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INHERITANCE PATTERN OF DROUGHT TOLERANCE ATTRIBUTES IN COTTON
(Gossypium hirsutum L.)
By
AZWAR RAZA SHAH GILLANI
A thesis submitted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
IN
PLANT BREEDING AND GENETICS
DEPARTMENT OF PLANT BREEDING AND GENETICS. UNIVERSITY OF AGRICULTURE, FAISALABAD,
PAKISTAN. 2010
i
TO
THE CONTROLLER OF EXAMINATIONS,
UNIVERSITY OF AGRICULTURE,
FAISALABAD.
We, the supervisory committee, certify that the contents and form of this
thesis submitted by Azwar Raza Shah Reg. No. 86-ag-1107 have been found
satisfactory, and recommend that it be processed for evaluation by the external
examiner(s) for award of the degree.
SUPERVISORY COMMITTEE
CHAIRMAN :_______________________________ (Dr. Tariq Manzoor Khan)
MEMBER :_______________________________
(Dr. Hafeez Ahmad Sadaqat)
MEMBER :_______________________________ (Dr. Ashfaq Ahmad Chatha)
ii
Declaration
I hereby declare that the contents of the thesis, “Inheritance pattern of drought
tolerance attributes in cotton (Gossypium hirsutum L.) ” are product of my own
research and no part has been copied from any published source (except the references,
standard mathematical and genetic models/ equations/ formulae/ protocols etc.). I further
declare that this work has not been submitted for award of any other diploma/degree. The
University may take action if the information provided is found inaccurate at any stage.
(In case of any default the scholar will be proceeded against as per HEC plagiarism
policy).
Signature of the student
iii
DEDICATED
TO
My late father,, aa symbol of love and affection! Whose mature, valuable guidance
and financial assistance enabled me to perceive and pursue high ideas in life. I believe, he
is still with me and with him fondness. My endearing mother, who enlightened me with
a learning spirit. My wife, brother and late brothers and sister,, whose prayers and
sympathies steer my way towards success. What ever am I, is due to the efforts of all my
family, I have a great gratitude and pride.
iv
Acknowledgements
I indebted to Almighty Allah, the auspicious, the compassionate and sovereign
whose blessing and glory flourished my thoughts and thrived my ambitions, giving me
talented teachers, affectionate parents, sweet brothers and sister and the sincere friends.
Trembling lips and wet eyes praise for Holy Prophet Muhammad (P.B.U.H.) for
enlightening our conscience with the essence of faith in Allah, converging all His
kindness and mercy upon him.
I am grateful to my worthy supervisor, Dr. Tariq Manzoor Khan, Assistant
Professor, Department of Plant Breeding and Genetics. His kind guidance and help in the
completion and presentation of this thesis. I thank to Dr. Hafeez Ahmad Sadaqat,
Professor, Department Plant Breeding and Genetics and Dr. Ashfaq Ahmad Chatha,
Associate Professor, Department of Agronomy, University of Agriculture, Faisalabad. I
am also obliged to Dr. Shahzad Maqsood Ahmad Basra, Professor, Department of
Crop Physiology, for his cooperation and help during the entire degree programme. I
would like to convey my deepest and sincere gratitude to my friends Jehanzaib,
Naeem Fiaz and Farhan Khalid who cooperated well in the preparation of my thesis.
AZWAR RAZA SHAH
v
Contents
Chapter No. Title Page No.
1 Introduction 1
2 Review of Literature 4
3 Materials and Methods 17
4 Results 33
5 Discussion 193
6 Summary 203
Literature Cited 206
Appendix 221
vi
List of Tables
Table No. Title
Page No.
1 Selected diverse thirty genotypes 18
2 Mean survival rate (%) of 30 cotton genotypes grown in three moisture levels.
34
3 Ranking of different genotypes on the basis of their tolerance level under waters stress condtions.
35
4 Six drought tolerant and susceptible parents selected. 35
5 F-value and coefficient of variation (CV %) for various seedling traits under normal and water stress conditions.
36
6 Mean and statistical significance of seedling of cotton genotypes under normal and water stress conditions.
37
7 Mean and statistical significance of seedling of cotton genotypes under normal and water stress conditions.
38
8 Analysis of variance of six cotton genotypes and their all possible crosses for various characters
47
9 Scaling tests for adequacy of additive-dominance model for various plant traits under normal conditions of cotton (Gossypium hirsutum L.)
50
10 Scaling tests for adequacy of additive-dominance model for various plant traits under water stress conditions of cotton (Gossypium hirsutum L.)
51
11 Mean squares of components of variation of plant height under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
53
12 Mean squares of components of variation of plant height under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
54
13 Mean squares of components of variation of monopodial per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
56
14 Mean squares of components of variation of monopodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
57
15 Mean squares of components of variation of sympodial per 59
vii
plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
16 Mean squares of components of variation of sympodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
60
17 Mean squares of components of variation of bolls per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
62
18 Mean squares of components of variation of bolls per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
63
19 Mean squares of components of variation of boll weight under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
65
20 Mean squares of components of variation of boll weight under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
66
21 Mean squares of components of variation of yield under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
68
22 Mean squares of components of variation of yield under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
69
23 Mean squares of components of variation of staple length under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
71
24 Mean squares of components of variation of staple length under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
72
25 Mean squares of components of variation of staple fineness under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
74
26 Mean squares of components of variation of staple fineness under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
75
27 Mean squares of components of variation of staple strength under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
77
28 Mean squares of components of variation of staple strength under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
78
29 Mean squares of components of variation of GOT (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
80
viii
30 Mean squares of components of variation of GOT (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
81
31 Mean squares of components of variation of seed index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
83
32 Mean squares of components of variation of seed index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
84
33 Mean squares of components of variation of lint index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
86
34 Mean squares of components of variation of lint index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
87
35 Mean squares of components of variation of relative water contents (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
89
36 Mean squares of components of variation of relative water content (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
90
37 Mean squares of components of variation of leaf temperature under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
92
38 Mean squares of components of variation of leaf temperature under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
93
39 Mean squares of components of variation of relative cell injury (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
95
40 Mean squares of components of variation of relative cell injury (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
96
41 Estimates of components of variation for plant height under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
98
42 Estimates of components of variation for plant height under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
99
43 Estimates of components of variation for number of monopodial branches under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
101
ix
44 Estimates of components of variation for monopodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
102
45 Estimates of components of variation for number of sympodial branches under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
104
46 Estimates of components of variation for sympodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
105
47 Estimates of components of variation for number of bolls per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
108
48 Estimates of components of variation for number of bolls per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
109
49 Estimates of components of variation for boll weight under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
111
50 Estimates of components of variation for boll weight under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
112
51 Estimates of components of variation for yield under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
114
52 Estimates of components of variation for yield under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
115
53 Estimates of components of variation for staple length under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
117
54 Estimates of components of variation for staple length under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
118
55 Estimates of components of variation for staple fineness under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
120
56 Estimates of components of variation for staple fineness under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
121
57 Estimates of components of variation for staple strength under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
123
58 Estimates of components of variation for staple strength under water stress conditions in a 6×6 diallel cross of
124
x
Gossypium hirsutum L.
