FUSARIUM HEAD BLIGHT IN WHEAT: STUDY OF FIELD RESISTANCE AND
MYCOTOXIN ACCUMULATION IN MILLED FRACTIONS OF SOFT RED AND SOFT
WHITE WINTER WHEAT
By
Swasti Mishra

A THESIS
Submitted to
Michigan State University
in partial fulfillment
of the requirements for the degree of
MASTER OF SCIENCE
Plant Breeding, Genetics and Biotechnology- Crops and Soil Science
2012

ABSTRACT
FUSARIUM HEAD BLIGHT IN WHEAT: STUDY OF FIELD RESISTANCE AND
MYCOTOXIN ACCUMULATION IN MILLED FRACTIONS OF SOFT RED AND SOFT
WHITE WINTER WHEAT
By
Swasti Mishra
Fusarium head blight, caused by Fusarium spp. is a major threat to the wheat industry, affecting
both yield and grain quality. Additionally, production of the mycotoxin deoxynivalenol (DON)
in infected grain is a cause of severe losses. A higher proportion of toxin accumulates in the
outer layers of kernel which is often removed by milling. To evaluate genotypic differences for
toxin accumulation in different tissues of infected grain, we compared 39 locally adapted soft
winter lines (21 white and 18 red) representing range of FHB resistance. DON was quantified in
a whole grain sample and milled fractions of bran and flour. On average, white lines
accumulated significantly higher quantities of DON in all fractions compared to red, while the
ratio DON-bran/ DON-flour was lower (p<0.05). The 39 lines were also evaluated for different
measures of FHB resistance in the field using two different artificial inoculation methods. Since
the amount and duration of moisture available to infected wheat heads influences visible
infection and toxin accumulation, a bagging method after spray inoculation (SB) was compared
with misting treatment that followed grain spawn inoculum (GS). The methods were compared
based on measures of Incidence, Severity, Index, Fusarium damaged kernels (FDK) and DON
accumulation in whole grain. The methods were significantly correlated (p<0.05) in ranking
genotypes for DON accumulation and FDK, while environmental and genotypic effects were
observed in the other measures.

ACKNOWLEDGEMENTS
I would like to acknowledge Dr. Janet Lewis and Dr. Russell Freed for their guidance and
support, and serving as my major professors. The guidance of my committee members Drs.
James Kelly, Frances Trail and Dechun Wang are greatly appreciated. I thank Dr. Perry Ng ,
Michigan State University and Dr. Yanhong Dong, University of Minnesota for their
collaboration and support in milling and mycotoxin analysis. I am grateful to Lee Siler, Sue
Hammar, and Randy Laurenz for their friendship and support in inoculum preparation and
completion of field experiments. I would also like to thank Cen Wu, Michigan State University
for statistical support. Special thanks to friends and colleagues in the Plant Breeding, Genetics
and Biotechnology program and Department of Crops and Soil Science at Michigan State
University who have supported me.

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TABLE OF CONTENTS
LIST OF TABLES……………….……………….……………….………………………….iv
LIST OF FIGURES……………….……………….……………….………………….……...v
INTRODUCTION……………….……………….……………….…………………………..1
REVIEW OF LITERATURE ……….……………….……………….………………………5
1. Wheat……………….……………….……………….……………….……………….5
1.1.Wheat: Evolution, History and Importance……………….………………………5
1.2.Introducing Genetic Variability in Wheat……………….………………………...7
1.3.Classes of Wheat and Economic Importance……………….………………….....8
1.4.Differences between Red and White Wheat……………….………………….....10
1.5.Wheat Kernel composition and Milling Fractions……………….………..…….10
1.6.Wheat Bran and Whole Grain Products……………….…………………......….11
2. Fusarium Head Blight……………….……………….………………………...…….12
2.1.History and Economic Importance………………..……………..….………...…12
2.2.Causal Organisms……………….……………….………………………………12
2.3.Infection and Progress of FHB……………….………...……….……………….13
2.4.Production of Mycotoxins……………….…………………...….……………….15
2.5.Deoxynivalenol……………….……………….…………..…….……………….17
2.6.Types of Host Resistance……………….……………….……………………….18
MATERIALS AND METHODS……………….……………….…………………….…….21
1. Plant Materials……………….……………….……………….…………….……….21
2. Planting of Field Trial……………….……………….……………….…...…………21
2.1.Inoculation ………………………………………………………………..……..22
2.2. Spray Inoculation- Liquid Inoculum…………………………………...….…….22
2.3. Grain spawn inoculum………………………………………………….….……23
3. Visual Evaluation of Disease……………….……………….…………………....….24
4. Post-Harvest Sample Handling and analysis……………….………………….…….24
4.1. Fusarium Damaged Kernel (FDK) Scoring………………………………….…25
4.2. Milling and Toxin Analysis……………………………………..………………25
5. Statistical Analysis……………….……………….………...……….……………….26
RESULTS……………….……………….……………….……………….…………...…….28
1. Comparison of Inoculation Methods……………….……………….……………….28
1.1.Comparison of Response Variables……………………………………………...29
1.2.Comparison of Methods………………………………………………………….30
1.3.Comparison of Genotypes……………………………………………………….30
1.4.Effect of Grain Color…………………………………………………………….31
2. DON Accumulation in Milled Fractions……………………………….…………….31

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DISCUSSION……………….……………….…………………….……………….…..……33
1. Choice of Inoculation Method……………….…………………….……….……..…33
2. DON Accumulation in Milled Fractions……………….…………………………….38
REFERENCES……………….……………….……………….……………….……………58

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LIST OF TABLES
Table 1: Different mechanisms of host resistance to FHB described in wheat………...……….18
Table 2: List of 39 lines planted in the study and their pedigree information……………….…..44
Table 3: List of sites for artificial inoculation using Grain Spawn or spray and Bag methods over
three years of field study…………………………………………………………………45
Table 4: Correlation of different response measures within GS, SB and SB1 methods………....45
Table 5: Correlation between GS, SB and SB1 methods for the response variables…………….46
Table 6: Estimates of the response variables DON, FDK, Index, Incidence and Severity across
the inoculation methods GS, SB and SB1 for 18 red genotypes…………………….…..47
Table 7: Estimates of the response variables DON, FDK, Index, Incidence and Severity across
the inoculation methods GS, SB and SB1 for 21 white genotypes…………………..…..48
Table 8: Overall ranking of 18 red genotypes by different response measures………….………49
Table 9: Overall ranking of 21 white genotypes by different response measures…………….…50
Table 10: DON partitioning ratio in flour and DON accumulation levels in whole grain sample
(DON-std), bran (DON-bran), flour (DON-flour) and total (DON-kernel) of red
genotypes……………….……………….……………….……………….…………...…51
Table 11: DON partitioning ratio in flour and DON accumulation levels in whole grain sample
(DON-std), bran (DON-bran), flour (DON-flour) and total (DON-kernel) of white
genotypes……………….……………….……………….……………….…………..….52

iv

LIST OF FIGURES
Figure 1: Schematic diagram for evolution of the hexaploid T. aestivum………………….…….6
Figure 2: Modifications in spray equipment: Adjustments in the hand held spray boom…….…23
Figure 3: Linear regression relationship between DON-kernel and DON-std in A. red and B.
white genotypes……………………….……………………….……………………...…53
Figure 4: Linear regression relationship between DON levels in milled fractions (DON-bran and
DON-flour) with DON-kernel in A. red and B. white genotypes………………………..54
Figure 5: Relationship between DON partitioning ratio and DON kernel in A. red and B. white
genotypes……………………….……………………….……………………….………55
Figure 6: Comparison of DON-std, DON-kernel, DON-bran and DON-flour between red and
white genotypes……………………….……………………….………………………...56

v

INTRODUCTION
Wheat is one of the most important agricultural crops, with a global production of 690
million tons in 2011 (International Grains Council., 2012). In Michigan, both soft red and soft
white winter wheat are grown; with a production value estimated at 341.7 million dollars in 2011
(National Agricultural Statistics Service, 2012). Michigan is one of the largest producers of
white wheat in the Great Lakes region. Wheat bran is a vital source of dietary fiber, and
increasing numbers of consumers are choosing whole wheat or bran-rich products for their health
benefits. A fiber rich diet promotes digestion, reduces cholesterol, minimizes the risk of diabetes
and helps in weight control (Kroon et al., 1997). White wheat bran lacks the bitter tannin present
in red wheat and therefore can be used to produce more appealing whole wheat products
(Boswell and Shroyer, 2000). More flour can be produced from white wheat because it can be
milled closer to the bran without negatively affecting color or flavor. Bran from white wheat is a
valuable commodity as it provides better flavor and color characteristics in the finished product,
and is preferred by consumers (Zhang and Moore, 1999).
Fusarium head blight (FHB), caused by the fungus Fusarium graminearum (Schwabe), is
one of the major fungal diseases affecting small grains. A number of severe and moderate FHB
epidemics over the past two decades have caused severe economic losses to the wheat and barley
industry worldwide (McMullen et al., 1997; Snijders, 1990). In addition to yield loss due to
shriveled kernels, FHB also results in poor grain quality and mycotoxin contamination
(McMullen et al., 1997; Parry et al., 1995). Accumulation of mycotoxins such as deoxynivalenol
(DON), nivalenol, and zealerone is a major concern with Fusarium contaminated wheat.
Exposure to DON may cause diarrhea, vomiting, leukocytosis, gastrointestinal hemorrhage, and
even shock like syndrome or death at higher doses. Long-term chronic exposure can affect
growth, immune function and reproduction (Pestka and Smolinski 2005). The Food and Drug
1

Administration (FDA, 1993) has established guidelines for the use of mycotoxin-contaminated
grain, with DON contamination higher than 1ppm considered unfit for human consumption.
Wheat pericarp color (red or white) has been found to play a role in DON contamination, with
reports of high toxin accumulation in white genotypes (Knott et al., 2008).
Recent studies have shown that a higher fraction of toxins accumulates in the outer layers
or the bran (pericarp and aleurone layers) of infected kernels (Nishio et al., 2010; Edwards et al.,
2011, Abbas et al., 1985). Processes such as cleaning of infected grain to remove chaff, and
milling to remove bran reduce DON quantities.in the flour. Bran and shorts, the byproducts of
milling that have high DON contamination, are often used as a part of animal feed. Toxin
residues can remain in animal products that can be harmful to humans (D'Mello et al., 1999).
Wheat heads are most susceptible to FHB when they are at anthesis (Feekes stage 10.510.5.1) when the anthers are extruded. Climate conditions play an important role in determining
the incidence and severity of the disease, with warm moist conditions in the spring increasing the
risk of FHB epidemics. No-tillage cropping systems, crop rotations with non-host crops and
fungicides have been used as ways of reducing FHB disease and subsequent DON accumulation.
Although agronomic and chemical methods of FHB control are widely utilized, an integrated
approach with the use of genetic resistance provides the best solution for control. Breeding
programs use artificial inoculation systems to screen varieties for resistance to incidence (type I)
and spread of infection (type II) (Schroeder and Christensen, 1963). Several methods have been
described to measure the visual symptoms of FHB in the field to rank and select varieties for
FHB resistance. Measurements such as % incidence, % severity and % Fusarium-damaged
kernels (FDK – a post-harvest measure) assess multiple types of host resistances. Reports from
state performance trials and uniform scab nurseries do not always agree on correlation of these

2

measures with mycotoxin accumulation in the grain, which is the biggest problem associated
with FHB. Moreover, field screening methods and measurements may not be accurate in
prediction of disease because they often represent a confounded effect from different resistance
types. Some disadvantages of these methods include the variable effect of environment and the
inability to measure or control the exact amount of inoculum per spike. It has been shown that
the availability of moisture after inoculation affects disease severity and toxin accumulation in
grains (Cowger et al., 2009; Culler et al., 2007; Gautam and Dill-Macky, 2008). Thus, choice of
screening method and misting treatment post-inoculation can have a variable effect on the
ranking of genotypes based on resistance to FHB or DON accumulation.
A higher fraction of DON accumulates in the bran compared to the flour (Abbas et al.,
1985; Dexter et al., 1996; Nishio et al., 2010). Although it is standard practice to measure DON
contamination in whole grain, toxin accumulation in bran or flour fractions has not been studied
to identify presence of genotypic differences. Since white wheat varieties are more valuable for
their bran, comparison of red and white genotypes is necessary to evaluate and identify white
genotypes with low level DON accumulation in the bran.
For this study, 39 commercial varieties and advanced experimental lines, including 21
soft white and 18 soft red winter lines, representing a range of FHB resistance based on
Michigan state performance trials (MSPT, 2007), were selected for the following objectives:
1. To estimate variability for toxin accumulation in bran and flour fractions of milled wheat
in locally adapted commercial and MSU experimental lines, and evaluate differences in
the proportion of toxin in these fractions based on toxin accumulation in whole grain
within groups of red and white soft winter wheat.

