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Genotype X environment interactions of triticale in Florida

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Genotype X environment interactions of triticale in Florida
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Calhoun, D. Steven, 1957-
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vii, 63 leaves : ill. ; 28 cm.

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Subjects / Keywords:
Arithmetic mean ( jstor )
Correlation coefficients ( jstor )
Crops ( jstor )
Genotypes ( jstor )
Grains ( jstor )
Heritability ( jstor )
Linear regression ( jstor )
Tillers ( jstor )
Triticale ( jstor )
Wheat ( jstor )
Agronomy thesis Ph. D
Dissertations, Academic -- Agronomy -- UF
Grain -- Florida ( lcsh )
Triticale -- Florida ( lcsh )
City of Quincy ( local )
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bibliography ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 1988.
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Includes bibliographical references.
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Typescript.
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Vita.
Statement of Responsibility:
by D. Steven Calhoun.

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Full Text


GENOTYPE X ENVIRONMENT INTERACTIONS OF
TRITICALE IN FLORIDA
By
D. STEVEN CALHOUN
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1988


To the God I know too little of
To the sons I knew too briefly,
and
To Diane, my love and my friend


ACKNOWLEDGMENTS
I would like to express my genuine appreciation to my
chairman, Dr. Paul L. Pfahler, for his expert guidance in the
preparation of this dissertation, and for his not-for-credit
course, "The Theory and Practice of Science, Writing, and
Academic Administration." I also offer sincere thanks to my
co-chairman, Dr. Ron D. Barnett, for his guidance and
logistical support in conducting this research, for dragging
me around the country looking at wheat, for his plant
breeding insights, and for his friendship.
Thanks are also due my committee members, Dr. Joe E.
Funderburk, Dr. Kuell Hinson, Dr. David D. Baltensperger, and
Dr. David A. Knauft, who at various times have given moral
support and contributed greatly to my academic training.
I cannot neglect those whose labor made this research
possibleDr. Ann Zimet, Mr. Alex Thompson, Mr. David Castro,
and Mr. Craig Bundy.
I would like to thank James Pier Muir whose fishing and
philosophy helped me retain what little grip on reality I can
now claim.
Finally, I thank my wife, Diane, whose love, support,
and typing skills I cannot live without.
iii


TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS i i i
LIST OF TABLES V
LIST OF FIGURES vii
ABSTRACT viii
INTRODUCTION 1
MATERIALS AND METHODS 7
Analysis of Pure Lines 8
Analysis of Blends 10
RESULTS 12
Stability of Pure Lines 12
Grain Yield 12
Kernel Weight 18
Tiller Number 22
Plant Height 24
Test Weight 26
Kernel Number per Tiller 28
Stability of Blends 28
Grain Yield 28
Test Weight 34
DISCUSSION 38
Pure Lines 3 8
Blends 48
APPENDIX
DATA TABLES 52
REFERENCES 58
BIOGRAPHICAL SKETCH 62
IV


LIST OF TABLES
Table Page
1. Mean squares for the analysis of variance and
regression partition for grain yield (GY),
kernel weight (KW), and tiller number (TN), and
plant height (PH) 13
2. Mean grain yield (GY), kernel weight (KW),
tiller number (TN), plant height (PH) and test
weight (TW) of six triticale genotypes by year,
location and N level 14
3. Stability parameters [mean, regression coeffi
cient (b) and deviation mean square (DMS)] for
grain yield (GY), kernel weight (KW) tiller
number (TN), plant height (PH) and test weight
(TW) of six hexaploid triticale genotypes 15
4. Means and regression coefficients (b) of six
triticale genotypes in above average and below
average environments 17
5. Simple correlation coefficients between sta
bility parameters [regression coefficient (b)
and deviation mean square (DMS)] for grain yield
(GY) and other traits [kernel weight (KW),
tiller (TN), plant height (PH), and test weight
(TW) ] 19
6. Simple correlation coefficients among kernel
weight (KW), tiller number (TN), and Kernel
number per tiller (KN) by genotype
7. Simple correlation coefficients among kernel
weight (KW), tiller number (TN), and kernel
number per tiller (KN) (as a proportion of their
environmental means) by genotype
8. Mean squares for the analysis of variance and
regression partition for test weight of six
triticale genotypes
v


Table Page
9.Mean squares (x 107 for analysis of variance and
regression partition for grain yield of three
groups of populations 30
10. Mean grain yield (GY) and test weight (TW) of
three groups of triticale populations (each
group consisting of two cultivars and one blend
made up half from each cultivar) by year,
location, and N level 31
11. Observed (O) and expected (E) stability param
eters [mean, regression coefficient (b) and
deviation mean square (DMS)] for grain yield of
three groups of triticale populations, each
group consisting of two cultivars and one blend
made up half from each cultivar 33
12. Mean squares for analysis of variance and
regression partition for test weight of three
groups of triticale populations 35
13. Observed (O) and expected (E) stability param
eters [mean, correlation coefficient (b) and
deviation mean square (DMS)] for test weight of
three groups of triticale populations, each
consisting of two cultivars and one blend made
up half from each cultivar 36
vi


LIST OF FIGURES
Figure Page
1. Kernel weight of six triticale genotypes vs.
environmental mean grain yield 23
2. Tiller number of six triticale genotypes vs.
environmental mean grain yield 25
3. Seed number per tiller of six triticale geno
types vs. environmental mean grain yield 29
4. Comparison of regression equations of Florico
and IRA triticale 41
5. Comparison of the performance of genotype A (b >
1.0), genotype B (b < 1.0), and an ideal geno
type with b > 1.0 in above average environments
and b < 1.0 in below average environments 4 2
Vll


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
GENOTYPE X ENVIRONMENT INTERACTIONS OF
TRITICALE IN FLORIDA
by
D. Steven Calhoun
April 1988
Chairman: Dr. P.L. Pfahler
Cochairman: Dr. R.D. Barnett
Major Department: Agronomy
Six triticale (XTriticosecale Wittmack) genotypes
('Florida 201', 'Beagle 82', 'Florico', Ira(2)M2AxCml/Ia-Trr,
FL7845-TQ-G32-J2, CT3477) were evaluated for mean performance
and environmental stability of grain yield (GY), kernel
weight (KW), tiller number m-2 (TN), plant height (PH) and
test weight (TW). Three 1:1 mechanical seed blends (Florida
201 + Beagle 82, Florida 201 + Florico, Beagle 82 + Florico)
were tested for mean performance and environmental stability
of GY and TW. Sixteen environments (E) including two years
(1985-86, 1986-87), two locations (Quincy30 35' N Lat,
Marianna30 46' N Lat), and four N topdress levels (0, 55,
110, 165 kg ha--*-) were tested. Analysis of variance and
linear regression techniques were applied to the data from
the six genotypes and each individual blend combination (the
blend and both component cultivars).
Significant genotype (G) effects were observed for all
traits. Among the 6 genotypes, significant GE interactions
viii


and differences among genotypes in their linear response (b)
to E were observed for all traits. Differences among
genotypes for deviation mean squares (DMS) were observed for
GY, TN, PH, and TW. No association was found between
stability parameters (b and DMS) for GY and the same
parameters for PH or TW. Means and b values for GY, KW, and
PH were positively correlated. Stability of individual yield
components (KW and TN), rather than yield component
compensation, seemed to be the major factor in GY stability.
Florida 201 + Florico yielded approximately equal to its
higher yielding component, Florico. Other blends had yields
intermediate between their component cultivars. Significant
GE effects on GY and TW were observed in two of the blend
combinations. All blend combinations exhibited differences
among populations in b values for GY while one blend
combination exhibited differences in b values for TW. Beagle
82 + Florico had a lower DMS than the component average,
while Florida 201 + Beagle 82 had a higher DMS than the
component average. The use of blends appeared to be a
desirable method to enhance yield and environmental
stability. However, not all blends showed enhanced
performance.
ix


INTRODUCTION
Triticale (XTriticosecale Wittmack) is a man-made small
grain species which combines the high grain yield potential
of wheat with the broad environmental adaptability and high
lysine content of rye. Early triticales were produced by
crossing and chromosome doubling of hexaploid wheat (Triticum
aestivum L.) and rye (Secale cereale L. ) but progeny of
these crosses were generally meiotically unstable. After
techniques were developed to excise and artificially culture
immature embryos, it became possible to produce hexaploid
triticale by crossing and chromosome doubling tetraploid
wheat (Triticum turgidum L. var durum L.) and rye.
Currently, all commercially acceptable cultivars are
hexaploid (Varughese et al., 1987).
Triticale has not been tested extensively in the
southeastern United States, but excellent potential for human
and animal feed production has been shown in preliminary
trials (Barnett and Luke, 1979; Kalmbacher et al., 1987;
Meyer et al. 1987). 'Beagle 82', the first cultivar
recommended for grain production in the area, was released in
1982 (Barnett et al., 1982). By 1985, over 12,000 ha of
Beagle 82 were grown in the Florida-Georgia-Alabama area.
1


2
'Florida 201', released in 1985, is more productive and has a
higher test weight than Beagle 82. Over a six-year period,
Florida 201 triticale has yielded at the same level as
'Florida 301', a leading wheat cultivar in Florida (Calhoun
and Barnett, 1986).
Little is known, however, regarding the response of
triticale genotypes to the diverse environmental conditions
encountered in this region. Florida is marginal for small
grain production because of many factors including large
yearly fluctuations in temperature and precipitation during
the winter months. Thus, it is necessary that cultivars be
adapted to a broad range of environments.
Selection among genotypes in a crop improvement program
is based primarily on performance in breeding nurseries and
yield tests on research stations. Such tests are normally
conducted under intensive management which may or may not
exist in the environment where the cultivar will ultimately
be grown. A genotype which gives excellent performance under
very favorable conditions often is not the best genotype when
grown under less favorable conditions due to genotype x
environment interactions. Selection based on environmental
stability as well as mean performance can ensure that
cultivars which are released will perform adequately under a
wide range of growing conditions.
Numerous measures of stability and methods of analysis
have been developed to deal with genotype x environment


3
interactions [e.g. ecovalence, stability index, multi-
criteria clustering, and independent environmental
measurement proposed by Wricke (1962), Langer et al. (1979),
Lefkovitch (1985), and Freeman and Perkins (1971),
respectively]. While most stability analysis methods are
based on the linear regression of genotype performance on
some measure of environmental value, the regression technique
proposed by Breese (1969) and others is the best known and
most widely used. For the Breese (1969) analysis, yield (or
other parameters) of each genotype tested is regressed on an
environmental index which is the mean of all genotypes in a
specific environment. Three values are used to represent the
genotypic response over all environments: 1) overall mean, 2)
regression coefficient (b)the genotypic response to the
environment attributable to linear regression which is
predictable, and 3) deviation mean square (DMS)the
deviation of the genotypic response from linear regression
which can be considered unpredictable.
From linear regression analysis, a desirable cultivar
would have a high mean yield, regression coefficient
approaching unity, and DMS equal to zero. A genotype with
b<1.0 may be undesirable because it would not respond well to
better environments or improved management practices with
increased yield. In addition, very low b values are often
associated with low yield (Eberhart and Russell, 1966). On
the other hand, a genotype with b much greater than 1.0 would


4
be undesirable because it would be subject to severe yield
losses when growing conditions were unfavorable. A small DMS
value, indicating predictable response to environment, would
be desirable.
Verma and Chahal (1978) proposed an "ideal" genotype
which would be relatively insensitive to unfavorable
environments but would be responsive to favorable environ
ments. Such a genotype would have b<1.0 in unfavorable
environments and b>1.0 in favorable environments.
The use of multilines, or mechanical seed blends of two
or more genotypes has been proposed as a method of improving
the environmental stability of yield and other quantitative
traits by increasing the genetic heterogeneity of the
resulting population (Pfahler and Linskens, 1979; Singh and
Bains, 1984; Norden et al., 1986). Most studies of multiline
performance have shown that genetic diversity does not
necessarily insure enhanced stability. Rather, each proposed
blend must be tested in a number of environments. The
stability of triticale blends has not been examined.
Few stability analyses of triticale grain yield have
been reported; all found significant genotype x environment
interactions and identified lines which were both high
yielding and stable (Sandha et al. 1980; Kaltsikes, 1971;
Sapra, 1985; and Sinha et al., 1986). However, most of these
studies were conducted outside the southeastern United States


5
using material unadapted to this region. Knowledge of the
grain yield stability of genetic material adapted to this
area would facilitate the development of high yielding,
stable cultivars.
Even less is known regarding stability of other
characters in triticale, such as plant height and test
weight, or the relationships among stability parameters for
these traits. An understanding of these relationships would
help breeders develop effective strategies for improving
stability of these traits.
The response pattern of yield components (kernel weight,
tiller number per unit area, and kernel number per tiller)
which results in the desired grain yield response pattern is
also poorly understood. There is considerable debate as to
whether stable grain
yield is a
result
of stable
yield
components
or a
result of compensatory
shifts in
yield
component
levels
as
proposed by
Grafius
(1956) .
More
information on the relationship between grain yield stability
and stability of yield components would be desirable.
The present study was undertaken to evaluate the mean
performance and environmental stability of representative
triticale cultivars and advanced breeding lines for grain
yield, kernel weight, tiller number, plant height, and test
weight. Broad sense heritability estimates for these traits
also were determined. In addition, mean performance and


6
environmental stability of grain yield and test weight for
three mechanical seed blends were determined.


MATERIALS AND METHODS
Three cultivars ('Florida 201', 'Beagle 82', 'Florico'),
three advanced breeding lines from the Florida triticale
program [Ira(2)M2A-Cml/Ia-Trr (IRA), FL7845-TQ-G32-J2
(FL7845), CT3477], and the three possible two-way blends of
the cultivars (i.e. Florida 201 + Beagle 82, Florida 201 +
Florico, Beagle 82 + Florico) were tested. Blends were
composed of a mechanical mixture of an equal number of viable
seeds from both component cultivars. All triticale genotypes
were hexaploid and had a spring growth habit.
Entries were tested in two cropping years (1986 and
1987) at two Florida locations (Quincy30 35' N Lat,
Marianna--30 46' N Lat). A uniform seeding rate of 360
viable seed m-2 was used in all environments. Planting dates
at Quincy were 11 December 1985 and 18 December 1986 for the
cropping years 1986 and 1987, respectively. Marianna
planting dates were 20 December 1985 and 4 February 1987 for
the cropping years 1986 and 1987, respectively. The Quincy
plantings were irrigated as needed. Pre-plant or early post
plant fertilizer was broadcast mechanically on all plots at a
rate of 25, 22, and 62 kg ha-1 of N, P and K, respectively.
7


8
Experimental units consisted of 6 rows 20 cm apart and
3.6 m long, trimmed to 3.0 m before harvest. Plots were
arranged in a randomized complete block of three replications
with four N topdress levels (0, 55, 110, and 165 kg ha-1 of N
as ammonium nitrate, applied during the tillering stage) as
main plots and genotypes or populations as subplots.
Plant height (PH) and tiller number (TN) were determined
prior to harvest. Grain yield (GY) was determined by combine
harvest of entire subplots. Kernel weight (KW) was based on
weight of a 200 seed sample from each subplot. Test weight
(TW) was not measured at Marianna in 1987 due to insufficient
seed from many plots. Kernel number per tiller (KN) was
calculated from GY, KW, and TN.
Analysis of Pure Lines
Analyses of variance (AOV) and linear regressions were
calculated for GY, KW, TN, PH, and TW of the six pure lines
(three cultivars and three breeding lines).
The AOV for all characters except TW included 16
environments (E) [4 N topdress levels (N) x 2 locations (L) x
2 years (Y) ] as well as the main and interaction effects of
N, L and Y, all tested with the main plot error. For the
analysis of TW, three location-year combinations (Quincy
1986, Marianna 1986, Quincy 1987) were considered three sites
(S) For TW, the AOV included 12 E (4 N x 3 S) and the
effects S, N, and SN, all tested with the main plot error.


9
For all characters measured, the effects of genotype (G) and
its interactions were tested with the subplot error.
Linear regressions of individual genotype performance
vs. environmental means were used to partition genotype x
environment (GE) effects into effects due to heterogeneity of
slopes and deviations from linear regressions as proposed by
Breese (1969). Significance of heterogeneity and deviation
effects were tested with the subplot error. Regression AOV
was performed for each genotype separately to test signifi
cance of individual DMS. All regression coefficients (b)
were tested for significant difference from 1.0 using t-
tests.
Simple correlation coefficients (df=4) were calculated
for mean vs. b and DMS for all parameters measured and for GY
stability (b and DMS) vs. stability of KW, TN, PH, and TW.
For each genotype, simple correlation coefficients (df=14)
among KW, TN, and KN were calculated, and KW, TN, and KN were
regressed against environmental means for GY.
Adjusted values for KW, TN, and KN were calculated by
dividing KW, TN, or KN for each genotype at each environment
by the corresponding environmental mean. Simple correlation
coefficients among adjusted values for KW, TN, and KN were
then calculated for each genotype.
The environments were divided into two subsets, as
proposed by Verma and Chahal (1978), one which represented
favorable or above average environments and one which


10
represented poor or below average environments. Linear
regressions were calculated for each genotype in both sub
sets. A genotype with b<1.0 would be desired in poor
environments, whereas a genotype with b>1.0 would be desired
in favorable environments. A genotype which had b<1.0 in
poor environments but b> 1.0 in favorable environments would
be considered ideal for a broad range of environments.
Broad sense heritability was estimated for GY, KW, TN,
PH, and TW by dividing the total phenotypic variance (sum of
variances of G, E, and GE) into the variance of G (Falconer,
1983) .
Analysis of Blends
Analysis of variance and linear regressions were
calculated for GY and TW of three groups of populations
involving blends: Group 1 = Florida 201, Beagle 82, and
Florida 201 + Beagle 82; Group 2 = Florida 201, Florico, and
Florida 201 + Florico; Group 3 = Beagle 82, Florico, and
Beagle 82 + Florico. Sources of variation and F tests in the
AOV and regression partition for each group were the same as
described above for the analysis of pure lines. For each
group, genotypic performance was regressed on the environ
mental means of the three populations involved, and the GE
interaction was partitioned into heterogeneity and deviations
effects. Significance of DMS and b values were tested as
described above for pure lines. Expected values for


11
stability parameters (mean, b, DMS) of each blend were the
mean of the corresponding values of the two component
cultivars.