59 Estimates of components of variation for GOT (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
126
60 Estimates of components of variation for GOT (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
127
61 Estimates of components of variation for seed index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
129
62 Estimates of components of variation for seed index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
130
63 Estimates of components of variation for lint index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
132
64 Estimates of components of variation for lint index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
133
65 Estimates of components of variation for relative water content (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
135
66 Estimates of components of variation for relative water content under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
136
67 Estimates of components of variation for leaf temperature under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
138
68 Estimates of components of variation for leaf temperature under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
139
69 Estimates of components of variation for relative cell injury (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
142
70 Estimates of components of variation for relative cell injury (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
143
71 Mean squares attributed to general and specific combining abilities and reciprocal effects of six cotton genotypes under normal conditions
146
72 Mean squares attributed to general and specific combining abilities and reciprocal effects of six cotton genotypes under
147
xi
water stress conditions
73 Estimates of variance components relative to general and specific combining ability and reciprocal effects of six cotton genotypes under normal conditions
148
74 Estimates of variance components relative to general and specific combining ability and reciprocal effects of six cotton genotypes under water stress conditions
149
75 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for plant height under normal conditions
150
76 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for plant height under water stress conditions
150
77 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for monopodial branches per plant under normal conditions
153
78 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for monopodial branches per plant under water stress conditions
153
79 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for sympodial branches per plant under normal conditions.
156
80 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for sympodial branches per plant under water stress conditions.
156
81 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls per plant under normal conditions.
159
82 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls per plant under water stress conditions.
159
83 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for
162
xii
bolls weight under normal conditions.
84 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls weight under water stress conditions.
162
85 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for yield under normal conditions.
165
86 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for yield under water stress conditions.
165
87 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple length under normal conditions.
168
88 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple length under water stress conditions.
168
89 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple fineness under normal conditions.
171
90 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple fineness under water stress conditions.
171
91 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple strength under normal conditions.
174
92 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple strength under water stress conditions.
174
93 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for GOT (%) under normal conditions.
177
94 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal
177
xiii
values) and reciprocal effects (below diagonal values) for GOT (%) under water stress conditions.
95 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for seed index under normal conditions.
180
96 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for seed index under water stress conditions.
180
97 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for lint index under normal conditions.
183
98 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for lint index under water stress conditions.
183
99 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative water content under normal conditions.
186
100 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative water content under water stress conditions.
186
101 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for leaf temperature under normal conditions.
189
102 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for leaf temperature under water stress conditions.
189
103 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative cell injury (%) under normal conditions.
192
104 Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative cell injury (%) under water stress conditions.
192
xiv
List of Figures
Figure
No. Title Page No.
1 Chlorophyll a, b determination under normal and water stress conditions.
41
2 Total carotenoids determination under normal and water stress conditions.
42
3 Chl a/b ratio determination under normal and water stress conditions.
43
4 Ployphenols determination under normal and water stress conditions.
44
5 Yield (g) of six drought tolerant and susceptible parents under normal and water stress conditions.
45
6 Relative cell injury (%) of six drought tolerant and susceptible parents under normal and water stress conditions.
46
7 Wr/Vr graph for plant height under normal conditions.
98
8 Wr/Vr graph for plant height under water stress conditions.
99
9 Wr/Vr graph for monopodial branches per plant under normal conditions.
101
10 Wr/Vr graph for monopodial branches per plant under water stress conditions.
102
11 Wr/Vr graph for sympodial branches per plant under normal conditions.
104
12 Wr/Vr graph for sympodial branches per plant under water stress conditions.
105
13 Wr/Vr graph for No. of bolls per plant under normal conditions.
108
14 Wr/Vr graph for No. of bolls per plant under water stress conditions.
109
15 Wr/Vr graph for bolls weight under normal conditions.
111
16 Wr/Vr graph for bolls weight under water stress conditions.
112
xv
17 Wr/Vr graph for yield under normal conditions.
114
18 Wr/Vr graph for yield under water stress conditions.
115
19 Wr/Vr graph for staple length under normal conditions.
117
20 Wr/Vr graph for staple length under water stress conditions.
118
21 Wr/Vr graph for staple fineness under normal conditions.
120
22 Wr/Vr graph for staple fineness under water stress conditions.
121
23 Wr/Vr graph for staple strength under normal conditions.
123
24 Wr/Vr graph for staple strength under water stress conditions.
124
25 Wr/Vr graph for GOT % under normal conditions.
126
26 Wr/Vr graph for GOT % under water stress conditions.
127
27 Wr/Vr graph for seed index under normal conditions.
129
28 Wr/Vr graph for seed index under water stress conditions.
130
29 Wr/Vr graph for lint index under normal conditions.
132
30 Wr/Vr graph for lint index under water stress conditions.
133
31 Wr/Vr graph for relative water content under normal conditions.
135
32 Wr/Vr graph for relative water content under water stress conditions.
136
33 Wr/Vr graph for leaf temperature under normal conditions.
138
34 Wr/Vr graph for leaf temperature under water stress conditions.
139
35 Wr/Vr graph for relative cell injury %under normal conditions.
142
36 Wr/Vr graph for relative cell injury % under water stress conditions.
143
xvi
Abstract
Experiments were conducted during the crop season 2005-06 to evaluate cotton germplasm under irrigated and drought regimes. The germplasm was evaluated for different physiological and morphological traits. The accessions showing higher cotton yield were used as a criterion for selection of drought tolerant and susceptible parents. Three susceptible and three tolerant parents were planted during cotton growing season and crossed on flowering stage. The hybrids were evaluated at seedling and mature plant stage during the crop season 2006-07 under irrigated and drought regimes. All traits under study were subjected to analyses of variance. Traits showing significant genotypic variation were analyzed following simple additive dominance model to estimate heritability and inheritance pattern. The objective of study is to explore genes having potential for high yield and fiber quality under drought environments in genetic material available by crossing the genotypes in diallel fashion that may be used in future breeding program. Gene action and combining ability were studied by analyzing diallel cross data between six cotton varieties viz., FH-113, PB-899, MNH-789, (drought tolerant), and CIM-506, FH-901, CRIS-466, (drought susceptible). A considerable reduction in almost all parameters was shown under stress conditions. Diallel analysis showed that characters like monopodial branches, sympodial branches and staple strength showed additive genetic effects and traits like plant height, number of bolls, boll weight, yield., staple length, staple fineness, GOT, seed index, lint index, relative water content, leaf temperature and relative cell injury showed additive and dominant genetic effects under normal conditions and water stress conditions, traits like staple strength and relative cell injury showed additive genetic effects and traits like plant height, monopodial branches, sympodial branches, number of bolls, boll weight, yield, staple length, staple fineness, GOT, seed index, lint index, relative water content, leaf temperature showed additive and dominant (non-additive) genetic effects. PB-899 proved the best general combiner for traits like plant height and staple length, FH-113 proved the best general combiner for traits like monopodial branches, sympodial branches, number of bolls, yield , seed index, relative water content, leaf temperature and relative cell injury, MNH-789 proved the best general combiner for traits like boll weight, GOT and CIM-506 proved the best general combiner for staple fineness, staple strength and lint index under normal and water stress conditions. Heritability estimates for yield and yield related traits and most of traits were high under normal and water stress conditions and had maximum ability to transfer genes to the next generation. So, selection of desirable parents and gene combinations for high yield on the basis of these traits under both conditions will be effective for future breeding programs. Breeders may utilize good general combiners in breeding programs for improvements of cotton traits. It is recommended that breeders should breed for superior combining ability aimed at improving overall GCA for yield and fiber quality.