3

2. To evaluate and compare two different methods of inoculation in the field: grain spawn
(GS) inoculum followed by misting, and spray inoculation followed by bagging (SB). I
hypothesized that since the SB method provides a more controlled environment postinoculation, with a fixed amount of inoculum applied over each plot at the time of
flowering, and each plot receiving the same humidity treatment through bagging, a more
consistent measure of type 1 and type 2 resistance can be obtained.
3. To evaluate the role of grain color in the prediction of FHB resistance as assessed
visually, toxin accumulation, and DON partitioning between bran and flour.
4. To identify lines with visible resistance to FHB, and lower toxin accumulation in whole
grain and milled fractions among soft white and soft red winter wheat cultivars and elite
experimental lines adapted to Michigan.

4

REVIEW OF LITERATURE
1. Wheat
1.1.Wheat: Evolution, History and Importance
Wheat (Triticum aestivum) originated approximately 10,000 years ago in the
Fertile Crescent (present day southeastern Turkey –northern Syria) (Lev-Yadun et al.,
2000). Wheat and barley constituted the principal grain stock upon which old world
agriculture was founded. Wheat is one of the earliest plants to be domesticated (Harlan
and Zohary, 1966); Nevo, 2002), and it contributed to the emergence of agriculture and
was ultimately responsible for the increase in human population by enabling the
production of food in large quantities (Nevo, 2002).
The common hexaploid wheat (T. aestivum) (AABBDD) and its several subspecies contain 21 pairs of chromosomes with seven pairs belonging to each of A, B, and
D genomes. Triticum aestivum came into existence after the domestication of tetraploid
wheat species, due to a hybridization event, followed by chromosome doubling between
the tetraploid T. turgidum (AABB) and T. tauschii (Ae. tauschii) (DD) contributing the D
genome (Kihara 1944) (Figure 1). Triticum zhukovskyi (AABBGG) evolved from T.
timopheevi (AAGG), and they comprise another evolutionary lineage. The A genomes of
all tetraploid species were contributed by T. monococcum) and T. urartu. The second
genome of tetraploid wheat, B in T. turgidum and F in T. timopheevi, were contributed by
T. speltoides (Drovak and Zhang 1990). Tetraploid wheat species in cultivation today are
durum wheat and emmer, which share the AABB genome and are inter-fertile. The
genetic structure and self-pollination behavior of polyploid wheat species facilitates

5

genetic diversity through mutation or hybridization. Being polyploid, common wheat can
tolerate the presence of gene loci in multiple doses.

Figure 1. Schematic diagram for evolution of the hexaploid T. aestivum. Taken from
Feldman et al. (1997)

Wheat is one of the world’s largest and most important food crop, with a global
production of 690 million tons in 2011 (International Grains Council, 2012). The United
States is the fourth largest producer of wheat, with approximately 2 billion bushels (54.41
million tons) produced in 2011 (NASS 2011). In the US, wheat production has declined
since the 1980s, as wheat acreage began to decrease. Projections from the USDA-ERS
(2011) suggest that although higher yields are obtained, this trend will continue over the
next few years as yield gains have not managed to offset the declining acreage ( USDAERS, 2011). Thus, to meet the growing demands for wheat, continued improvements are
necessary in yield and resistance to biotic and abiotic stresses. However, continual
selective breeding has resulted in accelerated loss of genetic diversity in the recent
decades. For wheat improvement programs to succeed, traditional breeding approaches
6

should be complemented by non-traditional methodologies that enhance the existing
genetic variation. Gene banks have been established to store and utilize the available wild
genetic resources. Dynamic in situ hybridization (within gene pools and geographic
regions) is suggested as a way to maintain, screen, and efficiently utilize the diversity of
wild relatives.
1.2.Introducing Genetic Variability in Wheat
Finding sources of germplasm with desired traits among cultivated crops has been
a limiting factor in making suitable improvements. One of the consequences of founder
effect in hexaploid wheat is the restricted genetic variability, i.e. only a fraction of
genetic variability from the parental population is represented (Feldman, 2001). Useful
genetic resources can be found among uncultivated (wild) plants, and these genes for
resistance to biotic and abiotic stresses can be introduced into existing food crops through
wide crosses (Mujeeb-Kazi, 1995). Researchers at International Maize and Wheat
Improvement Center (CIMMYT) have pursued bread wheat improvement through
interspecific and intergeneric hybridization.
Interspecific hybrids are produced by crossing bread wheat with diploids from
Triticum/Aegilops spp. that have high genetic proximity to the A, B, and D genomes.
Related diploid species include T. tauschii for the D genome; T. monococcum for the A
genome; and T. speltoides and related accessions for the B genome. Triticum tauschii has
been utilized for making interspecific crosses, as a wide range of resistances or tolerances
to biotic or abiotic stresses have been identified: Karnal bunt (Tilletia indica), scab
(Fusarium graminearum), spot blotch (Helminthosporium sativum syn. Bipolaris
sorokiniana), leaf rust (Puccinia recondita), stripe rust (P. striiformis), salinity, and

7

drought (Valkoun et al. 1990; Gilchrist 1999; Mujeeb- Kazi 1999). Variability from T.
tauschii (DD) has been effectively exploited by screening the accessions for desired traits
and making direct transfers by crossing selected accessions with T. aestivum (AABBDD)
or by crossing with T. turgidum (AABB) (Mujeeb-Kazi, 1995). Synthetic hexaploid
populations have been created via crosses between T. tauschii and T. turgidum.
Intergeneric crosses have been explored as a source of introducing genetic
variability for wheat improvement by crossing between annual bread wheat species
(Triticum and Aegilops spp.) with different perennial species of Triticae. The perennials
are important because their natural habitats provide the possibility that they could be
potent sources of resistance for several biotic and abiotic stresses. Very low success rate
is associated with intergeneric hybridizations (Mujeeb-Kazi, 1995) as the species
involved in intergeneric crosses are genomically diverse and rather difficult to cross.
Even when successfully combined, the resulting hybrids exhibit little or no intergenomic
chromosome association (Mujeeb-Kazi, 1995). Other disadvantages include cross
incompatibility, poor vigor in F1, and hybrid sterility. It is general practice to use the
higher ploidy species as the female parent to avoid imbalances between the chromosome
numbers of the embryo and endosperm and it is generally easier to emasculate the
hexaploid T. aestivum than the tightly invested florets of most alien species.
1.3. Classes of Wheat and Economic Importance
Wheat is classified based on observable physical distinctions. Wheat is described
as either red or white, based on the color of the outer layers of the kernel. A bran color of
reddish brown is referred to as red, while yellow or tan colored bran is referred to as
white. Hard and soft wheat are classified based on the texture or hardness of seed, usually

8

explained by allelic differences at a single locus Ha on chromosome 5D (Worland and
Snape, 2001). The Spring/ Winter growing habit is determined by the sensitivity of the
alleles present at the vernalization response loci (Vernalization sensitivity gene- Vrn3,
Vernalization insensitivity genes- Vrn-A1, Vrn-B1, Vrn D-1, Vrn-B4). Spring type alleles
are dominant and are insensitive to cold treatment, while the recessive winter alleles
normally require at least six weeks of vernalizing temperatures before flowering
(Worland and Snape 2001). Spring varieties are planted in spring and harvested in fall.
Winter wheat is planted in the fall and harvested in the following summer.
Different classes of wheat are identified for marketing efficiency by exporting
countries. The US system recognizes six broad classes of wheat: Hard Red Winter
(HRW), Soft Red Winter (SRW), Hard Red Spring (HRS), Soft White (SW), Hard White
(HW), and Durum. In Michigan, winter varieties of soft white (SWW) and soft red are of
great economic importance, cultivated over 560,000 acres and with production value
estimated at $164 million in 2009 (National Agriculture Statistics Service, 2009).
Production of SWW in North America is limited to the Great Lakes area (Michigan, New
York and Ontario) and the Pacific North-west (Washington, Oregon and Idaho). Given
declining acreage of white wheat in New York and Ontario (Johnson, 2011), Michigan is
now the major producer of white wheat in the eastern part of the country, catering to the
needs of the local cereal industry. Comparison of genetic diversity in SRW and SWW
wheat grown in the Eastern US shows the SWW gene pool has developed from a narrow
genetic background which is due to the use of only a few elite parents in breeding SWW
(Ward 1997).

9

1.4.Differences between Red and White Wheat
Red and white colored wheat differ in the color of the seed coat. Pericarp color is
controlled by three independent homoeologous genes R-A1, R-B1, and R-D1, located on
chromosomes 3A, 3B, and 3D (Metzger and Silbaugh, 1970). The color genes are
additive, and red color is dominant to white. Thus, presence of a single dominant allele is
sufficient for red color. The alleles R-A1b, R-B1b, and R-D1b result in red color while
white grain color is controlled by the recessive alleles R-A1a, R-B1a, and R-D1a. In
many regions, white wheat suffers from the lack of desirable traits and poor agronomic
performance due, in part, to limited breeding efforts in white wheat. The genetics of
kernel color makes it difficult to breed white wheat, and introduce desirable traits from
red lines (Sherman et al., 2008). For example, in a cross between a three gene red line
and a white seeded line, there is only a 1/64 chance of getting a progeny which has
recessive alleles at all three loci. White wheat suffers from relatively lower yield and
higher susceptibility to diseases. White grain color is also genetically associated with
susceptibility to Pre-harvest Sprouting (PHS) and subsequent starch degradation
compared to Red (Bassoi and Flintham, 2005; Groos et al., 2002; Himi et al., 2002) and
is also more susceptible to accumulation of mycotoxins produced during Fusarium
infection. However, superior color characteristics (Miskelly, 1984) and possible
advantages in milling and end use quality (Taylor et al., 2005) make white wheat
attractive for a variety of products.
1.5. Wheat Kernel Composition and Milling Fractions
The wheat kernel is composed of three parts: bran, endosperm and germ. The bran
consists of 7 layers that make up the hard protective coating on the kernel and is a