RESULTS
Appendix Tables A-l, A-2, A-3, A-4, A-5, and A-6 present
means (over three replications) for GY, KW, TN, KN, PH, and
TW, respectively, of each genotype or blend in sixteen
environments.
Stability of Pure Lines
Grain Yield
The AOV and regression partition for GY are shown in
Table 1. Environmental indices (i.e. environmental means)
used in linear regressions are presented in Table 2.
Genotype means differed significantly and ranged from 2550 kg
ha-1 for Florico to 1460 kg ha-1 for FL7845 (Table 3).
Environment had a highly significant (P<0.01) effect on
GY with environmental means ranging from 3610 kg ha--1- in 165
kg ha-1 N plots at Quincy in 1987 to 230 kg ha-1 in 0 kg ha-1
N plots the same year at Marianna (Table 2). The effects of
Y and L represented uncontrollable E effects and were highly
significant (P<0.01) as were the various interaction effects
among the environmental factors (Table 1) The mean square
for L was the largest for this trait. Nitrogen topdress
level was a management practice and thus represented a
12


Table 1. Mean squares for the analysis of variance and regres
sion partition for grain yield (GY), kernel weight
(KW), tiller number (TN), and plant height (PH).
Source
df
GYa
KW
TNb
PH
Genotype (G)
5
8,732**
1,296**
817**
7,641**
Environment (E)
15
24,320**
680**
357**
2 199**
Year (Y)
1
23,726**
1,667**
1,924**
5,236**
Location (L)
1
278,109**
7,892**
820**
8,633**
YL
1
7,369**
99
249*
6,044**
N level (N)
3
12,790**
1 3
191**
2,790**
YN
3
7 7 0 *
44
92
226
LN
3
1,583**
14
243**
213
YLN
3
1,910**
22
193**
91
GE
75
4 10**
10**
96**
98**
Variance partition
GY 5
1,627**
15**
342**
151**
GL
5
2,015**
62**
203**
587**
GYL
5
582**
5
366**
273**
GN
1 5
272**
8
100**
37
GYN
15
2 0 3 *
6
23
25
GLN
15
49
5
30
37
GYLN
15
137*
3
27
53
Regression partition
Heterogeneity 5
1,481**
67**
122**
623**
Deviation
70
333**
6
94**
60*
Replicationc
8
7 9 3 *
33
25
388*
Main plot error1*
24
217
20
36
136
Subplot errore
160
98
5
20
40
*
a
b
c
d
e
** Significant at the 0.05 and 0.01 probability level, respectively.
Mean square x 10-3.
Mean square x 10~2.
Replication within years and locations.
Used to test significance of E, Y, h, N, YL, YU, LN, YLN, Replication.
Used to test significance of G, GE, GY, GL, GR, GYL, GYN, GLN, GYLH,
Heterogeneity, Deviation .


tr cu
Table 2
Mean grain yield (GY), kernel weight (KW), tiller number (TN),
plant height (PH), and test weight (TW) of six triticale genotypes by
year, location, and N level. Values represent environmental
indices used in regression analysis.
Year3 Location
N level*3
GY
KW
TN
PH
TW
kg ha-1
kg ha-1
mg seed-1
no. m-2
cm
iQ
f
1
H
1986 Quincy
0
2720
46
271
97
700
55
3360
43
336
101
710
110
3160
43
371
102
700
165
3480
45
334
103
690
Marianna
0
1060
36
295
89
680
55
1620
37
318
104
680
110
1780
34
308
100
670
165
1690
34
330
103
650
1987 Quincy
0
1850
40
267
91
650
55
2850
42
282
103
660
110
3400
41
307
105
660
165
3610
41
324
108
650
Marianna
0
230
29
254
68

55
890
30
280
87

110
750
29
245
86

165
700
29
190
84

LsdQ.05
350
3
5
9
10
Year harvested.
Topdress N applied.


Table 3
Stability parameters [mean, regression coefficient (b), and
deviation mean square (DMS)] for grain yield (GY) kernel weight
(KW), tiller number (TN), plant height (PH), and test weight (TW)
of six hexaploid triticale genotypes.
Stability
parameter
Genotype
GY
KW
TN
PH
TW
kg ha-1
mg seed-1
no. m-2
cm
g L-1
Mean
Florida 201
2290
42
288
102
690
Beagle 82
1730
39
240
103
660
Florico
2550
43
299
106
720
IRA
2440
31
362
79
670
FL7845
1460
40
262
104
650
CT3477
1970
31
317
80
670
LSD. 05
150
1
21
3
10
ba
Florida 201
1.05
1.06
0.83
0.95
1.10
Beagle 82
0.97
0.96
1.42**
1.10
1.07
Florico
1.27**
1.33**
0.50*
1.19
1.15
IRA
1.02
0.77**
0.69
0.53**
0.96
FL7845
0.83**
1.05
1.24
1.48**
0.48**
CT3477
0.88*
0.83**
1.31
0.67**
1.20*
LSD.05
0.05
0.07
0.21
0.11
0.11
DMSb
Florida 201
126,453
4.830
1,116
40.32
252**
Beagle 82
215,139**
1.803
4,621**
27.27
150
Florico
363,582**
6.165
10,041**
34.29
256**
IRA
222,369
5.964
12,073**
59.16
498**
FL7845
244,779**
6.999
9,520**
103.26
749**
CT3477
516,867**
3.504
9,866**
38.55
115
a *, ** Significantly different than 1.0 at the 0.05 and 0.01 probability
levels, respectively.
b *, ** Significantly different than 0.0 at the 0.05 and 0.01 probability
levels, respectively.


16
controllable E effect. The influence of N was highly
significant, but
in
most
cases, GY did not
respond to N
topdress levels
above
55
kg ha-1 (Table 2) .
However, at
Quincy in 1987,
GY
in
the 110 kg ha-1
N plots was
significantly greater than in 55 kg ha-1 N plots.
The GE effect was also highly significant, and G
interacted significantly with all environmental factors and
most interactions among environmental factors (Table 1). In
the regression partition, the effect of heterogeneity of
slopes was highly significant (P<0.01) with b values ranging
from 0.83 for FL7845 to 1.27 for Florico (Table 3).
Individual DMS were also significantly different (Table 1)
with DMS values ranging from 126,453 for Florida 201 to
516,867 for CT3477 (Table 3).
Mean GY was significantly (P<0.05) correlated with
corresponding b values (r=0.83, df=4), but not DMS (r=0.02,
df=4).
Table 4 presents regression coefficients calculated for
each genotype in above and below average environments. Only
Florida 201 fit the criteria for an "ideal" genotype over a
broad range of environments (i.e. b>1.0 in above average
environments and b<1.0 in below average environments).
The broad sense heritability estimate for GY (+ standard
error) was 0.11 + 0.03.


Table 4. Means and regression coefficients (b) for grain yield of six
triticale genotypes in above average and below average
environments.
Environment set
Genotype
Above
average
Below
average
Mean
b
Mean
b
kg ha-1
kg ha-1-

Florida 201
3380
0.96
1200
0.69*
h-1
Beagle 82
2680
0.91
770
T. 00
Florico
3890
1.06
1210
0.88
IRA
3340
1.31*
1530
1.37*
FL7845
2310
0.58**
610
0.79**
CT3477
2730
1.19
1210
1.27
LSD0.05
140
0.17
100
0.13
* *
/
Significantly different than 1.0 at the 0.05 and 0.01
probability levels, respectively.


18
Kernel Weight
The AOV and environmental indices for KW are presented
in Tables 1 and 2, respectively. The effect of G was highly
significant (Table 1) with genotype means ranging from 31 mg
seed-1 for CT3477 to 43 mg seed-1 for Florico (Table 3).
The AOV for KW indicated highly significant (P<0.01)
effects of E, Y, and L (Table 1) In the absence of
significant interaction effects, comparisons can be made
between years and locations. KW was consistently higher at
the Quincy location and in 1986 (Table 2) Means over the
location-year combinations ranged from 44 mg seed-1 at Quincy
in 1986 to 29 mg seed-1 at Marianna in 1987.
The effect of GE was highly significant (P<0.01)
although G interacted only with Y and L (Table 1) The
regression partition indicated that genotypes differed
significantly (P<0.01) in b values but not in DMS (Table 1) .
The b values ranged from 0.77 for IRA to 1.33 for Florico
(Table 3).
Mean KW was significantly (P<0.05) correlated with b
(r=0.88, df=4), but not DMS (r=0.15, df=4). The correlation
coefficient between b for KW and b for GY was relatively high
(r=0.65, df=4), but not statistically significant (P>0.05)
(Table 5). KW had a significant positive correlation with KN
in all genotypes and with TN in Florida 201 and Beagle 82
(Table 6). Correlation coefficients were not significant for
adjusted KW vs. adjusted TN or adjusted KN (Table 7).


19
Table 5. Simple correlation coefficients between stability
parameters [regression coefficeint (b) and
deviation mean square (DMS)] for grain yield (GY)
and other traits [kernel weight (KW), tiller (TN),
plant height (PH), and test weight (TW)] .
Trait
Stability
parameter
b
GY
DMS
KW
b
0.65
-0.06
DMS
0.18
-0.11
TN
b
-0.83*
0.15
DMS
-0.03
0.56
PH
b
-0.02
-0.21
DMS
-0.53
-0.18
TW
b
0.52
0.33
DMS
-0.32
-0.35
* Significantly different than 0.0 at the 0.05
probability level, df=4.


Table 6. Simple correlation coefficients among kernel weight
(KW), tiller number (TN), and kernel number per
tiller (KN) by genotype.
Genotype
KW vs. TN
KW vs. KN
TN vs. KN
Florida 201
0.53*
0.85**
0.30
Beagle 82
0.61*
0.83**
0.30
Florico
0.23
0.89**
0.11
IRA
-0.10
0.60*
-0.39
FL7845
0.39
0.79**
0.41
CT3477
0.27
0.71**
0.21
*, ** Significantly different than 0.0 at the 0.05 and 0.01
probability levels, respectively, df=14.


Table 7. Simple correlation coefficients among kernel weight
(KW), tiller number (TN), and kernel number per
tiller (KN) (as a proportion of their environmental
means) by genotype.
Genotype
KW vs. TN
KW vs. KN
TN vs. KN
Florida 201
0.19
0.24
-0.39
Beagle 82
-0.13
-0.31
-0.10
Florico
-0.34
1
o
ro
-0.60*
IRA
0.01
-0.11
-0.46
FL7845
0.31
-0.18
0.33
CT3477
-0.21
-0.23
-0.24
* Significantly
different than
0.0 at the
0.05 probability
level, df=14.


22
Regression of KW vs. environmental mean for GY (Fig. 1)
indicated that KW of all genotypes increased with increasing
GY, but genotypes differed in their rate of increase.
The broad sense heritability (+ standard error) for KW
was 0.41 + 0.26.
Tiller Number
The AOV and environmental indices for TN are presented
in Tables 1 and 2, respectively. Genotypes differed
significantly in TN and ranged from 240 tillers m-2 for
Beagle 82 to 362 tillers m-2 for IRA (Table 3).
TN was significantly (P<0.01) influenced by E, Y, L, and
N, and by the interaction effects LN and YLN (Table 1). The
effect of YL was significant at P<0.05. Environmental means
ranged from 190 to 371 tillers m-2 (Table 2).
The effect of GE was highly significant and G interacted
significantly (P<0.01) with Y, L, YL, and N (Table 1) .
Regression partition indicated highly significant (P<0.01)
effects of heterogeneity and deviation (Table 1) Regression
coefficients ranged from 0.50 for Beagle 82 to 1.42 for
Florico, and DMS ranged form 12,073 for IRA to 1,116 for
Florida 201 (Table 3).
Regression coefficients tended to decrease (r=-0.57,
df=4) and DMS tended to increase (r=0.57, df=4) with
increasing mean TN, though correlation coefficients for these
relationships were not significant. There was a significant


Kernel weight of six triticale genotypes vs. environmental mean grain
yield. ** indicates b is significantly different than average at the
0.01 probability level.
NJ
u>
Figure 1.


24
negative relationship for b of GY vs. b of TN (Table 5). TN
and KN were not significantly correlated in any genotype,
though in 5 of 6 genotypes, TN tended to increase with
increasing KN (Table 6) The adjusted TN of Florico was
negatively correlated with adjusted KN of Florico (Table 7).
Regression of TN vs. environmental means for GY (Fig. 2)
indicated that, in all genotypes except IRA, TN increased
with increasing mean GY.
The broad sense heritability estimate (+ standard error)
for TN was 0.27 + 0.11.
Plant Height
The AOV and regression partition for PH are shown in
Table 1. Genotype means ranged from 79 cm for IRA to 106 cm
for Florico (Table 3).
The effect of E was highly significant (P<0.01) with
environmental means ranging from 68 to 108 cm (Table 2). The
main effects of Y, L, and N were also highly significant as
was the YL interaction (Table 1) The effect of N was
independent of Y and L. With the exception of Quincy in
1986, PH in 0 kg ha-1 N plots was significantly (P<0.05) less
than in plots receiving topdress N (Table 2).
The GE effect was highly significant (P<0.01), but G
interacted only with Y, L, and YL (Table 1) Regression
partition indicated highly significant (P<0.01) effects of


Tiller number (no.
0 1000 2UU0 30U ^000
Mean grain yield (kg ha- 1 )
igure 2. Tiller number of six trit icale genotypes vs. environmental mean grain yield.
*, * indicates b is signil icant !y different than average at the 0.05 and 0.01
probability levels, respectively.
to
U1


26
heterogeneity and deviation (Table 1). Regression
coefficients ranged from 0.53 for IRA to 1.48 for FL7845, and
DMS ranged from 27.27 for Beagle 82 to 103.26 for FL7845
(Table 3).
Mean PH was significantly (P<0.05) correlated with b
(r=0.88, df=4), but not DMS (r=0.05, df=4). Stability
parameters for PH were not significantly correlated with
stability parameters for GY (Table 5).
The broad sense heritability estimate (+ standard error)
for PH was 0.55 + 0.31.
Test Weight
The AOV and regression partition for TW are presented in
Table 8. Environmental indices used in regression analyses
are shown in Table 2. Genotype means ranged from 650 g L-1
for FL7845 to 720 g L-1 for Florico (Table 3).
As shown in Table 8, the effect of E and all
environmental component effects were highly significant
(P<0.01) Environmental means ranged from 650 to 710 g L-1
(Table 2).
The effect of GE was highly significant (P<0.01) as were
the interactions of G with S and N (Table 8). The regression
partition indicated that heterogeneity and deviation effects
were also highly significant (Table 8). Regression


27
Table 8. Mean squares for the analysis of variance and
regression partition for test weight of six
triticale genotypes.
Source
df
Mean Square
Genotype (G)
5
21,478**
Environment (E)
11
8,564**
Site (S)
2
38,917**
N level (N)
3
2,971**
SN
6
1,243**
GE
55
469**
Variance partition
SG
10
1,743**
NG
15
315**
SNG
30
121
Regression partition
Heterogeneity 5
1,130**
Deviation
50
403**
Replication3
6
280
Main plot error*3
18
130
Subplot errorc
120
134
** Significant at the 0.01 probability level.
a Replication within sites.
b Used to test significance of E, S, N, SN,
Replication.
c Used to test significance of G, GE, GS, GN, GSN,
Heterogeneity, Deviation.


28
coefficients ranged from 0.48 for FL7845 to 1.20 for CT3477,
and DMS ranged from 115 for CT3477 to 498 for IRA (Table 3).
The b values for TW tended to increase with increasing b
value for GY (Table 5) and with increasing mean TW (r=0.59,
df=4) but neither relationship was statistically significant.
The broad sense heritability estimate (+ standard error)
was 0.44 + 0.24.
Kernel Number per Tiller
Regression of KN vs. environmental means for GY
indicated that, in all genotypes, KN increased with higher
yielding environments, but the rate of increase differed
among genotypes (Fig. 3).
Stability of Blends
Grain Yield
Table 9 presents the AOV and regression partition for
the three groups of triticale populations (P) which included
blends (i.e. Group 1: Florida 201, Beagle 82, and Florida
201 + Beagle 82; Group 2: Florida 201, Florico, and Florida
201 + Florico; Group 3: Beagle 82, Florico, and Beagle 82 +
Florico). Environmental indices used in linear regressions
are shown in Table 10. In all groups, GY was significantly
(P<0.01) influenced by P (Table 9). The blend in Group 2
yielded equal to its higher yielding component, Florico,


34
CD
Genotype
( standard error)
1.
Florida 201
0.00554
(0.00058)
2 .
Beagle 82
0.00784
(0.00056)
1
3.
Florico
0.00621
(0.00052)
1
4.
IRA
0.00732
(0.00087)
o 26
5.
FL7845
0.00507
(0.00044)
6.
CT3477
0.00596
(0.00063)
Average
0.00614
18
o
c
CD
XI
E
3
C
2 io
o
1000
tsj
Mean
2000 1 3000
grain yield (kg ha )
4000
Seed number per tiller of six triticale genotypes vs. environmental mean grain
yield. indicates b is significantly different than average at the 0.05
probability level.
Figure 3


Table 9. Mean squares (x 10 ) for analysis of variance and regression
partition for grain yield of three groups of populations.
Each group consisted of two cultivars and one blend made up
half from each cultivar (Group 1: 'Florida 201', 'Beagle 82',
and Florida 201 + Beagle 82; Group 2: Florida 201, 'Florico',
and Florida 201 + Florico; Group 3: Beagle 82, Florico, and
Beagle 82 + Florico.
Group
Source
df
1
2
3
Population (P)
2
3,813**
1,269**
9,082**
Environment (E)
15
1 1,580* *
17,200**
16,000**
Year (Y)
1
20,414**
3,453**
8,460**
Location (L)
1
125,628**
214,877**
202,381**
YL
1
654 *
6,362**
4 397**
N level (N)
3
6,219**
5,815**
4,890**
YM
3
74
923**
523*
LN
3
954**
818**
882**
YU1
3
394 *
1, 347**
883**
PE
30
3 0 7 *
151
373**
Variance partition
PY
2
1,772**
186
1,572**
PL
2
1,313**
751**
1,983**
PYL
2
85
360*
712**
PM
6
156
51
104
PYN
6
208*
45
153
PLN
6
57
65
28
PYLM
6
59
164
170*
Regression partition
Heterogeneity
2
2 10* *
912**
1,726**
Deviation
28
3 14 *
97
279*
Replication3
8
508**
764**
395*
Main plot errorb
24
122
151
127
Subplot errorc
64
82
103
73
*, ** Significant at the 0.05 and 0.01 levels, respectively.
a Replication within years and locations.
b Used to test significance of 12, Y, h, N, YI,, YN, LN, YLN, Replication.
c Used to test significance of P, PE, PY, PL, PM, PYL, PYN, PLN, PYLM,
Heterogeneity, Deviation .