Abstract
Experiments were conducted during the crop season 2005-06 to evaluate cotton germplasm under irrigated and drought regimes. The germplasm was evaluated for different physiological and morphological traits. The accessions showing higher cotton yield were used as a criterion for selection of drought tolerant and susceptible parents. Three susceptible and three tolerant parents were planted during cotton growing season and crossed on flowering stage. The hybrids were evaluated at seedling and mature plant stage during the crop season 2006-07 under irrigated and drought regimes. All traits under study were subjected to analyses of variance. Traits showing significant genotypic variation were analyzed following simple additive dominance model to estimate heritability and inheritance pattern. The objective of study is to explore genes having potential for high yield and fiber quality under drought environments in genetic material available by crossing the genotypes in diallel fashion that may be used in future breeding program. Gene action and combining ability were studied by analyzing diallel cross data between six cotton varieties viz., FH-113, PB-899, MNH-789, (drought tolerant), and CIM-506, FH-901, CRIS-466, (drought susceptible). A considerable reduction in almost all parameters was shown under stress conditions. Diallel analysis showed that characters like monopodial branches, sympodial branches and staple strength showed additive genetic effects and traits like plant height, number of bolls, boll weight, yield., staple length, staple fineness, GOT, seed index, lint index, relative water content, leaf temperature and relative cell injury showed additive and dominant genetic effects under normal conditions and water stress conditions, traits like staple strength and relative cell injury showed additive genetic effects and traits like plant height, monopodial branches, sympodial branches, number of bolls, boll weight, yield, staple length, staple fineness, GOT, seed index, lint index, relative water content, leaf temperature showed additive and dominant (non-additive) genetic effects. PB-899 proved the best general combiner for traits like plant height and staple length, FH-113 proved the best general combiner for traits like monopodial branches, sympodial branches, number of bolls, yield , seed index, relative water content, leaf temperature and relative cell injury, MNH-789 proved the best general combiner for traits like boll weight, GOT and CIM-506 proved the best general combiner for staple fineness, staple strength and lint index under normal and water stress conditions. Heritability estimates for yield and yield related traits and most of traits were high under normal and water stress conditions and had maximum ability to transfer genes to the next generation. So, selection of desirable parents and gene combinations for high yield on the basis of these traits under both conditions will be effective for future breeding programs. Breeders may utilize good general combiners in breeding programs for improvements of cotton traits. It is recommended that breeders should breed for superior combining ability aimed at improving overall GCA for yield and fiber quality.
1
CHAPTER I
INTRODUCTION
Upland cotton (Gossypium hirsutum L.) is a very important textile fibre, currently
accounting for 90% of the commercially grown cotton worldwide. Cotton is the second
most important oilseed crop in the world averaging one fourth that of soybean (Cherry
and Leffler, 1984; Zhang, 2001;, Jones and Kersey, 2002) while in Pakistan, it is the
leading oil seed crop. Cotton waste can also be converted into ethanol (Border et al.,
1992) to be used as environmentally friendly fuel. Cotton sticks are also used to improve
the soil organic matter. Burying of cotton sticks at the rate of five tones per hectare
increases organic matter from 0.44 to 0.83%. It also increases the soil fertility as reported
an increase of available phosphorus and exchangeable potassium in the soil.
Cotton is harvested as seed cotton, which is then ginned to separate the seed and
lint. The long lint fibres are processed by spinning, to produce yarn that is knitted into
fabrics. The short fibres (fuzzy) covering the seeds are known as ‘linters’. The first cut
linters have a long fibre length and are used in the production of belts and mattresses. The
second cut linters have a much shorter fibre length and are a major source of cellulose for
both the chemical and food industry.
Delinted cotton seed can be processed to produce oil, meal and hulls. Cotton seed
oil has been in common use since the middle of the nineteenth century and achieved
GRAS (Gradually Recognized as Safe) status. Cotton seed contains 30% starch, 18.5% to
22.4% oil and 16.20% protein (Cobley and Steele, 1976). Cotton seed oil is used in a
variety of products including margarine, soap and plastics. Cotton seed cake, meal flour
or hulls derived from it, is used in food products and for animal feed as carbohydrate
roughages, but it is limited by the presence of natural toxicants in the seeds (gossypols)
(Pillay and Myers, 1999).
Cotton is cultivated in the tropical and sub-tropical regions and a wide range of
soil types as an annual crop, though it is basically a tropical perennial crop. Cotton is
primarily used to produce lint which is the unicellular, outgrowth of the cotton seed.
2
Cotton fibre is made up of secondary cellulose wall which develops after cell elongation
(Prentice, 1972; Poehlman, 1987; Kim and Triplett, 2001).
The species Gossypium barbadense originated in Central and South America,
accounts for about 90% of the world fibre. It produces cotton fibres of the highest quality.
The dominant position of cotton in the world has been snatched by the synthetic fabrics,
but due to its natural quality, cotton is still preferred. Cotton is grown on an area of about
3.1 million hectares with production of about 11.8 million bales (GOP, 2008-09).
Cotton crop is most important cash crop in the economy of Pakistan. It accounts
for 7.3% of the value added in agriculture sector and about 1.6% of GDP (GOP, 2008-
09). Cotton crop is cultivated on 13% of the total cultivated area in Pakistan. Cotton crop
in Pakistan feeds 500 textile mills, 1139 ginning factories, 443 spinning mills, 8.45
million spindles, 2585 oil expelling units and over 5 million labour engaged in cotton and
cotton related business. Punjab contributes 70% of total cotton area and production in
Pakistan (GOP, 2008-09). Dried cotton sticks are an important source of fuel to domestic
use. Yield of seed cotton on unit area basis is still lower than other cotton producing
countries like USA, Australia and Israel etc.
Improved cotton varieties are urgently needed to improve the cotton market
though cotton yield, high ginning percentage and good cotton quality as these factors
affect lint price in the world market.
We are moving from water abundant to water scarce country. Existing shortage is
11.69 MAF (GOP, 2008-09) which is likely to increase to 30 MAF by the year 2025. Due
to global warming, rise in sea level and prolonged drought, shortage of water supply also
limits the production potential of cotton plant. Despite the existence of good irrigation
canal system in Pakistan, it also suffers from wastage of large quantity of water in the
irrigation processes. Water management and conservation projects like brick lining are
likely to reduce this shortage. In view of the present situation of water shortage a serious
thought is needed to develop cultivars, especially in cotton. To develop a drought
tolerance genotype the physiological and biochemical markers should be considered that
could give economic yield under drought conditions.
Abiotic stresses such as drought stress is a serious threat to agriculture. Abiotic
stress is the primary cause of crop loss world wide reducing average yield of most of
3
major crop plants by more than 50% (Boyer, 1982; Bray et al., 2000). The success of a
breeding program is mainly due to knowledge on the available germplasm especially
genetic diversity (Meredith and Bridge, 1984; Pillay and Myers, 1999). A breeding
programme to improve cotton genotypes for drought conditions was undertaken with the
following objectives:
a. Screening of cotton germplasm for yield and drought tolerance.
b. Development of cotton breeding material i.e., hybrid populations.
c. Undertaking the genetic and physiological mechanisms and drought tolerance
and also their inheritance pattern.
The studies will yield scientific information on cotton yield components and
drought tolerance mechanisms and would generate plant material useful to breeding
cotton for high yield under water deficit conditions.