10

concentrated source of dietary fibers. The aleurone layer is the largest portion of bran and
contains concentrated vitamins, minerals and other nutrients. The endosperm constitutes
83-84% of the weight of the grain and consists of starch granules embedded in a protein
matrix within large thin walled cells (Yamazaki 1981). The germ contains the plant
embryo and accounts for only 2-3% of the grain’s dry weight. In the milling process the
pericarp and aleurone layer are removed from the wheat kernel and these
anatomical structures are separated into the milling fractions known as bran and
shorts, respectively. Commercial milling produces multiple fractions of flour and bran
and respective particle sizes vary depending on the mill producing it. Milling procedure
and efficiency may vary for hard and soft wheat, as when using roller mills, hard wheat is
milled easily leaving the bran intact and using successive rolls to scrape the endosperm
away from the bran (Yamazaki 1981).
1.6. Wheat Bran and Whole Wheat Products
Consumers today are health conscious, and the consumption of high fiber foods
increasing. Wheat bran is recognized as an important source of fiber, and use of refined
flour has shifted to whole grain flour. A number of whole wheat and wheat bran products
are available in the market, with several dietary and nutritional benefits. Higher fiber
content in the diet promotes digestion, reduces cholesterol, minimizes risk of diabetes,
and helps in weight control. Bran also supplies a high amount of antioxidants and
micronutrients, and reduces the risk of colon cancer (Kroon et al. 1997).
Addition of bran to flour for bread making results in altered loaf volumes, changes
in texture and crumb size (Galliard and Gallagher, 1988; Gan et al., 1992; Moder et al.,
1984; Zhang and Moore, 1999). While the bran particle size contributes to different

11

dough rheological properties and bread making quality, other factors are important for
consumer acceptance. Based on a consumer panel, general acceptability of products
using bran from white wheat was significantly higher, as it results in bread with a
uniform and lighter crust color (Zhang and Moore, 1999). Seed coat from red wheat
contains a higher concentration of tannin-catechin and polyphenol oxidase (Park et al.,
1997) that may have a negative effect on the flavor of the end product.
2. Fusarium Head Blight
2.1. History and Economic Importance
Fusarium head blight (FHB) or “Scab” is an important fungal disease affecting small
grains (Yi et al., 2001). With a number of severe and moderate epidemics over the past
two decades, FHB has caused severe economic losses to the wheat and barley industry
worldwide (McMullen et al., 1997; Snijders, 1990). Occurrence of FHB results in loss of
yield, poor grain quality resulting from shriveled and “tombstone” kernels, and
mycotoxin accumulation (McMullen et al., 1997; Parry et al., 1995).
2.2. Causal Organisms
Causal organisms of natural infection have been identified as Fusarium spp., such
as F. graminearum, F. culmorum, F. avenaceum, F. moniliforme, F. equisetti, F.
acuminatum and F. semitectum, and Microdochium nivale. The most prominent of these
are: F. graminearum (Schwabe), which is responsible for FHB in North America, and F.
culmorum (W. G. Smith), which has been identified as the cause of FHB in Europe.
Fusarium spp. have a wide host range including maize and small grain cereals like wheat,
oats and barley.

12

2.3. Infection and Progress of Disease
Conidia (asexual spores) of F. culmorum, and conidia and ascospores (produced
by sexual reproduction in perithecia) of F. graminearum growing on debris (grain, straw
etc.) from previous infected crops provide the primary source of inoculum (Birzele et al.,
2002; Snijders and Perkowski, 1990) that are dispersed (to the wheat head) through rain
splashes (Fernandez et al., 1993; Parry et al., 1995). The role of cultural practices in FHB
infections has been reviewed by Champeil et al. (2004). Higher frequency of FHB is
associated with the use of maize, durum wheat or oats in the crop rotation cycle prior to
wheat (Champeil et al., 2004; Dill-Macky and Jones, 2000). Limited soil tillage increases
the frequency of head blight, whereas deep tillage (ploughing) decreases it (Dill-Macky
and Jones, 2000; McMullen et al., 1997). Effects of irrigation and canopy density have
been investigated since as inoculum is primarily dispersed by splashing, a low canopy
density and presence of irrigation can favor spore dispersal (Mesterhazy, 1995). Wheat
plants have a restricted period of susceptibility during which F. graminearum requires
relatively high temperatures (20-30°C) and 48-60h surface wetness (Lacey et al., 1999;
Snijders and Perkowski, 1990). Rainfall also assists in conidial dispersal and, coinciding
with the availability of wheat heads at anthesis in late spring, provides suitable conditions
for colonization. FHB infection is highest for inoculation during the short period of
anthesis; little or no disease is observed for inoculations between head emergence and
anthesis; and visible disease symptoms decrease steeply for inoculations after mid
anthesis (Lacey et al., 1999).
Fusarium spores germinate on the abaxial surface of the glumes and in the floral
cavity within 12 hours post inoculation (hpi), giving rise to unbranched hyphae that

13

frequently come in contact with stomata (Pritsch et al., 2000; Wanjiru et al., 2002).
Fusarium graminearum has been reported to penetrate the adaxial surface and the
stomatal opening of the floral brackets such as the glume, lemma and palea (Pritsch et al.,
2000; Zhang et al., 2008). The ovary and the floral brackets are invaded by 36 hpi, while
the host cell walls and middle lamella in the vicinity of the hyphae demonstrate reduced
cellulose, xylan and pectin (Wanjiru et al., 2002). Inter- and intracellular hyphae are
present throughout the ovary and floral brackets by 76 hpi (Pritsch et al., 2000; Wanjiru
et al., 2002; Zhang et al., 2008). Hyphae spread down, and to a lesser extent up, the rachis
(Jansen et al., 2005; Wanjiru et al., 2002), and then spread down the plant (Kang and
Buchenauer, 2000; Zhang et al., 2008).
It has been suggested that initial FHB infection occurs on extruded anthers (Pugh
et al., 1933; Strange and Smith, 1969; Strange and Smith, 1971), where the presence of
growth stimulants promotes F. graminearum growth (Strange and Smith 1969; Strange
and Smith 1971). Structure of florets, either open (induced by hot temperatures and
drought conditions) or closed (induced by low temperatures and moist conditions) play an
important role in infection process. However, the role of anther extrusion (AE) is not
absolute and in spite of correlations, selecting for low AE could result in loss of some
resistant sources (Skinnes et al., 2008).
Before maturity, wheat heads infected with FHB appear water-soaked, lose their
chlorophyll and become straw-colored. When the heads are mature, it is difficult to
distinguish infection symptoms from natural senescence. In case of severe infection,
pinkish-red mycelium and conidia develop and the infection spreads through the entire
head. Infected kernels become shriveled and discolored with a white, pink or light brown

14

pericarp (Bottalico and Perrone, 2002). Infections occurring in early stages of growth
may result in seeds not being formed while late infections do not exhibit visual symptoms
of colonization. A large amount of mycotoxin can be produced even in the lack of visible
infection (Del Ponte 2007; Cowger 2010).
Several agronomic, chemical and biological methods of control have been
suggested for FHB management in wheat. Growing resistant germplasm, use of non-host
crops in rotation cycle, tillage practices are known to reduce the risk of Fusarium head
blight. Triazole fungicides containing tebuconazole are currently the most effective
(Homdork et al., 2000; Mesterhazy and Bartok, 1997), when applied during heading to
cover all the spikes. Biological control agents offer another strategy of FHB management.
2.4. Production of Mycotoxins
Many Fusarium spp. have the potential to produce secondary metabolites,
including mycotoxins in grain (Parry et al., 1995) reducing grain quality and rendering it
unfit for consumption. Mycotoxins produced by Fusarium spp. are typically Type A or
Type B trichothecenes and zearalenone (ZON). The major Type A trichothecenes
associated with Fusarium include T-2 toxin (T-2) and HT-2 toxin (HT-2).
Deoxynivalenol (DON), a Type B trichothecene, is the most prevalent toxin associated
with FHB and is often regarded as an “indicator-toxin” for FHB. Several countries
including Austria, Canada, Romania, Russia and the USA have established tolerance
limits for DON in cereals and finished products (FAO/WHO 2001). Other Type B
trichothecenes such as Nivalenol (NIV), 3-O-acetyl 4-deoxynivalenol (3-ADON) and 15acetyldeoxynivalenol (15-ADON) have also been identified in FHB infected grains.
Zearalenone is an uterotropic and estrogenic compound (Foroud and Eudes 2009). Based

15

on the major trichothecene produced by a given strain, Fusarium spp. are classified as
DON or NIV chemotypes, with NIV chemotypes mainly associated with maize
(Sydenham et al., 1991). DON chemotypes of F. graminearum are classified into two
types: DON-chemotype IA, producing DON and 3-ADON (from warmer regions,
including European strains) and DON-chemotype IB, producing DON and 15-ADON
(from slightly cooler regions, including mostly American strains) (Miller et al., 1991).
Different levels of aggressiveness and pathogenicity of F. graminearum isolates have
been attributed to the trichothecene producing potential of the isolate (Mesterhazy, 2002).
Isolates without the ability to produce trichothecenes were able to cause initial FHB
infection, but spread of the disease across a wheat spike was not observed (Bai et al.,
2002). Toxin contamination is also influenced by time of harvest. A study of mycotoxin
concentrations in wheat after inoculation with F. culmorum showed that DON was
detected in higher concentrations in earlier stages of development and it did not increase
in mature grain, while ZON was produced at a later stage of growth and in lower amounts
(Matthaus et al., 2004).
Trichothecene biosynthesis is well studied and the responsible TRI gene cluster is
known (Kimura et al., 2007). Mutants with disrupted TRI5 gene (coding for trichodiene
synthase) have dramatically reduced virulence and are no longer able to spread through
the rachis. Ilgen et al. (2009) studied the induction of trichothecene biosynthesis using
lines that were GFP tagged under the control of endogenous promoter of TRI5. Most
fluorescence was observed at the rachis nodes, suggesting an important role of these
structures in spread of infection. TRI5 mutants were not able to overcome the heavy cell
wall thickenings of the rachis node (Ilgen et al., 2009).

16

2.5. Deoxynivalenol
The US Food and Drug Administration (FDA) regulates the use of mycotoxin
contaminated wheat, and grain with DON concentration higher than 1 ppm is declared
unfit for human consumption. For use as livestock feed, DON concentration in the
finished feed should not be higher than 5 ppm (swine, dairy) and 10 ppm (poultry). Long
term surveys of DON contamination of naturally infected wheat grain have shown
presence of DON in quantities higher than the allowable limits. Mycotoxins have been
associated with acute mycotoxicoses in livestock, and to a lesser extent in human
(IARC1993). Acute administration of DON causes decreased feed consumption and
emesis (Creppy, 2002). Symptoms of exposure to DON in animals include diarrhea,
vomiting, leukocytosis, and gastrointestinal hemorrhage, with extremely high doses
resulting in shock-like syndrome and death (Pestka and Smolinski, 2005). Toxins in diet
not only cause serious health risks the animals, but toxin residues can remain in products
that can be harmful to humans (D'Mello et al., 1999). On a molecular level, DON
inhibits protein synthesis by binding to ribosomes and activating critical cellular kinases
(Pestka et al., 2004). Long-term chronic exposure can affect growth, immune function
(enhancement or suppression) and reproduction (Pestka, 2007; Pestka and Smolinski,
2005). Because it is heat tolerant, DON is not eliminated in finished food products
through cooking, baking or brewing (Scott 1991). Dry milling of wheat (process of
removing bran) results in DON reduction of up to 40% in flour, and 60% for highly
contaminated grains (Pestka, 2007). Quantification of DON in different fractions of
milled wheat shows that a higher fraction of the toxin accumulates in the bran (Rupp,
2002; Thammawong et al., 2010; TrigoStockli et al., 1996).

