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Variation In Soft Winter Wheat I. Characteristics
Measured By The Single Kernel Characterization
System II. Grain Functional Quality

presented by

Samuel P. Hazen

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VARIATION IN SOFT WINTER WHEAT: I. CHARACTERISTICS MEASURED
BY THE SINGLE KERNEL CHARACTERIZATION SYSTEM II. GRAIN
FUNCTIONAL QUALITY

By

Samuel P. Hazen

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Plant Breeding and Genetics - C88

1996

ABSTRACT

VARIATION IN SOFT WINTER WHEAT: I. CHARACTERISTICS MEASURED
BY THE SINGLE KERNEL CHARACTERIZATION SYSTEM II. GRAIN
FUNCTIONAL QUALITY
By

Samuel P. Hazen

Soft winter wheat (Tn'ticum aestivum) is used as an ingredient in a broad range
of end-use product, yet it is part of a single market class. The objective of this
research was to characterize the effects of cultivar, environment , and cultivar by
environment interaction on soft winter wheat grain functional quality. Eleven soft
winter wheat cultivars were grown in replicated trials in nineteen environments in
Michigan. Samples were analyzed for kernel hardness, width, and weight using
the Single Kernel Characterization System. A subset of these samples
comprised of ﬁve cultivars grown in nine environments were evaluated for flour
yield, protein content, cookie height, cookie diameter, mixograph peak time, and
mixograph peak height. The results suggest that a reasonable degree of
accuracy can be obtained from evaluating bulked replications of each genotype

from multiple locations per year.

To my family

ACKNOWLEDGMENTS

I would like to thank Dr. Rick Ward for his vital support and friendship and
for serving as my major professor. I would also like to thank Drs. Perry Ng and
Brian Diers for their friendship, guidance and for serving on my graduate
committee and Vince Rinaldi for assistance in wheat quality evaluation. I would
most like to thank my wife Amy who has made this manuscript possible by

providing love and friendship.

TABLE OF CONTENTS

LIST OF FIGURESV

CHAPTER1

GENERAL INTRODUCTION

1.1 INTRODUCTION

1.2 LITERATURE REVIEW
1.2.1 StatisticalAnalySIs
1.22ClualityAnaIysis....

1.3 REFERENCES

mmbw‘

A

CHAPTER2

VARIATION IN SOFT WINTER WHEAT CHARACTERISTICS MEASURED BY
THE SINGLE KERNEL CHARACTERIZATION SYSTEM

2.1 INTRODUCTION... . 23
2.2MATERIAL AND METHODS 25
2.4 DISCUSSION41

2.5 REFERENCES46

CHAPTERS

VARIATION IN GRAIN FUNCTIONAL QUALITY FOR SOFT WINTER WHEAT
3.1 INTRODUCTION... . 49
3.2MATERIAL AND METHODS 50
3.4 DISCUSSION69
3.5 REFERENCES73

APPENDIX A: DATA DERIVED FROM THE SINGLE KERNEL
CHARACTERIZATION SYSTEM75

APPENDIX B: AGRONOMIC AND QUALITY EVALUATION DATA...... .87

LIST OF TABLES

CHAPTER 2

Table 2.1. Name, grain color, origin, and year of release for the 11 cultivars
involved in this study ................................................................................... 26

Table 2.2. Mean values of kernel hardness index, kernel width, and kernel
weight of all cultivars in each environment measured by the Single Kernel
Characterization System .............................................................................. 30

Table 2.3. Mean square from ANOVA for kernel characteristics of 7 cultivars
grown in 19 replicated trials in 1991 -1 994 in Michigan ............................... 31

Table 2.4. Percentage of the total variance for each source of variation for
kernel characteristics of 7 cultivars grown in 19 replicated trials in 1991-
1994 in Michigan ......................................................................................... 32

Table 2.5. Mean values for kernel hardness index, kernel weight, and kernel
width of 11 cultivars across 19 environments measured by the Single Kernel
Characterization System .............................................................................. 33

Table 2.6. Adjusted means (x,') and mean rank (R,), mean absolute rank
difference (8,) and test statistic (2,) for kernel hardness and kernel weight

..................................................................................................................... 40
Table 2. 7. Correlations (n=209) among kernel hardness, width, and test weight
CHAPTER 3
Table 3.1. Name, grain color, origin, and year of release for the 5 cultivars

involved in this study ................................................................................... 50
Table 3.2. Environment mean values of quality traits for ﬁve cultivars .............. 51

Table 3.3. Mean squares from ANOVA for quality traits for 5 cultivars grown in 9
replicated trials in 1993 and 1994 in Michigan ............................................ 55

vi

Table 3.4. Percentage of the total variance for each source of variation for
quality characteristics of 7 cultivars grown in 19 replicated trials in 1993 and
1994 in Michigan ......................................................................................... 56

Table 3.5. Means values for quality traits of cultivars grown in 9 locations ....... 57

Table 3.6. The number of cultivars with signiﬁcant correlation coefﬁcients
(P<0.05) (above diagonal) and the range of r values (below diagonal) ...... 58

Table 3.7. Adjusted means (x?) and mean rank (n), mean absolute rank
difference (8,) and test statistic (2,) for flour yield, cookie diameter, and flour
protein content ............................................................................................. 67

Table 3.8. Adjusted means (x,') and mean rank (R,), mean absolute rank

difference (8,) and test statistic (2,) mixograph peak time and mixograph
peak height. ................................................................................................. 68

vii

-... ..- 1:!

LIST OF FIGURES

CHAPTER 2

Figure 2.1. Biplot of the principal components of the genotype by environment
effects on kernel hardness of 11 cultivars grown in 19 environments.
Cultivars are represented by a closed circle and three characters (AGS =
‘Augusta’, CRD = ‘Cardinal’, CLS = ‘Chelsea’, DNS = ‘Dynasty’, FKM =
‘Frankenmuth’, FDM = ‘Freedom’, HRS = ‘Harus’, HLS = ‘Hillsdale’, MND =
‘Mendon’, P2548 = Pioneer ® variety 2548, and TWN = ‘Twain’).

Environments are represented by an open circle and four or ﬁve characters.
The ﬁrst two describe growing year and the next two or three describe
county (HN = Huron, IM = Ingham, KO = Kalamazoo, LE1 = Lenawee1, LE2
= Lenawee2, ME = Monroe, SW = Saginaw, and SC = Sanilac). ................ 36

Figure 2.2. Biplot of the principal components of the genotype by environment
effects on kernel weight of 11 cultivars grown in 19 environments. Cultivars
are represented by a closed circle and three characters (AGS = ‘Augusta’,
CRD = ‘Cardinal’, CLS = ‘Chelsea’, DNS = ‘Dynasty', F KM = ‘Frankenmuth’,
FDM = ‘Freedom’, HRS = ‘Harus’, HLS = ‘Hillsdale’, MND = ‘Mendon’,
P2548 = Pioneer ® variety 2548, and TWN = ‘Twain’). Environments are
represented by an open circle and four or five characters. The ﬁrst two
describe growing year and the next two or three describe county (HN =
Huron, N = Ingham, KO = Kalamazoo, LE1 = Lenawee1, LE2 = Lenawee2,
ME = Monroe, SW = Saginaw, and SC = Sanilac ........................................ 37

Figure 2.3. Biplot of the principal components of the genotype by environment
effects on kernel width of 11 cultivars grown in 19 environments. Cultivars
are represented by a closed circle and three characters (AGS = ‘Augusta’,
CRD = ‘Cardinal’, CLS = ‘Chelsea’, DNS = ‘Dynasty’, F KM = ‘Frankenmuth’,
FDM = ‘Freedom’, HRS = ‘Harus’, HLS = ‘Hillsdale’, MND = ‘Mendon’,
P2548 = Pioneer ® variety 2548, and TWN = ‘Twain’). Environments are
represented by an open circle and four or ﬁve Characters. The ﬁrst two
describe growing year and the next two or three describe county (HN =
Huron, IM = Ingham, KO = Kalamazoo, LE1 = Lenawee1, LE2 = Lenawee2,
ME = Monroe, SW = Saginaw, and SC = Sanilac). ..................................... 38

viii

CHAPTER 3

Figure 3.1. Biplot of the principal components of the cultivar by environment

interaction effects on ﬂour yield. Cultivars are represented by a closed
circle and three characters (AGS = ‘Augusta’, CLS = ‘Chelsea’, F DM =
‘Freedom’, MND = ‘Mendon’, and TWN = ‘Twain‘). Environments are
represented by an open circle and four or ﬁve characters. The ﬁrst two
describe growing year and the next two or three describe county (HN =
Huron, lM = Ingham, LE1 = Lenawee1, LE2 = LenaweeZ, SW = Saginaw,
and SC = Sanilac). 57.3 and 29.1 percent of the variation is accounted for
in the x and y-axis, respectively ................................................................... 61

Figure 3.2. Biplot of the principal components of the cultivar by environment

interaction effects on wire-cut cookie diameter. Cultivars are represented
by a closed circle and three characters (AGS = ‘Augusta’, CLS = ‘Chelsea’,
FDM = ‘Freedom’, MND = ‘Mendon’, and TWN = ‘Twain’). Environments are
represented by an open Circle and four or ﬁve characters. The ﬁrst two
describe growing year and the next two or three describe county (HN =
Huron, IM = Ingham, LE1 = Lenawee1, LE2 = Lenawee2, SW = Saginaw,
and SC = Sanilac). 50.8 and 24.2 percent of the variation is accounted for
in the x and y-axis, respectively ................................................................... 62

Figure 3.3 . Biplot of the principal components of the cultivar by environment

interaction effects on ﬂour protein content. Cultivars are represented by a
closed circle and three characters (AGS = ‘Augusta’, CLS = ‘Chelsea’, FDM
= ‘Freedom’, MND = ‘Mendon’, and TWN = ‘Twain’). Environments are
represented by an open circle and four or ﬁve characters. The ﬁrst two
describe growing year and the next two or three describe county (HN =
Huron, IM = Ingham, LE1 = Lenawee1, LE2 = Lenawee2, SW = Saginaw,
and SC = Sanilac). 45.6 and 37.4 percent of the variation is accounted for
in the x and y-axis, respectively ................................................................... 63

Figure 3.4. Biplot of the principal components of the cultivar by environment

interaction effects on mixograph peak time. Cultivars are represented by a
Closed circle and three characters (AGS = ‘Augusta’, CLS = ‘Chelsea’, F DM
= ‘Freedom’, and TWN = ‘Twain’). Environments are represented by an
open circle and four or ﬁve characters. The ﬁrst two describe growing year
and the next two or three describe county (HN = Huron, IM = Ingham, LE1 =
Lenawee1, LE2 = Lenawee2, SW = Saginaw, and SC = Sanilac). 71.6 and
21.5 percent of the variation for cultivars is accounted for in the x and y-
axis, respectively. 69.0 and 3.5 percent of the variation for environments is
accounted for in the x and y-axis, respectively. ........................................... 64

Figure 3.5. Biplot of the principal components of the cultivar by environment
interaction effects on mixograph peak height. Cultivars are represented by
a closed circle and three characters (AGS = ‘Augusta’, CLS = ‘Chelsea’,
F DM = ‘Freedom’, and TWN = ‘Twain’). Environments are represented by
an open circle and four or ﬁve characters. The first two describe growing
year and the next two or three describe county (HN = Huron, IM = Ingham,
LE1 = Lenawee1, LE2 = Lenawee2, SW = Saginaw, and SC = Sanilac).
63.1 and 33.1 percent of the variation for cultivars is accounted for in the x
and y-axis, respectively. 63.1 and 30.7 percent of the variation for
environments is accounted for in the x and y—axis, respectively .................. 65

CHAPTER 1

General Introduction

1.1 INTRODUCTION

Cereals provide 68% of the world’s food supplies. Wheat accounts for
32% of that portion making it the most important food crop (Olson, 1994). Wheat
quality, i.e., it’s capacity to be milled and function as a food ingredient, is of
great signiﬁcance. Wheat must be of particular quality to meet industry
standards. Millers desire grain that will yield relatively high amounts of ﬂour
when milled. When wheat is used in food production it is often the primary
ingredient. Wheat flour with poor or variable physical and chemical properties
may not be suited for processing, baking, packaging, or meet consumer
standards.

To ensure the development and production of wheat cultivars with
positive functional quality attributes, the factors that affect wheat quality should
be researched. Factors that may inﬂuence quality are genotype of the wheat
line, the environment in which the wheat line is grown, and the genotype by
environment interaction (GEI). Often, environmental effects on phenotypes are
dependent on genotype. The differential between genotype performance across

environments is statistically known as GEI. Understanding the factors that

inﬂuence wheat quality is of great practical importance. Understanding GEI is
important for choosing breeding environments and establishing sampling
strategies. Knowbdge of the magnitudes Of error and GEI variances assists
breeders in determining the number of replications, locations, and years of
testing required to reach the level of precision necessary to measure differences
between genotypes (Rasmussen and Lambert, 1961 ). The optimum number of
replications, locations, and years of testing is partly dependent on the magnitude
of the variation attributed to error and the interactions. Many environments are
required to accurately predict the rank for a trait that is signiﬁcantly affected by a
crossover interaction, i.e., the ranking of genotypes change across
environments. On the other hand, if interaction is mild or not present, only one
or a few test sites are required.

Genotype by environment interaction involves polymorphisms among both
the lines evaluated and the circumstances used for evaluation. In the case of
line or genetic variation, the traits or underlying genetic systems involved in a
GEI may or may not be resolved as causative factors. Environmental variation
contributing to GEI also vary in their degree of repeatability in a given physical
location. This latter phenomena leads to a high degree of unpredictability of GEI
(Allard and Bradshaw, 1964). Analysis of GEI effects can lead to identiﬁcation of
sets of either lines or environments which exhibit similar patterns of responses.
That in turn can lead to identiﬁcation of the line and environmental factors
causing GEI. If important environmental effects are spatially repeatable then the

system becomes more predictable.

‘ 5' 1"
~.

1.2 LITERATURE REVIEW

1.2.1 Statistical Analysis

The complex and intricate nature of GEI can be revealed by many
different elaborate statistical methods. The method used to analyze GEI is
dependent on the objectives of the research. Several different types of
approaches are available to analyze the additive and interaction terms,
including: analysis of variance (ANOVA), multivariate analysis, additive main
effects and multiplicative Interaction model (AMMI), parametric stability, non-
parametric stability, and tests for crossover interaction.

Multivariate statistics allow the user to simultaneously analyze the
response of individuals for multiple variables. Methods such as principal
component analysis (PCA) and cluster analysis characterize the structure of the
experiment-wise GEI (Ghadehri et al., 1980). They reveal a genotype’s
response to‘environment as well as the patterns of environmental and genetic
contributions to GEI.

Analysis of variance determines whether potential sources of variation
have a signiﬁcant effects on a trait (Steel and Torrie, 1980). A potential source
of variation is GEI. The relative magnitude of each source’s contribution to total
variance can be determined by calculating the estimated components of
variance. The presence of GEI diminishes the reliability of the estimates of
genotype performance. The optimum number of replications, locations, and
years of testing depends on the magnitude of the variance components for the

error and interaction terms (Rasmusson and Lambert, 1961). The overall effects

of genotype by location interaction decreases by increasing the number
locations tested and the effects genotype by year interaction is decreased by
increasing the number of years tested. Experimental error is assumed to
decrease by testing a greater number of replications, locations, and years.
Analysis of variance methods are important for quantitative genetics purposes as
well. Estimates of genetic variance can be used to predict the genetic
improvements possible from selection (Lin and Sims, 1994). The additive main
effects and multiplicative interaction model combines the additive terms of
ANOVA and PCA of the GEI to construct a model to select genotypes (Gauch
and Zobel, 1995).

The deﬁnition of stability relies on the method of statistical analysis used
to measure it. Lin and Binns (1994) divide the statistical methods of stability
analysis into four distinct groups of methodology. The ﬁrst classiﬁes a genotype
as stable when the variation of the trait of interest over environments is small.
Coefﬁcient of variation and the regression method outlined by Finlay and
Wilkinson (1963) fall into this category. Romagosa and Fox (1993) describe the
Finlay and Wilkinson approach as the most widely used and abused statistical
method in plant breeding. This approach entails the regression of the individual
cultivar mans on the environmental index or the environmental mean. A slight
variation of this method calculates the environmental index that includes all
genotypes other than the one being regressed (Moll et al., 1978). Genotypes
with regression coefﬁcients that are approximately zero are considered stable.

Some problems with this methodology do exist. The environmental index is not

independent of the regressed variable. Also, the trait values are correlated with
the regression coefﬁcient, it has no real test of signiﬁcance, and it assumes that
the GEI can be explained by a simple linear model (Lin and Binns, 1994).

The second type of genotype stability is when the genotypic performance
is parallel to the mean of all other genotypes. These analyses include Plaisted
and Peterson’s (1959) mean variance component for pairwise GEI, Wricke’s
(1962) ecovalence, and Shukla’s (1972) stability variance. Ecovalence and
stability variance are advocated by Kang (1991), however, results are highly
dependent upon the cultivars used in comparison (Lin and Binns, 1994).

Eberhart and Russell’s (1966) second statistic comprises the third group.
This statistic declares stability when the deviation mean square from regression
is small. Linn and Binns’ (1988) method comprise the fourth group where a
genotype is considered stable when it’s variance in one location is small across
years. This type of analysis has the advantage of being virtually independent of
the other genotypes tested, but it is only possible in situations where multi-year
data is available (Linn and Binns, 1994).

Another type of statistic is used to measure crossover interaction or a
change in rank order. A crossover interaction occurs when the GEI is so severe
that a change in rank order is observed across environments. Baker (1988)
describes a method for a pair-wise comparison across independent
environments. Another stability statistic based on rank stability is Hahn’s
nonparametric stability statistics (Nasser and mm, 1987; Hahn and Nasser,

1989). Genotypes are assigned a rank according to their adjusted mean and the

mean of the absolute rank distribution is tested across environments based on a
7;: distribution. Two null hypothesis are tested: H.': all cultivars show similar
stability; and H.’: a speciﬁc cultivar is stable. It is important to differentiate
between changes in rank and changes in the magnitude of the differences of
genotype rank across environment.

1.2.2 Qualig Analysis

A great deal of research has been done to understand the physical and
chemical aspects of wheat quality. Micro and macro procedures have been
established to characterize and evaluate grain characteristics (AACC, 1983).
These tests are used to evaluate breeding lines, and to grade grain at the point
of purchase for milling and baking potential.