4
CHAPTER II
REVIEW OF LITERATURE
1. Drought
Faced with scarcity of water resources, drought is the single most crucial threat to
world food security. It was the catalyst of the great famines of the past. Because the
world’s water supply is limiting, future food demand for rapidly increasing population
pressure is likely to further aggravate the effects of drought (Somerville and Briscoe,
2001). The severity of drought is capricious as it depends on many factors such as
amount and distribution of rainfall, evaporative demands and moisture storing capacity of
soils (Wery et al., 1994). Inquiries carried out in the past provide considerable impending
into the mechanisms of drought tolerance in plants at molecular level (Hasegawa et al.,
2000). Three main mechanisms lessen crop yield by soil water deficit (i) reduced canopy
absorption of photosynthetically active radiation, (ii) decreased radiation-use efficiency
and (iii) reduced harvest index (Earl and Davis, 2003). A slow rate in revealing drought
tolerance mechanisms has hindered both traditional breeding efforts and use of modern
genetics approaches in the improvement of drought tolerance of crop plants
(Xiong et al., 2006). Although plant responses to drought are relatively well known, plant
performance under a more complex environment where multiple stresses are there that’s
why, the plants have to respond simultaneously to multiple stresses, e.g., drought,
extensive light, heat which may coincide in the fields. These kinds of investigations are
usually not predictable from single factor studies (Zhou et al., 2007). Currently, there are
no economically feasible technological means to smooth the progress of crop production
under drought. However, development of crop plants tolerant to drought stress might be a
promising approach, which helps in meeting the food demands. Development of crops for
enhanced drought resistance, among other things, entails the knowledge of physiological
mechanisms and genetic control of the contributing traits at different plant developmental
stages. Valuable work has been done on drought tolerance in plants (Ingram and Bartels,
5
1996) reviewed those appreciable efforts. More recent reviews deal with specific aspects
of plant drought tolerance (Penna, 2003; Reddy et al., 2004; Agarwal et al., 2006).
2. Effects of Drought on Plants:
The effect of drought range from morphological to molecular levels and are
apparent at all phenological phases of plant growth at whatever stage the water
scarceness takes place.
2.1. Crop growth and yield
The first effect of drought is reduce germination and poor stand establishment
(Harris et al., 2002). Drought stress has been reported to reduce germination and seedling
stand (Kaya et al., 2006). In alfalfa (Medicago sativa), germination potential, hypocotyl
length and shoot and root fresh and dry weights were lowered by polyethylene glycol
induced water shortage, while the root length was increased (Zeid and Shedeed, 2006).
In rice, during the vegetation growth stage, drought stress reduced the plant growth and
development to a great extent (Tripathy et al., 2000; Manikavelu et al., 2006). Growth is
accomplished through cell division, cell enlargement and differentiation and involves
genetic, physiological, ecological and morphological incidents and their complex
interactions. The quality and quantity of plant growth depend on these events which are
influenced by water deficit. Cell growth is one of the most drought responsive
physiological processes due to decrease in turger pressure (Taiz and Zeiger, 2006).
Under severe water deficit, cell elongation of higher plants can be inhibited by disruption
of water flow from the xylem to the surrounding elongating cells (Nonami, 1998).
Impaired mitosis, cell elongation and development result in reduced plant height, leaf
area and crop growth under drought (Nonami, 1998; Kaya et al., 2006; Hussain, 2008).
Many yield influential physiological processes in plant respond to water stress. For water
shortfall, severity, duration and timing of stress as well as responses of plants after stress
exclusion and interaction between stress and other factors are significant (Plaut, 2003).
Water stress at pre-anthesis reduced time to anthesis, while at post-anthesis it shortened
the grain filling period in triticale genotypes. Number of tillers, spike and grains per plant
and individual grain weight reduced the grain yield under drought stress. Post-anthesis
drought stress was harmful to grain yield regardless of the stress severity (Samarah,
2005). In wheat, drought had minor effect on the rate of kernel filling but its duration
6
(time from fertilization to maturity) was shortened and dry weights reduced at maturity
(Wardlaw and Willenbrink, 2000). Moisture deficit lowered cotton (Gossypium hirsutum)
lint yield, although the timing, severity, duration had roles in determining, how the plant
reacted to moisture deficit. Lint yield was generally reduced due to reduced boll
production because of fewer flowers and greater boll abortions when the stress intensity
was greater during reproductive growth (Pettigrew, 2004).
2.2. Water relations
Relative water content, leaf water potential, leaf temperature, stomatal resistance,
rate of transpiration and canopy temperature are important traits that influence plant
water relation. Relative water content of wheat leaves was higher primarily during leaf
development and decreased at the dry matter accumulation and leaf matured
(Siddique et al., 2001). Apparently, water-stress wheat and rice plants had lesser relative
water content than non-stressed ones. Hussain (2009) reported additive type of gene
action for leaf temperature and relative water content. Exposure of these plants to drought
stress decreased leaf water potential, relative water content and transpiration rate, with a
raise in leaf temperature (Siddique et al., 2001). In another study under drought stress,
relative water content, transpiration, stomatal conductance, turger potential and water use
efficiency were decreased (Egilla et al., 2005). Infact, although components of plant
water relations are affected by lowered availability of water, stomatal opening and
closing is more strongly influenced. Moreover, under water stress, change in leaf
temperature may be an important factor in controlling leaf water status. Drought tolerant
species maintain water use efficiency by reducing the water loss. However, the lower
plant growth leads to lower water use efficiency.
2.3. Nutrient relations
Decreasing water availability under drought generally results in restricted nutrient
uptake. An important effect of water shortfall is on the requisition of nutrient by the root
and their transport to shoots. As nutrients and water requirements are strongly related,
fertilizer application is probably to increase the efficiency of crops in utilizing available
water. It was shown that N and K uptake was hindered under drought stress in cotton
(McWilliams, 2003). A reduced transpiration rate due to water deficit results in reduction
of nutrient absorption and efficiency of their utilization.
7
2.4. Photosynthesis
A major effect of drought is reduction in photosynthesis which takes place by a
decrease in leaf expansion, impairs photosynthetic machinery, premature leaf senescence
and associated reduction in food production (Wahid and Rasul, 2005). The role of
drought induced stomatal closure, which limits CO2 uptake by leaves, is very important.
In such events reduced CO2 availability could lead to increased susceptibility to photo-
damage (Cornic and Massacci, 1996). Drought stress produced changes in photosynthetic
pigments and components (Anjum et al., 2003), damaged photosynthetic apparatus (Fuj
and Huang, 2001) and reduced activities of calvin cycle enzymes, which are important
causes of reduced crop yield (Monakhova and Chernyadev, 2002). Another important
effect of drought stress is the loss of balance between the production of reactive oxygen
species and the antioxidant defense (Fuj and Huang, 2001; Reddy et al., 2004), causing
accumulation of reactive oxygen species which reduces oxidative stress in cellular
components, lipids and proteins.
2.4.1. Stomatal oscillations
There is a continued debate for a long time as to whether drought stress limits
photosynthesis through stomatal closure or metabolic impairment (Sharkey, 1990; Tezara
et al., 1999). Stomatal closure was usually accepted to be the mean determinant for
decreased photosynthesis under drought stress conditions (Cornic and Massacci, 1996;
Yokota et al., 2002). When the amount of available water is limiting, the first option for
plants is the closure of stomata (Cornic and Massacci, 1996). It is clear that stomata close
progressively as drought progresses followed by a decline in net photosynthesis.
However, stomatal conductance is not controlled by soil moisture availability alone but
by complex interaction of factors.
2.4.2. Photosynthetic enzymes
Dehydration results in cell shrinkage, a decline in cellular volume. This makes
cellular contents more viscous. Increased concentration of solutes leading to increased
viscosity of cytoplasm may become toxic and may be harmful to the functioning of
enzymes, including those of the photosynthetic machinery (Hoekstra et al., 2001).
8
2.5. Assimilate partitioning
Drought stress frequently enhances allocation of dry matter to the roots, which
can enhance water uptake (Leport et al., 2006). De Souza and Da Silv (1987) while
analyzing the separation and distribution of photo-assimilates in annual and perennial
cotton under drought stress reported that the root-to-shoot dry matter ratio was high in
perennial cotton, showing accumulation of starch and dry matter in roots as an adaptation
to drought.
