(MILK) Symposium: The Dairy Cow in 50 Years


(MILK) Symposium: The Dairy Cow in 50 Years
ADSA Multidisciplinary and International Leadership (MILK) Symposium:
The Dairy Cow in 50 Years
272 The Dairy Cow in 50 Years: A symposium for all ADSA
members and especially for graduate students in dairy production. Michael VandeHaar*, Michigan State University, East
Lansing, MI.
Dairy cattle have changed considerably in the past 100 years. They
now are generally taller and thinner with larger udders that produce
considerably more milk. Genomic selection and the ability to modify
specific genes will enable us to make changes even faster in the future.
The discipline of animal breeding is generally concerned with genetic
change; however, all disciplines must adapt to the animals that are
produced. Moreover, dairy cattle genetics are related across countries.
Thus, it is imperative that we consider the future of the dairy cow from
a multidisciplinary and international approach. Although the presentations in this symposium are led mostly by geneticists, this symposium
is for all dairy scientists, especially for graduate students in the production division. You are the ones who will discover how to best feed and
manage the cows of the future!
Key Words: genomics, selection, dairy production
273 A vision of the dairy farm and dairy cow in 50 years. J. H.
Britt*, Jack H Britt Consulting, Etowah, NC.
Dairy-based foods will increase in importance in human diets because of
dairy’s role in meeting protein needs sustainably. Dairy enterprises will
relocate to regions that have adequate rainfall or water resources and suitable climates. Technologies that will be used have emerged conceptually
at basic scientific levels. Dairy enterprises will use laterally-integrated
systems comprising physically-separated facilities for pre-weaned heifers, replacement heifers, early dry cows, transition cows, milk cows and
dairy beef. Each unit will be managed as if it were a superorganism.
Scale of dairy enterprises will increase and manual labor will decrease
through automation, robotics and sensors. Resources will be harvested
from manure and reused. Perennial crops, including perennial maize
and high-starch energy grasses, will replace annual maize as major
feed sources. Dairy enterprises will be subjected to more regulations
and will put greater emphasis on sustainable agro-ecological systems.
Milk output will be contracted with processors and manufacturers, and
milk will be separated automatically into pools at farms according to
processing characteristics. Dairy cows worldwide will be gene-based
rather than breed-based and will comprise genes that have been edited,
synthesized or transferred. Cows will be smaller and healthier and
selected for their environmental region. Milk yield will exceed 25,000
kg per cow per year in North America. Genetic introductions into herds
will move from semen to embryos. Developmental programming and
precision management will be used to regulate epigenetic and other
gene-regulatory processes to control traits expressed in cattle weeks
to years later. Microbiomes of cattle, crops and farmsteads will be
manipulated strategically and a herd’s genomic profiles will include
genes of its cows and their microbiomes. Dairy beef with lower GHG
footprints will grow in importance as proportion of dairy cows delivering male calves increases.
Key Words: future, dairy, cow
J. Dairy Sci. Vol. 100, Suppl. 2
274 Possibilities in an age of genomics: The future of the
breeding index. J. B. Cole*, Animal Genomics and Improvement
Laboratory, ARS, USDA, Beltsville, MD.
Selective breeding has been practiced since domestication, but early
breeders commonly selected on appearance (e.g., coat color) rather than
quantitative phenotypes (e.g., milk yield). A breeding index converts
information about several traits into 1 number used for selection and
also to predict an animal’s own performance. Calculation of selection
indices is straightforward when phenotype and pedigree data are available. Prediction of economic values 3 to 10 years in the future, when the
offspring of matings planned using the index will be lactating, is more
challenging. The first USDA selection index included only milk and
fat yield, while the latest version of the lifetime net merit (LNM) index
includes 13 traits, with some traits actually composites of other traits.
Selection indices are revised to reflect improved knowledge of biology,
new sources of data, and changing economic conditions. Single-trait
selection often suffers from antagonistic correlations with traits not in
the selection objective. Multiple-trait selection avoids those problems at
the cost of less-than-maximal progress for individual traits. How many
and which traits to include is not simple to determine because traits
are not independent. Many countries use indices that reflect the needs
of different producers in different environments. While the emphasis
placed on trait groups differs, most indices include yield, fertility,
health, and type traits. Addition of milk composition, feed intake, and
other traits is possible but are more costly to collect, and many are not
yet directly rewarded in the marketplace, such as with incentives from
milk processing plants. As the number of traits grows there is increasing
interest in custom selection indices for closely matching genotypes to the
environments in which they will perform. Traditional selection required
recording lots of cows across many farms, but genomic selection favors
collecting more detailed information from cooperating farms. A similar
strategy may be useful in less developed countries. Recording important
new traits on a small fraction of cows can quickly benefit the whole
population through genomics. Gene editing may be used to increase the
frequency of high-value Mendelian traits, such as polled.