The ﬁrst step of wheat processing is milling. The endosperm of a wheat
kernel consists of starch molecules surrounding a protein matrix (Simmonds et
al., 1973). Softness is caused by the protein friablin, which has a negative effect
on the adhesion between starch granules and the protein matrix. (Greenwell and
Schoﬁeld, 1989). When milled, the kemel’s endosperm is separated from other
wheat kernel components and reduced in size to make ﬂour. Soft wheat kernels
must be soft in texture and yield relatively high amounts of ﬁrst break ﬂour. Soft
wheat has a discontinuous protein matrix with a weak adhesion to the starch
granules in the endosperm (Simmonds et al., 1973). The starch granules are
readily separated from the endosperm while sustaining little damage by the mill

rolls. Hard wheat has a continues protein matrix that bonds strongly to the

starch granules. Rather than separate from the protein, the starch granules
shatter when milled.

Interestingly, there is little consensus on the true nature of kernel
hardness and how it should be quantiﬁed. A number of methods have been
employed in order to measure kernel hardness (see Pomeranz and Williams,
1990 for review). Cobb in 1896 (sited in Pomeranz and Williams, 1990) was the
ﬁrst to report a method to quantify kernel hardness. He measured the amount of
force required to ‘bite’ through a kernel. Methods have been developed to
quantify hardness by measuring force or time to grind to a speciﬁc particle size,
pearling time, and the amount of force or time required to crush a kernel (Taylor
at al., 1939; Kosmolak, 1978; Miller et al., 1981; Martin et al., 1993). It was also
observed that there were differences in the physical nature of the crushed or
ground kernels (Cutler and Brinson, 1935). Softer kernels more readily yield
small flour particles. Tests have been developed which determined particle size
distributions (AACC, 1983; F inney and Andrew, 1986). “Hardness,” therefore,
may refer to a kemel’s resistance to crushing or to the kemel’s propensity to
yield flour in the early steps of grinding. Flour yield capabilities are often
estimated using one of various small scale laboratory mills.

Rheological attributes, the dough’s ability to resist ﬂow and deformation
when force is applied, can be measured using one of several techniques,
including the alveograph, amylograph, extensigraph, farinograph, and mixograph
(AACC, 1983). Water absorption, mixing time, and mixing tolerance (protein

strength) are estimated from these rheological tests. The mixograph measures

the time to reach dough development and overrnixing, and the dough’s ability to
resist mixing. When ﬂour is mixed with water, the surface of the flour particles
become hydrated and then the hydrated portion is whipped away exposing a dry
surface. Ultimately the flour becomes entirely hydrated at which point it is
considered optimally mixed dough. Continued mixing of developed dough
results in a wet and sticky ‘overmixed" dough that is no longer suitable for
baking. Overmixing breaks gluten disulﬁde bonds. Overmixing occurs when
activated double bond compounds react with the thiyl radicals. (Hoseney, 1994).
Optimal levels of protein strength vary with targeted end-use product properties.
Flour with good mixing quality will not become overrnixed rapidly.

Many of the end-use functionality tests involve judging the quality of an
actual end-use product. These include laboratory analysis of sugar-snap and
wire—cut formula cookies, sponge-dough, pound-loaf, self rising biscuits, saltine
crackers, high ratio cakes, angel cake, sweet yeast products, pie crust, and
bread loaf (AACC, 1983; Pizzinatto and Hoseney, 1980). The volume and
texture of the product are usually the characteristics of interest. These groups
are assumed to be direct measurements of functional end-use quality. However,
not all products, ingredient combinations, and processing techniques can be
tested. The diverse array of products utilizing wheat as a main ingredient are
largely represented by the lab procedures available. However, more simple and
rapid tests are key to making further advances in wheat breeding and food

technology.

Wheat storage proteins, known collectively as gluten, have received a
great deal of attention from both cereal chemists and wheat breeders. Grain and
flour percent protein content can be determined by measuring nitrogen content
or by near-infrared reﬂectance, which is calibrated to nitrogen content (AACC,
1983). Protein content and its individual components have been correlated with
many other quality attributes (Ng and Bushuk, 1988; Graybosch, 1993; Kaldy et
al., 1993; Souza et al., 1994; Hou, 1995).

Studies among different classes of wheat reveal a signiﬁcant linear
relationship among different quality traits. One study of hard red spring wheat
revealed a signiﬁcant (P<0.05) correlation for ﬂour yield vs. ﬂour protein (r = -
0.22) and ﬂour yield vs. loaf volume (r = 0.61) (Baker et al., 1971 ). No
signiﬁcant correlation was found for ﬂour yield or ﬂour protein vs. amylograph,
farinograph dough development time, extensigraph length, or extensigraph
resistance. In a similar study of hard red spring wheat ﬂour yield was negatively
correlated (P<0.05) with hardness (PSI) (r = -.55) while ﬂour protein was
positively correlated with baking score (r = 0.73) and baking volume (r = 0.77)
(Bhatt and Derera, 1975). No signiﬁcant correlation existed for ﬂour yield vs.
ﬂour protein, ﬂour yield vs. baking score or baking volume, ﬂour protein vs.
hardness (PSI), or hardness (PSI) vs. baking score or baking volume.

Graybosch et al. (1993) determined that for hard red winter wheat there is
a signiﬁcant (P<0.05) positive correlation for ﬂour protein vs. mixing time (r =
0.27), mixing tolerance (r = 0.37), and loaf volume (r = 0.82). No correlation was

found for ﬂour protein vs. ﬂour yield. Similar results were obtained in another

10

study of hard red winter wheat (Peterson et al., 1992). Flour protein was
signiﬁcantly (P<0.05) correlated with mixing time (r = -0.54) and mixing tolerance
(r = 0.29). Mixing time (r = -0.26) and mixing tolerance (r = -0.22) were both
correlated with kernel hardness. No signiﬁcant correlation was found for ﬂour
protein vs. kernel hardness or mixing time vs. mixing tolerance. Basset et al.
(1989) found signiﬁcant correlations (P<0.05) in their study of four soft white
winter wheat cultivars. The correlations were: ﬂour yield vs. percent protein (r =
-0.60 to -0.77), flour yield vs. hardness (NIR) (r = 0.72 to 0.81 ), ﬂour yield vs.
cookie diameter (r = 0.49 to 0.73), and cookie diameter vs. percent protein (r = -
0.42 to -0.66). Both signiﬁcant (P<0.05) and nonsigniﬁcant (P>0.05) correlations
were found among cultivars for hardness (NIR) vs. cookie diameter (r = 0.03 to
0.42) and hardness (NIR) vs. percent protein (r = -o.24 to -0.52).

Kernel morphology appears to have little relationship with ﬂour yield,
softness equivalence (SEQ), and protein content (Schuler et al., 1995).
Between kernel density, length, and width, and the quality traits (SEQ and
protein content), only kernel density was signiﬁcantly (P<0.05) correlated with
ﬂour protein (r = 0.48). Understanding the relationships among lab tests leads to
a greater understanding of individual measurement and the properties they
quantify. Efﬁciency of quality labs can be improved by eliminating redundant
analysis as well as realizing relationships between physical and chemical
properties and their ability to be milled, mixed, and baked.

In some instances, analysis of quality characteristics is conducted on

unreplicated grain samples that were obtained from different and diverse

11

environments. This type of analysis does not account for environmental effects
on wheat quality. In Gaines’ (1985) analysis of soft red and white winter wheat,
signiﬁcant (P<0.05) correlations were noted for: cake volume vs. hardness (PSI)
(r = -0.61), cake volume vs. protein content (r = -0.25); cookie diameter vs.
hardness (PSI) (r = -0.34), cookie diameter vs. protein content (r = -0.32). No
signiﬁcant correlations were noted for ﬂour yield vs. cake volume or vs. cookie
spread, or for cake volume vs. cookie spread. However, break ﬂour yield was
signiﬁcantly correlated with cake volume (r = 0.45), cookie diameter (r = 0.30),
protein content (r = -0.61), and hardness (PSI) (r = 0.73). In an analysis of each
of the US soft wheat classes, PSI was found to have a strong relationship with
sugar-snap cookie diameter (r = 0.78) (Yamamoto et al., 1996). Mixograph peak
height and farinograph peak time were negatively correlated with Japanese
sponge cake volume, r = -0.69 and -0.49 respectively. Sugar-snap cookie
diameter was positively correlated with mixograph peak time (r = 0.58) but
negatively correlated with mixograph peak height (r = -0.59). On another study
of soft white winter wheat, signiﬁcant (P<0.05) correlations were found for cookie
diameter vs. protein content (r = -0.69), but not for cake volume vs. protein
content or cake volume vs. cookie diameter (Kaldy et al., 1993).

Relatively few studies have been done concerning the effects of GEI
(Busch, et al. 1969; Bhatt and Derera, 1975; Baenziger et al., 1985; Peterson et
al., 1986; Basset et al., 1989; Lukow and McVetty, 1991; Cox and Shelton, 1992;
Peterson et al., 1992). Each wheat growing region with unique cultivars,

environmental conditions, and wheat processing industry warrants its own

12

analysis. All studies to data revealed a signiﬁcant genotype, environment, and
GEI effect on all quality traits measured. In instances of crossover interactions,
genotypes have different ranks across growing environments. It may be prudent
to divide a growing region into smaller regions where different cultivar
recommendations are made in the presence of a crossover interaction that is
predictable by geographic region (Homer and Frey, 1957). The magnitudes of
each of the interactions and error variance are of great importance in
determining the optimum number of replications, locations, and years of testing.
The importance of a GEI depends on its severity.

Bhatt and Derera (1975) studied hard spring wheat grown in Australia and
found genotype, environment, and GEI to have a highly signiﬁcant (P<0.01)
inﬂuence on ﬂour yield, ﬂour protein content, baking score, baking volume, and
hardness (PSI). The magnitude of genotypic variance associated with genotype
was larger than that associated with GEI. The effects of environment and year
were confounded in the results and therefore relative effects of these sources of
variance were unattainable.

Miller et al. (1984) and Pomeranz et al. (1985) found a signiﬁcant
inﬂuence of both environment and genotype on kernel hardness in an analysis of
soft and hard wheat grown in diverse climates. Hard wheat cultivars grown in
traditional salt wheat regions had harder kernels in every case than the soft
wheat cultivars grown in traditional hard wheat regions.

Baenziger et al.’s (1985) study of soft red winter wheat concluded that

genotype, environment, and GEI signiﬁcantly (P<0.05) inﬂuenced ﬂour yield,

13

whole grain protein content, and hardness (PSI). The interactions were non-
crossOver in nature, i.e., the ranking of cultivars did not change across
environrmnts. Estimated components of variance for genotype, environment,
and GEI were nearly equal for ﬂour yield. Environment accounted for more than
11 times the variation than genotype or GEI for whole grain protein. Hardness
(PSI) variance attributed to cultivar was 2.3 times greater than that of
environment and 4.6 times that of GEI.

Basset et al. (1989) found signiﬁcant effects of genotype, environment,
year, and all interactions for ﬂour yield, protein content, hardness (NIR), and
cookie diameter. Analysis of estimated components of variance revealed that for
ﬂour yield, year contributed 21.0% of the total variance that was second in
importance to year by environment with 34.0%. Year by environment interaction
accounted for 40.0% of the total variance for cookie diameter with cultivar by
year by environment interaction (20.0%) accounting for the remaining non-error
variation. Variance components for hardness were more evenly distributed
across sources than other traits: years (15.4%), environments (16.8%), year by
environment interaction (22.6%), replication (8.9%), cultivar by year interaction
(3.6%), and cultivar by year by environment interaction (16.5%).

In a study of mineral and protein concentrations of the 12" lntemational
Winter Wheat Performance Nursery in 1980, genotype and environment and
their interactions were signiﬁcant (P<0.01) (Peterson et al., 1986). For ﬂour
protein, the magnitude of variance component for environment was 3.2 times

that for genotype and 6.6 times that due to GEI.

14

Eight hard red spring wheat cultivars grown in Manitoba, Canada were
signiﬁcantly (P<0.01) inﬂuenced by the effects of genotype, environment, and
GEI for all quality traits: hardness (grinding time), ﬂour yield, protein content,
mixograph development time, farinograph dough development time, farinograph
mixing tolerance time, extensigraph extensibility, extensigraph resistance to
extension, and remix loaf volume (Lukow and McVetty, 1991). Total variance
attributed to cultivar ranged from 3.2 to 10.6 greater than GEI depending on the
trait being considered.

Hard red winter wheat grown in Nebraska and Arizona was signiﬁcantly
(P<0.01) inﬂuenced by genotype, environment and GEI for all quality traits
measured: protein content, mixing time, mixing tolerance, and kernel hardness
(microscopic observation of crushed kernels). Unlike other studies, components
of variance due to environment were greater than genotype for all traits
(Peterson, 1992).

Very little is known about the effects of farming management practices on
quality. Cox and Shelton (1992) examined the effects of conventional versus no-
till practices on various quality traits (test weight, protein content, ﬂour yield, loaf
volume, and mixogram score). Environment, cultivar, cultivar by environment
interaction, and tillage by environment interaction signiﬁcantly (P<0.01) affected
all quality traits. Neither tillage, or cultivar by tillage interaction was signiﬁcant
(P>0.05). However, when considering only those environments that experienced
winter kill, cultivar by tillage had a signiﬁcant effect on test weight and ﬂour yield

and a signiﬁcant effect (P<0.05) for protein content. When winter kill occurred it

15

was considerably higher in conventional-till situations rather than no-till. In only
one case, two genotypes had a signiﬁcant change in rank. Evaluation of
breeding material can be done effectively in conventional-till and no-till ﬁelds.

In an analysis of soft red winter wheat cultivars where grain samples
where heavily aspirated before evaluation, genotype, environment, and GEI
signiﬁcantly affected ﬂour yield, SEQ, and ﬂour protein content (Schuler et al.,
1995). Samples were heavily cleaned with the intention of removing
environrmmal effects from quality analysis. Evidently, quality factors are
affected by environment by other means than changes in kernel morphology.
This study also concluded that milling yield of soft winter wheat can not be
predicted by kernel morphology.

Genotype by environment interaction signiﬁcantly affected all quality traits
in every study to date. All interactions are believed to amount largely to a
change in the magnitude of the differences between cultivars and not a change
in their ranking. Therefore, it is not necessary to test breeding material in
multiple locations and years in order to accurately measure the differences
between breeding because of GEI. The appropriate number of replications,
locations, and years to test genotypes varies according to the size of the
interaction and error variance terms.

Millers and bakers are forced to make processing and ingredient changes
as wheat quality changes. Cultivars may differ for stability of quality
characteristics. Stability measured using regression analysis outlined by both

Eberhart and Russell (1966) and Moll et al. (1978) has shown that few cultivars

16

have either a high or low response to environments for ﬂour yield, protein
content, mixograph properties, and cookie spread (Busch et al., 1965; and
McGuire and McNeal, 1974; Peterson et al., 1992; Lukow and McVetty, 1991;
Basset et al. 1989; Baenziger et al., 1985). The regression coefﬁcient from this
approach is perceived by many as a measure of genotypic response to favorable
conditions rather than a stability measurement (Becker and Leon, 1988). A
cultivar may be adaptive to a wide range of environments while others may only
perform well under certain environmental conditions.

Busch et al. (1969) found that six hard red spring cultivars had signiﬁcant
differences in response to environment for ﬂour protein content, ﬂour yield,
mixogram score, and loaf volume. Ten hard red spring wheat cultivars were
tested in 25 Montana locations. Cultivars also had a signiﬁcant response to
differences in environments (McGuire and McNeal, 1974). These studies
suggest that comparison of genotypes with check lines across many locations
and years is necessary.

It is important that further analysis and characterization of wheat
genotypes are conducted using a suitable experimental design. This begins with
the origin of the grain samples. Grain samples should be collected from
replicated ﬁeld trials where variation can be partitioned between potential
sources. This cannot be compensated for by growing plants in a greenhouse or
sieving samples in an attempt to remove environment effects. Conclusions

made about the relationship between physical and chemical kernel constituents

17

and functional quality need to be rooted in genetic studies to facilitate further
progress in wheat breeding.

Ultimately, the results reviewed here suggest that further division of wheat
growing regions with more speciﬁc cultivar recommendation would not improve
wheat quality. The most efﬁcient and effective sampling strategies for evaluating

quality differences among genotypes is not clear.

18

1 .3 REFERENCES

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Baenziger, S.P., R.L. Clements, MS. McIntosh, W.Y. Yemazaki, T.M. Starling,
D.J. Sammons, and J.W. Johnson. 1985. Effects of cultivar,
environment, and their interaction and stability on milling and baking
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Baker, R.J. 1988. Tests for crossover genotype-environment interactions. Can.
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Baker, KH. Tipples, and AB. Campbell. 1971. Heritabilities of and correlations
among quality traits in wheat. Can. J. Plant Sci. 51:441-448.

Basset, L.M., R.E. Allan, and G.L. Rubenthaler. 1989. Genotype x environment
interactions on soft white winter wheat quality. Agron. J. 81:955-960.

Becker, HO, and J. Leon. 1988. Stability analysis in plant breeding. Plant
Breeding. 10121-23.

Bhatt, GM. and NF. Derera. 1975. Genotype by environment interactions for,
heritabilities of, and correlations among quality traits in wheat. Euphytica
24:597-604.

Busch, R.H., W.C. Shuey, and RC. Frohberg. 1969. Response of hard red
spring wheat (Triticum aestivum L.) to environments in relation to six
quality characteristics. Crop Sci. 92813-817.

Cox, D.J., and DR. Shelton. 1992. Genotype-by-tillage in hard red winter
wheat quality evaluation. Crop Sci. 84:627-630.

Cutler, G.H., and GA. Brinson. 1935. The granulation of whole wheat meal and
a method of expressing it numerically. Cereal Chem. 12:120—129.

Eberhart, SA, and WA Russell. 1966. Stability parameters for comparing
varieties. Crop Sci. 6:36-40.

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Finlay, KW., and GM. Wilkinson. 1963. The analysis of adaptation in a plant-
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F inney, PL, and LC. Andrews. 1986. Revised microtesting for soft wheat
quality evaluation. Cereal Chem. 63: 1 77-1 82.

