Determination of Gibberella ear rot resistance in new

Institute for Biotechnology in Plant
production, IFA-Tulln
Determination of Gibberella ear rot
resistance in new maize hybrids
Master thesis
Written by
Paulina Georgieva
Student ID: 1141840
Submitted for a diploma degree at:
Department for Agrobiotechnology, IFA-Tulln
University for Natural Resources and Life Sciences, Vienna, Austria
Supervisor: Ao.Univ.Prof.Dipl-Ing. Dr.nat.techn. Marc Lemmens
Vienna, 2015
Acknowledgement
Firstly, I would like to express my sincere gratitude to my advisor Prof. Marc Lemmens for the
continuous support of my master thesis and related research, for his patience, motivation, and
immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I
could not have imagined having a better advisor and mentor for my master thesis.
Besides my advisor, I express my warm thanks to Imer Maloku and Dr. Reza Omidvar for the
assistance with the maize assessment during the different weather conditions.
In particular, I take this opportunity to express my gratitude to my family: my parents, my sister and
my grandmother for supporting me constantly throughout my study and my life in general. Your
prayer for me was what sustained me thus far. Therefore, I want to dedicate this thesis to my dearest
family.
Last but not the least, I would like express my deepest appreciation to my beloved boyfriend Valentin
who spent sleepless nights with me and was always my spiritual support in the moments when I
encountered difficulty during the writing process of this thesis.
1
Abstract
The pathogen Fusarium graminearum Schwabe (teleomorph Gibberella zeae) is the main causal
agents of Gibberella ear rot (GER) in maize in the Austria. The quality of the harvested grain is heavily
impaired due to reduced kernel size, reduced marketability and the toxin contamination with the
immunsuppressive mycotoxin deoxynivalenol (DON). The goal of this master thesis was to evaluate
the GER resistance of 90 new candidate maize hybrids for registration in Austria. The resistance to
infection through the silk channel was assessed. The hybrids were grown in a randomized complete
block and mist irrigation was used to promote infection. The maize plants were artificially inoculated
with Fusarium graminearum (500.000 conidia/mL) by spraying 3 ml of the suspension directly on the
silks without wounding at BBCH67 stage. Three main parameters (disease incidence, disease severity
and disease intensity) were assessed and additionally the infestation of the European Corn Borer
(Ostrinia nubilalis, Hübner) was recorded. Although all genotypes showed GER susceptibility, a large
variation in resistance was found including some hybrids with a high level of GER resistance. The
obtained data showed moderate but significant relationships between disease severity and disease
incidence (r=0.31) and between the maturity group and the disease severity (r=0.59) of the hybrids.
Based on the hybrid performance a standard competition ranking was carried out. The results enable
to select hybrids with good GER resistance for registration in Austria and to discard the very sensitive
candidates.
Key words: maize, Gibberella ear rot, resistance assessment, hybrids, disease severity
2
Zusammenfassung (in German):
Kolbenfusariose, verursacht durch den Pilz Fusarium graminearum Schwabe (Teleomorph Gibberella
zeae), gehört zu den wichtigsten Krankheiten bei Mais in Österreich. Durch Fusarium-Befall treten
neben Ernterückgang durch vermindertes Korngewicht auch Qualitätsbeeinträchtigungen der Ernte
auf, vor allem durch starke Belastung mit Mykotoxinen. Im Rahmen dieser Masterarbeit wurde eine
Auswahl von 90 Maishybridsorten-Kandidaten für deren Zulassung hinsichtlich ihrer Anfälligkeit
gegenüber Kolbenfusariosen untersucht. In einem komplett randomisierten Block-Design mit
Nebelbewässerungssystem erfolgte die Inokulation mittels Besprühen mit Konidiensuspension
(500.000 Konidien/ml) von Fusarium graminearum. Drei Milliliter des Inokulums wurden direkt auf
den Seidenkanal des Maises im Stadium BBCH67 gesprüht. Um Vergleiche zwischen dem
Resistenzverhalten der Sorten zu ermöglichen, wurde eine lineare Boniturskala verwendet und drei
Hauptparameter (Prozentanteil erkrankter Kolben, Prozentanteil erkrankter Fläche der befallenen
Kolben, Prozentanteil erkrankter Fläche gemessen über alle Kolben) sowie der Befall durch Larven
des Maiszünslers (Ostrinia nubilalis, Hübner) evaluiert. Es wurde eine große Variabilität in der
Anfälligkeit im untersuchten Sortiment gefunden. Manche Hybriden zeigten ein hohes
Resistenzniveau. Es wurden auch moderate aber signifikante Korrelationen zwischen der kranken
Fläche der kranken Kolben und dem Prozentanteil erkrankter Kolben (r = 0,31) sowie zwischen den
Reifegruppen und dem Prozentanteil erkrankter Fläche der befallenen Kolben (r = 0,59) ermittelt. Auf
Grund der Hybridleistung wurde eine Resistenzreihung festgelegt. Die Ergebnisse lassen zu, Hybriden
mit einem guten Resistenzniveau aus den Hybridkandidaten zu selektieren und gleichzeitig die sehr
anfälligen Genotypen zu eliminieren.
Schlüsselwörter: Mais, Zea mays, Fusarium graminearum, Kolbenfusariose, Resistenz
3
Table of Contents
Acknowledgement................................................................................................................................... 1
Abstract ................................................................................................................................................... 2
Zusammenfassung (in German): ............................................................................................................. 3
Table of Contents .................................................................................................................................... 4
List of Abbreviations ................................................................................................................................ 8
Introduction............................................................................................................................................. 9
Purpose of the present work ................................................................................................................... 9
Literature review ................................................................................................................................... 10
1.1
Maize ......................................................................................................................................... 10
1.1.1.
The origin of maize ........................................................................................................... 10
1.1.2.
Cultivation practices and commercial use ....................................................................... 10
1.1.3.
The structure and chemical composition of maize .......................................................... 11
1.1.3.1.
Phenology.................................................................................................................. 11
1.1.3.2. Morphology................................................................................................................... 11
1.1.3.3. Reproductive morphology ............................................................................................ 11
1.1.3.4. Structure and nutrient components of the maize kernel ............................................ 12
1.1.4.
Gibberella ear rot and Fusarium ear rot .......................................................................... 14
1.1.4.1. The Pathogen ................................................................................................................ 14
1.1.4.2. Host plants .................................................................................................................... 14
1.1.4.3. Fusarium species of cereals .......................................................................................... 14
1.1.4.4. Disease development ................................................................................................... 17
1.1.4.5. Survival of Fusarium species ........................................................................................ 18
1.1.4.6. Mode of entry ............................................................................................................... 19
1.1.4.7. Symptoms/ Pathology and Epidemiology .................................................................... 20
a)
Red Ear Rot or Gibberella Ear Rot (GER) .......................................................................... 20
b)
Pink Fusariosis or Fusarium ear rot (FER) ........................................................................ 21
1.1.5.
Factors influencing the infection by Fusarium spp. of maize and mycotoxin production
22
1.1.5.1. Biological factors ........................................................................................................... 22
a)
Fungal damage .................................................................................................................. 22
b)
Vectors .............................................................................................................................. 22
European corn borer ............................................................................................................. 22
4
Storage insects ...................................................................................................................... 24
1.1.5.2. Physical factors ............................................................................................................. 25
a)
The aerobiology of Gibberella zeae ................................................................................. 25
b)
Impact of environmental factors...................................................................................... 25
Moisture Requirements........................................................................................................ 25
The effect of temperature on Fusarium Growth ................................................................. 25
c)
Effect of pH on Fusarium Growth (Hydrogen Ion Content, pH) ...................................... 26
d)
Other Growth Factors ....................................................................................................... 26
1.1.6.
Mycotoxins in maize ......................................................................................................... 27
1.1.6.1. Occurrence of mycotoxins in maize after natural infection with different Fusarium spp.
in Austria ....................................................................................................................................... 27
1.1.6.2. Maximum levels and health hazards for Fusarium mycotoxins in maize...................... 29
Trichothecenes...................................................................................................................... 29
Zearalenone .......................................................................................................................... 30
Fumonisins ............................................................................................................................ 30
1.2.
Breeding of maize ..................................................................................................................... 32
1.2.2.
Cytogenetics ...................................................................................................................... 32
1.2.3.
Source of resistance for breeding programmes .............................................................. 34
1.2.4.
Resistance to FER and GER ............................................................................................... 34
1.2.4.1. Types of Resistance ......................................................................................................... 34
a)
Qualitative host resistance / hypersensitivity resistance ........................................... 34
b)
Quantitative host resistance/ partial resistance ......................................................... 35
1.2.5.
Assessment of quantitative resistance ............................................................................ 36
1.2.5.1. Techniques for phenotypic characterization of resistance ......................................... 36
a)
Inoculation methods/ techniques ............................................................................... 37
b)
Traditional Evaluation of Fusaria ................................................................................. 37
c)
Indirect evaluation of Fusaria ...................................................................................... 37
1.2.5.2. Austrian methodology for the classification of the maize assortment .................. 39
1.2.6.
Maize-Fusarium pathosystem .......................................................................................... 41
Major components of defence response ............................................................................. 41
1.2.6.1. Physical barriers ........................................................................................................ 42
a)
Maize silk....................................................................................................................... 42
b)
Maize kernel.................................................................................................................. 43
1.2.6.2. Defence signalling pathways in maize against Fusarium ........................................ 45
5
Pathogenesis-related (PR) proteins ..................................................................................... 45
Plant antioxidants ................................................................................................................. 45
Fusarium infection ................................................................................................................ 46
1.2.6.3. Genetic characterization of resistance to GER and FER .......................................... 47
Resistance genes of maize .................................................................................................... 47
Pathogenicity genes in Fusarium spp................................................................................... 47
Fusarium graminearum ........................................................................................................ 47
1.2.7.
Biotechnology and plant breeding ................................................................................... 50
QTL mapping ......................................................................................................................... 50
Near-isogenic line (NIL)......................................................................................................... 51
Bt maize................................................................................................................................. 53
Association mapping............................................................................................................. 53
SNP (Single-Nucleotid Polymorphism) ................................................................................. 54
Heritability and correlation .................................................................................................. 54
1.3.
Integrated Maize Management to control GER ....................................................................... 56
1.3.1.
Preventive control of ear rot ............................................................................................ 56
Disease forecasting ............................................................................................................... 56
1.3.2.
Agricultural practices ........................................................................................................ 57
1.3.2.1. Crop rotation ................................................................................................................. 57
1.3.2.3. Maturity group .............................................................................................................. 58
1.3.2.4. Crop residue management and soil tillage .................................................................. 58
1.3.2.5. Sowing and Seed quality .............................................................................................. 60
Sowing time ........................................................................................................................... 60
Crop structure ....................................................................................................................... 60
1.3.2.6. Fertilization ................................................................................................................... 60
1.3.2.7. Chemical control of Fusarium infection ....................................................................... 61
Fungicides ............................................................................................................................. 61
Mode of action - DMI fungicides.......................................................................................... 61
Dithiocarbamates ................................................................................................................. 62
1.3.2.8. Biological control measures ......................................................................................... 62
1.3.2.9. Harvest time and storage ............................................................................................. 63
1.3.3.
Post-harvest measures ..................................................................................................... 64
1.3.4.
Mycotoxin reduction in grain chains ................................................................................ 64
6
Material and methods ........................................................................................................................... 66
2.
Field experiments .......................................................................................................................... 66
2.1. Inocolum production ................................................................................................................. 66
2.2. Artificial inoculation .................................................................................................................. 67
2.3.
Maize disease assessment .................................................................................................... 68
2.4.
Statistical analysis ................................................................................................................. 69
Results ................................................................................................................................................... 72
3.1.
Analysis of variance .............................................................................................................. 72
3.1.1.
Disease incidence DI (%) ................................................................................................ 73
3.1.2.
Disease severity DS (%) .................................................................................................. 73
3.1.3.
Disease intensity DINT (%) ............................................................................................. 74
3.2.
Visual symptoms after silk channel inoculation .................................................................. 75
3.3. Correlation analysis and scatterplots ....................................................................................... 79
3.3.1. Correlation between disease severity and disease incidence .......................................... 79
3.3.2. Correlation between maize maturity group and disease severity.................................... 80
3.3.3. European Corn Borer (Ostrinia nubilalis) ........................................................................... 81
Discussion .............................................................................................................................................. 83
4.1. Disease resistance ranking ........................................................................................................ 90
4.2. The influence of the ear structure on the GER incidence and severity ................................... 93
Appendix I. BBCH growth stages of maize (Meier 2001) ...................................................................... 96
Appendix II. Table with the experimental setup ................................................................................... 97
Appendix III. Gibberella ear rot disease evaluation scale…………………………………………………………………..98
Appendix IV. Maturity groups of the maize genotypes with disease severity (%) ................................ 99
Appendix V. Disease incidence (n=2)………………………………………………………………………………………………100
Appendix VI. Disease severity (n=2)………………………………………………………………………………………………..101
Appendix VII. Disease intensity (n=2)……………………………………………………………………………………………..102
Appendix VIII. Disease resistance ranking of 90 genotypes according to sum of the standard
competition ranking of the main three parameters DI%, DS% and DINT% (heavy + slightly= total)…103
Figures: ................................................................................................................................................ 105
Tables: ................................................................................................................................................. 108
Reference ............................................................................................................................................ 110
7
List of Abbreviations
µ
AGES
AGES1-90
ANOVA
BBCH-scale
BBM
CRB
DI
DIheavy
DINT
DINTheavy
DINTslighly
DINTtotal
DIslighly
DItotal
DS
DSheavy
DSslighly
DStotal
DTS
e
ECB
e.g.
ER
F.
FER
G
GER
𝐻0
H1
IPM
mL
LSD
p
r
RCB
SCL
%
grand mean
Austrian agency for health and food safety
Abbreviation of genotypes used
Analysis of variance
Phenological development stages of a plant
Bubble breeding method
Small-plot inoculation
Disease incidence
Disease incidence heavy
Disease intensity
Disease intensity heavy
Disease intensity slightly
Disease intensity heavy
Disease incidence slightly
Disease incidence total
Disease severity
Disease severity heavy
Disease severity slightly
Disease severity total
Number of days to silking
Random error
European Corn Borer
For example
Ear rot
Fusarium
Fusarium ear rot
Genotype
Gibberella ear rot
Null hypothesis
Alternative hypothesis
Integrated pest management
Milliliter
Least significant Difference
Probability
Correlation coefficient
Randomized complete block
Silk channel length
Percentage
8
Introduction
Numerous ear rot disease caused by fungi have been reported on maize, including Gibberella ear rot
caused by Fusarium graminearum (teleomorph Gibberella zeae), and Fusarium ear rot, caused by F.
verticillioides, F. proliferatum, and F. subglutinans (Payne 1999). They can cause significant yield loss,
and downgrade grain quality due to discoloration, loss of nutritional value, as well as the health
hazards due to consumption of grains contaminated with the mycotoxins produced by this pathogen.
Specifically, mycotoxins are secondary metabolic compounds, which, at sufficient concentrations,
may be toxic to domesticated animals and humans (Bennett 2003). It is of major importance to
agriculture to prevent the ear rot disease development. Among all agronomic practices that reduce
the growth of the fungi, the best control strategy remains the planting of resistant maize hybrids.
Purpose of the present work
The Institute for Biotechnology in Plant Production at the IFA supports the AGES (Austrian Agency for
Health and Food Safety) with testing of new submitted maize hybrids for Fusarium ear rot resistance
using artificial inoculation. It is the strategy of AGES to grow new candidates on several locations
using natural infection and artificial inoculation for Fusarium ear rot.
The purpose of this master thesis was to test 90 Austrian maize cultivars with regard to resistance
behaviour against the ear rot (Fusarium disease) of maize. It was specifically concerned with the
effect of the Fusarium infection through the silk channel on different maize hybrids.
In order to assess the field silk resistance an artificial inoculation technique on the hybrids, growing
in a randomized complete block was performed by spraying conidial suspension of Fusarium
graminearum directly on the silk without wounding. The crop was kept humid using a mist irrigation
system to promote infection. At the end of the growth season the ears were dehusked and the
diseased ear area was assessed.
9
Literature review
1.1 Maize
1.1.1. The origin of maize
The genus Zea belongs to the tribe Andropogoneae in the subfamily Panicoideae in the family
Poaceae or Gramineae. Zea mays ssp. mays is the only cultivated species; known as maize (“corn”,
“Mais”). The other species and subspecies of this family are wild grasses, referred to as teosintes.
Collective information during the past 60 years suggests that teosinte (Zea mays L.: ssp. Mexicana)
was the putative parent of modern-day maize (Wilkes 2004; Hallauer 2009).
Maize originated in the Western Hemisphere in the highlands of southern Mexico and Guatemala
about 7,000 to 10,000 years ago, from where it spread rapidly. At the end of the fifteenth century,
after the discovery of the American continent by Christopher Columbus, maize was introduced into
Europe through Spain and spread through the warmer climates of the Mediterranean and later to
northern Europe (Wilkes 2004).
1.1.2.
Cultivation practices and commercial use
Maize is an annual plant and reproduces exclusively by seed. The main parameters are characterized
by some cultural practices such as the vegetation period, required humidity, irrigation and time of
harvest.
The production, storage and processing of maize is of great importance from breeding activities to
industrial use. Maize is a very important crop for Austria. Recent data showed that the Austrian
cultivation and production of maize increased in the last years. In 1950 it was registered 58 600 ha
land area planted with annual total yield of 119 892 tons. The maize production drastically increased
and in 2012 was 219 700 ha with 2 351 370 tons (Statistik 2014). Moreover, the worldwide demand
for maize is projected to increase by 50% to over 800 million tons per year by the year 2020 and will
surpass both rice and wheat in global demand (Pingali and Pandey 2000).
Maize has been processed for a range of uses as major bulk commodity, a feedstuff for animals,
especially the corn-cob-mix (CCM), starch production, ethyl alcohol (ethanol) production, food and
seed-maize (Gyori 2000). The maize kernel finds its way into our life as edible products, including the
use of corn-on-cob for human nutrition as boiling the whole cob in water, tortilla, hominy, flat
breads, beverages, different fermented and non-fermented porridges, snakes, corn cakes and
polenta (Gyori 2000). In addition, maize is also used in the manufacture of non-food products
including ceramics, drugs, paints, paper goods, and textiles (Jones 2003). Interesting fact is that the
maize leaves and silks could be transformed into beautiful puppets and souvenirs originating from
the Bulgarian tradition (Figure 1).
10
Figure 1. Puppet (left) and flowers (right) from maize husks (Bulphoto 2014)
1.1.3. The structure and chemical composition of maize
1.1.3.1. Phenology
The development of the plant may be divided into two physiological stages:
the vegetative stage (V) where different tissues develop and differentiate until the flower
structures appear;
the reproductive stage (R) that begins with the fertilization of the female structures, which
will develop into ears and grains.
Further descriptions of the main stages of maize development are based on the BBCH-scale of maize
shown in detail in Appendix I (Meier 2001).
1.1.3.2. Morphology
The typical maize plant is a tall (1 – 4 m) annual grass (monocot) which forms a seasonal root system
bearing a single erect stem (culm) made up of nodes and internodes. Many temperate cultivars are
shorter than tropical (and subtropical) cultivars. Each leaf consists of a sheath surrounding the stalk
and an expanded blade connected to the sheath by the blade joint, or collar (Bennetzen and Hake
2009).
1.1.3.3. Reproductive morphology
According to the reproductive morphology, the maize is a monoeicious and a cross-pollinated species
with unique and separate male (tassel) and female (ear) organs shown in Figure 2, which represent
Inflorescence emergence (Heading) or BBCH 51 till end of flowering, when stigmata is completely dry
at BBCH 67 – 69 stages.
The typical mature male inflorescence (Figure 2a), or tassel, is terminated by several long branches at
the base of the central spike. The mature female inflorescence, or ear (Figure 2b), arise from axillary
buds and bear flowers in rows of ovaries along the cob covered with husk leaves. The number of
ovules that will develop into kernels ranges from 300- 1000 and is dependent on the cultivar (variety)
as factors occurring later in the development.
11
The silks of maize ear are the stylar canals of the mature ovaries (Kiesselbach 1949; J. H. Bennetzen
2009; Purseglove 1976). The silks have short hairs, trichomes, which form an angle to the stylar
canals and help harbouring pollen grains. Receptive silks are moist and sticky (Paliwal 2000). In
addition, maize is often considered as protandrous, as anthers on the spikelets protrude out of the
florets and start shedding pollen one or two days before the silks emerge above the husks. However,
the gynoecium matures and the silks become receptive before they appear above the husk tips
(Paliwal 2000).
Figure 2: General morphology of maize inflorescences. (Bennetzen and Hake 2009)
1.1.3.4. Structure and nutrient components of the maize kernel
Maize kernels develop through accumulation of the products of photosynthesis, root absorption and
metabolism of the maize plant on the female inflorescence (ear). Maize is known botanically as a
caryopsis and is similar to other cereal grains in that its grain consists of the four main parts: the
pericarp, hull or bran; the endosperm; the germ or embryo and the tip cap (dead tissue found where
the kernel joins the cob). As regards its dimensions, the average corn grain is 8–17 mm long, its width
is 4–6 mm and the weight of 1000 grains is 250–400 grams.
The kernel is composed of the following main groups of constituents: water (moisture content),
carbohydrates including fibre as non-starch polysaccharides, proteins, amino acids, lipids, as well as
vitamins, enzymes, mineral substances, coloured components, and compounds that produce the
flavour and aroma characteristics. The proportions of these constituents differ from one grain type to
another (Gyori 2000). The precise structure and chemical composition of the kernel depend on the
type the kernel itself belongs to. The kernel types of endosperm are divided into dent (Horse-tooth),
flint, flour, sweet, pop and pod (Ramstad 1991).
12
The determination of maize quality includes physical properties and characteristics of kernel and bulk
maize. Furthermore, the most important properties are purity, the evenness of kernels, hectoliter
weight, hundred-kernel weight and roundness, heat conductivity and combustion heat (Ramstad
1991). The main maize characteristics are kernel soundness, moisture content, hardness and density
or vitreousness (Gyori 2000).
However, the four major quality attributes are broken maize and foreign material, damaged kernels
and test weight (Gyori 2000). These parameters are also listed in the Directive of EU According to
food-standard specifications for the quality of maize and the foods made from it (EC 824/2000)
divided into two categories establishing nutritional and feed values and testing industrial processing
standards.
Also, toxins can be present in maize kernels. In general, all parts of the maize plant are susceptible to
certain diseases (Payne 1999). As a consequence, much attention is paid to diseases of the maize ear,
because fungal metabolism within the kernels results in a net decrease in grain dry matter content,
kernel density, and total grain yield (Seitz 1982). Since those toxins are produced by plant-associated
microorganisms rather than the plant itself, they are discussed in Section 1.1.6.
13
1.1.4.
Gibberella ear rot and Fusarium ear rot
Among all ear rot diseases invading the gramineous hosts especially the maize plants are Diplodia ear
rot, Fusarium ear rot, Gibberella ear rot, Nigrospora ear rot, Gray ear rot, Penicillium rot, Black ear
rot, Hormodendrum kernel rot, Rhizopus ear mold and Aspergillus ear rot (Koehler 1959). They affect
grain yield and particularly grain quality. Among the various ear rot diseases of maize, the most
prevalent in Europe are those caused by the genus Fusarium. Contamination with mycotoxins
produced by Fusaria is a potential threat that downgrades the agriculture products and is toxic to
human and animal consumption.
1.1.4.1. The Pathogen
The genus Fusarium includes a range of toxigenic fungi, which give cause for concern in many maize
growing regions in the world. Occurrence of different Fusarium species on maize in Europe is very
much dependent on the climatic conditions (Logrieco, Mule and Moretti, et al. 2002). Outbreaks of
diseases caused by intoxication of humans and livestock were consistently reported (Saggese 2009).
Based on the sexual stage, Gibberella zeae (Schwein) Petch (anamorph: Fusarium graminearum
Schwabe), the major causal agent of ear rot can be classified in the Kingdom Fungi, Phylum
Ascomycota, Subphylum Pezizomycotina, Class Sordariomycetidae, Subclass Hypocreomycetidae,
Order Hypocreales, Family Nectriaceae, and genus Gibberella (Goswami and Kistler 2004). The main
causative agents of the ear rots are members of section Discolor, Roseum, Elegans, MatriellaVentricosum, Liseola, Gibbosum and Sporotrichiella of the Fusarium genus.
1.1.4.2. Host plants
Almost all cultivated plants in the Gramineae are a host of a Fusarium species (Parry, Jenkinson and
Mcleod 1995). The genus Fusarium can attack wheat (Triticum aestivum), durum wheat (Triticum
durum), barley (Hordeum vulgare) and oat (Avena sativa) (Atanasoff 1920; Agrios 2005).
1.1.4.3. Fusarium species of cereals
The most prevalent Fusarium species, which are associated with stalk and ear rot of maize listed in
Table 1. The names are divided into Fusarium anamorphs and Fusarium teleomorphs.
In order to understand the routes of infection, the life cycle of Fusarium must be considered. Most
species have a full development cycle with both perfect stage (sexual spores) and imperfect stage
(asexual spores). They are both important sources of infection. This form of reproduction is found in
most Fusarium species, including F. graminearum.
Furthermore, the teleomorph is the sexual part of the development cycle, in which the fungus forms
the overwintering fruiting bodies called perithecia with sexually formed ascospores for persistence
on infested straw (Agrios 2005). Gibberella is accepted as the correct name for the perithecial state
of some anamorphs. In other words, to the teleomorph forms belong peritecia, asci and ascospores.
Therefore some species like F. culmorum, F. cerealis and F. poae lack a Gibberella teleomorph. In
addition, F. verticillioides, F. proliferatum and F. subglutinans are heterothallic species, but in
14
contrast to F. graminearum, sexual reproduction does not play a role as an important dispersal
factor.
The anamorphic stage contains sporodochia with macro- and microconidia, and the chlamidospores,
which location in either in the macro- or microconidia or in the mycelia (Reis 1990). For instance,
macroconidia (asexual spores) of Fusarium graminearum are derived from conidium-producing cells
called phialides, which are clustered together in cushion-shaped masses known as sporodochia. The
macroconidia are hyaline, canoe-shaped spores usually with five or more septa (Figure 3 and Figure
4). In addition, spore morphology (see Figure 4) is the major character in the identification of Fusaria
(Booth 1971).
Figure 3. Microscopic photo of macroconidia of Fusarium graminearum
The research of the spectrum of the Fusarium spp. of grains or plant parts show that more species
can always colonize the host at the same time, which is determined by the weather and
competitiveness of pathogens (Schweyda 1996).
Figure 4. The most common Fusarium species on maize: mycelium on agar plates (left) and the appropriate spores (right):
a) Fusarium verticillioides, b) F. proliferatum, c) F. subglutinans, d) F. graminearum, e) F. equiseti, f) F. crookwellense
(Fotos Brigitte Dorn, Agroscope ART; Andreas Hecker, Agroscope ART)
15
Table 1. The species found associated most frequently with Ear rots of and important mycotoxins in maize and other small-grain cereals (Brown 2013; Barug 2006)
Anamorph
Teleomorph
Sections
Associated mycotoxins
F. avenaceum
Gibberella avenacea (R. J. Cook)
Roseum
MON, BEA, ENN
F. cerealis (F. crookwellense) L.W. Burgess,
Nelson & Toussoun
unknown
Discolor
DON, ZON, NIV, FUS-X
F. culmorum (W. G. Smith) Sacc.
unknown
Discolor
F. equiseti (Corda.) Sacc.
Gibberella intricans Wollenw.
Gibbosum
F. graminearum Schwabe
Gibberella zeae (Schweinitz) Petch
Discolor
F. oxysporum
unknown
Elegans
MON, BEA, ENN
F. poae
unknown
Sporotrichiella
DAS, MAS, NIV, FUS, T2,
HT2, NEO
F. proliferatum (Matsush.) Nirenberg
Gibberella intermedia (Kuhlmann) Samuels, Nirenberg
& Seifert
Liseola
FB1, FB2, BEA, MON, ENN
F. solani (Mart.)
Nectria haematococca (Berk. & Br.)
Martiella
F. sporotrichioides
unknown
Sporotrichiella
T2, HT2, NEO, MAS, DAS
F. subglutinans (Wollenw. & Reinking) Nelson,
Toussoun & Marasas
Gibberella subglutinans Nelson, Toussoun & marsas
(syn.: G. fujikuroi var subglutinans Edwards
Liseola
BEA, MON, FUP, DAS,
FUM
F. tricinctum
Gibberella tricincta
Sporotrichiella
MON, ENN
F. verticillioides (Sacc.) Nirenberg
(synonym F. moniliforme = Sheld.)
Gibberella moniliformis Wineland
(= G. fujikuroi (Sawada) Wollenw.)
Liseola
FB1, FB2, FB3, BEA
DON, ZEN, NIV, FUS, ZOH,
AcDON
DAS, ZEN, ZOH, NIV,
DAcNIV, MAS, FUS, MON
DON, ZEN, NIV, FUS,
AcDON, DAcDON, DAcNIV
-
AcDON = mono-acetyldeoxynivalenol (3-AcDON, 15-AcDON); BEA = beauvericin; DAcDON = di-acetyldeoxynivalenol; DAcNIV = di-acetylnivalenol; DAS = diacetoxyscirpenol;
DON = deoxynivalenol; ENN = enniatins; FUM= fumonisins, FB1=fumonisin B1, FB2=fumonisin B2, FB3=fumonisin B3; FUP = fusaproliferin; FUS = fusarenone; HT2 = HT-2 toxin;
MAS = monoacetoxyscirpenol; MON = moniliformin; NEO = neosolaniol; NIV = nivalenol; T2 = T-2 toxin; ZEN = zearalenone; ZOH = zearalenols.
