteria for evaluating carcasses from grass and feedlot finish steers should be
different, and this process will help develop breeding strategies to select animals that will better perform in grass
finish systems.
Key words: beef cattle, grass fed, carcass traits
205
16 ASPECTS OF CARCASS COMPOSITION AND ORGAN WEIGHTS OF ANGUS STEERS
FINISHED ON GRASS AND HIGH GRAIN DIETS. G. Acetoze*, G. D. Cruz, and H. A. Rossow, University of
California, Davis, California, USA.
Interest in grass finished beef has been increasing. However more research is needed to apply grass finishing to
current beef production systems. A common method to predict carcass composition is based on the composition of
9-11th rib section. However, this method was developed with data from grain finished cattle. In addition, metabolic
rate and therefore energy requirements are closely linked to organ sizes of metabolically active organs such as liver,
gut and kidney. The objective of this study is to examine the effects of strictly forage or high grain diets on organ
weights and carcass composition of Angus steers. Carcass composition was estimated using the composition of 911th rib section from the right side, to compare the percentages of muscle, fat and bone of 14 grain-fed and 13
pasture-fed Angus steers. All internal organs were weighed and sampled to compare estimates of basal metabolic
rate among feedlot and grass finish cattle. Steers were slaughtered when their estimated quality grade by ultrasound
was greater than low select. Average yield and quality grades for grass finish steers were 58% and high select, and
63% and average choice for grain finish steers. Pasture was a mix of white clover and ryegrass (15.8% crude
protein, 49.8% NDF) and the feedlot diet was a 90% corn, 10% alfalfa finishing diet (10.0 % crude protein, 20%
NDF) on a dry matter basis. Data were analyzed with the general linear models procedure of SAS (SAS Institute,
2004). Muscle and fat % were significantly different (P< 0.01) among diet groups with means for muscle of 56.84%
and 61.46% and fat of 24.66% and 20.70% for grain finish and grass finish, respectively. Bone % was not
significantly different (P > 0.01) and the means were 14.17% and 15.82% for grain finish and grass finish,
respectively. The following organs were not statistically different (P > 0.01) and their averages were: spleen 1.14 kg
(SE = 0.062) and 1.06 kg (0.041), liver 7.04 kg (0.196) and 6.83 kg (0.166), kidney 0.52 kg (0.016) and 0.55 kg
(0.017), gallbladder 0.41 kg (0.041) and 0.49 kg (0.034), heart 2.66 kg (0.074) and 2.56 kg (0.083), lungs 8.65
(0.425) and 8.83 kg (0.232), for grain finish and grass finish, respectively. These results show that grass and grain
finish diets differ in carcass composition but organ weights are not significantly different.
Key words: grass finish, carcass composition, organ weight
206
17 PERFORMANCE AND EFFICIENCY ON PASTURE OF CATTLE WITH DIVERGENT
PHENOTYPES FOR RESIDUAL FEED INTAKE. T.D.A. Forbes*1, A.D. Aguiar2, L.O. Tedeschi3, F.M.
Rouquette, Jr.4, G.E. Carstens3, and R.D. Randel4, 1Texas AgriLife Research, Uvalde, TX, USA, 2University of
Florida, Gainsville, FL, USA, 3Texas A&M University, College Station, TX, USA, 4Texas AgriLife Research,
Overton, TX, USA.
The objectives were to 1) examine performance and efficiency on pasture of cattle with divergent phenotypes for
residual feed intake (RFI) and to compare RFI obtained under grazing (RFIg) with previously determined RFI
obtained under confinement feeding (RFIc) using Braunveih-sired crossbred steers and Brahman bulls; 2) assess the
feasibility of determining DMI of Brahman bulls on Coastal bermudagrass [Cynodon dactylon (L.) Pers.] pasture
using alkane-marked corn gluten individually-fed via Calan gates; and 3) compare the use of bermudagrass forage
C31 or C33 alkane in the DMI calculation. Braunveih steers (n = 169) were fed a high roughage diet (ME = 2.2
Mcal/kg DM) in Calan gates for 77 d, and RFIc calculated as the residual from the linear regression of DMI on midtest BW0.75 and ADG. Steers were ranked by RFIc, and those with low (LRFI), medium (MRFI) and high (HRFI; n
= 9, respectively) RFI were subsequently placed on annual ryegrass (Lolium multiflorum) pasture for an 84-d
grazing trial. Forage DMI was determined on two separate occasion s using continuous release capsules containing
C32 and C36. Fecal samples were collected twice daily for 10 d, and forage samples collected daily for analysis of
n-alkane concentrations via gas chromatography. Brahman bulls (n=53) were fed a high roughage diet for 70 d in
Calan gates, and bulls with low and high RFIc (n = 8) allotted to 4 replicate Coastal bermudagrass pastures (2 low
and 2 high RFI bulls per pasture) and grazed for 56 d. Bulls were individually fed twice daily with 400 g corn gluten
marked with C32 for four 10-d periods, using Calan gates in pasture. Fecal samples were collected 4 times daily
(0700, 1100, 1500, and 1900 h) for the last 5 d of each period, and forage samples collected over the last 7 d to
determine forage n-alkane concentrations. Braunveih steers with LRFI gained similarly (P = 0.26) to MRFI and
HRFI steers (1.10, 1.06, and 1.00 ± 0.04 kg/d, respectively), and DMI did not differ (P = 0.28; 6.4, 6.4, and 7.0 ±
0.28 kg DM/d). Despite lack of difference in forage DMI, RFIc was moderately correlated (P < 0.05) with RFIg (r =
0.39), and the ratio of forage DMI to ADG was lower (P = 0.001) in LFRI and MRFI than HRFI (5.8, 6.0, and 7.0 ±
0.28 respectively). In the Brahman bull study, four calculations (CALC) were used to estimate DMI: C31 or C33
with adjustment for forage C32 (C31 and C33), and C31 and C33 without adjustment for C32 (C31_0 and C33_0).
Within a CALC, treatments (TRT) were assumed to be either individual or combination of different times of daily
fecal collection. Statistical analyses included the fixed effects of TRT and RFIc, and the random effects of period, d
within period, animal, and pasture. Bulls did not consistently consume all the corn gluten in period 1 which was
dropped from further analysis. Gain did not differ between HRFI and LRFI bulls while grazing. Predicted DMI
(kg/d) using C31 (9.7), C33 (6.4), C31_0 (8.9), and C33_0 (5.9) alone or in combination [C31 and C33 (7.4), or
C31_0 and C33_0 (7.4)] differed (P = 0.011). Morning and afternoon fecal collections differed (P < 0.0010) in
predicting DMI with C31 (9.44), C33 (6.29), C31_0 (8.68), and C33_0 (5.74) for the morning and C31 (10), C33
(6.66), C31_0 (9.20), and C33_0 (6.09) for the afternoon. There were no differences in predicted DMI between
Brahman bulls ranked as efficient or inefficient using any n-alkane CALC (P > 0.90). A nonparametric analysis
indicated that pre-ranking animals for RFI under confinement did not predict (P < 0.0001) similar RFI rankings in
the Brahman bulls. These results suggest that using the combination of Calan gates, alkane-marked corn gluten and
twice daily fecal collection (0700 and 1500 h) it is possible to accurately estimate DMI of cattle grazing Coastal
bermudagrass pastures. Further studies are planned to explore possible explanations for the correlation between
RFIc and RFIg in the Braunveih steer study and the lack thereof in the Brahman bull study.