Gaines, CS. 1985. Associations among soft wheat ﬂour particle size, protein
content, chlorine response, kernel hardness, milling quality, white layer
cake volume, and sugar-snap cookie spread. Cereal Chem 62:290-292.

Gauch, H.G., and R.W. Zobel. 1996. AMMI analysis of yield trials, p. 85-122. In
M.S. Kang and HG. Gauch (eds.) Genotype-by-environment interaction.
CRC Pres, Boca Raton, Florida.

Ghaderi, A, E.H. Everson, and CE. Cress. 1980. Classiﬁcation of
environments and genotypes in wheat. Crop Sci. 20:707-710.

Graybosch, R., C.J. Peterson, K.J. Moore, M. Steems, and DI. Grant. 1993.
Comparative effects of wheat ﬂour protein, lipid, and pentosan
composition in relation to baking and milling quality. Cereal Chem.
70:95-101.

Greenwell, P., and JD. Schoﬁeld. 1989. The chemical basis of grain hardness
and softness, p. 59-72. In H. Salovaara (ed.) Wheat end-use properties.
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Homer, T.W., and K Frey. 1957. Methods for determining natural areas for cat
varietal recommendations. Agron. J. 49:313-315.

Hoseney, RC, 1994. Principles of cereal science and technology. 2" edition.
American association of cereal chemists, St. Paul.

Hou, G. H. 1995. Isolation and quantiﬁcation of storage proteins in US soft
wheat ﬂours and their relationship to rheological and baking properties.
Ph.D. diss. Michigan State Univ., East Lansing.

Hahn, M., and R. Nasser. 1989. On tests of signiﬁcance for nonparametric
measures of phenotype stability. Biometrics 45:997-1000.

Kang, MS, and H.N. Pham. 1991. Simultaneous selection for high yielding and
stable crop genotypes. Crop Sci. 83:161-165.

Kaldy, M.S., G.R. Kereliuk, and GO. Kozub. 1993. Inﬂuence of gluten
components and ﬂour lipids on soft white wheat quality. Cereal Chem

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70:77-88.

Kosmolack, F .G. 1978. Grinding time - a screening test for kernel hardness In
wheat. Can. J. Plant Sci. 58:415.

Linn, CS, and MR. Binns. 1994. Concepts and methods for analyzing
regional trial data p. 273-298. In Jules Janick (ed) Plant Breeding
Reviews. Vol. 13. John Wiley and Sons, Inc. New York.

Linn, CS, and MR. Binns. 1988. A method of analyzing cultivar x location x
year experiments: a new stability parameter. Theor. Appl. Genet.
76:425-430.

Lukow, OM. and P.B.E. McVetty. 1991. Effect of cultivar and environment on
quality characteristics of spring wheat. Cereal Chem. 68(6):597-601.

Martin, C.R., R. Rousser, and DI. Barbec. 1993. Development of a single-
kernel wheat characterization system. Trans. ASAE. 36(5):1399-1404.

McGuire, CF, and F .H. McNeal. 1974. Quality response of 10 hard red spring
wheat cultivars to 25 environments. Crop Sci. 14:175-178.

Miller, 8.8., J.W. Hughes, 8. Afework, and Y. Pomeranz. 1981. A method to
determine hardness and work of grinding of wheat. J. Food Sci.
46:1851-1854.

Miller, 8.8., Y. Pomeranz, and S. Afework. 1984. Hardness(texture) of hard red
winter wt'leat grown in a soft wheat areas and of soft red winter wheat
grown in a hard wheat area. Cereal Chem. 61 :201-203.

Moll, R.H., C.C. Cockerrnan, C.W. Stuber, and WP. Williams. 1978. Selection
responses, genetic-environmental interactions, and heterosis with
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Nasser, R., and M. Hahn. 1987. Studies on estimation of phenotypic stability:
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Ng, P.KW., and W. Bushuk. 1988. Statistical relationship between high
molecular weight subunits of glutenin and breadmaking quality of
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Olson, B.T. 1994. World wheat production utilization and trade. P. 1-11., In W.
Bushuk and V.F. Rasper (eds) Wheat production, properties, and
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21

Peterson, C.J., VA Johnson, and PI Mattem. 1986. Inﬂuence of cultivar and
environment on mineral and protein concentration of wheat ﬂour, bran,
and grain. Cereal Chem. 632183-186.

Peterson, C.J., R.A. Graybosch, P.S. Baenziger, and AW. Grombacher. 1992.
Genotype and environment effects on quality characteristics of hard red
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Romagosa (eds) Plant Breeding: Principals and Prospects. Chapman
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Schuler, S.F., R.K Bacon, P.L. F inney, and EB. Gbur. 1995. Relationship of
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Simmonds, D.H., KK Barlow, and CW. Wrigley. 1973. The biochemical basis
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22

cookie quality with high molecular weight glutenin alleles in soft white
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properties and baking qualities of selected soft wheats grown in the
United States. Cereal Chem. 73:215221.

CHAPTER 2

Variation in Soft Winter Wheat Characteristics Measured by the Single

Kernel Characterization System

2.1 INTRODUCTION

Wheat (Triticum aestivum L.) milling and baking quality is a function of the
physio-chemical properties of the wheat kernel. One physical property of
particular importance is kernel hardness. Kernel hardness, i.e., the resistance of
a kernel to fracture, results from the bond strength between starch and protein
as well as the protein matrix continuity (Anjum and Walker, 1991; Simmonds et
al., 1973). Kernel hardness is one of the key grain attributes considered in grain
marketing in the US. Differences in kernel hardness affect ﬂour yield, damaged
starch content, water absorption, and dough mixing characteristics (Pomeranz
and Williams, 1990). Hard wheat ﬂour is predominantly used to make bread.
The products made from soft wheat are more diverse: cakes, cookies, crackers,
noodles, pastries, pretzels, etc. (Faridi et al. 1994).

Interestingly, there is little consensus on the true nature of kernel
hardness or how it should be quantiﬁed. A number of methods have been
employed in order to measure kernel hardness (see Pomeranz and Williams,

1990 for review). Cobb in 1896 (cited in Pomeranz and Williams, 1990) was the

23

24

ﬁrst to quantify kernel hardness by measuring the amount of force required to
‘bite’ through a kernel. It was later observed that there were differences in the
physical nature of the crushed or ground kernels (Cutler and Brinson, 1935).
Softer kernels more readily yield small ﬂour particles. Hardness tests were
developed which was based on determination of particle size distributions
(AACC, 1983; Finney and Andrews, 1986). “Hardness,” therefore, may refer to a
kemel’s resistance to crushing or to the kernels propensity to yield ﬂour in the
early steps of grinding. The Single Kernel Characterization System (SKCS)
(Martin et al., 1993), recently developed by the United States Grain Marketing
Research Laboratory and Perten Instruments North America, is targeted to
become part of the Federal Grain Inspection Service’s new wheat classiﬁcation
system (Small, 1995). The SKCS determines the average moisture, width,
weight, and hardness of samples comprised of 300 individual kernels. The
SKCS hardness index is a function of moisture, size, weight, and the force-
deforrnation curve derived from crushing individual kernels (Martin et al., 1993).
Soft wheat marketing may be affected by the use of the SKCS. Equally
important, variation in hardness within the soft wheat class may also inﬂuence
soft wheat product quality.

Plant breeders and agronomists identify three sources of variation in plant
characteristics: cultivar, environment, and cultivar by environment interaction
(CEI). Cultivar (i.e., genotype) is known to have a pronounced inﬂuence on
kernel hardness. Past research suggests that kernel hardness is simply

inherited primarily through the effects of one or two major genes with secondary

25

effects from additional minor genes (Symes, 1964; Sampson et al., 1983; Baker
and Sutherland, 1991, Geenwell and Schoﬁeld, 1989). A cultivar is generally
classiﬁed as having either soft or hard kernels, but a high level of variability
exists within these two classes and the distribution of cultivar hardness is in fact
continuous rather than discrete (Slaughter, 1989). Measurements of particle
size index, near infrared reﬂectance, grinding time, and microscopic analysis of
broken kernels have been used to show that cultivar, environment and their
interaction have a signiﬁcant (P<0.01) effect on kernel hardness and kernel
weight (Peterson et al., 1992; Lukow and McVetty, 1991; Basset et al. 1989;
Baenziger et al., 1985; Pomeranz et al. 1985, Bhatt and Derera, 1975).

The purpose of this research was to characterize the magnitude of
cultivar, environment, and CEI effects on SKCS measured kernel hardness of
soft winter wheat cultivars representative of those grown In Michigan. Data for
kernel width, kernel weight, and test weight were also analyzed.

2.2 MATERIALS AND METHODS

Grain samples were collected from multi-Iocation yield trials conducted in
Michigan by Michigan State University between 1991 and 1994. Cultivars tested
are shown in Table 2.1. Test locations changed with year and are identiﬁed by
county and year (Table 2.2). Fall nitrogen regime varied with local farming
practices. Spring nitrogen rates of 90 or 100 kg of actual Nlha were applied as

urea in late winter or early spring.

26

Table 2.1 - Name, grain color, origin, and year of release for the 11 cultivars
Involved In this study

 

 

Name Grain color Origin Year
Augusta white Michigan 1979
Cardinal red Ohio 1986
Chelsea white Michigan 1993
Dynasty red Ohio 1987
Frankenmuth white Michigan 1979
Freedom red Ohio 1991
Harus white Ontario 1985
Hillsdale red Michigan 1983
Mendon red Michigan 1994
Pioneer (9 red Pioneer Hi-Bred Int. Inc. 1988
variety 2548 Indiana

Twain red Agripro Seed inc. Indiana 1987

 

27

Seed was planted at rates of 4.94-5.19 million seeds/ha in 3.0-3.7 m long plots
with seven rows spaced 0.21 m apart. Each location was regarded a distinct
environment and differed in precipitation, elevation, thermal environment, and
soil type.

Single kernel hardness, weight, and width were determined with an SKCS
prototype kindly made available by the USDA-ARS Soft Wheat Quality Lab in
Wooster, OH. Samples were lightly aspirated to remove chaff and other debris.
Three hundred kernels were analyzed for each sample. The mean value of the
data for the three hundred kernels was used to represent a given sample.
Kernel weight is reported in grams, width is reported in millimeters, and
moisture, measured by conductance, is reported as percent water content.
Hardness index is expressed as a unitless score and is unique to the SKCS.
Higher hardness index values reﬂect increased hardness. Test weight was
measured according to AACC (1983) method 55-10.

Analysis of variance (ANOVA) was conducted using the Statistical
Analysis System’s PROC GLM procedure (SAS, 1988). Environments were
treated as random effects. Cultivars were treated as ﬁxed effects. Variance
components were calculated with PROC VARCOMP using replicated data of a
subset of seven cultivars (Cardinal, Chelsea, Frankenmuth, Freedom, Hillsdale,
Mendon, and Pioneer ® variety 2548) (SAS, 1988). In that analysis, cultivars
were treated as random effects. Environment mean data for these seven
replicated cultivars were combined with data for ﬁve additional cultivars

(Augusta, Dynasty, Harus, and Twain) where a single composite sample was

28

tested for each trait. That combined data set was subjected to ANOVA,
Duncan’s multiple range test, Pearson’s correlation, and principal component
analysis (PCA). The matrix of raw means (19 environments x 11 cultivars) was
converted to a CEI matrix by subtraction of the row, column and grand means
from each cell. Eigenvectors and eigenvalues were computed from a variance-
covariance similarity matrix derived from the CEI matrix. First and second
principal components were obtained from a projection of the CEI effects matrix
and the eigenvectors and eigenvalues using NTSYS-pc (Rohlf, 1992).

The stability of cultivars was examined using Lu’s (1995) SAS program for
estimating Hahn’s nonparametric stability statistics (Nasser and Hahn, 1987;
Hahn and Nasser, 1989). This analysis is based on rank stability. Cultivars are
ranked according to adjusted means calculated as,

X*i=XII'( }, - X7.)
where x", is the adjusted mean of cultivar I in environment j giving the cultivar
with the lowest mean value the ﬁrst rank. Stable cultivars are assumed to have
similar rank across environments. HUhn’s stability statistic is expressed by two
rank stability values: the mean of the absolute rank difference of a cultivar over
all environments and the common variance of rank (Nasser and Hahn, 1987).
For these data these two terms were highly correlated (r=0.99); therefore, only

one term, the mean of the absolute rank difference (Sr). is reported:

s.-=2 2 Z er-RrI/ININ-III.

f=1 J"=J'+1

29

where N is the number of environments, and R is the rank of cultivar I in
environment j. Cultivars with an S, that Is large indicates instability whereas a
small 8; indicates sound stability relative to other cultivars (Krenzer et al., 1992).
Z, is the test statistic for individual cultivar stability and has a 1’ distribution with
one degrees of freedom:

2, = (S, - E(S,)1’/var(s,),
where E(S,) is the expected mean of the absolute rank difference of cultivar I and
var(S,) is equal to the variance of S,. The mean of the absolute rank differences,
8,, is tested against the expected differences based on x2 distribution and
degrees of freedom equal to the number of cultivars tested. This value tests the
null hypothesis that cultivar I is stable, i.e. Z, = 0. The sum of the Z, values for
each cultivar has a x2 distribution with degrees of freedom equal to the number
of cultivars tested. This measure of stability tests the null hypothesis that
cultivars exhibit similar stability.

Composite samples were also analyzed annually by the USDA-ARS Soft
Wheat Quality Lab in Wooster, OH, where kernel hardness was measured as
softness equivalence (F inney et al., 1986). Softness equivalence is a measure
of break ﬂour yield which is a function of wheat kernel texture (Gaines et al.,
1996). Samples analyzed by the Soft Wheat Quality Lab were composite
sample of locations within each year. Locations used to make a given years
composite sample varied with year: 1991, Huron, Ingham, Kalamazoo, Lenawee,
and Monroe; 1992, Huron, Ingham, Lenawee, and Sanilac; 1993, Huron,

Saginaw, and Sanilac; 1994, Huron and Ingham.

30

Table 2.2 - Mean values of kernel hardness index, kernel width, and kernel
weight of all cultivars In each environment measured by the Single Kernel

 

 

 

Characterization System
Year-County Kernel hardness Year-County Kernel width Year-County Kernel weight
hardness index i mm mg
92-Seginaw 27.3 a 92-Lenawee 2.803 a 92-Lenawee 41.39 a
94-Huron 24.4 b 92-lngham 2.720 b 92-lngham 39.58 b
91-Lenawee 22.8 c 92-Huron 2.700bc 92-Huron 38.96 bc
92-Huron 22.7 c 92—Sanilac 2.649 cd 92-Sanilac 38.08 c
94-Senilac 22.6 c 92—Saginaw 2.597 de 92-Saginaw 37.53 c
91-Monroe 19.7 d 93-Lenawee2 2.561 ef 93-Sanilac 35.66 d
92-Lenawee 19.5 d 93-Sanilac 2.550 ef 93-Lenawee2 35.21 ed
93-lngham 18.1 e 91-Huron 2.519 of 91-Huron 34.58 def
93—Lenawee2 18.1 e 93—lngham 2.516 fgh 93-Lenawee1 33.97 efg
92-Sanllac 16.7 ef 91-Ingham 2.495 fghi 94-Sanllac 33.50 fgh
92-Ingham 16.6 f 91-Lenawee 2.494 fghi 91-lngham 33.44 fgh
93-Sanilac 16.6 f 93-Lenawee1 2.489 fghi 94-Huron 33.34 fghi
93-Lenawee1 15.7 f 91-Kalamazoo 2.449 ghij 91-Lenawee 33.31 fghi
91-Kalamazoo 15.6 f 94-Huron 2.448 ghij 93-lngham 32.70 ghij
91-Huron 15.4 f 91-Monroe 2.445 ghij 93-Saginaw 32.50 ghij
93-Saginaw 13.6 g 94-Sanilac 2.443 ghij 94-Lenawee 32.42 ghij
94-lngham 13.4 g 93-Saginaw 2.439 hij 94-lngham 32.20 hij
91-lngharn 13.0 g 94-lngham 2.434 ij 91-Kalamazoo 31.78 ij
94-Lenawee 9.3 h 94-Lenawee 2.409 j 91-Monroe 31.15j
Range 18.0 0.390 10.24
Mean 17.96 2.529 35.09

 

1' Values followed by the same letter are not signiﬁcantly different at P<0.05

based on Duncan's Multiple Range test.

31

2.3 RESULT§

Cultivar, environment, and CEI effects were signiﬁcant (P<0.01) for kernel

hardness, kernel width, and kernel weight (Table 2.3).

Table 2.3 - Mean square from ANOVA for kernel characteristics of 7
cultivars grown in 19 replicated trials In 1991-1994 in Michigan

 

Mean Squares of Kernel

 

Source of variation df Hardness Width Weight
hardness index mm mg
Environment 18 405.24“ 0.215” 164.28“
Replication (Environment) 38 4.68 0.005” 2.12
Cultivar 6 2912.44“ 0500" 440.25”
Cultivar x Environment 108 15.78” 0.015“ 769“
Error 223 5.42 0.003 1.48

 

** Signiﬁcant at the 0.01 probability level.

Percentages of the total variance of each source of variation were calculated
from the variance components based on 7 cultivars grown in replicated trials in
19 environments are summarized in Table 2.4. Cultivar had the largest variance
component for kernel hardness and weight. Kernel width was the only trait
where environmental variance was greater than cultivar variance. Cultivar by
environment interaction had a greater inﬂuence on kernel width and kernel

weight than on kernel hardness. Error variance was nearly equal to CEI

32

variance for kernel width and weight. Kernel hardness cultivar by environment
interaction variance was smaller than the error variance. The magnitude of the
variance among replications was negligible. There is a continuous and wide
range of kernel hardness among cultivars based on mean differences (Table
2.5). Cultivar means show no pattern of red wheat vs. white wheat cultivars or
those developed in Michigan vs. those that were not. Duncan’s multiple range
test separates the twelve cultivars into six distinct groups (Table 2.5). The
softest cultivar, Mendon, had a mean hardness index of 6.84. The hardest

cultivar, Pioneer ® variety 2548, had a hardness index of 27.06.