17

Surveys have identified DON as the most frequent contaminant associated with
FHB in wheat all over the world, although variation has been observed in quantities of
DON in infected grain (Bai et al., 2001; Creppy, 2002). Moisture levels, temperature and
the timing of infection also determine the levels of DON produced. A direct prediction of
toxin contamination cannot be made based on incidence of FHB, crop yield loss, or
scabby grain severity (Bottalico and Perrone, 2002). In inoculation experiments, FHB
infection and DON accumulation in grains varied with infection time. Contamination
with DON peaked for inoculations at mid-anthesis, and small amounts of DON were
produced in case of later inoculations, continuing up to the late milk- early dough stages,
even in the absence of visual symptoms (Lacey et al., 1999). Periods of rain soon after
anthesis and late infections favor low-symptom, high-DON scenario (Del Ponte 2007;
Cowger 2010).
2.6. Types of Host Resistance
Several mechanisms of resistance, both active (physiological) and passive
(morphological) are known to help against FHB infections. Passive mechanisms include
plant height (dwarfness increases susceptibility), presence of awns (presence increases
severity), flowering type (AE promotes susceptibility), and infection avoidance by early
flowering in the boot stage (Mesterhazy, 1995). Five types of active mechanisms are
summarized in Table 1.
Table 1. Different mechanisms of host resistance to FHB described in wheat
Resistance
Type I
Type II
Type III
Type IV
Type V

Description
Resistance to initial infection (Schroeder and Christensen, 1963)
Resistance to disease spread (Schroeder and Christensen, 1963)
Resistance to kernel infection (Mesterhazy, 1995)
Tolerance against FHB and trichothecenes (Mesterhazy, 1995)
Resistance to trichothecene accumulation (Miller et al., 1985)
18

Resistance to initial FHB infection (Type I) and resistance to the spread of disease
from the point of infection (Type II) have been studied extensively and are used in
breeding programs to evaluate genotypes in greenhouse and field. Type I resistance is
measured as the percentage of spikelets exhibiting symptoms upon exposure to the
pathogen. Plants are typically sprayed (at anthesis) with microconidial or ascosporic
suspensions and high humidity is maintained by bagging infected heads or by mist
irrigation for a few days after inoculation. An alternative method is using grain spawn
inoculum, where infected wheat or corn is dispersed in the field to mimic natural
conditions for infection (Rudd et al., 2001); Dill-Macky 2003). Resistance is measured as
“incidence” (percentage of diseased spikes), “severity” (percentage of infected spikelets
on diseased spikes) and “index” (product of incidence and severity). The disease index,
the best estimate of resistance to FHB in the field, is a combination of Type I and Type II
resistances (Rudd et al., 2001). Assessment of type I resistance is not accurate, and is
often confounded with Type II resistance in the field. Environmental conditions in the
field are variable and the amount of inoculum per spikelet is not consistent and cannot be
measured, making it difficult to precisely assess Type I resistance (Mesterhazy et al.,
2008; Rudd et al., 2001; Dill-Macky 2003). Availability of moisture post infection is
shown to enhance FHB, FDK and DON accumulation, suggesting an influence of
screening method on ranking and selection of genotypes (Cowger, 2009).
Type II resistance is evaluated on individual plants, by injecting a quantifiable
amount of inoculum into individual spikelets at anthesis, with high humidity maintained

19

for a few days after inoculation. Resistance is measured by the number of infected
spikelets, typically below the initial point of infection (Rudd et al., 2001).
Types III, IV and V of resistance cannot be quantified directly. Resistance to
kernel infection can be assessed and reported as Fusarium damaged kernel (FDK)
evaluation. DON quantification in grains gives a measure of tolerance against
trichothecenes. Since toxin accumulation is a major concern in FHB infected grain,
several studies have been conducted to correlate visible symptoms with DON
contamination (Beyer 2007). Type II resistance and FDK contamination are not
correlated with DON contamination (Cowger 2010, Beyer 2007) and a large effect of
environment and genotype are responsible for inconsistent results over years (Foroud and
Eudes 2009). Type V resistance, resistance to trichothecene accumulation can be
subdivided into two classes: a) class 1- genotypes are able to chemically modify or
degrade trichothecenes, and b) class 2- genotypes that have the ability to inhibit
trichothecene synthesis (Boutigny et al., 2008)

20

MATERIALS AND METHODS
1. Plant Materials
Thirty nine commercial varieties and MSU advanced lines were selected for the
study (Table 1). The lines included 18 soft red and 21 soft white winter lines well adapted
to Michigan. A wide range of FHB resistance levels (including DON contamination
levels) was represented within each group according to previous data from the Michigan
State Performance Trial (MSPT, 2007).
2. Planting of Field Trial
Field study was carried out over three years- 2009, 2010 and 2011, with two
locations planted each year (Table 1). The 39 lines were planted in each location in an
alpha lattice design with three replications. All locations had Capac loam type soil, and 03 percent slope, except the MSU Clarksville research center in 2009 which had a Lapeer
sandy loam soil type and 2-6 percent slope. A Hege 95 planter was used to plant 5 ft long
plots at MSU agronomy research farm in 2009 (4 rows at 15in row spacing, 0.5in seed
spacing) while 6 ft or 12 ft long plots were planted in all other locations using an Almaco
heavy duty grain drill (ALMACO, Nevada, Iowa) (6 rows at 7.5 in row spacing, 2 million
seeds per acre, ~4.2 in seed spacing). All plots were trimmed to a length of 6 ft before
heading began in spring. Fertilizer: 196lbs of 46-0-0 (urea) (corresponding to 90 lbs N)
and Herbicide: 0.5 oz Harmony Extra (dupont), plus non-ionic surfactant were applied
once in the spring at all locations. Flowering date was recorded for each plot as the
number of days past January 1 when 50% of the wheat spikes in the plot had reached
anthesis.

21

Each location was artificially inoculated with Fusarium graminearum using the
grain spawn (GS) inoculum or spray and bag (SB) method, as shown in Table 2 and
described below.
2.1.Inoculation
Plots were inoculated with Fusarium graminearum isolate PH1 (NRRL31084)
during flowering using one of two methods: grain spawn inoculum (F. graminearum
colonized cereal grain spread in the field) followed by artificial misting, or spray
inoculation with conidial spores followed by 48 hours of bagging (Table 2). See below
for details of the two different inoculum types.
2.2. Spray Inoculation- Liquid Inoculum
Inoculum was generated by culturing Fusarium graminearum isolate PH1
(NRRL31084) in Carboxymethyl Cellulose (CMC) liquid medium (Cappelli and
Peterson, 1965; Cappelli and Peterson, 1971). Ten microliters of stock culture was added
to autoclaved media (100 ml in 250 ml flasks). The culture was shaken at 200-250 rpm
and 25°C for four days. Mycelia were removed by filtration with a sterile cheese cloth.
Spore count was taken using a haemocytometer and the culture was diluted to a final
concentration of 50,000 spores/ml. One drop of Tween 20 was added per 100 ml culture
before inoculations.
Plots were sprayed uniformly with 15 ml of 50000spores/ml liquid inoculum on
the day of flowering using a sprayer from R&D sprayers (BELLSPRAY INC.,
Opelousas, LA) fitted with TeeJet 8002VS nozzles (TeeJet Technologies, Springfield,
IL). The nozzles were adjusted at a 20° angle and one nozzle at the extreme right end was
plugged to cover the plot width of 4ft. To ensure an even spread of inoculum per plot,

22

pressure was maintained at 40 psi and the spray-boom was held at a height of 2ft over the
wheat heads (Figure 2). Transparent plastic bags of 43 x 48in size and 0.98 mil (25
micron) thickness (Grainger Inc., Lake Forest, IL) (4 per plot) were used to cover the
plots and secured at the base of plants using twist ties. Before use, folded bags were
punched at the top to allow for air flow and ventilation to the plants. All bags were
removed after 48 hours.

2 ft

20
4 ft

Figure 2. Modifications in spray equipment: Adjustments in the hand held spray boom.
Figure not to scale.
2.3.Grain Spawn Inoculum
Grain spawn inoculum was prepared in two batches about 2 weeks apart, using
wheat and barley mixture in 2009 and 2010; and using only wheat in 2011. Per batch,
seed (30lbs per acre) was divided into 21 milk jugs (800-900 ml seeds per jug); covered
with distilled water and soaked overnight. The seed was then drained of excess water and
autoclaved for two 45 minutes cycles (seed was cooled overnight between runs). The
sterile grains were inoculated with liquid F. graminearum culture (as prepared in 2.2) at a
rate of 10-15 million spores/ jug. The jugs were shaken well and incubated at room
temperature for 3 weeks, with shaking provided every alternate day. The infected grain
23

was then transferred to sterile aluminum trays and allowed to dry in a biohazard hood for
a week (the grain was stirred every day to allow even drying and break any clumps).
Once dry, the inoculum was stored in buckets at room temperature until ready to spread
in the field.
The inoculum was spread evenly over the plots (two handfuls sprinkled over each
plot) twice: at approx. 2 weeks prior to anthesis, and at the start of anthesis. A misting
system was set up using overhead sprinklers, running for 2 minutes every hour from 7 am
to 10 pm and one run at 2 am. Misting was provided from the time of first inoculum
application to help in the development of spores and continued through anthesis to help in
spore dispersal.
3. Visual Evaluation of Disease
In the field, visual symptoms were noted at 14 and 21 days post inoculation (dpi) of
each plot. In case of the GS method, the day of flowering was treated as the day of
inoculation. In 2011, disease ratings were taken only at 21 dpi as visual symptoms were
not obvious at earlier days.
Incidence- Number of wheat heads that are infected with FHB out of an arbitrary
collection of 10 heads. Reading taken 3 times per plot on a single day, averaged and
reported as percentage.
Severity- Number of spikelets infected per infected wheat head. Reading taken 3 times
per plot on a single day, averaged and reported as percentage.
FHB Index-FHB index was calculated as Index = [% Incidence x % Severity]/100
4. Post-Harvest Sample Handling and Analysis

24

All plots were harvested after the plants had completely senesced, using an Almaco
SPC40 harvester (ALMACO, Nevada, Iowa). The air passing through the sieves was
lowered to prevent loss of scabby seeds. The grain was then cleaned using an air column
cleaner (CB-2A, AGRICULEX Inc., Guelph, Ontario, Canada) and the ventilation scale
was set at five. All chaff was picked out by hand before milling and toxin analysis.
Cleaned grain was stored at 4°C before milling.
4.1.Fusarium Damaged Kernel (FDK) Scoring
A scoop of approximately 1000 kernels was sub-sampled from the cleaned
samples, for FDK rating. Each sample was spread evenly in a 5.5in x 5.5in x .8in white
plastic weigh-boat, and compared to standard checks placed in similar containers.
Separate sets of checks (0-100%) were created for red and white lines, by mixing suitable
number of scabby and healthy heads. For example, 30% check has 300 scabby and 700
healthy seeds.
4.2.Milling and Toxin Analysis
A subsample of 10g of cleaned grain was taken for quantification of
deoxynivalenol (DON) in the whole grain. The whole grain sample was ground to an
approximate 20 mesh size in a Stein Mill (Model M-2, Fred Stein Laboratories Inc.,
Atchison, KS) at University of Minnesota. Another subsample of 100g was milled to
separate bran and flour fractions. A Quadrumat Jr. Mill (Brabender® GmbH & Co. KG,
Germany) with a screen with mesh size 240 microns was used for milling. Moisture
content was measured before milling, and was found to vary between 11-12%. % Flour
yield was calculated as 100 x [flour weight (g)]/ [flour weight (g) + bran weight (g)]
(Nishio, 2010). This calculation accounts for the loss of screenings after 10 min of

