Genotype X environment interactions of triticale in Florida

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Genotype X environment interactions of triticale in Florida
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vii, 63 leaves : ill. ; 28 cm.
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Calhoun, D. Steven, 1957-
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Triticale -- Florida   ( lcsh )
Grain -- Florida   ( lcsh )
Agronomy thesis Ph. D
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bibliography   ( marcgt )
non-fiction   ( marcgt )

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

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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.















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....................................... 38
Blends.... ....................................... 48

APPENDIX

DATA TABLES.................................... 52

REFERENCES ........................................... 58

BIOGRAPHICAL SKETCH ................... ............... 62














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................ 21

8. Mean squares for the analysis of variance and
regression partition for test weight of six
triticale genotypes............................. 27










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 (0) 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 (0) 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


Table


Page














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........ 42


vii









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--300 35' N Lat,

Marianna--300 46' N Lat), and four N topdress levels (0, 55,

110, 165 kg ha-1) 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.














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.








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--300 35' N Lat,

Marianna--300 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.








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-i, 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





































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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>l.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.





















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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).










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)].

GY
Stability
Trait parameter b 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
probability level, df=4.


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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
















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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

















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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










Table 8. Mean squares for the
regression partition
triticale genotypes.


analysis of variance and
for test weight of six


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**

Replicationa 6 280
Main plot errorb 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,














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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




















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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




















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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.








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"











ra
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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 b<1.0 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










such association in hexaploid wheat or wheat-Aqropyron

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.

FL7845, 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 components) 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
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REFERENCES


Baihaki, A., R.E. Stucker, and J.W. Lambert. 1976.
Association of genotype x environment interactions with
performance level of soybean lines in preliminary yield
tests. Crop Sci. 16:718-721.

Bains, K.S., and V.P. Gupta. 1972. Stability of yield and
yield components in wheat. Indian J. Genet. Plant.
Breed. 32:306-312.

Banik, A.K., and A.S. Islam. 1984. Genetics of some strains
of triticale and their hybrid populations. Indian
J. Agric. Sci. 54:359-361.

Barnett, R.D., and H.H. Luke. 1979. Grain yield and agronomic
characteristics of triticale in comparison with other
small grains in Florida. Soil Crop Sci. Soc. Proc.
38:48-51.

Barnett, R.D., D.D. Morey, H.H. Luke, and P.L. Pfahler. 1982.
Beagle 82 triticale, A new winter feed grain for
multiple cropping systems in the Coastal Plains Region
of South Georgia and North Florida. Fla. Agric. Exp.
Stn. Circular S-297. 8 pp.

Breese, E.L. 1969. The measurement and significance of
genotype environment interactions in grasses. Heredity
24:27-44.

Calhoun, D.S., and R.D. Barnett. 1986. New varieties make
triticale a good alternative feed grain. Fla. Coop. Ext.
Service. Agronomy Facts No. 200. 7 pp.

Eberhart, S.A. 1969. Yield stability of single cross
genotypes. Proc. Ann. Corn Sorghum Res. Conf. Am. Seed
Trade Assn. 24:22-25.

Eberhart, S.A., and W.A. Russell. 1966. Stability parameters
for comparing varieties. Crop Sci. 6:36-40.

Falconer, D.D. 1983. Introduction to Quantitative Genetics.
Longman Group Limited. Essex, England. 2nd ed.










Fatih, Moneim Babu. 1987. Genotypic stability analysis of
yield and related agronomic characters in wheat-
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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.









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 ade te, in scope and quality, as
a dissertation for the degree o Dock o of Philosophy.
I //1


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.




Dr. R.D. Barnett, Cochairman
Pro essor 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. 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.




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. Flnderburk
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


Dean, C olege of Agcibulture


Dean, Graduate School







































UNIVERSITY OF FLORIDA
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