Key words: cattle, efficiency, residual feed intake
207
18 EFFECTS OF SAND SAGEBRUSH CONTROL ON STOCKER CATTLE PERFORMANCE ON A
SOUTHERN MIXED PRAIRIE COMMUNITY. S. A. Gunter*1, T. L. Springer1, E. Thacker1, and R. L. Gillen2,
1
USDA-ARS, Southern Plains Range Research Station, Woodward, OK USA, 2Western Kansas Agricultural
Research Centers, Kansas State University, Hays, KS USA.
Research on sand sagebrush communities has focused primarily on sagebrush control methods, with minimal
attention to effects on livestock production. To evaluate the effects of sand sagebrush (Artemisia filifolia) control on
cattle production, 15 native pastures (10 to 21 ha each) were selected in 2003 at the Southern Plains Experimental
Range near Ft. Supply, Oklahoma. Five of the pastures had recently (< 5 yr) been sprayed with 2,4
dichlorophenoxyacetic acid to control sand sagebrush, 6 had received chemical treatment before 1994, and the
remaining 4 had received no treatment. The 15 pastures were categorized into 3 discrete sagebrush levels based on
past management: 1) High with no sagebrush control within the last 60 yr, 2) Medium with sagebrush control at
least 9 yr and older, and 3) Low with sagebrush sprayed in 2003 with 1.1 kg of 2,4 dichlorophenoxyacetic acid
(a.i.)/ha. The average sagebrush canopy cover from 2003 to 2008 in the High, Medium, and Low pastures was 26.4
(n = 4), 10.0 (n = 6), and 3.0% (n = 5), respectively, as determined annually by the line-transect method. The major
grass species in the pastures were blue grama (Bouteloua gracilis), sand dropseed (Sporobolus cryptandrus), little
bluestem (Schizachyrium scoparium), sand lovegrass (Eragrostis trichodes) and sand bluestem (Andropogon hallii).
From 2004 to 2008, approximately 106 steers (BW = 202 ± 6.1 kg) were stocked annually in late January and
subsequently removed in mid-August. High pastures were stocked at 47 animal-unit-d/ha and Medium and Low
pastures were stocked at 69 animal-unit-d/ha. Cattle were supplemented with oil seed meal based cubes (48% CP)
from January through mid-April at a rate of 0.68 kg/steer daily; no supplement was fed from April to August.
Mineral blocks (NaCl) were available during the entire trial. Initial BW in January did not differ (P = 0.57) among
treatment (203, 201, and 202 kg for High, Medium, and Low, respectively). Also, BW did not differ (P = 0.76) in
April (236, 235, and 235 kg for High, Medium, and Low, respectively, or at removal in August (P = 0.12; 352, 344,
and 346 kg for High, Medium, and Low, respectively). Average daily gains from January to April did not differ (P =
0.96), but did differ from April to August (P = 0.02). Steers grazing the High pastures gained BW faster (P < 0.01;
1.03 kg/d) than steers grazing Medium pastures (0.96 kg/d) and tended to gain faster (P = 0.08) than steers grazing
Low pastures (0.98 kg/d). Body weight gain per steer during the January to April grazing period did not differ (P =
0.80). However, steers in the High pasture had higher BW gain per steer from April to August (P < 0.01) than steers
in the Medium pastures. Steers in the Medium pastures tended (P = 0.08) to have higher BW gain per steer than the
Low pastures. Even with the differences in BW gain per steer during the summer, the overall BW gain per steer did
not differ (P = 0.14). Body weight gain per hectare was greatest (P < 0.01) for Low pastures (102 kg) compared to
High (67 kg) or Medium (81 kg) pastures; also, Medium pastures were greater (P < 0.01) than High pastures. No
sagebrush control (High), with an appropriate stocking rate resulted in the lowest BW gain hectare. However, the
use of intensive sagebrush control (Low; spraying every 5 yr) and increasing the stocking rate (47%) over the High
pastures resulted in a 26% increase in BW gain per hectare. However, the moderate sagebrush control (Medium;
spraying intervals of approximately 10 yr) resulted in performance similar to that of the Low pastures. These results
suggest that medium levels of sand sagebrush control with appropriate stocking rates may be the best management
option for stocker cattle production.
Key words: cattle, herbicides, sand sagebrush
208
19 PASTURE MANAGEMENT EFFECTS ON NONPOINT SOURCE POLLUTION OF
MIDWESTERN PASTURES. D.A. Bear*, J.R. Russell, and D.G. Morrical, Iowa State University, Ames, Iowa,
United States of America.
Grazing management practices that allow cattle to congregate near pasture streams may increase sediment, nutrient,
and pathogen loading of the streams by removing vegetation and depositing manure in and near the streams. To
assess effects of microclimate conditions and the size and/or shape, botanical composition, and shade distribution of
pastures on the congregation of cattle near pasture streams, grazing distribution of cattle on five beef cow-calf farms
in southern Iowa were analyzed during spring, summer, and fall of 2007 through 2009. Pastures ranged in size from
8 to 125 ha with varying proportions of cool-season grasses, legumes, weeds, brush, and bare ground. Cows were
Angus and Angus-Cross on four of the farms, and Mexican Corriente on the remaining farm. Two to 3 cows on each
farm were fitted with Global Positioning Systems (GPS) collars to record location within a pasture at 10 min
intervals for periods of 5 to 14 d in each season. HOBO data loggers recorded ambient temperature, black globe
temperature, dew point, relative humidity, and wind speed and direction at 10 min intervals during the three-year
project. Data were used to calculate 13 heat indices to be used to estimate cow distribution in or within 30.5 m
(streamside zone) of a water source within pastures. Water sources and fence lines were referenced on a geospatial
map and used to establish zones within the pastures. Designated zones were in the stream or pond (water source) or
lower or greater than 30.5 m (Uplands) from the water source. One hundred thirty-nine data sets were obtained
during the three grazing seasons. Farm and seasonal effects on cow distribution in pastures were analyzed with the
GLM procedure of SAS using years as replicates. Mean proportions of observations when cattle were in the water
source differed (P < 0.01) between seasons being 1.1, 1.8, and 1.0% in the spring, summer, and fall, respectively.