25

milling. An average flour yield of 67.84% was observed. A whole grain, bran and flour
sample of each plot were sent to the DON testing laboratory at University of Minnesota
where DON was quantified using Gas chromatography Mass Spectrometry (GC-MS)
(Fuentes et al. 2005;(Mirocha et al., 1998).
Apart from the measured DON quantities in bran (DON-bran), flour (DON-flour),
and whole grain (DON-std), DON in entire kernel (DON-kernel) and DON partitioning
ratio in flour were calculated for each variety as described by Nishio et al. (2010)
DON-kernel = [DON-flour x % flour yield /100] + [DON-bran x (100 - %flour
yield)/100]
DON partitioning ratio = [(DON-flour/ DON-kernel) x % Flour yield] (Nishio 2010)
5. Statistical analysis
From a total of 720 possible data points (120 plots x 6 environments), visual data
from 33 plots were missing due to flooding damage, error in or missed inoculation, or
excessive heat damage due to bagging. Four of 720 samples were missing for DON
quantification in whole grain (total DON response variable). Samples from the year 2009
were stored for a year at 4°C before being milled, and 55 samples from environment 1
(replication 3 and some random) 30 samples from environment 2 (random) had to be
discarded due to molding. Therefore, for the calculation of simple means for DON-bran,
DON-flour, DON-kernel and DON partitioning ratio, a separate data set consisting of
environments 2-6 (Table 1) was used.
Each year x location combination was treated as an environment (6 levels). To
maintain homogeneity of data during analysis, flowering dates were classified into five
levels (1-5) nested within each environment. The alpha lattice blocking, replication,

26

environment and flowering date were used as random factors in the model, to evaluate
the fixed effects of variety and method on the response variables. Simple means for effect
of variety (genotype) and method were calculated using proc mixed in SAS 9.2. Data
was found to be near normal; and variance was not homogenous across all years. Data
was transformed according to the λ value predicted for the variable by box-cox
transformation, when required. A total of 9 entries were removed as outliers based on the
Cook’s distance calculated for each variable by the “influence” command in proc mixed.
Data from 2009 spray and bag method was found to heavily influence the mean estimates
of varieties, and hence was treated as a third method “SB1”. For comparisons between
Red and White groups, student’s t test was used.

27

RESULTS
1. Comparison of Inoculation Methods
Weather conditions varied substantially over the period of study. Considerable
effect of environment, due to atypical temperatures in 2010 and 2011, was observed on
flowering dates and FHB infection. The average daytime temperature during the period
of inoculation was 70.9°F in 2009, 76,7°F in 2010, and 82.05°F in 2011. A maximum
recorded temperature during this period was 75.02°F in 2009, 87.8 °F in 2010 and
87.59°F in 2011. In 2011, the temperature and relative humidity in bagged and un-bagged
plots over the period of inoculation was measured using data loggers. Temperature inside
bags crossed 100°F, when the outside temperature was above 85°F. Four such hot days,
when the temperature crossed 100°F in bagged plots, were observed in 2011. Data
loggers were not used for temperature measurement in 2010, but from data obtained from
the Michigan automated weather network for stations nearest to the field locations, it is
estimated that there were three hot days during the period of inoculation. Heat stress was
observed in plots inoculated with SB method in both years. Heads with visible signs of
heat damage were not included in the calculation of visual symptoms, and removed from
the plot before harvest.
The conditions in 2009 were cooler and more suited for the SB treatment making
it was easier to see differences in visual symptoms between plots in the field. For the
measured response variables, mean values for all response variables observed in 2009
were much higher, and had a higher variance than in years 2010 and 2011. In order to
maintain the integrity of data and reduce errors, SB inoculation in 2009 at Clarksville
(environment 1) was treated as a third method – SB1. Overall analysis of variance

28

(ANOVA) was significant for the effect of variety and the interaction term variety x
inoculation method at α=0.01, but was not significant for inoculation method alone.
Grain color was not treated as a fixed effect in the analysis. However, varieties and their
simple means for the disease response measures were sorted by grain color, and the two
groups were compared using student’s t test.
1.1.Comparison of Response Variables
Correlation coefficients between the different measures of diseases response within
each method are summarized (Table 3). For the GS method of inoculation, all visual
response measures except incidence were significantly correlated (p≤0.05) with DON
accumulation for red genotypes. Studying the rankings of red genotypes, the measures
index and severity were found to correlate significantly with DON, FDK and each other
(p≤0.05). Incidence was found to correlate significantly with index and FDK, but not
DON or severity (p≤0.05). In white genotypes, only FDK showed a significant
correlation with DON contamination (p≤0.05). Incidence and severity measures from the
field correlated with index, as expected from index calculation.
Methods SB and SB1 were expected to perform in a similar way, as they represent the
same method with possible heat stress in SB. Both SB and SB1 methods ranked white
genotypes consistently over all response measures. FDK, index, incidence and severity
showed a significant positive correlation (p≤0.05) with DON contamination in white
lines. All visual measures were also found to correlate with one another (p≤0.05).
Comparing red genotypes, DON contamination correlated with FDK, index, incidence,
and severity in SB1 and FDK, index and severity in SB. In SB1, index, incidence, and
severity correlated among themselves (p≤0.05) but not with FDK.

29

1.2.Comparison of Methods
Comparison of inoculation methods for each response variable is summarized in
Table 4. Significance of all correlation coefficients was tested at α=0.05. Methods GS,
SB and SB1 ranked varieties in a similar way for DON contamination. Correlation
coefficients for all three methods for DON are significant in both red and white
genotypes at α=0.05. FDK scores of white genotypes in the three methods show
significant correlation, while for red genotypes, SB method correlated with GS and SB1.
Methods GS and SB1 were not significantly correlated for FDK scores in red genotypes.
The three methods did not show much significant correlation with one another for field
visual measures of index, incidence and severity among red genotypes. When comparing
white genotypes, however, all methods are significantly correlated for measure of
severity. Methods SB and SB1 were not correlated with GS for a measurement of
incidence in red or white genotypes.
1.3.Comparison of Genotypes
Individual rankings of the 18 red and 21 white lines were not consistent across all
response variables and methods. On classifying the red and white lines into three
categories based on 33rd and 66th percentiles; it was found the same few lines were ranked
in the “high” and “low” categories for most response variables in all the methods (Tables
5,6). Genotypes ranked in the middle group showed a lot of variation and were classified
as better or worse depending on method of inoculation and measure of disease response.
Simple means for each genotype were calculated for all response variables (Tables 7, 8).

30

A possible effect of flowering date was identified on mycotoxin accumulation in
grain. Spray and Bagging method should provide a more controlled environment than
misting for inoculation studies in the field, reducing the effect of environment.
1.4.Effect of Grain color
There is a significant effect (α < 0.01) effect of color on the DON accumulation in
whole grain as well as bran and flour samples. White lines consistently accumulated
higher quantities of DON in the whole grain even though visual assessment ranked both
red and white lines similarly. Effect of grain color on FDK, index, incidence and severity
were not significant overall, or by method.
2. DON Accumulation in Milled Fractions
The calculated DON-kernel correlated positively (p<0.01) with DON-std (DON
contamination in whole grain), which is the DON accumulation test routinely carried out
by breeding programs for selection (Fig. 5). DON-std and DON accumulation in bran and
flour fractions were correlated at α=0.01. Regression analysis between DON-bran, DONflour and DON-kernel showed positive linear relationships between DON-bran and
DON-kernel (R2=0.97 in white and R2= 0.95 in red genotypes), and DON-flour and
DON-kernel (R2=0.94 in white and R2=0.96 in red genotypes) (Fig. 6). A higher DONkernel value corresponds to higher toxin concentrations in both bran and flour,
irrespective of grain color.
Red and white genotypes were categorized into groups based on each of DONbran, DON-flour, DON-kernel, Partitioning ratio and DON-std (Tables 9, 10). The
categories were consistent in ranking the same lines as low or high based on 95% LSD
values compared to lowest and highest values in each variable.

31

DON accumulation in bran fraction was consistently higher than in the flour in
both red and white genotypes. DON quantities in bran were approximately twice of that
in flour (on average, 2.24 in red and 1.95 in white genotypes). Genotypes tested showed a
range DON ratio between bran and flour fractions: 1.88 to 2.58 in red, and 1.51 to 2.36 in
white genotypes. ANOVA tests show a significant effect of genotype on DON ratio.
There are varietal differences in both groups for DON ratio but the trait is not correlated
with DON-std, DON-kernel, DON-bran and DON-flour (Tables 9, 10).
The partitioning ratio of DON in flour, calculated by weighing in the flour
recovery % was also not significantly different between red and white genotypes, and
could not be predicted by DON-std or DON-kernel (Fig. 7). The range of partitioning
ratio of DON in flour varied between 44.54 to 54.24 in red, and 50.65 to 60.61 in white
genotypes. White genotypes accumulated significantly higher (p<0.01) amounts of toxin
in whole grain, bran and flour fractions, and calculated total (Fig. 8).

32

DISCUSSION
1.

Choice of Inoculation Method
Resistance to FHB is multifaceted, best described as a combination of five
different types of resistance (Mesterhazy, 1995). Host resistance has been extensively
studied in wheat, using different inoculation methods and disease response measures in
laboratory, greenhouse and field. Various traits of the disease such as % incidence (type I
resistance), % severity (type II resistance), FHB index, FDK (type IV resistance) and
mycotoxin contamination in grain (type III, V resistance), and their interactions have
been studied extensively. A number of studies, including the uniform Fusarium nursery
trials conducted by the US Wheat and Barley Scab Initiative (Sneller et al., 2011)
measure these disease traits regularly and have reported a large effect of year and
environment (Lemmens et al., 2004). Environment is recognized as the major factor that
determines severity of natural and artificial infections (Parry et al., 1995).
Of all environmental factors, moisture/ humidity/ rainfall during and after
infection is critical in determining the level of FHB. In this study, two methods of
artificial inoculation in the field were explored. In GS, humidity is controlled by a
misting system similar to Snijders and Perkowski (1990). Bagging of inoculated heads
has been recommended by several researchers as an alternative method for studying FHB
resistance in the field by providing a more stable microclimate for each genotype studied
(Mesterhazy et al., 1999; Wang and Miller, 1988). A study of spray inoculation methods,
comparing humidity treatment by irrigation (misting) and bagging found that providing
optimal moisture conditions by bagging for a longer period of time resulted in more
disease as compared to the treatment without bags (Lemmens et al., 2004).