16
Two ear rots have been described on the maize ears: pink ear rot or Fusarium ear rot (FER), usually
caused by members of Liseola section, such as F. verticillioides, F. proliferatum, and F. subglutinans,
and red ear rot or Gibberella ear rot (GER), usually caused by species in the Discolor section, with F.
graminearum the most common species.
Occurrence of different Fusarium species on maize in Europe is very much dependent on the climatic
conditions (Logrieco and Mule 2002). Gibberella ear rot (GER) is prevalent in Central Europe and
especially in Austria. According to the project “Optimization of a reliable methodology to assess the
genetic determination and differentiation of susceptibility to ear fusariosis in the assortment of
maize in Austria” by Austrian Agency for Health and Food Safety (AGES) the most frequently isolated
species from maize for 2013 are F. subglutinans, F. proliferatum and F. verticillioides. The decrease of
F. graminearum is also observed in comparison with previous years.
1.1.4.4. Disease development
The main events of stages comprising the disease cycle include the production and dissemination of
primary inoculum, primary infection, growth and development of the pathogen, secondary infection
and overwintering (Drenth 2004).
Ear rot epidemics are normally completed in one infection cycle. Secondary spread and polycyclic
epidemics would require longer periods of favorable conditions and host susceptibility.
Once the infection process is accomplished, regardless of whether by ascospores or conidia,
mycelium is formed and mycelium can give rise to macroconidia initially, and later in the season to
perithecia (Sutton 1982). The fungus is released back to the soil through infected stalks or infected
seed. Epidemiologically, temperature and moisture appear to be the most influential factors affecting
the development of ear and stalk rot (Miller 1994). The disease cycle of F. graminearum is
summarized in Figure 5.
Figure 5: Disease cycle of F. graminearum (Pioneer)
17
1.1.4.5. Survival of Fusarium species
Fusarium graminearum mycelia overwinter on plant debris in the soil, where they exhibit a
saprophytical lifestyle (Goswami 2004). Colonized maize residues, as well as residues from a broad
range of other host crops, provide overwintering refuge for the fungus (Booth 1971). Moreover,
Fusarium verticillioides can be seedborne and grow as an endophyte within maize plants (Bacon,
Glenn and Yates 2008; Lee, Pan and May 2009). These Fusarium species, particularly F. verticillioides,
survive in maize stalks as thickened hyphae in moist soils that have poor aeration and little or no
competition with other fungi and bacteria. Munkvold reviewed the roles of residue size and burial
depth in the survival of F. verticillioides, F. subglutinans and F. proliferatum in maize (Munkvold and
Cotten 1998). They concluded that Fusarium strains can survive at least 630 days on the surface of
buried residues (Munkvold and Cotten 1998).
The reproductive structures produced in nature by Fusarium species are micro- and macroconidia,
and chlamydospores. Furthermore, not all of them produce chlamydospores, for example F.
avenaceum. The homothallic F. graminearum forms dormant spores (chlamydospores), which do not
germinate immediately after their formation, overwinter in plant debris. F. proliferatum and F.
subglutinans, produce numerous microconidia, no chlamydospores, and relatively few macroconidia
(Brown 2013). They differ in the manner in which the microconidia are produced in that F.
verticillioides produces microconidia in long chains from monophialides, while F. proliferatum
produces microconidia in shorter chains, with occasional false heads, from both monophialides and
polyphialides.
Chlamydospores germinate in spring and the mycelium produce perithecia, which eject ascospores in
the air. This is the most important dispersal structure (Munkvold 2003).
Perithecia are fruiting bodies of the pathogen, spherical and borne on the surface of diseased maize
stab. Additionally, the specialized hyphae in mycelia have been shown to store significant quantities
of lipids, which are later used to fuel perithecium formation (Brown 2013). Therefore, perithecia
undergo morphogenic processes and four “tissue types”: perithecium wall, ascogenous hyphae,
paraphyses and periphyses, and asci with ascospores. Thus, perithecia are reported to retain viability
for up to 16 months on maize kernels (Reis 1990) or 23 months on wheat straw residue (Pereyra, DillMacky and Sims 2004) under field conditions, with viability usually lasting longer than the ability to
sporulate. Ascospores of G. zeae are preferentially released at night (Paulitz 1996) and the formation
of perithecia depends on light (Tschanz, Horst and Nelson 1976). As symptoms progress, F.
graminearum forms purplish-black perithecia due to a combination of two pigments (Gaffoor, et al.
2005) and F. verticillioides has blue-black perithecia (Brown 2013).
The ascogenous hyphae expand as the paraphyses recede, becoming the predominant tissue in the
centrum and producing asci and ascospores (Brown 2013). The asci mature under warm, wet
weather during the spring or summer, producing eight ascospores within each ascus. In all species,
ascospores are forcibly discharged into the air from the mature perithecia. For the infection of the
ascospores, it is important that the infested crop residues lie on the soil surface in order to dismiss
the airworthy ascospores from their fruiting bodies. The ascospores of G. zeae lost their viability after
being discharged from perithecia. This finding illustrates that ascospores need favourable conditions
18
for germination immediately after discharge. In fact, ascospore discharge increases with relative
humidity (Trail, et al. 2002).
1.1.4.6. Mode of entry
Mode of entry of the fungal pathogen into the host is really important for the successful
establishment of a parasitic relationship between the pathogen and the host. Spores can reach the
silks not only by anemochory or hydrochory, but also through entomochory. Hidrochory, which uses
kinetic energy of falling raindrops, or dispersal by water is the main autonomous dissemination of
spores during the silking period. In general, there are three main pathways for the infection:
Silk route: growth of mycelium, produced by germinated spores, down the silks to the
kernels and cob (rachis);
Entry through wounds created by a spore-carrying insects and/or birds (Koehler 1942).
Systemic growth of the pathogen via the stalk;
Fusarium graminearum enters into maize ears via the first two major routes. Where the F.
verticillioides can enter the maize ears not only via an air- or splash-borne infection by conidia
through silks or wounds, but also the soil borne hyphae germinate and infect the germinating seed
and roots and move up the plant through systemic growth (Munkvold, McGee and Carlton 1997b).
Thus, the primary infection pathway in maize kernels is via silk channel and susceptibility remains
high during the first 6 days after silk emergence (Reid and Hamilton 1996). During the flowering the
first anthers start to shed pollen from the upper third of the main spike of the tassel (BBCH 63) and
then spread out over the whole tassel down to the lower branches. This is the stage where Fusarium
may first intercept the ear structure. A pollen grain starts germinating within minutes of coming into
contact with a silk (Bennetzen and Hake 2009). Duncan summarized and discussed how Fusarium
entered ears, in the absence of vectors (Duncan and Howard 2010):
1.
2.
3.
the growth of hyphae along the outside of silks (stigmas) from outside the husk covering
to the inside of the silk channel;
penetration into the exposed silks that hang outside the husks and growth within stylar
tissue, perhaps following the path of pollen tubes down to the kernel;
movement of conidia from exposed silks to regions deep inside the husk-covering
facilitated by movement of free water, perhaps dictated under forces of capillarity.
Direct invasion of kernels can also occur through weak points such as stress cracks in the pericarp
and through the pedicel. Silks are highly susceptible 2-6 days after emergence; kernels are
susceptible until physiological maturity (Kant 2011) Premature ripening caused by early-season plant
stress also may play a role in stalk infection (Windels and Kommendahl 1984). Moreover stalk
infection plays an important role in the epidemiology of Gibberella ear and stalk rot because this
tissue is the primary substrate for overwintering and inoculum production the following season
(Konga and Sutton 1988).
Using the vegetative compatibility as a marker to track fungal movement in the maize, Munkvold et
al. (1997b) reported and obtained evidence for the systemic infection of Fusarium verticillioides
19
either carried inside the seeds or on the seed surface. The fungus develops inside the young plant,
moving from the roots to the stalk and finally to the cob and kernels (Kedera, Leslie and Claflin 1992).
The silk route is the most important pathway, but some infections to the kernels are clearly initiated
by direct injury through birds, lepidopteran insects or thrips. Additionally, insect transmission was
also observed and discussed in part “Vectors”.
1.1.4.7. Symptoms/ Pathology and Epidemiology
a) Red Ear Rot or Gibberella Ear Rot (GER)
Gibberella ear rot (GER) caused by Fusarium graminearum (teleomorph Gibberella zeae) is prevalent
in Central Europe and especially in Austria. The most frequently isolated species causing from GER
species complex are F. graminearum, F. culmorum, F. cerealis, and F. avenaceum (teleomorphGibberella avenacea). Next to this fungus, F. culmorum can also induce this disease (Logrieco 2002).
Figure 6. Severe symptoms of Gibberella Ear Rot on artificially infected maize ears
Red ear rot (RER) primary occurs on the tip of the cob, developing a pink to reddish mycelium, which
covers big parts of the cob (Figure 6). Additionally, the mold may be very pale in some cases, causing
it to be confused with other ear rots. Early, severely infected ears may rot completely, with husks
adhering tightly to the ear and the mold growing between the husks and ear (Payne 1999).
Sometimes the purplish-black perithecia or brownish perithecia can additionally be observed on
infected husks (Logrieco, Mule, et al. 2002; Gaffoor, et al. 2005).
20
In addition to ascospores, conidia, chlamydospores, or hyphal fragments of Fusarium graminearum
can also serve as inoculum for GER. Copious asexual spores (conidia) are produced on the surface of
infected plants or on crop residue during damp periods. The fusiform shape of the conidia are
produced in slimy masses borne on sporodochia (cushion-shaped hyphal structures) (Trail 2009),
which has been associated with rain-splash dispersal (Deacon 2006). F. graminearum requires
succulent silk tissue, less than 8 to 10 days old, for infection (Reid, Bolton, et al. 1992).
In contrast, colonization of silks by F. moniliforme occurs more frequently after the onset of silk
senescence (Reid, Nicol, et al. 1999). The spores germinate on the silks, form a germination tube and
grow through the silks to the cob.
Infection by F. graminearum often leaves grain contaminated with mycotoxins. The most frequently
encountered Fusarium mycotoxins associated with GER are deoxynivalenol (DON), type B
trichothecenes including nivalenol, and zearalenone. Colonization of developing seeds by Fusarium
graminearum is accompanied by DON accumulation. Moreover, DON also functions as a virulence
factor in maize (Harris, et al. 1999).
b) Pink Fusariosis or Fusarium ear rot (FER)
Pink Fusariosis or Fusarium ear rot (FER) is caused primarily by members of the Liseola section, such
as F. verticillioides, F. proliferatum, and F. subglutinans, which were a part of the F. moniliforme
species complex (Snyder and Hansen 1954).
The heterothallic F. verticillioides can infect both through the mode of systemic infections from
contaminated seed, as well as through the silk channel by airborne spores. The fungus also produces
airborne spores from sporulation on the tassels of the previous crop residue. They are translocated
via wind, rain or insect transmission on the cobs.
Silk colonisation by F. verticillioides starts from the tip of the ear, and progresses down the ear during
grain fill. Infection is enhanced not only at the base of the ear by late-season rainfall, but also by the
physiological state of the silks after pollination. A random kernel rot phase of the disease can also
occur, appearing as randomly scattered individuals or group of kernels, usually tan to brown.
(Logrieco and Mule 2002). Silk infection seems to be the most important pathway for the fungus to
enter the plant (Munkvold, McGee und Carlton 1997b).
The most visible symptoms of Fusarium ear rot (FER) on ears are a white-pink mold colony on the
silks that may grow with the silks from the tip of the ear to its base (Payne 1999). In severe cases of
FER, the entire ear may be whitish on and between kernels. As the ear ripens, the fungal mycelia
darken and become less prominent (Payne 1999). In some host genotypes and environments, the
“starburst” symptom is more prevalent; this symptom is characterized by white-colored streaks
beneath the pericarp, radiating basipetally from the kernel silkscar (where the silks were attached)
(Koehler 1942; Payne 1999).
In addition, on wounded maize ears, F. verticillioides seems to act as an opportunistic saprophyte but
this fungus is also known to have an endophytic development in maize. In that case, the fungal
development is symptomless but may become pathogenic under certain conditions (Bacon, Glenn
21
and Yates 2008). Based on these results, Picot hypothesized that this change in the biological
comportment of F. verticillioides may be induced by the presence of F. graminearum or damage
caused by this fungus (Picot, Hourcade-Marcolla, et al. 2012).
Production of fumonisins in grain before harvest has been documented under many environmental
conditions. Both F. proliferatum and F. verticillioides are capable of synthesizing large amounts of
fumonisins and are widely distributed in most places where maize is grown. The severity of the FER
disease, and the occurrence and prevalence of the causal species, may vary by region and year.
1.1.5.
Factors influencing the infection by Fusarium spp. of maize and mycotoxin production
Several factors may influence the presence of Fusarium species on maize and their capacity to
produce mycotoxins. They can be divided into biological, physical and chemical factors. Disease
development can be favoured or decreased by the presence of pollen, anthers, silks and senescent
floral bracts that act as a potential substrate for establishment of infections (Sutton 1982).
1.1.5.1. Biological factors
a) Fungal damage
Maize is susceptible to infection of different ear rots, as was mentioned in section 1.1.5. In particular,
it is possible the interaction between Fusaria and different other species causing ear rots on maize.
b) Vectors
Many different insect species are associated with GER and FER. Insects serve as vectors and damage
the kernels, which can favour the fungal infection through kernel wounds by Fusarium species, not
only in the field, but also in the storage.
European corn borer
In particular, the damage produced by the second-generation larvae (from the middle of July) of
European corn borer (ECB) population, Ostrinia nubilalis (Hübner), (Lepidoptera: Crambidae) has an
impact on maize ear rot. During the larvae movements and feeding it favors the fungal development
through tunnels into the stalk and kernel wounds, which leads to the Fusarium easier access to
susceptible crop tissues (Dowd 1998; Papst, Utz, et al. 2005). Therefore the colonization of the plants
is supported by vectoring microconidia of F. verticillioides from leaves into ears, causing kernel
infection (Sobek and Munkvold 1999). Following European corn borer (ECB) attacks, kernels damaged
by the insects often are covered by fungal growth as it is shown in Figure 7 and Figure 8.
22
Figure 7. Different types of larvae entrances by ECB (Tulln, 2013)
Figure 8. Damage by the second generation of the European corn borer (ECB), (Tulln, 2013)
Thrips
Both the western flower thrips (Frankliniella occidentalis (Pergande)), and the corn thrips
(Frankliniella williamsi Hood) have been reported on maize (Hudson 1999; Godfrey, et al. 2006).
Intra-ear thrips populations with fumonisins kernels, Frankliniella spp., especially F. occidentalis and
F. williamsi, risk to maize posed by the insect (Parsons 2012). For maize, the most widely reported
thrips damage occurs at the early vegetative crop stage. Populations grow on early developing weed
or crop hosts like alfalfa in the spring, and then move to maize when these alternate hosts are cut or
senesce (Hudson 1999). Thrips infestation of maize ears has been reported only in California, USA
(Farrar and Davis 1999; Parsons 2008). Although, the western flower thrips has not been reported to
infest maize in Austria, field observations are required regularly after maize pollination.
23
Storage insects
During the storage time wounding by insects may provide an opportunity for the fungus to
circumvent the natural protection and establish infection sites in the vulnerable interior. Moreover, if
left unchecked, infestations of Situphilus spp. can cause devastating damage to stored grain such as
lot of heat and moisture, subsequently, extensive quality loss, mould growth and growth of
populations of other insects.
Sitophilus granaries, S. oryzae and S. zeamais are members of genus Sitophilus, family Curculionidae
or true weevils, are among the most important pests of stored maize kernels. Feeding by larvae
leaves large cavities inside the maize grains and newly emerging adults leave behind large ragged
emergence holes. Adults also cause further damage by feeding (Rees 2004).
24
1.1.5.2. Physical factors
a) The aerobiology of Gibberella zeae
Viable propagules (ascospores and macroconidia) of G. zeae exist in the air before, during, and after
maize flowering. Generally, the majority of spores dispersed from crop residues travel only short
distances, but given appropriate weather conditions and wind, spores may spread over long
distances. Researchers are studying the long-range aerial dispersal of the pathogen. Spores of G. zeae
have been collected hundreds of km in the air over agricultural fields, forests, and lakes (Schmale and
Bergstrom 2007).
The periodicity of aerial dispersion has also been studied. Furthermore, Paulitz (1996) reported that
spore release often occurs between 18:00 h and early morning (04:00–08:00 h), mostly before
midnight, with a peak at around 23:00 h. For F. graminearum, the maximum ascospore concentration
observed is of the order of 4333 ascospores/mm3 in 1 h. Paulitz also reported spores of other species
of Fusarium to be continuously present in the air, but with densities varying according to the period
of the day (Paulitz 1996; Champeil, Doré and Fourbet 2004).
b) Impact of environmental factors
Moisture Requirements
Water activity (aw) has a significant impact on growth and mycotoxin production. Cool, wet weather
(rainfall or high relative humidity) during and after anthesis (BBCH 63- 69) provides optimal
conditions for the development of ear rot (GER). Exposure of heads of F. graminearum to moisture
reduces conidial formation time to 1-2 days and conidial number increases with humidity. Moreover,
conidia dispersal is strongly related to rain-splash, so that conidia continue to be produced for some
hours after rainfall (Rossi, et al. 2002). The germination of macroconidia, ascospores and
chlamydospores of F. graminearum is maximal between 0 and −20 bar and is inhibited between −60
and −80 bar (Sung and Cook 1981). Ascospore germination is inhibited beyond a threshold of −30 bar
(−3MPa) of water potential after 8 h of drought (Paulitz 1996). Nevertheless, optimal conditions for
the development of FER by F. verticillioides are warm to hot temperatures and dry conditions at silk
emergence and during grain-filling (Munkvold 2003).
The effect of temperature on Fusarium Growth
F. graminearum requires periods of warm temperatures with persistent wetness during July and
August (silking and early kernel development) (Sutton 1982; Reid, Nicol, et al. 1999). Doohan
reported that macroconidia of F. graminearum are produced at an optimum temperature of 30°C
and their production is severely inhibited below 15°C (Doohan, Brennan and Cooke 2003). Perithecial
production increases with the high in temperature up to 29°C and decreases sharply at temperatures
over 20°C. Fusarium species differ in their temperature requirements: optical growth of F.
graminearum occurs at 20- 25°C, and for F. culmorum and F. poae at 20°C (Table 2).
25
Table 2: Minimum, maximum and optimum temperature of the main Fusarium species (Bottalico, 1999)
Species
Fusarium culmorum
Fusarium graminearum
Fusarium sporotrichoides
Fusarium moniliforme
Fusarium proliferatum
Temperature
min (°C)
2
2
-2
5
5
Temperature
max (°C)
37
40
35
42
42
Temperature
optimum (°C)
25
24-26
12-15
25
25
c) Effect of pH on Fusarium Growth (Hydrogen Ion Content, pH)
As temperature, pH also plays a role in determining the ability of Fusarium spp. to grow or thrive in
particular environments. The pH dependence of germination may be interpreted as evidence for the
involvement of enzyme activity in the process of ascospore germination (Beyer and Verreet 2005).
For example, Clausen and Green observed the maximum activity of fungal polygalacturonase
between pH 3.4 and 3.9 (Clausen and Green 1996). Pigmentation, however, is the most markedly
affected by the pH of the medium although some species have the capacity to modify the pH of the
medium to their optimum requirements (Booth 1971).
However, low environmental pH (approximately 2.0) is also a prerequisite for optimal fumonisin
production, and this condition is most readily achieved in senescent or decaying host tissues where
starch is being metabolized (Miller 2001).
d) Other Growth Factors
Apart from optimum temperature, moisture, pH, etc. various other growth factors are required for
normal fungal growth. During the cob formation, when the plant assimilates are converted into sugar
in order the growing grains to be transported, the Fusarium species are encouraged by the high sugar
concentration in accelerated growth. Rintelen (1966) speaks in this context of increased "plant
susceptibility", which increases significantly with the transport of assimilates. Since Fusarium easy
utilizes soluble carbohydrates as a food source and the parenchyma cells of the stem lose its strength
by the decreasing sugar concentration, it can be assumed that both factors favour the spread of
Fusarium.
Nutrients can also influence disease development. Moreover, fungi can utilize a wide variety of
nitrogen sources (Marzluf 1997). Martin and MacLeod founded that extra nitrogen in form of
ammonium nitrate, as well as the use of plant growth regulator, increase the incidence of disease on
cereal grains (Martin and MacLeod 1991). It was also confirmed by Onken and Warren (1979) and it
was further concluded that ammonium forms of nitrogen cause greater disease intensity than forms
of nitrogen (Onken and Warren 1979). Unlike DON, though similar to zearalenone, fumonisins are
most readily produced under conditions of high oxygen tension, usually associated with senescent
host tissues (Miller 2001).
26
1.1.6.
Mycotoxins in maize
1.1.6.1. Occurrence of mycotoxins in maize after natural infection with different Fusarium spp. in
Austria
GER produces fungal metabolites known as mycotoxins. Strains of Fusarium can synthesize literally
hundreds of different secondary metabolites, most of whose function is completely unknown (Brown
2013). According to Barug (2006), the agriculturally important mycotoxins produced by Fusarium spp.
in maize are fumonisins, deoxynivalenol and zearalenone listed in Table 1. Regarding to the reduction
of the mycotoxin contamination of the maize kernels, the European Union (EU) established specific
maximum limits (see Table 3, Table 4 and Table 5) for certain food contaminants in unprocessed
maize, including DON, ZEA and FUM.
There is no doubt that species of Fusarium are widely distributed geographically and associated toxin
formation must be expected under specific conditions (see Table 2). In addition, mycotoxins are
commonly generated under favourable humidity and temperature, especially if the water content (or
more accurately, water activity) of the product is favourable for fungal growth. Other factors
affecting the mycotoxin production include substrate aeration, inoculum concentrations, microbial
interactions, mechanical damage, and insect infestation. Mycotoxin production in agricultural crops
and plants can occur at each point in the food chain (Figure 9), such as pre-harvest, harvest and
drying, and storage. In particular, poor agricultural and harvesting practices, such as improper drying,
handling, packaging, storage, and transport conditions, can promote fungal growth and increase the
risk of mycotoxin production (Gong, Jianga and Feng Chenb 2014). Avoiding mycotoxin occurrence in
the food chain involves understanding elements of strategies to manage mycotoxins, as visualised in
Figure 9 (CAST 2003).
Figure 9. Factors affecting mycotoxin occurrence in the food
and feed chain (Pestka and Casale 1989).
27
The dominant species in any given year depends upon the meteorological conditions in the regions of
cultivation, as stated by Leslie et al. (2014). Additionally, the project “KOFUMA” (2014) investigated
the spread of different Fusarium species and the mycotoxin situation in grain maize during the last
few years in Austria and showed that despite the overall low levels of DON, ZON and FUM some
varieties showed a higher risk of mycotoxin contamination with medians above 1000 μg/kg. The
results varied depending on the variety, location and year and were regularly beyond the legal
threshold level set by the European Commission. In addition, the variability of different mycotoxin
concentrations during the years in Austria could be seen in Figure 10.
Mycotoxin content of maize hybrids in Austria
Mycotoxin concentration in μg/kg
4500
DON- Mean
4000
DON- Median
3500
ZEA- Mean
3000
ZEA- Median
2500
FUM- Mean
2000
FUM- Median
1500
1000
500
0
2011
2012
2013
2014
Figure 10. Overview of medians and means of deoxynivalenol (DON), zearalenone (ZEA) and fumonisins (FUM) after
natural infection with different Fusarium spp. of different maize hybrids (2004-2010: Dersch and Krumphuber, 2011;
2011–2013: KOFUMA-Projekt; AGES, EMYKOM 2014). The arrows represent the maximum limits for DON (pink), ZEA
(lilac), FUM (green) in unprocessed maize grain (Commission Regulation (EC) 1881/2006).
28
1.1.6.2. Maximum levels and health hazards for Fusarium mycotoxins in maize
Maize may be colonized by strains from numerous Fusarium spp. (Table 1). Fusarium infection in
maize does not only impair the yield, it is also responsible for the contamination of food and
feedstuff with mycotoxins. Therefore, Commission Regulation (EC) No 1881/2006 of 19 December
2006 laid down maximum levels for certain contaminants in foodstuffs, including maximum levels for
deoxynivalenol, zearalenone and fumonisins (see Table 3, Table 4, and Table 5) (EFSA 2014) .
The maximum level applies to unprocessed cereals placed on the market for first-stage processing.
The regulation specifies that “first-stage processing” shall mean any physical or thermal treatment,
other than drying, of or on the grain. Cleaning, sorting and drying procedures are not considered to
be “first-stage processing” insofar no physical action is exerted on the grain kernel itself and the
whole grain remains intact after cleaning and sorting. In integrated production and processing
systems, the maximum level applies to the unprocessed cereals in case they are intended for firststage processing (EFSA 2014).
Although, of the Fusarium mycotoxins as thrichothecenes, ZEA and FUM have received the most
attention, there are also the possibilities of other fungal metabolites, which are naturally present in
maize grain and have been reported from range of Fusarium species, as summarized in Table 1.
Trichothecenes
The trichothecene derivates most commonly found in cereal grains and feed stuffs are T-2 toxin,
diacetoxysciprenol (DAS), deoxynivalenol (DON, vomitoxin) and nivalenol (NIV) (EFSA 2014). The
main trichothecens isolated from strains of Fusarium species are referred in Table 1. Trichothecenes
may inhibit protein synthesis in plants by binding to ribosomes (Pestka and Casale 1989).
Deoxynivalenol (DON) also commonly known as vomitoxin (see Figure 11), is often the most
prevalent trichothecene because of its high incidence in cereals, including maize in temperate
climates. DON is mainly produced by Fusarium graminearum and Fusarium culmorum and frequently
occurs together with other mycotoxins listed in Table 1 (EFSA 2014).
Figure 11. Chemical structure of deoxynivalenol
The distribution of mycotoxins and fungal biomass in maize ears which are infected with F.
graminearum is not completely known (Nutz 2010). Reid et al. (1996) investigated the distribution of
DON in infected maize ears, and found the highest concentration of DON in the rachis, the second
most in the symptomatic kernels, and the least concentration in the symptomless kernels (Reid, et al.
1996.
29
Zearalenone
Zearalenone (ZEA, ZON, see Figure 12) is a plant pathogenic mycotoxin produced by multiple
Fusarium species, including Fusarium graminearum and also Fusarium culmorum, Fusarium equiseti
and Fusarium verticillioides, found in cereals on the field (in particular maize) but also as a postharvest contaminant (EFSA 2014).
Figure 12. Chemical structure of zearalenone
Fumonisins
FUMO (Figure 13) are a group of structurally related plant pathogenic mycotoxins produced mainly
by Fusarium verticillioides and Fusarium proliferatum growing on cereals, in particular on maize.
The role fumonisin production plays in pathogenic interactions with maize seems to vary based on
the specific disease interaction and maize genotype (EFSA 2014). In relation to corn seedling blight,
fumonisins were suggested to increase the virulence of F. verticillioides but were not necessary or
sufficient for disease development (Desjardins, Plattne and Nelson 1994). But the relationship
between the development of symptoms, the fungal growth and the mycotoxin accumulation is still
unclear (Nutz 2010).
Figure 13. Chemical structure of fumonisin B1
Pascale et al. (2002) found a good correlation between ear rot symptoms and mycotoxin
concentration after inoculation with F. verticillioides and F. proliferatum, respectively. In addition,
Reid et al. (1999) demonstrated that F. verticillioides is able to colonize plants, which were previously
artificially inoculated with F. graminearum, and to suppress this fungi (Nutz 2010).
30
Table 3. Maximum levels for deoxynivalenol (DON) in foods. Extract from Regulation (EC) 1881/2006 and EFSA (2014)
Deoxynivalenol
Unprocessed maize, with the exception of
unprocessed maize intended to be processed by
wet milling
Maize and maize milling products intended for
direct human consumption
Bread (including small bakery wares), pastries,
biscuits, cereal snacks and breakfast cereals
Processed cereal-based foods and baby foods for
infants and young children
Maximum
levels (μg/kg)
Requested temporary
“derogation value” (μg/kg)
for products produced
before 31/12/2014
1 750
2250
750
1000
500
750
200
-
Table 4. Maximum levels for zearalenone (ZON) in foods. Extract from Regulation (EC) 1881/2006 and EFSA (2014)
Zearalenone
Unprocessed maize with the exception of
unprocessed maize intended to be processed by
wet milling
Maize and maize milling products intended for
direct human consumption
Refined maize oil
Maize intended for direct human consumption,
maize-based snacks and maize-based breakfast
cereals
Processed maize-based foods for infants and
young children
Maximum
levels (μg/kg)
Requested temporary
“derogation value” (μg/kg)
for products produced
before 31/12/2014
350
500
100
200
400
-
100
200
20
-
Table 5. Maximum levels for fumonisins (FUM) in foods. Extract from Regulation (EC) 1881/2006 and EFSA (2014)
Fumonisins (Sum of B1 and B2)
Unprocessed maize, with the exception of
unprocessed maize intended to be processed by
wet milling
Maize intended for direct human consumption,
maize-based foods for direct human
consumption, with the exception of maize-based
breakfast cereals and maize-based snacks
Maize-based breakfast cereals and maize-based
snacks
Processed maize-based foods and baby foods for
infants and young children
Maximum
levels (μg/kg)
Requested temporary
“derogation value” (μg/kg)
for products produced
before 31/12/2014
4000
4500
1000
1500
800
1300
200
-
31
1.2. Breeding of maize
Maize breeding methods can be divided in a few groups:
Conventional breeding,
Breeding open-pollinated maize includes mass selection, ear-to-row breeding and variety
hybridization,
Hybrid maize,
Mutation breeding (spontaneous mutation and chemical mutagenesis).