Mean proportions of observations when cattle were within the streamside zone did not differ (P > 0.05) between
seasons, but differed (P < 0.01) between farms. Heat indices effects on the probability of cattle being in or within
30.5 m of the water were estimated using the LOGISTIC procedure of SAS. Ambient temperature best predicted
cattle location in the streamside zone compared to the other twelve indices, and determined the probability of
finding cattle in the streamside zones of pastures by farms ranged from 2.6 to 4.9% for every 1 Celsius degree
increase in ambient temperature. The proportion of time cattle were within the streamside zone increased with
decreasing pasture size (y = 32.88 - 0.79x + 0.0054x2, r2 = 0.49; y = 35.73 - 0.73x + 0.0043x2, r2 = 0.77; y = 36.49 0.89x + 0.0058x2, r2 = 0.66), increasing the proportion of streamside zone within a pasture (y = -3.05 + 2.34x 0.059x2, r2 = 0.47; y = 5.75 - 0.32x + 0.075x2, r2 = 0.37; y = 0.70 + 0.93x - 0.0094x2, r2 = 0.39), and increasing the
proportion of shade located within the streamside zones of pastures (y = 1.76 + 0.89x - 0.011x2, r2 = 0.36; y = 7.08 +
0.40x - 0.0031x2, r2 = 0.25; y = 2.11 + 0.82x - 0.0091x2, r2 = 0.31) in the spring, summer, and fall, respectively.
Botanical composition of pastures were evaluated and regressed on cattle locations within the streamside zone, but
no relationship existed. Consequently, the proportion of time cattle were located within the streamside zone
increased with increasing ambient temperature, decreasing pasture size, increasing the proportion of streamside zone
within a pasture, and proportion of shade located within the streamside zone. Implementation of grazing
management practices, such as implementation of a rotational grazing system, flash grazing of the streamside zones
of pastures, altering pasture size, shape, and shade distribution within a pasture altering the location, timing,
intensity, and length of grazing for the protection of pasture streams will likely be most effective on small and/or
narrow pastures in which cattle have less opportunity to locate in upland locations.
Key words: environment, grazing behavior, water quality
209
20 SUPPLEMENTATION OF RUMINALLY UNDEGRADABLE PROTEIN TO MAINTAIN
ESSENTIAL AMINO ACID SUPPLY DURING NUTRIENT RESTRICTION ALTERS CIRCULATING
ESSENTIAL AMINO ACIDS OF BEEF COWS IN EARLY TO MID-GESTATION. A. M. Meyer*1, J. S.
Caton1, M. Du2, and B. W. Hess2, 1Center for Nutrition and Pregnancy, Department of Animal Sciences, North
Dakota State University, Fargo, ND, USA, 2Department of Animal Science, University of Wyoming, Laramie, WY,
USA.
Early to mid-gestation is a critical period for placental growth and fetal organogenesis, thus maternal nutritional
insults during this period may be particularly detrimental to offspring development. We hypothesize that
supplementation of ruminally undegradable protein (RUP) to provide essential AA will offset negative
consequences of maternal nutrient restriction on offspring. Our specific objective was to determine the effect of
RUP supplementation on serum essential AA concentration in beef cows nutrient restricted during early to midgestation. Angus  Gelbvieh cows (n = 36) were blocked by parity and randomly allocated by BW to 1 of 3 dietary
treatments for 140 d, beginning on d 45 of gestation. Treatments included a native grass hay-based control (CON)
diet formulated to meet NRC requirements for mid-gestation; a nutrient restricted (NR) diet providing 70% of CON
NEm ; or an NR diet fed with a RUP supplement (NRP; 68.7% menhaden fish meal, 24.5% hydrolyzed feather meal,
and 6.8% porcine blood meal, DM basis) formulated to provide similar essential AA flow to the duodenum as CON.
Cows were individually fed, and diets were adjusted biweekly for BW change and increasing NEm requirements of
gestation. Blood samples were obtained and serum collected on d 0, 42, 84, and 140 of the treatment period for AA
analyses. Data were analyzed using a mixed model containing effects of treatment, sampling day, and their
interaction, where sampling day was used as a repeated effect. There were treatment main effects for total serum AA
(P = 0.002), His (P = 0.001), Lys (P = 0.06), and Trp (P = 0.10). Total AA were greater (P < 0.006) for cows fed
NRP compared with CON and NR (2.48 vs. 2.3 and 2.25 ± 0.04 mmol/L). Serum His was less (P < 0.002) for CON
than NR and NRP, whereas CON had greater (P < 0.04) Lys and Trp than NR. A treatment  sampling day
interaction was detected (P  0.08) for total essential AA, all other essential AA (mol/L), and all relative essential
AA (% of total essential AA) concentrations. On d 42 of the treatment period, cows fed NRP had greater (P < 0.001)
total essential AA, Arg, Val, Leu, and Phe than CON and NR, whereas NR had less (P < 0.05) Ile than CON and
NRP. Cows fed NRP continued to have greater (P < 0.04) serum Leu and Phe on d 84. Total essential AA, Arg, and
Val on d 84 and 140 and Leu on d 140 were greatest (P < 0.03) for cows fed NRP, intermediate for CON, and least
for NR. On d 84 and 140, NR cows had less Ile (P < 0.02) compared with CON and NRP. Cows fed the CON diet
had greater (P < 0.007) Met than NR and NRP on d 140, whereas NRP had greater (P = 0.005) Phe than NR. When
expressed as a percent of total essential AA, NRP cows had less (P  0.003) Thr, Met, and Trp from d 42 to 140,
Lys on d 42 and 84, Arg on d 42, and Ile on d 140 than CON and NR. Cows fed the NR diet had greater (P < 0.001)
His than CON and NRP from d 42 to 140, whereas NR had greater (P < 0.009) Phe than NRP on d 42 and 140.
Serum Ile was greatest (P < 0.005) for CON, intermediate for NR, and least for NRP on d 42 and 84. Cows fed the
CON diet also had greater (P = 0.001) Lys than NRP on d 140. Conversely, NRP had greater (P < 0.001) Val and
Leu compared with CON and NR from d 42 to 140. Providing nutrient restricted beef cows with RUP during
gestation can increase serum essential AA, but may also alter their relative concentrations. Increasing circulating
essential AA in nutrient restricted cows may protect the developing fetus from intrauterine growth restriction,
although more research is necessary to determine these effects.
Key words: amino acids, gestation, nutrient restriction
210
21 EFFECT OF FORAGE TYPE ON PERFORMANCE OF WEANLING BEEF STEERS. G. Scaglia*1,
B. Corl2, W. S. Swecker, Jr.3, and A. Lillie4, 1Louisiana State University Agricultural Center, Iberia Research
Station, Jeanerette, LA, USA, 2Department of Dairy Science, Virginia Polytechnic Institute and State University,
Blacksburg, VA, USA, 3Virginia Maryland Regional College of Veterinary Medicine,Virginia Polytechnic Institute
and State University, Blacksburg, VA, USA, 4Department of Animal and Poultry Sciences, Virginia Polytechnic
Institute and State University, Blacksburg, VA, USA.