Table 2.4 - Percentage of the total variance for each source of variation for
kernel characteristics of 7 cultivars grown In 19 replicated trials In 1991-
1994 in Michigan

 

 

 

Source of variation Kernel hardness Kernel width Kernel weight
%

Environment 23.8 37.6 39.5

Replications 0.0 1 .3 0.5

Cultivar 65.0 34.3 41 .2

Cultivar by Environment 4.4 15.4 11.0

Error 6.9 1 1.4 7.8

 

Table 2.5 shows that the differences in kernel width among cultivars was
small. Kemal width ranged from 2.39 mm for Pioneer ® variety 2548 to 2.62 mm

for Mendon. Variation for kernel weight was large and continuous. Cultivar

33

means for kernel weight ranged from 30.60 mg for Pioneer ® variety 2548 to
39.58 mg for Mendon. Cultivar means for width and weight revealed no pattern
of white vs. red grain cultivars, or for those developed in Michigan vs. those that
were not.

Environments had a large inﬂuence on kernel hardness. Environment
hardness indexes (i.e., means of all cultivars at each environment) ranged from
9.3 for Lenawee 1994 to 27.3 for Saginaw 1993. Variation among environments
was continuous but small in magnitude for width and weight. Counties or
regions did not appear to have consistent effects on any trait.

Principal component analysis of the CEI effects are presented in ﬁgures
2.1-2.3. Cultivar and environment principal components are expressed as
vectors on a biplot and are to be considered separately in each graph. Vectors
have two properties: direction and length. Points of the same type (cultivars or
environments) that are close to each other have similar CEI values across the
other factor (cultivars 0r environments). Points with similar direction behaved
relatively alike whereas points with opposite direction behaved conversely.
Points whose vector directions are perpendicular behaved independently of one
another, i.e., there is no negative or positive relationship between the two. For

cultivars, 11 objects (cultivars) are measured for 19 variables (environments).

Table 2.5 - Mean values for kernel hardness index, kernel weight, and
kernel width of 1 1 cultivars across 19 environments measured by the
Single Kernel Characterization System

 

 

Cultivar Kernel hardness Kernel width Kernel weight
Hardness index 1' mm mg
Augusta 18.13 d 2.636 a 35.39 ode
Cardinal 13.03 e 2.590 ab 36.64 0
Chelsea 12.86 a 2.635 a 35.39 ode
Dynasty 13.61 a 2.489 c 32.28 g
Frankenmuth 20.04 c 2.532 be 34.23 ef
Freedom 21.71 c 2.416 d 33.38 fg
Harus 16.38 d 2.552 b 36.11 00
Hillsdale 23.67 b 2.552 b 35.73 bod
Mendon 6.84 f 2.621 a 39.58 a
Pioneer ® 2548 27.06 a 2.392 d 30.60 h
Twain 17.64 d 2.489 c 34.56 def
Range 20.21 0.240 8.98
Mean 17.96 2.529 35.09

 

1' Values followed by the same letter are not signiﬁcantly different at P<0.05
based on Duncan’s Multiple Range test.

35

The converse is true when discussing environments, i.e., 19 environments are
analyzed for 11 variables (cultivars). For instance, the CEI effects associated
with Twain’s kernel hardness are unlike those of any other cultivars (Figure 2.1).
No other cultivar has a vector with similar direction. In other words, the pattern
of CEI effects for Twain was unique among the cultivars tested.

Augusta, Chelsea, and Hillsdale, cluster for kernel hardness CEI (Figure
2.1). All 3 cultivars were developed in Michigan and are highly related by
pedigree. The other Michigan cultivar, Mendon, does not associate with these
cultivars. It’s CEI pattern contrasts that of the other Michigan cultivars. Harus is
highly related to the Michigan cultivars, but lies opposite of the cluster of
Augusta, Chelsea, and Hillsdale and is perpendicular to Mendon (Figure 2.1).
Red wheat cultivars have no consistent PCA pattern for kernel hardness CEI.
Hillsdale and Augusta cluster for kernel hardness CEI as well as weight and
width CEI (Figures 2.2 and 2.3). Cardinal associates with this group when
considering kernel width and kernel weight, but not for kernel hardness. Twain,
Mendon, Rams, and Freedom have similar direction and distance for kernel
weight (Figure 2.2). There is no consistent association of cultivars for all three
traits other than that of Hillsdale and Augusta. Even though some clusters of
cultivars exist, there is no evidence that a particular pattern can be associated

with known variables such as pedigree or grain color.

36

 

Second Principal Component
0

 

 

 

First Prlclpsl Component

 

 

Figure 2.1 - Biplot of the principal components of the genotype by environment
effects on kernel hardness of 11 cultivars grown in 19 environments. Cultivars are
represented by a closed circle and three characters (AGS = ‘Augusta', CRD I
‘Cardinal’, CLS I ‘Chelsea’, DNS - ‘Dynasty’, FKM = ‘Frankenmuth’, FDM -
‘Freedom', HRS - ‘Harus’, HLS = ‘Hillsdale’, MND - ‘Mendon’, P2548 . Pioneer is
variety 2548, and TWN - ‘Twain’). Environments are represented by an Open
circle and four or five characters. The ﬁrst two describe growing year and the
next two or three describe county (HN = Huron, IM 8 Ingham, KO - Kalamazoo,
LE1 - Lenawee1, LE2 - Lenawee2, ME = Monroe, SW = Saginaw, and SC 8

Sanilac).

37

 

Second Prlnobel Component
0

 

 

 

First PrIpraI Component

 

 

Figure 2.2 - Biplot of the principal components of the genotype by environment
effects on kernel weight of 11 cultivars grown In 19 environments. Cultivars are
represented by a closed circle and three characters (AGS I ‘Augusta’, CRD I
‘Cardinal', CLS I ‘Chelsea’, DNS I ‘Dynasty’, FKM I ‘Frankenmuth', FDM I
‘Freedom’, HRS I ‘Harus', HLS I ‘Hillsdale', MND I ‘Mendon’, P2548 I Pioneer 0
variety 2548, and TWN I ‘Twain’). Environments are represented by an open
circle and four or ﬁve characters. The ﬁrst two describe growing year and the
next two or three describe county (HN I Huron, IM I Ingham, KO I Kalamazoo,
LE1 I Lenawee1, LE2 I Lenawee2, ME I Monroe, SW =- Saginaw, and SC I

Sanilac).

38

 

 

 

_b
2

_b
8

alsLLlalilalala+alnlalsja

0.10 cos 0% -0.04 -0.02 0.00 0.02 0.04 0.08 0.08 0.10 0.12
dePrIc'paICornponert

 

 

 

 

 

Figure 2.3 - Biplot of the principal components of the genotype by environment
effects on kernel width of 11 cultivars grown in 19 environments. Cultivars are
represented by a closed circle and three characters (AGS I ‘Augusta’, CRD I
‘Cardinal’, CLS I ‘Chelsea’, DNS I ‘Dynasty’, FKM I ‘Frankenmuth’, FDM I
‘Freedom’, HRS I ‘Harus’, HLS I ‘I-IIIlsdale’, MND I ‘Mendon’, P2548 I Pioneer 0
variety 2548, and TWN I ‘Twaln’). Environments are represented by an open
circle and four or ﬁve characters. The ﬁrst two describe growing year and the
next two or three describe county (HN I Huron, IM I Ingham, KO I Kalamazoo,
LE1 I Lenawee1, LE2 I Lenawee2, ME I Monroe, SW I Saginaw, and SC I

Sanilac).

39

Cultivar by environment interaction patterns that exist among
environments are not afﬁliated with deﬁnable variables such as year or
geographical proximity (Figures 2.1 - 2.3). Counties within the same year
occasionally clustered such as the Ingham county locations in 1991 and 1992
(Figure 2.1), but other county locations in 1991 or Ingham county 1993 and 1994
locations did not cluster with them. This type of inconsistency implies a lack of
predictability of CEI effect on these traits.

Hahn’s nonparametric stability statistics were used to estimate the relative
rank stability of cultivars (Table 2.6). The sums of Z, for kernel hardness, width,
and weight were each less than the critical value of 19.68. Cultivars therefore
did not exhibit differences in rank stability. Inspection of the individual 2, statistic
reveals that all cultivars scored less than the critical value of 8.05. No cultivar

was signiﬁcantly more or less stable than the others.

40

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41

Correlation analysis showed little relationship of kernel hardness versus
kernel weight, width, or test weight (Table 2.7). Kernel weight and kernel width
were signiﬁcantly correlated. Test weight exhibited a highly signiﬁcant
correlation with both kernel size and kernel weight. No signiﬁcant correlation at
the 5% level occurred between softness equivalence and kernel hardness

index (data not shown).

Table 2. 7 - Correlations (n=209) among kernel hardness, width, and test
weight

 

Kernel width Kernel weight Kernel moisture Test weight

 

Hardness index -0.10 —0.14** 0.18” 0.03
Kernel width 0.89“" 029*” 0.44“"
Kernel weight 0.21“ 056*“

 

*, **, ..., signiﬁcant at 0.05, 0.01, 0.001 respectively.

2.4 DISC 8 ION

Cultivar by environment interaction is important for sampling purposes
and designation of breeding environments. Knowledge of the magnitude and
cause of CEI assists breeders in determining the number of locations and
years of testing required to reach the level of precision necessary to accurately
measure differences between cultivars. Many environments are required to

accurately predict the value of a breeding lines for a trait that exhibits a

42

signiﬁcant CEI. On the other hand, if interaction is very mild or not present,
only one or a few test sites are required to rank lines. In conjunction with the
importance of variation caused by interactions, the magnitude of the error
variance must be considered when determining experimental design. Results
reported here are in agreement with other studies and suggest that samples
from a single location or a composite sample from multiple locations are
sufﬁcient to predict relative cultivar performance for kernel hardness (Peterson
et al., 1992; Lukow and McVetty, 1991; Basset et al. 1989; Baenziger et al.,
1985; Pomeranz et al. 1985, Bhatt and Derera, 1975). This does not mean that
the same degree of kernel hardness can be achieved by {a cultivar regardless
of environment, but rather that a cultivar’s kernel hardness relative to other
cultivars should be relatively constant. A check cultivar which has been
analyzed over many seasons should be used as a benchmark.

Allard and Bradshaw (1964) discuss genotype by environment
interaction effects as being either predictable or unpredictable. Here, principal
component analysis showed environmental factors that elicit a differential
response across genotypes are not associated with year or geographical
proximity. This lack of association shows that their effects are unpredictable
when considering geography or year. It would not be prudent to divide
Michigan into micro growing regions due to a lack of consistent CEI for
cultivars in any one region. Kernel weight and width have been shown to have

no relationship to ﬂour yield, ﬂour protein, alkaline water retention capacity, or

43

softness equivalence (Schuler et al., 1995). Therefore, these traits appear to
be of little importance in cultivar development.

There were no differences in rank stability among cultivars across
environments for either kernel width, weight, or hardness. Stability measured
using regression analysis outlined by both Eberhart and Russell (1966) and
Moll at al. (1978) have shown few cultivars have either a high or low
responsive to environments for kernel hardness (Peterson et al., 1992; Lukow
and McVetty, 1991; Basset et al. 1989; Baenziger et al., 1985). The regression
coefﬁcient from this approach is perceived by many as a measure of genotypic
response to favorable conditions rather than a stability measurement (Becker
and Leon, 1988). Given that there were no differences in overall rank stability
and no cultivar was unstable, CEI does not diminish a breeders ability to
accurately characterize genetic differences among cultivars. The appropriate
number of replications, locations, and years of testing is determined by the size
of the variance components of the interactions and the error variance
components.

Our results imply that relative differences between cultivars for SKCS
values can be obtained by testing in only a few environments. Absolute
relative values can be predicted by comparing cultivars against a well tested
check cultivar. Ultimately it is important to test cultivars across at least a few
locations in a few years to be assured of the absolute SKCS attributes.

The use of the SKCS should not affect marketing of wheat grown in

Michigan since all samples were scored as soft. Wheat is treated as a

44

commodity that is classiﬁed by bran color (red vs. white), growth habit (spring
vs. winter), and kernel hardness (hard vs. soft). The continuous and broad
range of hardness for the 11 cultivars suggests that a single classiﬁcation of
these cultivars as soft oversimpliﬁes the true situation. Due to the signiﬁcant
effect of cultivar and lack of signiﬁcant change in ranking of cultivars across
environments for kernel hardness, knowledge of cultivar rather than growing
region may provide a great deal of insight to grain purchasers about kernel
hardness. However, environmental effects will be large and unpredictable.

Kernel hardness is used as a limited tool to predict end-use quality.
Hardness is often used to judge whether a cultivar is suitable as an ingredient
for a particular class of baked goods. A more detailed understanding of the
effects of kernel hardness on quality of a variety of end-use products may show
that different levels of kernel hardness prove more or less appropriate for
speciﬁc end-use products. Protein level is a trait that is useful in this manner.
For example, a low protein hard wheat ﬂour may be preferred as an ingredient
to make tortillas as opposed to high protein hard wheat ﬂour as an ingredient to
make bagels. It is possible that hardness as measured by the SKCS may be of
similar predictive value as a value of speciﬁc end-use.

It has been shown by Gaines et al. (1996) that raw values that are used
to compute hardness index can also be used to predict softness equivalence.
It was also shown by Gaines et al. (1996) that moisture content may inﬂuence
SKCS measure of kernel hardness. The samples analyzed in our study had a

narrow range of moisture with a mean of 13.9% with a standard deviation of

45

0.8. The correlation coefﬁcient for percent moisture content vs. hardness index
was signiﬁcant, but small (r = 0.18). Therefore, it appears that kernel moisture
had a negligible effect on kernel hardness in this study.

Millers and bakers should expect signiﬁcant variation of kernel hardness
due to both cultivar and environment effects. Neither growing region within
Michigan nor bran color will provide insight into the degree of kernel hardness
grain may have. Relationships of milling and baking performance with kernel
width, weight, and hardness, may be revealed by further research evaluating
the same samples for flour yield, ﬂour protein content, mixing properties, and

baking quality.

2.5 REFERENCE§

American Association of Cereal Chemists. 1983. Approved methods of the
AACC. 8" Ed. AACC, St. Paul, MN.

Allard, R.W., and AD. Bradshaw. 1964. Implications of genotype-
environmental interactions in applied plant breeding. Crop Sci. 4:503-
508.

Anjum, F .M., and CE. Walker. 1991. Review on the signiﬁcance of starch and
protein to wheat kernel hardness. J. Sci. Food Agric. 56: 1 -1 3.

Baenziger, S.P., R.L. Clements, M.S. McIntosh, W.Y. Yamazaki, T.M. Starling,
D.J. Sammons, and J.W. Johnson. 1985. Effects of cultivar,
environment, and their interaction and stability on milling and baking
quality of soft red winter wheat. Crop Sci. 25:5-8.

Baker, R.J., and KA. Sutherland. 1991. Inheritance of kernel hardness in ﬁve
spring wheat crosses. Can. J. Plant Sci. 71:179-181.

Basset, L.M., R.E. Allan, and G.L. Rubenthaler. 1989. Genotype x environment
interactions on soft white winter wheat quality. Agron. J. 81:955-960.

Bhatt, GM. and NF. Derera. 1975. Genotype by environment interactions for,
heritabilities of, and correlations among quality traits in wheat. Euphytica
24:597-604.

Becker, H.C., and J. Leon. 1988. Stability analysis in plant breeding. Plant
Breeding. 10121-23.

Cutler, G.H., and GA. Brinson. 1935. The granulation of whole wheat meal and
method of expressing it numerically. Cereal Chem. 122120-129.

Eberhart, SA, and WA Russell. 1966. Stability parameters for comparing
varieties. Crop Sci. 6:36-40.

Faridi, H., C. Gaines, and P. Finney. 1994. Soft wheat quality on production of
cookies and crackers. In W. Bushuk and V.F. Rasper. Balckie Academic
and Professional, Glasgow (eds), pp. 1-11. Wheat production,

properties, and quality.

Finney, P.L., and LC. Andrews. 1986. Revised microtesting for soft wheat
quality evaluation. Cereal Chem. 632177-182.

47

Gaines, C.S., P.F. Finney, L.M. Fleege, and LC. Andrews. 1996. Predicting a
hardness measurement using the single-kernel characterization system.
Cereal Chem. 73:278-283.

Greenwell, P., and JD. Schoﬁeld. 1989. The chemical basis of grain hardness
and softness, p. 59-72. In H. Salovaara (ed.) Wheat end-use properties.
Proc ICC ’89 Symposium. University of Helsinki.

Hiihn, M., and R. Nasser. 1989. On tests of signiﬁcance for nonparametric
measures of phenotype stability. Biometrics 452997-1000.

Krenzer, Jr., E.G., J.D. Thompson, and BF. Carver. 1992. Partitioning of
genotype x environment interactions of winter wheat forage yield. Crop
Sci. 32:1143-1147.

Lin, C.S., MR. Binns, and LP. Lefkovitch. 1986. Stability analysis: where do
we stand? Crop Sci. 26:894-900.

Lu, H.Y. 1995. PC-SAS program for estimating Hahn’s nonparametric stability
statistics. Agron. J. 87:888-891.

Lukow, OM. and P.B.E. McVetty. 1991. Effect of cultivar and environment on
quality characteristics of spring wheat. Cereal Chem. 68(6):597-601.

Martin, C.R., R. Rousser, and D.L. Barbec. 1993. Development of a single-
kemel wheat characterization system. Trans. ASAE 36(5):1399-1404.

Moll, R.H., C.C. Cockarman, C.W. Stuber, and WP. Williams. 1978. Selection
responses, genetic-environmental interactions, and heterosis with
recurrent selection for yield in maize. Crop Sci. 18:641-645.

Nasser, R., and M. Hahn. 1987. Studies on estimation of phenotypic stability:
tests of signiﬁcance for nonparametric measures of phenotype stability.
Biometrics 43245-53.

Peterson, C.J., R.A. Graybosch, P.S. Baenziger, and AW. Grombacher. 1992.
Genotype and environment effects on quality characteristics of hard red
winter wheat. Crop Sci. 32:98-103.

Pomeranz, Y., C.J. Peterson, and P.J. Mattem. 1985. Hardness of winter
wheats grown under widely different climatic conditions. Cereal Chem.
2(6):463-467.

Pomeranz, and PC. Williams. 1990. Wheat hardness: its genetic, structural,

48

and biochemical background, measurement, and signiﬁcance. Y.
Pomeranz (ed.) pp. 471-548 In Advances in Cereal Science and
Technology. American Association of Cereal Chemists: St. Paul,
Minnesota.