All the present day cultivated varieties of maize grown are hybrids. In detail, they have been
developed for commercial cultivation through the use of: modified single-crosses, three-way crosses,
doubled haploid (DH), double-crosses, top crosses and multiple cross. An important consideration in
hybrid breeding is the combining ability of individual inbred lines as generally few combinations
results in superior hybrids.
1.2.1.Breeding targets
The aim of plant breeding is to direct selection towards increasing the frequency of desirable gene
combination which best suit agricultural systems (Brown and Caligari 2008).The fact that maize has
high commercial value means that there is much to be gained by the breeding programmes.
Furthermore, in order to achieve perfect quality, maize breeding has been, and is being, used to
improve or alter traits such as kernel properties, high grain and fodder yield, adaptation, stalk
quality, resistance to ear dropping and water lodging, husk covering, rapid dry-down, various
maturity duration, disease and insect resistance (Paliwal 2000). Speciality maize varieties are also
being bred for industrial quality such as sweet corn, high-oil content, high-quality protein, popcorn
and silage (Paliwal 2000a).
Since the European Union released legal limits (EC No. 1126/2007) for mycotoxin content in
unprocessed maize grain the breeding for resistance to ear rots (GER and FER) become an important
criterion for selecting better inbred lines for commercial cultivars, in order to minimize the maize
contamination by mycotoxins.
1.2.2.Cytogenetics
All types of resistance are generally controlled by genes located in the plant chromosomes in the cell
nucleus (Agrios 2005). Therefore, cytogenetic is the study of the behaviour and properties of
chromosomes being the structural units which carry the genes that govern expression of all the traits
such as the resistance (Lanubile, Bernardi, et al. 2012).
Maize is a diploid species with 10 chromosomes (2n=20), which are visualized in Figure 14. The
combined length of the chromosomes is 1500 cM. Maize has a moderate genome size (about 2,400
Mb), and 1 cM on the genetic map corresponds to 1,500 Kb on the physical map (Civardi, et al. 1994).
Some of the maize chromosomes have what are known as "chromosomal knobs" considered as
highly repetitive heterochromatic domains that stain darkly. Individual knobs are polymorphic among
strains of both maize and teosinte (Anonymous 2014).
32
Figure 14. Analysis of maize chromosomes, then and now. Maize chromosomes are large and easily visualized by light
microscopy. (a) (Rhoades 1952). (b) This image is comparable to that in part a except that the spindle is shown in blue
(stained with antibodies to tubulin), the centromeres are shown in red (stained with antibodies to a centromere-associated
protein), and the chromosomes are shown in green (Dawe, et al. 1999).
High numbers of knobs are positively correlated with the following external features of the ear and
plant: high row numbers, denting, absence of husk leaves, many seminal roots, and irregular rows of
kernels (Brown 1949).
Walbot (2009) reminded that phenomena such as transposons, paramutation, and imprinting were
all discovered in maize, and described precisely why the maize plant is so special and why we should
read about maize genome. In addition, several databases and web resources have been established
to support genome comparisons and orthologous gene studies between grass species: Gramene,
PlantGDB, EGO (Eukaryotic gene orthologs), GrainGenes, MaizeGDB, ZmDB, TIGR Maize database (Yu
and Wing 2005). For example, the orthologous genes of a waxy gene on rice chromosome 6 are
found on chromosome 9 of maize and chromosome 7 of wheat, which are proved to be a syntenic
part of these genomes (Devos and Gale 1997).
Detailed knowledge of the molecular organization of the pathogen is also important. The Fusarium
comparative genomics database contains draft sequences for F. graminearum and F. verticillioides
genomes and tools that facilitate analysis of individual genomes and comparative analysis among
these fungal species (Reinprecht, et al. 2011) shown in Table 6.
Table 6: Fusarium genomes (Reinprecht, et al. 2011)
Species
F. graminearum
F. verticillioides
Genome size (Mb)
36.45
41.78
Chromosomes
4
12
Genes
13 332
14 179
Genome coding (%)
49.04
42.82
The complete processes of interaction between the maize with the Fusarium spp. remain still
without definitive answers. Therefore, the chromosomes definitely contain the key to discovery not
only the plant-pathogen interaction between ear rots and the maize, but also the mechanisms of
maize resistance and Fusarium virulence. This finding could be of direct practical relevance for the
maize breeding and, subsequently, the reduction of the potential mycotoxin treat in the food and
feed supply chains.
33
1.2.3. Source of resistance for breeding programmes
In maize breeding, this feature is common to all aspects related to maize improvement. Disease
resistance may be obtained from different sources like other cultivated species (Teosinte),
germplasm collections and mutations, for example, natural mutations (Mohanan 2010). In detail, the
introduction and adaptation of exotic germplasm, improvement of germplasm resources, pedigree
selection to develop improved inbred lines, backcrossing to incorporate alleles and/or allelic
combinations into otherwise desirable inbred lines, and conversion programs to improve and/or
change the chemical composition of either the grain (Carena 2009).
Germplasm includes inbred lines, land races, open pollinated varieties, exotic accessions, wild species
(e.g., teosinte), cultivars, and other breeding stocks. The background of the stock is everything in
these types of germplasm. The Plant Genetic Resource Center is specially designed vaults currently
hold some 28,000 samples of maize and teosinte, a wild relative of maize (CIMMYT). Maize
germplasm is available from the North Central Regional Plant Introduction Station (NCRPIS; NC7)
located in Ames, IA, USA. Maize germplasm can also be obtained from Centro Internacional para
Mejoramiento de Maiz y Trigo (CIMMYT) located in Mexico City, MEXICO.
1.2.4. Resistance to FER and GER
The extent to which a plant prevents the entry or subsequent growth of the pathogen within its
tissues or the extent to which a plant is damaged by a pathogen is used to measure the resistance or
susceptibility (Singh 1986).
The best strategy for controlling FER and GER and reducing the incidence of mycotoxin
contamination of grain still remains the development and deployment of maize hybrids with genetic
resistance (Zila, et al. 2013). The inheritance of traits will be of great importance in determining, not
only whether selection is to be carried out, but also the complexity of experimentation needed in
order to identify the desirable types (Brown and Caligari 2008).
1.2.4.1. Types of Resistance
The resistance based on mode of inheritance is separated into two categories and will be revealed
more precisely below. In short, monogenic resistance is based on single genes whereas quantitative
resistance depends on two or more genes (Keller, Feuillet and Messmer 2000; Van der Plank 1968).
Polygenic resistance has also been designated by many names, including partial resistance
(Niederhauser, Cervantes and Servia 1954), field resistance, general resistance, generalized
resistance, tolerance (Singh 1986).
a) Qualitative host resistance / hypersensitivity resistance
Qualitative genetics means that the inheritance is controlled by alleles at a single locus, or at very
few loci (Brown 2012).
34
b) Quantitative host resistance/ partial resistance
The quantitative nature of resistance resembled what is commonly referred to as horizontal or
general resistance (Singh 1986), as listed in Table 7. Quantitative genetics for traits are where the
variation is determined by alleles at more than a few loci, traits that are said to be controlled by
polygenic systems. Quantitative genetics is concerned to describe the variation present in terms of
statistical parameters such as progeny means, variances and co-variances (Brown 2012), also called
rate- reducing resistance (Miedaner 2011).
Fusarium ear rot resistance is under polygenic control and strongly influenced by environmental
factors; no fully immune genotypes have been discovered (King and Scott 1981; Nankam and Pataky
1996; Clements, et al. 2004). The complexity of this resistance trait has impeded breeding, such that
most commercial maize hybrids have lower levels of resistance than are desirable (Bush, et al. 2004).
According to Miedaner (2011) there are only quantitative resistance in maize based on many genes
and are highly environmentally dependent. The breeding of resistant maize varieties is complicated
by the fact that many different Fusarium species can infect the corn plant and also consists mostly of
mixed infections. It should be emphasised, that resistance to a certain Fusarium species does not
necessarily mean a resistance to a different Fusarium species (Reid, et al. 2009).
Additionally, quantitative resistance is characterized by a continuous distribution in the level of
resistance (Keller, Feuillet and Messmer 2000), which results from the interaction of the plant
genotype, the pathogen population and environmental effects. Due to the complex interaction
between the host and pathogen, the level of resistance has to be defined in terms of development
stage of the plant (adult plant), assessed traits (necrotic tissue vs. sporulation frequency), assessed
organs (ear), pathogen population (isolate vs. mixture) and growing conditions (field vs. growing
chamber).
Thus, breeders are faced with the difficulty of introducing polygenic resistance alleles of generally
small effect linked to inferior polygenic alleles for agronomic performance if they attempt to
incorporate improved genetic resistance from unadapted lines into elite breeding gene pools (Zila, et
al. 2013).
Table 7. Types of resistance (Singh 1986)
Resistance Based on:
Mode of Inheritance
Effect of Genes
Growth Stages of the Host Plant
Epidemiological Terms
Cytoplasmic Resistance
Resistance Based on Defence Mechanisms
Characteristic:
Monogenic (oligogenic, specific), polygenic
Major and minor genes
Seedling resistance, adult plant resistance
Vertical, horizontal
Cytoplasmic male sterility
Role of wax and cuticle, etc.
The major components of resistance which affect the reproduction rate of the pathogen are the
reduction of infection frequency, lengthening of latent period and decrease of spore production
(Keller, Feuillet and Messmer 2000).
35
1.2.5.Assessment of quantitative resistance
Crop disease assessment called also phytopathometry involves the measurement and quantification
of plant diseases (Vidhyasekaran 2004). Nevertheless, the disease assessment data must be
quantified by the growth stage of the crop at the time of the assessment and should be related to a
stage of plant development that determines an important physiological function- for example
BBCH89 in maize (see Appendix I) (Jones 1998). The evaluation and selection of quantitative
resistance is complex. Therefore, the basic requirements should be followed (Miedaner 2011):
Examination in larger ranges with standard genotypes with known resistance,
Examination in several locations and if possible in several years; more accurate tests should
occur with at least two randomised grown repetitions,
Quantitative traits detection,
High environmental stability of the resistance.
The most programs for resistance breeding of maize against FER and GER analyze the following
parameters, which are recorded for each hybrid: number of days to silking (DTS), silk channel length
(SCL) at pollination, husk covering at maturity, harvested maize ears for visual disease severity, grain
yield, mycotoxin concentration, e.g. deoxynivalenol (DON), and fungal biomass by quantitative
polymerase chain reaction (PCR) and/or ergosterol quantification (Mesterházy, Lemmens and Reid
2012; Reid, Hamilton and Mather 1996). In order to investigate the influence of kernel characteristics
could also be examined the colour (white and yellow), type (dent and flint), use (popcorn, sweet corn
and field corn), mutation (waxy, opaque), and heterotic group (European, Reid, Lancaster, Northern
Flint, Minnesota No 13, other Corn Belt, and miscellaneous) on GER and FER and mycotoxin
accumulation (Santiago, et al. 2013).
The severity of the disease, and the occurrence and prevalence of the causal species, may vary by
region and year. This variation depends primarily on climatic parameters, for example, temperature,
relative humidity, rain, and location, and also on the farming system, for example, tillage, crop
rotation, fertilization, planting area, irrigation, and disease and pest control.
1.2.5.1.
Techniques for phenotypic characterization of resistance
The most important prerequisites for a successful resistance breeding program are presence of
genotypic differences for host-plant response to the pathogen and availability of techniques to
reliably detect these differences (Bolduan, et al. 2009). In order to select for plant resistance to pests
it is necessary to have a well-established disease testing scheme, one that truly mimics the disease as
it exists in an agricultural crop (Brown and Caligari 2008).
Two types of resistance to GER and FER have been identified in maize (Reinprecht, et al. 2011):
Silk resistance prevents the fungus invading through silk channel down to the kernel.
Kernel resistance blocks the spread of the fungus from the kernel to kernel.
Resistance to GER and FER in maize has two conceptual components: resistance to initial penetration
and resistance to spreading by the pathogen in the host tissue (Hou, Hue, et al. 2002). Identifying
factors associated with kernel resistance to infection by Fusarium and fumonisin accumulation helps
36
in the understanding of genetic mechanisms controlling disease resistance and also facilitates maize
breeding (Sampietro, et al. 2009).
a) Inoculation methods/ techniques
There are described several ways to inoculate maize plants with the casual-agent of GER and FER, in
order to be confirmed by the field test, which include natural infection, direct techniques and special
laboratory techniques (Ram 2014). Papst et al. (2007) summarized the direct inoculation methods,
which are visualized in Figure 15. Moreover, inoculation methods can be divided into two types: with
(type 1) and without (type 2) mechanical assistance (Hou, Hue, et al. 2002; Balconi, et al. 2008):
wounding silk channel inoculation assay or toothpick inoculation,
non-wounding or the spore suspension inoculation.
Figure 15. Different inoculation methods (Papst, et al. 2007)
Detailed information for the methodology for each inoculation method is provided by Mesterházy et
al. (2012).
b) Traditional Evaluation of Fusaria
For the evaluation of Gibberella ear rot three major parameters are usually assessed: disease
incidence, disease severity and disease intensity. Despite the majority of disease assessment keys
Cooke (2006) discussed the advantages only of the percentage scale as: the upper and lower limits
are always uniquely defined; the scale is also flexible and can be divided and subdivided. The same
statement is confirmed by Mesterházy et al. (2012), where it was stated that for scientific purposes,
the percentage of infected kernels may give more precise data.
c) Indirect evaluation of Fusaria
Besides this, it is often the case that data from the visual assessment of plant disease severity do not
correlate with the amount of fungal biomass colonizing host tissue, which leads to inaccurate
disease-yield loss relationship (Cooke 2006). Several evaluation techniques are developed to quantify
fungal biomass within host tissue, either by measuring fungal chitin, or ergosterol as biomarkers.
Of particular interest in the quantitative assessment of plant disease are the immunological and
nucleic acid-based techniques. For instance, enzyme-linked immunosorbent assay (ELISA) kits for use
in the field and the polymerase chain reaction, particularly PCR (qPCR), for determining infection in
the plant material, respectively (Cooke 2006).
37
So far, the conventional mycotoxin analyses are relatively expensive (HPLC, NIRS, ELISA) therefore,
their determination is not always necessary. The fact was well stated by (Martin, Miedaner, et al.
2012a). Here he explained that the DON concentration and GER severity showed a strong positive
relationship in various maize materials and visual GER rating is the cheapest alternative and should
be given priority. Similar suggestions were obtained by Reid et al. (1999) and Miedaner et al. (2008),
where Fusarium verticillioides caused a lower level of disease and toxins content than Fusarium
graminearum. High correlation between visual symptoms of ear rot disease and DON and FUM
content was found (0.76 and 0.78, respectively) what was confirmed in another study (Robertson et
al., 2006).
In environmentally controlled plots (Lemmens, personal communication), 36 genotypes were
evaluated for GER disease resistance and mycotoxin content for three year period (2010, 2011,
2012). Thus, it was tested the effect of mycotoxin content on the disease intensity parameter and it
was found a close relation between Fusarium graminearum symptoms and toxin content. An
example is illustrated in Figure 16. Moreover, a highly significant correlation coefficient was found
between disease intensity (%) and the DON content (r= 0.83 ***).
Disease intensity
Relation between DON content and disease intensity after silk
channel inoculation with F. graminearum (means over 3 years)
90
80
70
60
50
40
30
20
10
0
0
20000
40000
60000
80000
100000
[DON] in microgram/kg
Figure 16. Relation between the data for the disease index of F. graminearum (mean over 3 growth seasons each with 3
replications) after toothpick inoculation and the DON contamination. Pearson correlation coefficient between both data
sets is 0.83***.
The trichothecenes may act as virulence factors to enhance the spread of F. graminearum on maize,
which indicates that there is a relation between disease severity caused by the F. graminearum
isolates and the DON concentrations in the infected ear (Harris, et al. 1999). In this connection it
must be emphasized once more that the correlation between the GER and the respective mycotoxin
content exists, therefore the visual assessment of the maize varieties after artificial inoculation of the
ear rot severity is a beneficial method for evaluation of the susceptibility level of different
commercial maize varieties.
38
1.2.5.2.
Austrian methodology for the classification of the maize assortment
As a part from the Austrian system for variety testing, registration and seed certification for maize is
the evaluation of the resistance level of some important diseases, for instance GER and FER. The
screening system for testing the maize hybrids is normally carried out by Institute for Plant Varieties,
Austrian Agency for Health and Food Safety (AGES) in Vienna, Austria. The evaluation of the
susceptibility of maize varieties to GER and FER includes screening methods combining results of
natural infection with artificial inoculation techniques, which are assessed for each cultivar at least
two locations (testing stations). Additionally, the method comprises the following steps in brief:
verified field inspection prior harvest, variety-specific classifications in each environment for each
evaluation criterion and calculating a final score of the total marks of the evaluation criteria.
Several artificial inoculations are performed in order to be tested the silk and kernel susceptibility:
Conidial suspension in sprayed directly on the silk
Stab inoculation
Injected the inoculum in the silk channel.
The algorithm of the new method to variety evaluation includes two parts (AGES, 2013). In both trails
the maize phenological stage during the field inspection is at BBCH 89 approximately prior
harvesting. In the first trial is investigated the resistance to spread – spore suspension injection
among the inoculation at BBCH 71 and BBCH 73 stage. If the yield is also evaluated than the
screening must be performed before harvesting in order to be avoided any influence of the water
content of the maize ears.
Further, in the second trial the results are verified with spore suspension spraying and show the
correlation with DON and/ or other mycotoxins. In addition, the mycotoxin content analysis (DON,
ZEA and/or FUM) are usually carried out in an authorized seed certification laboratory through ELISA
Test-kits. Each probe includes 1.5 kg maize grain.
The GER and FER disease assessment uses pictorial key for estimating the disease severity on 0-100 %
scale.
The rest of the overall scores for ZEA, FUM and KOBF (FER and/or GER) is calculated analogously. In
detail, the final grade for each maize variety for these overall grades of each criterion is calculated as
the severity grades (German: Ausprägungsstufe-APS) agent according to the following formula with
abbreviation (KOBF, ZUNS, DONF, ZEAF, FUMF):
𝑥̅𝑗𝑤 =
∑𝑛𝑖 = 1𝑥𝑗𝑖 𝑤𝑖
∑𝑛𝑖 = 1 𝑤𝑖
The 𝑥𝑗𝑖 represents the DON values of the species j at site i and i in environment (with i from 1 to n)
and 𝑤𝑖 for the standard deviation (A-weighted) or the squared standard deviation the locations and
environments i. n is the number of locations and environments, was investigated on or in which the
DON content of the variety. To confirm the findings from this experiment, the formula including the
ER grade (German: Ausprägungsstufe, APS) and mycotoxin content is calculated for each particular
maize variety and the relative values are then transferred into a 9-strage scale.
39
In conclusion, all data is published in the Austrian Descriptive List of Varieties with other beneficial
traits, for instance yield. On the base of these results must be decided, which maize varieties will be
registered and listed in the National Catalogue according to the Austrian Federal Constitutional Law
(B-VG), (§65 Saatgutgesetz 1997, BGBl. I Nr. 72/1997 zgd BGBl. I Nr. 83/2004) (Anonymous, 2014;
AGES 2015).
On the following Figure 17 are illustrated the results of some registered maize hybrids in Austria
(AGES, 2015).
Figure 17. Effect of the grades (APS3 to APS7) on the DON content. The mean DON content of the hybrids classified with
the score 3 (APS3) was about a factor 4 lower as compared to the hybrids with the grade 7 (APS7) (Source EMYKOM,
AGES) (AGES 2014)
40
1.2.6.Maize-Fusarium pathosystem
The invasion of a plant by a pathogenic microorganism is in reality continuous process, but certain
key strategies can be identified as shown below (Lukas 1998):
Locating the host
Attachment/adhesion to host surface
Penetration and entry into host
Colonization of the host tissues
Suppression or avoidance of the host defence mechanisms
Reproduction of the pathogen
Dispersal of pathogen from the host.
In Chapter 1 these steps of the maize- Fusarium interaction were already discussed.
Major components of defence response
Maize could inhibit the spread of GER and FER by reinforcing physical and chemical barriers,
inhibiting the activities of fungal- degradative enzymes and reducing toxin effects. In addition, the
plant cell wall composition plays an important role in defence mechanisms that inhibit fungal
penetration or hyphal spread (Reinprecht, et al. 2011). However, secondary metabolites in maize
kernel are thought to play also an important role in plant’s resistance against ear rot fungi. Several
layers of interaction (see Figure 18) have been uncovered between plants and pathogens leading to
plant disease or resistance (Kant and Reinprecht 2011).
During disease or resistance reaction, extensive crosstalk among pathogen and plant components
occurs. Moerschbacher and Mendgen (2000) raised the question whether a given structural feature
of a plant, whether preformed or pathogen induced, is causally involved in making this plant
resistant, for instance maize to GER. In order to discover if a given preformed structure is essential in
protecting a plant from a pathogen Moerschbacher and Mendgen (2000) proposed to compare the
susceptibility of different plants which do or do not possess the structure under investigation.
Considering the fact, that the most structural features of a plant serve multiple purposes, probably
none can be expected to be involved in the defence against pathogens alone, for example the cuticle
protects not only from pathogens, but also from desiccation. For the above reason, they also
suggested that it would be much easier to compare pathogens that have or do not have the ability to
overcome the structural barrier under debate. For instance, the deletion of a given cell wall
degrading enzyme (CWDE) in a macro-organism results in loss of its pathogenicity (i.e. if the
pathogen becomes less virulent and causes less severe symptoms) than the cell wall component
degraded by that CWDE may tentatively be considered a structural feature involved in pathogen
resistance of this plant (Moerschbacher and Mengen 2000).
Nevertheless, the maize and Fusarium spp. are two different organisms with different structure and
metabolisms. As you can see below (see Figure 18), we attempted to summarize some of the most
recent findings for the components involved in resistance of maize to FER and GER.
41
Figure 18. Overview of plant-pathogenic interactions; MAMP-mitogen-associated molecular pattern; RLK- receptor-like
kinases; LRR-leucine-rich repeats; NB-LRR- nucleotide-binding LRR; PTI-pattern-triggered immunity; ROS- reactive oxygen
species; ETI- effector triggered immunity; ethylene (ET) and jasmonate (JA)- mediated signalling pathways; nitric oxide
(NO) synthesis; pathogenesis related (PR) proteins; (Kant and Reinprecht 2011)
1.2.6.1.
Physical barriers
As traits providing resistance to ear rots can be considered several morphological and physiological
factors, for example the pericarp thickness and husk covering (Warfield and Davis, 1996). They are
classified as passive or constitutive plant defence against Fusarium as pre-existing features of the
maize (Lukas 1998). The Fusarium pathogens have no specialized structures for penetration of host
cell, like appressoria or haustoria. Instead, the fungus either enters the host through natural
openings, or penetrates the epidermal cell walls directly with short infection-hyphae (Kikot, Hours
and Alconada 2009). As mentioned in Chapter 1, Fusarium graminearum forms ascospores and
conidia for long-distance dispersal, which are believed to be the main inoculum infecting the maize
silk (Trail, Xu, et al. 2002; Parry, Jenkinson and Mcleod 1995).
a) Maize silk
Maize silk infection and development of F. gramineraum in silk tissues are believed to be influenced
by morphological factors such as husk coverage, physiological factors such as flowering time and silk
age, and diverse biochemical factors (Reid, Bolton, et al. 1992; Logrieco, et al. 2003; Sutton 1982).
Miller and associates (2007) followed the progress of F. graminearum mycelium down the silks
toward the developing kernels and rachis; they observed mycelia growth on the silk surface and
inside the epidermal cells of the silk from the second and third days, respectively, after macroconidia
42
attachment. The time it took for the mycelium to reach the kernels was different in resistant and
susceptible genotypes, suggesting the existence of resistance mechanisms in the silks (Miller, Reid
and Harris 2007).
According to Butron et al. (2006), hybrids with tight, adherent husks and less open apical parts of the
ear are more resistant. To sum up, maize silks and silk channels are often the first tissue to come in
contact with the pathogen. Therefore husk covering and tightness are factors contributing to initial
fungal pathogen resistance against this fungus, according to Sekhon et al. (2006).
Changes in silk flavone content were observed after artificial infection with F. graminearum (Reid,
Mather, et al. 1992b) but no association between flavone content and resistance was established.
Similarly, genotypic differences in the alkane contents of silk wax did not satisfactorily explain
differences in resistance to F. graminearum (Miller, Reid and Butler, et al. 2003; Cao 2011).
However, Cao (2011) investigated the role of induced or constitutive hydroxycinnamic acids of the
silk tissues in F. graminearum resistance. In view of the fact that silks are the tissue where the initial
fungal development takes place and represent a key infection pathway to the ear, evaluation of
hydroxycinnamic acid contents and Fusarium spp. development on silk tissues is of significant
interest (Cao 2011).
Secondly, physiological factors may influence resistance. Kernels that mature faster shorten the
period of susceptibility (Butron, et al. 2006). Colonization of silks by fungi depends on the conditions
of the silks. For instance, symptomatic and asymptomatic infection of kernels by F. verticillioides is
less common for inbred lines with silks that are green and actively growing at inoculation than it is
when the silks are brown (Hou, Hue, et al. 2002).
b) Maize kernel
The first line of defence and possibly the single most important in the maize kernel is the excretion
and polymerization of cuticle at outside of the outer perticlinal plant cell walls, as shown in Figure 19.
The waxes incorporated into the cuticle proper, and the epicuticular waxes on the very surface,
higher diffusion of cell wall degrading enzymes into the wall (and prevent the diffusion of nutrients
from the leaf cells to the micro-organisms on the surface) (Keller, Feuillet and Messmer 2000).
Moreover, the cuticle protects not only from pathogens, but also from desiccation (Keller, Feuillet
and Messmer 2000).
The pericarp is the outermost layer of the maize kernel and provides effective protection from fungal
invasion. It consists of several layers of cells which differ in their degree of degradation and cell wall
thickness (Wolf 1952). Sampietro et al. (2009) suggested that the pericarp and its wax content are
resistance factors to fumonisin accumulation in most genotypes assayed. In detail, removing wax
from the pericarp significantly increased fumonisin concentration and a higher wax content on
kernels was associated to lower fumonisin accumulation (Sampietro, et al. 2009).
The surface of the pericarp has been reported to be covered by a thin layer of cutin (Johann 1935;
Wolf 1952; Heslop-Harrison, Reger and Heslop-Harrison 1984) and, as such, might represent a barrier
to penetration by some pathogens. These defensive structures usually protect the host fruit from
what may be a constant and massive onslaught of inoculum. Fusarium has no appressoria to
penetrate the kernel tissue. Therefore, Duncan et al. (2010) hypothesized the passive movement of
43
conidia along the surface of silks, perhaps via capillarity, as a possible mechanism for pathogen
access to the infection court. Thus, the production of a range of enzymes may be advantageous for
infection of maize kernels by Fusarium spp. pre- or post-harvest (Marin 1998).
Figure 19. Maize kernel structure. The mature maize kernel is comprised of multiple tissues and organs within the
embryo and endosperm, in addition to maternally derived structures (Bennetzen and Hake 2009)
Last but not least, very little fumonisin accumulation was detected in kernels with low starch or
where the amylase:amylopectin ration was high meaning that the fumonisin production during the
infection of maize ears is triggered by amylopectin, a component of starch (Bennetzen and Hake
2009; Bluhm and Woloshuk 2005).
44
1.2.6.2.
Defence signalling pathways in maize against Fusarium
Currently, the best way to reduce or prevent the mycotoxin contamination is to limit their
biosynthesis during the cultivation of maize plants (Picot, et al. 2013). The identification of naturally
occurring mechanisms in plants that lead to less Fusarium infection and subsequently reduced
mycotoxin accumulation is of particular importance.
The active defence in plants is a multicomponent process (Lukas 1998). The induced defence
mechanisms start with plant-pathogen recognition, which is based on the interaction between
pathogen elicitors and plant receptors. This interaction as a signal is subsequently transmitted
through different signalling pathways, and eventually leads to the expression of plant defence genes.
Pathogenesis-related (PR) proteins
Plants defend themselves against fungal infection by physical strengthening of the cell wall through
lignification, suberization, and producing various pathogenesis-related (PR) proteins such as
chitinases and glucanases (Agrios 2005). PR proteins are proteins with antimicrobial or hydrolytic
properties that are synthesized in the early events of the plant defence response (Kant and
Reinprecht 2011) and are thought to inhibit growth, multiplication and/or spread of the invading
pathogen (Sekhon, et al. 2006).