In two consecutive years, 42 weanling crossbred Angus steers (BW = 266 ± 5.2 kg) were purchased at auction and
transported to the Virginia Tech Kentland Farm, VA. The experimental design was a completely randomized design
with 3 replicates. In each block there were 6 paddocks and of those, three paddocks were planted with each of the
two forages used. Three paddocks were planted (September 2, 2004) with Jesup endophyte-free tall fescue (Lolium
arundinaceum (Schreb.) S.J. Darbyshire = Festuca arundinacea Schreb) at a rate of 11.3 kg/ha and the other three
paddocks were planted (September 5, 2004) with AmeriStand 403 alfalfa (Medicago sativa) variety at a rate of 9
kg/ha. Tall fescue pastures received 68 kg/ha of diammonium phosphate (DAP) and the alfalfa pastures received 68
kg/ha of DAP + 45.5 kg/ha of Potash . Nitrogen was applied to the tall fescue paddocks at 13.6 kg/ha in October
(2004) and then again in March (2005) to promote vigorous growth and tillering. Each year 42 weanling steers were
blocked by weight into 6 groups of 7 steers each and randomly allotted and rotationally stocked to alfalfa or tall
fescue within block, where they would remain until the end of the grazing period. All paddocks had an area of 0.61
ha (stocking rate of 3.8 steers/ha) . The steers grazing the tall fescue paddocks were provided with trace mineralized
salt. The mineral blocks in the alfalfa pastures contained poloxalene in addition to the trace mineral in order to help
prevent bloat. All steers were weighed on d 0, 14, 28 and every 28 d thereafter until the conclusion of the grazing
period. Cattle rotation between paddocks depended upon forage availability. When the tall fescue was grazed to a
height of approximately 5 cm the cattle were moved to the next paddock. When the alfalfa was grazed to a height of
10 to 15 cm cattle were moved to the next paddock.
Forage mass for the fescue and alfalfa paddocks were determined by measuring and cutting 18 double samples using
wire circles of 0.25 m2 at 2.5 cm above ground level. Additionally 30 measurements using a falling plate meter to
determine canopy height per paddock were taken. Nutritive value samples were taken on d 0 and every 14 d
thereafter. Nutritive value samples were collected by walking two diagonal strips in an X pattern in the designated
paddock and grab samples were obtained approximately every ten steps.
Data was analyzed using PROC MIXED of SAS. For data analysis period was used as repeated measures and the
experimental unit was pasture within treatment (replicate). Treatment was the fixed effect and year the random
effect. The model included treatment, replicate, period, and treatment x period interaction. In all cases level of
significance was declared at P < 0.05. Trends were declared at 0.05 P0.10. Forage mass was different among
years (P = 0.002), sampling day (P = 0.023) and treatments (P = 0.006). No interactions were detected (P > 0.05) for
forage mass determination. Less forage was produced in year 1 than in year 2 (2526 vs. 3244 kg DM/ha for
weanlings, in year 1 and 2, respectively). Both forage stands were new stands so it is normal to see an increase in
forage mass in the second year of production. Alfalfa had greater DM production (3203 kg DM/ha) than tall fescue
(2568 kg DM/ha). A possible explanation for these results is that the alfalfa variety used in this experiment was
selected for grazing; hence we can expect higher DM production and resistance to grazing. Forage mass was lowest
(2359 kg DM/ha) on d 56 and different (P < 0.05) than on d 28 (3387 kg DM/ha) and 84 (3133 kg DM/ha). As
expected, alfalfa presented a larger concentration of CP but lower NDF and ADF than tall fescue at all sampling
times (P < 0.01). The interaction of treatment x day of sampling was significant (P = 0.001) for NDF and ADF.
There is an increase on NDF and ADF concentrations from d 0 to d 84 for tall fescue and to d 112 for alfalfa,
declining thereafter. The differences in ADG between d 28 and 84 explained the ADG differences for the whole
grazing period (d 0 to 140) as well as the differences in final BW and in kg of beef produced per unit of area (kg/ha).
Steers grazing alfalfa gained 0.37 kg/d more than those grazing tall fescue (0.99 vs. 0.62 kg/d). It resulted in 332 and
524 kg/ha of beef produced for tall fescue and alfalfa, respectively. Even though alfalfa is known to be a better
forage, steers grazing tall fescue gained adequately.
Key words: alfalfa, tall fescue, steers
211
22 ESTIMATES OF DRY MATTER INTAKE OF BEEF STEERS GRAZING HIGH QUALITY
PASTURES USING ALKANES. G. Scaglia*1 and H. Boland2, 1Louisiana State University Agricultural Center,
Iberia Research Station, Jeanerette, LA, USA, 2Prairie Research Unit, Mississippi State University, Prairie, MS,
USA.
Pastures remain the most important source of nutrients for ruminant livestock and nutrition is critical to optimize
animal production. The daily quantity of dry matter that is consumed by an animal is a critical measurement to make
nutritional inferences about feed and subsequent animal response. Researchers are facing the dilemma that, while
estimates of individual animal performance are readily obtained, it is still difficult to estimate the herbage intake of
individual animals. The objectives of this experiment were to estimate forage intake in beef steers grazing tall fescue
[Lolium arundinaceum (Schreb.) Darbysh. = Festuca arundinacea Schreb.] or alfalfa (Medicago sativa)/tall fescue
mixed pastures and to measure the recovery rate of artificial alkanes from a controlled release device under these
conditions. Six steers (330±11 kg) were allotted to each pasture (0.61 ha). Steers were previously trained to the use
of a harness and fecal collection bags, so that the expected negative effect on animal behavior could be minimized.
A controlled-released capsule (Nufarm, Auckland, NZ) containing alkanes (C32 and C36; release rate: 400 mg/d) was
administered on d 0 to all 12 steers. During the experimental period (d 8 to 14) fecal collection bags were fitted to all
steers in the fescue pasture and to three steers in the fescue/alfalfa pastures. Collection bags were emptied twice
daily (at 0830 and 1630). Feces were weighed, mixed, and an aliquot of 0.5 kg frozen. Rectal grab samples were
obtained twice daily from each animal at the time of switching bags. Forage mass in each pasture was estimated
from two 3 meters strips obtained on d 8 using a push mower. From d 6 to 12 forage samples for alkane
determination were obtained by walking the pasture in an X and clipping every 20 steps with a set of handheld
clippers at a height of 2.54 cm from the ground. Fecal and forage samples were freeze dried and ground through a
0.5 mm screen. Alkane determination and dry matter intake estimation used C31 as odd chain alkane following the
method. The effect of pasture on herbage intake was estimated by analyses of variance. A paired t-test was used to
compare herbage intake estimated from grab samples vs. samples taken from the fecal collection bag. All statistical
analyses were conducted using SAS. Forage availability (3078 vs. 3088 kg DM) and quality (CP: 19 vs. 17.5%;
NDF: 50 vs. 55%; ADF: 31 vs. 29%) were similar for fescue/alfalfa and fescue pastures, respectively. Similar
concentration of C32(mg/kg of feces) was found in fecal samples taken directly from the steers (grab samples) and
from the fecal collection bags, with an average ratio (C32 grabs/ C32 bags) of 0.99 and 0.92 for alfalfa/fescue and
fescue pastures, respectively. The recovery rate of C32 in feces was higher than previously reported with an average
of 0.985 and 0.991 for fescue/alfalfa and fescue pastures, respectively. Using information obtained from fecal
samples collected from bags there was no difference (P > 0.05; SEM=0.48) in the average daily dry matter intake of
steers grazing fescue/alfalfa (10.8 kg DM) and fescue pastures (10.3 kg DM). Similarly, no differences (P >0.05)
were detected in average daily dry matter intake when using C32 from grab samples (9.9 vs. 9.6 kg DM for steers
grazing fescue/alfalfa and fescue pastures, respectively; SEM=0.39). When sampling method was compared (grab
fecal samples vs. fecal samples from bags) there was no difference (P >0.05) in the estimation of dry matter intake
for steers grazing fescue or fescue/alfalfa pastures. The results of the present study demonstrate that knowing the
recovery rate of the marker used (in this case C32) fecal grab samples can be used to estimate dry matter intake in
beef steers under grazing conditions. However, the recovery rate for C32 observed in this experiment was 5 to 15%
higher than those found in the literature. Therefore, further research is needed to study the effectiveness of the
controlled-release capsules under different conditions and factors that might be affecting the release rate of the
marker.