Rohlf, F .J. 1992. NTSYS-pc: numerical taxonomy and multivariate analysis
system. Version 1.70. Exeter software. Setauket, New York.

Sampson, D.R., D.W. Flynn, and P. Jui. 1983. Genetic studies on kernel
hardness in wheat using grinding time and near infrared reﬂectance
spectroscopy. 1983. Can. J. Plant Sci. 63:825-832.

SAS Institute. 1988. SAS user’s guide. Statistics. SAS Inst, Cary, NC.

Schuler, S.F., RK Bacon, P.L. Finney, E.E. Gbur. 1995 Relationship of test
weight and kernel properties to milling and baking quality in soft red
winter wheat. 1995. Crop Sci. 35:949-953.

Simmonds, D.H., KK Barlow, and CW. Wrigley. 1973. The biochemical basis
of grain hardness in wheat. Cereal Chem. 50:553-563.

Slaughter, DC. 1989. Guide to wheat hardness. USDA-ARS, ARS-79. Natl.
Tech. Inform. Serv., Sringﬁeld, VA.

Smail, V.W. 1995. Improving grain quality through rapid prediction systems.
Cereal Foods World. 40:5-6.

Symes, KJ. 1964. The inheritance of grain hardness in wheat as measured by
the particle size index. Aust. J. Agric. Res. 16:113-123.

CHAPTER 3

Variation in Grain Functional Quality for Soft Winter Wheat

3.1 INTRODUCTION

Wheat (Triticum aestivum L.) grain quality is determined by the chemical
and physical constituents of the kernels themselves. Those chemical
constituents are assembled following anthesis in a manner dependent on the
genetic make up of the cultivar and the growing environment. Past research
has indicated that genotype, environment and genotype by environment
interaction all inﬂuence the milling and baking quality of all classes of wheat
(Baenziger et al., 1985; Basset et al., 1989; Bhatt et al., 1975; Lukow and
McVetty, 1989; Peterson et al., 1992).

Cultivars are classiﬁed in the US based on kernel hardness (soft or
hard), bran color (red or white), and growth habit (spring or winter). Each grain
class is expected to function as an ingredient in a broad range of class-speciﬁc
end-use products. The products made from soft wheat are numerous and
include cakes, cookies, crackers, noodles, pastries, and pie crusts (Faridi et
al., 1994). Some of the quality criteria used in cultivar development are milling

yield, protein content, kernel hardness, rheological properties, and volume and

49

50

texture of actual end-use products. There is a great deal of variability within
and among market classes for functional quality. It is important to recognize
which traits for each market class are desirable, and the most efﬁcient and
effective selection and sampling strategy to facilitate genetic improvements. In
order to establish sampling strategies, it is important to realize the causes of
variation among and within samples.

The purpose of this research was to quantify the variation in soft wheat
quality of cultivars representative of those grown in Michigan. Speciﬁcally, we
sought to partition quality variation into cultivar, environment, and cultivar by
environment sources.

3.2 MATERIAL AND METHODS

Grain samples were collected from multi-location yield trials conducted

in Michigan in 1993 and 1994. Cultivars tested are shown in Table 3.1. Test

locations changed with years and are identiﬁed by county and year (Table 3.2).

Table 3.1 - Name, grain color, origin, and year of release for the 5 cultivars
involved in this study

 

 

Name Grain color Origin Year
Augusta white Michigan State University 1979
Chelsea white Michigan State University 1993
Freedom red Ohio State University 1991
Mendon red Michigan State University 1994

Twain red Agripro Seed, Inc., Indiana 1987

 

51

Fall nitrogen regime varied with local farming practices. Spring nitrogen rates
of 90 or 100 kg of actual Nlha were applied as urea in late winter or early
spring. Seed was planted at rates of 4.94-5.19 million seeds/ha in 3.0-3.7 m
long plots with seven rows spaced 0.21 m apart. Each location was considered
a distinct environment and differed in precipitation, elevation, thermal
environment, and soil type.

Grain samples from each plot were lightly aspirated to remove chaff and
other debris and were sieved on a 1.79 mm by 12.7 mm slot dockage tester.
Flour yield was determined by milling three hundred gram samples at
14.011096 grain moisture content on a Brabender Quadromat Jr. experimental
mill (C.W. Brabender Instruments, Inc., South Hackensack, NJ; AACC
approved method 26-50, 1983). Protein was determined according to the
AACC approved Kieldahl method 46-13 (AACC, 1983) and expressed on a
14% moisture basis. Flour mixograph properties were measured using a
National Manufacturing Co. mixograph with a 359 bowl (Lincoln, NE; AACC
approved method 54-40, 1983). Mixograms were evaluated for both the time to
reach the peak of the curve and the height of the peak. Cookie baking quality
was determined using the wire-cut formulation of the AACC approved method
10-54 (AACC, 1992). Milling, mixograph, and baking properties were

measured at 19.5:I:O.4 °C and 42.0:t1.0% relative humidity.

52

Table 3.2 - Environment mean values of qualIty traits for ﬁve cultivars

 

Year-County FYT PC MPT MXH CH CD

 

% % min cm mm mm
93-lngham 63.26 b: 8.32 cd 3.38 od 4.98 e 0.96 7.70 cd
93-Lenawee1 65.90 a 8.24 cd 3.37 od 4.97 e 0.96 7.79 bc
93-Lenawee2 63.89 b 9.12 b 3.58 od 5.22 d 1.00 7.68 od
93-Saginaw 65.44 a 7.79 e 3.23 d 4.70 f 0.93 7.92 a
93-Sanilac 65.65 a 8.15 d 3.31 d 4.93 e 0.94 7.88 ab
94-Huron 63.87 b 9.46 b 3.69 be 5.48 c 0.97 7.64 d
94-Ingham 63.24 b 9.88 a 3.96 ab 5.79 b 0.96 7.69 cd
94-Lenawee 61.59 c 8.51 c 3.43 cd 5.18 d 0.96 7.63 d

94-Sanilac 64.53 ab 10.02 a 4.05 a 6.19 a 0.97 7.5 e

 

Range ' 4.31 2.23 0.82 1.49 0.04 0.42

Mean 64.18 8.76 3.56 5.27 0.96 7.73

 

f Values within a column followed by the same letter are not signiﬁcantly
different at P<0.05 based on Duncan’s Multiple Range test.

1: FY, 96 ﬂour yield; PC, % protein content; CD, cookie diameter; CH, cookie
height; MPT, mixograph peak time; MXH, mixograph peak height.

53

Analysis of variance (ANOVA) was conducted using the Statistical
Analysis System’s GLM procedure (SAS Institute, 1988). Environments were
treated as random effects. For ANOVA F tests, cultivar was treated as a ﬁxed
effects variable. Variance components were calculated using the SAS
procedure VARCOMP (SAS Institute, 1988). In that analysis, cultivar was
treated as a random effects variable. Pearson correlation coefﬁcients were

The stability of cultivars was examined using Lu’s (1995) SAS program
for estimating Huhn’s nonparametric stability statistics (Nasser and Hﬂhn,
1987; Hahn and Nasser, 1989). This analysis is based on rank stability.
Cultivars are ranked according to adjusted means calculated as,

x*;=x;-( i. - x...)
where x‘, is the adjusted mean of cultivar i in environment j giving the cultivar
with the lowest mean value the ﬁrst rank. Stable cultivars are assumed to have
similar rank across environments. Hahn's stability statistic is expressed by two
rank stability values: the mean of the absolute rank difference of a cultivar over
all environments and the common variance of rank (Nasser and Hahn, 1987).
For these data these two terms were highly correlated (r=0.99); therefore, only

one term, the mean of the absolute rank difference (8;), is reported:

N-l N

s.=2 Z er‘RriliNiN-U].

I=I J'=r‘+1
where N is the number of environments, and R is the rank of cultivar i in

environment j. Cultivars with an S, that is large indicates instability whereas a

small S, indicates sound stability relative to other cultivars (Krenzer et al.,

54

1992). 2, is the test statistic for individual cultivar stability and has a 1*
distribution with one degree of freedom:
2, = is, - E(Si)]’Ivar( 8,),

where E(Si) is the expected mean of the absolute rank difference of cultivar i
and var(S,) is equal to the variance of S). The mean of the absolute rank
differences, 8,, is tested against the expected differences based on 3;”
distribution and degrees of freedom equal to the number of environments
tested. This value tests the null hypothesis that cultivar i is stable, i.e. L = 0.
The sum of the L values for each cultivar has a X2 distribution with degrees of
freedom equal to the number of cultivars tested. This measure of stability tests
the null hypothesis that cultivars exhibit similar stability.
3.3 RE§ULT§

Analysis of variance revealed that cultivar, environment, and CEI effects
were signiﬁcant (P<0.01) for ﬂour yield, protein content, cookie diameter, and
mixograph peak height (Table 3.3). Only cultivar signiﬁcantly (P<0.05) affected
cookie height. Mixograph peak time was signiﬁcantly (P<0.01) affected by
cultivar and environment, but not CEI. The variation among replications
signiﬁcantly affected protein content, mixograph peak height, and cookie
diameter. Mendon did not form dough as evidenced by a ﬂat mixogram
Mendon data were therefore not included in the analysis of mixograph (data not

shown).

55

Table 3.3 - Mean squares from ANOVA for quality traits for 5 cultivars
grown In 9 replicated trials In 1993 and 1994 In Michigan

 

Mean squares

 

Source of Variation df FYT PC CD CH MPT MXH

 

Environment 8 24.36“ 6.54“ 0.17” 0.02 0.94“ 2.30"“r

Replication (env) 18 3.54 0.64” 0.03" 0.01 0.23 0.19“

Cultivar 4 46.31“ 13.73“ 0.28“ 0.05" 2.99“ 2.30"
CEI 31 6.71 ** 0.45“ 0.03” 0.02 0.24 0.27”
Error 56 3.10 0.17 0.02 0.02 0.14 0.20

 

*, ”Signiﬁcant at the 0.05 and 0.01 probability levels, respectively.
t FY, 96 flour yield; PC, 96 protein content; CD, cookie diameter; CH, cookie
height; MPT, mixograph peak time; MXH, mixograph peak height.

Percentage of the total variance for each source of variance for each
trait was estimated through the calculation of variance components (Table 3.4).
Variance components attributed to cultivar were greater than those of
environment and CEI for all traits except mixograph peak height. Environment
and CEI had nearly equal variation for ﬂour yield. Error variance was the
largest term for ﬂour yield, cookie diameter, cookie height, and mixograph peak

time. Error contributed least to the variance of mixograph peak height.

56

Table 3.4 - Percentage of the total variance for each source of variation
for quality characteristics of 7 cultivars grown In 19 replicated trials In
1903 and 1994 In Michigan

 

 

 

Source of Variation FY'f PC CD CH MPT MXH
96

Environment 18.3 28.2 20.1 3.4 15.0 44.7

Replication 1.3 7.5 8.9 0.0 6.5 9.3

Cultivar 23.8 46.0 28.3 8.6 30.1 17.5

Cultivar by Environment 17.6 7.5 10.7 8.0 8.7 18.7

Error 39.0 10.9 32.1 80.0 39.7 9.7

 

7 FY, ﬂour yield; PC, protein content; CD, cookie diameter; CH, cookie height;
MPT, mixograph peak time; MXH, mixograph peak height.

Cultivar by environment interaction variance was much less than cultivar
variance as well as less than error variance for protein content. Cultivar means
for protein content ranged from 8.1 to 8.4 percent with the exception of the
cultivar Twain which had a mean protein content of 10.2 percent (Table 3.5).
Twain also had unique rheological properties compared to the other cultivars.
It’s mean mixograph peak time and'mixograph peak height were the shortest
and highest, respectively. Augusta, Chelsea and Freedom had similar
mixograph peak times, but differed in mixograph peak height. The range for

mixograph peak height for environments was greater than twice that of cultivars

57

(data not shown) (Table 3.2 and 3.5). The two white bran cultivars, AUgusta

and Chelsea, had the largest cookie diameter and the smallest cookie height.

Table 3.5 - Means values for quality traits of cultivars grown In 9 locations

 

Cultivar FYT PC MPT MXH CH CD

 

96 % min cm cm cm
Augusta 61.51 b1: 8.41 c 3.75 a 5.15 c 0.93 b 7.83 a
Chelsea 64.72 a 8.14 d 3.65 a 5.30 b 0.93 b 7.89 a
Freedom 65.40a 8.74 b 3.82 a 5.01 d 0.96 ab 7.64 b
Mendon 64.30 a 8.22 c - - 0.99 a 7.61 b

Twain 64.68 a 10.18 a 3.08 b 5.68 a 0.98 a 7.67 b

 

Range 3.89 2.04 0.74 0.67 0.06 0.28

Mean 64.17 8.76 3.56 5.27 0.96 7.73

 

1' Values within a column followed by the same letter are not signiﬁcantly
different at P<0.05 based on Duncan’s Multiple Range test.

:1: FY, % ﬂour yield; PC, % protein content; CD, cookie diameter; CH, cookie
height; MPT, mixograph peak time; MXH, mixograph peak height.

The range of average protein content for environments was nearly equal
to that of cultivars (Table 3.2). Environment means for protein content ranged
from 7.8 percent in Saginaw county in 1993 to 10.0 percent in Sanilac county in

1994. There was a broader range of cookie diameter values among

58

environments than among cultivars, but there were no signiﬁcant differences in
cookie height among environments (Table 3.2, 3.3, and 3.5).

Correlation coefﬁcients (n=9) were calculated for each cultivar using
environment means (Table 3.6). Mixograph peak height vs. protein content
was the only trait combination with a signiﬁcant (P<0.05) correlation for all
cultivars. Some signiﬁcant (P<0.05) within-cultivar r values were observed for
ﬂour yield vs. protein content, ﬂour yield vs. cookie diameter, ﬂour yield vs.
mixograph peak time, ﬂour yield vs. mixograph peak height, cookie diameter

vs. cookie height, and cookie diameter vs. mixograph peak time.

Table 3.8 - The number of cultivars with signiﬁcant correlation
coefﬁcients (P<0.05) (above diagonal) and the range of r values (below
diagonal)

 

 

FYT PC MPT MXH CH CD
FY - 2 2 1 0 1
PC -0.53 In -0.73 - 0 4 0 1
- MPT -0.52 It -O.72 NS - 0 0 1
MXH -0.71 0.38 to 0.98 NS - 0 0
CH NS NS -0.57 NS - 3
CD 0.06 0.65 NS NS -0.68 to -0.72 -

 

TFY, 96 flour yield; PC, 96 protein content; CD, cookie diameter, CH, cookie height;
MPT, mixograph peak time; MXH, mixograph peak height.

There was an absence of a signiﬁcant r value for any cultivar for ﬂour yield vs.
cookie height, protein content vs. cookie height, protein content vs. mixograph
peak time, cookie diameter vs. mixograph peak height, cookie height vs.

mixograph peak time, and cookie height vs. mixograph peak height.

59

Correlation analysis of the combined cultivar results (n = 45) showed signiﬁcant
(P<0.05) r values for protein content vs. cookie diameter (r = -0.37), mixograph
peak time (r = —0.33), and mixograph height (r = 0.90). Signiﬁcant rvalues
were also found for ﬂour yield vs. mixograph peak time (r = -0.37), cookie
diameter vs. cookie height (r = -0.65), and cookie diameter vs. mixograph peak
height (r = -0.39).

Principal component analysis of the CEI effects are presented in Figs.
3.1 - 3.5. Cultivar and environment principal components are expressed as
vectors on a biplot and are to be considered separately in each graph. Vectors
have two properties: direction and length. Points of the same type (cultivars or
environments) that are close to each other have similar CEI values across the
other factor (cultivars or environments). Points with similar direction behaved
relatively alike whereas points with opposite direction behaved conversely.
Points whose vector directions are perpendicular behaved independently of
one another, i.e., there is no negative or positive relationship between the two.
For cultivars, 5 parameters (cultivars) are measured for 9 variables
(environments). The converse is true when discussing environments, i.e., 9
environments are analyzed for 5 variables (cultivars). For instance, Twain and
Freedom are close to each other in protein content and mixograph height (Figs.
3.2 and 3.5). This means that the CEI effects across environments or the
differential response to environment for these two cultivars are similar for
protein content and mixograph height. It is also true that Chelsea is either

independent of or opposite to all other cultivars for CEI pattern (Figs. 3.1 - 3.5).

60

Freedom and Mendon are largely independent of each other for each trait. The
cultivar arrangement in the biplots for protein content and mixograph peak
height are very similar in that the relationship among cultivars is alike (Figs. 3.3
and 3.5). These two traits have a signiﬁcant linear correlation (r = 0.90). The
amount of variance due to CEI for these two traits is relatively low. It appears
as though CEI has a signiﬁcant effect on protein content and mixograph peak
height, but the interaction is very mild.

The relationship between protein content and mixograph peak height
biplots does not exist when considering cultivars as the variables and
environment as the parameter (Figs. 3.3 and 3.5). There are little or no
patterns of counties or years for any traits (Figs. 3.1 - 3.5). It appears as
though the differences in environments that contribute to the interaction are
associated with random variables, not those related to spatial or temporal
characteristics of each environment. Ingham and Huron counties in 1994
cluster on each biplot. Interaction effects in these two environments were
highly correlated. The interaction effects in Lenawee 1993 were contrary to
those that occurred in Ingham and Huron 1994 in all cases. Other Lenawee

and Ingham ﬁelds did not have a consistent relationship across traits.

61

 

2.0 F

f.
O
i

p
o
I

Second Principal Component
9
0|

 

 

 

 

 

Figure 3.1 - Biplot of the principal components of the cultivar by environment
interaction effects on flour yield. Cultivars are represented by a closed circle
and three characters (AGS I ‘Augusta’, CLS I ‘Chelsee’, FDM I ‘Freedom', MND
I ‘Mendon', and TWN I ‘Twain’). Environments are represented by an open
circle and four or ﬁve characters. The ﬁrst two describe growing year and the
next two or three describe county (HN I Huron, IM I Ingham, LE1 I Lenawee1,
LE2 I Lenawee2, SW I Saginaw, and SC I Sanilac). 57.3 and 29.1 percent of the

variation ls accounted for in the x and y-axis, respectively.