Table 10
Mean grain yield (GY) and test weight (TW) of three groups of
triticale populations (each group consisting of two cultivars
and one blend made up half from each cultivar) by year,
location, and N level. Values represent environmental indices
used in regressions analysis.
Year3
Location
N level*3
GY
TW
Group
Group
1
2
3
1
2
3
- g L --
1986
Quincy
0
2620
3340
3090
700
740
720
55
3290
3790
3580
710
740
730
110
3390
3490
3420
700
740
710
165
3650
3940
3750
690
730
700
Marianna
0
1040
1150
1060
670
700
690
55
1590
1740
1540
700
700
690
110
1800
1800
1590
660
700
680
165
1600
1770
1610
650
690
700
1987
Quincy
0
1660
2620
2220
640
670
660
55
2290
3620
3420
650
680
670
110
3070
4330
3850
650
690
670
165
3170
4440
3850
650
670
670
Marianna
0
230
410
290



55
850
1210
880



110
690
1020
730



165
700
910
' 560



LSD.05
370
410
380
10
8
10
a Year harvested,
k N topdress level.


32
while the blends in Groups 1 and 3 had GY intermediate
between their component cultivars (Table 11).
The grain yield of all groups was significantly
influenced by E, Y, L, and N (Table 9) In most cases, GY
did not respond to N levels greater than 55 kg ha-1, however,
at Quincy in 1987, GY in 110 kg ha-1 N plots was
significantly greater than in 55 kg ha-1 N plots (Table 10).
Interactions among environmental factors were all significant
at P<0.05 or 0.01 with the exception of the YN interaction in
Group 1 (Table 9) Environmental means ranged from 230 to
3650, from 410 to 4440, and from 290 to 3850 kg ha-1 in
Groups 1, 2, and 3, respectively (Table 10). The highest
yielding environment for Group 1 was at Quincy in 1986, while
the highest yielding environment for Groups 2 and 3 was at
Quincy in 1987.
The interaction PE was significant (P<0.01) in groups 1
and 3 (Table 9). Although the PE interaction was not
significant in Group 2, the interaction effects PL and PYL
were significant (P<0.01 and P<0.05, respectively). P did
not interact significantly (P>0.05) with N in any group. The
regression partition indicated that the effect of
heterogeneity was highly significant in all groups (Table 9).
The deviation effect was significant (P<0.05) in Groups 1 and
3. The greatest range in b values was observed in Group 3
(0.83 for Beagle 82 to 1.12 for Florico) (Table 11). No DMS
value in Group 2 was significantly different from zero. DMS


Table 11. Observed (O) and expected (E) stability parameters [mean,
regression coefficient (b), and deviation mean square (DMS)] for
grain yield of three groups of triticale populations, each group
consisting of two cultivars and one blend made up half from each
cultivar.
Group
Population
Mean
ba
DMS
b
0
E
0 E
0
E
kg
ha-1
1
Florida 201
2290
1.06
293,104
Beagle 82
1730
1.01
77,403
Blend 1
1970
2007
0.93 1.04
261,441**
185,254
LSD.05
130
0.06
2
Florida 201
2290
0.89**
48,267
Florico
2550
1.10**
65,697
Blend 2
2590
2420
1.01 0.95
80,529
56,982
LSD.05
150
0.04
3
Beagle 82
1730
0.83**
274,803**
Florico
2550
1.12**
213,464
Blend 3
2380
2139
1.05 0.98
71,049
244,134*
LSD.05
130
0.04
a ** Significant from 1.0 at the 0.01 probability level.
b ** Significant from 0.0 at the 0.01 probability level.


34
for the blend in Group 1 and Beagle 82 in Group 3 were
significantly (P<0.01) greater than zero.
Test Weight
The AOV and regression partition for Groups 1, 2, and 3
are presented in Table 12. The effect of P was highly
significant (P<0.01) in all groups (Table 12). Florico had
the highest TW, 720 g L_1, and Beagle 82 had the lowest, 660
g LT1 (Table 13). The blends in all groups consistently had
TW close to the mean TW of their component cultivars.
The effects of E, S, and N were significant (P<0.01) in
all groups (Table 12). The interaction SN was significant at
P<0.05 in Groups
1 and
2, and
at
P<0.01 in
Group 3.
Environmental mean
ranges
were 640
to
710, 670 to
740, and
660 to 730 g L-1 in Groups 1, 2, and 3, respectively (Table
10). In all groups, the highest TW was observed in 55 kg ha~
1 N plots at Quincy in 1986, and the lowest TW was observed
in 0 kg ha-1 plots at Quincy in 1987.
Highly significant (P<0.01) PE interaction was observed
only in Group 3 (Table 12). The PE interaction in Group 2
was significant at P<0.05. In all groups, however, P
interacted significantly (P<0.01) with S. The regression
partition indicated that Group 2 had a significant (P<0.05)
deviations effect with DMS values ranging from 50 to 158
(Table 13). Group 3 had highly significant (P<0.01)
heterogeneity and deviation effects (Table 12) with b values


Table 12. Mean squares for analysis of variance and regression partition
for test weight of three groups of triticale populations. Each
group consisted of two cultivars and one blend made up half from
each cultivar (Group 1: 'Florida 201', 'Beagle 82', and Florida
201 + Beagle 82; Group 2: Florida 201, 'Florico', and Florida
201 + Florico; Group 3: Beagle 82, Florico, and Beagle 82 +
Florico).
Group
Source
df
1
2
3
Population (P)
2
9,384**
7,272**
32,642**
Environment (E)
11
5,657**
6,113**
4,577**
Site (S)
2
28,423**
31,818**
21,894**
N level (N)
3
874**
780**
1,128**
SN
6
460*
210*
529**
PE
22
168
144*
311**
Variance partition
PS
4
341*
332**
919**
PN
6
126
71
284*
PSN
12
131
133
122
Regression partition
Heterogeneity
2
80
49
431**
Deviation
20
177
154*
299**
Replication3
6
103
111
132
Main plot error*3
18
147
58
89
Subplot error0
48
133
77
122
*, ** Significant at the 0.05 and 0.01 probability levels, respectively.
a Replication within sites.
b Used to test significance of E, S, N, SN, Replication.
c Used to test significance of P, PE, PS, PN, PSN, Heterogeneity,
Deviation.


Table 13. Observed (O) and expected (E) stability parameters [mean,
correlation coefficient (b), and deviation mean square (DMS)] for
test weight of three groups of triticale populations, each
consisting of two cultivars and one blend made up half from each
cultivar.
Group
Population
Mean
b
DMS
0
E
0
E
0
E
g L-1
1
Florida 201
690
0.99
161**
Beagle 82
660
0.95
111
Blend 1
670
671
1.07
0.97
82
136
LSD.05
10

2
Florida 201
690
0.95
158**
Florico
720
1.01
50
Blend 2
710
701
1.04
0.98
116
104
LSD.05
10

3
Beagle 82
660
1.06
94
Florico
720
1. 12
226**
Blend 3
690
685
0.82
1.09
279**
160
LSD.05
10
0.09
**
Significant from 0.0 at the 0.01 probability level.


37
ranging from 0.82 for the blend to 1.12 for Florico and DMS
ranging from 94 for Beagle 82 to 279 for the blend (Table
13) .


DISCUSSION
Pure Lines
For most traits, the wide range of environmental means
and relatively uniform distribution across the range provided
a basis for comparing the response of genotypes to different
environments. A possible exception was PH where seven
environments were in the 100 to 104 cm range, while the other
nine environments were distributed more uniformly between the
68 and 108 cm extremes. It should be noted that the large L
effect, seen particularly for GY, was due to differences in
planting date and water management as well as edaphic
differences between Quincy and Marianna.
Genetic variation for a trait is required in order to
alter that trait by phenotypic selection. Therefore, to
improve GY performance and stability, variation among
genotypes must exist for GY mean, b value, and/or DMS. In
this study, genotypes were found to differ for all three
stability parameters. Other workers have also found
triticale genotypes differing in GY mean, b value, and DMS
(Sandha et al., 1980; Sapra, 1985; Sinha et al., 1986). Thus
it should be possible to select for enhanced GY stability in
triticale.
38


39
Eberhart and Russell (1966) have described a desirable
genotype as one which combines high mean yield with average b
value and low DMS. A positive correlation among stability
parameters would seem to preclude the combination of high
yield with average b value and low DMS. In this study, b
values and means for GY were positively correlated. A
positive relationship between b value and mean for GY has
been reported by some workers (Fischer and Maurer, 1978;
Baihaki et al., 1976; Eberhart, 1969), while others (Gama and
Hallauer, 1980) found no such relationship. The association
between b values and mean performance of GY observed here and
reported by other researchers could result, in part, from the
nature of the analysis and the genotypes involved. Since
observed yield cannot fall below zero, the intercept of a
linear regression will not be very much less than zero.
Thus, it would be difficult to envision a situation where low
mean yield would be associated with a large b value. The low
b values thus associated with low yielding genotypes could
influence the correlation between b and mean. However, the
critical issue is whether some (however few) genotypes
combine high yield with average b value. In this study, one
genotype, IRA, was high yielding and had an average b value
as well as a low DMS. Other workers have also found
triticale genotypes which combined high yield and desired
stability (Kaltsikes, 1971; Sandha et al., 1980; Sapra,


40
1985). Thus, selection can apparently be made for stability
without sacrificing high yield.
A graphical presentation of regression equations of
Florico and IRA (Fig. 4) illustrate the significance of this
type of analysis. Florico would be expected to yield more
than IRA in high yielding environments, such as in breeding
yield nurseries. Thus, IRA could be easily overlooked if no
consideration is given to production in a broad range of
environments. IRA would be expected to yield higher than
Florico in low yielding environments. No yield data are
available for commercial triticale production in Florida.
However, wheat and triticale yield levels are similar in
experimental plots, and commercial wheat production in the
state averages about 2000 kg ha-1 (Florida Department of
Agriculture, Division of Marketing, 1986). In such
environments, IRA and Florico would be expected to yield
about equal and other considerations such as disease
resistance, plant height, or grain quality would become more
important. Further, IRA had a low DMS, whereas Florico had a
large DMS.
Verma and Chahal (1978) have proposed an alternative to
the idea that b=1.0 is the desired level of linear response.
Given two genotypes with equal mean yield and different b
values, the genotype with the higher b value would be higher
yielding in favorable environments and lower yielding in less
favorable environments (Fig 5.) A theoretical "ideal"


5000
Comparison of regression slopes of 'Florico' and IRA triticale. Equations
are presented in Table 3.
igure 4 .


'1000
Ideal
Figure 5. Comparison of the performance of genotype A (b>1.0), genotype B (b<1.0), and
an ideal genotype with b>l 0 in above average environments and bcl.O in below
average environments. (Adapted from Verma and Chahal, 1978.)
M


43
genotype would have a low b value in less favorable
environments, but a high b value in favorable environments.
To identify genotypes which had this response pattern, linear
regressions were calculated for genotypes in this study in
two environmental subsets as described by Verma and Chahal
(1978). Florida 201 exhibited the ideal b values proposed by
Verma and Chahal (1978) (i.e. b > 1.0 in above average
environments and bcl.O in below average environments).
However, its mean yield was surpassed by Florico in above
average environments and by IRA in below average
environments. Thus, the advantage Florida 201 had in terms
of b values was moderated by its low mean yield relative to
Florico and IRA. The present example does not detract from
the value of this approach since it is theoretically possible
to find genotypes which combine the desired b values with
high mean performance in both sets of environments.
It has been reported that b value for GY is related to
yield and plant height (Laing and Fischer, 1977; Purvis,
1973). While a positive correlation between b value for GY
and mean GY was observed in the present study, there was no
such association between b value for GY and mean PH. The
genotypes with the highest and lowest b values (Florico and
FL7845, respectively) were both tall, and both of the short
genotypes (IRA and CT3477) had average or below average b
values. Sinha et al. (1986) likewise found no relationship
between b values for GY and mean PH in triticale. An


44
association between these parameters would be expected in a
set of genotypes where b value and mean for GY are highly
correlated and there is a close association between yield
potential and plant height, as was the case when Laing and
Fischer (1977) and Purvis (1973) compared semi-dwarf wheats
to their taller counterparts.
While b=1.0 is generally accepted as desirable for GY
(Eberhart and Russell, 1966), this is not the case for
quality traits such as TW. For quality traits, a minimum b
value, in addition to low DMS, would be the goal since a
uniform product, regardless of production environment, is
generally desired. Unfortunately, the only genotype which
had a relatively low b value for TW (FL7845) had the lowest
mean TW. Beagle 82 and CT3477 had low DMS for TW and low
mean TW.
A low b value and DMS=0.0 for PH would also be desirable
since uniform plant height, even across variable field
conditions, would enhance acceptability and facilitate
mechanical harvest. Low mean values were again associated
with low b values and the two short genotypes, IRA and
CT3477, had b<1.0.
Although it is accepted that an average b value (i.e.
b=1.0) for GY is desirable, there seems to be no clear
understanding of how, in terms of yield components, this
level of linear response is achieved. Low b values for GY
could be achieved in a genotype by two means: 1) yield


45
components could resist change or 2) yield component levels
could change in a compensating manner such that the final
product remained relatively constant.
Grafius (1956) has presented a geometrical
interpretation of yield component compensation in widely
adapted genotypes. Reports by other researchers are quite
conflicting in this regard. Vaid et al. (1985) and
Rathnaswamy and Jagathesan (1982), working with dry bean and
sesame, respectively, found that b values for fruiting body
number were significantly correlated with b values for GY.
Saeed and Francis (1983) found that b for GY was
significantly correlated with b for seeds m-2 in all grain
sorghum genotypes tested and with b for KW in late maturing
genotypes. They concluded that stability for yield
components contributed to GY stability. Heinrich et al.
(1983) also concluded that yield component compensation was
not the major mechanism of GY homeostasis in grain sorghum.
In hexaploid wheat, Talukdar and Bains (1982) observed a
significant positive correlation between b values for GY and
b values for KW. However, they concluded that responsiveness
of KN and TN to changes in environment was the chief means by
which GY levels were maintained across diverse growing
conditions.
Singh and Bains (1984) found no association between b
values for GY and b values for yield components in chickpea.
Bains and Gupta (1972) and Fatih (1987) likewise found no


46
such association in hexaploid wheat or wheat-Agropyron
derivatives, respectively. These three reports attributed
low b values for GY to yield component compensation.
If yield component compensation was the mechanism of
maintaining GY across diverse environments, a decrease in one
yield component should be accompanied by an increase in
another yield component. In general, this was not the case
for genotypes tested in this study. All genotypes showed a
positive correlation between KW and KN. In addition, two
genotypes showed a positive correlation between KW and TN.
Only IRA gave any indication of yield component compensation
(i.e. a negative, nonsignificant correlation between KW and
TN, and between TN and KN).
Given that all yield components were adversely affected
in less favorable environments, the data were examined for
the possibility of a relative increase in one yield component
accompanying a relative decrease in another. When correla
tion coefficients were calculated for adjusted values yield
components, there was an apparent trend for yield component
compensation, but only the r value for adjusted TN vs.
adjusted KN of Florico was significant. In FL7845,the
genotype with the lowest b value for GY, positive, though
nonsignificant (P<0.05), correlation coefficients for
adjusted KW vs. adjusted TN and adjusted TN vs. adjusted KN
were observed. Therefore, yield component compensation did


47
not seem to be the major factor controlling GY responsive
ness .
The b value for GY would then appear to depend on the b
values of individual yield components. In the present study,
b values for GY and KW had a relatively high, though
nonsignificant, correlation coefficient. Responsiveness for
GY and TN were significantly correlated, but surprisingly,
the correlation was negative.
Since environments were ranked differently for GY, KW,
and TN, the functional relationships between GY and yield
components could be better understood by regressing KW, TN,
and KN against environmental means for GY rather than against
their own environmental means. Florico, the genotype with
the highest b value for GY, had a b value for KW vs. mean GY
significantly (P<0.01) greater than average (Fig. 1), but TN
vs. mean GY (Fig. 2) and KN vs. mean GY (Fig. 3) b values
were equal to the average. Therefore, the high b value for
GY of Florico was due to the high b value of KW vs. mean GY.
FL784 5, the genotype with the lowest b value for GY, had
above average b value for TN vs. mean GY and below average b
value for KN vs. mean GY. Therefore, the relatively low b
value for GY of this genotype was due to the tendency of KN
to remain constant despite environmental conditions. IRA had
remarkably constant TN across environments (Fig. 2) though
the genotype showed an average b value for GY. For IRA, the
b value for KN vs. mean GY (Fig. 3) was above average, though


48
not statistically so. In the case of IRA, the low b value of
TN vs. mean GY was counterbalanced by the high b value for KN
vs. mean GY. Thus, b values for GY seemed to depend on the b
value of one or more yield components vs. mean GY, and
genotypes differed in which yield component(s) had the major
impact.
Heritability estimates depend greatly upon the range of
genetic variation in the material tested and upon the
magnitude of environmental variance. Broad sense
heritability (H2) estimates reported here were lower than
estimates reported for triticale elsewhere in the literature,
due, probably, to the large range of environments sampled.
Kamboj and Mani (1982) reported H2 estimates of 0.91, 0.54,
0.72, and 0.74 for GY, KW, TN, and PH, respectively. Banik
and Islam (1984) reported H2 estimates of 0.66 and 0.71 for
TN and PH, respectively.
Blends
Genetic diversity in a population has the potential to
enhance yield and yield stability, particularly when extreme
environmental fluctuations occur or in the presence of
sporadic disease or insect outbreaks. In such situations,
plants affected by adverse conditions can be compensated for
by neighboring plants which are more tolerant to
environmental pressure. Population diversity also has the
potential to improve utilization of available resources when


49
component individuals differ in their environmental
requirements. Yet, acceptance of commercial cultivars in
most species requires uniformity for such characters as plant
height and maturity, especially for machine harvest.
The three blends considered here would meet accepted
uniformity standards. Although all cultivars were visually
similar in growth habit and maturity, and no disease or
insect pressure was evident, two of the combinations
apparently benefited from genetic diversity in terms of GY.
The blends, Florida 201 + Florico and Beagle 82 + Florico
yielded higher than the mean of their component cultivars,
and Florida 201 + Florico yielded equal to its higher
yielding component. Both of these blends approached unity
regression more closely than their component cultivars, and
Beagle 82 + Florico had a lower DMS than expected. The
common cultivar in these two blends was Florico. Florico had
the highest yield in this study, but also had an extremely
high b value. The interaction of Florico with the lower
yielding and more stable cultivars, Florida 201 and Beagle
82, resulted in enhanced performance in their blends.
Genetic diversity itself did not ensure enhanced
performance. One blend (Florida 201 + Beagle 82) did not
yield higher than the mean of its component cultivars, and
the blend had a significant DMS, even though both component
cultivars had DMS=0.0. Pfahler and Linskens (1979) also
found that only certain blends approached desired stability


50
(b=1.0 and DMS=0.0) more closely than their component pure
lines. Singh and Bains (1984) found no blend which yielded
equal to its higher yielding component, and only a few blends
equaled or exceeded their component lines in terms of
stability.
While the use of blends appears to offer a means of
enhancing yield performance over a range of environments, it
cannot be assumed that increased genetic diversity will
necessarily have the desired effect. Each proposed
combination must be tested. As Pfahler and Linskens (1979)
have pointed out, blends which include high yielding, rather
stable lines would be more likely to perform well in diverse
environments.