Key words: alkanes, intake, pastures
212
23 EFFECT OF ESTABLISHMENT METHOD OF WHEAT PASTURE AND FALL STOCKING
RATE ON PERFORMANCE OF GROWING STEERS. P Beck*, M Morgan, T Hess, D Hubbell, M Anders,
and B Watkins, University of Arkansas Division of Agriculture, Little Rock, AR USA.
Agricultural practices have impacted the environment for many centuries. In past generations, agricultural disasters
like the Dust Bowl in the 1930’s increased awareness about tillage practices and their impact on the environment.
Passage of the Clean Water Act in 1972 set in motion actions that have greatly impacted land use by farms. Previous
research concerning the impact of tillage systems on environmental health has focused on crop production with little
research to determine the interaction of livestock grazing and tillage system on resource sustainability of farms.
Rainfall is critical for forage production and can be an important concern when either inadequate or overabundant.
Dry conditions limit forage establishment and growth while wet conditions can delay establishment due to crusting
of the soil surface, will cause footing problems for livestock, and hoof action of livestock will disturb wheat stands.
This research was conducted over 3-yr from fall of 2006 to spring of 2009 to determine the effect of fall stocking
rate (SR) of small grain pastures on animal performance during the fall and subsequent spring for 3 establishment
methods: 1) no-till (NT) seeding into undisturbed stubble with approximately 85% residue cover, 2) reduced-till
(RT) - disking once followed by broadcast seeding with approximately 50% residue cover, and 3) conventional-till
(CT) – drilling into a prepared seedbed with approximately 4% residue cover. Each year soft-red winter wheat was
sown in the first wk of September (136 kg/ha). During the fall 60 steers (BW = 226 ± 17.9 kg) were stocked to
pastures at 1.8, 2.4, and 3.6 steers/ha (n = 2 pastures/stocking rate and establishment method) and grazed from early
November until mid-February (depending on forage availability). Each spring graze-out period 180 steers (BW =
246 ± 21.0 kg) were stocked to pastures at 10 steers/ha in late February or early March (depending on forage
availability) and removed in early May. Animal performance was analyzed with regression using SAS; indicator
variables were used to construct orthogonal contrast in order to separate the effects of tillage (NT vs RT and CT)
and CT vs RT. There was no establishment method effects (P  0.24) on fall BW gain, but fall BW gain decreased
linearly with increasing SR (Total BW gain, kg = 166 18*SR, R2 = 0.24, P < 0.001). Effect of fall SR on spring
graze-out BW gain and ADG were quadratic (P < 0.01), with interactions (P < 0.05) present for tillage system
comparisons with NT BW gain = 162 - 91.5*SR + 16.2*SR2 (P < 0.01, R2 = 0.11), CT BW gain = 160 - 85.9*SR +
13.9*SR2 (P < 0.01, R2 = 0.13), and RT BW gain = 125.8 – 52.8*SR + 7.3*SR2 (P < 0.01, R2 = 0.21); and NT ADG
= 3.50 – 2.01*SR + 0.37*SR2 (P < 0.01, R2 = 0.13), CT ADG = 3.40 – 1.88*SR + 0.32*SR2 (P < 0.01, R2 = 0.10),
and RT ADG = 2.46 – 0.98*SR + 0.14*SR2 (P = 0.05, R2 = 0.15). Total BW gain/ha of NT increased in a quadratic
(P < 0.01, R2 = 0.80) fashion with increasing SR (BW gain/ha = 1040 – 438*SR + 96*SR2). Stocking rate during the
fall did not affect (P > 0.50) gain/ha of RT (612 kg/ha) or CT (579 kg/ha), neither of which differed (P > 0.70) from
NT. Reduced tillage tended (P = 0.07) to have greater BW gain/ha than CT. Results from this study indicate that
tillage system has no affect on animal performance during the fall. Although animal performance is reduced in the
fall grazing period with increasing SR, gains would still be considered adequate for most producers at the greatest
SR. Increasing SR in the fall reduces animal performance the following spring, except for NT, indicating there was
less impact on forage production during the spring with NT than with CT or RT. Contrary to conventional wisdom
there was no difference in BW gain/ha between CT and NT or RT. Selection of an establishment method is
ultimately dependent on economic conditions and producer’s sensitivity to environmental impacts.
Key words: tillage system, growing cattle, stocking rate
213
24 GROWTH PERFORMANCE EFFECTS OF MINERAL VS. SALT SUPPLEMENTATION ON
STOCKER CATTLE GRAZING SPRING-SUMMER PASTURES OVER TWO CONSECUTIVE YEARS.
P. J. Guiroy*, S. E. Showers, and B. McMurry, Cargill Inc., Hopkins, MN.
The objective was to evaluate growth performance in yearling crossbred steers grazing native spring-summer
pastures in Osage County, Oklahoma for the effects of mineral vs. salt (NaCl) supplementation over two consecutive
years. Year 1 consisted of 2,132 steers grazed for 85 days (mid April to mid July 2008). Year 2 consisted of 2,225
steers grazed for 123 days (late March to mid July 2009). Each year animals were randomly assigned to one of 8
pasture paddocks, 4 paddocks (replicates) per treatment. The same pastures were utilized in years 1 and 2, with the
mineral treatment paddocks in year 1 becoming the salt treatment paddocks in year 2. The mineral used in this study
(NutreBeef® Stocker Summer Mineral) contained a minimum of 18% calcium, 8% sodium, 4% phosphorus, 1%
magnesium, trace minerals and vitamins, and was formulated to provide 350 mg/hd/day of Chlortetracycline. Data
obtained from this experiment were analyzed by analysis of variance using Proc GLM as complete randomized
design. The model included year, treatment, and the interaction year x treatment effects, with pasture as the
experimental unit.