Key Words: dairy cattle, genetic improvement, selection index
275 Building a better cow: The Australian experience and
what’s next. J. E. Pryce*1,2 and M. Shaffer3, 1Agriculture Victoria,
Bundoora, VIC, Australia, 2La Trobe University, Bundoora, VIC,
Australia, 3DataGene, Bundoora,VIC, Australia.
Genomic selection has opened up opportunities for developing new
breeding values that rely on phenotypes that use dedicated reference
populations of genotyped cows. There are also opportunities to advance
phenotype collection through automation and identifying predictor traits
that can be measured cost-effectively. One model is to identify the best
phenotypes to measure in research herds and then increase observations
(perhaps using predictors) in genotyped commercial herds. Further
advances in the accuracy of genomic prediction can be gained from
the use of sequence data, in addition to gene expression studies, which
can lead to improved persistence of genomic breeding values across
generations. In Australia integrating data collection with a research and
implementation platform is the platform for delivering new methodologies and breeding values. For example, we have recently delivered the
Feed Saved breeding values to industry and are soon to deliver genomic
breeding values for Heat Tolerance. Identifying traits to include in the
national objective will be the focus of future breeding value research,
such as expanding the number of health traits breeding values available.
However, industry, market and social drivers may see the emergence of
new breeding values, such as cow level methane emissions, gestation
length or niche milk products. To date, selection objectives have been
similar globally, but it is possible that they may diverge into the future.
Selection index methodology is still needed to ensure that the weights
on each trait in the index are appropriate, although the weights are subtly
altered to respond to respective industry and consumer requirements.
So far nationwide indices remain standard practice, but this may change
in the future, especially as tools to deliver information back to farmers
become more sophisticated. Already bull selection tools and personalised
genetic trends are available, but the capture of economic and farm data
will see the emergence of even more tools. Increasing the rate of genetic
gain in the genomics era remains a challenge in Australia, so industry
engagement is paramount.
Key Words: genomic selection, novel traits, selection index
276 Building a better cow. How can we be sure she is adaptable? D. P. Berry*, Teagasc Moorepark, Fermoy, Co. Cork, Ireland.
Intuition suggests that if a trait is under genetic control, then selection for
improved performance will increase the frequency of the genetic variant conferring that advantage (as well as co-inherited genetic variants).
If selection persists then eventually all of the individuals carrying the
unfavorable variant may be culled thus resulting in only the one variant
of the mutation in the population and thus logically an exhaustion of
genetic variability. Because sustainable genetic gain, or the ability to
adapt to a changing environment (in light of changing economic and
social policy as well as changes in weather and climate), is predicated
on the presence of genetic variability, this expected loss in genetic variation, suggest that the response to selection or ability to adapt genetically
will reduce and eventually halt. Although genetic variance is expected
to reduce in the initial generations of selection, empirical evidence, in
general, does not support the thesis of an eventual exhaustion of genetic
variability; arguably the most recognized evidence originates from
the Illinois corn lines selected for high and low content of oil in the
kernel. The quantity of exploitable genetic variation in a population is
dictated by evolutionary forces such as selection, migration, mutation
and genetic drift. The extent of variability introduced by mutations is
actually high and is thought to represent 0.1% of the environmental
variance; this is equivalent to approximately 0.3% for a trait with a
coefficient of genetic variability of 10%. Moreover, developments in
genomic tools and approaches, both through whole genome selection
and genome editing has the potential to reduce the demise of standing
genetic variation and even (strategically) introduce genetic variability.
For example, genome editing has recently been used to edit the CD163
gene in pigs to increase resistance to the recently isolated arterivirus
porcine reproductive and respiratory syndrome (PRRS). Reality, however, does not always reflect potential, and pressures to capture market
share may unduly place greater emphasis on short-term genetic gain to
the determinant of long-term gain.
Key Words: genetic, evolution, dairy
J. Dairy Sci. Vol. 100, Suppl. 2

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