Lanubile et al. (2012) found several pathogen-related genes (PR genes) in resistant and susceptible
genotypes of maize, in the kernels surrounding the point of F. verticillioides penetration. Overall,
these evidences indicated that the resistant line may activate more efficiently a battery of defence
genes, before the invasion by the fungus in non-damaged tissues. The expression data indicated that
the fungal growth could be inhibited in resistant kernels by earlier activation of defence and
regulatory genes, such as chitinases, glucanases, PR proteins, positive and negative transcriptional
factors MYB-like and WRKY1. This finding revealed that the resistant seeds activated a defence
programme in which expression of defence-related genes was both controlled by the host genotype
and induced by the pathogen (Lanubile, Bernardi, et al. 2012).
Plant antioxidants
The kernel content in antioxidant compounds may represent a common selectable trait for the
control of mycotoxins in the field. The potential involvement of antioxidants (α-tocopherol, lutein,
zeaxanthin, β-carotene, and ferulic acid) in the resistance of maize varieties to Fusarium ear rot was
the focus of a lot of studies. In addition, tocopherols and carotenoids have been reported to naturally
occur at varying levels in mature maize kernels (Kurilich and Juvik 1999). However, the composition
of maize kernel is a major determinant of mycotoxin accumulation, like fumonisins, for instance
(Bennetzen and Hake 2009).
Among the plant natural antioxidants, phenolic compounds have been widely studied for their in
vitro ability to inhibit mycotoxins production such as aflatoxin, trichothecenes B, and fumonisins and
for their involvement in disease resistance (Picot, et al. 2013). The majority of grain phenolics are
located in the outer layers of the grain, the pericarp and aleurone layers, and the germ (Figure 19).
Ferulic acid (FA) is the predominant phenolic compound of maize tissue (Bily, et al. 2003). Lower
concentrations of ferulic acid (FA) in the cell wall of the inoculated silks could be due to cell wall
45
degradation from fungal attack (Cao 2011). Assabgui et al. (1993) proposed that fungal growth was
decreased when pure FA was added to artificial media. Based on the results of this study, it was
suggested that conventional breeding programs aimed at attaining maize germplasm resistant to G.
zeae should incorporate genotypes of maize containing high concentration of grain FA. Differential
decrease of FA in resistant and susceptible genotypes suggested also a possible role of
hemicelluloses in F. graminearum resistance (Cao 2011). Overall, the data by Picot et al. (2013)
supported the fact that ferulic acid (FA) may contribute to Fusarium ear rot and/ or fumonisins
accumulation.
Phenolic acids thus operate in defence response through direct interference with the fungus or
through the reinforcement of plant structural components to act as a mechanical barrier against the
pathogen (Siranidou 2002).
In addition, chlorogenic acid has been attracting also widespread interest in maize resistance. The
data, which was provided by Atanasova-Penichon et al. (2012), suggested strongly that free
chlorogenic acid could form a part of the plant response to F. graminearum infection. They also
demonstrated that the main phenolic acids that F. graminearum is likely to encounter at the
beginning of maize ear colonization and DON production are chlorogenic acid and, to a lesser extent,
ferulic acid.
Moreover, a new class of sesquiterpenoid phytoalexins, called zealexin, with inhibitory activities
against F. graminearum has been identified in maize, responding to attack by F. graminearum as well
as synergistically in jasmonate-treated plants (Huffaker, et al. 2011). Purified zealexin inhibited F.
graminearum growth in physiologically active concentrations, indicating that transgenic
enhancement of the levels of this phytoalexin can be beneficial to reduce ear rot in maize (Kazan
2012).
Fusarium infection
Plant-pathogenic fungi produce an array of extracellular enzymes that enable them to penetrate and
infect the host tissue: these enzymes are collectively called cell wall-degrading enzymes (CWDE).
They may contribute to pathogenesis by degrading wax, cuticle and cell walls, thus aiding tissue
invasion and pathogen dissemination (Kikot, Hours and Alconada 2009).
Marin et al. (1998) investigated, that the dominant three enzymes produced by the fungi on whole
colonised maize kernels were a-D -galactosidase, b-D-glucosidase, and N-acetyl-b-D-glucosaminidase.
There were significant increases in the total production of the three predominant enzymes between
3–9 days colonisation. This study suggested that these hydrolytic enzymes may play an important
role in enabling these important fumonisins producing Fusarium spp. to rapidly infect living maize
grain over a wide a range (water activity, aw ; 0.98–0.93) with parallel moisture contents during
silking and during post-harvest drying and storage (Marin 1998).
Additionally, DON acts as translational inhibitor and damages plasma membranes, chloroplasts, and
ribosomes and ultimately causes plant cell death. During the cellular colonisation the fungus
Fusarium graminearum can detoxify and counteract the activities of plant toxins and defence
proteins (Kant and Reinprecht 2011).
46
1.2.6.3.
Genetic characterization of resistance to GER and FER
In general, there are two genomes that participate in the outcome of GER infection: the host (maize)
and the pathogen (Fusarium spp.) genomes. Identifying genes on the maize genome that confer
resistance can help improve the survival of the host.
Resistance genes of maize
Resistance genes (R-Genes) are genes in plant genomes that convey plant disease resistance against
pathogens by producing R proteins (Knepper and Day 2010). Once the R protein has detected the
presence of a pathogen, the plant can mount a defence against the pathogen. Because R genes
confer resistance against specific pathogens, it is possible to transfer an R gene from one plant to
another and make a plant resistant to a particular pathogen (Anonymous 2010).
Pathogenicity genes in Fusarium spp.
Many of the regulated genes encode effector proteins or proteins involved in the synthesis of
secondary metabolites (SM), both of which can contribute to pathogenicity. For example, during
growth on live maize plants Fusarium verticilllioides can synthesize a number of toxic SM, including
fumonisins, fusarins, and fusaric acid, that can contaminate kernels and kernel-based food and feed
(Brown, Busman and Proctor 2014). Moreover, some fungal genes involved in trichothecene
biosynthesis have been shown also to encode regulatory proteins. However, the global regulation of
toxin biosynthesis and the kernel metabolites potentially involved in this synthesis are still enigmatic
(Merhej, Urban, et al. 2012; Atanasova-Penichon, et al. 2012). A summary of genes involved in the
mycotoxin biosynthesis is beyond the scope of this review. More detailed information can be found
in Kant and Reinprecht 2011, and Woloshuk and Shim 2013.
Fusarium graminearum
Atanasova-Penichon et al. (2012) observed that Fusarium graminearum infection, which started as
early as the blister stage and proceeded slowly until the dough stage. Then, a peak of trichothecene
accumulation occurred and infection progressed exponentially until the final harvest time.
It is generally accepted that the content of Fusarium genomic DNA is correlated to the corresponding
mycotoxin concentration, but gene expression is regulated by various specific transcription factors
for the RNA polymerase binding to promoters (Gong, Jianga and Feng Chenb 2014). In addition, the
correlation between the DNA content and mycotoxin concentration was examined also by Schnerr et
al. (2001). In detail, essential to the biosynthesis of each mycotoxin is a specific regulatory gene
encoding a protein that binds to cis-elements in the promoters of biosynthetic pathway genes. These
transcription factors act as positive regulators that assist in recruiting RNA polymerase II to initiate
transcription (Woloshuk and Shim 2013).
In the review of more than 110 scientific articles by Geng et al. (2014), the secondary metabolite
synthesis, hyphal development and pathogenicity related genes in F. graminearum were thoroughly
summarized. They highlighted 36 genes involved in the secondary metabolites production in F.
graminearum, 23 genes and proteins associated with sexual and asexual reproduction, 15 genes and
proteins involved in energy metabolism, as well as 14 genes and proteins participated in the
virulence, resistance and growth in F. graminearum (Geng, et al. 2014).
47
Recently, Merhej, Urban, et al. (2012) identified the velvet gene from F. graminearum, FgVe1.
Disruption of FgVe1 causes several phenotypic effects. However, the complementation of this
mutant with the FgVe1 gene restores the wildtype phenotypes. The in vitro phenotypes include
hyperbranching of the mycelium, suppression of aerial hyphae formation, reduced hydrophobicity of
the mycelium and highly reduced sporulation. Their data also showed that FgVe1 modulated the
production of the aurofusarin pigment and was essential for the expression of Tri genes and the
production of trichothecenes. Pathogenicity studies performed on flowering wheat plants indicated
that FgVe1 is a positive regulator of virulence in F. graminearum.
If we pay attention to one of the transcription genes belonging to F. graminearum, we can observe
also some part of the mycotoxin biosynthesis as well. For instance, in F. graminearum, the
transcription factor gene (TRI6) is located in the gene cluster on chromosome 2 and the disruption of
TRI6 reduces transcription of genes involved in trichothecene biosynthesis (Proctor, Hohn and
McCormick 1995; Woloshuk and Shim 2013). Woloshuk and Shim (2013) visualized in Figure 20 the
regulatory components involved in the transcription of mycotoxin biosynthetic genes. In detail, a
variety of regulatory factors (in dotted oval) ultimately influence RNA polymerase II complex for the
transcription of genes in the mycotoxin gene cluster. Epigenetic factors also play a critical role in
structural modification of chromatin, which ultimately promotes the expression of the mycotoxin
gene.
There is a common response of mycotoxin accumulation in Fusarium species induced by addition of
hydrogen peroxide. Supplementation of H2O2 into the liquid cultures of F. graminearum leads to the
increased toxin production and up-regulated Tri genes expression (Gong, Jianga and Feng Chenb
2014).
Besides the oxidative stress, the pH possesses a key role in the regulation of mycotoxin biosynthesis.
Gariner et al. (2009) demonstrated that the low extracellular pH both promotes and is required for
DON production in F. graminearum. Moreover, a combination of low pH and amines results in
significantly enhanced expression of the TRI5 gene and increased DON production during axenic
growth. Gong and associates (2014) reported that the pH value in the field wheat and maize samples
might have to be considered to investigate the linear correlation between DNA content and
mycotoxin concentration. Unfortunately, Merhej et al. (2011) ascertained that the impact of pH
during the infection of F. graminearum on to wheat and maize is unknown.
In addition to the role of the pathway-specific regulators, many “external” factors (called also
regulatory factors in Figure 20), such as carbon and nitrogen sources, oxidative stress by H2O2,
phenolic acids, fungicides, temperature and magnesium, have been shown to modulate the
expression of Tri genes and the production of trichothecenes (Boutigny et al. 2010; Covarelli et al.
2004; Jiao et al. 2008; Miller and Greenhalgh 1985; Pinson-Gadais, et al. 2008; Ponts et al. 2007;
Schmidt-Heydt et al. 2008). However, the results for the expression of Tri genes and the
pathogenicity of trichothecene are still not sufficient to understand the whole mechanism underlying
the regulation of biosynthesis during the infection in the field (Merhej, Urban, et al. 2012).
48
Figure 20. Schematic overview of the regulatory components involved in the transcription of mycotoxin biosynthetic
genes. POLII, polymerase II; TF, general transcription factors; MED, mediator complex; ssTF, sequence-specific
transcription factors (Woloshuk and Shim 2013).
In summary, when the interaction between these organisms (maize and mycotoxin-producing
Fusarium species) is completely understood by the scientists, a breeding program could be
developed, it would lead to reliable results for the agriculture and subsequently to improved
molecular detection of mycotoxins (Gong, Jianga and Feng Chenb 2014).
49
1.2.7.Biotechnology and plant breeding
Molecular techniques have been developed to investigate and handle both qualitative and
quantitative characters (Lanubile, Bernardi, et al. 2012). Based on the problem of conventional
breeding, the modern molecular breeding gives complete insight into the diseases and cost effective
PCR based markers can be easily used for development of disease resistant varieties and hybrids (Ali
and Yan 2012). Molecular breeding offers an integrative summary of subjects from basic theories to
their application for crop improvement and comprises of molecular marker technology, gene
mapping, genetic transformation, quantitative genetics and breeding strategies (Ali and Yan 2012).
Moreover, genes found to have significant associations with target traits can be resequenced in a
diverse panel of germplasm to identify causal mutations and the most favourable alleles for trait
improvement and to develop simple PCR-based markers for MAS (Yan, Warburton and Crouch 2011).
QTL mapping
QTL-mapping provides a powerful method to understand the genetic relationships between
correlated traits, although meaningful results are hard to obtain for traits with moderate heritability
traits (Beavis 1998).
QTL mapping factors:
Population size,
Number of locations / year (environments),
Scale quantitative-genetic parameters,
Correspondence between mapping and validation population, type of environments (cities,
geographic distance, years), lines- or testcrosses of hybrids,
Verification of the QTL in different genetic background (Miedaner 2011).
Finding loci for different quantitative traits is analogous to finding molecular markers corresponding
to different chromosomes (Bernardo 2010).
Once the molecular markers are available as gene tags, the information and diagnostic technology
can be incorporated into a molecular breeding strategy to accelerate variety production or to
assemble complex genotypes, which would be otherwise difficult with conventional crossing
involving usual selections (Gupta and Varshney 2005). However, the large number of progeny has to
be screened to find the rare genotype that carry the desired allele at each resistance locus (Keller,
Feuillet and Messmer 2000).
With the aid of molecular markers, it is now possible to dissect the quantitative resistance into
individual genes. This information will assist breeders to choose the appropriate crossing partners in
order to combine the different sources of resistance in one genotype (Keller, Feuillet and Messmer
2000). Cloning genes conditioning quantitative resistance is much more challenging because of their
modest phenotypic effects (Wisser, Balint-Kurti and Nelson 2006).
Wisser et al. (2006) outlined that the disease QTL (dQTL) mapping studies conducted in maize thus
have provided information on the genetic architecture of disease resistance, including the number,
location, and the action of chromosomal segments conditioning the trait. In addition, they derived
also a consensus map of maize resistance loci for each chromosome.
50
Several quantitative trait loci (QTL) controlling disease resistance have been mapped in maize (Table
8). Disease resistance QTLs and mechanisms through molecular breeding are PCR-based DNA
markers. Three chromosomes carrying major resistance genes (36%) were identified by Miedaner
(2011). These studies considered that the QTL with the highest effect for GER resistance was located
at end of the Chromosome 1. In addition, QTL located at similar positions were detected across three
populations in two chromosomal regions and across two populations in additional two regions. The
results suggested a combination of classical phenotypic selection and MAS as a promising strategy in
breeding maize for resistance to GER and reduced DON contamination (Martin, Miedaner, et al.
2012a).
Lanubile et al. (2002) recently summarized a lot of publications reporting the mapping of disease
resistance genes in maize, which are listed in Table 8. Wisser et al. (2006) determined that reported
disease resistance QTL covered 89 % of the maize genome. This high degree of coverage is partly
reflective of the relatively low precision and accuracy of QTL mapping (Bennetzen and Hake 2009).
In their review, Wisser et al. (2006) also demonstrated that the maize karyotype is ordered in
generally decreasing physical size, and chromosome size is generally related to the estimated
number of all maize genes. Therefore, the number of dQTL per chromosome tended to decrease with
the number of genes per chromosome. For instance, Wisser, Balint-Kurti and Nelson (2006)
summarized 51 dQTL on the largest chromosome (chromosome 1) and 17 on the smallest
chromosome (chromosome 10). Considering this conclusion and the recent chromosomal
investigations reviewed by Lanubile et al. (2002), it can be pointed out that dQTL for Fusarium are
reported through the all maize chromosomes as detailed in Table 8.
Near-isogenic line (NIL)
Wisser et al. (2006) emphasized the need of genetic dissection of QTL which will improve the
production and refinement of near-isogenic lines (NILs). As NILs are developed and refined through
successive generations of backcrossing, the set of genes carried on the introgressed chromosomal
segment is serially reduced and the questions regarding the phenotypic effects of certain loci can be
refined to pertain to a defined set of genes, and eventually to an individual gene. Moreover, the
availability of NILs will allow many unresolved questions regarding quantitative disease resistance to
be addressed, for instance the multiple disease resistance. Wisser et al. (2006) also rised the
question for future research: do certain loci condition resistance to multiple pathogens?
In hybrids such as maize, the question of the correlation between line and hybrid breeding was
raised. The line population is always mapped and the test crosses must be phenotyped. Miedaner
(2011) recommended also that the QTL-mapping in maize should be always in the line populations
(see Table 8). If the correlation between two population types is low, the hybrids in the inoculated
trials should be selected (Miedaner 2011).
51
Table 8: Summary of chromosomal distribution of quantitative trait loci (QTLs) for resistance to ear rots caused by Fusarium verticillioides (FER), Fusarium graminearum (GER), and
Aspergillus flavus (AER) and for reduction in accumulation of fumonisins (Fum), deoxynivalenol (Don), zearalenone (Zea), and aflatoxin (Afl) in different population of maize (Hou, Hue, et
al. 2002)
Population
Parents c
Sized
Typee
Cross 3 x 18
238
F 2:3
CIM
Cross 5 x 18
206
F 2:3
CIM
GE440 x FR1064
213
BC1F1:2
CIM
NC300 x B104
143
BC1F1:2
CIM
87-1 x Zong3
187
RIL
CIM
CO387 x CG62
144
RIL
CIM
UH006 x UH007
150
DH
CIM MR
UH007 x D152
180
DH
ANOVA
UH006 x UH009
101
DH
ANOVA
UH007 x UH009
227
DH
ANOVA
NC300 x B104
48
RIL
Chromosome b
QTL
analysis a
CIM
1
2
3
4
5
6
7
8
9
10
FER
FER
FER
FER
-
FER
FER
-
-
FER
FER
-
FER
FER
FER
FER
FER
-
-
-
FER
Fum
Fum
FER
Fum
FER
-
FER
Fum
FER
Fum
-
Fum
FER
Fum
-
GER
GER f
GER
Don
Zea
-
GER
FER
Fum
FER
Fum
FER
GER
GER
GER f
GER
Zea
GER
GER f
-
GER
GER
Don
Zea
GER
Don
GER
Don
GER
Afl
-
-
FER
Fum
FER
GER
GER f
-
GER
Fum
Fum
-
FER
GER
Zea
Don
Don
GER
-
-
Don
-
-
-
-
-
GER
Don
AER
FER
Fum
GER
-
AER
AER
FER
Afl
Fum
FER
GER
Reference
Perez-Brito et al.
(2001)
Perez-Brito et al.
(2001)
Robertson-Hoyt et al.
(2006)
Robertson-Hoyt et al.
(2006)
Ding et al. (2008)
Ali et al. (2015)
GER
-
Don
Martin et al. (2011)
--
-
Martin et al. (2012)
-
GER
Don
-
GER
Martin et al. (2012)
-
-
-
GER
AER
Fum
-
FER
Afl
Fum
Afl
GER
Don
GER
Don
FER
Afl
Martin et al. (2012)
Robertson-Hoyt et al.
(2007)
a CIM, composite interval mapping; ANOVA, analysis of variance; CIM MR, composite interval mapping with multiple regression.
b Stable QTLs in multiple year–location environments are indicated in bold. c Resistant genotype is indicated in bold.
d Number of progeny. e RIL, recombinant inbred line; BC, backcross; DH, doubled haploid; S, self-pollinated. f Silk resistance.
52
Therefore, a resistance breeding must take into account (Entrup, Schwarz and Heilman 2013) both
the pathogen and toxin formation. Bolduan et al. (2010) have demonstrated a close relationship
between the visible mold growth and toxin load. Miedaner et al. (2010) and Bouldan et al. (2010)
found that dent material has a higher resistance levels than flint material. However, resistance
selection must be parallel in both gene pools. Bouldan et al. (2010) recommended a two-stage
selection:
on line personal contribution by artificial infection at one place;
between test hybrids in several (2-3) locations.
Only the best performing hybrids will be investigated for toxin levels in the grain using methods such
as ELISA or HPLC. Martin et al. (2011, 2011) determined using SSR markers several QTL for resistance
to FER and GER and a reduced toxin levels which were frequently mapped for both traits in the same
genomic region. Thus, even with this resistance trait the way for a marker-assisted selection is open
(Entrup, Schwarz and Heilman 2013).
Bt maize
Resistance of the maize plant against insects causes less Fusaruim infection. In general, it was
reported that the hybrids with good husk cover show a greater resistance to insect damage and in
turn accumulate lower levels of mycotoxins (Betran, Isakeit and Odvody 2002). Genetically modified
Bt maize (maize with genes from Bacillus thuringiensis with insecticidal activity due to Bt toxin) had a
significantly lower mycotoxin content, especially DON content (Wu, Miller and Casman 2004). Ostry
et al. (2010) reviewed the comparative data concerning Fusarium mycotoxins in Bt maize and non-Bt
isogenic maize and concluded that nineteen out of 23 studies on Bt maize confirmed that Bt maize is
less contaminated with mycotoxins (FUM, DON, ZEA) than the conventional control variety in each
case (Ostry, et al. 2010).
Association mapping
Association mapping is a method for finding QTL in the genome based on naturally occurring linkage
disequilibrium between a marker locus and the QTL in a random mating population. Because it uses
linkage disequilibrium, the method is also called linkage-disequilibrium mapping. Moreover, this
method identifies statistical associations between molecular markers and phenotypic variation for
complex traits. Linkage disequilibrium in a population between the marker locus and a functional
variant in a gene can cause the association (Griffiths, et al. 2012). It is a powerful tool to identify QTL
and has the capability of identifying a single polymorphism within a gene trait is responsible for the
phenotypic variation of a specific trait (Ali and Yan 2012).
Association mapping offers several advantages over QTL mapping (Griffiths, et al. 2012):
Since it is performed with random-mating populations, there is no need to make controlled
crosses or work with known parent–offspring relationships;
It tests many alleles at a locus at once. In QTL-mapping studies, there are two parents and so
only two alleles are being compared;
All the alleles in the population are being assayed at the same time;
53
Lead to the direct identification of the genes at the QTL without the need for subsequent
fine-mapping studies. This is possible because the SNPs in any gene that influences the trait
will show stronger associations with the trait than SNPs in other genes.
Studies of molecular polymorphisms between resistant and susceptible crop cultivars and
segregating populations produced by crossing cultivars with different resistance levels or sources
have identified the genomic locations (often called quantitative trait loci (QTL)) conditioning natural
resistance to Fusarium (Reinprecht, et al. 2011).
Based on the article by Walbot (2009), it was summarized that the gene flow from teosinte and
between maize populations (enhanced by the outcrossing nature of maize), farmer and natural
selection (especially following introduction into new growing regions), recombination, drift, and
mutation have all contributed to diversity seen in maize germplasm (Yan, Warburton and Crouch
2011). Therefore, utilizing the vast range of phenotypic variation found in diverse maize germplasm,
association genetic analysis correlates molecular polymorphisms within candidate gene sequences to
quantitative phenotypic variation (Bennetzen and Hake 2009). However, the choice of germplasm for
association mapping, composed of elite inbred lines, diverse inbred lines or land races, is the vital
concern for success of association analysis (Ali and Yan 2012).
SNP (Single-Nucleotid Polymorphism)
In population genetics, a locus is simply a location in the genome; it can be a single nucleotide site or
a stretch of many nucleotides. The simplest form of variation one might observe at a locus is a
difference in the nucleotide present at a single nucleotide site whether adenine, cytosine, guanine,
or thymine. This phenomenon is called a single nucleotide polymorphism (SNP). SNPs are the
smallest type of genetic change that can occur within a given gene and the most prevalent types of
polymorphism in most genomes (Griffiths, et al. 2012) .
Using commercially available maize SNP chips with more than 60,000 SNPs (Miedaner 2011) enables
the research on genetic resistance to different pathogens of maize to identify the genetic differences,
termed alleles, which are associated with resistance/susceptibility. Zila et al. (2013) performed
association tests with 47 445 SNPs with controlling for background genomic relationships with mixed
model and identified three marker loci significantly associated with disease resistance in at least one
subset of environments (Zila, et al. 2013).
Heritability and correlation
How maize susceptibility to GER and FER can be minimized depends on its heritability and whether
new genes and alleles for these traits are available. The degree to which variation for a trait in a
population is explained by genetic factors is measured by the broad-sense heritability (H2) of the
trait. In detail, H2 is the ratio of the genetic variance to the phenotypic variance (Griffiths, et al.
2012).
Bolduan et al. (2009) proposed that heritability and selection gain can be enhanced by increasing the
number of test environments at the cost of number of replications and plants per plot. They found
also for GER and DON that heritability estimates were moderate to high.
54
It has been reported that although phenotypic correlations between levels of ear rot and fumonisin
are moderate to low (Clements 2004), the genotypic correlations are significantly higher. Relatively
high heritabilities for both traits have also been reported by Robertson, et al. 2006, and Bennetzen
and Hake 2009.
Löfller et al. (2011) investigated the resistance to ear rot and mycotoxin accumulation caused after
silk channel inoculation with F. graminearum and F. verticillioides. High genotypic correlation
between ear rots and mycotoxins (r> 0.90), and similar heritabilities of both traits, revealed the
effectiveness of indirect selection for mycotoxin content based on ear rot rating after inoculation
(Löfller, et al. 2011).
Martin et al. (2012b) showed that correlations of resistance to GER and DON contamination with
important agronomic traits (grain yield under non-inoculated conditions, number of days to silking,
plant height) were not significant. This indicated that selection for resistance and these agronomic
traits can be carried out simultaneously. Holley (1989) reported that tropical hybrids crossed with US
Corn Belt testers had better resistance to kernel ear rot (Fusarium moniliforme).
Hallauer and Miranda (1988) stated that environmental factors such as weather, soil, and pests may
have large effect on maize genotypes. These effects are notoriously higher on single crosses than
other types of hybrids because the single hybrids have more interaction with the environment than
have the double-cross hybrids (Hallauer and Miranda 1988; Troyer 1996). Since these factors differ
across the locations or planting dates because of the environmental conditions, this fact additionally
contributes to the genotype-by-environment interactions and to the experimental errors (Vieira
2009a; Viera 2012).
Santiago et al. (2013) showed that it is not necessary to transfer resistance from a given kernel
colour, type or use to another since acceptable levels of resistance were found in all of the different
groups they studied. Finally, to take advantage of the adaptation and heterosis of the heterotic
patterns ‘Reid Dent’דEuropean Flint” and “Lancaster Dent”דEuropean Flint”, they proposed to
initiate a pedigree breeding program for obtaining field corn inbreds with the following crosses:
EP31×EP39 and F575×EP65 (European Flint), B93×Oh43 and A670×H95 (Lancaster Dent) and
A630×A635 and A654×A666 (Reid Dent).
Unfortunately, most commercial hybrids currently grown worldwide have little or no resistance to
infection by F. graminearum. However, inbred lines with improved silk and/or kernel resistance to F.
graminearum infection have been developed in recent years and were summarized by Lanubile,
Maschietto and Marocco 2014.
It is unlikely that we will have completely disease-free maize plants. But it is possible that many of
the genetic traits that confer the plant resistance will be completely determined. This could lead to
better understanding the mechanisms for disease control and will improve the plant breeding
programmes. In the future, genomics will hopefully hold the promise of providing powerful DNA
markers for the desired breeding traits such as GER resistance, as well as selection for markers aimed
at minimizing these disease effects resulting in reduced mycotoxin contamination in maize.
55
1.3. Integrated Maize Management to control GER
The first line of defence against the contamination of mycotoxins is at the farm level and starts with
implementation of good agricultural practices (GAPs) to prevent infection (Murphy, et al. 2006).
Several practices could be implemented to avoid Fusarium infection and toxin contamination.
Due the multiple possible origins of fungi infection, any prevention strategy for fungal and mycotoxin
contamination must be carried out at an integrative level all along the food production chain (Jouany
2007). Main factors determining plant quality are shown in Table 9. Grain contamination was
reduced by 86% using an integrated programme involving three factors, which were plant
maturation, nutrition, and insect control (Blandino, Reyneri and Colombari, et al. 2009).
Table 9. Main factors determining plant quality (Gyori 2000)
Genetic properties of the plant
Biological resources
Varieties
Hybrids
Environmental influences
Environmental resources
Climate
Soil
Water
1.3.1.
Crop management practice
Technical resources
Plant sequence
Sowing time
Amelioration
Plant population
Plant protection
Plant growth regulations
Fertilization
Irrigation
Drying at harvest
Transportation
Storage
Preventive control of ear rot
Prevention of mycotoxin contamination in grain depends on the prevention of kernel infection,
subsequently the prevention on the field. The severity of the FER and GER disease, and their
occurrence and prevalence of the causal species (Chapter 1.1.4.), may vary by region and year,
therefore different prediction tools could facilitate the appropriate maize protection strategy.
Disease forecasting
Disease forecasts can be used to help producers make decisions about the application of a fungicide
or biological control, establish harvest priorities, and pursue markets for grain (De Wolf, Madden and
Lipps 2003) and subsequently to optimize the management strategies. Grain buyers can use this
information to make preparations for mycotoxin testing, grain cleaning, storage, and processing
(Gilbert and Fernando 2004).
The majority of the models are based on prediction tools and typically include temperature, rainfall
and other weather inputs, insect populations, maize hybrid and maturity, previous crop, planting
date, flowering date, disease observations and concentrations of DON and other crop production
practices, and sometimes economic factors (Munkvold 2014). They could be divided into preflowering (weather variables observed prior to flowering to predict disease) and post-flowering
56
(weather variables observed both prior to and during the flowering period) models (De Wolf,
Madden and Lipps 2003).
Several disease forecasting systems have been established for Fusarium disease in recent years,
especially for Fusarium head blight (FHB) in wheat, for instance FusaProg in Switzerland, Fusarium
graminearum and deoxynivalenol forecasting system (Musa, et al. 2007)
Unfortunately, there is no available forecasting model for maize up to date. Additional improvements
to, and increased grower awareness of, existing models will help manage FHB, subsequently FER and
GER, and will reduce Fusarium inoculum (Gilbert and Fernando 2004). However, it is likely that they
will become available in the near future. The prediction models may become widely available as risk
assessment tools to assist in mycotoxin management (Munkvold 2014).