Average daily gain and final weights were higher in year 1 than year 2 (P<0.01), and attributed to pasture burning
before the start of grazing in year 1 but not in year 2. There were no interactions between treatments and no other
year effects for any of the variables measured (P>0.1). No difference in mineral consumption between years
(P=0.13) was noted. Average mineral and salt consumption across years was 147 and 59 g/day, respectively. Year 1
and 2 combined ADG for the mineral treatment animals was 0.14 kg higher than those in the salt treatment (P<0.01).
Total weight gain improvement for the mineral vs. salt treatment was 11 and 19 kg, or 9 and 16%, for year 1 and 2,
respectively. During year 1, animals treated for foot rot decreased from 38 to 12 by mineral supplementation
(P=0.04). Foot rot treatments were not recorded in year 2.
Providing a mineral supplement to steers grazing native spring-summer pastures in the Osage County, Oklahoma
increased ADG and total weight gain that justified the cost of the mineral supplementation.
Year 1
Year 2
(85 days grazing)
(123 days grazing)
Mineral
Salt
Mineral
Salt SEM P-value
Initial BW, kg
261
261
257
259 1.92 0.49
Final BW, kg
398
388
388
372 3.45 <0.01
ADG, kg
1.62
1.49
1.07
0.92 0.03 <0.01
Total Gain, kg
138
127
132
113 3.03 <0.01
61
138
57
Supplement Consumption, g/d 156
Key words: beef, grazing, mineral
214
6.74 <0.01
25 USE OF DRIED DISTILLERS GRAINS AS A SUPPLEMENTAL FEEDSTUFF FOR GROWING
CATTLE ON WHEAT PASTURE. E. D. Sharman*, P. A. Lancaster, G. W. Horn, and J. T. Edwards, Oklahoma
Agricultural Experiment Station, Stillwater, OK, USA.
Two experiments were conducted to evaluate the efficacy of dried distillers grains and solubles (DDGS) as a
supplemental feedstuff for stocker cattle grazing hard red winter wheat pasture (Triticum aestivum). In Exp. 1, 172
fall-weaned steer calves (BW = 255 ± 29.9 kg) were stratified by BW and assigned randomly within BW groups to
16, 7.3- to 9.7-ha, pastures in a randomized complete block design on November 15 at a stocking rate of 1.37
steer/ha for 113 d. Treatments were (1) non-supplemented, control (CON); (2) 1.13 kgsteer-1d-1 of whole corn
(CORN); (3) 1.13 kgsteer-1d-1 of pelleted soybean hulls (SBH); and (4) 1.13 kgsteer-1d-1 of DDGS. All steers
had free-choice access to a monensin-containing mineral (1764 mg monensin/kg). Data were analyzed by ANOVA
using the Mixed procedure of SAS. Experimental units were pastures and sampling units were steers. Orthogonal
contrasts included: (a) energy supplementation vs the CON; (b) type of supplement (i.e., high-starch, CORN, vs the
average of the two high-fiber supplements, SBH and DDGS); and (c) SBH vs DDGS. Mean daily monensin intakes
were 121, 76, 83 and 84 mg/steer for the CON, CORN, SBH, and DDGS treatments, respectively. Consumption of
energy supplements averaged about 0.35% of mean BW for treatment 2-4. Energy supplementation tended to
increase ADG (1.12, 1.11, 1.20, 1.20 ± 0.026 kg/d for treatments 1-4, respectively; P = 0.10), and was greater (P =
0.01) for SBH + DDGS vs CORN. There was no difference (P = 0.91) between SBH and DDGS. Supplement
conversion, expressed as kg of as-fed supplement per kg of increased BW gain, was poor and averaged 11.9 and
13.2 for SBH and DDGS, respectively. Ultrasound measurement of intramuscular fat tended to be increased (3.55 vs
3.72%; P = 0.09) and backfat was increased (0.51 vs 0.57 cm; P = 0.03) by energy supplementation. In Exp. 2, 157
fall-weaned steer calves (BW = 242 ± 16.2 kg) were stratified by BW and assigned randomly within BW groups to
18, 7.3- to 9.7-ha, pastures in a randomized complete block design on December 4 at a stocking rate of 1.10 steer/ha
for 50 d. Treatments were: (1) high-calcium, non-medicated free-choice mineral mix (control); (2) 0.91 kgsteer-1d1
of a monensin-containing energy supplement; and (3) 0.91 kgsteer-1d-1 of a monensin-containing supplement
with 65% DDGS. Treatment 2 supplement contained (% as-fed): ground corn, 44.2; wheat middlings, 43.3; pellet
binder, 5.0; salt, 1; dicalcium phosphate, 2.0; limestone, 3.7; magnesium oxide, 0.5; copper sulfate, 0.025; vitamin A
to provide 29,964 IU/kg; and Rumensin 80® to provide 176 mg monensin/kg of supplement. Treatment 3
supplement contained 65% DDGS and 22.6% wheat middlings, while other ingredients were similar to treatment 2.
Both energy supplements were pelleted in order to improve handling characteristics and minimize waste of the
DDGS. Data were analyzed as described for Exp. 1. Orthogonal contrasts included the effect of energy
supplementation and type of energy supplement. Mean mineral consumption was 0.18 ± 0.07 kgsteer-1d-1.
Supplementation increased ADG (0.97, 1.12, and 1.20 ± 0.04 kg/d for treatments 1-3, respectively; P = 0.001) but
was not influenced (P = 0.15) by substitution of DDGS into the corn/wheat middling supplement. Supplement
conversions were improved compared with Exp. 1 and were 6.06 and 3.92 kg of supplement per kg of increased BW
gain for the corn/wheat middling and DDGS-based supplements, respectively. Results of these experiments indicate
that DDGS can be used as an alternative feedstuff in energy supplements fed at relatively low amounts to wheat
pasture stocker cattle without compromising ADG or causing polioencephalomalacia. Ionophores are generally
thought of as a technology for improving ADG of growing cattle, yet they also improve supplement conversion
rates. The greatly improved supplement conversions observed in Exp. 2 accentuate the importance of including
monensin in energy supplements for wheat pasture stocker cattle rather than providing energy supplements alone.
The substantially improved supplement conversion for DDGS compared with corn/wheat middling supplement in
Exp. 2 needs further evaluation.
Key words: distillers grains, growing cattle, wheat pasture
215
26 A GENETIC MARKER FOR RESISTANCE TO FESCUE TOXICOSIS IN BEEF CATTLE. C.J.
Kojima1, J.C. Waller*1, D.E. Spiers2, R.L. Kallenbach2, B.T. Campbell1, T.A. Cooper1, and J.K. Bryant2, 1University
of Tennessee, Knoxville, TN, USA, 2University of Missouri, Columbis, MO, USA.
Tall fescue is the prevalent grass in the Southeastern United States and can harbor an endophytic fungus (N.
coenophialum) which causes symptoms known collectively as fescue toxicosis (FT): reduced feed intake and
weight gain, decreased fertility, vasoconstriction and decreased ability to thermoregulate. The hallmark signs of
fescue toxicosis are decreased serum prolactin concentrations and a rough, overly thick hair coat. A single
nucleotide polymorphism (SNP) was discovered within the dopamine receptor D2 gene (DRD2) where a
guanine/adenine substitution existed, creating two alleles, G and A. Two experiments were performed to assess the
informativeness of this SNP for resistance to fescue toxicosis. For both experiments, data were analyzed by mixed
model analysis of variance of SAS with and without repeated measures as appropiate. Forty-two Angus-based
steers were grazed on Kentucky 31 tall fescue containing a toxic form of the endophyte (endophyte-infected
fescue or EIF; n = 21), or Jesup tall fescue containing a non-toxic variety of endophyte (Jesup MaxQ; n = 21).