APPENDIX
DATA TABLES


Ul
K>
Table A-1. Grain yield in kg ha ^ of nine tiiticale populations in sixteen environments
(averacjed over three replications) .
Year: 1985-86 1986-87
Location: Quincy Marianna Quincy Marianna
N level3:
Popul at i on'
165
D
110
55
0
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201 3,861
3,315
3,484
3,022
1,478
1,627
1,623
1,178
3,959
3,905
3,187
2,279
1,066
1,059
1,198
372
Beagle 82
3,400
3,131
3,189
2,592
1,379
1,516
1,322
818
2,521
2,733
2,516
1,362
291
279
457
112
Florico
4,045
3,500
4,021
3,198
1,889
1,651
1,601
1,223
4,889
4,791
3,821
2,833
848
1,034
1, 162
308
IRA
3,601
3,608
3,702
3, 183
2,487
2,560
2,064
1 265
4,282
3,586
3,145
1,615
1,106
1,151
1,285
314
F L 7845
2,266
1 700
2,575
2,107
972
1,086
1 290
723
3,002
2,779
2,383
1,699
136
257
321
91
CI3477
3,692
3,728
3,204
2,244
1,946
2,244
1,810
1,122
3,015
2,580
2,052
1,334
756
722
906
192
Blend 1
3,461
3,732
3,316
2,944
1,788
1,664
1,496
869
3,726
3, 774
3, 164
2,008
634
827
939
401
Blend 2
3,922
3,648
3,875
3,803
1,957
2,137
2,010
1,050
4,456
4,296
3,859
2,748
801
978
1,270
564
Blend 3
3,803
3,763
3,527
3,489
1,563
1,593
1,695
1,150
4,138
4,031
3,924
2,477
540
872
1,033
455
a lopdress nitrogen (as ammonium nitrate) applied in kg ha
k Blend 1 = florida 201 + Beagle 82, Blend 2 = Florida 201 + Florico, Blend 5 = Beagle 82 Florico.


Table A-2.
Kernel weight in mg kernel ^ of nine triticale populations in sixteen
environments (averaged over three replications).
Year:
1905-86
1986-87
Location: Quincy Marianna Quincy Marianna
N level3: 165 1 10 55 0 165 1 10 55 0 165 1 10 55 0 165 1 10 55 0
Population^
Florida 201
50.
.5
48
.3
48.
.7
49.
.4
36.
.7
37.
3
38,
.5
38,
.3
46,
.5
45.
. 1
47.
.0
44.
.7
33
.8
33.
.1
34.
.7
31
.6
Beagle 62
46.
, 1
45 .
.0
44.
.0
48.
.4
36.
.7
36.
.6
39.
.3
38.
. 1
40.
,3
43,
.3
43.
.3
41.
.8
31
.3
30.
.2
31.
.5
31.
.1
Florico
54.
,5
49.
.9.
49.
.0
54.
.5
40.
.0
39.
,2
41 .
. 1
38.
.4
48.
.0
48,
.5
46.
.9
43.
.7
32.
.1
32.
.3
31.
.7
29.
.4
IRA
36.
0
35.
.5
35.
.7
37.
.8
27.
.9
27.
,3
31 .
. 1
34.
.6
33.
.8
32.
.9
34.
.2
31 .
.4
23
.6
24.
.8
24.
.8
24.
.2
F L 7845
45.
.9
43.
.8
47.
. 1
48.
.8
36.
.6
33.
,2
41 .
.7
38,
.3
41.
.2
43.
.8
43.
.8
44.
.5
31,
.3
30.
.8
31 .
.2
29.
.6
CT3477
38.
.0
37.
.8
34.
.4
37.
.8
27.
.4
27.
,7
28.
.2
29.
.5
32.
.9
34.
.4
34.
.5
32.
.9
23
.2
23.
.8
24.
.3
25.
.7
Blend 1
47.
.5
52.
.7
49.
,2
48.
.9
36.
.3
39.
.4
40.
.9
36.
.5
45.
.8
45.
.9
45.
.2
43.
.7
33,
.6
35.
.2
35.
.4
33
.8
Blend 2
52.
.6
52.
.9
51.
.1
53.
.7
40.
. 1
41.
,4
41 .
.0
38.
. 1
48.
.3
50.
.2
47.
.0
46.
.5
32
.6
31.
.3
32.
.9
30.
.5
Blend 3
53.
.5
52.
.2
46.
.9
49.
.4
37,
.4
38.
,0
42.
. 1
36.
.5
49.
.4
49,
.5
47.
.4
46.
.8
32
. 1
34.
.5
29.
.0
31.
.9
3 lopdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend V = Florida 201 + Beagle 82, Blend 2 = Florida 201 Florico, Blend 3 = Beagle 82 + Florico.


Table A-3. Tiller number m of nine Lritj.ca.lo populations in sixteen environments
(averaged over three replications).
rear: 1985-86 1986-87
Location: Quincy Marianna Quincy Marianna
N level0:
Population^
165
110
55
0
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201
308.7
346.0
310.0
248.7
327.0
321.0
305.7
288.0
299.0
303.0
318.7
299.3
194.7
237.3
279.7
229.0
Beagle 82
286.3
338.0
342.3
264.0
257.0
269.7
314.3
310.0
260.0
247.7
194.3
140.3
122.2
133.3
188.7
169.7
Florico
232.3
289.3
314.0
247.7
290.3
287.7
294.7
203.7
398.0
370.3
3B6.7
350.3
158.7
273.7
293.7
319.7
1 RA
457.7
482.7
350.3
347.7
391.0
399.0
371.0
322.3
325.3
352.0
229.3
237.7
311.7
391.0
407.7
407.7
F L 7845
277.7
284.0
374.0
247.3
2 76.3
241.7
282.0
300.3
329.3
311.7
329.3
333.7
97.5
124.9
194.3
188.7
0 34 77
441.0
484.0
328.3
272.3
438.0
332.3
340.7
766.7
329.7
260.0
232.0
242.0
256.0
321.3
314.0
208.7
Blend 1
347.7
356.0
33 7.7
354.7
279.3
274.0
322.7
289.0
310.3
292.0
216.7
228.0
164.0
197.3
261.3
229.3
Blend 2
302.0
315.7
282.3
250.3
280.7
261.0
307.3
207.7
353.3
325.3
356.0
269.7
148.7
203.0
322.7
324.0
Blend 3
356.3
325.3
365.7
271.0
299.0
283.7
303.0
203.7
288.0
271.3
290.7
193.0
137.5
181 .0
212.7
206.7
0 Topdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend 1 = Florida 201 Beagle 82, Blend 2 = Florida 201 Florico, Blend 3 = Beagle 82 Florico.


Table A-4.
Kernel number tiller of nine triticale populations in sixteen environ
ments (averaged over three replications) .
Year:
1985-86
1986-87
Location:
Quincy
Marianna
Quincy
Mari anna
N level*1:
Popu 1 a t i on*1
165 110 55
0
165 110 55 0
165
110 55 0
165
110
Florida 201
25.
.03
19.
.70
23.
.77
24.
.73
12.
.21
13.
.57
13.
.60
10,
.67
28.
.63
29.
.20
21 .
.93
18.
,00
15.
.87
13.
.30
12.
.20
5.
.17
Beagle 82
26,
.07
20.
.33
21.
,63
20.
.23
14.
.87
15.
.40
10.
.70
6.
.94
24.
.07
25.
.67
29.
.73
23.
.17
6.
.87
6.
.90
8.
.42
2.
.27
Florico
33.
.17
24.
.53
26.
.80
23.
,63
16.
.60
14.
.27
13
.04
11 .
.34
25.
.70
26.
.83
21.
.07
19.
.27
16.
.70
11.
.73
12.
.37
3,
.24
IRA
22,
.77
21.
.30
29.
.93
24.
.33
22.
.53
23.
.67
18,
.18
10,
.84
38.
.90
31.
.27
40.
.30
22.
.03
14.
.77
11.
.90
12,
.60
3.
.27
F L 7845
18.
.20
14.
.09
14.
. 73
17.
.53
9.
.62
14.
.07
11
.39
6.
.22
23.
.03
20.
.73
16.
.57
11 .
.52
4.
.54
6.
.70
5,
.33
1.
.59
CT3477
23
.23
20.
.63
28.
.77
21 .
.90
16.
.03
24.
.90
18,
.47
13
.79
27.
.73
29.
.30
25.
.60
17.
.20
12.
.34
9.
.41
11.
.63
3.
.53
Blend 1
21
. 10
19.
.93
20.
.50
17.
.20
17.
.21
15.
.37
11
.25
8,
.22
26.
.60
28.
.13
33.
.10
21.
.03
11 ,
.34
12.
.44
10.
.42
5.
.06
Blend 2
24,
.93
21.
.53
27.
. 73
28.
.43
17.
.77
19.
.27
15,
.63
9.
.38
26.
.07
26.
.53
23.
.20
22.
. 10
17.
.53
15.
.33
12.
.17
5.
.86
B1 end 3
20,
.13
22.
.07
20.
.50
26.
.67
14.
.11
14,
.70
13,
.13
11,
.29
29.
.20
29.
.83
28,
.43
27.
.73
12.
.51
14.
.20
17,
.87
6.
.72
u Topdress nitrogen (as ammonium nitrate) applied in kg ha
*J Blend 1 Florida 201 + Beagle 82, Blend 2 = Florida 201 + Florico, Blend 3 = Beagle 82 + Florico.


Table A-5
Plant heiyht in cin of nine tritj.cale populations in sixteen environments
(averaged over three replications).
Tear:
1985 86
1986-87
Location: Quincy Marianna Quincy Marianna
N level3: 165 110 55 0 165 110 55 0 165 110 55 0 165 110 55 0
Popul at i on^
Florida 201
106,
.33
106
.33
110.
.67
108,
.00
99.
.73
105.
.80
110,
.00
93.
.90
112,
.33
11A.
.67
109.
.67
96.
.67
87.
.50
93.
.33
97.
.50
7A.
.17
Beagle 82
113,
.00
113,
.00
110.
.67
105.
.80
107,
.67
110.
.80
112.
.33
97.
.33
113.
.00
113.
.00
11A.
.00
96.
.67
88.
.33
90.
,83
86.
.67
75.
.83
Florico
116,
.33
108
.00
113.
.00
111.
.33
121 .
.00
115.
.00
116.
.00
98.
.80
116.
.67
116,
.67
111.
.67
103.
.17
96.
.50
93.
.33
9A.
.10
70.
. 83
IRA
83.
.73
81 .
.13
78.
.57
78.
.60
86.
. 10
86.
.27
BA .
.57
69.
.17
89.
.17
81 .
.67
80.
.00
65.
.83
76.
.67
78.
.33
77.
.37
65.
.00
F L 784 5
115,
.00
115,
.67
115.
.67
103.
.23
118.
.33
98.
.00
111 .
33
102.
27
12A.
.00
120.
.00
118.
.00
108.
.00
81 .
.67
85.
.83
85.
.83
65
.00
Cl 3477
82.
.00
89,
.70
79.
A3
76.
.90
89.
.70
83.
.70
87.
. 10
7 A ,
.30
90.
.83
83.
.33
8A ,
.17
7A .
.17
73.
.33
75.
.00
81 .
.67
60.
.00
Blend 1
112.
.33
109.
.67
112.
.33
111.
.67
105,
.60
111.
.77
111.
.33
83 .
.83
11A.
.00
119.
.00
113.
.00
99.
.83
92.
.50
96.
.67
93
.33
73.
.33
Blend 2
107.
.00
109.
.67
111.
.33
109.
.67
116.
.33
115.
.00
116.
.67
91 .
, A 0
118,
.67
119.
.00
11A.
.00
101 .
.50
93.
.33
99.
.17
93.
.33
70.
.00
Blend 3
115.
,00
115.
.67
111.
,33
107.
,00
IK.
.00
IK.
,27
116.
,67
100.
.57
120.
.33
117.
.00
116.
.33
10A .
.67
93
.33
96.
.67
93,
.33
75
.00
d Topdress nitrogen (as ammonium nitrate) applied in kg ha'.
Blend 1 = Florida 201 Beagle 82, Blend 2 = Florida 201 + Florico, Blend 3 = Beagle 82 + Florico.


Table A-G Test weiyht in i) I, ot nine triticnle populaL ions in twelve
environments (averaged over three replications).
tear: 1985-86 1986-87
Location: Quincy Marianna Quincy
N level8:
Populationk
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201
714.3
718.7
725.3
720.7
654.0
680.3
684.7
680.0
673.7
671.3
662.7
660.7
Beagle 82
669.0
676.0
694.0
695.0
646.0
639.3
656.3
660.7
628.7
633.0
641.7
622.0
florico
740.3
749.0
755.0
742.7
703.3
716.3
716.3
710.0
684.3
699.3
684.3
678.0
1 RA
671.3
680.0
708.0
686.3
654.0
673.7
692.7
703.7
637.3
648.0
650.0
641.3
FL7845
637.0
643.7
667.0
678.0
605.0
657.3
641.3
662.7
646.0
654.3
650.0
641.7
CT3477
686.7
703.7
710.0
695.7
652.0
667.0
667.0
678.0
637.3
644.0
643.7
628.7
Blend 1
684.3
695.0
708.0
699.3
637.3
667.0
673.7
678.0
660.7
662.7
662.7
650.0
Blend 2
731.7
736.0
744.7
744.7
697.0
703.3
705.7
707.7
663.0
684.3
688.7
671.3
Blend 3
690.7
710.0
727.3
725.3
656.3
676.0
690.7
691.0
689.0
682.3
682.0
675.7
a i
Topdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend 1 = Florida 201 Beagle 82, Blend 2 = Florida 201 Florico, Blend 3 = Beagle 82 Florico.
Ln


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BIOGRAPHICAL SKETCH
Steve Calhoun was born in Harlingen, Texas, on 9 October
1957, and raised mainly in the West Texas oilpatch town of
Midland. After graduating from Robert E. Lee High School,
Steve attended Texas A&M University where he became an Aggie;
got acquainted with Mary Diane McDaniel; and completed, with
honors, a B.S. in agronomy.
From 1980 to 1982, he worked for the Southern Baptist
Foreign Mission Board in Jos, Nigeria, where he taught
agriculture in a mission high school, managed the school
farm, and corresponded with Diane. Upon his return, Steve
worked on a ranch in Southwest Kansas.
In March 1983, Steve and Diane married and moved to
Florida where Steve began work on a M.S. degree with Dr.
Gordon Prine. In 1985, Steve completed the epic work,
"Elephantgrass Performance in a Warm Temperate Environment,"
and began work on his Ph.D. in plant breeding with Drs.
Pfahler and Barnett.
On 12 September 1987, Diane gave birth to their sons,
Rossi Rutledge, who was still born, and Heathman Divilbiss,
who died shortly after premature birth.
62


63
Upon graduation, Steve and Diane plan to move to
Texcoco, Mexico, where Steve has a postdoctoral fellowship
with Centro International de Mejoramiento de Maiz y Trigo.