1.3.2.
Agricultural practices
Since the early days of IPM systems, cultural controls have been considered a first line of defence in
many pest-management systems (Bajwa and Kogan 2004). Some of the agricultural practices involve
many aspects of crop management, such as the use of pest-free seed, good sanitation and
destruction of the plant residue to limit the spread of the pests, crop rotation and good nutrition
supply for the plants (Blandino, Reyneri and Colombari, et al. 2009).
Munkvold (2014) reviewed the crop management practices in order to minimize the risk of
mycotoxin contamination in temperate-zone maize and outlined pre- and post-planting management
decisions, as it can be seen in Table 10.
Table 10. Pre- and post-planting management decisions relevant to the maize IPM
Pre-planting management decisions
Hybrid selection
Crop rotation and tillage practices
Planting date
Fertilization
Planting density
Seed treatment
Post-planting management decisions
Insect management
Irrigation management
Weed management
Harvest date and practices
1.3.2.1. Crop rotation
Since the maize was grown as monocrop or in rotation with other cereals as wheat, crop rotation
practices should be considered in the integrated plant management (IPM) strategy. In general,
monocrops often suffer significant yield losses as the result of damages due to diseases or pests
(Ciancio and Mukerji 2008).
A close rotation between cereals (e.g. wheat) and maize which are susceptible for the same Fusarium
species causing Fusarium head blight and FER must be avoided (Leslie 2014).
Munkvold (2014) suggested that the maize rotation with legumes or other dicot crops could reduce
the build-up of fungal inoculum in crop residue. For instance, a soybean/maize rotation can provide
an excellent control for some insects (Bajwa and Kogan 2004). Particularly, maize rootworm,
57
Diabrotica virgifera Le Conte, have been effectively controlled by 2-year rotation of maize with
soybeans. Unfortunately, there are areas where the rootworms have developed strains that can
diapauses for more than 1 year (Bajwa and Kogan 2004). Moreover, the crop rotation with plant, that
are not susceptible to Fusarium spp. has been advocated as possible managing strategy.
1.3.2.2. Resistant plant varieties
Resistant cultivars are highly cost-effective components of IPM systems, and many examples
demonstrate how they provide substantial returns on economic investment (Bajwa and Kogan 2004).
Moreover, host resistance is a critical factor determining epidemic development (Cookie, Johnes and
Kaye 2006). Therefore, resistance breeding remains a promising strategy against the Fusarium
damage in maize fields, as well as an essential element of the sustainable agriculture (Cookie, Johnes
and Kaye 2006). Resistance breeding was revised more into detail in the Breeding section (Chapter
2.1.).
1.3.2.3. Maturity group
FAO numbers are generally calculated from the grain moisture at harvest, which has decreased
substantially in recent decades. Later harvest at lower grain moisture content is required if drying
costs are to be reduced. However, the lower the grain moisture at harvest, the smaller the difference
in grain moisture between the maturity groups (Marton, Szieberth and Csürös 2004). This study
reports an improved design for determining the maize maturity group including, in addition to grain
moisture at harvest, two additional grain moisture contents, measured in a relatively high grain
moisture range, and the flowering date. An advantage of their model is that breeders can calculate
the FAO numbers of their hybrids more accurately from their own results and can thus enter them
for trials in the appropriate maturity group (Marton, Szieberth und Csürös 2004).
In detail, deoxynivalenol and zearalenone are both increased by the higher length of the ripeness
stage, as reported by Reid and Sinha (1998). Of all the different agricultural factors Blandino et al.
(2009) stated that the hybrid maturity played the least important role on fumonisin prevention,
compared to other Fusarium toxins aforementioned. They also confirmed that a significant reduction
of contamination could probably only be obtained for fumonisins with the use of an earlier maturity
hybrid (FAO rating 200–300), which would be able to assure a further anticipation of the anthesis and
to avoid the activities of 2nd generation ECB larvae during the milk and dough stage (Derridj, et al.
1989).
1.3.2.4. Crop residue management and soil tillage
As outlined in Chapter 1.1.4.5., the crop residue is the most important source of inoculum for many
Fusarium species. In general, residue management has been shown to reduce populations of
Fusarium in soil, prevent germination, or decrease disease in several crops, for example wheat.
Despite of this, Munkvold (2014) pointed out that within-field residue management alone is not
sufficient to reduce infection levels or mycotoxin contamination. Maiorano et al. (2009) and McGee
et al. (1998) confirmed also that inoculum production due to crop rotation or tillage is likely to be
offset by airborne inoculum arriving from outside the field.
In cereal production late developing tillers and volunteer plants provide a “green bridge” which
enables such pathogens to survive the gap between the harvesting of one crop and the emergence of
58
the next. Infected weeds can also act as strong sources of inoculum for nearby crop plants that share
their susceptibility to particular diseases (Cookie, Johnes and Kaye 2006).
Sanitation involves removing and destroying overwintering substrates for pathogen inoculum. After
harvest destroying the stubble of maize is an important measure in various corn borers. Other
sanitation techniques include using pest-free seeds and decontaminating equipment (Bajwa and
Kogan 2004).
Hence, the tillage practices can disrupt the insect’s life cycle or inoculum production that occur in the
soil or in crop residues. Tillage timing and depth should be also taken into consideration, especially
for the weed management in the early stage during the maize development. Deep ploughing after
harvest buries infested plant parts and stubble and destroys the larvae of pests such as ECB. In the
case of the ECB, the ploughing of stubble result in a 90% reduction of hibernating larvae (Horn, 1988;
Bajwa and Kogan 2004; Marocco, et al. 2008).
Nowadays a lot of new machines for soil cultivation are available (see Figure 21), which are designed
to slice up the entire soil surface, mix in harvest trash and loosen to depth, all in a single pass.
Creating a seedbed in a single pass and, at the same time, working at shallow depth or down 30 cm
(deeper in the subsoil) can save a lot of time and fuel as well.
Finally, a high proportion of maize in crop rotation, especially in no-till, is the biggest risk factor for
the occurrence of the stalk and ear rot. A reduction of the infestation can already be achieved with
careful incorporation of infested stubble residues of maize (Munkvold 2014).
Figure 21. Soil cultivation after maize harvest (2012, Riben, Bulgaria)
Microorganisms are naturally active in controlling pathogen propagules on plant debris. This is one of
the benefits of tillage, which provides for greater microbial contact and degradation of the pathogen
and it takes the form of mycoparasitism, antibiosis, or the production of volatile compounds that are
inhibitory to the pathogen (Gilbert and Fernando 2004).
59
Last but not least, the fine condition of the soil before sowing is very important factor for the easy
maize germination. In order to level the seedbed, appropriate field procedures should be applied in
accordance with the particular field and weather conditions.
1.3.2.5. Sowing and Seed quality
Considering the fact that Fusarium spp. are seed-borne fungi, an appropriate seed testing is required
in order to ensure the health of the seed stocks. By testing for the presence of the pathogen in the
used maize seeds, and rejecting the intensively contaminated seed material, if it is found, this could
lead to reduction of the inoculum. Thus, Fusarium spp. are almost impossible to eliminate once they
are present in the seed material. If necessary, the application of a fungicidal seed treatment is
recommended (Chapter 1.3.2.7) (Cookie, Johnes and Kaye 2006).
Sowing time
Blandino et al. (2009) confirmed that an early sowing date could significantly reduce ECB injury
because the first part of the grain filling, the most sensitive stage, takes place before the occurrence
of the 2nd generation larvae peak (Pilcher and Rice 2001). ECB larvae break the pericarp and give the
fungus a direct point of entry into the kernel; moreover the same larvae can be vectors of inoculum
(see Chapter 1.1.5.1. b) (Sobek and Munkvold 1999). Furthermore, an early sowing date, anticipating
the harvest, increases grain dry down and reduces the stage length that is most involved in fungal
infection and development (Blandino, Reyneri and Colombari, et al. 2009).
Crop structure
The high plant density affects the development of Fusarium disease (Blandino, Reyneri and Vanara
2008a). Therefore the moderate plant density could also be achieved with an appropriate planting
technique (considering the row width and spacing). In this case, a precise sowing machine can ensure
a uniform planting depth and an exact seed placement, which is a prerequisite for the good maize
emergence.
1.3.2.6. Fertilization
Healthy plants are essential for optimal agricultural production. In general, vigorous plants are better
able to tolerate pests, subsequently to maximize the yield of high-quality production. Excessive
nutrients (particularly nitrogen) and relative nutrient balance (ratios of nutrients) in soils affect the
pest response to plants. In addition, imbalances in the soil can make the plant more attractive to
insect pests, for instance ECB, and less able to recover from pest damage or more susceptible to
secondary infestations by plant pathogens (Bajwa and Kogan 2004). Soil fertility levels influence the
maize susceptibility to mycotoxin contamination (Munkvold 2014) and lower rates of nitrogen
application resulted in lower levels of fumonisins, aflatoxins and ochratoxin A as indicated by
Blandino et al. (2008b). However, the plant health during the vegetation period will improve the stalk
integrity, subsequently it will minimize the stalk cannibalization and plant lodging during the maize
harvest (Figure 22).
60
Figure 22. Liquid fertilization of maize field (2011, Riben, Bulgaria)
1.3.2.7. Chemical control of Fusarium infection
Curtis et al. (2011) outlined the importance of treatment with fungicides carried out at the flowering
of maize (the critical phenological phase) and presented results on the use of synthetic fungicides
applied at silk emission and at the milky-waxy ripening phase to the foliage and to root, in
combination with insecticide treatment, which showed a significant reduction of the Fusarium ear rot
severity and fumonisin kernel contamination.
Indeed, the foliage treatment carried out in a crucial phenological phase could play a key role in
reducing the insidious airborne microconidia as an inoculum for the silks infection and the
subsequent mycotoxin contamination (Curtis, et al. 2011). In this regard, recent research performed
by Duncan et al. (2010) on the biology of maize kernel infection by F. verticillioides clearly
demonstrated the silk penetration by fungus and could more effectively elucidate the spray
treatment effect with some fungicides in reducing the kernel infections in their experiments.
Fungicides
The commercial fungicides available in Austrian market for seed treatment and foliar application are
given in Table 11.
Mode of action - DMI fungicides
These are systemic compounds acting at a single site in the fungal cell. In general, sterol biosynthesis
inhibitors (SBIs) affect membrane structure and function, with widespread consequences for the cell.
The specific interaction is an enzyme protein catalysing a single demethylation step in the sterol
biosynthesis pathway, which are referred as demethylation inhibitors (DMIs) in class III according to
FRAC classification (Lucas 1998). An available fungicide on the Austrian market is listed in Table 11,
including the active ingredients prothioconzole + tebuconazole.
61
Dithiocarbamates
Multi-site contact activity (class M) belongs to the dithiocarbamates and relatives. The active
ingredient (a. i.) belonging to this group available on the market is thiram for seed treatment (see
Table 11). It is a part from the monoalkyldithiocarbamates and possess a reactive hydrogen on the
nitrogen atom which permits the formation of a highly toxic isothiocyanate. These fungicides alter
permeability of fungal cells and inactivate thiol groups of essential enzymes (Vidhyasekaran 2004).
Table 11. Commercial fungicides available against FER and GER in Austria (AGES 2015)
Fungicides
Class
Trade name
FRAC-M3/
Aatiram
Dithiocarbamates 65/Flowsan FS
FRAC-G1/ DMI
1.3.2.7.1.
Prosaro 250
Application time
Harvest
Restriction
thiram
Seed treatment
-
prothioconzole +
tebuconazole
BBCH32-69/71
35 days
Active ingredients
Efficacy of fungicides
Since fungicides are widely used to control crop diseases, it is pertinent to consider the effects of
these agents on mycotoxin production (De Mello, et al. 1998).
The Azole fungicides (a.i. prothioconazole) have been reported to be the most effective active
substances in the control of Fusarium Head Blight (FHB) and in the reduction of the main mycotoxins
that occur in cereal grain, such as deoxynivalenol (DON) (Scaprino, et al. 2014).
On the other hand, Rodriquez-Brljevich et al. (2010) have clearly shown that seed treatments (a.i.
fludioxonil + azoxystrobin + mefenoxam + thiamethoxam) suppress infection by Fusarium spp., and
seedling disease development in maize, resulting in enhanced photosynthesis and increased plant
vigour. Furthermore, early-season protection of germinating seedlings may contribute to reduction
of the mid- to late season disease.
Ronchi et al. (1997) observed that the fungicide tetraconazole affects the phenylpropanoid-flavonoid
biosynthesis and the hydroxyprolinerich glycoprotein gene in maize plant, increasing the maize plant
defence responses to abiotic and biotic stresses as drought and plant pathogens.
Field application of fungicides may reduce fungal infection, thus reducing mycotoxin contamination
potential, but is often not economically feasible (Channaiah and Maier 2014).
1.3.2.8. Biological control measures
The potential for using microorganisms to detoxify mycotoxins has shown promise. Several fungal
and bacterial species were found to act as antagonists of Fusarium spp. in maize and the most
effective associated with F. verticillioides have been discussed in a review provided by Kant and
Reinprecht (2011).
Additionally, Owolade et al. (2000) investigated the efficacy of certain indigenous plant extracts
against seed-borne infection of Fusarium moniliforme on maize in south western Nigeria. They
62
demonstrated that DON production in general was inhibited by all essential oils at 30°C and an
inhibitory trend was observed when cinnamon and oregano oils were added to maize grain. Studies
on the efficacy of indigenous plant extracts against seed-borne infection of F. moniliforme on maize
demonstrated that aqueous extracts of leaves of O. gratissimum, Acalypha ciliate Forssk., V.
amygdalina, M. indica L. (mango tree) and A. indica had significant inhibitory growth effects on the
fungal pathogen (Owolade, Amusa and Osikanlu 2000). Their results validated the significance of
natural products to control the seed-borne inoculum of this pathogen.
At present, although many investigations have been carried out, biological control of ear rot in maize
still remains at an experimental stage with very difficult field application.
1.3.2.9. Harvest time and storage
Maize crops left to dry in the field can be exposed to temperature and moisture conditions that are
favourable for continued growth of toxigenic fungi and the accumulation of their mycotoxins in grain.
Therefore, harvest timing can have a major impact on the final levels of mycotoxins accumulated in
the grain (Munkvold 2014).
Efficient maize harvesting depends on the following factors:
Harvest time
Moisture content
Stalk integrity
Appropriate header equipment (Munkvold 2014).
Figure 23. Harvest header for maize with stalk chopper rotor (Riben, Bulgaria, 2014)
In addition to harvesting at an optimum grain moisture content, achieving proper combine settings
(cylinder speed setting; airflow to clean grain; deck plated/snapping rollers; ear savers) can help to
increase combine efficiency, maximize grain quality (minimizing seed coat damage; reduction of the
occurrence of broken kernels, glumes, ear tips, and cob pieces), and minimize field losses
(Anonymous 2015; Munkvold 2014). Harvest header with stalk chopper rotor (visualized in Figure 23)
enables faster harvesting with considerable reduction of the broken maize kernels. The integration of
stalk chopper eliminates any need for second-pass chopping operations and facilitates the faster
inoculum degradation. Nevertheless, harvesting at lower moistures can increase mechanical losses
due to ear drop, stalk lodging, and kernel shattering (Anonymous 2015).
63
Ideally, grain should be harvested and dried to 15.5% moisture content or less (depending on storage
intentions) as quickly as possible (Munkvold 2014).
1.3.3.
Post-harvest measures
In their review, Channaiah and Maier (2014) summarized and discussed the best management
practices for the successful storage of maize and stressed the importance of SLAM- based strategy
(sanitation, loading, aeration, and monitoring) proposed by Mason and Woloshuk (2010). Namely,
sanitation, screening, aeration, monitoring, sampling, and testing of stored grain lead to reduction of
the mycotoxins entering grain-based food and feed chains (Channaiah and Maier 2014).
General hygiene and sanitation measures of maize are important to reduce spoilage in storage and
increase the shelf-life of the mass production. Insect infestation or microbial infection (see Chapter
1.1.5.) in incoming maize affects the overall plant sanitation, reduces the storage life of the maize
and shelf-life of the product, increases the risk of mycotoxin production and decreases product
palatability (Hall 1996).
1.3.4.
Mycotoxin reduction in grain chains
Leslie et al. (2014) summarized pre-harvest techniques for mycotoxin reduction:
Grain sorting and sizing, corn screening (small broken pieces of corn kernels)
Wet (to produce starch) and dry milling of contaminated corn
Fermentation of mycotoxin-contaminated corn to produce ethanol, which did not
significantly degrade the toxins
The effect of a variety/ chemical processing operations on mycotoxin content
Heat application to reduce mycotoxin levels.
Table 12. Physical and chemical methods for mycotoxin decontamination and inactivation in maize (Jouany 2007;
Bullerman and Bianchini 2014)
Physical methods
Removal of contaminated
materials
Sorting, sizing
Chemical methods
Washing (sodium carbonate
solution, sodium bisulfite)
Adsorbents
Grain and flour processing
Sieving-cleaning
Ozonation
Extrusion cooking (≥160°C)
Flotation and density segregation
(gravity tables)
Irradication
Milling (dry, wet), Dehulling
Steeping
Nixtamination
Flaking
Citric acid
Other chemical treatments
Roasting
Toasting
Canning
Flour milling
Dehulling
Mycotoxin are heat stable and no toxin degradation occurs under normal boiling, frying, or
pastreurization condition. Limited research suggested the possibility that the mycotoxin may be
metabolized by corn enzymes; and decline in DON levels in grains stored at – 180C to 40C and
trichothecens at temperatures greater than 00C (Murphy, et al. 2006). Several treatment strategies of
grain for food and feed consumption are summarized in Table 12. The methods listed in Table 12 for
64
the reduction of mycotoxin levels are not always economically feasible and are often impractical or
logistically difficult to implement in large scale production.
Indeed, good processing techniques listed in Table 10 may reduce mycotoxin concentration, but
mycotoxins are never completely destroyed during processing and can contaminate finished
processed foods and feeds (Bullerman and Bianchini 2014).
IPM technology advocates the combination of a variety of control measures as it can be seen in this
chapter (see Table 10). Successful disease management often leads to profitable crop production. For
that reason, farmers need to be active on a community basis, and practice crop health monitoring on
a regular basis (Ciancio and Mukerji 2008).
It should be emphasized that prevention is better than cure. Hence, it is important to follow good
agricultural practices as much as possible in the pre- and postharvest components of the chain to
maintain grain quality and reduce the need for intervention in storage (Magan, Aldred and Baxter
2014).
65
Material and methods
2.
Field experiments
As previously mentioned, the main aim of the experiment was the evaluation of new Austrian maize
hybrids for resistance to Fusarium graminearum after silk channel inoculation. The trials were
conducted during the vegetation period of 2013 in the field of the Institute of Biotechnology in Plant
Production, Tulln, Austria, 30 km west of Vienna, at 180 m above sea level. The maize accessions
consisted of 90 genotypes provided by Austrian Agency for Health and Food Safety (AGES)
represented different FAO maturity groups (FAO230-250, FAO260-300, FAO310-350, FAO360-440),
some of them are listed in Appendix IV. For the accurately identification of the maize phenology
BBCH-scale was applied (Appendix I).
The experimental design was a randomized complete block (RCB) with in total two replications as it is
shown in Appendix II. The lines were labelled with appropriate numbers, sown in twin row plots, 8 m
long with approximately 50 plants per row. Additionally, to ensure adequate humid conditions during
the inoculation season an automatic mist irrigation device was installed and irrigation was started
two days before the beginning of the inoculation period. Water was supplied on three days per week
(Monday, Wednesday and Friday) during 24 hours (starting at 8:00 in the morning). During the
irrigation period the misting system was switched on every 20 minutes for 20 seconds (during the
day) or every hour (at night) for 10 seconds.
Damage produced by the second-generation larvae (from the middle of July) of European corn borer
(ECB) population, Ostrinia nubilalis (Hübner), (Lepidoptera: Crambidae) has an impact on maize ear
rot. These injuries can act as portals of entry for the fungus and therefore support colonization of the
plants, as it is known for the European corn borer (Ostrinia nubilalis) and other insects (Papst, Utz, et
al. 2005). During the larvae movements and feeding it favours the fungal development through
tunnels into the stalk and kernel wounds, which leads to easier access of the Fusarium to susceptible
crop tissues (Dowd 1998). To avoid the ECB infestation, Trichograma field traps have been used.
2.1. Inocolum production
Figure 25. Bubble breeding method
Figure 24. Concentrated suspensions of macroconidia
stored frozen in small aliquots
66
In the case of F. graminearum, it was very helpful to ensure satisfying and homogenous infection
pressures by performing artificial silk channel inoculation as described by Reid et al. (1996), which
more closely simulates the natural infection. This requires a sufficient amount of inoculum with a
suitable level of aggressiveness. Therefore, a suitable amount of macroconidia was mass-produced
in liquid media with aeration using the bubble breeding method according to (Mesterházy 1978)(see
Figure 25). A Fusarium graminearum isolate (IFA166) was selected. Mung bean broth medium was
prepared as follows: 20 gr mung beans (Vigna radiate L.) were added to 1 L of boiling water. The
beans were cooked for ca. 21 minutes until the pericarb of the seeds start to burst. Thereafter the
medium was immediately decanted and the beans were removed. The bottles containing the
medium (9 Liter) were autoclaved and after cooling at room temperature seeded with the Fusarium
strain. The flasks were aerated with sterile air for five days at room temperature. Thereafter the
bottles were stored overnight at 2-5°C to allow the macroconidia to settle down at the bottom.
Subsequently the clear supernatant was removed and the conidia collected. The number of
macroconidia was counted with a Bürker-Türk hematocytometer. The spores were frozen at -80°C in
small aliquots for storage (Figure 24) until use for the field experiment. The frozen aliquots were
quickly thawed in luke-warm water and the content was suspended in 1L water resulting in a final
concentration of 500.000 conidia/mL.
2.2. Artificial inoculation
The appropriate timing of spore application was related to the date of anthesis of the maize,
approximately five to seven days after 50% silking, when the silks are elongated, pollinated and still
green, which represents stage BBCH 67 (Figure 26). In order to inoculate all hybrids at the same stage
of development, they were regularly checked for silk emergence in order to avoid the rapid silk
senescence of the maize after pollination, which alters the suitability of silk for growth of ear-rotting
organisms (Reid et al. 1996).
Figure 26. Maize during inoculation at BBCH67 with the mist irrigation system (26.07.2013, Tulln)
67
The inoculum was applied using hand-held sprayers and 3.0 ml of the Fusarium suspension
containing 5x 105 macroconidia per ml-1 was sprayed on the silks close to the opening of the silk
channel. Only the main ear was treated and evaluated. The different maize hybrids were inoculated
consecutively, starting with the first plant in a row between the July 26 and the August 8 in 2013.
2.3. Maize disease assessment
For the evaluation of Gibberella ear rot three major parameters were assessed: disease incidence,
disease severity and disease intensity. Therefore, during the first week in October all maize plants
were examined on the basis of ear symptoms after ripening directly on the field (BBCH 89). Ears were
hand-dehusked and only the main cob was scored for disease incidence and severity assessment
based on visual estimation of the infected kernels by F. graminearum. The observation was carried
out according to procedure given by Lemmens (personal communication), which is listed in the Table
13 and also visualized in Appendix III. Disease severity was estimated as the percentage of ear area
colonized by Fusarium.
%𝐷𝑆 𝑡𝑜𝑡𝑎𝑙 = %𝐷𝑆 𝑠𝑙𝑖𝑔ℎ𝑡 + %𝐷𝑆 ℎ𝑒𝑎𝑣𝑦
For more precise results, it was recorded using single ear ratings and detailed linear scale as the
percentage of diseased ears varying from zero to 100%. For the average ear size 500 kernels were
taken as the unit, subsequently one diseased kernel representing 0.2 % disease severity. The
expression of the symptom was determined also as light, heavy and total diseased area, where the
heavy damage was calculated in Excel as difference between the previous mentioned. Kernels which
were heavily damaged were completely colonized by the fungus and exhibit strong discoloration and
are usually overgrown by the fungus. Light infection is characterized by light mycelial growth mainly
observed between the kernels. The kernels do not show discoloration and look visually healthy, but
the fungus colonized the kernel via the placenta of the ear and the embryos of the kernels are
infected.
In addition, the occurrence of the mechanical damage of the European Corn Borer (ECB) was
assessed. ECB infested ears were excluded from the statistical analyses, because the promotion of
fungal infections through the larvae physical injuries falsifies the results. In order to verify the
Fusarium resistance evaluation, only ears without insect or other wound-mediated infection should
be considered for the results (Mesterházy, Lemmens and Reid 2012). In order to eliminate as far as
possible operator error (subjectivity) the visual assessment of the main disease parameters was
carried out by two persons for each row per genotype at the same time.
Table 13. Ear rot rating scale
Rating
0
0,2
0,4
0,6
1
3
5
10
% Diseased kernels
0 % = no disease visible
0,2 % = 1 diseased kernel
0,4 % = 2 diseased kernels
0,6 % = 3 diseased kernels
1% = 5 diseased kernels
3%
5%
10% (1/10)
Rating
15
25
35
50
75
90
100
z
% Diseased kernels
15%
25% (1/4)
35%
50% (1/2)
75% (3/4)
90%
100%
European Corn Borer
68
Assessment of the percentage disease incidence (DI) was calculated by recording the number of
infected ears sampled as a percentage of the total number of inoculated plants, averaged over both
replications. DI as defined above was then calculated according to the following formula:
𝐷𝐼(%) =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑 𝑒𝑎𝑟𝑠
× 100
𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑎𝑟𝑠 𝑎𝑠𝑠𝑒𝑠𝑠𝑒𝑑
The disease severity (DS) was expressed as a percentage of infected ear tissue (diseased kernels) per
number of total kernels in the diseased ears. DS (%) is figured as follows:
𝐷𝑆(%) =
𝐴𝑟𝑒𝑎 𝑜𝑓 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑 𝑡𝑖𝑠𝑠𝑢𝑒
× 100
𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑠𝑠𝑢𝑒 𝑎𝑟𝑒𝑎
The disease intensity (DINT) as a parameter is a multiplicative product of the percentage of ears
diseased (DI %) and the area infected (DS %).
𝐷𝐼𝑁𝑇(%) =
𝐷𝑖𝑠𝑒𝑎𝑠𝑒 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 (𝐷𝐼) × 𝐷𝑖𝑠𝑒𝑎𝑠𝑒 𝑠𝑒𝑣𝑒𝑟𝑖𝑡𝑦 (𝐷𝑆)
100
2.4. Statistical analysis
The test of significance helps in determining whether the difference between the two samples
(replicates) are actually due to a chance factor or the difference is really significant among samples
(Kar 2010). Testing of hypothesis is the procedure which approaches comparison between means.
The test of a sample mean (𝑥̅ ) was done between the two means of two samples (𝑥̅1 and 𝑥̅2 ). The
hypothesis or a statement was that 𝑥̅1 = 𝑥̅2 , therefore the examination was done to determine
whether there was no difference between 𝑥̅1 and 𝑥̅2 , which is called null hypothesis (𝐻0 ). It was
expressed in the following way:
𝐻0 : 𝑥̅1 = 𝑥̅2
𝑥̅1 = Mean of sample 1
𝑥̅2 = Mean of sample 2.
The statistical technique, analysis of variance (ANOVA) for comparing the two means, is required
when analysis of data of tree or more field samples are involved. Alternative hypothesis (𝐻1 ) can be
defined as the hypothesis which is complementary to null hypothesis. Rejection of null hypothesis
leads to acceptance of alternative hypothesis (Kar 2010). Generally it can be written as,
𝐻1 : 𝑥̅1 ≠ 𝑥̅2
This states that there is difference between two samples means, for example either:
𝑥̅1 < 𝑥̅2 or 𝑥̅1 > 𝑥̅2 .
Data on ear damage was combined over two replicates of the respective year without ECB date of
damage and the significance of results was measured by conducting one-way analysis of variance
(ANOVA). To compare the total Fusarium infection of maize hybrids, data were pooled for each
69
hybrid and thereafter ranked according to infection level. A design matrix was constructed to
partition the variances due to:
𝑌𝑖𝑗 = 𝜇 + 𝐺𝑖 + 𝑒𝑖𝑗
In which “μ” stands for the grand mean, “G” for the maize genotype (i = 1...90) and “e” for
random error.
Probability value (P-values) in Table 14 is always between 0 and 1 and it represents the probability of
the difference in the data being due to sampling error. P-value should be lower than a threshold
significance level (0.05) meaning that the results are significant and are not accidental.
Table 14. Statistical significance
P-value
Description
Identification
P≤ 0,001
Highly significant
***
P≤ 0,01
Significant
**
P≤ 0,05
Low significant
*
P>0,05
Non-significant
n.s.
2.5. Least Significant Difference (LSD)
Plot means were made on least square means for genotypes by using Fisher’s protected least
significant difference (LSD) at α = 0,05 (5%) to lower the rate of Type III errors and increase the ability
of the T-test to detect significant differences among hybrid reactions. If the difference between the
mean values of lines is lower than the LSD, they are equal.
The ANOVA tables as well LSD for different scoring parameters were performed and calculated with
SAS® Version 9.2 for Windows (Little 1978).