Genotypic frequencies for this population were 0.33 (AA), 0.45 (AG) and 0.22 (GG). While genotype had no effect
on serum prolactin or hair coat score in Jesup MaxQ steers, homozygous GG steers grazing EIF had decreased
serum prolactin concentrations in April and May compared to AA steers (P < 0.05). Effect of genotype was then
assessed in 53 Angus-based steers grazed on EIF. Genotypic frequencies for this population were 0.38 (AA), 0.36
(AG) and 0.26 (GG). Genotype was associated with serum prolactin concentrations (P = 0.004) and hair coat score
(P = 0.0102) such that GG animals had decreased prolactin and increased hair coat scores relative to AA and AG
animals. As most herds in Tennessee are spring-calving and therefore more prone to the effects of FT, we
hypothesized that non-intentional selection for the advantageous allele was occurring in spring-calving herds
relative to fall-calving herds, herds that are not exposed to FT, and herds of heat-resistant breeds. Genotypic and
allelic frequency was assessed in 92 Tennessee Angus bulls, 78 Missouri Fall-calving Angus cows, 12 Missouri
Spring-calving Angus steers, 10 Oklahoma Angus steers and 11 Florida Romosinuano steers. These data suggest
that the advantageous allele is less prevelant in herds of cattle which are less affected by FT (eg. fall-calving, not
grazed on tall fescue, or heat-tolerant breeds) than are spring- calving Angus cattle grazed on tall fescue. This may
be a by-product of selection for longevity, fertility, and growth in cow-calf herds affected by FT. The DRD2 SNP
may have potential use in the selection of animals resistant to fescue toxicosis.
Genopytic and allelic frequencies of cattle from different herds and calving seasons.
Genotypic
Alleilc
Frequency
Frequency
Calving
Herd
Season
n
TN Angus Steers I
Spring
TN Steers II
AA AG
GG A
G
42 0.33 0.45
0.22 0.56
0.45
Spring
53 0.38 0.36
0.26 0.56
0.44
TN Bulls
Spring
92 0.30 0.49
0.21 0.55
0.45
MO Angus Cows
Fall
78 0.23 0.51
0.25 0.49
0.51
MO Angus Steers
Spring
12 0.50 0.25
0.25 0.63
0.38
OK Angus Steers
Unknown 10 0.20 0.40
0.40 0.40
0.60
FL Romosinuano Steers Unknown 11 0.33 0.33
0.33 0.50
0.50
Key words: cattle, dopamine receptor, tall fescue toxicosis
216
27 PERFORMANCE OF CATTLE GRAZING SWITCHGRASS OR COMBINATIONS OF BIG
BLUESTEM AND INDIANGRASS DURING THE SUMMER IN THE MID-SOUTH. W.M. Backus, J.C.
Waller*, P.D. Keyser, G.E. Bates, C.A. Harper, F.N. Schrick, and B.T. Campbell, University of Tennessee,
Knoxville, TN, USA.
The Tennessee Biofuels Initiative and others have selected switchgrass, a native warm-season perennial grass, as the
main feedstock for cellulosic ethanol production. To meet the proposed level of ethanol production thousands of
acres of pastures in Tennessee will be replaced with switchgrass. If beef producers could use the switchgrass as
summer forage and later harvest it as a biofuel crop, the impact of this shift in grazing land would be reduced. Cattle
grazing switchgrass or other warm-season native grasses should have greater weight gain than those grazing tall
fescue during the summer. Tall fescue is a cool-season forage that has limited growth and reduced quality in the
summer. Animal performance response to grazing switchgrass in this region is very limited. The objectives of this
study are to graze switchgrass and other warm-season grasses that have the potential for cellulosic ethanol
production to determine animal performance during summer. Two grazing studies were conducted in 2009 to
compare animal performance of cattle grazing pastures of switchgrass (Panicum virgatum L.) or a combination of
big bluestem (Andropogon gerardii Vitman) and indiangrass (Sorghastrum nutans L. Nash). Study 1 was conducted
from May 29 to August 3 at the Highland Rim Research and Education Center near Springfield TN in which Angus
and Angus cross steers (269±5.7 kg) were used in a randomized block design with two forage treatments: 1)
Switchgrass (SG) or 2) a combination of big bluestem and indiangrass (BB/IG). All pastures used were two-yr old
stands. Prior to initiation of grazing all steers were fed a high roughage filler diet for 4 d with individual BW taken
in the early AM each day. The average BW for those 4 d was used for initial BW for the grazing period. Intake of
the filler diet on a dry matter basis was about 2.5% BW. At the end of the grazing period all steers were fed the same
filler diet for 4 d and those weights were used as the final weights for the study. Four steers (testers) were allotted to
1.2-ha paddocks with five replications per treatment. Additional steers were used in a put-and-take manner to keep
forage in a vegetative state. Steers had free choice access to pasture, water, mineral, and shade. Steers were weighed
on 21-d intervals to determine ADG. Data were analyzed using the MIXED procedure of SAS. Least square means
for ADG of steers were not different (P > 0.05). The ADG of steers grazing SG and BB/IG, was 0.95 and 1.06 kg/d,
respectively. Study 2 was conducted from June 5 to August 10 at the Middle Tennessee Research and Education
Center near Spring Hill, TN in which bred Holstein replacement heifers (485±15.0 kg) were used in a randomized
block design with four forage treatments: 1) switchgrass (SG), 2) a combination of big bluestem and indiangrass
(BB/IG), 3) switchgrass plus red clover (Trifolium pratense L.) and 4) a combination of big bluestem and
indiangrass plus red clover. The red clover was drilled in March at a rate of 5.6 kg/ha. All pastures used were two-yr
old stands. Initial and final BW of heifers was established using the same filler diet and technique used in Study 1.
Three heifers (testers) were allotted to 1.2-ha paddocks with four replications per treatment. Additional heifers were
used in a put-and-take manner to keep forage in a vegetative state. Heifers had free choice access to pasture, water,
mineral, and shade. Heifers were weighed on 21-d intervals to determine ADG. Data were analyzed using the
MIXED procedure of SAS. Least square means for ADG of heifers were different (P < 0.05) when grazing SG or
BB/IG without red clover. The ADG of heifers grazing SG and BB/IG, was 0.75 and 0.97 kg/d, respectively. Least
square means for ADG of heifers were different (P < 0.05) when red clover was present stands of SG and BB/IG.
Heifer ADG was 0.80 and 0.99 kg/d for SG + red clover and BB/IG + red clover, respectively. The results of these
two grazing studies demonstrate the ability of native warm season grasses to provide suitable summer forage and
animal performance for beef steers and replacement heifers within the tall fescue growing region.