2.6. Respiration
Drought tolerance is a cost-intensive phenomenon, as a substantial quantity of
energy is spent to deal with it. The fraction of carbohydrate that is vanished through
respiration determines the overall metabolic efficiency of the plant (Davidson et al.,
2000). The root is a major consumer of carbon fixed in photosynthesis and uses it for
growth and maintenance, as well as dry matter production (Lambers et al., 1996).
However, the rate of photosynthesis limits the plant growth when soil water availability is
reduced (Huang and Fu, 2000). In wheat, depending on growth stage, cultivar and
nutritional status, more than 50% of the daily assembled photosynthates were transported
to the roots and around 60% of this fraction was respired (Lambers et al., 1996). Shoot
and root biomass, photosynthesis and root respiration rate were reduced under severe
drought stress.
2.7. Oxidative damage
The generation of reactive oxygen species occurs when plants exposed to certain
environmental stresses. Reactive oxygen-species may react with proteins, lipids
deoxyribonucleic acid, causing oxidative damage and impairing the normal functions of
cells (Foyer and Fletcher, 2001). Many cell compartments produce reactive oxygen
species, of these, chloroplasts are important source. Mechanism for the generation of
reactive oxygen species in biological systems are represented by both non-enzymatic and
enzymatic reactions. Reactive oxygen species are formed as by products in the electron
transport chains of chloroplasts (Apel and Hirt, 2004), mitochondria and plasma
membrane (Sairam et al., 2005). Oxidative stress may cause protein oxidation with a loss
of enzyme activity. Oxidatively damaged proteins accumulate in pea leaves subjected to
waster stress (Moran et al., 1994). Overall, the production of reactive oxygen species is
9
linear with the severity of drought stress, which leads to enhanced peroxidation of
membrane lipids and degradation of nucleic acids and both structural and functional
proteins.
3. Drought Resistance Mechanisms
Plants respond and adapt to survive under drought stress by the induction of
various morphological, biochemical and physiological responses. Drought tolerance is
defined as the ability to grow, flower and display economic yield under water stress.
3.1. Morphological mechanisms
Plant drought tolerance involves changes at whole-plant, tissue, physiological and
molecular levels. Various morphological mechanisms under drought conditions are given
below:
3.1.1. Escape
Escape from drought is attained through a shortened life cycle or growing season,
allowing plants to reproduce before the environment becomes dry. Flowering time is an
important trait related to drought adaptation, where a short life cycle can express to
drought escape (Araus et al., 2002). Drought escape takes place when phenological
development is successfully coordinated with periods of soil moisture availability, where
growing is shorter and terminal drought stress predominates (Araus et al., 2002).
Developing short duration varieties has been an effective strategy for minimizing
yield loss from terminal drought as early maturity helps the crop to avoid the period of
stress (Kumar and Abbo, 2001). However, yield is correlated with the length of crop
duration.
3.1.2. Avoidance
Drought avoidance consists of mechanisms that decrease water loss from plants,
due to stomatal control of transpiration, and also maintain water uptake through an
extensive root system (Turner et al., 2001; Kavar et al., 2007). The root characteristics
such as biomass, length, density and depth are the main drought avoidance traits that
contribute to final yield under terminal drought environments (Subbarao et al., 1995;
Turner et al., 2001). A deep and thick root system is helpful for extracting water from
considerable depths (Kavar et al., 2007). The possession of a deep and thick root system
allowed access to water deep in soil which was considered important in determining
10
drought resistance in upland rice (Kavar et al., 2007). Fresh root weight is the best and
the easiest character for determination of drought tolerance (Nour and Weibal, 1978;
Agarwal and Sinha, 1983; Horgenboom et al., 1987; Dai et al., 1990, and Xu and Bland,
1993). Dry root weight was increased under water under water stress conditions
(Mehdi et al., 2001). Glaucousness or waxy bloom on leaves helps with maintenance of
high water potential, and is therefore, considered as desirable trait for drought tolerance
(Richards et al., 1986; Ludlow and Muchow, 1990). Determination of leaf temperature
indicated that, compared with non-glaucous leaves, glaucous leaves were 0.7 oC cooler
and had lower rate of leaf senescence (Richards et al., 1986).
3.1.3. Phenotypic flexibility
Plant growth is affected to greater extent by water deficit. At a morphological
level, the shoots and roots are the most affected and both are the key components of plant
adaptation to drought. Plants generally limit the number and area of leaves in response to
drought stress, just to cut down the water at the cost of yield loss (Schuppler et al., 1998).
Since roots are the only source to acquire water from soil, root growth, its density and
size are key responses of plants to drought stress (Kavar et al., 2007). It has been
established that plants bearing small leaves are typical of xeric environments. Such plants
withstand drought very well.
Hairy leaves have reduced leaf temperatures and transpiration (Sandquist and
Ehleringer, 2003). Under high temperature and radiation stress, hairiness increases the
light reflectance and minimizes water loss by increasing the boundary layer resistance to
water vapor movement away from the leaf surface. Roots are the key plant organ for
adaptation to drought. The possession of a deep and thick root system allowed access to
water deep in soil which was considered important in determining drought resistance in
upland rice (Kavar et al., 2007). It is quality i.e., the distribution and structure, the net
quantity of roots that determines the extraction of water during the crop growth. The
drought tolerance of tea, onion and cotton was increased by improved root growth.
3.2. Physiological mechanisms
Osmotic adjustment, anti-oxidation and a scavenging defense system have been
the most important bases responsible for drought tolerance.
11
3.2.1. Cell and tissue water conservation
Under drought stress, sensitive pea genotypes were more affected by a decline in
relative water content than tolerant ones (Upreti et al., 2000). The determination of leaf
water status in the morning and water content in leaves in the afternoon was useful for
screening drought tolerance in chickpea (Pannu et al., 1993). Wild melon plant survived
drought by maintaining its water content without wilting of leaves even under severe
drought. Osmotic adjustment is an important trait in delaying damage in water deficit by
maintenance of cell turgor and physiological processes (Taiz and Zeiger, 2006).
3.2.2. Antioxidant defense
The antioxidant defense system in the plant cell constitutes both enzymatic and
non-enzymatic components. In environmental stress tolerance, such as drought, high
activities of antioxidant enzymes and high contents non-enzymatic constituents are
important. The reactive oxygen species in plants are removed by a variety of antioxidant
enzymes and water soluble scavenging molecules (Hasegawa et al., 2000). The
antioxidant enzymes being the most efficient mechanisms against oxidative stress
(Farooq et al., 2008). Antioxidant genes such as glutathione reductase were higher during
recovery from a water deficit period and emerge to play a role in the protection of cellular
machinery against damage by reactive oxygen species (Ratnayaka et al., 2003). Oxidative
damage in the plant tissue is increased by action of both enzymatic and non-enzymatic
antioxidant systems. These include β-carotenes, ascorbic acid, α-tocopherol and enzymes
including superoxide, peroxidase, catalase, polyphenol oxidase (Hasegawa et al., 2000).
Carotenes form a key part of the plant antioxidant defense system (Havaux, 1998; Wahid
et al., 2007) but they are very susceptible to oxidative destruction. The ß-carotenes
present in the chloroplast of all green plants are bound to the core complexes of photo-
system-I and photosystem-II. β-carotene as a necessary pigment acts as an effective
antioxidant and plays a unique role in protecting photochemical processes and sustaining
them (Havaux, 1998). As regarding polyphenols (which are secondary metabolites) are
produced under stress. Polyphenols are increased in all cotton genotypes under waters
stress conditions. Increase in polyphenols contents in different tissues under stress has
been reported in a number of plants (Agastian et al., 2000; Muthukumarasamy et al.,
2000).