33

The SB method was studied in a total of four environments, three of which were
affected by heat stress. SB method in environment 1 (SB1), which is expected to be a
more typical representation of the attributes of this inoculation treatment, resulted in
significantly higher levels of visual disease as well as toxin compared to the GS method
in the same year (p<0.05), following the same trend as Lemmens et al. (2004). Effects of
inoculation method on any of the disease traits measured were not significant. However,
a significant effect (p<0.05) of genotype and genotype-method interaction was detected
for all response variables.
The methods GS, SB, and SB1 were compared for two groups of genotypes based
on grain color (Table 4). In both red and white genotypes, the three methods were
significantly correlated with one another for a measure of DON accumulation. The
methods were also correlated when comparing FDK scores in both red and white
genotypes (with the exception of GS and SB1 in red genotypes).
Within each method, DON accumulation was always found to strongly correlate
(p<0.05) with FDK scores, contradictory to some reports (Mesterhazy et al., 1999;
Perkowski et al., 2008). Severity was correlated with DON accumulation for both red and
white genotypes in SB and SB1 methods. Incidence was significantly correlated (p<0.05)
with DON in SB and SB1 method for white, and in only SB1 method for red genotypes.
Similar studies, comparing various disease traits and measurements, have reported
varying results, attributed mainly to the climate in particular years. Severity and DON
accumulation were found to be significantly correlated by Wang and Miller (1988),
Gilbert et al. (1993), which was not confirmed by Mesterhazy (1999). All disease traits
were correlated (p<0.05) in SB and SB1 methods for white genotypes, while in GS

34

method, significant correlations (p<0.05) were observed between most disease traits in
the case of red genotypes.
Disease symptoms such as incidence and severity, as well as DON accumulation,
are positively influenced by the period of moisture availability during and following
infection (Lacey et al., 1999; Cowger et al., 2009; Culler et al., 2007; Gautam and DillMacky, 2008). With the GS method, differences in flowering dates could have altered the
misting duration for genotypes. The SB method allows a greater control on the timing of
infection and humidity treatment in each plot (Prange et al., 2005). This can explain the
consistency of FHB incidence in SB and SB1 methods over GS. It is possible that the SB
method provides a better estimation of type I and type II resistance, as measured by
incidence and in the field, especially in the case of white genotypes (incidence and
severity significantly correlated with all other measures). The highest visible amount of
disease was observed with the SB1 method, with presence of shriveled as well as scabby
kernels in harvested samples. This could have played a role in biased FDK scores for red
genotype.
Average flowering dates were obtained for each genotype, over all six
environments and genotypes were classified into five groups of very early, early,
moderate, late, very late flowering times. White genotypes had an even distribution of
genotypes over the flowering groups, while red genotypes flowered relatively early, with
the majority of genotypes flowering in early and moderate groups. This could explain
some of the differences between methods when comparing red and white genotypes. SB
and SB1 methods were more consistent in the ranking of white lines for both visual
disease traits and DON accumulation. The flowering time of red lines, unfortunately,

35

coincided with the hot days observed in years 2010 and 2011. These differences in
flowering dates could explain the difference in GS and SB methods for the visual
measures of disease between red and white genotypes. The effect of only flowering date
on the two methods is vague, as it is also confounded with genetic resistance and
susceptibility to FHB. Lack of correlation between visible disease and toxin accumulation
could be explained by infection in premature heads in the early flowering genotypes
(Lemmens et al., 2004; Bai and Shaner, 1994). In premature wheat heads, FHB infection
can cause wilting above the point of infection, giving the appearance of higher visual
infection, but cause a reduction in DON accumulation (Bai and Shaner, 1994)
Interestingly, superior white lines (multiple low rankings across different responses and
methods), such as E6003 and E6055, had the earliest flowering dates in the group
suggesting escape as a mechanism of resistance. Red genotypes Truman, and OH-02-126,
ranking consistently in the low disease response group, were the last ones to flower in the
red group.
SB method, with heat stress, resulted in very low estimates of all visible traits of
FHB. However, significant amount of DON still accumulated in the grain showing that
high level DON accumulation could result in grain with lower visible symptoms of
disease. This observation is in agreement with results from Mesterhazy (1999), who
suggested different modes of resistance (type III or V) could be playing a role in toxin
accumulation.
Another aim of this study was to compare the red and white genotypes to each
other. Effect of grain color was not significant (α=0.05) on the visual disease traits such
as incidence, severity, index and FDK. Even though white and red genotypes were ranked

36

equivalent based on visual symptoms, white genotypes were found to accumulate
significantly higher quantities of DON than red consistently over the three inoculation
methods. Lastly, the genotypes were ranked into three categories of low, moderate and
high-levels for each disease trait measured based on 25th and 75th percentile (Tables 7, 8).
The best and poorest performing lines were found to be stable in their rankings across all
disease traits in both grain color groups, while the varieties that ranked as moderate were
found to be highly variable (Lewis, 2012). This behavior could be explained by presence
of only partial resistance types as opposed to presence or absence of multiple types of
resistance in the varieties ranked at extremes.
Bagging protocol in the field has been demonstrated by Prange et al.(2005) by
bagging plots immediately after liquid inoculation for a period of 12h. (Prange et al.,
2005). Mesterhazy (1999) used a similar bagging protocol after inoculating selected
heads within a plot at early morning hours (Mesterhazy et al., 1999). But in our
experience, SB method could be more useful and escape potential heat stress when
inoculation and bagging are carried out later in the day when the temperature starts
falling, and removed before noon the next day. This might ensure sufficient humidity
treatment and avoid very high temperatures that could damage wheat heads as well as
fungal spores. In summary, the SB method gave a fair estimate of DON accumulation and
FDK, comparable to those obtained from the more traditional GS inoculum and misting
method. There seems to be a lesser effect of environment as explained by the flowering
date (in case of white genotypes), as was expected. SB method could be advantageous for
testing in locations where misting systems are not available, and may be more consistent

37

in measuring type I and type II resistances, provided cool temperatures occur during the
flowering period.
2. DON Accumulation in Milled Fractions
Most commercial applications of wheat involve milling and processing steps, as
flour is the primary product of interest. Effects of cleaning, milling and other processing
steps such as baking and extrusion have been extensively studied (Kushiro, 2008).
Cleaning of contaminated wheat reduces DON levels down by removing chaff, husk,
screenings, dust and other debris (Abbas et al., 1985; Lancova et al., 2008; Young et al.,
1984). Plant tissue other than grain (e.g., husk, chaff and stems) can accumulate
considerable quantities of fungal biomass and high amounts of toxin, which are removed
during the process of cleaning (Young and Miller, 1985). Cleaning steps remove the
majority of infected seeds, which are of lower density, thus reducing DON levels. In this
experiment, samples were cleaned thoroughly to remove all unwanted plant tissue. Air
settings were controlled during harvest and cleaning to avoid the loss of low-weight
infected seeds and to obtain an accurate measure of toxin levels across genotypes. The
process of milling, by the removal of bran and shorts, reduces DON levels considerably.
Effect of milling on DON content in wheat has been widely studied, with multiple studies
reporting a reduction of DON contamination after milling, suggesting that a higher
concentration of DON accumulates in the bran (Abbas et al., 1985; Cheli et al., 2010;
Dexter et al., 1996; Edwards et al., 2011; Lancova et al., 2008). Kushiro (2008) published
a review of studies on the effects of cleaning, milling and cooking methods on mycotoxin
content in wheat.

38

Commercial milling produces multiple streams of flour including break flour,
reduction flour, bran and shorts. Study of industrially milled soft winter wheat in Canada
concluded that milling led to a fractionation of DON, with increased levels in the outer
kernel, with DON quantities decreasing in inner flour portions (Young et al., 1984).
Comparing DON quantities in various milling fractions, Thammawong et al (2011) found
that DON accumulation was highest in bran and shorts fractions at low, moderate and
high-level contaminated grains. A high amount of DON remained in the flour, in highlevel contaminated grains, even after milling (Thammawong et al., 2010). Reported DON
quantities can vary substantially due to environment in a particular year, wheat
characteristics, level of contamination, and mill type (Abbas et al., 1985; Edwards et al.,
2011). However, all studies consistently reported higher DON concentrations in the bran
(or shorts) fraction, consistent with our results (Abbas et al., 1985; Edwards et al., 2011;
Lancova et al., 2008; Trucksess et al., 1996).
The relationship between location of fungal biomass and DON levels remains
unclear. High levels of toxin in the bran are attributed to the presence of F. graminearum
in the outer layers of the kernel (pericarp and aleurone tissues) (Bechtel et al., 1985).
Young et al. (1984) reported a strong correlation between ergosterol (indicator of fungal
biomass) and DON levels in infected grain, and suggested that greater fungal infection
occurred at or near the surface of the kernel. In heavily infected kernels, hyphae were
found throughout the starchy endosperm, which may explain the higher DON-flour
obtained in such lines (Bechtel et al., 1985). Further investigation is needed to clearly
understand the relationship between fungal biomass localization on the grain and the
mechanism of DON accumulation and distribution.

39

Nishio et al. (2010) studied the DON level in 16 different lines, and found
positive linear correlations (p<0.01) between DON level in bran and in kernels (R2=0.94)
and DON level in flour to DON level in kernel (R2=0.93). Our findings are similar, with
correlation of DON-bran to DON-kernel, and DON-flour to DON-kernel. For both red
and white genotypes, DON-bran and DON-flour increased with an increase in DONkernel. However, DON-bran levels increased at a higher rate with respect to DON-kernel
2

2

(y = 1.4833x + 0.087, R = 0.98 in red, y = 1.2747x+0.2877, R = 0.94 in white)
2

2

compared to DON-flour (y = 0.7384x -0.053, R = 0.98 in red, y = 0.8551x-0.1215, R =
0.97 in white).
Correlation of DON partitioning ratio with the total DON content in kernel has
been reported (Nishio et al., 2010). Our results did not show a significant correlation
between DON partitioning ratio in flour to DON-kernel (Fig. 7). DON partitioning ratio
between bran and flour was not significantly affected by genotype (α=0.05) and was
found to be fairly constant within groups of red and white.
A significant effect of grain color was observed in DON-bran, DON-flour and
DON-kernel (p<0.01), with higher quantities of DON accumulating in the white
genotypes (Fig. 8). This finding agrees with results from Lewis et al. (2008) and Knott et
al. (2008) studying toxin accumulation and FHB infection between the two grain colors
groups (Knott et al., 2008; Lewis et al., 2008). Although quantities of DON in all
fractions were higher, average DON-ratio (DON-bran/DON-flour) of white genotypes
was significantly lower than red at α=0.05. It was anticipated that the DON quantity in
the bran of red wheat would be much lower, proportionally, since phenolic compounds
present in the outer layers of red wheat have an anti-fungal property (Boutigny et al.,
40

2008; McKeehen et al., 1999). However, DON can be translocated away from the actual
growth of the fungus. Since we did not quantity the fungus in these studies, we don’t
know if the accumulation of the DON in the bran of the red wheat is also associated with
a similar proportion of the fungus.
Individual genotypes showed a range of DON ratio (DON-bran/ DON-flour), with
overlapping values between the two grain color groups, ranging from 1.88 to 2.58 in red,
and 1.51 to 2.36 in white genotypes. Previously, a ratio of 2.3-2.4 between DON-bran
and DON-flour has been reported, from a study of two spring wheat cultivars (Tanaka,
1999).
Nishio et al. (2010) reported DON partitioning ratio in flour to be primarily
related to the level of DON-kernel. In our study, no significant correlation was found
between DON partitioning ratio and DON-kernel levels. The calculation of partitioning
ratio takes the % flour recovery into account, suggesting that the amount of total DON
retained in the flour, or removed in the form of bran, was not correlated with percent
flour recovery (range of 54.03% to 71.44%), consistent with the findings of Nishio et al.
(2010). Flour yield from a Quadrumat Jr mill was shown to be similar to that from an
experimental or large scale mill (Meredith, 1967). Hence percent flour yield and DON
partitioning ratio reported in this study are expected to be similar to those that would
have been obtained from commercial milling. It has been previously shown that flour
yield is not influenced by an increase in FHB damaged kernels in Canadian hard winter
wheat (Dexter 1996), suggesting that the possibility of altered percent flour recovery due
to presence of high-level infected (scabby) kernels, which in turn could have an effect on
DON partitioning ratio, is unlikely.