2.6. Measures of Correlation
Pearson's correlation coefficient when applied to a sample is commonly represented by the letter r
and may be referred to as the sample correlation coefficient or the sample Pearson correlation
coefficient. So if we have one dataset {x1,...xn} containing n values and another dataset {y1,...yn}
containing n values then that formula for r is:
𝑟 = 𝑟𝑥𝑦 =
∑𝑛𝑖=1(𝑥𝑖 − 𝑥̅ )(𝑦𝑖 − 𝑦̅)
√∑𝑛𝑖=1(𝑥𝑖 − 𝑥̅ )2 √∑𝑛𝑖=1(𝑦𝑖 − 𝑦̅)2
where:
n , 𝑥𝑖 , 𝑦𝑖 are defined as above
1
𝑛
𝑥̅ = ∑𝑛𝑖=1 𝑥𝑖 (this is the sample mean: the term for y is similar) (Wikipedia, 2015)
Linear correlation coefficient could be interpreted as follows (Few 2012):
All values fall between +1 and -1.
A value of 0 indicates that there is no linear correlation.
A value of +1 means a perfect linear correlation.
70
The greater the value, either positive or negative, the stronger the linear correlation is.
The formula for r is used for the Microsoft Excel (2007) function PEARSON. Data correlations
coefficients and scatterplots were calculated and drawn in Microsoft Excel 2007.
Regression analysis was performed using SAS PRO CORR to check if the correlation coefficients were
significant. The aim of the method is to measure the discordance of the observation, subsequently to
detect outliers and provide stable estimates in case they are present.
Residual gives the residuals, which are the differences between actual and predicted values.
Residuals are calculated by subtracting the predicted value from the actual value. You might find it
helpful to think of the residual as the amount by which the fitted regression line “missed” the actual
data point.
The steps for performing the regression analysis were as follows (Schlotzhauer 2009):
Creating of a SAS data set,
Checking the data set for errors,
Choosing the significance level,
Checking the assumptions,
Performing the test,
Making conclusions from the results.
In addition, the differences between the values of the outcome predicted by the model and the
values of the outcome observed in the sample are called residuals (also known as outliers),
(Schlotzhauer, 2009).Using the SAS PRO PEG procedure 95% confidence interval was calculated to
show which genotypes were located outside the linear relation.
71
Results
The behaviour of 90 Austrian maize hybrids to Gibberella ear rot (GER) was assessed after artificial
inoculation in completely randomized block design with 2 replications. The results for all screenings
for GER resistance performed for the master thesis at hand will be presented in the upcoming
chapters (Figure 27). Disease incidence and disease severity were assessed at the end of the season
and the disease intensity was calculated. All ears showing wounding by ECB damage were omitted
from the analysis. Data is divided as follows: the general statistic distribution parameters, visual
symptoms, correlation analysis and ranking.
Figure 27. Visual examination of the maize plants on the field at BBCH89 (11.10.2013, Tulln)
3.1. Analysis of variance
Based on results of one season, the resistance differences in sensitivity for the trait %GER between
the 90 genotypes were detected through analysis of variance (ANOVA) and can be seen in Table 15,
Table 16, Table 17 for each parameter separately (disease incidence, disease severity and disease
intensity). Data collected were subjected to ANOVA using PROC GLM model of SAS to compute mean
squares of each parameter. The degree of variation was determined using % coefficient of variation
P< 0.05. Differences in parameter means were also measured using least significant difference (LSD).
To sum up, significant differences, revealed below, were found for the factor Genotype for all disease
72
parameters DI, DS and DINT (%). Therefore, we can reject the H0 hypothesis and the maize genotypes
do show difference, which leads to acceptance of the alternative hypothesis.
3.1.1. Disease incidence DI (%)
The results of the ANOVA analysis with the data for disease incidence are illustrated in Table 15.
Highly significant differences for maize genotypes are present for the traits “disease incidence heavy”
(p=0.0001***) and “DItotal” (p=0.0001***), as well as “DIlightly” (p=0.0006***).
Table 15. ANOVA analysis of RCBD for the data of Disease incidence %DI hea , %DIsli, %DItot after silk inoculation with F.
graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square)
Disease incidence %
Parameter
Disease
incidence
heavy %
Disease
incidence
slightly %
Disease
incidence
total %
Source of
variance
DF
SS
MS
F-value
P-value
Genotype
90
33383.35
375.09
3.25
<0.0001
Error
89
10385.26
115.39
Genotype
90
17463.44
196.22
2.01
0.0006
Error
89
8806.62
97.85
Genotype
90
44435.13
499.27
3.39
<0.0001
Error
89
13262.91
147.35
3.1.2. Disease severity DS (%)
For the disease trait disease severity, overall highly significant differences could be determined
among genotypes, as summarized in Table 16. The results of the 90 genotypes are highly statistically
significant in the parameters “heavy” (%DShea, p=0.001***), and “total” (%DStot, p=0.0001***). In
contrast, the characteristic “slightly” is low significant (%DSsli, p=0.0468*).
Table 16. ANOVA analysis of RCBD for the data of Disease severity %DShea , %DSsli , %DStot after silk inoculation with F.
graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square)
Disease severity %
Parameter
Disease
severity
heavy %
Disease
severity
slightly %
Disease
severity
total %
Source of
variance
DF
SS
MS
F-value
P-value
Genotype
90
372.89
5.86
2.43
<0.001
Error
89
264.10
2.41
Genotype
90
521.87
4.19
1.43
0.0468
Error
89
217.05
2.93
Genotype
90
919.87
10.34
2.22
<0.0001
Error
89
418.17
4.65
73
3.1.3. Disease intensity DINT (%)
The analysis of variance for the calculated trait DINTheavy (%), (p=0.0001***) and DINTtotal (%),
(p=0.0001***) revealed that there were overall highly significant differences among genotypes, also
for %DINTslight (p=0.00974*), as shown in Table 17. The summarizing parameter DINT (%) shows
highly significant performance referring to strong spread of the disease.
Table 17. ANOVA analysis of RCBD for the data of Disease intensity %DINThea ,%DINTsli , %DINTtot after silk inoculation
with F. graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square)
Disease intensity %
Parameter
Disease
intensity
heavy %
Disease
intensity
slightly %
Disease
intensity
total %
Source of
variance
DF
SS
MS
F-value
P-value
Genotype
90
159.29
1.79
2.65
<0.0001
Error
89
60.80
0.68
Genotype
90
75.52
0.85
1.65
0.0097
Error
89
46.40
0.52
Genotype
90
379.37
4.26
2.15
0.0002
Error
89
178.08
1.98
Overall it can be concluded that the results are highly significant according to the characteristic
“heavy” for all parameters %DIhea (p=0.0001***), %DShea (p=0.0001***) and %DINThea (p=0.0001***).
Highly significant differences in resistance between the maize genotypes were present. The same
conclusion can be drawn for the sum of both heavy and light symptoms for all three disease
parameters. In contrast, the results related to symptom characteristic “slightly” (%DSsli, p=0.0468*)
are only slightly significant in case of disease severity, which may be due to the rapid spread of the
light infection in the rainy autumn weather in 2013. In conclusion we can state that the different
resistance levels indicate that the genotypes react differently on the same Fusarium isolate under the
same environmental conditions.
74
3.2. Visual symptoms after silk channel inoculation
The prime source for the phytopathological data of GER in maize was the silk channel inoculation
with 3.0 ml of a suspension containing an inoculum concentration of 5x 105 macroconidia per ml-1 of
F. graminearum strain IFA166. In addition, disease incidence was defined as the proportion of the
diseased maize cobs in CRB obtained after the visual evaluation of the main ear of each plant and
represented by the DI % parameter. Subsequently, the severity symptoms are the amount of disease
affecting the maize ears. Therefore, the disease intensity (DINT %) was calculated as the mean grade
value with the both parameters DI% and DS% presented in the following Table 19.
Overall, few disease symptoms were observed on the maize ears in the CRB, where the pre-harvest
disease rating was calculated at phonological stage BBCH89. Out of a sum of 14422 maize main ears
over all sample blocks were evaluated and only 522 were infested by European Corn Borer (ECB).
From these ears, maize smut (Ustilago maydis) and corn leaf aphid (Rhopalosiphum maidis) were also
identified, but were not recorded. Only the infestation of the European Corn Borer (ECB), (Ostrinia
nubilalis) was recorded and will be discussed in Chapter 3.3.
The minimum, mean, median and maximum values of all parameters can be seen also in Table 18.
The full range of the diseased maize cobs (%DItot) is quite large, extending from 6.2% (AGES57) to
90.1% (AGES29), whereas 1.0% (AGES8) to 14.1% (AGES76) is the distribution of parameter %DStot
(infected ear area %) of Fusarium for all 90 genotypes. The results for total disease intensity of maize
were also illustrated in Table 19 and their coefficients (%DINTtot) vary in the range of 0.1 to 7.0%. A
detailed overview with all individual data is presented in Appendix V, Appendix VI and Appendix VII.
Table 18. Descriptive statistics for the traits Disease incidence, Disease severity and Disease incidence (Heavy + slightly=
total). Results are depicted for all genotypes and are given in %.
%DIhea
Minimum
Maximum
Median
Mean
LSD 0.05
2.5
79.4
25.6
28.2
21.34
%DIsli
2.6
52.4
21.1
22.5
19.65
%DItot
6.2
90.1
40.1
41.7
24.12
%DShea
1.0
8.7
4.1
4.1
3.09
%DSsli
1.1
8.2
3.6
3.8
3.40
%DStot
1.0
14.1
4.5
4.9
4.29
%DINThea
0.1
5.3
1.1
1.3
1.63
%DINTsli
0.1
3.5
0.8
0.9
1.43
%DINTtot
0.1
7.0
2.0
2.2
2.79
The means over 2 replications for the disease parameters %DItot, %DStot and %DINTtot of all tested
hybrids are summarized in Table 19. In this table we took the total sum of all symptoms being the
sum of the heavy and slightly infected area of the ears to describe the GER resistance of the tested
hybrids. For the interested reader we have also summarized in detail the individual data for heavy
and light infection level for each individual hybrid in Appendix V, Appendix VI and Appendix VII. The
hybrids in Table 19 are ranked according decreasing resistance according to the parameter %DINTtot
which was calculated out of the %DItot and the %DStot data as explained in the section 2.4 “Maize
disease assessment”. The overall means and the LSD0.05 values for each disease parameter are listed
in the last lines of the Table 19.
The disease parameter %DItot which equals the percentage of diseased ears in the plot varied from
6.2% up to 90.1% for hybrid AGES57 and AGES29, respectively. Hence for this disease parameter a
large variability was present among the hybrids tested in this nursery. The LSD0.05 value equalled
75
24.1%. The disease parameter %DStot, which describes the diseased area of the ears showing
symptoms, showed a range from 1% up to 14.1% for the hybrids AGES8 and AGES76, respectively.
The LSD0.05 value was 4.28.
It should be noted that the good result of AGES57 is mainly due to a low percentage of diseased ears
(%DItot) and not as much on a low level of the percentage of infected ear area of the diseased ears
(%DStot). AGES8 on the other hand, which is not statistically different from AGES57 for the parameter
%DINTtot, relies more on a better %DStot level and does not show a good performance for the
percentage of diseased ears.
We also identified hybrids which performed bad for both %DItot and %DStot, resulting in a high
sensitivity for GER (as described by the %DINTtot). Examples are AGES83, AGES29 and AGES76 which
can be found at the bottom of Table 19 and Table 20. The maximum level of FER severity
(%DStot=14.1%) was obtained from AGES76. It was followed by AGES68 and AGES73 (%DStot=10.4%
and %DStot=10.0%, respectively). Nevertheless, AGES 29 has 90.1% diseased plants represented its
total disease incidence, which represented 144 cobs out of 158. The DINTtot (%) up to 2.0% (Median
level) exhibited 46 genotypes, whereas only 30 maize genotypes showed %DStot level under 3.6%
(Median level).
Last but not least, both previous disease parameters were used to calculate %DINTtot which offers a
further possibility for data reduction and gives an overall overview of the FER resistance equalling the
percentage of Fusarium colonized kernels in the harvested material. Overall AGES57 performed best
with %DINTtot equalling 0.1% while hybrid AGES76 had about 7.1% diseased kernels considering both
healthy and diseased ears.
The importance of DINTheavy is because of the heavy infected kernels containing the most toxin
content. Therefore total amount of heavy symptoms is considered as very important parameter. In
our data the mean heavy symptoms over two replicates varied from 0.1% (AGES57, AGES36, AGES87)
to 5.3% (AGES29), which illustrates the large variability present in the resistance towards the maize
genotypes (see also LSD0.5 values in Table 20). As mentioned above the separate ANOVA analyses
showed that the differences in resistance of the maize genotypes are highly significant (see Table 17).
76
Table 19. Disease incidence, disease severity and disease intensity after silk channel inoculation with F. graminearum.
Total (sum of light and heavy) infection are listed. The genotypes are ranked according to increasing mean disease
intensity over 2 replications.
Genotype
AGES57
AGES8
AGES11
AGES32
AGES59
AGES7
AGES36
AGES53
AGES69
AGES24
AGES87
AGES88
AGES66
AGES72
AGES82
AGES18
AGES27
AGES48
AGES1
AGES3
AGES12
AGES52
AGES4
AGES35
AGES85
AGES51
AGES47
AGES75
AGES21
AGES34
AGES67
AGES60
AGES50
AGES42
AGES15
AGES13
AGES54
AGES5
AGES89
AGES49
AGES55
AGES62
AGES26
AGES33
AGES28
AGES6
%DItot
6.2
29.6
20.6
38.5
15.0
24.9
16.7
28.1
18.7
35.1
19.8
16.7
20.2
26.8
15.6
36.5
48.7
27.2
33.4
40.2
33.8
31.6
24.3
42.3
35.8
28.3
20.8
24.6
33.0
50.5
36.2
36.5
34.0
40.1
27.5
58.8
31.5
41.3
34.7
41.2
45.7
37.7
35.9
34.8
47.1
67.8
%DStot
%DINTtot
2.4
1.0
1.9
1.3
3.6
2.3
3.6
2.4
3.6
1.9
3.9
4.5
4.1
2.9
3.4
2.0
1.7
2.8
2.5
2.3
2.9
3.1
3.9
2.5
3.3
3.3
6.1
5.1
3.9
2.5
3.6
3.5
3.9
3.4
4.8
2.5
4.5
3.3
4.4
3.8
3.4
4.6
4.3
4.9
4.1
3.0
Genotype
0.1
AGES81
0.3
AGES2
0.4
AGES58
0.5
AGES90
0.6
AGES61
0.6
AGES46
0.6
AGES78
0.7
AGES37
0.7
AGES17
0.7
AGES30
0.7
AGES71
0.8
AGES10
0.8
AGES45
0.8
AGES16
0.8
AGES56
0.8
AGES44
0.8
AGES86
0.9
AGES25
0.9
AGES84
0.9
AGES14
1.0
AGES79
1.0
AGES77
1.0
AGES80
1.1
AGES64
1.2
AGES20
1.2
AGES9
1.2
AGES43
1.3
AGES22
1.3
AGES68
1.3
AGES40
1.3
AGES38
1.3
AGES70
1.3
AGES19
1.4
AGES63
1.5
AGES39
1.5
AGES31
1.5
AGES23
1.5
AGES41
1.5
AGES74
1.6
AGES65
1.7
AGES73
1.7
AGES83
1.7
AGES29
1.7
AGES76
1.9 Mean
2.0 LSD 0.05
%DItot
38.7
51.6
40.1
37.2
35.0
47.1
40.6
43.7
48.8
60.1
40.7
45.9
34.9
59.8
34.7
46.0
48.3
55.3
45.9
50.1
38.4
31.6
75.3
59.2
41.7
37.0
42.9
69.0
30.4
50.1
54.2
66.2
67.1
72.4
58.9
64.1
50.0
48.0
62.5
73.8
47.9
73.0
90.1
47.3
41.7
24.116
%DStot
5.4
4.0
5.2
5.9
6.5
4.7
5.6
5.4
4.5
4.0
5.9
5.3
7.5
4.4
8.2
5.3
5.8
5.2
6.4
6.0
7.9
7.0
4.2
5.6
8.7
9.2
7.5
5.2
10.4
6.9
6.9
5.7
6.0
5.8
6.9
6.7
8.3
9.1
7.4
6.1
10.0
8.1
7.1
14.1
4.9
4.2824
%DINTtot
2.1
2.1
2.2
2.2
2.3
2.3
2.3
2.4
2.4
2.5
2.5
2.5
2.6
2.6
2.7
2.8
2.9
2.9
2.9
3.0
3.0
3.0
3.1
3.3
3.4
3.4
3.5
3.6
3.6
3.7
3.7
3.9
4.1
4.2
4.3
4.4
4.5
4.5
4.6
4.7
4.9
6.0
6.4
7.0
2.2
2.7946
77
Table 20. Disease intensity after silk channel inoculation with F. graminearum. The genotypes are ranked according to
increasing mean disease intensity (heavy + slightly= total) over 2 replications. The data is given in percentage using scale
of 1 (no rot) to 100 % (fully rotten ear).
Genotype
AGES57
AGES8
AGES11
AGES32
AGES59
AGES7
AGES36
AGES53
AGES69
AGES24
AGES87
AGES88
AGES66
AGES72
AGES82
AGES18
AGES27
AGES48
AGES1
AGES3
AGES12
AGES52
AGES4
AGES35
AGES85
AGES51
AGES47
AGES75
AGES21
AGES34
AGES67
AGES60
AGES50
AGES42
AGES15
AGES13
AGES54
AGES5
AGES89
AGES49
AGES55
AGES62
AGES26
AGES33
AGES28
AGES6
%DINThea
0.1
0.2
0.2
0.4
0.4
0.2
0.1
0.5
0.2
0.4
0.1
0.5
0.2
0.3
0.4
0.6
0.5
0.6
0.3
0.6
0.3
0.4
0.4
0.6
0.5
0.5
0.6
0.8
0.4
0.9
0.6
0.9
0.5
0.7
0.9
0.9
0.8
0.7
1.2
0.7
0.7
1.2
0.9
1.0
1.3
1.3
%DINTsli
0.1
0.1
0.2
0.1
0.2
0.4
0.5
0.1
0.5
0.3
0.6
0.3
0.5
0.4
0.4
0.2
0.3
0.3
0.6
0.3
0.6
0.6
0.6
0.4
0.7
0.7
0.6
0.4
0.9
0.4
0.6
0.4
0.8
0.6
0.5
0.5
0.6
0.8
0.3
0.8
0.9
0.4
0.8
0.7
0.6
0.7
%DINTtot
0.1
0.3
0.4
0.5
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.8
0.8
0.8
0.8
0.8
0.8
0.9
0.9
0.9
1.0
1.0
1.0
1.1
1.2
1.2
1.2
1.3
1.3
1.3
1.3
1.3
1.3
1.4
1.5
1.5
1.5
1.5
1.5
1.6
1.7
1.7
1.7
1.7
1.9
2.0
Genotype
AGES81
AGES2
AGES58
AGES90
AGES61
AGES46
AGES78
AGES37
AGES17
AGES30
AGES71
AGES10
AGES45
AGES16
AGES56
AGES44
AGES86
AGES25
AGES84
AGES14
AGES79
AGES77
AGES80
AGES64
AGES20
AGES9
AGES43
AGES22
AGES68
AGES40
AGES38
AGES70
AGES19
AGES63
AGES39
AGES31
AGES23
AGES41
AGES74
AGES65
AGES73
AGES83
AGES29
AGES76
Mean
LSD 0.05
%DINThea
1.1
1.0
0.9
1.1
1.3
1.7
1.5
1.3
1.6
1.5
1.5
1.7
1.1
1.6
1.4
1.9
2.2
1.8
2.2
1.9
1.8
1.4
1.5
2.1
2.0
1.0
2.3
2.2
1.7
2.5
1.6
3.1
2.5
2.3
2.7
2.6
2.3
1.8
2.8
3.4
2.5
2.9
5.3
3.5
1.3
1.633
%DINTsli
1.0
1.1
1.2
1.0
0.9
0.6
0.8
1.1
0.8
1.0
1.0
0.8
1.5
1.0
1.3
0.9
0.6
1.1
0.7
1.1
1.2
1.6
1.6
1.1
1.3
2.4
1.1
1.4
1.9
1.1
2.1
0.9
1.5
1.9
1.6
1.7
2.1
2.7
1.7
1.2
2.4
3.0
1.0
3.5
0.9
1.427
%DINTtot
2.1
2.1
2.2
2.2
2.3
2.3
2.3
2.4
2.4
2.5
2.5
2.5
2.6
2.6
2.7
2.8
2.9
2.9
2.9
3.0
3.0
3.0
3.1
3.3
3.4
3.4
3.5
3.6
3.6
3.7
3.7
3.9
4.1
4.2
4.3
4.4
4.5
4.5
4.6
4.7
4.9
6.0
6.4
7.0
2.2
2.795
78
3.3. Correlation analysis and scatterplots
3.3.1. Correlation between disease severity and disease incidence
Using the method of interpretation explained in Chapter 2, the following Figure 28 displays a
relationship between the variable Disease severity total % plotted along the X axis and the variable
Disease incidence total plotted along the Y axis for all genotypes. It depicts the correlation that is
positive (r=0.31, p=0.0029**, upward from left to right), but not extremely strong. There is a weak
tendency that a higher DI level is associated with a higher DS level. However, interesting differences
in the combination of both disease parameters can be found among the hybrids.
If we take a closer look at Figure 28, it can be seen that more than 8 maize genotypes with a disease
severity for F. graminearum higher than 8.0 % are responsible for the low correlation. Especially the
hybrid AGES76 and AGES68 show a very high disease severity level. Both genotypes show a medium
level of DI (number of infected ears), but if the ear is infected the area over which the Fusarium has
spread is over-proportionally high.
On the other hand, there also exists hybrids which have a similar disease incidence level, but the area
of the ear infected with F. graminearum remains low (below 2%). These hybrids are AGES8, AGES 11
and AGES32. The latter hybrids show different resistance behaviour as compared to AGES76 and
AGES68. Either the infection reaches the tip of the ear later or the disease is not spreading fast after
the ear tip was infected (see discussion). Furthermore in Figure 28 (at the left) it can be seen that one
hybrid, AGES57 (see also Table 19), shows a very low disease incidence. This genotype carries
another resistance component that results in a low number of diseased ears (see also discussion). It
is able to inhibit symptoms (so the establishment of the disease on the ear) to a higher extend than
the other genotypes can.
Relation between DS total % and DI total %
Disease severity total %
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
0.0
20.0
40.0
60.0
80.0
100.0
Disease incidence total %
Figure 28. Relation between Disease severity total (DS total %) and Disease incidence total (DI total %) after silk channel
inoculation with Fusarium graminearum in CRB design (r=0.31, p=0.0029**)
A similar relation can be found between the DI and the DS for the heavy symptoms only. A closer
examination of the data shows that there is also moderately positive linear relation between both
disease parameters for this symptom class (r=0.36, p=0.0005***). The diseased ears of hybrids
79
AGES9 and AGES76 show a high level of heavily infected kernels. AGES29 (at the right in Figure 29)
does not only have 80% diseased ear showing heavy symptoms. But on top of that also about 7% of
the ear area of the diseased ears was covered with heavy infected kernels. Since heavily infected
kernels can be massively contaminated with mycotoxins, such genotypes represent a high risk.The
SAS programme has also confirmed to 5 outliers.
Disease severity heavy %
Relation between DS heavy% and DI heavy%
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0.0
20.0
40.0
60.0
80.0
100.0
Disease incidence heavy %
Figure 29. Relation between Disease severity heavy (DS total %) and Disease incidence heavy (DI total %) after silk
channel inoculation with Fusarium graminearum in CRB design (r= 0.36, p=0.0005***)
3.3.2. Correlation between maize maturity group and disease severity
We also correlated the maturity group of each hybrid with the visual observation data, and the
results are summarized in Figure 30. Furthermore, the maturity classification is important for GER
due the duration of the vegetation period of each hybrid and its possible effect on their Fusarium
susceptibility. The examined maize cultivars fall into maturity classification according to FAO groups
shown precisely in Chapter 2. Thus, the most resistant hybrids were ranked with cultivars that were
early in maturity. For example, AGES8 (FAO 250) and AGES32 (FAO260) were ranked with average
rating 1.0 and 1.3 % respectively. However, cultivars that require a long growing season (FAO 440)
were always considered as the most susceptible to GER. The two most susceptible maize hybrids
were AGES68 (FAO370) and AGES76 (FAO440) and were very late maturing, rated with %DStot 10.4%
and 14.1% respectively. It was observed also that there were two outliers at p=0.05.
80
Maturity group
Relation between DS total and hybrid maturity group
600
550
500
450
400
350
300
250
200
150
100
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Disease severity total (%) mean
Figure 30. Relation between the disease severity total (%) and the maturity group after the silk channel inoculation with
Fusarium graminearum; Pearson correlation coefficient between both data sets is 0.59, p=0.0001***.
3.3.3. European Corn Borer (Ostrinia nubilalis)
During the visual assessment of the Completely Randomized Block (CRB) with 90 cultivars were
evaluated also the infested maize ears by European corn borer (Ostrinia nubilalis), which were
excluded from the results and the ANOVA analysis (Table 21). It should be noted, that only AGES44
was not infested by ECB. Moreover, the correlation between the number of the infested ears and
disease incidence heavy (%DIhea) is moderately negative (r= -0.067) shown in Figure 31, which
indicated no influence of the ECB after the IPM on the experiment results. During the evaluation we
observed some ears without fungal damage but with larvae damage (Figure 32).
Table 21. Number of the ECB (Ostrinia nubilalis) damaged ears per genotype (mean between two replicates)
ECB damaged
ears (%)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
Genotypes
AGES44
AGES10, AGES49, AGES66, AGES70, AGES71, AGES81
AGES23, AGES37, AGES51, AGES56, AGES74, AGES77, AGES90
AGES1, AGES21, AGES39, AGES4, AGES41, AGES45, AGES60, AGES63, AGES76, AGES82,
AGES87
AGES15, AGES25, AGES30, AGES38, AGES42, AGES47, AGES50, AGES52, AGES59,
AGES73
AGES11, AGES3, AGES43, AGES62, AGES64, AGES65, AGES75, AGES78, AGES88
AGES13, AGES16, AGES22, AGES24, AGES26, AGES32, AGES48, AGES68, AGES69,
AGES72, AGES79, AGES85
AGES27, AGES33, AGES36, AGES46, AGES5, AGES53, AGES57, AGES6, AGES67, AGES7,
AGES84, AGES9
AGES12, AGES19, AGES31, AGES55, AGES58
AGES17, AGES18, AGES28, AGES54, AGES61, AGES80
AGES14, AGES34, AGES83, AGES89
AGES86
AGES29, AGES35
81
6.5
7
8.5
AGES40
AGES8, AGES2
AGES20
Correlation analysis was used to test the relationship between the hybrid maturity group and the
subsequent Fusarium “heavy” severity (%) after the European Corn Borer damage showing a weak
negative correlation coefficient (r=-0.52, p=0.0002***) in Figure 31. In our experiment the later
hybrids showed a higher level of corn borer damage. Since corn borer damage of the ear is very often
associated with Fusarium on the ear, we omitted the corn borer infested ears from the analyses.
Otherwise the results for Fusarium ear rot resistance would be biased.
European Corn Borer severity
Correlation between Maturity group and European Corn Borer
sensitivity
10
8
6
4
2
0
200
250
300
350
400
450
500
Maturity group
Figure 31. Relation between the maturity group of each hybrid and the European Corn Borer (Ostrinia nubilalis) severity;
Pearson correlation coefficient between both data sets in r= -0.52, p=0.0002***.
Figure 32. Infested maize ear by European Corn Borer larvae (Ostrinia nubilalis) (08.10.2013, Tulln)
82
Discussion
The thesis was carried out in the project founded by Institute for Plant Varieties, Austrian Agency for
Health and Food Safety (AGES) in Vienna, Austria in collaboration with the Institute of Biotechnology
in Plant Production, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources
and Life Sciences Vienna (BOKU). In a research for behaviour of variety candidates to Gibberella ear
rot (GER), a set of 90 Austrian maize hybrids was tested in 2013 at IFA, Tulln.
Figure 33. Flowchart of the experiment
Seed material provided
by AGES
Using Fusarium
graminearum strain
IFA66
Start
Planting of the maize
hybrids in RCB
Field monitoring
Inoculum production
through BBM
Preventing the
infestation of the ECB
Artificial inoculation at
BBCH 67
Maize disease
assessment at BBCH 89
Transfer of the field
measurements in Excel
AGES
Mycotoxin analysis
alysis
Yes
Interpretation of the
results
Field work
Laboratory work
Evaluation report
Final
disea
se
asses
smen
83
This project was undertaken to evaluate GER disease in 90 maize hybrids as a part from the Austrian
system for variety testing, registration and seed certification. The present thesis was part of the
established system for evaluating the susceptibility of maize varieties to GER. This research was
focused on the silk channel resistance in maize and the main steps during working process could be
followed on the flowchart displayed in Figure 33. Therefore, the information gained from the maize
evaluation data should be connected with the screening results from the previous years and
mycotoxin analysis (DON, ZEA and/or FUM) usually carried out in an authorized seed certification
laboratory in Austria.