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
fvU
Dr.'P.L. Pfahler, Chairman
Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Q
DrR. D. Barnett, Cochairman
Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
uu//
H
1^
Dr. K. Hinson
Professor of Agronomy


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Dr. D.D. Baltensperger
Associate Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
k) A Wu//
Dr. D.A. Knauft
Associate Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Dr. J.E. Funderburk
Assistant Professor of Entomology
and Nematology
This dissertation was submitted to the Graduate Faculty
of the College of Agriculture and to the Graduate School and
was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.
April 1988 (j
'(cloA pf .
Dean, CoiAege of Agriculture
Dean, Graduate School




UNIVERSITY OF FLORIDA
3 1262 08556 7864


Full Text

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GENOTYPE X ENVIRONMENT INTERACTIONS OF
TRITICALE IN FLORIDA
By
D. STEVEN CALHOUN
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1988

To the God I know too little of
To the sons I knew too briefly,
and
To Diane, my love and my friend

ACKNOWLEDGMENTS
I would like to express my genuine appreciation to my
chairman, Dr. Paul L. Pfahler, for his expert guidance in the
preparation of this dissertation, and for his not-for-credit
course, "The Theory and Practice of Science, Writing, and
Academic Administration." I also offer sincere thanks to my
co-chairman, Dr. Ron D. Barnett, for his guidance and
logistical support in conducting this research, for dragging
me around the country looking at wheat, for his plant
breeding insights, and for his friendship.
Thanks are also due my committee members, Dr. Joe E.
Funderburk, Dr. Kuell Hinson, Dr. David D. Baltensperger, and
Dr. David A. Knauft, who at various times have given moral
support and contributed greatly to my academic training.
I cannot neglect those whose labor made this research
possible—Dr. Ann Zimet, Mr. Alex Thompson, Mr. David Castro,
and Mr. Craig Bundy.
I would like to thank James Pier Muir whose fishing and
philosophy helped me retain what little grip on reality I can
now claim.
Finally, I thank my wife, Diane, whose love, support,
and typing skills I cannot live without.
iii

TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS iii
LIST OF TABLES V
LIST OF FIGURES vii
ABSTRACT viii
INTRODUCTION 1
MATERIALS AND METHODS 7
Analysis of Pure Lines 8
Analysis of Blends 10
RESULTS 12
Stability of Pure Lines 12
Grain Yield 12
Kernel Weight 18
Tiller Number 22
Plant Height 24
Test Weight 26
Kernel Number per Tiller 28
Stability of Blends 28
Grain Yield 28
Test Weight 34
DISCUSSION 38
Pure Lines 3 8
Blends 48
APPENDIX
DATA TABLES 52
REFERENCES 58
BIOGRAPHICAL SKETCH 62
IV

LIST OF TABLES
Table Page
1. Mean squares for the analysis of variance and
regression partition for grain yield (GY),
kernel weight (KW), and tiller number (TN), and
plant height (PH) 13
2. Mean grain yield (GY), kernel weight (KW),
tiller number (TN), plant height (PH) , and test
weight (TW) of six triticale genotypes by year,
location and N level 14
3. Stability parameters [mean, regression coeffi¬
cient (b) , and deviation mean square (DMS)] for
grain yield (GY), kernel weight (KW) , tiller
number (TN), plant height (PH) , and test weight
(TW) of six hexaploid triticale genotypes 15
4. Means and regression coefficients (b) of six
triticale genotypes in above average and below
average environments 17
5. Simple correlation coefficients between sta¬
bility parameters [regression coefficient (b)
and deviation mean square (DMS)] for grain yield
(GY) and other traits [kernel weight (KW),
tiller (TN), plant height (PH), and test weight
(TW) ] 19
6. Simple correlation coefficients among kernel
weight (KW), tiller number (TN), and Kernel
number per tiller (KN) by genotype 20
7. Simple correlation coefficients among kernel
weight (KW), tiller number (TN), and kernel
number per tiller (KN) (as a proportion of their
environmental means) by genotype
8. Mean squares for the analysis of variance and
regression partition for test weight of six
triticale genotypes
v

Table Page
9.Mean squares (x 107 for analysis of variance and
regression partition for grain yield of three
groups of populations 30
10. Mean grain yield (GY) and test weight (TW) of
three groups of triticale populations (each
group consisting of two cultivars and one blend
made up half from each cultivar) by year,
location, and N level 31
11. Observed (O) and expected (E) stability param¬
eters [mean, regression coefficient (b) , and
deviation mean square (DMS)] for grain yield of
three groups of triticale populations, each
group consisting of two cultivars and one blend
made up half from each cultivar 33
12. Mean squares for analysis of variance and
regression partition for test weight of three
groups of triticale populations 35
13. Observed (O) and expected (E) stability param¬
eters [mean, correlation coefficient (b) , and
deviation mean square (DMS)] for test weight of
three groups of triticale populations, each
consisting of two cultivars and one blend made
up half from each cultivar 36
vi

LIST OF FIGURES
Figure Page
1. Kernel weight of six triticale genotypes vs.
environmental mean grain yield 23
2. Tiller number of six triticale genotypes vs.
environmental mean grain yield 25
3. Seed number per tiller of six triticale geno¬
types vs. environmental mean grain yield 29
4. Comparison of regression equations of Florico
and IRA triticale 41
5. Comparison of the performance of genotype A (b >
1.0), genotype B (b < 1.0), and an ideal geno¬
type with b > 1.0 in above average environments
and b < 1.0 in below average environments 4 2
Vll

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
GENOTYPE X ENVIRONMENT INTERACTIONS OF
TRITICALE IN FLORIDA
by
D. Steven Calhoun
April 1988
Chairman: Dr. P.L. Pfahler
Cochairman: Dr. R.D. Barnett
Major Department: Agronomy
Six triticale (XTriticosecale Wittmack) genotypes
('Florida 201', 'Beagle 82', 'Florico', Ira(2)M2AxCml/Ia-Trr,
FL7845-TQ-G32-J2, CT3477) were evaluated for mean performance
and environmental stability of grain yield (GY), kernel
weight (KW), tiller number m-2 (TN), plant height (PH), and
test weight (TW). Three 1:1 mechanical seed blends (Florida
201 + Beagle 82, Florida 201 + Florico, Beagle 82 + Florico)
were tested for mean performance and environmental stability
of GY and TW. Sixteen environments (E) including two years
(1985-86, 1986-87), two locations (Quincy—30° 35' N Lat,
Marianna—30° 46' N Lat), and four N topdress levels (0, 55,
110, 165 kg ha--*-) were tested. Analysis of variance and
linear regression techniques were applied to the data from
the six genotypes and each individual blend combination (the
blend and both component cultivars).
Significant genotype (G) effects were observed for all
traits. Among the 6 genotypes, significant GE interactions
viii

and differences among genotypes in their linear response (b)
to E were observed for all traits. Differences among
genotypes for deviation mean squares (DMS) were observed for
GY, TN, PH, and TW. No association was found between
stability parameters (b and DMS) for GY and the same
parameters for PH or TW. Means and b values for GY, KW, and
PH were positively correlated. Stability of individual yield
components (KW and TN), rather than yield component
compensation, seemed to be the major factor in GY stability.
Florida 201 + Florico yielded approximately equal to its
higher yielding component, Florico. Other blends had yields
intermediate between their component cultivars. Significant
GE effects on GY and TW were observed in two of the blend
combinations. All blend combinations exhibited differences
among populations in b values for GY while one blend
combination exhibited differences in b values for TW. Beagle
82 + Florico had a lower DMS than the component average,
while Florida 201 + Beagle 82 had a higher DMS than the
component average. The use of blends appeared to be a
desirable method to enhance yield and environmental
stability. However, not all blends showed enhanced
performance.
ix

INTRODUCTION
Triticale (XTriticosecale Wittmack) is a man-made small
grain species which combines the high grain yield potential
of wheat with the broad environmental adaptability and high
lysine content of rye. Early triticales were produced by
crossing and chromosome doubling of hexaploid wheat (Triticum
aestivum L.) and rye (Secale cereale L. ) , but progeny of
these crosses were generally meiotically unstable. After
techniques were developed to excise and artificially culture
immature embryos, it became possible to produce hexaploid
triticale by crossing and chromosome doubling tetraploid
wheat (Triticum turgidum L. var durum L.) and rye.
Currently, all commercially acceptable cultivars are
hexaploid (Varughese et al., 1987).
Triticale has not been tested extensively in the
southeastern United States, but excellent potential for human
and animal feed production has been shown in preliminary
trials (Barnett and Luke, 1979; Kalmbacher et al., 1987;
Meyer et al. , 1987). 'Beagle 82', the first cultivar
recommended for grain production in the area, was released in
1982 (Barnett et al., 1982). By 1985, over 12,000 ha of
Beagle 82 were grown in the Florida-Georgia-Alabama area.
1

2
'Florida 201', released in 1985, is more productive and has a
higher test weight than Beagle 82. Over a six-year period,
Florida 201 triticale has yielded at the same level as
'Florida 301', a leading wheat cultivar in Florida (Calhoun
and Barnett, 1986).
Little is known, however, regarding the response of
triticale genotypes to the diverse environmental conditions
encountered in this region. Florida is marginal for small
grain production because of many factors including large
yearly fluctuations in temperature and precipitation during
the winter months. Thus, it is necessary that cultivars be
adapted to a broad range of environments.
Selection among genotypes in a crop improvement program
is based primarily on performance in breeding nurseries and
yield tests on research stations. Such tests are normally
conducted under intensive management which may or may not
exist in the environment where the cultivar will ultimately
be grown. A genotype which gives excellent performance under
very favorable conditions often is not the best genotype when
grown under less favorable conditions due to genotype x
environment interactions. Selection based on environmental
stability as well as mean performance can ensure that
cultivars which are released will perform adequately under a
wide range of growing conditions.
Numerous measures of stability and methods of analysis
have been developed to deal with genotype x environment

3
interactions [e.g. ecovalence, stability index, multi-
criteria clustering, and independent environmental
measurement proposed by Wricke (1962), Langer et al. (1979),
Lefkovitch (1985), and Freeman and Perkins (1971),
respectively]. While most stability analysis methods are
based on the linear regression of genotype performance on
some measure of environmental value, the regression technique
proposed by Breese (1969) and others is the best known and
most widely used. For the Breese (1969) analysis, yield (or
other parameters) of each genotype tested is regressed on an
environmental index which is the mean of all genotypes in a
specific environment. Three values are used to represent the
genotypic response over all environments: 1) overall mean, 2)
regression coefficient (b)—the genotypic response to the
environment attributable to linear regression which is
predictable, and 3) deviation mean square (DMS)—the
deviation of the genotypic response from linear regression
which can be considered unpredictable.
From linear regression analysis, a desirable cultivar
would have a high mean yield, regression coefficient
approaching unity, and DMS equal to zero. A genotype with
b<1.0 may be undesirable because it would not respond well to
better environments or improved management practices with
increased yield. In addition, very low b values are often
associated with low yield (Eberhart and Russell, 1966). On
the other hand, a genotype with b much greater than 1.0 would

4
be undesirable because it would be subject to severe yield
losses when growing conditions were unfavorable. A small DMS
value, indicating predictable response to environment, would
be desirable.
Verma and Chahal (1978) proposed an "ideal" genotype
which would be relatively insensitive to unfavorable
environments but would be responsive to favorable environ¬
ments. Such a genotype would have b<1.0 in unfavorable
environments and b>1.0 in favorable environments.
The use of multilines, or mechanical seed blends of two
or more genotypes has been proposed as a method of improving
the environmental stability of yield and other quantitative
traits by increasing the genetic heterogeneity of the
resulting population (Pfahler and Linskens, 1979; Singh and
Bains, 1984; Norden et al., 1986). Most studies of multiline
performance have shown that genetic diversity does not
necessarily insure enhanced stability. Rather, each proposed
blend must be tested in a number of environments. The
stability of triticale blends has not been examined.
Few stability analyses of triticale grain yield have
been reported; all found significant genotype x environment
interactions and identified lines which were both high
yielding and stable (Sandha et al. , 1980; Kaltsikes, 1971;
Sapra, 1985; and Sinha et al., 1986). However, most of these
studies were conducted outside the southeastern United States

5
using material unadapted to this region. Knowledge of the
grain yield stability of genetic material adapted to this
area would facilitate the development of high yielding,
stable cultivars.
Even less is known regarding stability of other
characters in triticale, such as plant height and test
weight, or the relationships among stability parameters for
these traits. An understanding of these relationships would
help breeders develop effective strategies for improving
stability of these traits.
The response pattern of yield components (kernel weight,
tiller number per unit area, and kernel number per tiller)
which results in the desired grain yield response pattern is
also poorly understood. There is considerable debate as to
whether stable grain
yield is a
result
of stable
yield
components
or a
result of compensatory
shifts in
yield
component
levels
as
proposed by
Grafius
(1956) .
More
information on the relationship between grain yield stability
and stability of yield components would be desirable.
The present study was undertaken to evaluate the mean
performance and environmental stability of representative
triticale cultivars and advanced breeding lines for grain
yield, kernel weight, tiller number, plant height, and test
weight. Broad sense heritability estimates for these traits
also were determined. In addition, mean performance and

6
environmental stability of grain yield and test weight for
three mechanical seed blends were determined.

MATERIALS AND METHODS
Three cultivars ('Florida 201', 'Beagle 82', 'Florico'),
three advanced breeding lines from the Florida triticale
program [Ira(2)M2A-Cml/Ia-Trr (IRA), FL7845-TQ-G32-J2
(FL7845), CT3477], and the three possible two-way blends of
the cultivars (i.e. Florida 201 + Beagle 82, Florida 201 +
Florico, Beagle 82 + Florico) were tested. Blends were
composed of a mechanical mixture of an equal number of viable
seeds from both component cultivars. All triticale genotypes
were hexaploid and had a spring growth habit.
Entries were tested in two cropping years (1986 and
1987) at two Florida locations (Quincy—30° 35' N Lat,
Marianna--30° 46' N Lat). A uniform seeding rate of 360
viable seed m-2 was used in all environments. Planting dates
at Quincy were 11 December 1985 and 18 December 1986 for the
cropping years 1986 and 1987, respectively. Marianna
planting dates were 20 December 1985 and 4 February 1987 for
the cropping years 1986 and 1987, respectively. The Quincy
plantings were irrigated as needed. Pre-plant or early post¬
plant fertilizer was broadcast mechanically on all plots at a
rate of 25, 22, and 62 kg ha-1 of N, P and K, respectively.
7

8
Experimental units consisted of 6 rows 20 cm apart and
3.6 m long, trimmed to 3.0 m before harvest. Plots were
arranged in a randomized complete block of three replications
with four N topdress levels (0, 55, 110, and 165 kg ha-1 of N
as ammonium nitrate, applied during the tillering stage) as
main plots and genotypes or populations as subplots.
Plant height (PH) and tiller number (TN) were determined
prior to harvest. Grain yield (GY) was determined by combine
harvest of entire subplots. Kernel weight (KW) was based on
weight of a 200 seed sample from each subplot. Test weight
(TW) was not measured at Marianna in 1987 due to insufficient
seed from many plots. Kernel number per tiller (KN) was
calculated from GY, KW, and TN.
Analysis of Pure Lines
Analyses of variance (AOV) and linear regressions were
calculated for GY, KW, TN, PH, and TW of the six pure lines
(three cultivars and three breeding lines).
The AOV for all characters except TW included 16
environments (E) [4 N topdress levels (N) x 2 locations (L) x
2 years (Y) ] as well as the main and interaction effects of
N, L and Y, all tested with the main plot error. For the
analysis of TW, three location-year combinations (Quincy
1986, Marianna 1986, Quincy 1987) were considered three sites
(S) . For TW, the AOV included 12 E (4 N x 3 S) and the
effects S, N, and SN, all tested with the main plot error.

9
For all characters measured, the effects of genotype (G) and
its interactions were tested with the subplot error.
Linear regressions of individual genotype performance
vs. environmental means were used to partition genotype x
environment (GE) effects into effects due to heterogeneity of
slopes and deviations from linear regressions as proposed by
Breese (1969). Significance of heterogeneity and deviation
effects were tested with the subplot error. Regression AOV
was performed for each genotype separately to test signifi¬
cance of individual DMS. All regression coefficients (b)
were tested for significant difference from 1.0 using t-
tests.
Simple correlation coefficients (df=4) were calculated
for mean vs. b and DMS for all parameters measured and for GY
stability (b and DMS) vs. stability of KW, TN, PH, and TW.
For each genotype, simple correlation coefficients (df=14)
among KW, TN, and KN were calculated, and KW, TN, and KN were
regressed against environmental means for GY.
Adjusted values for KW, TN, and KN were calculated by
dividing KW, TN, or KN for each genotype at each environment
by the corresponding environmental mean. Simple correlation
coefficients among adjusted values for KW, TN, and KN were
then calculated for each genotype.
The environments were divided into two subsets, as
proposed by Verma and Chahal (1978), one which represented
favorable or above average environments and one which

10
represented poor or below average environments. Linear
regressions were calculated for each genotype in both sub¬
sets. A genotype with b<1.0 would be desired in poor
environments, whereas a genotype with b>1.0 would be desired
in favorable environments. A genotype which had b<1.0 in
poor environments but b> 1.0 in favorable environments would
be considered ideal for a broad range of environments.
Broad sense heritability was estimated for GY, KW, TN,
PH, and TW by dividing the total phenotypic variance (sum of
variances of G, E, and GE) into the variance of G (Falconer,
1983) .
Analysis of Blends
Analysis of variance and linear regressions were
calculated for GY and TW of three groups of populations
involving blends: Group 1 = Florida 201, Beagle 82, and
Florida 201 + Beagle 82; Group 2 = Florida 201, Florico, and
Florida 201 + Florico; Group 3 = Beagle 82, Florico, and
Beagle 82 + Florico. Sources of variation and F tests in the
AOV and regression partition for each group were the same as
described above for the analysis of pure lines. For each
group, genotypic performance was regressed on the environ¬
mental means of the three populations involved, and the GE
interaction was partitioned into heterogeneity and deviations
effects. Significance of DMS and b values were tested as
described above for pure lines. Expected values for

11
stability parameters (mean, b, DMS) of each blend were the
mean of the corresponding values of the two component
cultivars.