41

Breeding programs select for DON contamination traits based on quantities
estimated based on whole grain samples (DON-std). We found that DON-std can be used
as a selection tool for lower quantities of DON-kernel, DON-flour as well as DON-bran.
Comparing four different levels of FHB contaminated wheat, Thammawong (2011)
concluded that at higher contamination levels, higher quantities of DON remained in the
flour and were not removed by milling (Thammawong et al., 2010). Results from this
study indicate the presence of toxins in the flour even at low-level DON contamination,
in both red and white genotypes. This is consistent with the findings of (Lancova et al.,
2008), who showed that although substantial part of DON accumulates in the bran,
almost 40% of its original content is still left in the flour. The first break flour, obtained
from the first set of break rolls in a commercial mill, was found to be the most
contaminated (Lancova et al., 2008).
Even though wheat flour is the primary product of interest, bran from white wheat
is an attractive commodity due to its superior flavor and taste characteristics. In
Michigan, one of the major producers of white wheat in the great lakes region, wheat
bran is valuable for the local cereal industry. Bran is used as a source of dietary fiber, as a
component of high fiber breakfast cereals or mixed in with flour for bread recipes
(Edwards, 2011). This report rejects the concept that DON fractionation in bran is higher
in white genotypes. Although white genotypes accumulate significantly higher quantities
of DON, DON ratios are comparable for red and white genotypes. Estimates of average
DON ratio in white genotypes was found to be lower than in red genotypes. The white
lines with low-level DON-std, MSU lines E6003 and E6055 and Pioneer 25W43, also
had significantly lower levels of DON-bran. MSU lines E6003 and E6055 contained

42

DON levels in all fractions comparable to those in the best red genotypes Roane and
Truman. These white lines are recommended for use as parents for breeding programs
intending to improve FHB resistance in soft white lines.
Genetics of FHB resistance in white genotypes should be further investigated
using populations derived from the use of the resistant lines identified in the study. The
role of grain color related genes in attributing resistance to DON accumulation should be
studied using a red x white population. Correlation of toxin accumulation in genotype
with presence of fungal biomass in different kernel tissues can provide further insight
into how DON is produced and translocated within the kernel in infected red and white
wheat.

43

Table 2. List of 39 lines planted in the study and their pedigree information
Color
White

Red

Variety
Ambassador
Aubrey
Augusta
Caledonia
Coral
Crystal
Envoy
Jewel
MSU D6234 (D6234)
MSU D8006 (D8006)
MSU Line E3024 (E3024)
MSU Line E5011B (E5011B)
MSU Line E5017 (E5017)
MSU Line E5024
(E5024)
MSU Line E5028 (E5028)
MSU Line E5038 (E5038)
MSU Line E6003 (E6003)
MSU Line E6055 (E6055)
Pioneer 25W43 (25W43)
Arena
Bravo
Cooper
FT Wonder
Hopewell
Hyland Emmit (Emmit)
MCIA Oasis
OH02-12686
OH02-7217
OH04-213-39
OH04-264-58
Pioneer 25R47 (25R47)
Pioneer 25R62 (25R62)
Red Ruby
Roane
Tribute
Truman

Pedigree
Pioneer 2737W/MSU Line D1148
NA
Genesee/Redcoat/A2747//Yorkstar
Ross Selection /3/( NY5207aB-2B-34 ) Burt // Genesee / CI
12658 /4/ Genesee
MSU Line D3913/MSU Line D0331
Pioneer 2737W/MSU Line D1148
MSU Line DC076/Pioneer 2552
Pioneer 2552/Pioneer 2737W
X1291/C5107
Pioneer 2555/Lowell
(Caledonia, Geneva / Geneva)//NY85020-395
Caledonia / NY88024-117
D6206 / Pioneer Brand 2552
MSU D6234 / Pioneer Brand 25W33
D6234 / Pioneer Brand 25W33
VA96W-403WS / D6277
VA96W-403-WS / W14
VA96W-403-WS / W14
SC8021V2 (PI554142)/25W33//WBL0305A1
NASW84-345/Coker9835//OH419/OH389
Madison/Twain
Pio.2510/Jackson
KANRED/SEVIER//RIDIT/3/YOGO[1318]
Logan / Hart // 32270A / Rousalka /3/ TN1685 / IA22 // 6767
/ 216-6-3
Pio.2510/Marilee
IL85-3132-1/Irena//OH449/VA86-54-290
Foster/Hopewell//OH581/OH569
NA
Hopewell/IL94-6858
OH645/Hopewell
WBE2190B1(Frankenmuth/2555 sib//2551
sib)/WBA416H2(Houser/MO9545//W4034D/Augusta)//2552
WBN0686C/WBJ0249B1
Pioneer 2552/Pioneer 2737W
VA71-54-147(CI17449)/C68-15//IN65309C1-18-2-3-2
VA98W-593=VA92-51-39(IN71761A4-31-5-48//71-54147/MCN1813)/AL870365 (CK747*2/Amigo)
MO 11769/Madison

Note: NA= Not Available

44

Table 3. List of sites for artificial inoculation using Grain Spawn or spray and Bag methods over
three years of field study
Environment

Year

1
2009
2
3
2010
4
5
2011
6

Location

Inoculation
Method

MSU Agronomy Research Farm
East Lansing, MI
MSU Clarksville Research Center
Clarksville, MI
MSU Agronomy Research Farm
East Lansing, MI
“Ingham” test location
Webberville, MI
MSU Agronomy Research Farm
East Lansing, MI
MSU Wheat Research Farm
Mason, MI

Flowering Dates

GS

11-Jun to 25-Jun

SB

1-Jun to 6-Jun

SB

24-May to 1-Jun

SB

27-May to 3-Jun

GS

1-Jun to 8-Jun

SB

3-Jun to 8-Jun

Table 4. Correlation of different response measures within GS, SB and SB1 methods
Method=GS
DON-std
FDK
Index Incidence Severity
DON-std
0.70*
0.19
0.12
0.42
FDK
0.63*
0.10
0.09
0.33
Index
0.56*
0.79*
0.55*
0.49*
Incidence
0.34
0.39
0.57*
0.58*
Severity
0.53*
0.53*
0.77*
0.26
Method=SB
DON-std
FDK
Index Incidence Severity
DON-std
0.80*
0.74*
0.60*
0.81*
FDK
0.67*
0.67*
0.72*
0.83*
Index
0.85*
0.93*
0.49*
0.61*
Incidence
0.82*
0.46
0.60*
0.90*
Severity
0.48*
0.63*
0.80*
0.61*
Method=SB1
DON-std
FDK
Index Incidence Severity
DON-std
0.80*
0.76*
0.73*
0.67*
FDK
0.70*
0.48*
0.67*
0.59*
Index
0.69*
0.95*
0.62*
0.36
Incidence
0.54*
0.52*
0.09
0.72*
Severity
0.59*
0.38
0.99*
0.65*
Note: Correlation coefficients for red genotypes are listed below the diagonal and those for white
are above. * Significant with α<0.05

45

Table 5. Correlation between GS, SB and SB1 methods for the response variables
GS

SB

SB1

a.DON-std
GS
0.52*
0.64*
SB
0.67*
0.69*
SB1
0.58*
0.69*
b.FDK
GS
0.44*
0.61*
SB
0.63*
0.53*
SB1
0.39
0.47*
c.Index
GS
0.44*
0.15
SB
-0.04
0.42
SB1
0.39
0.46
d.Incidence
GS
0.21
0.13
SB
-0.07
0.60*
SB1
0.44
0.61*
e.Severity
GS
0.70*
0.54*
SB
0.46
0.54*
SB1
0.37
0.29
Note: Correlation coefficients for red genotypes are
listed below the diagonal and those for white are above.
*Significant with α<0.05

46

Table 6. Estimates of the response variables DON, FDK, Index, Incidence and Severity across the inoculation methods GS, SB and
SB1 for 18 red genotypes

GS
SB
SB1
Genotype
DON FDK Index Inc.
Sev. DON FDK Index Inc.
Sev. DON FDK Index Inc.
Sev.
25R47
2.52 16.73 6.80 34.82 13.73 1.36 6.53 5.86 29.84 14.60 8.56 23.09 23.69 93.93 26.37
25R62
1.48 20.49 8.77 43.77 12.98 1.41 4.18 2.41 21.18 8.59 8.94 5.08 19.94 96.83 20.39
Arena
2.92 18.56 13.67 38.48 24.74 3.36 7.99 5.19 33.42 10.40 12.74 33.26 68.32 101.14 71.52
Bravo
1.18 22.51 12.64 40.11 26.97 1.51 5.59 6.94 38.99 14.54 12.08 26.86 31.30 90.74 34.04
Cooper
2.78 25.57 11.78 45.34 19.66 1.65 8.15 4.67 29.10 13.68 11.01 30.51 27.56 88.95 30.99
Emmit
0.98 18.05 15.19 47.51 20.33 1.10 4.30 2.26 18.11 8.08 11.88 36.58 33.28 90.22 35.92
FT Wonder
1.33 10.27 11.28 44.98 19.20 0.90 3.61 1.36 13.53 7.99 9.93 43.53 12.68 68.58 18.81
Hopewell
2.35 21.48 16.74 43.17 22.85 1.75 6.59 2.87 21.58 11.10 15.62 24.03 34.72 94.22 38.41
MCIA Oasis 1.35 25.70 12.88 43.34 25.24 1.89 5.11 3.25 22.12 11.20 11.16 12.09 30.32 83.97 36.43
OH02-126
1.22 10.29 14.33 39.44 22.85 0.50 2.51 2.73 18.44 9.37 5.40 5.21 15.15 73.74 19.05
OH02-721
1.09 15.02 7.73 39.64 12.83 1.45 4.24 4.54 25.21 12.41 13.73 11.27 36.94 99.82 37.65
OH04-213
0.95 14.61 11.08 37.04 17.58 0.95 4.08 3.58 23.53 11.55 12.29 16.75 35.09 89.68 39.30
OH04-264
2.14 16.73 14.90 48.27 18.39 1.21 4.87 3.11 24.65 12.09 9.26 12.98 27.09 93.88 28.49
Red Amber
3.24 20.51 17.50 44.29 35.23 2.66 7.49 6.31 27.95 20.68 18.96 35.20 37.59 96.61 37.27
Red Ruby
3.54 26.59 19.85 52.73 24.68 1.73 5.03 3.61 20.89 14.36 16.28 40.91 41.75 97.86 43.77
Roane
0.74 5.94 8.87 46.06 11.00 0.65 3.85 4.81 32.14 12.72 6.97 9.58 27.81 97.06 31.01
Tribute
0.74 5.32 11.13 32.04 15.01 1.40 5.02 3.61 28.82 12.28 10.21 14.17 31.46 91.66 32.54
Truman
1.04 4.74 8.64 26.40 16.76 0.39 1.06 2.29 15.72 6.76 6.51 11.72 8.30 56.21 13.06
Mean
1.83 16.60 12.91 42.08 20.84 1.51 5.00 3.98 25.27 12.13 11.20 20.39 30.84 88.16 34.73
Std Error
0.22 1.65 0.94 1.66 1.64 0.17 0.44 0.33 1.42 0.74 0.83 2.65 2.94
3.35 3.00
Note: Inc. and Sev. stand for percent Incidence and Severity respectively. DON quantities are in ppm. FDK values are in percent.