62

 

0.10

0.“

v ' vi f' r

l...
gm:
5::

«0.04

1 t ft ‘7 ' T

 

 

[LlalalJlsJalslelalslsLJla

-0.10 one one -0.04 -0.02 0.00 0.02 0.04 0.06 0.03 0.10 0.12
FlretPrInpreIConeaonent

 

 

 

Figure 3.2 - Biplot of the principal components of the cultivar by environment
interaction effects on wire-cut cookie diameter. Cultivars are represented by a
closed circle and three characters (AGS I ‘Augusta’, CLS I ‘Chelsea’, FDM I
‘Freedom’, MND I ‘Mendon’, and TWN I ‘Twain’). Environments are represented
by an open circle and four or ﬁve characters. The ﬁrst two describe growlng
year and the next two or three describe county (HN I Huron, IM I Ingham, LE1 I
Lenawee1, LE2 I Lenawee2, SW I Saginaw, and SC I Sanilac). 50.8 and 24.2

percent or the variation is accounted for in the x and y-axls, respectively.

63

 

9
u.
1

Second Prlncklel Component
.6

 

 

 

-o.2 -
-o.3 - yeti
i
-o.4 i
-o.5 -
L l L l I l A .l 1 4L J
-o.5 -o.3 -o.1 0.1 0.3 0.5 0.7

Flu Principd Componerl

 

 

 

Figure 3.3 - Biplot of the principal components of the cultivar by environment
interaction effects on ﬁour protein content. Cultivars are represented by a
closed circle and three characters (AGS I ‘Augusta’, CLS I ‘Chelsea’, FDM I
‘Freedom’, MND I ‘Mendon’, and TWN I ‘Twain’). Environments are represented
by an open circle and four or ﬁve characters. The ﬁrst two describe growing
year and the next two or three describe county (HN I Huron, IM I Ingham, LE1 I
Lenawee1, LE2 I Lenawee2, SW I Saginaw, and SC I Sanilac). 45.6 and 37.4

percent of the variation is accounted for in the x and y-axis, respectively.

 

0.2 I'

 

 

 

-0.2 '
-0 3 . l I 1 A e A 1 A l . s . l . I
-0.4 .03 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Fir! Prindpd Componem

 

 

 

Figure 3.4 - Biplot of the principal components of the cultivar by environment
interaction effects on mixograph peak time. Cultivars are represented by a
closed circle and three characters (AGS I ‘Augusta’, CLS I 'Chelsea’, FDM I
‘Freedom’, and TWN I ‘Twain’). Environments are represented by an open circle
and four or ﬁve characters. The ﬁrst two describe growing year and the next two
or three describe county (HN I Huron, IM I Ingham, LE1 I Lenawee1, LE2 I
Lenawee2, SW I Saginaw, and SC I Sanilac). 71.6 and 21.5 percent of the
variation for cultivars is accounted for in the x and y-axis, respectively. 69.0 and
3.5 percent of the variation for environments is accounted for in the x and y-axis,

respectively.

65

 

0.3 '-

o.2 r

0.1 l'

0.0 F

-0.1 P

 

SecondPrhcipelComponerl

 

-0.2 P $4$

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4
Fire Principal Component

 

 

 

Figure 3.5 - Biplot of the principal components of the cultivar by environment
interaction effects on mixograph peak height. Cultivars are represented by a
closed circle and three characters (AGS I ‘Augusta’, CLS I ‘Chelsea’, FDM I
‘Freedom’, and TWN I ‘Twain’). Environments are represented by an open circle
and four or ﬁve characters. The ﬁrst two describe growing year and the next two
or three describe county (HN I Huron, IM I Ingham, LE1 I Lenawee1, LE2 I
Lenawee2, SW I Saginaw, and SC I Sanilac). 63.1 and 33.1 percent of the
variation for cultivars is accounted for in the x and y-axis, respectively. 63.1 and

30.7 percent of the variation for environments is accounted for in the x and y-

axis, respectively.

66

Relative rank stability was estimated using Hithn’s nonparametric
stability statistic (Table 3.7 and 3.8). The sum of the test statistic ,Z,, has a 12
distribution with degrees of freedom equal to the number of cultivars tested, 5.
It tests the null hypothesis that there is no difference in overall cultivar rank.
The null hypothesis was rejected for ﬂour yield (2, =16.27) alone (Table 3.7).
Upon observation of the 2, values for each cultivar, Freedom differed from the
other cultivars. Freedom’s relatively low S, value for ﬂour yield reveals that it
behaved more stable than Augusta, Chelsea, Mendon, and Twain. Neither the
sum of the stability test statistic nor the individual cultivar test statistic
exceeded the critical values for protein content, mixograph peak time,
mixograph peak height, cookie height or cookie diameter. Cultivars had overall
rank stability differences for ﬂour yield with Freedom exhibiting greater rank
stability than other cultivars. For other quality traits, cultivars did not exhibit

differences in rank stability overall, or when considering cultivars individually.

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69

3.4 DISCQSSION

In order to breed cultivars with improved plant characteristics, it is
necessary to accurately characterize the relative differences among breeding
material. An interaction between genotype and environment can diminish a
breeders ability to recognize those differences. Change of cultivar rank for a
particular trait across environments is indicative of a severe CEI. A mild CEI
results in a change in the magnitude of the differences among cultivars rather
than a change in rank order. In instances of crossover interaction, rank order
of the mean performance of cultivars is not a true representation of each
cultivars phenotypic behavior in each environment tested. Understanding the
nature of the interactions and error variance leads to a more informed strategy
of sampling and possibly more defined regions of cultivar production.

Cultivars may differ for stability of quality characteristics. Stability
measured using regression analysis outlined by both Eberhart and Russell
(1966) and Moll et al. (1978) have shown few cultivars have either a high or
low response to environments for ﬂour yield, protein content, mixograph
properties, and cookie diameter (Busch et al., 1965; and McGuire and McNeal,
1974; Peterson et al., 1992; Lukow and McVetty, 1991; Basset et al. 1989;
Baenziger et al., 1985). The regression coefficient from this approach is
perceived by many as a measure of genotypic response to favorable conditions
rather than a stability measurement (Becker and Léon, 1988). A cultivar may
be adaptive to a wide range of environments while others may only perform

well under certain environmental conditions. Stability was estimated here

70

using nonparametric methods by measuring the absolute rank differences of
cultivars across environments. Cultivars exhibited signiﬁcant differences in
rank stability for ﬂour yield alone. Further analysis of the individual test
statistics shows that the cultivar Freedom in this instance was signiﬁcantly
more stable than the other cultivars. Freedom’s rank across environments was
relatively ﬁxed. In every other instance, no cultivar signiﬁcantly exceeded or
fall below the expected amount of rank change based on )8 distribution. Thus,
stability issues are not important to consider for the cultivars tested.

Data in this study suggest that there are signiﬁcant (P<0.01) cultivar,
environment, and CEI effects on ﬂour yield, protein content, cookie diameter,
and mixograph peak height. The interactions are, however, mild and consist
primarily of variation in the magnitude of the differences between cultivars
across environments. The signiﬁcant differences among replications for
protein content, mixograph peak height, and cookie diameter inﬂuences the
number of replications that are needed to recognize the differences among
9900M)”-

Mixograph properties are not traditional characters that are often
selected for in soft wheat breeding programs. The results reported here show
a diverse range in mixograph peak height and peak time. The area under
mixograph peak curve has been shown to be related to dough viscosity and
cookie diameter in soft wheat (Morris et al., 1943). The results reported show
mixograph peak height is strongly related to protein content. Mixograph peak

time was not related to other quality characters, but it remains important to

71

processors. Mendon’s mixogram curve was ﬂat where as Twain had a high
peak that took approximately 3.08 minutes to reach. Hard wheat has relatively
high protein content and gives a well deﬁned mixograph peak that is generally
higher and has a more dramatic ‘breakdown’ than that of soft wheat. Soft
wheat such as Twain may be used as a substitute ingredient for hard wheat or
to expand the range of soft wheat products. Protein content is negatively
correlated to cookie diameter, which is in agreement with previous studies
(Gaines, 1985; Basset et al., 1989, Kaldy et al., 1993; Yamamoto et al., 1996).
Mixograph peak height is also signiﬁcantly related to cookie diameter, however
the correlation coefﬁcients of these two traits are not large enough to warrant
substitution of one test for another. The minimum characteristics that grain
samples should be evaluated for are ﬂour yield, hardness, protein content, and
baking quality. Further analysis of rheological properties and aptitude as an
ingredient in diverse end-use products should be done in advanced stages of
cultivar development.

It is possible that cultivars change rank in response to particular
environmental factors associated with regions or management practices (Allard
and Bradshaw, 1964). It may be prudent to divide a growing region into
smaller regions where different cultivar recommendations are made in the
presence of a crossover interaction that is predictable by geographic region
(Homer and Frey, 1957). Here, principal component analysis showed
environmental factors that elicit a differential response across genotypes are

not associated with year or geographical proximity. This lack of association

72

shows that their occurrence is unpredictable. It would not be prudent to divide
Michigan into micro growing regions due to a lack of consistent CEI for
cultivars in any one region.

In order to accurately estimate the relative differences between cultivars
in non-crossover interaction situations, analysis of composite samples of
environments within a year is the most efﬁcient manner. Quality analysis is
both labor and technically intensive as well as expensive. A check cultivar
which has been analyzed over many seasons should be used as a benchmark
in quality evaluation. Sampling strategies based on these data should account
the signiﬁcant effects of CEI and replication and the magnitude of the error
variance. In order to obtain optimum accuracy, it is necessary to sample
multiple replications from each location. Random selection of locations within a
year is sufﬁcient. However, the analysis of multiple replications from each site
is often not an option because of resource constraints. A reasonable degree of
accuracy can be obtained by evaluating bulked replications from a few
locations per year. When purchasing grain, knowledge of the cultivar is useful
in predicting quality relative to other cultivars. Knowledge of local

environments is of little use when attempting to predict grain quality.

73

3.5 REFERENCES

American Association of Cereal Chemists. 1983. Approved methods of the
AACC. 8th Ed. AACC, St. Paul, MN.

Allard, R.W., and AD. Bradshaw. 1964. Implications of genotype-
environrnental interactions in applied plant breeding. Crop Sci. 4:503-
508.

Baenziger, S.P., R. L. Clements, M.S. McIntosh, W.Y. Yamazaki, T.M. Starling,
D.J. Sammons, and J.W. Johnson. 1985. Effects of cultivar,
environment,
and their interaction and stability on milling and baking quality of soft red
winter wheat. Crop Sci. 25:5-8.

Basset, L.M., R.E. Allan, and G.L. Rubenthaler. 1989. Genotype x environment
interactions on soft white winter wheat quality. Agron. J. 81:955-960.

Becker, H.C., and J. Léon. 1988. Stability analysis in plant breeding. Plant
Breeding. 10121-23.

Bhatt, GM. and NF. Derera. 1975. Genotype by environment interactions for,
heritabilities of, and correlations among quality traits in wheat. Euphytica
24:597-604.

Busch, R.H., W.C. Shuey, and RC. Frohberg. 1969. Response of hard red
spring wheat (Triticum aestivum L.) to environments in relation to six
quality characteristics. Crop Sci. 9:813-817.

Eberhart, SA, and WA. Russell. 1966. Stability parameters for comparing
varieties. Crop Sci. 6: 36-40.

Faridi, H., C. Gaines, and P. F inney. 1994. Soft wheat quality on production of
cookies and crackers. pp. 1-11 In W. Bushuk and V.F. Rasper (eds)
Wheat production, properties, and quality. Blackie Academic and
Professional, Glasgow.

Finney, PL, and LC. Andrews. 1986. Revised microtesting for soft wheat
quality evaluation. Cereal Chem. 632177-182.

Homer, T.W., and K Frey. 1957. Methods for determining natural areas for cat
varietal recommendations. Agron. J. 49:313-315.

74

Hahn, M., and R. Nasser. 1989. On tests of signiﬁcance for nonparametric
measures of phenotype stability. Biometrics 452997-1000.

Kaldy, M.S., G.R. Kereliuk, and GO. Kozub. 1993. Inﬂuence of gluten
components and ﬂour lipids on soft white wheat quality. Cereal Chem
70:77-88.

Krenzer, Jr., E.G., J.D. Thompson, and BF. Carver. 1992. Partitioning of
genotype x environment interactions of winter wheat forage yield. Crop
Sci. 32:1143-1147.

Lu, H.Y. 1995. PC-SAS program for estimating Hiihn’s nonparametric stability
statistics. Agron. J. 87:888-891.

Lukow, 0M. and P.B.E. McVetty. 1991. Effect of cultivar and environment on
quality characteristics of spring wheat. Cereal Chem. 68(6):597-601.

McGuire, CF, and PH. McNeal. 1974. Quality response of 10 hard red spring
wheat cultivars to 25 environments. Crop Sci. 14:175-178.

Morris, V.H., C.E. Bode, and HK Heizer. 1943. The use of the mixogram in
evaluating quality in soft wheat varieties. Cereal Chem. 21:49-57.

Moll, R.H., C.C. Cockerman, C.W. Stuber, and WP. Williams. 1978. Selection
responses, genetic-environmental interactions, and heterosis with
recurrent selection for yield in maize. Crop Sci. 18:641-645.

Nasser, R., and M. Huhn. 1987. Studies on estimation of phenotypic stability:
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Biometrics 43:45-53.

Peterson, C.J., RA Graybosch, P.S. Baenziger, and AW. Grombacher. 1992.
Genotype and environment effects on quality characteristics of hard red
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Rohlf, F.J. 1992. NTSYS-pc: numerical taxonomy and multivariate analysis
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SAS Institute. 1988. SAS/STAT User’s Guide. Statistics. SAS Institute Inc,
Cary.NC.

Yamamoto, H, S.T. Worthington, G. Hou, and P.KW. Ng. 1996. Rheological
properties and baking qualities of selected soft wheats grown in the
United States. Cereal Chem. 73:215221.

APPENDICES

APPENDIX A

DATA DERIVED FROM THE SINGLE KERNEL CHARACTERIZATION

SYSTEM

75

 

 

 

APPENDIX A

Cultivar Lac/year Rep. Kernel Kernel Kernel

hardness wejight width

hardness mg m

index

Augusta lng-‘91 1 10.8 34.9 2.60
Augusta Hur-‘91 1 17.8 33.0 2.50
Augusta Kala-'91 1 18.0 32.9 2.60
Augusta Len—'91 1 26.1 34.3 2.60
Augusta Mon-'91 1 25.5 32.4 2.60
Augusta lng-‘92 1 13.0 40.8 2.90
Augusta Hur-‘92 1 17.9 38.2 2.80
Augusta Len-'92 1 19.4 43.8 2.90
Augusta Sanl-‘92 1 16.2 39.5 2.80
Augusta Sagw-‘92 1 23.4 37.8 2.70
Augusta Ing-‘93 1 21 .7 31 .2 2.50
Augusta Lem-’93 1 15.5 34.0 2.60
Augusta Len2-‘93 1 19.2 31.1 2.40
Augusta Sam-'93 1 13.7 35.6 2.60
Augusta Sagw-‘93 1 15.2 30.5 2.50
Augusta lng-‘94 1 16.0 34.4 2.60
Augusta Hur-‘94 1 24.8 37.2 2.70
Augusta Len-'94 1 7.3 34.0 2.50
Augusta Sam-'94 1 23.0 36.9 2.70
Cardnial lng-‘91 1 8.2 36.2 2.61
Cardnial lng-‘91 2 7.5 36.7 2.61
Cardnial lng-‘91 3 6.4 36.0 2.58
Cardnial Hur-‘91 1 8.7 39.3 2.67
Cardnial Hur-‘91 2 9.4 38.5 2.66
Cardnial Hur-‘91 3 10.0 37.0 2.61
Cardnial Kala-'91 1 9.0 33.3 2.52
Cardnial Kala-'91 2 13.1 31.4 2.46
Cardnial Kala-'91 3 1 1 .1 32.6 2.52
Cardnial LenJ91 1 16.7 36.3 2.59
Cardnial Len-'91 2 15.8 36.4 2.60
Cardnial Len-'91 3 15.1 36.5 2.55
Cardnial Mon-'91 1 17.4 31.2 2.42
Cardnial Mon-'91 2 16.5 32.1 2.45
Cardnial Mon-'91 3 15.1 32.3 2.47
Cardnial Ing-‘92 1 14.9 41.9 2.76

Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Cardnial
Chelsea
Chelsea
Chelsea
Chelsea

AQN-‘(ﬂM-‘UN-‘QN-‘UN-ﬁ(JON-‘0)N-‘O’NddewN-‘UN-‘QNAQNJUNAQN

76

16.7
17.3
22.9
24.4
23.7
16.6
11.4
10.1
13.3
13.9
14.8
27. 2
26.4
27. 2
11.3
9. 0
11.8
8.5
9.8
7. 8
15. 9
11.2
14.2
15.3
13.0
13.1
6.0
5.4
6.2
6.0
3.3
3. 3
22.9
15.6
14.8
3.7
3.3
1. 7
18. 8
13.2
16.8
6.5
6.1
7.7
5.7

40. 8
42. 7
39.1
37.8
40.4
43.3
43.7
42.7
40.2
40.7
39.6
40.2
41.5
40.0
35.7
35.2
32.2
35. 4
34. 3
35.0
36.8
39.1
39.1
39.1
37.0
35.6
34.5
34. 0
33. 2
32.7
33.3
33.4
34.3
34.9
34.6
34. 5
33.9
34. 6
35.1
35.5
35.4
33.6
34. 5
33. 1
36.3

2.75
2. 77
2. 64
2. 62
2.75
2.84
2. 84
2. 78
2.69
2.75
2.70
2.67
2. 69
2. 68
2. 61
2. 57
2. 44
2. 51
2. 43
2. 48
2. 57
2. 68
2. 65
2. 65
2. 56
2. 52
2.46
2.48
2. 44
2. 43
2.42
2. 45
2. 46
2. 47
2.46
2.48
2. 46
2. 51
2.51
2.49
2.49
2.59
2. 57
2. 54
2. 63