RESULTS
Appendix Tables A-l, A-2, A-3, A-4, A-5, and A-6 present
means (over three replications) for GY, KW, TN, KN, PH, and
TW, respectively, of each genotype or blend in sixteen
environments.
Stability of Pure Lines
Grain Yield
The AOV and regression partition for GY are shown in
Table 1. Environmental indices (i.e. environmental means)
used in linear regressions are presented in Table 2.
Genotype means differed significantly and ranged from 2550 kg
ha-1 for Florico to 1460 kg ha-1 for FL7845 (Table 3).
Environment had a highly significant (P<0.01) effect on
GY with environmental means ranging from 3610 kg ha--1- in 165
kg ha-1 N plots at Quincy in 1987 to 230 kg ha-1 in 0 kg ha-1
N plots the same year at Marianna (Table 2). The effects of
Y and L represented uncontrollable E effects and were highly
significant (P<0.01) as were the various interaction effects
among the environmental factors (Table 1) . The mean square
for L was the largest for this trait. Nitrogen topdress
level was a management practice and thus represented a
12

Table 1. Mean squares for the analysis of variance and regres
sion partition for grain yield (GY), kernel weight
(KW), tiller number (TN), and plant height (PH).
Source
df
GYa
KW
TNb
PH
Genotype (G)
5
8,732**
1,296**
817**
7,641**
Environment (E)
15
24,320**
680**
357**
2 , 199**
Year (Y)
1
23,726**
1,667**
1,924**
5,236**
Location (L)
1
278,109**
7,892**
820**
8,633**
YL
1
7,369**
99
249*
6,044**
N level (N)
3
12,790**
1 3
191**
2,790**
YN
3
7 7 0 * *
44
92
226
LN
3
1,583**
14
243**
213
YLN
3
1,910**
22
193**
91
GE
75
4 10**
10**
96**
98**
Variance partition
GY 5
1,627**
15**
342**
151**
GL
5
2,015**
62**
203**
587**
GYL
5
582**
5
366**
273**
GN
1 5
272**
8
100**
37
GYN
15
2 0 3 * *
6
23
25
GLN
15
49
5
30
37
GYLN
15
137*
3
27
53
Regression partition
Heterogeneity 5
1,481**
67**
122**
623**
Deviation
70
333**
6
94**
60*
Replicationc
8
7 9 3 * *
33
25
388*
Main plot error1*
24
217
20
36
136
Subplot errore
160
98
5
20
40
*
a
b
c
d
e
** Significant at the 0.05 and 0.01 probability level, respectively.
Mean square x 10-3.
Mean square x 10~2.
Replication within years and locations.
Used to test significance of E, Y, h, N, YL, YU, LN, YLN, Replication.
Used to test significance of G, GE, GY, GL, GN, GYL, GYN, GLN, GYLM,
Heterogeneity, Deviation .

tr &
Table 2
Mean grain yield (GY), kernel weight (KW), tiller number (TN),
plant height (PH), and test weight (TW) of six triticale genotypes by
year, location, and N level. Values represent environmental
indices used in regression analysis.
Year3 Location
N level*3
GY
KW
TN
PH
TW
kg ha-1
kg ha-1
mg seed-1
no. m-2
cm
g L-1
1986 Quincy
0
2720
46
271
97
700
55
3360
43
336
101
710
110
3160
43
371
102
700
165
3480
45
334
103
690
Marianna
0
1060
36
295
89
680
55
1620
37
318
104
680
110
1780
34
308
100
670
165
1690
34
330
103
650
1987 Quincy
0
1850
40
267
91
650
55
2850
42
282
103
660
110
3400
41
307
105
660
165
3610
41
324
108
650
Marianna
0
230
29
254
68
—
55
890
30
280
87
—
110
750
29
245
86
—
165
700
29
190
84
—
lsd0.05
350
3
5
9
10
Year harvested.
Topdress N applied.

Table 3
Stability parameters [mean, regression coefficient (b), and
deviation mean square (DMS)] for grain yield (GY), kernel weight
(KW) , tiller number (TN) , plant height (PH), and test weight (TW)
of six hexaploid triticale genotypes.
Stability
parameter
Genotype
GY
KW
TN
PH
TW
kg ha-1
mg seed-1
no. m-2
cm
g L-1
Mean
Florida 201
2290
42
288
102
690
Beagle 82
1730
39
240
103
660
Florico
2550
43
299
106
720
IRA
2440
31
362
79
670
FL7845
1460
40
262
104
650
CT3477
1970
31
317
80
670
LSD. 05
150
1
21
3
10
ba
Florida 201
1.05
1.06
0.83
0.95
1.10
Beagle 82
0.97
0.96
1.42**
1.10
1.07
Florico
1.27**
1.33**
0.50*
1.19
1.15
IRA
1.02
0.77**
0.69
0.53**
0.96
FL7845
0.83**
1.05
1.24
1.48**
0.48**
CT3477
0.88*
0.83**
1.31
0.67**
1.20*
LSD.05
0.05
0.07
0.21
0.11
0.11
DMSb
Florida 201
126,453
4.830
1,116
40.32
252**
Beagle 82
215,139**
1.803
4,621**
27.27
150
Florico
363,582**
6.165
10,041**
34.29
256**
IRA
222,369
5.964
12,073**
59.16
498**
FL7845
244,779**
6.999
9,520**
103.26
749**
CT3477
516,867**
3.504
9,866**
38.55
115
a *, ** Significantly different than 1.0 at the 0.05 and 0.01 probability
levels, respectively.
b *, ** Significantly different than 0.0 at the 0.05 and 0.01 probability
levels, respectively.

16
controllable E effect. The influence of N was highly
significant, but
in
most
cases, GY did not
respond to N
topdress levels
above
55
kg ha-1 (Table 2) .
However, at
Quincy in 1987,
GY
in
the 110 kg ha-1
N plots was
significantly greater than in 55 kg ha-1 N plots.
The GE effect was also highly significant, and G
interacted significantly with all environmental factors and
most interactions among environmental factors (Table 1). In
the regression partition, the effect of heterogeneity of
slopes was highly significant (P<0.01) with b values ranging
from 0.83 for FL7845 to 1.27 for Florico (Table 3).
Individual DMS were also significantly different (Table 1)
with DMS values ranging from 126,453 for Florida 201 to
516,867 for CT3477 (Table 3).
Mean GY was significantly (P<0.05) correlated with
corresponding b values (r=0.83, df=4), but not DMS (r=0.02,
df=4).
Table 4 presents regression coefficients calculated for
each genotype in above and below average environments. Only
Florida 201 fit the criteria for an "ideal" genotype over a
broad range of environments (i.e. b>1.0 in above average
environments and b<1.0 in below average environments).
The broad sense heritability estimate for GY (+ standard
error) was 0.11 + 0.03.

Table 4. Means and regression coefficients (b) for grain yield of six
triticale genotypes in above average and below average
environments.
Genotype
Environment
set
Above
average
Below
average
Mean
b
Mean
b
—kg ha-1—
—
kg ha-1-
—
Florida 201
3380
0.96
1200
0.69*
Beagle 82
2680
0.91
770
T. 00
Florico
3890
1.06
1210
0.88
IRA
3340
1.31*
1530
1.37*
FL7845
2310
0.58**
610
0.79*
CT3477
2730
1.19
1210
1.27
LSD0.05
140
0.17
100
0.13
*, ** Significantly different than 1.0 at the 0.05 and 0.01
probability levels, respectively.
-j

18
Kernel Weight
The AOV and environmental indices for KW are presented
in Tables 1 and 2, respectively. The effect of G was highly
significant (Table 1) with genotype means ranging from 31 mg
seed-1 for CT3477 to 43 mg seed-1 for Florico (Table 3).
The AOV for KW indicated highly significant (P<0.01)
effects of E, Y, and L (Table 1) . In the absence of
significant interaction effects, comparisons can be made
between years and locations. KW was consistently higher at
the Quincy location and in 1986 (Table 2) . Means over the
location-year combinations ranged from 44 mg seed-1 at Quincy
in 1986 to 29 mg seed-1 at Marianna in 1987.
The effect of GE was highly significant (P<0.01)
although G interacted only with Y and L (Table 1) . The
regression partition indicated that genotypes differed
significantly (P<0.01) in b values but not in DMS (Table 1) .
The b values ranged from 0.77 for IRA to 1.33 for Florico
(Table 3).
Mean KW was significantly (P<0.05) correlated with b
(r=0.88, df=4), but not DMS (r=0.15, df=4). The correlation
coefficient between b for KW and b for GY was relatively high
(r=0.65, df=4), but not statistically significant (P>0.05)
(Table 5). KW had a significant positive correlation with KN
in all genotypes and with TN in Florida 201 and Beagle 82
(Table 6). Correlation coefficients were not significant for
adjusted KW vs. adjusted TN or adjusted KN (Table 7).

19
Table 5. Simple correlation coefficients between stability
parameters [regression coefficeint (b) and
deviation mean square (DMS)] for grain yield (GY)
and other traits [kernel weight (KW), tiller (TN),
plant height (PH), and test weight (TW)] .
Trait
Stability
parameter
b
GY
DMS
KW
b
0.65
-0.06
DMS
0.18
-0.11
TN
b
-0.83*
0.15
DMS
-0.03
0.56
PH
b
-0.02
-0.21
DMS
-0.53
-0.18
TW
b
0.52
0.33
DMS
-0.32
-0.35
* Significantly different than 0.0 at the 0.05
probability level, df=4.

Table 6. Simple correlation coefficients among kernel weight
(KW), tiller number (TN), and kernel number per
tiller (KN) by genotype.
Genotype
KW vs. TN
KW vs. KN
TN vs. KN
Florida 201
0.53*
0.85**
0.30
Beagle 82
0.61*
0.83**
0.30
Florico
0.23
0.89**
0.11
IRA
-0.10
0.60*
-0.39
FL7845
0.39
0.79**
0.41
CT3477
0.27
0.71**
0.21
*, ** Significantly different than 0.0 at the 0.05 and 0.01
probability levels, respectively, df=14.

Table 7. Simple correlation coefficients among kernel weight
(KW), tiller number (TN), and kernel number per
tiller (KN) (as a proportion of their environmental
means) by genotype.
Genotype
KW vs. TN
KW vs. KN
TN vs. KN
Florida 201
0.19
0.24
-0.39
Beagle 82
-0.13
-0.31
-0.10
Florico
-0.34
1
o
M
N>
-0.60*
IRA
0.01
-0.11
-0.46
FL7845
0.31
-0.18
0.33
CT3477
-0.21
-0.23
-0.24
* Significantly
different than
0.0 at the
0.05 probability
level, df=14.

22
Regression of KW vs. environmental mean for GY (Fig. 1)
indicated that KW of all genotypes increased with increasing
GY, but genotypes differed in their rate of increase.
The broad sense heritability (+ standard error) for KW
was 0.41 + 0.26.
Tiller Number
The AOV and environmental indices for TN are presented
in Tables 1 and 2, respectively. Genotypes differed
significantly in TN and ranged from 240 tillers m-2 for
Beagle 82 to 362 tillers m-2 for IRA (Table 3).
TN was significantly (P<0.01) influenced by E, Y, L, and
N, and by the interaction effects LN and YLN (Table 1). The
effect of YL was significant at P<0.05. Environmental means
ranged from 190 to 371 tillers m-2 (Table 2).
The effect of GE was highly significant and G interacted
significantly (P<0.01) with Y, L, YL, and N (Table 1) .
Regression partition indicated highly significant (P<0.01)
effects of heterogeneity and deviation (Table 1) . Regression
coefficients ranged from 0.50 for Beagle 82 to 1.42 for
Florico, and DMS ranged form 12,073 for IRA to 1,116 for
Florida 201 (Table 3).
Regression coefficients tended to decrease (r=-0.57,
df=4) and DMS tended to increase (r=0.57, df=4) with
increasing mean TN, though correlation coefficients for these
relationships were not significant. There was a significant

Kernel weight of six triticale genotypes vs. environmental mean grain
yield. ** indicates b is significantly different than average at the
0.01 probability level.
NJ
u>
Figure 1.

24
negative relationship for b of GY vs. b of TN (Table 5). TN
and KN were not significantly correlated in any genotype,
though in 5 of 6 genotypes, TN tended to increase with
increasing KN (Table 6) . The adjusted TN of Florico was
negatively correlated with adjusted KN of Florico (Table 7).
Regression of TN vs. environmental means for GY (Fig. 2)
indicated that, in all genotypes except IRA, TN increased
with increasing mean GY.
The broad sense heritability estimate (+ standard error)
for TN was 0.27 + 0.11.
Plant Height
The AOV and regression partition for PH are shown in
Table 1. Genotype means ranged from 79 cm for IRA to 106 cm
for Florico (Table 3).
The effect of E was highly significant (P<0.01) with
environmental means ranging from 68 to 108 cm (Table 2). The
main effects of Y, L, and N were also highly significant as
was the YL interaction (Table 1) . The effect of N was
independent of Y and L. With the exception of Quincy in
1986, PH in 0 kg ha-1 N plots was significantly (P<0.05) less
than in plots receiving topdress N (Table 2).
The GE effect was highly significant (P<0.01), but G
interacted only with Y, L, and YL (Table 1) . Regression
partition indicated highly significant (P<0.01) effects of

Tiller number (no.
0 1000 2UUU _ 30UÜ ^000
Mean grain yield (kg ha- 1 )
igure 2. Tiller number of six trit icale genotypes vs. environmental mean grain yield.
*, * * indicates b is signil icant !y different than average at the 0.05 and 0.01
probability levels, respectively.
to
U1

26
heterogeneity and deviation (Table 1). Regression
coefficients ranged from 0.53 for IRA to 1.48 for FL7845, and
DMS ranged from 27.27 for Beagle 82 to 103.26 for FL7845
(Table 3).
Mean PH was significantly (P<0.05) correlated with b
(r=0.88, df=4), but not DMS (r=0.05, df=4). Stability
parameters for PH were not significantly correlated with
stability parameters for GY (Table 5).
The broad sense heritability estimate (+ standard error)
for PH was 0.55 + 0.31.
Test Weight
The AOV and regression partition for TW are presented in
Table 8. Environmental indices used in regression analyses
are shown in Table 2. Genotype means ranged from 650 g L-1
for FL7845 to 720 g L-1 for Florico (Table 3).
As shown in Table 8, the effect of E and all
environmental component effects were highly significant
(P<0.01) . Environmental means ranged from 650 to 710 g L-1
(Table 2).
The effect of GE was highly significant (P<0.01) as were
the interactions of G with S and N (Table 8). The regression
partition indicated that heterogeneity and deviation effects
were also highly significant (Table 8). Regression

27
Table 8. Mean squares for the analysis of variance and
regression partition for test weight of six
triticale genotypes.
Source
df
Mean Square
Genotype (G)
5
21,478**
Environment (E)
11
8,564**
Site (S)
2
38,917**
N level (N)
3
2,971**
SN
6
1,243**
GE
55
469**
Variance partition
SG
10
1,743**
NG
15
315**
SNG
30
121
Regression partition
Heterogeneity 5
1,130**
Deviation
50
403**
Replication3
6
280
Main plot error*3
18
130
Subplot errorc
120
134
** Significant at the 0.01 probability level.
a Replication within sites.
b Used to test significance of E, S, N, SN,
Replication.
c Used to test significance of G, GE, GS, GN, GSN,
Heterogeneity, Deviation.

28
coefficients ranged from 0.48 for FL7845 to 1.20 for CT3477,
and DMS ranged from 115 for CT3477 to 498 for IRA (Table 3).
The b values for TW tended to increase with increasing b
value for GY (Table 5) and with increasing mean TW (r=0.59,
df=4) but neither relationship was statistically significant.
The broad sense heritability estimate (+ standard error)
was 0.44 + 0.24.
Kernel Number per Tiller
Regression of KN vs. environmental means for GY
indicated that, in all genotypes, KN increased with higher
yielding environments, but the rate of increase differed
among genotypes (Fig. 3).
Stability of Blends
Grain Yield
Table 9 presents the AOV and regression partition for
the three groups of triticale populations (P) which included
blends (i.e. Group 1: Florida 201, Beagle 82, and Florida
201 + Beagle 82; Group 2: Florida 201, Florico, and Florida
201 + Florico; Group 3: Beagle 82, Florico, and Beagle 82 +
Florico). Environmental indices used in linear regressions
are shown in Table 10. In all groups, GY was significantly
(P<0.01) influenced by P (Table 9). The blend in Group 2
yielded equal to its higher yielding component, Florico,

34
o
Genotype
(± standard error)
1.
Florida 201
0.00554
(±0.00058)
2 .
Beagle 82
0.00784
(±0.00056)
1—
3.
Florico
0.00621
(±0.00052)
1
4.
IRA
0.00732
(±0.00087)
o 26
5.
FL7845
0.00507
(±0.00044)
6.
CT3477
0.00596
(±0.00063)
Average
0.00614
18
o
c
o
_Q
E
3
C
2 io
o
1000
ÍVJ
V£>
Mean
2000 _ 1 3000
grain yield (kg ha )
4000
Seed number per tiller of six triticale genotypes vs. environmental mean grain
yield. * indicates b is significantly different than average at the 0.05
probability level.
Figure 3

Table 9. Mean squares (x 10 ) for analysis of variance and regression
partition for grain yield of three groups of populations.
Each group consisted of two cultivars and one blend made up
half from each cultivar (Group 1: 'Florida 201', 'Beagle 82',
and Florida 201 + Beagle 82; Group 2: Florida 201, 'Florico',
and Florida 201 + Florico; Group 3: Beagle 82, Florico, and
Beagle 82 + Florico.
Group
Source
df
1
2
3
Population (P)
2
3,813**
1,269**
9,082**
Environment (E)
15
1 1,580* *
17,200**
16,000**
Year (Y)
1
20,414**
3,453**
8,460**
Location (L)
1
125,628**
214,877**
202,381**
YL
1
654 *
6,362**
4 , 397**
N level (N)
3
6,219**
5,815**
4,890**
Ytl
3
74
923**
523*
LN
3
954**
818**
882**
YU1
3
394 *
1, 347**
883**
PE
30
3 0 7 * *
151
373**
Variance partition
PY
2
1,772**
186
1,572**
PL
2
1,313**
751**
1,983**
PYL
2
85
360*
712**
PH
6
156
51
104
PYN
6
208*
45
153
PLN
6
57
65
28
PYLN
6
59
164
170*
Regression partition
Heterogeneity
2
2 10* *
912**
1,726**
Deviation
28
3 14 * *
97
279*
Replication3
8
508**
764**
395*
Main plot errorb
24
122
151
127
Subplot errorc
64
82
103
73
*, ** Significant at the 0.05 and 0.01 levels, respectively.
a Replication within years and locations.
b Used to test significance of 12, Y, I., n, YI,, YN, LN, YLN, Replication.
c Used to test significance of P, PE, PY, PL, PN, PYL, PYN, PLN, PYLN,
Heterogeneity, Deviation .