47

Table 7. Estimates of the response variables ON, FDK, Index, Incidence and Severity across the inoculation methods GS, SB and SB1
for 21 white genotypes
GS
SB
SB1
Genotype
DON FDK Index Inc
Sev
DON FDK Index Inc
Sev
DON FDK Index Inc
Sev
25W43
1.43 8.31 9.84 38.62 16.55 1.14 4.79 5.60 29.76 14.17 14.22 25.16 28.32 93.43 31.60
Ambassador
3.91 22.03 26.28 48.30 46.31 4.30 6.92 9.40 33.96 23.87 31.01 50.51 52.61 89.08 58.92
Aubrey
2.03 9.01 12.25 38.62 23.72 2.17 3.83 3.43 28.94 10.24 14.74 21.97 40.75 87.63 46.58
Augusta
3.95 12.09 20.94 45.61 48.10 5.17 7.17 10.78 41.45 24.57 16.34 28.69 43.75 68.98 63.86
Caledonia
3.94 23.62 25.88 54.22 43.24 3.31 7.02 3.99 23.02 13.18 17.23 24.21 50.91 81.38 63.63
Coral
2.07 18.65 19.13 44.74 28.77 2.24 4.65 3.66 27.30 13.32 14.36 25.41 39.32 84.44 45.02
Crystal
4.66 21.10 16.24 47.49 28.01 2.46 4.09 3.85 21.98 11.36 16.63 26.41 31.31 76.63 37.33
D6234
2.43 12.28 21.65 54.87 32.34 2.43 6.28 5.34 30.52 15.60 11.28 7.61 17.89 72.99 24.02
D8006
3.98 8.93 19.56 44.90 29.54 2.61 3.19 4.65 25.40 15.06 21.73 23.79 55.94 98.70 56.80
E3024
2.67 24.21 14.58 46.24 38.02 2.70 4.41 4.00 21.18 17.38 13.38 34.25 33.03 73.03 38.86
E5011B
1.87 18.14 10.58 49.63 15.23 2.43 5.32 5.48 34.95 16.48 16.82 23.47 50.77 95.00 52.99
E5017
2.51 13.88 13.58 54.40 20.57 3.26 5.89 4.56 25.38 13.88 16.46 20.94 19.51 91.26 20.36
E5024
3.59 23.27 21.76 53.34 30.43 2.06 5.25 3.50 24.39 13.08 17.30 11.96 19.74 82.92 21.45
E5028
3.43 18.26 20.31 38.62 30.49 2.36 4.40 3.29 25.02 12.23 16.44 24.25 29.85 89.17 31.93
E5038
8.52 36.19 9.98 38.62 30.74 3.46 6.45 2.90 20.58 10.82 24.84 51.55 63.63 91.02 70.29
E6003
0.75 12.74 9.13 38.62 14.46 0.41 0.82 0.63 6.39 2.80 3.76 8.53 0.22 13.13 3.55
E6055
0.88 11.83 13.38 38.62 18.75 0.82 1.87 2.42 18.19 7.09 4.68 8.77 -0.69 25.09 1.77
Envoy
4.15 21.85 15.28 44.17 29.26 1.72 4.94 4.64 26.49 13.15 14.93 26.95 37.57 97.54 38.21
Frankenmuth 2.73 10.06 19.86 54.97 34.30 2.28 3.34 3.65 23.13 13.99 9.93 17.78 24.83 50.26 48.87
Jewel
5.20 15.63 15.92 50.08 22.14 1.85 4.39 3.45 27.65 11.31 14.97 16.96 26.11 91.51 29.27
Lowell
1.80 12.03 13.57 36.84 25.44 1.78 4.13 4.86 29.03 12.48 16.25 21.94 64.57 88.64 71.58
Mean
3.10 16.87 16.24 46.07 28.16 2.36 4.74 4.37 25.48 13.34 15.59 25.09 34.18 79.06 39.37
Std Error
0.39 1.51 1.13 1.16 2.11 0.25 0.35 0.50 1.63 1.07 1.28 2.64 4.08 4.78 4.47
Note: Inc. and Sev. stand for percent Incidence and Severity respectively. DON quantities are in ppm. FDK values are in percent.

48

Table 8. Overall ranking of 18 red genotypes by different response measures
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

DON
Truman
OH02-126
Roane
OH04-213
FT Wonder
OH02-721
Tribute
MCIAOasis
Emmit
25R62
25R47
Cooper
Hopewell
Bravo
OH04-264
Arena
Red Ruby
Red Amber

FDK
Emmit
Truman
OH02-126
OH04-213
Bravo
Tribute
Red Amber
Hopewell
25R62
Roane
Red Ruby
FT Wonder
25R47
OH04-264
OH02-721
Arena
MCIA Oasis
Cooper

Index
25R62
Truman
FT Wonder
Tribute
Roane
25R47
OH02-721
OH04-213
MCIA Oasis
OH02-126
Emmit
Cooper
Hopewell
Arena
OH04-264
Bravo
Red Ruby
Red Amber

49

Incidence
Truman
OH02-126
FT Wonder
Tribute
OH04-213
25R62
Hopewell
Emmit
MCIA Oasis
25R47
OH02-721
Red Ruby
Arena
OH04-264
Red Amber
Cooper
Roane
Bravo

Severity
25R62
Emmit
25R47
OH02-721
MCIA Oasis
OH02-126
Tribute
OH04-213
FT Wonder
Truman
Hopewell
Roane
OH04-264
Bravo
Red Ruby
Cooper
Red Amber
Arena

Table 9. Overall ranking of 21 white genotypes by different response measures
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

DON
E6003
E6055
25W43
Lowell
E5011B
Aubrey
Coral
D6234
Frankenmuth
E5024
E3024
Envoy
E5028
E5017
D8006
Jewel
Crystal
Caledonia
Ambassador
Augusta
E5038

FDK
E6003
E6055
Aubrey
25W43
Crystal
E5024
Lowell
E3024
E5011B
D8006
D6234
Coral
Jewel
E5017
Augusta
Frankenmuth
Caledonia
Envoy
Ambassador
E5028
E5038

Index
E6003
E5038
25W43
Aubrey
Lowell
E6055
Jewel
E5011B
E5017
E3024
Envoy
Crystal
Coral
D8006
E5028
Frankenmuth
E5024
D6234
Caledonia
Augusta
Ambassador

50

Incidence
E6003
E6055
E5038
Lowell
D8006
E3024
25W43
Jewel
Caledonia
Crystal
Coral
E5024
Ambassador
Aubrey
Envoy
E5028
E5017
Frankenmuth
Augusta
D6234
E5011B

Severity
E6003
E6055
25W43
Jewel
E5011B
Aubrey
E5017
Lowell
Crystal
Coral
E5038
Envoy
E5028
D8006
E5024
Frankenmuth
D6234
E3024
Caledonia
Ambassador
Augusta

Table 10. DON partitioning ratio in flour and DON accumulation levels in whole grain sample
(DON-std), bran (DON-bran), flour (DON-flour) and total (DON-kernel) of red genotypes
Variety Name DON-bran DON-flour
DON-kernel
Partitioning Ratio
DON-std
Roane
1.50
0.74
0.97
50.72
0.78
Truman
1.45
0.68
0.95
48.27
0.88
OH02-126
1.98
0.81
1.23
44.54
0.96
Tribute
2.04
0.90
1.22
50.05
1.13
FT Wonder
2.23
1.08
1.45
48.30
1.24
OH04-213
2.03
0.99
1.33
50.84
1.25
Emmit
2.45
1.14
1.56
48.57
1.27
OH02-721
2.42
1.20
1.61
51.82
1.56
Bravo
2.68
1.50
1.84
53.44
1.57
25R62
2.44
1.16
1.58
51.96
1.64
MCIA Oasis
3.18
1.59
2.07
50.90
1.72
OH04-264
3.39
1.77
2.30
52.31
1.95
25R47
2.99
1.64
2.01
52.96
1.98
Hopewell
3.79
1.96
2.55
50.09
2.25
Cooper
3.53
1.57
2.21
49.41
2.31
Red Ruby
4.46
2.67
3.24
54.24
2.77
Arena
5.18
2.36
3.26
47.90
3.05
Red Amber
5.17
2.40
3.26
49.74
3.37
Mean
2.94
1.45
1.92
50.34
1.76
Std. Error
0.26
0.14
0.18
0.55
0.18
LSD (95%)
0.96
0.56
0.64
4.2
0.62
Note: Varieties are arranged in ascending order of DON-std. DON quantities are in ppm,
Partitioning ratio is a percentage value.

51

Table 11. DON partitioning ratio in flour and DON accumulation levels in whole grain sample
(DON-std), bran (DON-bran), flour (DON-flour) and total (DON-kernel) of white genotypes
Variety Name
DON-bran DON-flour DON-kernel Partitioning Ratio
DON-std
E6003
1.27
0.69
0.85
56.41
0.67
E6055
1.86
1.32
1.47
60.36
0.99
25W43
2.56
1.90
2.26
54.39
1.30
Lowell
3.28
1.83
2.29
54.27
1.70
E5011B
4.19
2.24
2.89
53.48
1.91
Aubrey
3.11
1.80
2.24
55.47
2.05
Coral
3.51
1.65
2.22
50.65
2.19
D6234
3.36
1.74
2.24
54.62
2.34
Frankenmuth
3.66
2.09
2.67
52.73
2.49
E5024
3.36
1.91
2.37
56.84
2.65
E3024
4.81
2.36
3.13
52.04
2.76
Envoy
4.45
2.62
3.21
54.95
2.84
E5028
3.84
2.34
2.83
52.67
2.84
E5017
4.54
3.41
3.91
60.41
2.92
D8006
5.22
3.00
3.82
55.28
3.23
Jewel
4.12
2.67
3.15
56.94
3.39
Crystal
4.44
2.90
3.40
56.21
3.40
Caledonia
5.33
2.46
3.37
50.93
3.57
Ambassador
6.25
4.32
4.97
58.23
4.01
Augusta
6.98
3.89
5.07
50.89
4.58
E5038
6.65
4.48
4.99
60.61
5.86
Mean
4.13
2.46
3.02
55.16
2.75
Std. Error
0.32
0.21
0.24
0.66
0.26
LSD (95%)
1.42
0.90
0.96
3.8
1.08
Note: Varieties are arranged in ascending order of DON-std. DON quantities are in ppm,
Partitioning ratio is a percentage value

52

A

DON-kernel (ppm)

4.0

3.0

2.0
y = 0.9834x + 0.1936
R² = 0.9517

1.0

0.0
0.0

0.5

1.0

1.5
2.0
2.5
DON-std (ppm)

3.0

3.5

4.0

B

DON-kernel (ppm)

6.0
5.0
4.0

3.0
y = 0.8384x + 0.7136
R² = 0.8348

2.0
1.0
0.0
0.0

1.0

2.0

3.0
4.0
DON-std (ppm)

5.0

6.0

7.0

Fig. 3. Linear regression relationship between DON-kernel and DON-std in A. red and B. white
genotypes

53

DON level in milled fraction (ppm)

A

DON-flour

6.0

DON-bran
5.0
4.0

R² = 0.9804
3.0
2.0
R² = 0.9813

1.0
0.0
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

DON -kernel (ppm)

B
DON level in milled fraction (ppm)

8.0

DON-flour

7.0

DON-bran

6.0
5.0
R² = 0.9426

4.0
3.0

R² = 0.9701

2.0
1.0
0.0
0.0

1.0

2.0
3.0
DON- kernel (ppm)

4.0

5.0

6.0

Fig. 4. Linear regression relationship between DON levels in milled fractions (DON-bran and
DON-flour) with total DON levels (DON-kernel) in A. red and B. white genotypes

54

A
A

DON partitioning ratio

65.0
60.0

55.0
50.0
45.0
40.0
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

DON-kernel (ppm)

B
DON partitioning ratio

65.0
60.0
55.0
50.0
45.0
40.0
0.0

1.0

2.0

3.0

4.0

5.0

6.0

DON-kernel (ppm)
Fig. 5. Relationship between DON partitioning ratio and DON-kernel in A. red and B. white
genotypes

55

4.5

*

DON accumulation (ppm)

4.0

Red

White

3.5
*
3.0

*
*

2.5
2.0
1.5
1.0
0.5
0.0
DON-std

DON-kernel

DON-bran

DON-flour

Fig. 6. Comparison of DON-std, DON-kernel, DON-bran and DON-flour between red and white
genotypes.* shows statistically significant difference between the two groups (α=0.05).

56

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