(nxusea
(Huﬂsea
(Huﬂsea
(nuﬂsea
(Nuﬂsea
(ﬁxﬂsea
(nuﬂsea
(nxﬂsea
(ﬁxﬂsea
(nunsea
(nausea
(Huﬂsea
(nuusea
(nuﬂsea
(nausea
(nausea
(nausea
(ﬁxﬂsea
(nuﬂsea
(ﬂuﬂsea
(”vases
(Nuﬂsea
(Huﬂsea
(nausea
(nausea
(uuﬂsea
(nausea
(nausea
(nxﬂsea
(nuﬂsea
(nuﬂsea
(nausea
(nausea
(”vases
Gnuﬂsea
(nxﬂsea
(nuﬂsea
(nausea
(Huﬂsea
(nausea
(ﬂuusea
(nausea
(ﬁxﬂsea
(nausea
(”Muses

tﬁuUQ1
rou91
h9ﬂa491
Kala-'91
uuua291
LenJ91
LenJ91
Len491
IWonJ91
IwonJ91
lﬂonJ91
lngJ92
Ing492
lngJ92
rﬁxJ92
lﬁuﬁ92
tuu392
Len192
LenJ92
LenJ92
Sank1n!
SanLKKZ
£huﬂ392

Sagww92
Sagww92
SkumNJQZ

lngJ93

lngJ93

lngJ93

Len1393
Len1393
Len1393
Len2-‘93
Len2- '93
Len2393
Sanh1ﬁ3
Sanhﬂxs
Sanhﬁﬁl

Sang93
Sagw—‘93
Sang93

lngJ94
lng- '94
lngJS4
rﬁu394

JmNdwN-‘wN-‘QMAQNAQNdwN-‘wM-twN-bOJN-in-‘wN-‘wN-‘QNAUM

77

7.44
11.'7
1215
11:4
1212
1731
2015
1557
1313
1714
1614
11.1
815
8.7'
14.13
1731
1615
1613
1639
2414
141)
1C15
7K6
22.12
21.'7
20.44
8.‘I
12.13
12.13
8.13
11.15
7.1)
14.1)
1(15
11.1
11.8
1(24
1215
722
9.‘I
13.13
915
11..2
1231
2315

361'
34.12
31.44
3015
301)
3513
3213
34.13
3215
3013
30.13
4015
3715
36.55
40.44
391'
3814
421)
4331
43JI
3513
3615
3715
3613
3715
3712
3212
331'
32]’
35.44
34.12
3512
33.‘I
36.‘I
3513
36.‘I
3713
37JI
3514
3313
3313
3514
3314
3512
3612

1268
1256
12.54
12.51
12.44
12.65
12.56
1257
12.59
12.54
1249
12.83
2.71
1263
1288
1275
1270
21“
1295
2.5”
12.58
12.62
1267
1265
1275
1265
1259
1263
12.60
12.66
1255
1266
12.58
12.69
1263
12.62
1270
1267
1259
1253
1250
1266
12.54
12.67
1265

(Huﬂsea
(nausea
(Huﬂsea
(”Muses
(nausea
(nausea
(Nausea
(nausea
Dynashr
Dynashr
Dynashr
Dynasty
Dynasty
Dynashr
Dynasty
Dynashr
Dynashr
Dynashr
Dynashr
Dynasur
Dynasty
Dynashr
Dwnashr
Dynashr
Dynashr
Dynashr
Dynashr
FWankennunh
F rankenmuth
F rankenmuth
Frankenmuth
FWankennunh
FWankennnnh
Fkankennunh
FWankennunh
FWankennunh
FWankennnnh
FWankennunh
F rankenmuth
F rankenmuth
FWankennunh
FWankennunh
F rankenmuth
Fnuﬂauunuﬂt
anﬂuuvnuﬂt

rhuJ94
PkuJ94
LenJ94
LenJ94
LenJ94
ShuﬂJ94
Sanhﬁﬂl
Sank1¥l
lngJ91

rou91

Kala-'91
LenJ91
ImonJ91
Ina-'92

fﬂu992
Len- '92
Shun-'92

SangQZ

lngJ93

Len1J93
Len2J93
Skou93

Sagw993

lngJ94
ru»- '94
Len-'94
Sank1¥1
lngJ91
lngJ91
lngJ91
FkuJ91
rkuJ91
PkNJ91
kuﬂaJ91
"Qua-'91
Huﬂa- 91
Len- '91
Len-'91
LenJ91
IwonJ91
ImonJ91
IWonJ91
lngJ92
lngJ92
lngJ92

“NJwNAwNAmM-kwméwwd-ﬁ-L—t-A—t—I-L...LA—L—t-A—b-AA-A-L—AAdewN-hwm

78

201!
201)
31
213
().6
20.‘I
1726
17K5
1313
14.12
913
18.12
19.1)
9.44
2213
1133
151)
2013
1231
101)
1552
17KB
16.12
(3.7
155.2
1. 5
1C).5
191)
1213
1813
1731
211)
1613
1813
1812
251'
2313
2831
2012
2211
2212
1731
1313
1731
2113

3513
3313
3413
3311
3331
3531
3712
3313
291'
3114
2813
2813
2413
3715
3731
3712
3715
3531
3113
3215
3613
331)
3315
2813
30.13
311)
2812
321)
3112
3214
3212
3113
3114
3215
3231
3315
3415
3413
32.12
3112
3114
401)
3713
3713
3913

12.61
12.54
12.63
1254
1252
1260
1264
1256
12.50
1260
1230
12.30
12.20
1270
1270
1280
1270
1250
1250
1250
2.70
1250
12.50
12.40
12.30
12.40
1220
12.45
12.38
2.41
1242
1237
1245
21”
1247
1253
12.52
12.56
2.49
1243
1247
2]?
1267
1262
212

anﬂuuwnuﬁi
Frankenmuth
F rankenmuth
Fkankennunh
FWankennnnh
FWankennnnh
F rankenmuth
FWankennunh
F rankenmuth
FWankennunh
F rankenmuth
FWankennunh
FWankennunh
FWankennunh
Frankenmuth
F rankenmuth
Frankenmuth
Frankenmuth
FWankennunh
FTankennunh
F rankenmuth
Frankenmuth
FWankennunh
F rankenmuth
Frankenmuth
Frankenmuth
FWankennunh
FWankennunh
Frankenmuth
F rankenmuth
F rankenmuth
Frankenmuth
FWankennuuh
FWankennunh
F rankenmuth
FWankennunh
F rankenmuth
FWankennunh
Fkankennunh
Fnuukxn

Fnuukxn

Fnuukxn

Fnuukxn

Fnuukxn

FWeedont

lngJ93
Len1J93
Len1 J93
Len1 J93
Len2J93
Len2-93
Len2J93
Sam-'93
Sam-'93
Sam-'93
Sagw-‘93
Sagw-‘93
Sagw-‘93
IngJ94

UFO-lQNAQN-‘(DNdwN-ﬁ(ON-8031040)N-‘UN-ﬁwN-in-KQN-lmN-‘UN-JQN—l

79

23.7
18.4
25.2
23.5
25.2
18.2
17.4
17.3
28.5
28.0
27.6
23.7
28.2
24.1
18.7
22.2
19.2
18.4
16.6
14.5
15.5
19.9
15.8
19.9
14.8

11.3
16.9
18.6
17.9
27.7
26.5
23.8
13.2
9.6

8. 2

27. 0
27.0
20.7
13.4
18.1
14.7
18.7
18.3
19.4

37.2
38.1
39.8
40. 2
40. 4
40.5
40.5
39.4
37.9
38.9
36. 5
30. 7
31. 0
30. 7
32. 3
31.9
33.1
31.2
38 1
34. 0
35 7
33.2
35.2
26.9
30.1

31.8
32.0
31.4
32.8
35.1
35.1
33.8
32.9
32.6
31.8
35.7
35.0
33.3
32.6
30.3
28.5
33.5
33.5
33.8

2. 55
2. 68
2. 79
2.79
2.78
2.70
2.71
2.63
2. 63
2. 68
2. 61
2.42
2. 45
2. 44
2. 39
2. 42
2. 48
2. 39
2. 52
2. 54
2. 57
2.45
2.55
2.26
2.39

2.43
2.47
2. 49
2. 54
2. 59
2. 56
2. 51
2.47
2.43
2. 43
2. 60
2. 59
2.44
2. 38
2. 29
2.20
2. 43
2. 38
2. 40

Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
F reedorn
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom

Kala-'91
Kala- '91
Kala-'91
Len—'91
Len-91
Len-'91
Mon-'91
Mon-'91
Mon-'91
lngJ92
lngJ92
lngJ92
HurJ92
HurJ92
HurJ92
LenJ92
Len-'92
Len-92
SanlJ92
SanlJ92
SanI-92
Sagw-92
Sagw-‘92
Sang92
Ina-'93
lngJ93
lng-‘93
Len1 J93
Len1 J93
Len1J93
Len2J93
Len2J93
Len2-93
SanlJ93
SanIJ93
Sanl-93
Sang93
Sagw-93
Sagw-93
lng-94
lngJ94
lngJ94
Hur-94
HurJ94
Hut-'94

WN-wa-wadwN-ﬁwN-‘QN—le-‘QNAdewN-‘wN-‘UNAUN‘QN-‘UN-A

80

13.6
14.7
17.1
26.1
26.7
26.6
21. 5
22.3
19. 7
22.4
23.1
23.5
26.4
28.8
29.0
24.5
24.7
21.3
24.2
22.0
23.2
31.6
30. 2
28 1
16.4
20.6
22.5
18.2
17.3
20.4
21.3
26.7
22.2
18 1
19.3
21.9
22.3
18 0
20.3
20.2
17.2
15.7
27.0
33.0
26.7

31.2
32.0
30.6
32.8
31.7
31.2
29.3
30.1
29.8
39.1
38.7
36. 2
35.8
35.3
35 8
39.0
37. 8
36. 4
32. 6
36. 3
33. 8
32.4
35.0
34.9
34.1
35. 2
35.3
35 8
38. 2
35. 7
35.5
32.7
37.0
34.6
34.1
33.7
33.5
33.8
32.8
30.2
30.0
31.9
31.4
31.8
30.6

2.32
2. 37
2. 34
2. 39
2. 36
2. 32
2.28
2.33
2. 29
2. 58
2. 58
2.52
2.51
2.45
2.49
2.68
2. 60
2. 53
2. 36
2. 53
2. 42
2. 36
2. 40
2.36
2. 56
2. 59
2.60
2.48
2. 60
2. 51
2. 54
2. 41
2.64
2.46
2.44
2. 38
2. 41
2.42
2.39
2.32
2. 30
2. 35
2. 29
2.31
2.28

Freedom

Freedom
Freedom
Freedom
Freedom
Freedom
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Harus
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale
Hillsdale

LenJ94
Len-94
Len-'94
Sam-94
SanlJ94
SanI-94
lngJ91
HurJ91
Kala-'91
LenJ91
Mon-'91
Ing-92
HurJ92
Len-'92
Sent-'92

Sang92

lngJ93

Len1 J93
Len2-93
Saul-'93

Sang93

lng-94
HurJ94
Len-'94
Sam-'94
lngJ91
lngJ91
lngJ91
Hur-91
HurJ91
HurJ91
Kala-'91
Kala- '91
Kala-'91
Len-'91
LenJ91
Len-'91
Mon-'91
Mon-91
Mon-'91
lngJ92
lngJ92
lngJ92
HurJ92
HurJ92

N-AwN-LwN—LwN—LQN—hwN-hQNd-h-b-b-b—b-LA-AA-l—hd-t—lAdd‘dwNJwNA

81

16.1
11.8
19.3
26.4
27.5
26.2
12.4
13.7
16.2
21.2
15.9
19.3
19. 9
22.4
13. 0
28.7
21.6
17.1
15.3
18.9
12. 5
7. 6
17. 5
5. 5
12. 7
18.7
17.1
17.3
18.3
21.1
17.6
19.4
20.5
21.2
26.5
30. 5
28. 5
22.5
26.0
26.6
23. 8
22.6
21.8
25.7
26.2

30.1
30.2
27.8
32. 9
34.1
30. 5
35. 4
36. 4
31. 0
36.1
33. 8
41.8
40.4
45.0
40.7
36.8
34. 5
35.0
38.9
39.4
33.9
32.1
31.4
31.0
32.6
35.0
34.1
32. 4
34.3
34.0
34. 5
32. 0
32.7
30. 9
34. 5
35 7
34.9
34.2
33.7
32.2
38.6
40.4
38.3
41.1
40.3

2.26
2.29
2.18
2.35
2.41
2. 24
2. 60
2. 50
2. 40
2. 50
2. 50
2.80
2. 70
2. 90
2. 70
2.50
2.60
2. 50
2. 70
2.70
2.50
2. 40
2. 30
2.30
2.40
2. 52
2. 46
2. 43
2.42
2.43
2. 41
2. 46
2 48
2.40
2.51
2.59
2.53
2. 57
2. 55
2. 45
2.69
2. 73
2. 64
2. 79
2.72

tﬁusdakp
rﬁﬂsdaka
lﬁusdaka
tﬁusdaka
Hillsdale
rﬁusdaha
lﬁusdaka
fﬁusdaka
fﬁﬂsdakp
tﬁusdaup
tﬁﬂsdaua
rﬁusdauy
Hillsdale
tﬁﬂsdaut
tﬁusdaka
tﬁﬂsdak5
fﬁWamﬂe
rﬁusdaua
fﬁusdakp
rﬁusdak5
1ﬁﬂsdak5
rﬁusdakt
Hillsdale
Hillsdale
rﬂusdaua
1ﬁusdau5
rﬂﬂsdaka
rﬁusdakt
Pﬁusdaka
tﬁﬂsdaka
lﬁﬂsdaka
tﬁﬂsdak:
1ﬁﬂsdak5
tﬁusdaka
rﬁusdaka
fﬁﬂsdaka
fﬁusdaki
luendon
Ilendon

rou92
LenJ92
LenJ92
LenJ92
Sanhﬂxz
ShuﬂJQZ
ShuﬂJ92

SangSQ
Sang92

EMMmNJQZ

lngJ93

lngJ93

lngJ93

Len1J93
Len1J93
Len1J93
Len2J93
Len2J93
Len2J93
SanLKXB
SanLKNB
Sam-'93

Sagww98
SangQG
ShumNJQS

N-‘O’N-‘UN-‘WNJQN-‘UN-‘wM-le-FUN-‘QN-‘UN-‘QN-‘wN-‘UN-‘QN-‘w

82

2531
251)
2413
2714
2412
2113
2013
3512
3812
3213
2214
2313
2731
2031
2014
2013
23.44
2213
25.7'
2231
1913
2217
1913
2114
1522
20J’
21.1
2413
3717
3312
30.7'
11. 8
1532
1513
31.1
2813
2613
613
413
422
615
613
153.9
313
613

4112
4215
4212
4013
4213
4215
4113
3913
38.7'
38.7'
3231
3013
2913
34.‘I
35‘1
3414
341)
3453
321)
35.44
36.7'
3713
3115
3313
3115
3415
3413
3313
3513
3615
3514
3415
34.7'
3315
3513
3713
3113
39J'
4112
4112
4231
4015
3913
3913
3915

12.78
12.80
12.75
1269
1279
1276
1283
1270
1263
1269
1248
12.35
1240
12.49
1248
1247
1249
1250
1244
1250
21“
1264
1238
1248
1237
1252
1250
12.50
12.52
12.62
1249
1249
1253
1243
1252
1260
12.34
21”
12.64
12.69
12.69
12.60
12.58
1264
1268

Iwendon

IWendon

Imendon
lwendon

Ilendon
Imendon
Iwendon
Iwendon
Idendon

kuuaJ91
LenJ91
LenJ91
LenJ91
IWonJ91
IdonJ91
100nJ91
lngJ92
lngJ92
lngJ92
ruu292
ruu¢92
HurJ 92
LenJ92
LenJ92
LenJ92
Sanhﬂxz
SkuﬂJ92
Sanhﬁxz

Sang92
Sang92

Sagww92

lngJ93
lngJ93
lngJ93
Len1J93
Len1J93
Len1J93
Len2J93
Len2J93
Len2- '93
Sanl-9
SanLKXB
Sanhﬂxs

Sang93
SangQ3
SkunMJ93

|n0494
lngJ94
lngJ94
ruxc94
FkuJ94
PkuJ94
LenJ94
LenJ94

N-bwN-ﬁwN-‘QN—ﬁwnAQN-‘ON-‘UN-‘UN-‘QN‘CON-AUN-AQN-AQN-‘UN-bw

83

12.8
1413
1313
14.44

7'.9
11.44

813

813

713

844
1813
121)
111)

313

532

725
615
614

31)
14.1)
1712
1413

726

714

8J'

213

13.2
17.44
532

913

139
431

313

3J'

313
411

(37
421

132

2.44
13.44
1132
1138
—3J’
-313

4013
3615
3613
3613
3812
37.13
38.‘1
4413
45.55
45J'
4431
44.13
4513
4914
4913
4815
4012
4013
4213
4131
4113
4413
37.12
3613
36.55
38.55
3913
3613
39.‘1
4115
4213
3814
401)
4031
33J'
351)
3413
3413
35.44
3512
35.44
3413
33.44
3831
3615

1267
1255
12.55
12.58
1263
1261
12.61
12.80
12.77
1277
1275
1277
1277
1294
1300
12.98
12.57
1263
1268
12.57
1257
1276
12.60
1257
12.56
12.55
1263
12.46
21”
1274
1278
1253
1263
1262
1238
12.44
12.43
1245
12.47
12.50
12.45
12.38
1235
1257
1250

Mandon

Mendon
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548

Len-'94
Sam-‘94
Sam-'94
Sam-'94
Ina-'91
Hur-‘91
Kala-'91
Lon-'91
Mon-'91
Ina-'92
Hur-‘92
Len-'92
Sam-'92

Sagw-‘92

Ina-'93

Lan1 -'93
Lan2-‘93
Sam-'93

Snow-'93

Ina-'94
Hur—‘94
Len-'94
Sam-'94
Inc-'91
ing-‘91
inc-'91
Hot-'91
Hur-‘91
Hur-‘91
Kala-'91
Kala-'91
Kala-'91
Lon-'91
Len-'91
Len-'91
Mon-'91
Mon-'91
Mon-'91
ing-‘92
Inc-'92
Ina-'92
Hat-'92
Hur-‘92
Hur-‘92
Len192