According to Munkvold et al. (1997), to ensure equal distribution of the pathogen for all of the maize
plants in the field, artificial inoculation is needed. Artificial inoculation enhances infections and
overcomes the variability of certain years, when natural infection levels are too low to identify
genotypic differences (Reid, 1996a). Therefore, the evaluation of the varieties should occur over
multiple locations and seasons. The GER natural infection of maize is problematic due to inconsistent
natural disease pressure, which underlines the need to use environmentally stable, highly aggressive
isolates for resistance trials (Reid et al., 1993), as such could be considered isolates of Fusarium
graminearum and Fusarium culmorum, which are commonly highly aggressive (Miedaner, Bolduan
and Melchinger 2010). In contrast, natural infected maize ears vary a lot in their disease symptoms
and there significant differences regarding the GER infection degree per year. Eckard et al. (2011)
supposed this discrepancy was due to different conditions, mainly artificial silk channel infection with
one species versus natural infection by various species through many different means.
Additionally, the natural infection depends on the weather conditions and the actual damage of
European corn borer, (Ostrinia nubilalis). Rot, which was presumably not caused by Fusarium species,
but rather as a consequence of ECB larvae feeding or caused by other pathogens (Ustilago maydis),
was separately recorded and omitted from the statistical analysis. However, the screening of the
maize varieties for GER was performed in a small-plot inoculation (CRB) and in order to avoid the
environmental factors during the flowering time a mist irrigation system was applied.
Fusarium spp. infects maize kernels through silks and kernel tissue wounds (Munkvold et al., 1997a;
Plienegger and Lemmens 2002). The method applied in this thesis for resistance screening of the
maize was modified to test individual resistance components (Lemmens, personal communication),
namely, silk channel inoculation among spore suspension with isolate of F. graminearum, which was
sprayed in the middle of the blossom period (BBCH67) onto the maize silk of primary ears. The
resistance data after application of the silk inoculation reflects the natural infection process of the
fungus and represents as closely as possible the so called “field resistance”. Moreover, Munkvold et
al. pointed out that the kernel infection through silk is the best technique for evaluating genetic
resistance to Fusarium ear rot (Munkvold, Hellmich and Showers 1997a).
The most important aspect of this thesis was the disease rating. Ear rot severity was visually assessed
after physiological maturity (BBCH89) of the maize using a scale based on the percentage of (0- 100
%) the surface covered with mycelium of the primary ear (see Appendix III). Husks were opened by
hand and the main scored parameters were disease incidence, disease severity and the physical
damage of predators such as ECB (Ostrinia nubilalis). Than the disease intensity was calculated as a
multiplicative product of the percentage of disease incidence (%) and disease severity (%). The
method relayed upon direct observation by two trained observers in order to avoid the inaccurate
84
results. The differences between varieties were in many cases statistically significant (see Table 15,
Table 16 and Table 17).
Also important is that we assessed at the same time heavy and slight diseased kernels. As mentioned
before, the heavy severity represents the kernels intensively colonized by Fusarium resulting in
change in colour and structure, for instance the reddish kernel discoloration (Figure 34). However,
the slight infection is the area slightly affected by the fungus, which occur usually late in the season
and is dependent upon the availability of environmental factors such temperature, rain, humidity
and aeration. Especially the high amount of rain can promote the easy colonization of the kernels by
the fungus. In this area the fungal mycelium can be observed spreading between the kernels and
probably also infecting them via the placenta of the ear without kernel pigmentation (Figure 34).
Figure 34. Appearance of visually symptomless (left) and diseased (right) kernels after field evaluation (11.10.2013)
On Figure 34 it was demonstrated an example of visually evaluated as symptomless maize kernels
(on the left) on one hand, and extremely GER diseased kernels (right) on the other hand.
The first parameter included in the calculation of the final grade for “total disease intensity” deserves
also special attention. That is the “heavy” index. The number of heavy infected ears illustrated by the
“heavy disease incidence” (%DIhea) ranged from 2.5 to 79.4%. Detailed information for the resistance
of each genotype can also be found in Appendix V, Appendix VI and Appendix VII. It is important to
note that the minimum of %DIhea was evaluated for AGES87 with %DIhea = 2.5%, which equals 12
diseased maize plants in the two replications of the hybrid. In addition, AGES87 showed the lowest
total disease incidence (%DItot = 6.2%).
Another part of the “disease intensity” formula was the disease severity parameter. For the
evaluation criteria of the “severity” were only considered the symptoms with intensively colonized
maize kernels entirely covered with white to pink mycelium. The minimum GER severity was
observed in AGES8 (%DStot= 1.0%). Disease severity “heavy” above 2% was evaluated for 8 varieties
(AGES1, AGES7, AGES8, AGES11, AGES17, AGES27, AGES32 and AGES36). The LSD0.05 value for DShea
(%) equals 3.09, which makes the previous mentioned genotypes not statistically different from each
other. According to heavy severity the best performance demonstrated the genotypes AGES57
(DShea= 1.0%), AGES8 (DShea= 1.0%) and AGES32 (DShea= 1.1%). Whereas the genotype AGES57
85
achieved the best performance of GER severity (DShea=1.0 %) and pretty good results for the
percentage of diseased ears (DIhea= 15%).
Light infections are regarded as late infections during ear development. Normally Fusarium stops its
growth due to lack of water. During ripening the water content in the kernels and the ear cob
decreases. Adverse conditions resulting in humidity on the ear surface between the kernels can also
promote light infections (e.g. dew building due to low temperatures, rainfall resulting in wetting of
the ear). Lightly infected kernels have lower toxin content and this symptom is very depending on
the weather conditions (Lemmens, personal communication). This is probably also the reason why
there are only slightly significant differences between genotypes. In general we found a positive
correlation between heavy and light infection. AGES83, AGES76, AGES 41 and AGES9 showed a high
amount of slightly infected kernels. Late infections proceeded extensively in these genotypes.
To get an impression for different overall hybrid performance, the obtained results after the field
evaluation are summarised in Figure 35. As can be seen in Figure 35, all maize genotypes express
susceptibility and are noticeably different for all measured parameters. If we look closer to the
results on the left side of the Figure 35, there seems to be a tendency to high levels of silk channel
resistance, therefore a detailed comparison between the maize hybrids is needed and it will be
revealed bellow.
86
Performance of the maize hybrids resistance to GER
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
%DINTtot
%DStot
%DItot
Figure 35. GER data after silk channel inoculation with Fusarium graminearum for the 90 maize genotypes presenting the main three parameters, namely the total percentage of total disease incidence
(DItot%), which is displayed in blue, the total disease severity (DStot%) in orange, and the total disease intensity (DINTtot%), (heavy + slightly= total) included in green. DI%, DS% and DINT% are ordered
according to DINTtot% and given in percentage (%).
87
Figure 36 plots the difference between the performance of one of the most resistant (AGES27, in
blue) and susceptible (AGES76, in orange) genotypes. Disease intensity over all symptoms was 0.8%
and 7.0% infected kernels for the resistant and the susceptible variety, respectively. Interestingly,
the good resistance of the AGES27 is not caused by a low disease incidence since DItot of both
genotypes is comparable (47 and 49%). The essential difference between both maize varieties is the
difference in the percentage of the area colonized by the Fusarium on the diseased ears (%DStot). On
average 14% of the ear area of the diseased ears was colonized by the fungus in case of AGES76,
whereas only 1.7% was infected in case of the more resistant genotype. A difference in resistance
behaviour might explain this: either the fungus reaches the tip of the ear much later in the
development of the plant or the fungus is spreading much slower on the ear after it colonized the ear
tip.
AGES76 and AGES27 after silk channel inoculation
60.0
AGES76
50.0
AGES27
40.0
30.0
20.0
10.0
0.0
%DIhea
%DIsli
%DItot
%DShea
%DSsli
%DStot %DINThea %DINTsli %DINTtot
Figure 36 . Performance of AGES76 and AGES57 genotypes for all evaluated (Disease incidence, Disease severity and
Disease incidence, heavy+ slightly= total) after silk channel inoculation with Fusarium graminearum in CRB design. All
data is given in percentage (%) using scale of 1 (no rot) to 100 % (fully rotten ear).
A striking difference was noted according to the parameter heavy disease incidence between the
genotypes AGES8 (in orange) and AGES9 (in blue)(see Figure 37). Despite the high percentage of the
disease incidence for both genotypes, the overall performance according to the disease intensity
parameter was in favour of the AGES8. Hence, AGES9 demonstrated very high levels of GER severity,
namely DShea=8.5% and DStot=9.2%. Although AGES8 expressed twice higher DIheavy than AGES9,
differed tremendously according to the parameter disease severity, and showed far lower
susceptibility to GER than AGES8 (%DStot=1.0%). The example of Figure 37 seem to indicate that
despite of the increased number of diseased ears as in the case of AGES8, the high levels of
resistance can be observed because of the less spread of the disease.
88
AGES8 and AGES9 after silk channel inoculation
40
AGES9
35
AGES8
30
25
20
15
10
5
0
%DIhea
%DIsli
%DItot
%DShea
%DSsli
%DStot %DINThea %DINTsli %DINTtot
Figure 37. Performance of AGES8 and AGES9 genotypes for all evaluated (Disease incidence, Disease severity and Disease
incidence, heavy+ slightly= total) after silk channel inoculation with Fusarium graminearum in CRB design. All data was
given in percentage (%) using scale of 1 (no rot) to 100 % (fully rotten ear).
Another example of the differences in behaviour in resistance can be found in comparing for instance
hybrid AGES66 and AGES80 (see Figure 38). The diseased area of the diseased ears (%DStot) is similar
(4.1 and 4.2% for AGES66 and AGES80, respectively). But the amount of diseased ears is remarkably
different: AGES66 had 20.2% diseased ears and in case of AGES80 about ¾ of the ears showed
symptoms. This resulted in 0.8% diseased kernels (%DINTtot) for AGES66 but 3.1% for AGES80. Hence,
the better resistance for GER of AGES66 is based on a lower number of diseased ears and hence a
resistance towards infection and establishment of the disease after migration through the silk
channel.
AGES80 and AGES66 after silk channel inoculation
80.0
AGES80
70.0
AGES66
60.0
50.0
40.0
30.0
20.0
10.0
0.0
%DIhea
%DIsli
%DItot
%DShea
%DSsli
%DStot %DINThea %DINTsli %DINTtot
Figure 38. Performance of AGES80 and AGES66 genotypes for all evaluated (Disease incidence, Disease severity and
Disease incidence, heavy+ slightly= total) after silk channel inoculation with Fusarium graminearum in CRB design. All
data is given in percentage (%) using scale of 1 (no rot) to 100 % (fully rotten ear)
89
4.1. Disease resistance ranking
To investigate the classification of resistant and susceptible hybrids, we compared mean GER of each
hybrid using standard completion ranking ordered according to the sum of ranking places of the
%DItot, %DStot and %DINTtot (Table 22). Undoubtedly, genotype AGES57 can be considered as most
resistant hybrid (sum of ranks=11) among all and it ranks first according the parameters %DINThea,
%DIsli, %DINTsli, %DItot and %DINTtot, but according to the indicators %DStot remains on ninth place.
Thus, AGES57 gathers 11 ranking points. The same tendency of varying ranking can be observed with
the results of the other hybrids. However, we determined the large variability present in resistance
towards GER in the maize blocks (see yellow colour), but only selected genotypes are shown in Table
22. Detailed information for ranking performance of all genotypes can be found in Appendix VIII.
Table 22. Standard competition ranking of some genotypes, ordered according %DINTtot parameter (ranking order:
1224).
Genotype
AGES57
AGES11
AGES8
AGES7
AGES53
AGES59
AGES36
AGES82
AGES69
AGES72
AGES24
AGES32
AGES1
AGES18
AGES66
AGES88
AGES3
AGES85
AGES27
AGES47
AGES34
AGES13
AGES76
AGES74
AGES29
AGES83
Rank
%DItot
1
9
19
13
17
2
4
3
6
14
33
43
25
37
8
4
47
34
66
10
71
75
62
80
90
87
6.2
20.6
29.6
24.9
28.1
15.0
16.7
15.6
18.7
26.8
35.1
38.5
33.4
36.5
20.2
16.7
40.2
35.8
48.7
20.8
50.5
58.8
47.3
62.5
90.1
73.0
Rank
%DStot
9
4
1
7
9
27
27
23
27
16
4
2
11
6
38
44
7
20
3
68
11
11
90
78
77
82
2.4
1.9
1.0
2.3
2.4
3.6
3.6
3.4
3.6
2.9
1.9
1.3
2.5
2.0
4.1
4.5
2.3
3.3
1.7
6.1
2.5
2.5
14.1
7.4
7.1
8.1
Rank
1
3
2
5
8
5
5
12
8
12
8
4
18
12
12
12
18
25
12
25
28
35
90
85
89
88
%DINTtot
Sum
0.1
11
0.4
16
0.3
22
0.6
25
0.7
34
0.6
34
0.6
36
0.8
38
0.7
41
0.8
42
0.7
45
0.5
49
0.9
54
0.8
55
0.8
58
0.8
60
0.9
72
1.2
79
0.8
81
1.2
103
1.3
110
1.5
121
7.0
242
4.6
243
6.4
256
6.0
257
Different levels of resistance and susceptibility could be categorized according to the particular
performance of the hybrids. For instance, the distribution of high disease severity on less diseased
maize ears in a plot represents class 2. On the other hand there is class 3, which follows the opposite
pattern: bad performance for disease incidence parameter with less disease severity. The cross
between class 2 and class 3 could contribute to the hybrid improvement. The same suggestion could
90
have been given for a cross of class 2 and class 3 with a resistant genotype of class 1. Resistance of a
certain genotype could be improved by introgressing the complement resistance. Some examples of
the categorization are listed in Table 23.
Table 23. Categorization of some maize genotypes divided into four classes according to possible combinations between
the parameters disease incidence and disease severity
Category
Class 1
Class 2
Class 3
Class 4
Description
Good for both DI and DS
Good for DI, bad for DS
Bad for DI, good for DS
Bad for both DI and DS
Genotypes
AGES57, AGES11, AGES7
AGES59, AGES82, AGES88, AGES69
AGES8, AGES24, AGES32, AGES3, AGES34, AGES13
AGES83, AGES29, AGES76, AGES74
Figure 39. Gibberella ear rot symptoms: white to pinkish mold starting always at the ear tip (11.10.2013, Tulln)
If we take a closer look to the parameter DINT, one can see that there is strong variation among the
most resistance genotypes. For example AGES57 and AGES36, where the DINT heavy is less that 0.1,
but the total parameter has 0.5 % difference in favour of AGES57. Similar example is also the
difference between AGES8 and AGES72, where DINT total is equal (DINTtot=0.3 %), but the heavy
parameter alters with 0.6 %. In addition, Figure 39 shows some cobs after the field evaluation
ordened from the symptomless one (left) to the typical Fusarium symptoms (right), which do not
exceed 20% heavy diease severity.
In contrast, using again sum of the standard competition ranking (1224), Figure 40 represents the
genotypes occupied the last six places ordered by parameter %DINTtot, which ranges from 3.9 to
7.0%. A strong variation in the values of the individual indicators is observed. As mentioned above,
the largest number of diseased ears has been evaluated for genotype AGES29 precisely %DItot =
90.1%. It is remarkable that the indicator %DStot has the highest value in genotype AGES76 (%DStot =
14.1), which is twice more than the result of AGES29 (%DStot = 7.1%), although the parameter %DItot
equals 47.3%, which is less than half of the same value for AGES29.
91
Gibberella Ear Rot Data for the most susceptible maize genotypes
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
AGES70
%DIhea
AGES65
%DIsli
%DItot
AGES73
%DShea
%DSsli
AGES83
%DStot
AGES29
%DINThea
%DINTsli
AGES76
%DINTtot
Figure 40. Ranking of the 6 most susceptible genotypes inoculated through silk channel inoculation with Fusarium
graminearum at IFA, Tulln in 2013. Data is ordered according to the sum of standard competition ranking for each
parameter (heavy + slightly= total) and is given in percentage using scale of 1 (no rot) to 100 % (fully rotten ear).
Figure 41. Severely affected maize ears by Gibberella ear rot: largely rotted with husk parts and silks adhering tightly
(hybrid dependent) to the ear (11.10.2013, Tulln)
Further, 44 of the all 90 genotypes exceeded the median and mean level of the heavy disease
severity, namely 4.1 % and ranged from 4.1% to 8.7%. The high percentage of Fusarium severity of
these varieties could be seen in the previous Figure 41, where the typical Fusarium symptoms on the
maize ear surface covered with mycelium and also typical Fusarium graminearum pigmentation of
fully rotten maize cobs are demonstrated. It should be noted that the symptoms of silks adhered
tightly to maize cob were hybrid dependent and they were not observed for all varieties.
92
4.2. The influence of the ear structure on the GER incidence and severity
As mentioned previously, the aim of this thesis was to test the maize resistance to GER among field
inoculation with Fusarium graminearum performing silk channel inoculation. It is important to
reiterate that GER is a quantitative genetic trait, which is generally due to multiple genes contributing
quantitatively, called also quantitative resistance. Unfortunately, the mechanisms of pathogenicity
and maize resistance in GER disease are largely unclear (see Chapter 1.2.6. and 1.2.7.).
The infection process of the silk inoculation technique is through the silk channel. Therefore, the
resistance components include resistance to entrance and resistance to spread of symptoms, which
is related to many other factors such as tightness of husks, silk channel length, ear length, ECB
damage, environmental conditions and the maturity group (known also as FAO group).
On one hand the husk protection can lower the GER incidence. On the other hand husks that open
early will dry faster. In other words, when the husks do not cover the ear tip tightly, they could dry
out quickly instead of closed ones. Secondly, the long silk channel can predispose to less infection.
As an advantage for one genotype could be considered the small number of diseased ears per trial,
which respectively predisposes to less disease incidence percentage.
Another maize feature predisposing the GER intensive infection was the ear length and homogeneity
of the typical ear form among the small-plot trial. The smaller maize ears were subsequently more
infected after the artificial inoculation with Fusarium graminearum than the ears with normally
developed cobs (see Figure 42).
Figure 42. Maize ears with GER symptoms after silk channel inoculation with Fusarium graminearum, (08.10.2013, Tulln)
The hypothesis that the maturing of the maize ears hanging in a downward position usually has less
GER symptoms than ears with open husks or ears maturing in an upright position proposed by
Koehler (1959) could not be accepted. AGES69 is a good example, because it ranks with moderately
93
resistance and grows in upward position of the eras with DItot=18.7%, DStot=3.6%, DINTtot=0.7% (see
Figure 43).
Figure 43. Genotype AGES69 dehusked after evaluation on the field, (07.10.2013, Tulln)
Correlation (r=0.52***, p=0.0001***) between the maturity group and the disease severity
emphasized the influence of the duration period in the field on the maize resistance to GER. The
longer the growing period is, the more exposed to the environmental conditions the maize plants
are.
Therefore, any of the above mentioned factors may influence the crop resistance to GER. Moreover,
to the desirable characteristic for an “ideal” maize variety could be considered the fast dryness of the
tip of the ear and less disease severity. Although these factors investigate the resistance of maize to
GER, they do not exhaust the cause for resistance mechanisms, which was briefly summarised in
Chapter 1.2.6., and the interaction with other Fusarium species, which are also widely spread in
Austria, for instance Fusarium subglutinans and Fusarium proliferatum (AGES, 2014).
In should be noted that the GER assessment cannot be based solely on the silk channel inoculation
method. Moreover, in order to obtain more precise results for hybrid behaviour on the field against
GER for the subsequent maize hybrid, the data obtained after the field assessment can successfully
be combined with mycotoxin analysis and/or inoculation with other Fusarium spp., for instance with
Fusarium verticillioides.
However, the tillage practices, crop rotation, weed management, late seasonal rainfall, wind and
pest vectors can all influence the amount and source of fungal inoculum that maintains the disease
cycle in maize (Munkvold 2014). In addition to that, genetic factors also play a significant role in
maize resistance. But still, there is no efficient biological or chemical treatment against GER in maize
to date. Unfortunately, most commercial hybrids currently grown worldwide have little or no
94
resistance to infection by F. graminearum (Kant 2011). Therefore, the improvement of the variety for
resistance to GER remains a beneficial method in the integrated pest management in maize.
Fusarium spp.-resistant maize varieties with reduced mycotoxin content are still necessary.
In conclusion, the results of this study illustrated significant differences in the resistance behaviour of
the used genotypes. Therefore, visual assessment of GER is sufficient for the appropriate screening
for resistant maize varieties and it has been validated in the present study. The results demonstrated
that high levels of resistance are available in the maize registration candidates in Austria. Based on
analysis above, the study aims to facilitate the appropriate variety choice by growers with regard to
the environment in the growing region, which could be used as good strategy in controlling the
prevalence of this important crop disease.
The introduction of new less susceptible to GER maize cultivars in the Descriptive Variety List is
expected to lead to reduced Fusarium mycotoxin contamination of the harvested maize grain and,
subsequently, to increased maize quality.
95
Appendix I. BBCH growth stages of maize (Meier 2001)
0. Germination
5. Inflorescence Emergence, Heading
00
Dry seed (caryopsis)
51
01
Beginning of seed imbibition
53
03
Seed imbibition complete
55
05
Radicle emerged from caryopsis
59
06
Radicle elongated, root hairs/side roots
visible
07
Coleoptile emerged from caryopsis
61
09
Coleoptile penetrates soil
63
1. Leaf Development
10
First leaf through coleoptile
65
67
11
First leaf unfolded
12
2 leaves unfolded
71
13
3 leaves unfolded
73
Stages continuous till...