Table 10
Mean grain yield (GY) and test weight (TW) of three groups of
triticale populations (each group consisting of two cultivars
and one blend made up half from each cultivar) by year,
location, and N level. Values represent environmental indices
used in regressions analysis.
Year3
Location
N level*3
GY
TW
Group
Group
1
2
3
1
2
3
- g L --
1986
Quincy
0
2620
3340
3090
700
740
720
55
3290
3790
3580
710
740
730
110
3390
3490
3420
700
740
710
165
3650
3940
3750
690
730
700
Marianna
0
1040
1150
1060
670
700
690
55
1590
1740
1540
700
700
690
110
1800
1800
1590
660
700
680
165
1600
1770
1610
650
690
700
1987
Quincy
0
1660
2620
2220
640
670
660
55
2290
3620
3420
650
680
670
110
3070
4330
3850
650
690
670
165
3170
4440
3850
650
670
670
Marianna
0
230
410
290
—
—
—
55
850
1210
880
—
—
—
110
690
1020
730
—
—
—
165
700
910
' 560
—
—
—
LSD.05
370
410
380
10
8
10
a Year harvested,
k N topdress level.

32
while the blends in Groups 1 and 3 had GY intermediate
between their component cultivars (Table 11).
The grain yield of all groups was significantly
influenced by E, Y, L, and N (Table 9) . In most cases, GY
did not respond to N levels greater than 55 kg ha-1, however,
at Quincy in 1987, GY in 110 kg ha-1 N plots was
significantly greater than in 55 kg ha-1 N plots (Table 10).
Interactions among environmental factors were all significant
at P<0.05 or 0.01 with the exception of the YN interaction in
Group 1 (Table 9) . Environmental means ranged from 230 to
3650, from 410 to 4440, and from 290 to 3850 kg ha-1 in
Groups 1, 2, and 3, respectively (Table 10). The highest
yielding environment for Group 1 was at Quincy in 1986, while
the highest yielding environment for Groups 2 and 3 was at
Quincy in 1987.
The interaction PE was significant (P<0.01) in groups 1
and 3 (Table 9). Although the PE interaction was not
significant in Group 2, the interaction effects PL and PYL
were significant (P<0.01 and P<0.05, respectively). P did
not interact significantly (P>0.05) with N in any group. The
regression partition indicated that the effect of
heterogeneity was highly significant in all groups (Table 9).
The deviation effect was significant (P<0.05) in Groups 1 and
3. The greatest range in b values was observed in Group 3
(0.83 for Beagle 82 to 1.12 for Florico) (Table 11). No DMS
value in Group 2 was significantly different from zero. DMS

Table 11. Observed (O) and expected (E) stability parameters [mean,
regression coefficient (b), and deviation mean square (DMS)] for
grain yield of three groups of triticale populations, each group
consisting of two cultivars and one blend made up half from each
cultivar.
Group
Population
Mean
ba
DMS
b
0
E
0 E
0
E
—kg
ha-1
1
Florida 201
2290
1.06
293,104
Beagle 82
1730
1.01
77,403
Blend 1
1970
2007
0.93 1.04
261,441**
185,254
LSD.05
130
0.06
2
Florida 201
2290
0.89**
48,267
Florico
2550
1.10**
65,697
Blend 2
2590
2420
1.01 0.95
80,529
56,982
LSD.05
150
0.04
3
Beagle 82
1730
0.83**
274,803**
Florico
2550
1.12**
213,464
Blend 3
2380
2139
1.05 0.98
71,049
244,134*
LSD.05
130
0.04
a ** Significant from 1.0 at the 0.01 probability level.
b ** Significant from 0.0 at the 0.01 probability level.

34
for the blend in Group 1 and Beagle 82 in Group 3 were
significantly (P<0.01) greater than zero.
Test Weight
The AOV and regression partition for Groups 1, 2, and 3
are presented in Table 12. The effect of P was highly
significant (P<0.01) in all groups (Table 12). Florico had
the highest TW, 720 g L_1, and Beagle 82 had the lowest, 660
g L-1 (Table 13). The blends in all groups consistently had
TW close to the mean TW of their component cultivars.
The effects of E, S, and N were significant (P<0.01) in
all groups (Table 12). The interaction SN was significant at
P<0.05 in Groups
1 and
2, and
at
P<0.01 in
Group 3.
Environmental mean
ranges
were 640
to
710, 670 to
740, and
660 to 730 g L-1 in Groups 1, 2, and 3, respectively (Table
10). In all groups, the highest TW was observed in 55 kg ha~
1 N plots at Quincy in 1986, and the lowest TW was observed
in 0 kg ha-1 plots at Quincy in 1987.
Highly significant (P<0.01) PE interaction was observed
only in Group 3 (Table 12). The PE interaction in Group 2
was significant at P<0.05. In all groups, however, P
interacted significantly (P<0.01) with S. The regression
partition indicated that Group 2 had a significant (P<0.05)
deviations effect with DMS values ranging from 50 to 158
(Table 13). Group 3 had highly significant (P<0.01)
heterogeneity and deviation effects (Table 12) with b values

Table 12. Mean squares for analysis of variance and regression partition
for test weight of three groups of triticale populations. Each
group consisted of two cultivars and one blend made up half from
each cultivar (Group 1: 'Florida 201', 'Beagle 82', and Florida
201 + Beagle 82; Group 2: Florida 201, 'Florico', and Florida
201 + Florico; Group 3: Beagle 82, Florico, and Beagle 82 +
Florico).
Group
Source
df
1
2
3
Population (P)
2
9,384**
7,272**
32,642**
Environment (E)
11
5,657**
6,113**
4,577**
Site (S)
2
28,423**
31,818**
21,894**
N level (N)
3
874**
780**
1,128**
SN
6
460*
210*
529**
PE
22
168
144*
311**
Variance partition
PS
4
341*
332**
919**
PN
6
126
71
284*
PSN
12
131
133
122
Regression partition
Heterogeneity
2
80
49
431**
Deviation
20
177
154*
299**
Replication3
6
103
111
132
Main plot error*3
18
147
58
89
Subplot error0
48
133
77
122
*, ** Significant at the 0.05 and 0.01 probability levels, respectively.
a Replication within sites.
b Used to test significance of E, S, N, SN, Replication.
c Used to test significance of P, PE, PS, PN, PSN, Heterogeneity,
Deviation.

Table 13. Observed (O) and expected (E) stability parameters [mean,
correlation coefficient (b), and deviation mean square (DMS)] for
test weight of three groups of triticale populations, each
consisting of two cultivars and one blend made up half from each
cultivar.
Group
Population
Mean
b
DMS
0
E
0
E
0
E
g L-1
1
Florida 201
690
0.99
161**
Beagle 82
660
0.95
111
Blend 1
670
671
1.07
0.97
82
136
LSD.05
10
—
2
Florida 201
690
0.95
158**
Florico
720
1.01
50
Blend 2
710
701
1.04
0.98
116
104
LSD.05
10
—
3
Beagle 82
660
1.06
94
Florico
720
1. 12
226**
Blend 3
690
685
0.82
1.09
279**
160
LSD.05
10
0.09
**
Significant from 0.0 at the 0.01 probability level.

37
ranging from 0.82 for the blend to 1.12 for Florico and DMS
ranging from 94 for Beagle 82 to 279 for the blend (Table
13) .

DISCUSSION
Pure Lines
For most traits, the wide range of environmental means
and relatively uniform distribution across the range provided
a basis for comparing the response of genotypes to different
environments. A possible exception was PH where seven
environments were in the 100 to 104 cm range, while the other
nine environments were distributed more uniformly between the
68 and 108 cm extremes. It should be noted that the large L
effect, seen particularly for GY, was due to differences in
planting date and water management as well as edaphic
differences between Quincy and Marianna.
Genetic variation for a trait is required in order to
alter that trait by phenotypic selection. Therefore, to
improve GY performance and stability, variation among
genotypes must exist for GY mean, b value, and/or DMS. In
this study, genotypes were found to differ for all three
stability parameters. Other workers have also found
triticale genotypes differing in GY mean, b value, and DMS
(Sandha et al., 1980; Sapra, 1985; Sinha et al., 1986). Thus
it should be possible to select for enhanced GY stability in
triticale.
38

39
Eberhart and Russell (1966) have described a desirable
genotype as one which combines high mean yield with average b
value and low DMS. A positive correlation among stability
parameters would seem to preclude the combination of high
yield with average b value and low DMS. In this study, b
values and means for GY were positively correlated. A
positive relationship between b value and mean for GY has
been reported by some workers (Fischer and Maurer, 1978;
Baihaki et al., 1976; Eberhart, 1969), while others (Gama and
Hallauer, 1980) found no such relationship. The association
between b values and mean performance of GY observed here and
reported by other researchers could result, in part, from the
nature of the analysis and the genotypes involved. Since
observed yield cannot fall below zero, the intercept of a
linear regression will not be very much less than zero.
Thus, it would be difficult to envision a situation where low
mean yield would be associated with a large b value. The low
b values thus associated with low yielding genotypes could
influence the correlation between b and mean. However, the
critical issue is whether some (however few) genotypes
combine high yield with average b value. In this study, one
genotype, IRA, was high yielding and had an average b value
as well as a low DMS. Other workers have also found
triticale genotypes which combined high yield and desired
stability (Kaltsikes, 1971; Sandha et al., 1980; Sapra,

40
1985). Thus, selection can apparently be made for stability
without sacrificing high yield.
A graphical presentation of regression equations of
Florico and IRA (Fig. 4) illustrate the significance of this
type of analysis. Florico would be expected to yield more
than IRA in high yielding environments, such as in breeding
yield nurseries. Thus, IRA could be easily overlooked if no
consideration is given to production in a broad range of
environments. IRA would be expected to yield higher than
Florico in low yielding environments. No yield data are
available for commercial triticale production in Florida.
However, wheat and triticale yield levels are similar in
experimental plots, and commercial wheat production in the
state averages about 2000 kg ha-1 (Florida Department of
Agriculture, Division of Marketing, 1986). In such
environments, IRA and Florico would be expected to yield
about equal and other considerations such as disease
resistance, plant height, or grain quality would become more
important. Further, IRA had a low DMS, whereas Florico had a
large DMS.
Verma and Chahal (1978) have proposed an alternative to
the idea that b=1.0 is the desired level of linear response.
Given two genotypes with equal mean yield and different b
values, the genotype with the higher b value would be higher
yielding in favorable environments and lower yielding in less
favorable environments (Fig 5.) A theoretical "ideal"

5000
Comparison of regression slopes of 'Florico' and IRA triticale. Equations
are presented in Table 3.
Figure 4.

'1000
Ideal
Figure 5. Comparison of the performance of genotype A (b>1.0), genotype B (b<1.0), and
an ideal genotype with b>l . 0 in above average environments and bcl.O in below
average environments. (Adapted from Verma and Chahal, 1978.)
M

43
genotype would have a low b value in less favorable
environments, but a high b value in favorable environments.
To identify genotypes which had this response pattern, linear
regressions were calculated for genotypes in this study in
two environmental subsets as described by Verma and Chahal
(1978). Florida 201 exhibited the ideal b values proposed by
Verma and Chahal (1978) (i.e. b > 1.0 in above average
environments and bcl.O in below average environments).
However, its mean yield was surpassed by Florico in above
average environments and by IRA in below average
environments. Thus, the advantage Florida 201 had in terms
of b values was moderated by its low mean yield relative to
Florico and IRA. The present example does not detract from
the value of this approach since it is theoretically possible
to find genotypes which combine the desired b values with
high mean performance in both sets of environments.
It has been reported that b value for GY is related to
yield and plant height (Laing and Fischer, 1977; Purvis,
1973). While a positive correlation between b value for GY
and mean GY was observed in the present study, there was no
such association between b value for GY and mean PH. The
genotypes with the highest and lowest b values (Florico and
FL7845, respectively) were both tall, and both of the short
genotypes (IRA and CT3477) had average or below average b
values. Sinha et al. (1986) likewise found no relationship
between b values for GY and mean PH in triticale. An

44
association between these parameters would be expected in a
set of genotypes where b value and mean for GY are highly
correlated and there is a close association between yield
potential and plant height, as was the case when Laing and
Fischer (1977) and Purvis (1973) compared semi-dwarf wheats
to their taller counterparts.
While b=1.0 is generally accepted as desirable for GY
(Eberhart and Russell, 1966), this is not the case for
quality traits such as TW. For quality traits, a minimum b
value, in addition to low DMS, would be the goal since a
uniform product, regardless of production environment, is
generally desired. Unfortunately, the only genotype which
had a relatively low b value for TW (FL7845) had the lowest
mean TW. Beagle 82 and CT3477 had low DMS for TW and low
mean TW.
A low b value and DMS=0.0 for PH would also be desirable
since uniform plant height, even across variable field
conditions, would enhance acceptability and facilitate
mechanical harvest. Low mean values were again associated
with low b values and the two short genotypes, IRA and
CT3477, had b<1.0.
Although it is accepted that an average b value (i.e.
b=1.0) for GY is desirable, there seems to be no clear
understanding of how, in terms of yield components, this
level of linear response is achieved. Low b values for GY
could be achieved in a genotype by two means: 1) yield

45
components could resist change or 2) yield component levels
could change in a compensating manner such that the final
product remained relatively constant.
Grafius (1956) has presented a geometrical
interpretation of yield component compensation in widely
adapted genotypes. Reports by other researchers are quite
conflicting in this regard. Vaid et al. (1985) and
Rathnaswamy and Jagathesan (1982), working with dry bean and
sesame, respectively, found that b values for fruiting body
number were significantly correlated with b values for GY.
Saeed and Francis (1983) found that b for GY was
significantly correlated with b for seeds m-2 in all grain
sorghum genotypes tested and with b for KW in late maturing
genotypes. They concluded that stability for yield
components contributed to GY stability. Heinrich et al.
(1983) also concluded that yield component compensation was
not the major mechanism of GY homeostasis in grain sorghum.
In hexaploid wheat, Talukdar and Bains (1982) observed a
significant positive correlation between b values for GY and
b values for KW. However, they concluded that responsiveness
of KN and TN to changes in environment was the chief means by
which GY levels were maintained across diverse growing
conditions.
Singh and Bains (1984) found no association between b
values for GY and b values for yield components in chickpea.
Bains and Gupta (1972) and Fatih (1987) likewise found no

46
such association in hexaploid wheat or wheat-Agropyron
derivatives, respectively. These three reports attributed
low b values for GY to yield component compensation.
If yield component compensation was the mechanism of
maintaining GY across diverse environments, a decrease in one
yield component should be accompanied by an increase in
another yield component. In general, this was not the case
for genotypes tested in this study. All genotypes showed a
positive correlation between KW and KN. In addition, two
genotypes showed a positive correlation between KW and TN.
Only IRA gave any indication of yield component compensation
(i.e. a negative, nonsignificant correlation between KW and
TN, and between TN and KN).
Given that all yield components were adversely affected
in less favorable environments, the data were examined for
the possibility of a relative increase in one yield component
accompanying a relative decrease in another. When correla¬
tion coefficients were calculated for adjusted values yield
components, there was an apparent trend for yield component
compensation, but only the r value for adjusted TN vs.
adjusted KN of Florico was significant. In FL7845,the
genotype with the lowest b value for GY, positive, though
nonsignificant (P<0.05), correlation coefficients for
adjusted KW vs. adjusted TN and adjusted TN vs. adjusted KN
were observed. Therefore, yield component compensation did

47
not seem to be the major factor controlling GY responsive¬
ness .
The b value for GY would then appear to depend on the b
values of individual yield components. In the present study,
b values for GY and KW had a relatively high, though
nonsignificant, correlation coefficient. Responsiveness for
GY and TN were significantly correlated, but surprisingly,
the correlation was negative.
Since environments were ranked differently for GY, KW,
and TN, the functional relationships between GY and yield
components could be better understood by regressing KW, TN,
and KN against environmental means for GY rather than against
their own environmental means. Florico, the genotype with
the highest b value for GY, had a b value for KW vs. mean GY
significantly (P<0.01) greater than average (Fig. 1), but TN
vs. mean GY (Fig. 2) and KN vs. mean GY (Fig. 3) b values
were equal to the average. Therefore, the high b value for
GY of Florico was due to the high b value of KW vs. mean GY.
FL784 5, the genotype with the lowest b value for GY, had
above average b value for TN vs. mean GY and below average b
value for KN vs. mean GY. Therefore, the relatively low b
value for GY of this genotype was due to the tendency of KN
to remain constant despite environmental conditions. IRA had
remarkably constant TN across environments (Fig. 2) , though
the genotype showed an average b value for GY. For IRA, the
b value for KN vs. mean GY (Fig. 3) was above average, though

48
not statistically so. In the case of IRA, the low b value of
TN vs. mean GY was counterbalanced by the high b value for KN
vs. mean GY. Thus, b values for GY seemed to depend on the b
value of one or more yield components vs. mean GY, and
genotypes differed in which yield component(s) had the major
impact.
Heritability estimates depend greatly upon the range of
genetic variation in the material tested and upon the
magnitude of environmental variance. Broad sense
heritability (H2) estimates reported here were lower than
estimates reported for triticale elsewhere in the literature,
due, probably, to the large range of environments sampled.
Kamboj and Mani (1982) reported H2 estimates of 0.91, 0.54,
0.72, and 0.74 for GY, KW, TN, and PH, respectively. Banik
and Islam (1984) reported H2 estimates of 0.66 and 0.71 for
TN and PH, respectively.
Blends
Genetic diversity in a population has the potential to
enhance yield and yield stability, particularly when extreme
environmental fluctuations occur or in the presence of
sporadic disease or insect outbreaks. In such situations,
plants affected by adverse conditions can be compensated for
by neighboring plants which are more tolerant to
environmental pressure. Population diversity also has the
potential to improve utilization of available resources when

49
component individuals differ in their environmental
requirements. Yet, acceptance of commercial cultivars in
most species requires uniformity for such characters as plant
height and maturity, especially for machine harvest.
The three blends considered here would meet accepted
uniformity standards. Although all cultivars were visually
similar in growth habit and maturity, and no disease or
insect pressure was evident, two of the combinations
apparently benefited from genetic diversity in terms of GY.
The blends, Florida 201 + Florico and Beagle 82 + Florico
yielded higher than the mean of their component cultivars,
and Florida 201 + Florico yielded equal to its higher
yielding component. Both of these blends approached unity
regression more closely than their component cultivars, and
Beagle 82 + Florico had a lower DMS than expected. The
common cultivar in these two blends was Florico. Florico had
the highest yield in this study, but also had an extremely
high b value. The interaction of Florico with the lower
yielding and more stable cultivars, Florida 201 and Beagle
82, resulted in enhanced performance in their blends.
Genetic diversity itself did not ensure enhanced
performance. One blend (Florida 201 + Beagle 82) did not
yield higher than the mean of its component cultivars, and
the blend had a significant DMS, even though both component
cultivars had DMS=0.0. Pfahler and Linskens (1979) also
found that only certain blends approached desired stability

50
(b=1.0 and DMS=0.0) more closely than their component pure
lines. Singh and Bains (1984) found no blend which yielded
equal to its higher yielding component, and only a few blends
equaled or exceeded their component lines in terms of
stability.
While the use of blends appears to offer a means of
enhancing yield performance over a range of environments, it
cannot be assumed that increased genetic diversity will
necessarily have the desired effect. Each proposed
combination must be tested. As Pfahler and Linskens (1979)
have pointed out, blends which include high yielding, rather
stable lines would be more likely to perform well in diverse
environments.