12
3.2.3. Cell membrane stability
Biological membranes are the fist target of many abiotic stresses. It is generally
accepted that the maintenance of integrity and stability of membrane under water stress is
a major component of drought tolerance in plants (Bajji et al., 2002). Cell membrane
stability, reciprocal to cell membrane injury, is a physiological index widely used for the
evaluation of drought tolerance (Premachandra et al., 1991). Moreover, it is genetically
related phenomenon. Dhanda et al. (2004) showed that membrane stability of the leaf
segment was the most important trait to screen the germplasm for drought tolerance. In a
study on maize K nutrition improved the drought tolerance, mainly due to improved cell
membrane stability (Gnanasiri et al., 1991). The causes of membrane disruption are
unknown; a decrease in cellular volume causes crowding and increases the viscosity of
cytoplasmic components. This increases the chances of molecular interactions that can
cause protein denaturation and membrane fusion. For model membrane and protein
system, a range or compounds have been identified that can prevent such adverse
molecular interactions. Some of these are glutamate, glycinebetaine sucrose
(Folkert et al., 2001).
3.2.4. Plant growth regulators
Plant growth regulators when applied externally, and phytohormone, when
produced internally, are substances that influence physiological processes of plants at
very low concentrations (Morgan, 1990). Under drought endogenous contents of auxins,
gibberellins and cytokinin usually decrease while abscisic acid and ethylene increase
(Nilsen and Orcutte, 1996). Abscisic acid is a growth inhibitor and produced under a
variety of environmental stresses including drought. All plants respond to drought by
accumulating abscisic acid. Abscisic acid is generally recognized as a stress hormone. It
has been proposed that abscisic acid and cytokinin have opposite roles in drought stress.
Increase in abscisic acid and decline in cytokinins levels support stomatal closure and
limit water loss through transpiration under water stress (Morgan, 1990).
Among the other endogenously produced growth regulating factors, the role of
salicylic acid in the induction of tolerance against several abiotic stresses has been
brought out recently. Polyamines are known to have profound influence on plant growth
and development. Being cationic, polyamines can associate with anionic compounds of
13
the membrane such as phospholipids, thus protecting the lipid bilayer from deteriorating
effects of stress (Bouchereau et al., 1999). Compound with sensitive plants, stress
tolerant plants usually have a greater capacity to synthesize polyamines in response to
stress. Glycinebetaine is one of the ammonium compounds and compatible solutes in
plants, animals and bacteria (Wahid et al., 2007). Glycinebetaine plays an important role
in enhancing plant tolerance under range of abiotic stresses including drought
(Quan et al., 2004).
3.3. Molecular mechanisms
Plant cellular water deficit may occur under conditions of reduced soil water
content. Under these conditions, changes in gene expression take place. Various genes
are induced in response to drought at the transcriptional level and these gene products are
thought to function in tolerance to drought (Kavar et al., 2007). Drought tolerance is
complex phenomenon concerning the action of many genes (Agarwal et al., 2006;
Cattivelli et al., 2008). Aquaporins have the capability to facilitate and regulate the
passive exchange of water across membranes. They belong to membrane proteins. In
plants, aquaporins are present abundantly in plasma membrane. The aquaporins play a
specific role in controlling trans-cellular water transport. They are expressed in roots
where they mediate soil water uptake (Javot and Maurel, 2002).
4. Managing Drought Stress
Drought stress effects can be managed by production of the most appropriate
plant genotypes together with adjustment of agronomic practices. Efforts have been made
to produce drought tolerant genotypes using the knowledge of responses of plants to
drought stress. The two most important strategies may include:
a. Selecting the desirable materials as in traditional breeding using molecular and
biotechnological means, including production of genetically modified or
transgenic plants.
b. Inducing drought tolerance in susceptible plants by priming and hormonal
application.
4.1. Selection and breeding strategies
Conventional breeding has been based on empirical selection for yield (Atlin and
Lafitte, 2002). However, this approach is far from being optimal. Since yield is
14
quantitative trait and characterized by a low heritability and a high genotype x
environmental interaction (Babu et al., 2003), it is strongly believed that understanding of
physiological and molecular basis may help the key traits that limit yield.
Even the power of molecular biology for locating important gene sequences for
selecting or genetically transforming important quantitative trait loci depends upon
understanding of yield determining physiological processes (Araus et al., 2002; Kirigwi
et al., 2007).
Screening under natural drought stress conditions in the target environments is
tough because of the uneven drought response. But screening under controlled stress
environment and rain-out shelters is more manageable. Available reports showed that
drought tolerant species reduced the water loss either by reducing the leaf area or limiting
stomatal opening.
4.2. Induction of drought resistance
Drought resistance can be induced by adopting various strategies. Exogenous use
of growth regulating and other chemicals has proven important in producing drought
resistance at various growth stages in a number of plants.
4.2.1. Seed priming
One of the short term and most important approach to overcome the drought
stress effects is seed priming. Seed priming is a technique by which seeds are partially
hydrated to a point where germination related metabolic processes begin but radicle
emergence does not occur (Farooq et al., 2006). Primed seeds usually exhibit increased
germination rate, greater germination uniformity and greater total germination percentage
(Kaya et al., 2006; Farooq et al., 2007). While testing the effectiveness of different crops,
to improve the performance of direct-seeded rice, noted that priming with 4% KCl
solution and saturated CaHPO4 solution was successful in improving the seedling
emergence, crop stand establishment and yield under stress. Seed priming improved
performance of wheat seeds under drought stress in terms of germination and water-use
efficiency of drought stressed plants by 44% compared with unprimed seeds
(Ajouri et al., 2004).
15
4.2.2. Use of plant growth regulators
Foliar application of plant growth regulators, both natural and synthetic has
proven worth for improving growth against abiotic stresses. Exogenous application of
gibberellic acid increased the net photosynthetic rate, stomatal conductance and
transpiration rate in cotton (Kumar et al., 2001). Salicylic acid can improve plant growth
under drought conditions (Senaratna et al., 2000). In a recent study, exogenous
application of salicylic acid improved drought tolerance of winter wheat. In wheat,
salicylic acid was shown to increase the abscisic acid content.
4.2.3. Use of osmoprotectants
Osmoprotectants are involved in signaling and regulating plant responses to
multiple stresses, including reduced growth that may be part of plants’ adaptation against
stress. In plants common osmoprotectants are proline glycinebetaine. They play an
important role in protecting subcellular structures in stressed plants. Exogenously applied
glycinebetaine improves the growth and production of some plants under stress (Naidu
et al., 1998; Chen et al., 2000; Hussain et al., 2008).
4.2.4. Silicon
Silicon is the second most abundant element in soils and mineral substrate for
plant life. When silicon is available to plants, it plays a significant role in their growth,
mineral nutrition and resistance to several stresses (Epstein, 1994). Exogenously applied
silicon lowered the shoot to root ratio indicating the facilitation of root growth and
maintenance of higher photosynthetic rate and stomatal conductance compared with
plants grown without silicon application under drought stress (Hattori et al., 2005).
5. Conclusion
The drought tolerance mechanism involves a number of physiological and
biochemical processes at cell, tissue, organ and whole plant levels. Examples of these
mechanisms are reduction in water loss by increasing stomatal resistance, increased water
uptake by developing large and deep root systems, accumulation of osmolytes and
osmoprotectant synthesis. Among plant growth substances, salicylic acid, cytokinin and
abscisic acid have been reported to play an important role in drought tolerance.