Key words: switchgrass, big bluestem, indiangrass
217
28 BOTANICAL COMPOSITION, NUTRITIONAL VALUE, VOLUNTARY FEED INTAKE
ESTIMATION AND BEHAVIORAL HABITS OF CATTLE GRAZING ON RANGE PASTURES. D.
Rodriguez-Tenorio*1, R. Gutierrez-Luna1,2, R.D. Valdez-Cepeda1, F.G. Echavarria1,2, M.A. Salas1, J.I. AguileraSoto1, M.A. Lopez-Carlos1, C.F. Arechiga1, and J.M. Silva-Ramos1, 1Universidad Autonoma de Zacatecas,
Zacatecas, Zac., Mexico., 2Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, Morelos, Zac.,
Mexico.
The objective was to compare botanical composition, nutritional value, voluntary intake and behavioral habits of
cattle (Stobs, 1973; Chacon et al., 1976); under two different grazing systems in short grasslands of Zacatecas
State, Mexico. Experiments were carried out at two ranches: R1) with a short-duration grazing system and R2) with
a continuous grazing system. Samples were collected during the summer, autumn and winter for two consecutive
years. Data collected for nutritional value, botanical composition and voluntary intake were analyzed using
principal components analysis. Log-linear models for categorical variables were used to analyze grazing and
behavioral habits. Ether extract content was affected by season. Crude fiber was similar in both ranches, but
nutrient availability was reduced from 48% to 27%, and sometimes up to 62%. Nutrient availability was reduced
from 45 to 27% at R1 as maturity advanced, whereas for R2, it was reduced from 46% to 25%. Grazing activity
was different between ranches (P<0.05). Regarding behavioral habits in R1 cattle grazed 85 and 10% during winter
and summer, respectively. Whereas, in R2 cattle grazed 20 and 70%. A greater grazing activity was performed at
10, 12 and 16 h for both seasons. There was a 50%-60% crude protein decrease during winter and early spring at
both ranches. Results for NDF were higher than ADF at both ranches. Energy requirements for maintenance are
2.22 Mcal/kg for the growing season and 1.58 Mcal/kg for the period of dormancy. They are different to those
found in our study: 1.00 Mcal/kg during the summer 0.90 Mcal/kg during the winter at both ranches, due to their
poor range condition and the irregular rainfall.
Key words: grazing systems, botanical composition, arid highlands
218
Grazing Livestock Nutrition Conference
Index of Authors
Acetoze, G., 205, 206
Clark, C., 191
Aguiar, A. D., 207
Cooper, T. A., 216
Aguilera-Soto, J. I., 218
Corl, B., 211
Anders, M., 213
Cruz, G., 205
Anderson, D. M., 57, 192
Cruz, G. D., 206
Arechiga, C. F., 218
DelCurto, T., 1
Backus, W. M., 217
Detweiler, C., 192
Bates, G. E., 217
Dittmar, R. O., 202
Bear, D. A., 209
Dobson, L., 202
Beck, P., 213
Doniec, M., 192
Biermacher, J. T., 204
Dove, H., 31, 133
Blanton, J. R., 204
Du, M., 210
Boland, H. T., 194
Echavarria, F. G., 218
Boland, H., 212
Edwards, J. T., 215
Brulc, J. M., 10
Endecott, R. L., 152
Bryant, J. K., 216
Forbes, T. D. A., 207
Bu, D., 199
Gillen, R. L., 208
Bu, D. P., 200, 201
Glassey, C., 191
Butler, W. R., 93
Gregorini, P., 191
Butler, T. J., 204
Gruber, M., 195
Campbell, B. T., 216, 217
Guiroy, P. J., 214
Carstens, G. E., 207
Gunter, S. A., 208
Caton, J. S., 104, 210
Guo, Y. Q., 201
Chase, L. E., 203
Guretzky, J. A., 204
219
Gutierrez-Luna, R., 218
McLennan, S. R., 133
Harper, C. A., 217
McLeod, K., 191
Hensen, A., 197
McMurry, B., 214
Hess, B. W., 104, 210
Medeiros, F. S., 193
Hess, T., 213
Meyer, A. M., 210
Hoffman, K., 203
Milliniks, J. T., 180
Horn, G. W., 215
Morgan, M., 213
Hu, T., 201
Morrical, D. G., 209
Hubbell, D., 213
Murphy, E. J., 196
Jago, J., 191
Nelson, K. E., 10
Jonker, A.., 195
Nolen, B., 192
Kallenbach, R. L., 216
Olson, K. C., 1
Kering, M. K., 204
Patino, H. O., 193
Kerley, M., 125
Petersen, M. K., 180
Keyser, P. D., 217
Poppi, D. P., 133
Kojima, C. J., 216
Randel, R. D., 207
Kronberg, S. L., 196
Reuter, R. R., 204
Lambert, B. D., 202
Rimbey, N. R., 170
Lancaster, P. A., 215
Roberts, A. J., 180
Lardy, G. P., 152
Rodriguez-Tenorio, D., 218
Lillie, A., 211
Rogers, J. K., 204
Liu, K. L., 201
Romera, A., 191
Lodge-Ivey, S. L., 24
Rossow, H., 205
Lopez-Carlos, M. A., 218
Rossow, H. A., 206
Maddock, R. J., 196
Rouquette, Jr., F. M., 207
McCuistion, K. C., 202
Rus, D., 192
220
Russell, J. R., 209
van den Pol-van Dasselaar, A., 197, 198
Salas, M. A., 218
Waller, J. C., 216, 217
Scaglia, G., 194, 211, 212
Wang, Y., 195
Scholljegerdes, E. J., 196
Wang, J., 199
Schrick, F. N., 217
Wang, J. Q., 200, 201
Sharman, E. D., 215
Waterman, R. C., 93, 180
Shen, J., 199
Watkins, B., 213
Showers, S. E., 214
Wei, H., 199
Silva-Ramos, J. M., 218 Wei, H. Y., 200
Soder, K. J., 203
White, B. A., 10
Spiers, D. E., 216
Wickersham, T. A., 202
Springer, T. L., 208
Wiley, L., 202
Sun, P., 199, 200
Winters, C., 192
Swecker, Jr., W. S., 211
Yan, Z. H., 200
Tedeschi, L. O., 207
Yeoman, C. J., 10
Thacker, E., 208
Yu, P., 195
Torell, L. A., 170
Zhang, Y. D., 201
Valdez-Cepeda, R. D., 218
Zhou, L. Y., 200
221
Untitled-1
1
6/5/2003, 2:42 PM
Untitled-1
1
6/5/2003, 2:42 PM
Conference Sponsors
Western Section American Society of Animal Science
USDA – National Institute of Food and Agriculture
Alpharma Animal Health
Cargill
Kahne Animal Health
Agricultural Experiment Stations of
Arizona, Colorado, Montana, and Wyoming

Proceedings - Corona Range and Livestock Research

Transcription

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