75
9 or more leaves unfolded
79
Beginning of grain development: kernels at
blister stage, about 16% dry matter
Early Milk
Kernels in middle of cob yellowish-white
(variety-dependent), content milky, about 40%
dry matter
Nearly all kernels have reached final size
8. Ripening
30
Beginning of stem elongation
83
31
First node detectable
85
32
2 nodes detectable
87
33
3 nodes detectable
89
Early dough: kernel content soft,
45% dry matter
Dough stage: kernels yellowish to yellow
55% dry matter
Physiological maturity: black dot/layer
visible at base of kernels, 60% dry matter
Fully ripe: kernels hard & shiny,
65% dry matter
9. Senescence
Stages continuous till…
9 or more nodes detectable
Male: stamens in middle of tassel visible
Female: tip of ear emerging from leaf sheath
Male: beginning of pollen shedding
Female: tips of stigmata visible
Male: upper and lower parts of tassel in flower
Female: stigmata fully emerged
Male: flowering completed
Female: stigmata drying
End of flowering: stigmata completely dry
7. Development of Fruit
3. Stem Elongation
39
Tip of tassel visible
Middle of tassel emergence:
middle of tassel begins to separate
End of tassel emergence:
tassel fully emerged and separated
6. Flowering, Anthesis
69
19
Beginning of tassel emergence, tassel
detectable at top of stem
97
99
Plant dead and collapsing
Harvested product
96
Appendix II. Table with the experimental setup
Border
Border
Border
Border
Border
Border
Border
126
AGES79/2013/2
175
AGES86/2013/1
176
AGES71/2013/1
225
AGES74/2013/1
226
AGES53/2013/1
Border
275
127
AGES38/2013/2
174
AGES78/2013/1
177
AGES31/2013/1
224
AGES29/2013/1
227
AGES7/2013/1
274
273
125
77
124
AGES89/2013/2
78
123
AGES70/2013/2
128
AGES5/2013/2
173
AGES24/2013/1
178
AGES50/2013/1
223
AGES6/2013/1
228
AGES67/2013/1
79
122
AGES83/2013/2
129
AGES6/2013/2
172
AGES52/2013/1
179
AGES11/2013/1
222
AGES82/2013/1
229
AGES80/2013/1
272
80
121
AGES36/2013/2
130
AGES53/2013/2
171
AGES15/2013/1
180
AGES10/2013/1
221
AGES57/2013/1
230
AGES13/2013/1
271
81
AGES20/2013/2
120
AGES14/2013/2
131
AGES2/2013/2
170
AGES63/2013/2
181
AGES40/2013/1
220
AGES25/2013/1
231
AGES68/2013/1
270
82
AGES51/2013/2
119
AGES56/2013/2
132
AGES57/2013/2
169
AGES55/2013/2
182
AGES32/2013/1
219
AGES61/2013/1
232
AGES77/2013/1
269
83
AGES71/2013/2
118
AGES64/2013/2
133
AGES9/2013/2
168
AGES8/2013/2
183
AGES48/2013/1
218
AGES63/2013/1
233
AGES65/2013/1
268
84
AGES17/2013/2
117
AGES73/2013/2
134
AGES45/2013/2
167
AGES11/2013/2
184
AGES70/2013/1
217
AGES90/2013/1
234
AGES79/2013/1
267
85
AGES43/2013/2
116
AGES82/2013/2
135
AGES27/2013/2
166
AGES52/2013/2
185
AGES49/2013/1
216
AGES84/2013/1
235
AGES21/2013/1
266
86
AGES22/2013/2
115
AGES4/2013/2
136
AGES86/2013/2
165
AGES28/2013/2
186
AGES64/2013/1
215
AGES20/2013/1
236
AGES42/2013/1
265
87
AGES13/2013/2
114
AGES66/2013/2
137
AGES54/2013/2
164
AGES72/2013/2
187
AGES30/2013/1
214
AGES56/2013/1
237
AGES75/2013/1
264
88
AGES65/2013/2
113
AGES84/2013/2
138
AGES47/2013/2
163
AGES42/2013/2
188
AGES8/2013/1
213
AGES9/2013/1
238
AGES2/2013/1
263
89
AGES25/2013/2
112
AGES81/2013/2
139
AGES35/2013/2
162
AGES40/2013/2
189
AGES34/2013/1
212
AGES87/2013/1
239
AGES72/2013/1
262
90
AGES41/2013/2
111
AGES88/2013/2
140
AGES78/2013/2
161
AGES49/2013/2
190
AGES59/2013/1
211
AGES16/2013/1
240
AGES69/2013/1
261
91
AGES90/2013/2
110
AGES10/2013/2
141
AGES60/2013/2
160
AGES21/2013/2
191
AGES46/2013/1
210
AGES62/2013/1
241
AGES39/2013/1
260
AGES38/2013/1
92
AGES46/2013/2
109
AGES61/2013/2
142
AGES19/2013/2
159
AGES77/2013/2
192
AGES60/2013/1
209
AGES43/2013/1
242
AGES58/2013/1
259
AGES66/2013/1
93
AGES30/2013/2
108
AGES37/2013/2
143
AGES69/2013/2
158
AGES26/2013/2
193
AGES12/2013/1
208
AGES85/2013/1
243
AGES47/2013/1
258
AGES41/2013/1
94
AGES18/2013/2
107
AGES58/2013/2
144
AGES31/2013/2
157
AGES87/2013/2
194
AGES3/2013/1
207
AGES37/2013/1
244
AGES19/2013/1
257
AGES35/2013/1
95
AGES1/2013/2
106
AGES29/2013/2
145
AGES33/2013/2
156
AGES62/2013/2
195
AGES14/2013/1
206
AGES54/2013/1
245
AGES27/2013/1
256
AGES23/2013/1
96
AGES74/2013/2
105
AGES23/2013/2
146
AGES59/2013/2
155
AGES68/2013/2
196
AGES51/2013/1
205
AGES36/2013/1
246
AGES55/2013/1
255
AGES26/2013/1
97
AGES76/2013/2
104
AGES16/2013/2
147
AGES75/2013/2
154
AGES39/2013/2
197
AGES4/2013/1
204
AGES22/2013/1
247
AGES81/2013/1
254
AGES33/2013/1
98
AGES32/2013/2
103
AGES24/2013/2
148
AGES50/2013/2
153
AGES12/2013/2
198
AGES73/2013/1
203
AGES1/2013/1
248
AGES45/2013/1
253
AGES83/2013/1
99
AGES80/2013/2
102
AGES67/2013/2
149
AGES3/2013/2
152
AGES48/2013/2
199
AGES17/2013/1
202
AGES89/2013/1
249
AGES88/2013/1
252
AGES76/2013/1
100
AGES85/2013/2
101
AGES34/2013/2
150
AGES15/2013/2
151
AGES44/2013/2
200
AGES28/2013/1
201
AGES5/2013/1
250
AGES18/2013/1
251
AGES44/2013/1
Border
76
AGES7/2013/2
97
Appendix III. Gibberella ear rot disease evaluation scale
0%
20%
0,2%
35%
0,4%
50%
0,6%
1%
75%
3%
90%
5%
100%
10%
15%
Corn Borer Damage
98
Appendix IV. Maturity groups of the maize genotypes with disease severity (%)
FAO maturity group
Genotype
Maturity group 2
AGES7
230
AGES1
240
AGES3
240
AGES2
250
AGES8
250
Maturity group 3
AGES12
260
AGES21
260
AGES25
260
AGES32
260
AGES13
270
AGES14
280
AGES35
280
AGES15
290
AGES17
290
AGES27
290
AGES30
290
AGES33
290
AGES34
290
AGES37
290
AGES16
300
Maturity group 4
AGES38
310
AGES42
320
AGES50
320
%DStot Genotype
2.3 AGES51
2.5 AGES39
2.3 AGES45
4.0 AGES46
1.0 AGES59
%DStot
AGES49
2.9 AGES52
3.9 AGES61
5.2
1.3 AGES67
2.5 AGES68
6.0 AGES84
2.5 AGES90
4.8 AGES64
4.5 AGES83
1.7 AGES89
4.0 AGES77
4.9 AGES85
2.5 AGES88
5.4 AGES75
4.4 AGES62
%DStot
AGES86
6.9 AGES63
3.4 AGES76
3.9
FAO maturity group
Maturity group 4
330
340
340
340
340
350
350
350
Maturity group 5
360
370
370
370
380
380
380
390
390
390
400
410
410
440
440
%DStot
3.3
6.9
7.5
4.7
3.6
3.8
3.1
6.5
%DINTtot
3.6
10.4
6.4
5.9
5.6
8.1
4.4
7.0
3.3
4.5
5.1
4.6
5.8
5.8
14.1
99
Appendix V. Disease incidence (n=2)
Genotype
AGES57
AGES59
AGES82
AGES88
AGES36
AGES69
AGES87
AGES66
AGES11
AGES47
AGES4
AGES75
AGES7
AGES72
AGES48
AGES15
AGES53
AGES51
AGES8
AGES68
AGES54
AGES77
AGES52
AGES21
AGES1
AGES12
AGES50
AGES56
AGES89
AGES33
AGES45
AGES61
AGES24
AGES85
AGES26
AGES67
AGES60
AGES18
AGES9
AGES90
AGES62
AGES79
AGES32
AGES81
AGES42
AGES58
%DIhea
3.5
9.0
7.9
10.9
7.7
6.4
2.5
4.9
11.4
12.7
17.3
19.1
15.0
9.6
21.0
19.6
21.8
13.9
21.4
23.1
21.9
17.3
12.8
16.8
17.6
13.0
17.3
21.7
28.1
28.4
22.3
22.6
26.0
19.2
25.6
20.9
21.1
30.6
12.8
28.5
29.3
24.3
37.7
24.4
24.1
25.6
%DIsli
2.6
8.3
10.4
8.6
10.8
13.5
18.2
17.7
9.5
15.4
14.5
10.3
11.3
19.2
9.5
13.3
7.8
19.3
7.3
23.4
17.0
26.3
22.4
19.6
22.9
20.4
24.9
22.2
13.8
13.2
20.9
20.3
10.8
23.0
13.7
21.1
17.6
8.6
29.8
25.6
17.8
25.2
3.2
28.4
21.2
23.1
%DItot
6.2
15.0
15.6
16.7
16.7
18.7
19.8
20.2
20.6
20.8
24.3
24.6
24.9
26.8
27.2
27.5
28.1
28.3
29.6
30.4
31.5
31.6
31.6
33.0
33.4
33.8
34.0
34.7
34.7
34.8
34.9
35.0
35.1
35.8
35.9
36.2
36.5
36.5
37.0
37.2
37.7
38.4
38.5
38.7
40.1
40.1
Genotype
AGES3
AGES78
AGES71
AGES49
AGES5
AGES20
AGES35
AGES43
AGES37
AGES55
AGES10
AGES84
AGES44
AGES46
AGES28
AGES76
AGES73
AGES41
AGES86
AGES27
AGES17
AGES23
AGES14
AGES40
AGES34
AGES2
AGES38
AGES25
AGES13
AGES39
AGES64
AGES16
AGES30
AGES74
AGES31
AGES70
AGES19
AGES6
AGES22
AGES63
AGES83
AGES65
AGES80
AGES29
Mean
LSD 0.05
%DIhea
25.5
32.0
31.2
19.9
23.7
28.2
25.4
34.8
28.2
22.5
38.4
38.7
34.0
37.5
35.8
38.8
38.6
24.2
39.8
35.0
36.8
38.4
33.6
40.1
38.5
23.2
36.3
32.3
42.4
40.2
45.3
40.8
39.1
50.2
45.6
56.6
51.1
44.1
49.4
48.3
44.7
52.9
46.1
79.4
28.2
24.341
%DIsli
15.9
22.7
26.0
29.6
25.6
20.0
19.9
20.5
27.5
27.1
20.6
19.6
23.1
16.4
18.3
39.0
37.0
41.3
14.9
16.6
19.8
34.6
28.9
22.8
16.2
33.4
38.0
35.3
23.5
33.5
31.0
30.6
38.1
38.5
31.6
28.0
27.8
27.6
35.1
46.8
52.4
38.7
40.6
23.1
22.5
19.652
%DItot
40.2
40.6
40.7
41.2
41.3
41.7
42.3
42.9
43.7
45.7
45.9
45.9
46.0
47.1
47.1
47.3
47.9
48.0
48.3
48.7
48.8
50.0
50.1
50.1
50.5
51.6
54.2
55.3
58.8
58.9
59.2
59.8
60.1
62.5
64.1
66.2
67.1
67.8
69.0
72.4
73.0
73.8
75.3
90.1
41.7
24.116
100
Appendix VI. Disease severity (n=2)
Genotype
AGES8
AGES32
AGES27
AGES11
AGES24
AGES18
AGES3
AGES7
AGES53
AGES57
AGES13
AGES34
AGES1
AGES35
AGES48
AGES12
AGES72
AGES6
AGES52
AGES51
AGES5
AGES85
AGES82
AGES55
AGES42
AGES60
AGES67
AGES69
AGES36
AGES59
AGES49
AGES21
AGES50
AGES4
AGES87
AGES30
AGES2
AGES28
AGES66
AGES80
AGES26
AGES16
AGES89
AGES54
AGES88
AGES17
%DShea
1.0
1.1
1.4
1.6
1.6
1.8
2.5
1.0
2.4
3.3
2.1
2.3
1.7
2.3
2.2
2.5
3.3
3.0
3.0
2.8
2.6
2.4
3.3
3.2
3.0
4.0
3.3
2.9
1.3
3.7
3.7
2.4
2.8
2.4
3.3
3.7
4.4
3.4
4.4
3.2
3.4
4.0
4.2
3.6
4.3
4.0
%DSsli
1.2
1.1
2.0
2.1
2.8
2.0
1.8
3.6
1.7
2.3
2.3
2.4
2.5
2.3
2.8
3.1
2.3
2.4
2.8
2.9
2.8
3.0
2.6
3.0
2.9
2.3
2.9
3.4
5.5
2.4
2.8
4.4
3.4
3.9
3.9
2.5
3.1
3.7
3.1
4.1
4.7
3.2
2.2
3.6
3.1
3.9
%DStot
1.0
1.3
1.7
1.9
1.9
2.0
2.3
2.3
2.4
2.4
2.5
2.5
2.5
2.5
2.8
2.9
2.9
3.0
3.1
3.3
3.3
3.3
3.4
3.4
3.4
3.5
3.6
3.6
3.6
3.6
3.8
3.9
3.9
3.9
3.9
4.0
4.0
4.1
4.1
4.2
4.3
4.4
4.4
4.5
4.5
4.5
Genotype
AGES62
AGES46
AGES15
AGES33
AGES75
AGES58
AGES22
AGES25
AGES44
AGES10
AGES81
AGES37
AGES78
AGES64
AGES70
AGES86
AGES63
AGES90
AGES71
AGES19
AGES14
AGES65
AGES47
AGES84
AGES61
AGES31
AGES38
AGES39
AGES40
AGES77
AGES29
AGES74
AGES45
AGES43
AGES79
AGES83
AGES56
AGES23
AGES20
AGES41
AGES9
AGES73
AGES68
AGES76
Mean
LSD 0.05
%DShea
4.2
4.3
4.3
3.5
4.2
3.4
4.4
5.5
4.8
4.4
4.5
4.4
4.6
4.9
5.1
5.2
4.9
3.9
4.4
5.0
5.6
6.0
4.7
5.7
6.5
5.7
4.1
6.4
5.8
5.2
6.9
5.8
5.0
6.1
7.3
6.3
6.6
5.8
7.2
7.2
8.5
6.3
6.7
8.7
4.1
3.085
%DSsli
3.1
3.8
3.5
5.1
4.3
5.1
4.0
3.1
3.6
4.0
3.5
3.9
3.5
3.7
3.1
4.3
4.0
4.2
3.8
5.5
3.5
3.0
4.3
3.8
4.6
5.2
5.3
4.4
4.8
4.8
4.8
4.7
6.7
5.7
5.0
5.5
6.8
5.5
7.4
6.4
7.6
6.3
6.6
8.2
3.8
3.403
%DStot
4.6
4.7
4.8
4.9
5.1
5.2
5.2
5.2
5.3
5.3
5.4
5.4
5.6
5.6
5.7
5.8
5.8
5.9
5.9
6.0
6.0
6.1
6.1
6.4
6.5
6.7
6.9
6.9
6.9
7.0
7.1
7.4
7.5
7.5
7.9
8.1
8.2
8.3
8.7
9.1
9.2
10.0
10.4
14.1
4.9
4.282
101
Appendix VII. Disease intensity (n=2)
Genotype
AGES57
AGES8
AGES11
AGES32
AGES59
AGES7
AGES36
AGES53
AGES69
AGES24
AGES87
AGES88
AGES66
AGES72
AGES82
AGES18
AGES27
AGES48
AGES1
AGES3
AGES12
AGES52
AGES4
AGES35
AGES85
AGES51
AGES47
AGES75
AGES21
AGES34
AGES67
AGES60
AGES50
AGES42
AGES15
AGES13
AGES54
AGES5
AGES89
AGES49
AGES55
AGES62
AGES26
AGES33
AGES28
AGES6
%DINThea
0.1
0.2
0.2
0.4
0.4
0.2
0.1
0.5
0.2
0.4
0.1
0.5
0.2
0.3
0.4
0.6
0.5
0.6
0.3
0.6
0.3
0.4
0.4
0.6
0.5
0.5
0.6
0.8
0.4
0.9
0.6
0.9
0.5
0.7
0.9
0.9
0.8
0.7
1.2
0.7
0.7
1.2
0.9
1.0
1.3
1.3
%DINTsli
0.1
0.1
0.2
0.1
0.2
0.4
0.5
0.1
0.5
0.3
0.6
0.3
0.5
0.4
0.4
0.2
0.3
0.3
0.6
0.3
0.6
0.6
0.6
0.4
0.7
0.7
0.6
0.4
0.9
0.4
0.6
0.4
0.8
0.6
0.5
0.5
0.6
0.8
0.3
0.8
0.9
0.4
0.8
0.7
0.6
0.7
%DINTtot
0.1
0.3
0.4
0.5
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.8
0.8
0.8
0.8
0.8
0.8
0.9
0.9
0.9
1.0
1.0
1.0
1.1
1.2
1.2
1.2
1.3
1.3
1.3
1.3
1.3
1.3
1.4
1.5
1.5
1.5
1.5
1.5
1.6
1.7
1.7
1.7
1.7
1.9
2.0
Genotype
AGES81
AGES2
AGES58
AGES90
AGES61
AGES46
AGES78
AGES37
AGES17
AGES30
AGES71
AGES10
AGES45
AGES16
AGES56
AGES44
AGES86
AGES25
AGES84
AGES14
AGES79
AGES77
AGES80
AGES64
AGES20
AGES9
AGES43
AGES22
AGES68
AGES40
AGES38
AGES70
AGES19
AGES63
AGES39
AGES31
AGES23
AGES41
AGES74
AGES65
AGES73
AGES83
AGES29
AGES76
Mean
LSD 0.05
%DINThea
1.1
1.0
0.9
1.1
1.3
1.7
1.5
1.3
1.6
1.5
1.5
1.7
1.1
1.6
1.4
1.9
2.2
1.8
2.2
1.9
1.8
1.4
1.5
2.1
2.0
1.0
2.3
2.2
1.7
2.5
1.6
3.1
2.5
2.3
2.7
2.6
2.3
1.8
2.8
3.4
2.5
2.9
5.3
3.5
1.3
1.633
%DINTsli
1.0
1.1
1.2
1.0
0.9
0.6
0.8
1.1
0.8
1.0
1.0
0.8
1.5
1.0
1.3
0.9
0.6
1.1
0.7
1.1
1.2
1.6
1.6
1.1
1.3
2.4
1.1
1.4
1.9
1.1
2.1
0.9
1.5
1.9
1.6
1.7
2.1
2.7
1.7
1.2
2.4
3.0
1.0
3.5
0.9
1.427
%DINTtot
2.1
2.1
2.2
2.2
2.3
2.3
2.3
2.4
2.4
2.5
2.5
2.5
2.6
2.6
2.7
2.8
2.9
2.9
2.9
3.0
3.0
3.0
3.1
3.3
3.4
3.4
3.5
3.6
3.6
3.7
3.7
3.9
4.1
4.2
4.3
4.4
4.5
4.5
4.6
4.7
4.9
6.0
6.4
7.0
2.2
2.795
102
Appendix VIII. Disease resistance ranking of 90 genotypes according to sum of the standard competition ranking of the
main three parameters DI%, DS% and DINT% (heavy + slightly= total).
Genotype
AGES57
AGES11
AGES8
AGES7
AGES53
AGES59
AGES36
AGES82
AGES69
AGES72
AGES24
AGES87
AGES48
AGES32
AGES1
AGES18
AGES66
AGES88
AGES52
AGES12
AGES51
AGES4
AGES3
AGES85
AGES27
AGES21
AGES50
AGES35
AGES60
AGES67
AGES75
AGES15
AGES54
AGES42
AGES47
AGES5
AGES89
AGES34
AGES26
AGES55
AGES13
AGES33
AGES49
AGES62
AGES28
AGES58
AGES6
AGES81
Rank
%DItot
1
9
19
13
17
2
4
3
6
14
33
7
15
43
25
37
8
4
22
26
18
11
47
34
66
24
27
53
37
36
12
16
21
45
10
51
29
71
35
56
75
30
50
41
61
45
84
44
6.2
20.6
29.6
24.9
28.1
15.0
16.7
15.6
18.7
26.8
35.1
19.8
27.2
38.5
33.4
36.5
20.2
16.7
31.6
33.8
28.3
24.3
40.2
35.8
48.7
33.0
34.0
42.3
36.5
36.2
24.6
27.5
31.5
40.1
20.8
41.3
34.7
50.5
35.9
45.7
58.8
34.8
41.2
37.7
47.1
40.1
67.8
38.7
Rank
%DStot
9
4
1
7
9
27
27
23
27
16
4
32
15
2
11
6
38
44
19
16
20
32
7
20
3
32
32
11
26
27
51
49
44
23
68
20
42
11
41
23
11
50
31
47
38
52
18
57
Rank
2.4
1.9
1.0
2.3
2.4
3.6
3.6
3.4
3.6
2.9
1.9
3.9
2.8
1.3
2.5
2.0
4.1
4.5
3.1
2.9
3.3
3.9
2.3
3.3
1.7
3.9
3.9
2.5
3.5
3.6
5.1
4.8
4.5
3.4
6.1
3.3
4.4
2.5
4.3
3.4
2.5
4.9
3.8
4.6
4.1
5.2
3.0
5.4
1
3
2
5
8
5
5
12
8
12
8
8
18
4
18
12
12
12
21
21
25
21
18
25
12
28
28
24
28
28
28
35
35
34
25
35
35
28
41
41
35
41
40
41
45
49
46
47
%DINTtot
Sum
0.1
11
0.4
16
0.3
22
0.6
25
0.7
34
0.6
34
0.6
36
0.8
38
0.7
41
0.8
42
0.7
45
0.7
47
0.9
48
0.5
49
0.9
54
0.8
55
0.8
58
0.8
60
1.0
62
1.0
63
1.2
63
1.0
64
0.9
72
1.2
79
0.8
81
1.3
84
1.3
87
1.1
88
1.3
91
1.3
91
1.3
91
1.5
100
1.5
100
1.4
102
1.2
103
1.5
106
1.5
106
1.3
110
1.7
117
1.7
120
1.5
121
1.7
121
1.6
121
1.7
129
1.9
144
2.2
146
2.0
148
2.1
148
103
AGES90
AGES61
AGES2
AGES78
AGES46
AGES77
AGES17
AGES37
AGES10
AGES45
AGES71
AGES30
AGES56
AGES44
AGES16
AGES68
AGES25
AGES79
AGES84
AGES86
AGES9
AGES80
AGES14
AGES43
AGES64
AGES20
AGES22
AGES40
AGES70
AGES38
AGES19
AGES63
AGES39
AGES41
AGES23
AGES31
AGES73
AGES65
AGES76
AGES74
AGES29
AGES83
40
32
72
48
60
22
67
55
57
31
49
79
28
59
78
20
74
42
57
65
39
89
69
54
77
52
85
69
82
73
83
86
76
64
68
81
63
88
62
80
90
87
37.2
35.0
51.6
40.6
47.1
31.6
48.8
43.7
45.9
34.9
40.7
60.1
34.7
46.0
59.8
30.4
55.3
38.4
45.9
48.3
37.0
75.3
50.1
42.9
59.2
41.7
69.0
50.1
66.2
54.2
67.1
72.4
58.9
48.0
50.0
64.1
47.9
73.8
47.3
62.5
90.1
73.0
64
71
36
59
48
76
44
57
55
79
64
36
83
55
42
89
52
81
70
62
87
40
66
79
59
85
52
73
61
73
66
62
73
86
84
72
88
68
90
78
77
82
5.9
6.5
4.0
5.6
4.7
7.0
4.5
5.4
5.3
7.5
5.9
4.0
8.2
5.3
4.4
10.4
5.2
7.9
6.4
5.8
9.2
4.2
6.0
7.5
5.6
8.7
5.2
6.9
5.7
6.9
6.0
5.8
6.9
9.1
8.3
6.7
10.0
6.1
14.1
7.4
7.1
8.1
49
51
47
51
51
66
54
54
56
59
56
56
61
62
59
74
63
66
63
63
71
69
66
73
70
71
74
76
78
76
79
80
81
83
83
82
87
86
90
85
89
88
2.2
2.3
2.1
2.3
2.3
3.0
2.4
2.4
2.5
2.6
2.5
2.5
2.7
2.8
2.6
3.6
2.9
3.0
2.9
2.9
3.4
3.1
3.0
3.5
3.3
3.4
3.6
3.7
3.9
3.7
4.1
4.2
4.3
4.5
4.5
4.4
4.9
4.7
7.0
4.6
6.4
6.0
153
154
155
158
159
164
165
166
168
169
169
171
172
176
179
183
189
189
190
190
197
198
201
206
206
208
211
218
221
222
228
228
230
233
235
235
238
242
242
243
256
257
104
Figures:
Figure 1. Puppet (left) and flowers (right) from maize husks (Bulphoto) ............................................. 11
Figure 2: General morphology of maize inflorescences (Bennetzen 2009) .......................................... 12
Figure 3. Microscopic photo of macroconidia of Fusarium graminearum ........................................... 15
Figure 4. The most common Fusarium species on maize: mycelium on agar plates (left) and the
appropriate spores (right): a) Fusarium verticillioides, b) F. proliferatum, c) F. subglutinans, d) F.
graminearum, e) F. equiseti, f) F. crookwellense (Fotos Brigitte Dorn, Agroscope ART; Andreas Hecker,
Agroscope ART) ..................................................................................................................................... 15
Figure 5: Disease cycle of F. graminearum (Pioneer)............................................................................ 17
Figure 6. Severe symptoms of Gibberella Ear Rot on artificially infected maize ears .......................... 20
Figure 7. Different types of larvae entrances by ECB............................................................................ 23
Figure 8. Damage by the second generation of the European corn borer (ECB) .................................. 23
Figure 9. Factors affecting mycotoxin occurrence in the food and feed chain (Pestka and Casale
1989)...................................................................................................................................................... 27
Figure 10. Overview of medians and means of deoxynivalenol (DON), zearalenone (ZEA) and
fumonisins (FUM) after natural infection with different Fusarium spp. of different maize hybrids
(2004-2010: Dersch and Krumphuber, 2011; 2011–2013: KOFUMA-Projekt; AGES, EMYKOM 2014).
The arrows represent the maximum limits for DON (pink), ZEA (lilac), FUM (green) in unprocessed
maize grain (Commission Regulation (EC) 1881/2006). ........................................................................ 28
Figure 11. Chemical structure of deoxynivalenol.................................................................................. 29
Figure 12. Chemical structure of zearalenone ...................................................................................... 30
Figure 13. Chemical structure of fumonisin B1 ..................................................................................... 30
Figure 14. Analysis of maize chromosomes, then and now. Maize chromosomes are large and easily
visualized by light microscopy. (a) (Rhoades 1952). (b) This image is comparable to that in part a
except that the spindle is shown in blue (stained with antibodies to tubulin), the centromeres are
shown in red (stained with antibodies to a centromere-associated protein), and the chromosomes
are shown in green (Dawe 1999). ......................................................................................................... 33
Figure 15. Different inoculation methods (Papst, et al. 2007).............................................................. 37
Figure 16. Relation between the data for the disease index of F. graminearum (mean over 3 growth
seasons each with 3 replications) after toothpick inoculation and the DON contamination. Pearson
correlation coefficient between both data sets is 0.83***. ................................................................. 38
Figure 17. Effect of the grades (APS3 to APS7) on the DON content. The mean DON content of the
hybrids classified with the score 3 (APS3) was about a factor 4 lower as compared to the hybrids with
the grade 7 (APS7) (Source EMYKOM, AGES)........................................................................................ 40
Figure 18. Overview of plant-pathogenic interactions; MAMP-mitogen-associated molecular pattern;
RLK- receptor-like kinases; LRR-leucine-rich repeats; NB-LRR- nucleotide-binding LRR; PTI-patterntriggered immunity; ROS- reactive oxygen species; ETI- effector triggered immunity; ethylene (ET)
and jasmonate (JA)- mediated signalling pathways; nitric oxide (NO) synthesis; pathogenesis related
(PR) proteins; (Kant and Reinprecht 2011) ........................................................................................... 42
Figure 19. Maize kernel structure. The mature maize kernel is comprised of multiple tissues and
organs within the embryo and endosperm, in addition to maternally derived structures (Bennetzen
and Hake 2009) ..................................................................................................................................... 44
105
Figure 20. Schematic overview of the regulatory components involved in the transcription of
mycotoxin biosynthetic genes. POLII, polymerase II; TF, general transcription factors; MED, mediator
complex; ssTF, sequence-specific transcription factors (Woloshuk and Shim 2013). .......................... 49
Figure 21. Soil cultivation after maize harvest (2012, Riben, Bulgaria) ................................................ 59
Figure 22. Liquid fertilization of maize field (2011, Riben, Bulgaria) .................................................... 61
Figure 23. Harvest header for maize with stalk chopper rotor (Riben, Bulgaria, 2014) ....................... 63
Figure 24. Concentrated suspensions of macroconidia stored frozen in small aliquots ...................... 66
Figure 25. Bubble breeding method ..................................................................................................... 66
Figure 26. Maize during inoculation at BBCH67 with the mist irrigation system (26.07.2013, Tulln) .. 67
Figure 27. Visual examination of the maize plants on the field at BBCH89 (11.10.2013, Tulln) .......... 72
Figure 28. Relation between Disease severity total (DS total %) and Disease incidence total (DI total
%) after silk channel inoculation with Fusarium graminearum in CRB design (r=0.31, p=0.0029**) ... 79
Figure 29. Relation between Disease severity heavy (DS total %) and Disease incidence heavy (DI total
%) after silk channel inoculation with Fusarium graminearum in CRB design (r= 0.36, p=0.0005***) 80
Figure 30. Relation between the disease severity total (%) and the maturity group after the silk
channel inoculation with Fusarium graminearum; Pearson correlation coefficient between both data
sets is 0.59, p=0.0001***. ..................................................................................................................... 81
Figure 31. Relation between the maturity group of each hybrid and the European Corn Borer
(Ostrinia nubilalis) severity; Pearson correlation coefficient between both data sets in r= -0.52,
p=0.0002***. ......................................................................................................................................... 82
Figure 32. Infested maize ear by European Corn Borer larvae (Ostrinia nubilalis) (08.10.2013, Tulln) 82
Figure 33. Flowchart of the experiment ............................................................................................... 83
Figure 34. Appearance of visually symptomless (left) and diseased (right) kernels after field
evaluation (11.10.2013) ........................................................................................................................ 85
Figure 35. GER data after silk channel inoculation with Fusarium graminearum for the 90 maize
genotypes presenting the main three parameters, namely the total percentage of total disease
incidence (DItot%), which is displayed in blue, the total disease severity (DStot%) in orange, and the
total disease intensity (DINTtot%), (heavy + slightly= total) included in green. DI%, DS% and DINT%
are ordered according to DINTtot% and given in percentage (%). ....................................................... 87
Figure 36 . Performance of AGES76 and AGES57 genotypes for all evaluated (Disease incidence,
Disease severity and Disease incidence, heavy+ slightly= total) after silk channel inoculation with
Fusarium graminearum in CRB design. All data is given in percentage (%) using scale of 1 (no rot) to
100 % (fully rotten ear). ........................................................................................................................ 88
Figure 37. Performance of AGES8 and AGES9 genotypes for all evaluated (Disease incidence, Disease
severity and Disease incidence, heavy+ slightly= total) after silk channel inoculation with Fusarium
graminearum in CRB design. All data was given in percentage (%) using scale of 1 (no rot) to 100 %
(fully rotten ear). ................................................................................................................................... 89
Figure 38. Performance of AGES80 and AGES66 genotypes for all evaluated (Disease incidence,
Disease severity and Disease incidence, heavy+ slightly= total) after silk channel inoculation with
Fusarium graminearum in CRB design. All data is given in percentage (%) using scale of 1 (no rot) to
100 % (fully rotten ear) ......................................................................................................................... 89
Figure 39. Gibberella ear rot symptoms: white to pinkish mold starting always at the ear tip,
(11.10.2013, Tulln) ................................................................................................................................ 91
Figure 40. Ranking of the 6 most susceptible genotypes inoculated through silk channel inoculation
with Fusarium graminearum at IFA, Tulln in 2013. Data is ordered according to the sum of standard
106
competition ranking for each parameter (heavy + slightly= total) and is given in percentage using
scale of 1 (no rot) to 100 % (fully rotten ear). ....................................................................................... 92
Figure 41. Severely affected maize ears by Gibberella ear rot: largely rotted with husk parts and silks
adhering tightly (hybrid dependent) to the ear (11.10.2013, Tulln) ..................................................... 92
Figure 42. Maize ears with GER symptoms after silk channel inoculation with Fusarium graminearum,
(08.10.2013, Tulln) ................................................................................................................................ 93
Figure 43. Genotype AGES69 dehusked after evaluation on the field, (07.10.2013, Tulln) ................. 94
107
Tables:
Table 1. The species found associated most frequently with Ear rots of and important mycotoxins in
maize and other small-grain cereals (Brown 2013; Barug 2006) .......................................................... 16
Table 2: Minimum, maximum and optimum temperature of the main Fusarium species (Bottalico,
1999)...................................................................................................................................................... 26
Table 3. Maximum levels for deoxynivalenol (DON) in foods. Extract from Regulation (EC) 1881/2006
and EFSA (2014)..................................................................................................................................... 31
Table 4. Maximum levels for zearalenone (ZON) in foods. Extract from Regulation (EC) 1881/2006
and EFSA (2014)..................................................................................................................................... 31
Table 5. Maximum levels for fumonisins (FUM) in foods. Extract from Regulation (EC) 1881/2006 and
EFSA (2014)............................................................................................................................................ 31
Table 6: Fusarium genomes .................................................................................................................. 33
Table 7. Types of resistance (Singh 1986) ............................................................................................. 35
Table 8: Summary of chromosomal distribution of quantitative trait loci (QTLs) for resistance to ear
rots caused by Fusarium verticillioides (FER), Fusarium graminearum (GER), and Aspergillus flavus
(AER) and for reduction in accumulation of fumonisins (Fum), deoxynivalenol (Don), zearalenone
(Zea), and aflatoxin (Afl) in different population of maize (Hou, Hue, et al. 2002) .............................. 52
Table 9. Main factors determining plant quality (Gyori 2000) ............................................................. 56
Table 10. Pre- and post-planting management decisions relevant to the maize IPM .......................... 57
Table 11. Commercial fungicides available against FER and GER in Austria (AGES 2015) .................... 62
Table 12. Physical and chemical methods for mycotoxin decontamination and inactivation in maize
(Jouany 2007) (Bullerman and Bianchini 2014)..................................................................................... 64
Table 13. Ear rot rating scale ................................................................................................................. 68
Table 14. Statistical significance............................................................................................................ 70
Table 15. ANOVA analysis of RCBD for the data of Disease incidence %DIhea , %DIsli, %DItot after silk
inoculation with F. graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square) .. 73
Table 16. ANOVA analysis of RCBD for the data of Disease severity %DShea , %DSsli , %DStot after silk
inoculation with F. graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square) .. 73
Table 17. ANOVA analysis of RCBD for the data of Disease intensity %DINThea ,%DINTsli , %DINTtot after
silk inoculation with F. graminearum (DF, degrees of freedom, SS, sum of squares, MS, mean square)
............................................................................................................................................................... 74
Table 18. Descriptive statistics for the traits Disease incidence, Disease severity and Disease
incidence (Heavy + slightly= total). Results are depicted for all genotypes and are given in %. .......... 75
Table 19. Disease incidence, disease severity and disease intensity after silk channel inoculation with
F. graminearum. Total (sum of light and heavy) infection are listed. The genotypes are ranked
according to increasing mean disease intensity over 2 replications..................................................... 77
Table 20. Disease intensity after silk channel inoculation with F. graminearum. The genotypes are
ranked according to increasing mean disease intensity (heavy + slightly= total) over 2 replications.
The data is given in percentage using scale of 1 (no rot) to 100 % (fully rotten ear). .......................... 78
Table 21. Number of the ECB (Ostrinia nubilalis) damaged ears per genotype (mean between two
replicates) .............................................................................................................................................. 81
Table 22. Standard competition ranking of some genotypes, ordered according %DINTtot parameter
(ranking order: 1224). ........................................................................................................................... 90
108
Table 23. Categorization of some maize genotypes divided into four classes according to possible
combinations between the parameters disease incidence and disease severity ................................. 91
109
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