_lwN-th—leA“Ndwmémméwwé‘d—bd—L—L-L—L—l—b—l-L—L—hd-ﬁ—L-L-LmN-lw

-80
6.8
6.2

11.3
10.1
12.4
19.3
20.7
15.5
30.1
19. 5
18.6
28.7
15.9
14.7
18.5
21.0
17.5
12.0
23.4
7. 4
18. 7
23.0
23.7
23.9
26.3
24.1
25.6
27.0
23.6
22. 3
28. 9
31.5
31.9
28.9
26.9
29.6
22.5
21.4
17.9
30.3
37.0
29.4
25.8

35.0
38.8
37.2
34.3
36.0
34. 5
3‘1. 3
30.5
38. 2
40. 4
36.8
36.1
38.6
34. 9
31. 0
34.7
34.8
34. 9
31. 1
33.0
33.0
32.7
27.0
26.6
27.1
30.5
30.9
30.4
28.2
26.7
26.2
30.3
29.4
28.5

26.9 ,

26.4
26. 9
36. 2
35.1
37.3
35. 5
34. 5
35. 5
37.1

2.43
2.58
2.52

2.50
2.60
2.50
2.40
2.40
2. 60
2. 70
2.60
2.60
2.60
2. 60
2. 30
2.50
2.40
2.50
2. 30
2. 40
2.40
2. 40
2. 23
2.24
2.27
2.42
2.41
2. 43
2. 34
2. 26
2.18
2. 37
2. 36
2. 30
2.32
2.27
2.33
2.63
2.59
2. 62
2. 60
2. 55
2.63
2.70

P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548

P2548 '

P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548
P2548

LenJ92
Lon-'92
Sam-'92
Sam-'92
Sam-'92
Saw-'92
Saw-'92
Snow-'92
Inc-'93
lngJ93
lngJ93
Lan1493
Lan1 -'93
Len1 -'93
Lan2-‘93
Lan2-‘93
Len2-‘93
Sam-'93
Sam-'93
Sam-'93
Sagw-‘93
Saw-'93
Saw-'93
Inc-'94
Ina-'94
Inc-'94
Hur-‘94
Hur-‘94
Hur-‘94
Len-'94
LanJQ4
Len-'94
Sam-'94
Sam-'94
Sam-'94

wNAWN-‘wl’od00N-‘(DNAQN-‘wN-‘QN-th-th-ﬁwN-‘OON

85

24.9
24.7
24.2
22.9
24.8
33.9
35.0
36.8
27.0
25.8
29.0
25.4
26.8
24.6
30.7
31.5
28.8
25.1
27.1
26.2
25.6
24.2
21.5
25.7
22.3
21.6
36.9
33.1
31.7
22.6
23.3
23.7
30.1
30.4
33.3

34.9
36.4
33.7
33.2
31.6
33.0
34.2
33.3
29.3
30.1
27.8
32.4
31.5
34.3
32.5
32.3
32.4
31.4
30.9
31.6
29.8
31.4
31.4
26.3
27.3
27.2
28.8
30.3
28.6
26.6
27.8
26.8
28.5
28.1
25.5

2.63
2.72
2.54
2.54
2.46
2.41
2.52
2.46
2.44
2.46
2.32
2.45
2.46
2.54
2.51
2.51
2.50
2.42
2.41
2.42
2.31
2.39
2.35
2.13
2.19
2.24
2.26
2.31
2.25
2.15
2.23
2.17
2.29
2.20
2.08

APPENDIX B

AGRONOMIC AND QUALITY EVALUATION DATA

 

 

 

CM Loo/yea Repﬂcadon Flour yield Proteh Grain yield Test weight
% 94 bu/acre Mn

Angst Ina-93 1 . . 29.7 53
August big-93 2 61.04 8.11 34.8 54
Angst Ina-93 3 57.49 8.53 33. 8 52
Angst Len1-93 1 64.4 7. 35 60.8 55
Angst Len1-93 2 64. 36 7. 96 57.7 54
Angst Len1-93 3 68 .63 7. 33 56.8 54
Angst Len2-93 1 58.55 9 59.1 52
Angst Len2-93 2 59. 87 8.73 63.9 53
Angst LenZ-93 3 60. 46 8. 49 65 54
Angst Sag-93 1 64.18 7. 56 77 .6 55
Angst Sag-93 2 63.6 7. 15 73.1 56
Angst Sag-93 3 57.51 7.05 70.4 54
Angst Snl-93 1 63.31 7. 07 65.9 54
Angst Snl-93 2 65.92 7. 06 69.6 55
Angst Sui-93 3 62.85 7. 52 . .
Angst inc-94 1 61.29 9. 96 76.9 56
Angst Ina-94 2 63.38 9. 75 78.2 57
Angst Ina-94 3 62.22 9.6 74.1 57
Angst Hts-94 1 60.54 9. 72 73.6 59
Angst His-94 2 60. 4 8. 71 67.5 59
Angst Hut-94 3 58. 5 8. 54 70.7 57
Angst Len-94 1 . 8. 85 71.5 54
Angst Len-94 2 58.1 9.01 78.8 56
Angst Len-94 3 58.5 8 .67 67.7 55
Angst 8111-94 1 61.02 9. 56 81.8 57
Angst 8:11-94 2 62.37 9.08 56
Angst 8:11-94 3 59.26 10.35 . 57
Chelsea Inc-93 1 63.84 7 27.3 54
Chebea Ina-93 2 64.47 7. 48 27.7 55
Chesea Ina-93 3 65.32 7. 07 41.7 55
Chelsea Len1-93 1 68.81 6. 96 64.8 56
Chesea Len1-93 2 65.93 7 .21 61.8 57
Chelsea Len1-93 3 67.8 7. 02 62 56
Chelsea Len2-93 1 66.07 8. 74 72.1 56
Chelsea LenZ-93 2 63.63 8.44 76 55
Chelsea Len2-93 3 65.81 8. 67 57.6 57
Chelsea Sag-93 1 70.19 8.04 81.8 58
Chelsea Sag-93 2 67.03 7. 83 81.3 58
Chesea Sag-93 3 67.9 7. 27 83.4 59
Chelsea Snl-93 1 69.05 7.44 89 58
Chebea Snl-93 2 65.86 8.18 81.6 58
Chelsea 8nl-93 3 67.48 8. 49 80.9 59
Chelsea Ina-94 1 61.21 9. 94 76.9 56
Chelsea Ina-94 2 61.53 9. 79 78.2 57
Chebea Ina-94 3 80.51 9.76 74.1 57
Chebea Hts-94 1 63.25 9.1 73.6 59
Chesea Hts-94 2 81 .47 8.51 67.5 59
Chelsea Hts-94 3 61 .22 8.66 70.7 57

Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom

Len-94
Len-94
Len-94
Sill-94
Sid-94
Sui-94
hug-93
bra-93
Inn-93
Len1-93
Len1-93
Len1-93
Len2-93
LenZ-93
Len2-93
Sag-93
Sag-93
Sag-93
Std-93
Sid-93
Sui-93
lag-94
hag-94
819-94
Hts-94

Hts-94
Len-94
Len-94
Len-94
Sal-94
Sui-94
Sal-94
Ina-93
”93
ﬁlo-93
Len1 -93
Len1-93
Len1 -93
Lora-93
Len2-93
Ler12-93
Sag-93
Sag-93
Sea-93
Sui-93
Sal-93
8nl-93
hug-94
Ina-94

UN-‘UN-‘UN‘UN-‘UN-‘UNHUN‘UN-b“NdUN-‘UN-‘UN-ﬁUN-‘UN-‘UN-‘UN-‘UN-ﬁ

61.33
62.19
61.26
61.39
61.53
58.01
66.01
62.25
64.42
66.63
66.08

66.74
65.36
64.63
65.85
67.43
66.96
65.41
67.89
66.85
66.69
65 .6
64.5
67 .24
63. 37
60. 69
63. 36
66.33
65.12
65.5
63. 99
66.45
63 .6
59.53
65.72
68. 04
62.01
65.46
62.95
66.54
65.74
64.98
64.83
60.12
66.27
60.77
62.72
61.63

87

7. 69
8 .66
7. 39
9.64
9. 3
12.21
7.6
8. 65
8. 87

8.17
8.71
9.07
9.28
9.13
7.32
8.09
7. 99
8. 55
8.46
8.52
9.53
9.31
9.21
8. 73
8. 94
7. 75
8.53
7.94
8. 84
9.03
9.28
10.61
7.02
8.16
7.9
7. 84
7.05
8.91
8.82
9.07
8.72
7.83
6.76
7.19
7.56
7.4
7.22
9.22
10
9.65

78.3
75
75.1

27.8
44.4
47.8
55.1
67.9
66.7
66.7
74.1
69.5
84.2
79.1
87.1
91.1
94.3
80. 2
81.8
78 .2
84.5
82.7
73.6
73.2
81
71.6
74
72.8
87.3
91.6
24.8
42.8
41.9
69.7
67.4
57.8
76.4
80. 9
80. 3
76. 5
77.1
85.7
75.3
81.2
81). 9
90. 9
93.6
78

(”08010100040463
‘1‘! GOOONON

388833883338383883832383

asassassassaxaxaa

Hts-94
Hts-94
His-94
Len-94
Len-94
Len-94
Snl-94
Sui-94
Snl-94
Inc-93
lug-93
Ina-93
Len1-93
Len1 -93
Len1 ~93
Len2-93
LenZ-93
Len2-93
Sec-93
Sag-93

Sag-93
Sol-93

Sci-93
Sri-93

Ina-94

Ina-94
1111-94
His-94
Hts-94
Len-94
Len-94
Len-94
Snl-94
Sri-94
Snl—94

UN-‘UN-AUNduN-buN-‘UN-AUN‘UN-‘UN-‘UN-‘UN-‘UN‘

68.1
64.01
66.37
65.83

61 .49
65.68
67.24

65.15
61.7
64.42
64.31
63.79
66.05
66.08
65.18
63.66
66.67
65.09
63.43
64.42
65.99

64.16
64.88
65.16
64.94
64.51
69.68
61.78
60.65
62.51
65.27
65.3
62.78

10.03
8.63
8.67
8.38
8.34
8.04
8.44

8.2
8.8

10.48

10.86

10.59
9.94

10
10.3

10.31

10.03
9.63
9.02
8.29
9.49

10.47

10.95

1 1.08

10.89

10.58

1 1.01

10.14

10.31
9.98
9.83
8.85
10.93
10.09
12.47

77
74.6
60.6

85

89.5
100.2
26.8
37
35.7
52.6
55
70.5
64.6
72.9
69.8
89.7
83.7
82.7
83.9
91 .6
65
69.4
71 .1
61 .6
52.7
55.5
76.8

69.6
68.8
70.5
58.3

83388838888

59

833238888888°

 

 

 

Cultivar Loclyear Replication Cookie Cookie Mixograph Mixograph pk.
spread height pk time height
cm cm min cm
Augusta Ina-93 1 . . . .
Augusta Ina-93 2 7.85 0.94 3.7 4.8
Augusta inc-93 3 7.58 0.915 3.9 5.15
Augusta Len1-93 1 7.9 0.9 3 4.6
Augusta Len1-93 2 7.94 0.75 3.9 4.8
August Len1-93 3 7.84 0.95 3.7 4.3
Augusta Len2-93 1 7.7 1.015 4.1 5.3
Augusta Len2-93 2 7. 54 1 3.9 5.2
Augusta Len2-93 3 7. 59 1.1 3.5 5
Augusta Sag-93 1 8.09 0. 9 3.75 4.75
Augusta Sag-93 2 7. 88 0. 95 3.8 4.4
Augusta Sag-93 3 8. 01 0.95 3.4 4.3
August Sol-93 1 7.99 0.985 4 3.8
Augusta Sn1-93 2 7.9 1 .025 3.6 4.1
Augusta Sui-93 3 7.99 1 4.4 4.35
Augusta inn-94 1 7. 96 0.915 3.2 6.2
Augusta inc-94 2 7. 82 0.85 3.25 6.1
Augusta inc-94 3 8.08 0. 89 3.25 5.9
Augusta Hut-94 1 7. 8 0. 9 3.5 8.1
Augusta l-lnr-94 2 7. 72 0. 915 3.9 5.4
Augusta Her-94 3 7. 6 0.94 4.2 5.3
Augusta Len-94 1 7. 93 0.9 4.1 5.4
Augusta Len-94 2 7. 99 0.8 4.4 5.3
August Len-94 3 7. 74 0. 95 4 5
Augusta Snl-94 1 7. 6 0.925 3.4 8
Augusta Sni-94 2 7. 74 0.965 3.9 5.85
Augusta Snl-94 3 7.48 1 3.7 8.45
Chelsea inn-93 1 7. 88 0.95 3 4.4
Chelsea inc-93 2 7. 93 0.925 2.6 4.6
Chelsea inc-93 3 7.76 0.915
Chelsea Len1-93 1 7.85 0.975
Chelsea Len1-93 2 8.06 0.95
Chelsea Len1-93 3 7. 9 0.9 . .
Chelsea Len2-93 1 7. 78 1 3.6 5.15
Chelsea Len2-93 2 8.04 0.95 3.5 5.1
Chelsea Len2-93 3 7. 86 0.95 2. 6 4.5
Chelsea Sag-93 1 8. 31 0.925 3. 55 4.9
Chelsea Sag-93 2 8.33 0.775 3 4.5
Chelsea Sag-93 3 8.2 0. 89 2.8 4.2
Chelsea Sui-93 1 7. 94 0. 9 2.9 4.6
Chelsea Snl-93 2 8.01 0. 95 3.4 5.1
Chelsea Sni-93 3 8.08 0.89 3. 8 5.3
Chelsea inc-94 1 7. 79 0.9 3. 75 6.2
Chelsea inc-94 2 7. 65 0.99 4 5.8
Chelsea inc-94 3 7. 64 1 4 5.8
Chelsea Hnr-94 1 7. 52 1 3.8 5. 7
Chelsea Hut-94 2 7.93 0.89 5 5. 45

Chelsea

Chelsea

Chelsea

Chelsea

Chelsea

Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Fmedorn
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom
Freedom

Hur-94
Len-94
Len-94
Len-94
Sol-94
Sui-94
Sui-94
Ina-93
inc-93
lug-93
Len1-93
Len1-93
Len1-93
Len2-93
Len2-93
Len2-93
Sag-93
Sag-93
Sag-93
Sui-93
Sui-93
Sol-93
inc-94
inc-94
inc-94
Hur-94
Her-94
Her-94
Len-94
Len-94
Len-94
Snl-94
Sui-94
Sni-94
inc-93
inc-93
inc-93
Len1-93
Len1-93
Len1-93
Len2-93
Len2-93
Len2-93
Sag-93
Sag-93
Sag-93
Sui-93
Sui-93
Sni-93
inc-94
Ina-94

N-‘UN-AODNJUN-BUN-‘UN-AUN-AUN-‘UN-‘ON-‘QN‘UN-‘UN-‘ON-‘UN‘UN‘GN‘O

7.76
7.54
7. 68
7. 93
7. 7
8.11
7.59
7.69
7.81
7.46
7.65
7.86
7.36
7. 68
7. 61
7. 72
7. 48
7. 74
7.74
8.01
7.7
7. 64
7. 69
7.62
7.43
7.64
7.56
7. 36
7. 33
7. 88
7. 65
7. 69
7. 68
7. 27
7. 69
7. 64
7. 59
7. 72
7. 56
7. 79
7.59
7.63
7.71
7.85
7.64
7. 62
7. 64
7. 74
7.76
7.41
7.48

0.925
0.9

0.925
0.975
0.825
0.89
13 925
0. 9
13 95
1.05
0.925
0.95
0.95

1.025
0.965
0.99
0. 85
0.84
0.865
0.95
1.05
0.875
1.015
0.975
1.025
0.965
1.05
0.825
0.95
0.975
0.85
1.05
0. 95
1.1
0. 95
0. 9
1 625
0.875
1.04
0.865

1 .05
0.95

1.05
0.9
1.1

0. 95

4.5
3.8
4.8
3.8
3.9
3.8
3.8

3. 4
2. 75
3.5
4.5
3.8
3.7
3.4
3.5
4.2

4.3
3.5
3. 9
3. 45
3.5
3.6
5. 5
4. 35
3.6
3.9
3.5
4.8
4.3
3.8
3.5

5.55
5.15
5.2
4.8
6.2
6.4
7.3
4.4
4.8
5.1
4. 85
4. 7
4. 95
5. 3
4. 85
5.1
4.4
4.8
4. 8
4. 75
4.9
4.9
5. 4
4. 95
5.15
5.1
5.1
4.8
5.2
4.9
4.8
5. 3
5. 65
6.2

Mendon

Mendon

Mendon
Mendon
Mendon
Mendon
Mendon
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain

Twain
Twa1n
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain
Twain

Twain

inc-94
Her-94
Hnr-94
Hnr-94
Len-94
Len-94
Len-94
Sui-94
Snl-94
Sal-94
Ina-93
inc-93
Ina-93
Len1-93
Len1-93
Len1-93
Len2-93
Len2-93
Len2-93
Sag-93
Sag-93
Sag-93
Sui-93
Sui-93
Snl-93
inc-94
inn-94
inc-94
Hnr-94
Hut-94
Her-94
Len-94
Len-94
Len-94
Sni-94
Sni-94
Sni-94

UNdUN-‘QNddedeUNdUNdUNdQN-LUN-‘UN-‘UN-‘U

7.6
7.31
7.4
7.73
7.26
7.61
7.36
7.51
7.38

7.95
7.53
7.48
7.65
7.54
7.75
7.54
7.65
7.54
7.84
7.79
7.87
7.93
7.86
7.67
7.83
7.67
7.65
7.75
7.83
7.73
7.25
7.58
7.61
7.61
7.45
7.31

91

0.925
0.95
1.065
0.99
1.04
0.85
1.025
1.05
0.95

0.865
1.05
1.05

1.1
1.1

Add

0.85
0.9
0.9

0.94

0.85

0.94
0.925
0.975

1.05

0.95
0.925

1.15

0.9

0.99

0.95

1.05

0.99

2.9
2.6
2.4
2.7
2.7

2.7
2.8

3.5
3.7
3.3
2.9
2.9
2.7
2.8
2.9
3.6
3.9
3.3

3.2
3.5
3.4
3.6
3.2

5.65
5.9
5.5
5.6

. 5.4

5.7
5.6
5.8
5.4
5.1
4.8
5.3
6.1
5.95
6.1
5.8
6.05
5.6
5.9
5.7
5.4
5.5
5.5
6.5
5.8
6.6

   

RIES

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