Scavenging of reactive oxygen species by enzymatic and non-enzymatic systems, cell
membrane stability, expression of aquaporins and stress proteins are also vital
16
mechanisms of drought tolerance. Drought stress effects can be managed by production
of most appropriate plant genotypes, seed priming, plant growth regulators, use of
osmoprotectants, silicon and some other strategies. Mutants or transgenic plant exhibiting
differential capabilities for reactive oxygen species, formation and elimination could be
useful for this fundamental point. Molecular knowledge of response and tolerance
mechanisms is likely to pave the way for engineering plants that can withstand and give
economic yield under drought stress.
17
CHAPTER III
MATERIALS AND METHODS
3.1. EXPERIMENT 1
In the first experiment thirty genotypes were collected from the Department of
Plant Breeding and Genetics, University of Agriculture, Faisalabad, CRIS, CCRI, NIAB
and NIBGE. The initial research work has carried out in the experimental area of cotton
at PARS (Postgraduate Agriculture Research Station) of the Department of Plant
Breeding and Genetics, University of Agriculture, Faisalabad during the crop season
2005-06. Thirty genotypes were planted at PARS, University of Agriculture, Faisalabad
under irrigated (W1) and drought conditions (W2). Soil pH and EC of the field was 6.9
and 1.4 dSm-1 respectively, organic matter 1.42% and saturation percentage was 31%.
The data for temperature, relative humidity and rainfall during crop season was presented
in appendix-I.
A split plot design with two factors keeping water availability in the main plots
and genotypes in sub-plots was followed with three replications. Each genotype was
sown in three rows per replication. Ten plants were sown in each row. Row to row and
plant to plant distance was 75cm and 25cm respectively. The non stress regimes (W1)
were irrigated to maintain soil water contents close to field capacity. In the drought
experiment 50% irrigations of normal were applied (Kirda et al., 2005). Data for
morphological traits were taken at maturity, drought susceptible and drought tolerant
parents were selected on yield basis as described by Ullah et al., (2006)
18
Table-1: Selected diverse thirty genotypes
Sr. No. Genotypes
1 CIM-506 2 FH-901 3 CRIS-466 4 FH-167 5 CIM-707 6 CIM-496 7 CIM-541 8 FH-127 9 CIM-446 10 CRIS-134 11 NIAB-846 12 CRIS-342 13 NIBGE-4 14 CIM-538 15 CIM-534 16 NIAB-824 17 TH-84/99 18 CRIS-9 19 SLH-284 20 TH-35/99 21 MNH-732 22 MNH-786 23 BH-160 24 FH-1000 25 NIAB-111 26 MARVI 27 CIM-554 28 MNH-789 29 PB-899 30 FH-113
19
3.2. EXPERIMENT 2
The collected cotton accessions were evaluated on the basis of their survival rate
under three different moisture levels i.e. 50, 75 and 100 % of field capacity (FC). The
moisture levels at field capacity were measured by volume using moisture meter
(∆T – NH2, Cambridge, England) .
Polythene bags (18 x 9 cm) filled with sandy loam soil (pH 7.8 and EC 1.7 dSm-1)
were used as experimental units following a completely randomized design with three
replications. One seedling was established in one polythene bag. Thirty seedlings of each
accession were grown in green house. Moisture stress treatments were initiated on 25th
days of sowing and maintained the required moisture levels of 50, 75 and 100 % of FC by
irrigating the accessions with water on alternate days. Survived seedlings were counted
and survival rate of each accession was estimated using the following formula.
Survival rate % = No. of survived seedlings/ Total No. of seedlings × 100
Fifteen genotypes were selected on the basis of survival rate.
3.3. EXPERIMENT 3
Experiment was further evaluated for the following different morphological and
physiological traits for final selection. Drought response of selected varieties was further
assessed by growing them under normal and water stress conditions. Fifteen different
genotypes were sown in polythene bags (30 × 15 cm), following complete randomized
design with three replications under normal and water stress conditions. The plants were
irrigated every alternate day with normal tap water. After 45 days from sowing, a cycle of
drought was induced by stopping irrigation for seven days. Normal regime was
maintained by irrigating the plants regularly. Drought stress was maintained by irrigating
and restricting the irrigation to polythene bags.
The selected genotypes from the seedling experiment were further evaluated for
the following different morphological and physiological traits for final selection.
20
3.3.1 Morphological traits
1. Root length (cm)
2. Root weight (g)
3. Shoot length (cm)
4. Shoot weight (g)
5. Dry shoot weight (g)
6. Dry root weight (g)
3.3.1.1. Fresh root length (cm)
Fresh root length (cm) of seedlings was measured by using a measuring
tape.
3.3.1.2. Fresh root weight (g)
The seedlings of each genotype were washed carefully free from sand and blotted
dry fresh seedlings roots were weighed in grams by using an electronic balance
(CHYO, Japan JL-80).
3.3.1.3. Fresh shoot length (cm)
Fresh shoots obtained from seedlings were measured for their length by using
measuring tape.
3.3.1.4. Fresh shoot weight (g)
Fresh shoots separated from the seedlings were weighed in grams by using an
electronic balance (CHYO, Japan JL-80).
3.3.1.5. Dry shoot weight (g)
Shoots were also dried as illustrated above and their weights were
recorded.
3.3.1.6. Dry root weight (g)
Roots of seedlings genotype were put in Kraft paper bag and dried in an electric
oven at 65±5 0C for 72 hours. After drying, dry root weights were recorded by using an
electronic balance.
3.3.2. Physiological Traits
1. Chlorophyll a and b
2. Total Carotenoids
3. Total phenolics
21
3.3.2.1. Determination of chlorophyll and carotenoids:
The contents of chlorophyll and carotenoids have been analyzed respectively by
different methods in a conventional way. I studied simple method for simultaneous
determination of pigments. All pigments in sample were extracted with acetone at once,
then optical density of supernatant at 663nm, 645nm, 645nm, 505nm and 453nm are
measurer by spectrophotometer at the same time. From these values, the contents of
chlorophyll a, b and carotenoids were estimated using proposed equations
(Nippon, 1992).
Chlorophyll a (mg/100mL) = 0.999A663 - 0.0989A645
Chlorophyll b (mg/100mL) = -0.328A663 + 1.77A645
β – Carotene (mg/100mL) = 0.216A663-1.22A645 - 0.304A505+ 0.452A453
(A663, A645, A505 and A453 are absorbance at 663nm, 645nm, 505nm and 453nm each other.)
3.3.2.2. Folin-ciocalteu method for total phenol determination procedure:
Extraction:
Cut and weigh 0.5 g of leaf tissue and homogenize in 5mL 80% acetone. Rinse
with another 5 mL 80% acetone (total 10mL). Filter with Whatman’s No. 1 filter paper or
centrifuge for 10 minutes. Add acetone to make filtrate 10mL.
1. From each calibration solution sample, or blank (water), pipette 20uL
into separate plastic cuvettes.
2. Add 1.58 mL water.
3. Add 100 uL of the Folin-Ciocalteu reagent and mix well. Wait for
22
between 30 seconds and 8 minutes.
4. Add 300 uL of the sodium carbonate solution and mix well. Leave the
solution at 25 C for 2 hours, 30 minutes at 40 C.
5. Determine the absorbance of each solution at 760nm against the blank
(80% acetone) and plot absorbance vs. concentration.
6. Create a calibration curve with standards and determine the levels in the
samples.
3.4. EXPERIMENT 4
3.4.1 Development of genetic material
Three susceptible and three tolerant parents were planted in earthen pots in
green house during 2005-06. These accessions were hybridized, when these started to
flower in 6 × 6 diallel fashion. For crossing purpose, unopened flowers commonly
known as buds were hand-emasculated in the evening. Stamens were removed and
carpels were covered with soda straw tubes. Emasculated flowers were pollinated the
following morning with pollen grains from the male parents and respective soda straw
tubes were placed back on the style. Self pollination of parents (to develop selfed seed)
was achieved simply by trying a piece of thread around the buds in the evening. At
maturity, F1 seeds from all the crosses attempted were collected.