APPENDIX
DATA TABLES

Ul
M
Table A-1 . Grain yield in kg ha ^ of nine t: 1 iticale populations in sixteen environments
(averaged over three replications).
Year: 1985-86 1986-87
Location: Quincy Marianna Quincy Marianna
N level3:
Popul at i on'
165
D
110
55
0
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201 3,861
3,315
3,484
3,022
1,478
1,627
1,623
1,178
3,959
3,905
3,187
2,279
1,066
1,059
1,198
372
Beagle 82
3,400
3,131
3,189
2,592
1,379
1,516
1,322
818
2,521
2,733
2,516
1,362
291
279
457
112
Florico
4,045
3,500
4,021
3,198
1,889
1,651
1,601
1,223
4,889
4,791
3,821
2,833
848
1,034
1, 162
308
IRA
3,601
3,608
3,702
3, 183
2,487
2,560
2,064
1 , 265
4,282
3,586
3,145
1,615
1,106
1,151
1,285
314
F L 7845
2,266
1 , 700
2,575
2,107
972
1,086
1 , 290
723
3,002
2,779
2,383
1,699
136
257
321
91
C13477
3,692
3,728
3,204
2,244
1,946
2,244
1,810
1,122
3,015
2,580
2,052
1,334
756
722
906
192
Blend 1
3,461
3,732
3,316
2,944
1,788
1,664
1,496
869
3,726
3, 774
3, 164
2,008
634
827
939
• 401
Blend 2
3,922
3,648
3,875
3,803
1,957
2,137
2,010
1,050
4,456
4,296
3,859
2,748
801
978
1,270
564
Blend 3
3,803
3,763
3,527
3,489
1,563
1,593
1,695
1,150
4,138
4,031
3,924
2,477
540
872
1,033
455
a Topdress nitrogen (as ammonium nitrate) applied in kg ha .
k Blend 1 = florida 201 + Beagle 82, Blend 2 = Florida 201 + Florico, Blend 5 = Beagle 82 + Florico.

Table A-2.
Kernel weight in mg kernel ^ of nine triticale populations in sixteen
environments (averaged over three replications).
iear:
1985-86
1986-87
Location: Quincy Marianna Quincy Marianna
N level3: 165 1 10 55 0 165 1 10 55 0 165 1 10 55 0 165 1 10 55 0
Population^
Florida 201
50.
.5
48.
.3
48.
.7
49.
.4
36.
.7
37.
3
38,
.5
38,
.3
46,
.5
45.
. 1
47.
.0
44 .
.7
33
.8
33.
.1
34.
.7
31.
.6
Beagle 82
46.
, 1
45 .
.0
44.
.0
48.
.4
36.
.7
36.
.6
39.
.3
38.
. 1
40.
,3
43,
.3
43.
.3
41.
.8
31
.3
30.
.2
31.
.5
31.
.1
Florico
54.
,5
49.
.9.
49.
.0
54.
.5
40.
.0
39.
,2
41 .
. 1
38.
.4
48.
.0
48,
.5
46.
.9
43.
.7
32.
.1
32.
.3
31.
.7
29.
.4
IRA
36.
0
35.
.5
35.
.7
37.
.8
27.
.9
27.
.3
31 .
. 1
34.
.6
33.
.8
32.
.9
34.
.2
31.
.4
23
.6
24.
.8
24.
.8
24.
.2
F L 7845
45.
.9
43.
.8
47.
. 1
48.
.8
36.
.6
33.
,2
41 .
.7
38.
.3
41.
.2
43.
.8
43.
.8
44.
.5
31
.3
30.
.8
31 .
.2
29.
.6
CT3477
38.
,0
37.
.8
34.
.4
37.
.8
27.
.4
27.
,7
28.
.2
29.
.5
32.
.9
34.
.4
34.
.5
32.
.9
23
.2
23.
.8
24.
.3
25.
.7
Blend 1
47.
.5
52.
.7
49.
,2
48.
.9
36.
.3
39.
.4
40.
.9
36.
.5
45.
.8
45.
.9
45.
.2
43.
.7
33,
.6
35.
.2
35.
.4
33
.8
Blend 2
52.
.6
52.
.9
51 .
. 1
53.
.7
40.
. 1
41.
,4
41 .
.0
38.
. 1
48.
.3
50.
.2
47.
.0
46.
.5
32
.6
31.
,3
32.
.9
30.
.5
Blend 3
53.
.5
52.
.2
46.
.9
49.
.4
37.
.4
38.
,0
42,
. 1
36.
.5
49.
.4
49,
.5
47.
.4
46.
.8
32
. 1
34.
.5
29.
.0
31,
.9
3 lopdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend V = Florida 201 + Beagle 82, Blend 2 = Florida 201 ♦ Florico, Blend 3 = Beagle 82 + Florico.

Table A-3. Tiller number m ^ of nine triticalo populations in sixteen environments
(averaged over three replications).
rear: 1985-86 1986-87
Location: Quincy Marianna Quincy Marianna
w level0:
Population^
165
110
55
0
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201
308.7
346.0
310.0
248.7
327.0
321.0
305.7
288.0
299.0
303.0
318.7
299.3
194.7
237.3
279.7
229.0
Beagle 82
286.3
338.0
342.3
264.0
257.0
269.7
314.3
310.0
260.0
247.7
194.3
140.3
122.2
133.3
188.7
169.7
Florico
232.3
289.3
314.0
247.7
290.3
287.7
294.7
203.7
398.0
370.3
3B6.7
350.3
158.7
273.7
293.7
319.7
1 RA
457.7
482.7
350.3
347.7
391.0
399.0
371.0
322.3
325.3
352.0
229.3
237.7
311.7
391.0
407.7
407.7
FL 7845
277.7
284.0
374.0
247.3
2 76.3
241.7
282.0
300.3
329.3
311.7
329.3
333.7
97.5
124.9
194.3
188.7
0 34 77
441.0
484.0
328.3
272.3
438.0
332.3
340.7
766.7
329.7
260.0
232.0
242.0
256.0
321.3
314.0
208.7
Blend 1
347.7
356.0
337.7
354.7
279.3
274.0
322.7
289.0
310.3
292.0
216.7
228.0
164.0
197.3
261.3
229.3
Blend 2
302.0
315.7
282.3
250.3
280.7
261.0
307.3
207.7
353.3
325.3
356.0
269.7
148.7
203.0
322.7
324.0
Blend 3
356.3
325.3
365.7
271.0
299.0
283.7
303.0
203.7
288.0
271.3
290.7
193.0
137.5
181 .0
212.7
206.7
0 Topdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend 1 = Florida 201 + Beagle 82, Blend 2 = Florida 201 * Florico, Blend 3 = Beagle 82 ♦ Florico.

Table A-4.
Kernel number tiller ^ of nine triticale populations in sixteen environ¬
ments (averaged over three replications) .
Year:
1985-86
1986-87
Location:
Quincy
Marianna
Quincy
Mari anna
N level*1:
Popu 1 a t i on*1
165 110 55 0
165 110 55 0
165
110 55
0
165
110
Florida 201
25.
.03
19.
.70
23.
.77
24.
.73
12.
.21
13.
.57
13.
.60
10,
.67
28.
.63
29.
.20
21 .
.93
18.
,00
15.
.87
13.
.30
12.
.20
5.
.17
Beagle 82
26,
.07
20.
.33
21 .
.63
20.
.23
14.
.87
15.
.40
10.
. 70
6.
.94
24.
.07
25.
.67
29.
.73
23.
,17
6.
.87
6.
.90
8.
.42
2.
.27
Florico
33.
.17
24,
.53
26.
.80
23.
,63
16.
.60
14.
.27
13
.04
11 .
.34
25.
.70
26.
.83
21.
.07
19.
.27
16.
.70
11.
.73
12,
.37
3,
.24
IRA
22,
.77
21.
.30
29.
.93
24.
.33
22.
.53
23.
.67
18,
.18
10,
.84
38.
.90
31.
.27
40.
.30
22.
.03
14.
.77
11.
.90
12.
.60
3.
.27
F L 784 5
18.
.20
14.
.09
14.
.73
17.
.53
9.
.62
14.
.07
11
.39
6.
.22
23.
.03
20.
.73
16.
.57
11 .
.52
4.
,54
6.
.70
5,
.33
1.
.59
CT3477
23
.23
20.
.63
28.
.77
21 .
.90
16.
.03
24.
.90
18,
.47
13
.79
27.
.73
29.
.30
25.
.60
17.
.20
12.
.34
9.
.41
11.
.63
3.
.53
Blend 1
21
. 10
19.
.93
20.
.50
17.
.20
17.
.21
15 ,
.37
11
.25
8,
.22
26.
.60
28.
.13
33.
. 10
21.
.03
11 ,
.34
12.
.44
10.
.42
5.
.06
Blend 2
24,
.93
21.
.53
27.
. 73
28.
.43
17.
.77
19.
.27
15,
.63
9.
.38
26.
.07
26.
.53
23.
.20
22.
. 10
17.
.53
15.
.33
12.
.17
5.
.86
Blend 3
20,
.13
22.
.07
20.
.50
26.
.67
14.
.11
14.
.70
13
.13
11,
.29
29.
.20
29.
.83
28,
.43
27.
.73
12.
.51
14.
.20
17,
.87
6.
.72
u lopdrcss nitrogen (as ammonium nitrate) applied in kg ha
*J Blend 1 = Florida 201 + Beagle 82, Blend 2 = Florida 201 + Florico, Blend 3 = Beagle 82 + Florico.

Table A-5
Plant heiyht in cm of nine tritj.cale populations in sixteen environments
(averaged over three replications).
Tear:
1985 - 86
1986-87
Location: Quincy Marianna Quincy Marianna
N level3: 165 110 55 0 165 110 55 0 165 110 55 0 165 110 55 0
Popul at i on^
Florida 201
106,
.33
106.
.33
110.
.67
108,
.00
99.
.73
105.
.80
110,
.00
93.
.90
112.
.33
114.
.67
109.
.67
96.
.67
87.
.50
93.
.33
97.
.50
74.
.17
Beagle 82
113,
.00
113,
.00
110.
.67
105.
.80
107,
.67
110.
.80
112.
.33
97.
.33
113
.00
113,
.00
114,
.00
96.
.67
88.
.33
90.
,83
86.
.67
75.
.83
Florico
116,
.33
108
.00
113.
.00
111.
.33
121 .
.00
115.
.00
116.
.00
9B,
.80
116.
.67
116,
.67
111.
.67
103.
.17
96.
.50
93.
.33
94.
.10
70.
. 83
IRA
83.
.73
81 .
.13
78,
.57
78.
.60
86.
. 10
86.
.27
BA .
.57
69.
.17
89.
.17
81 .
.67
80.
.00
65.
.83
76.
.67
78.
.33
77
.37
65.
.00
F L 784 5
115,
.00
115,
.67
115.
.67
103.
.23
118.
.33
98.
.00
111 .
33
102.
,27
124.
.00
120.
.00
118.
.00
108.
.00
81 .
.67
85.
.83
85.
.83
65
.00
Cl 3477
82.
.00
89,
.70
79.
.A3
76.
.90
89.
.70
83.
.70
87.
. 10
74 .
.30
90.
.83
83.
.33
84 ,
.17
74.
.17
73.
.33
75.
.00
81 .
.67
60.
.00
Blend 1
112.
.33
109.
.67
112.
.33
111.
.67
105,
.60
111.
.77
111.
.33
83 .
.83
114.
.00
119.
.00
113.
.00
99.
.83
92.
.50
96.
.67
93.
.33
73.
.33
Blend 2
107.
.00
109.
.67
111.
33
109.
.67
116.
.33
115.
,00
116.
.67
91 .
.40
118,
.67
119.
.00
114.
.00
101 .
.50
93.
.33
99.
.17
93
.33
70.
.00
Blend 3
115.
,00
115.
.67
111.
33
107.
,00
IK.
.00
IK.
,27
116.
,67
100.
.57
120.
.33
117.
.00
116.
.33
104 .
.67
93
.33
96.
.67
93
.33
75,
.00
3 Topdress nitrogen (as ammonium nitrate) applied in kg ha"'.
Blend 1 = Florida 201 + Beagle 82, Blend 2 = Florida 201 + Florico, Blend 3 = Beagle 82 + Florico.

Table A-G . Tost weight in ij I, ' ol nine trilicale popula Lions in twelve
environments (averaged over three replications).
tear: 1985-86 1986-87’
Location: Quincy Marianna Quincy
N level3:
Popul at i on^
165
110
55
0
165
110
55
0
165
110
55
0
Florida 201
714.3
718.7
725.3
720.7
654.0
680.3
684.7
680.0
673.7
671.3
662.7
660.7
Beagle 82
669.0
676.0
694.0
695.0
646.0
639.3
656.3
660.7
628.7
633.0
641.7
622.0
Florico
740.3
749.0
755.0
742.7
703.3
716.3
716.3
710.0
684.3
699.3
684.3
678.0
1 RA
671.3
680.0
708.0
686.3
654.0
673.7
692.7
703.7
637.3
648.0
650.0
641.3
FL7845
637.0
643.7
667.0
678.0
605.0
657.3
641.3
662.7
646.0
654.3
650.0
641.7
CT3477
686.7
703.7
710.0
695.7
652.0
667.0
667.0
678.0
637.3
644.0
643.7
628.7
Blend 1
684.3
695.0
708.0
699.3
637.3
667.0
673.7
678.0
660.7
662.7
662.7
650.0
Blend 2
731.7
736.0
744.7
744 . 7
697.0
703.3
705.7
707.7
663.0
684.3
688.7
671.3
Blend 3
690.7
710.0
727.3
725.3
656.3
676.0
690.7
691.0
689.0
682.3
682.0
675.7
a • . i
Topdress nitrogen (as ammonium nitrate) applied in kg ha
^ Blend 1 = Florida 201 ♦ Beagle 82, Blend 2 = Florida 201 ♦ Florico, Blend 3 = Beagle 82 Florico.
Ol

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BIOGRAPHICAL SKETCH
Steve Calhoun was born in Harlingen, Texas, on 9 October
1957, and raised mainly in the West Texas oilpatch town of
Midland. After graduating from Robert E. Lee High School,
Steve attended Texas A&M University where he became an Aggie;
got acquainted with Mary Diane McDaniel; and completed, with
honors, a B.S. in agronomy.
From 1980 to 1982, he worked for the Southern Baptist
Foreign Mission Board in Jos, Nigeria, where he taught
agriculture in a mission high school, managed the school
farm, and corresponded with Diane. Upon his return, Steve
worked on a ranch in Southwest Kansas.
In March 1983, Steve and Diane married and moved to
Florida where Steve began work on a M.S. degree with Dr.
Gordon Prine. In 1985, Steve completed the epic work,
"Elephantgrass Performance in a Warm Temperate Environment,"
and began work on his Ph.D. in plant breeding with Drs.
Pfahler and Barnett.
On 12 September 1987, Diane gave birth to their sons,
Rossi Rutledge, who was still born, and Heathman Divilbiss,
who died shortly after premature birth.
62

63
Upon graduation, Steve and Diane plan to move to
Texcoco, Mexico, where Steve has a postdoctoral fellowship
with Centro International de Mejoramiento de Maiz y Trigo.

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
i-L
.
Dr. P.L. Pfahler, Chairman
Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
DrR. D. Barnett, Cochairman
Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Y
Dr. K. Hinson
Professor of Agronomy

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Dr. D.D. Baltensperger
Associate Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
k) A Wu//
Dr. D.A. Knauft
Associate Professor of Agronomy
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Dr. J.E. Funderburk
Assistant Professor of Entomology
and Nematology
This dissertation was submitted to the Graduate Faculty
of the College of Agriculture and to the Graduate School and
was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.
April 1988 (j
'jCclqA pf .
Dean, Coi/Lege of Agriculture
Dean, Graduate School

UNIVERSITY OF FLORIDA
3 1262 08556 7864

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