3.5. EXPERIMENT 5
3.5.1 Evaluation of genetic material
In order to investigate the genetics of drought tolerance in cotton,
responses of thirty F1 hybrids along with six parents were planted in irrigated and
drought regime during crop season 2006-07. The stress regime was given 50 %
irrigations of the normal regime (Kirda et al., 2005).
A split plot design with two factors keeping water availability in main
plots and genotypes in sub plots was followed with three replications. Ten plants were
sown in each row. Row to row and plant to plant distance was 75 cm and 25 cm
respectively. All recommended production practices and plant protection measures were
adopted to raise healthy population. Data were collected on ten guarded plants at
23
appropriate time for the following morphological and physiological traits in the field as
well as laboratory.
3.5.2. Morphological traits
1. Plant height (cm)
2. Monopodial branches
3. Sympodial branches
4. Number of bolls per plant
5. Boll weight (g)
6. Yield per plant (g)
7. Staple length (mm)
8. Staple fineness (ug/inch)
9. Staple strength (g/tex)
10. Ginning out turn (%)
11. Seed index (g)
12. Lint index (g)
3.5.3 Physiological traits
1. Leaf Temperature (°C)
2. Relative Water Content (%)
3. Relative cell injury (%)
3.5.2.1. Plant height (cm)
The final height of the plant was measured with measuring rod from the first
cotyledonary node to the apical bud, when the growth ceased.
3.5.2.2. No. of monopodial branches per plant
The monopodial branches are the vegetative types of branches in cotton. At
maturity the monopodial branches per plant were counted for all the selected plants.
3.5.2.3. No. of sympodial branches per plant
The sympodial branches are the direct fruit bearing branches. At maturity the
sympodial branches on each plant was counted for all selected plants.
3.5.2.4. No. of bolls per plant
The number of effective mature bolls from all the picks was counted and the
cumulative record was maintained for each plant separately.
24
3.5.2.5. Boll weight (g)
Average weight per boll was obtained by dividing the total yield of seed cotton
per plant by the numbers of bolls picked that very plant.
3.5.2.6. Yield of seed cotton (g)
The mature bolls were picked at three different picks and seed cotton was
collected in paper bags separately for all the plants in all three replications. Picking was
done after evaporation of dew. The harvest was weighed on electronic balance.
3.5.2.7. Fibre characters
The fibre characters like staple length, staple fineness and staple strength were
measured using the fibro graph HVI-900. It is a computerized high volume instrument
which provides the comprehensive profile of raw fiber. It measure the most important
fibre characteristics such as length (mm), fineness (ug/inch) and strength (g/tex)
according to the International Trading Standards
3.5.2.8. Ginning out turn (GOT %)
Clean and dry samples of the seed cotton were weighed and then ginned
separately with a single roller electric gin. The lint obtained from each sample was
weighed and lint percentage was calculated by the following formula:
GOT % = Weight of lint in a sample ×100 Weight of seed cotton in a sample
3.5.2.9. Seed index (g)
Seed index is the 100 seed weight in grams. 100 seeds were taken at random for
each sample and weighed in gram.
3.5.2.10. Lint index (g)
Lint index is weight of index in grams obtained from 100 seeds. Lint index of
each genotype was calculated by applying the following formula:
Lint index = Seed index × GOT_ 100 – GOT
25
3.5.3.1. Leaf temperature (°C)
Leaf temperature for ten selected plants per replication at 13.00-15.00 pm from
fully exposed leaves to sunlight. Data were recorded from three leaves of each
selected plant with infrared thermometer (RAYPRM30CFRG, RAYTEK, USA).
3.5.3.2. Relative water content (%)
Three fully developed leaf sample were taken from each of the selected plants.
When the plants were showing the symptoms of the drought stress. These samples were
covered with polythene bags soon after excision and fresh weight was recorded using
electronic balance. The leaf samples were dipped in water overnight for recording the
turgid leaf weight. The samples were oven dried at 70 0C for taking dry weight. RWC
was calculated using the following formula.
RWC = [{(Fresh weight - Dry weight)/Turgid weight – Dry weight)}] × 100
3.5.3.3. Relative cell injury percentage (RCI %)
Relative cell injury percentage is an indicator of cell membrane thermo stability
(CMT) and was determined by Sullivan (1972) method. Two leaf discs (10 mm diameter)
from each side of midrib, from one leaf of each tagged seedling were taken in glass vials
(containing 2 mL de-ionized water). The leaf discs were grouped in two sets on the basis
of sampling i.e. sides of midrib. The leaf discs were washed thrice with de-ionized water
for removal of adherent or already released electrolytes. One set was exposed to 50 0C
(treatment) temperature for one hour (treated) and second set at 25 0C for one hour
(control) in water bath (MEMMERT-WBI, Germany) with attached shaker
(MEMMERT-SVI 422, Germany). After treatment 10 mL de-ionized water was added to
each vial and place at 10 0C for 24 hours in an incubator (SANYO-MIR 253) to allow
diffusion of electrolytes. Then vials were placed on mechanical shaker (EYELA-MMS,
RIKAKIKAI CO., LTD.) to mix the contents at room temperature. Electrical
conductivity (EC) of sap in vials was recorded with an EC meter (TOA-CM-14P, Japan).
Then vials were autoclaved at 121 0C with 15 psi for 10 minutes to kill tissues completely
and to release all the electrolytes from the cells. Vilas were allowed to cool at room
temperature and EC of the sap was again measured. RCI % was calculated using the
following formula (Sullivan, 1972):
26
3.5.3.3.1. Formula for the calculation of relative cell injury %
Relative Cell Injury %age = 1-[{1-(T1/T2)}/{1-(C1/C2)}] × 100
Where,
• T1 = EC of sap of treated discs (50ºC) before autoclaving.
• T2 = EC of sap of treated discs (50ºC) after autoclaving.
• C1 = EC of sap of treated discs (25ºC) before autoclaving.
• C2 = EC of sap of treated discs (25ºC) after autoclaving.
3.6. STATISTICAL ANALYSIS
3.6.1. Analysis of Variance
Analysis of variances was computed to compare the genotypes for each trait in
experiment 1 (Steel et al., 1996).
3.6.2. Diallel Analysis
For diallel analysis the data were subjected according to Hayman (1954a,b)
and Mather and jinks (1982). Variations occurred in diallel cross occur due to the
differences among parental or maternal genotypes or due to interaction between them.
There are two phases of the analysis of diallel (Mather and Jinks, 1982). Formal
analysis of variance of the data is calculated which indicated whether significant
additive or non-additive genetic variation is present. In the absence of maternal
effects, the main items for the differences among the same set of genotypes should
yield the estimates of the same components of variation, the additive variation. Where
the additive-dominance model is adequate and there are no reciprocal differences, the
mean squares of most of the items in the analysis of the variance can be represented in
simple terms. Thus, a and b item tests the significance of the additive effects of the
genes significance of the dominance effects. The a item is a test of the additive genetic
component if b was non significant. The b1 item tests the mean deviation of the F1s
from their value mid parental value. It is significant only if the dominance deviations
of the genes are in one direction predominantly there is directional dominance effect.
The b2 item tests whether the mean dominance deviation of the F1 from their mid-
parental values within each array differs over arrays. It will differ, if some parents
27
have considerably more dominant alleles than other. The b3 items tests the important
effects of specific genes ability for a mixed model where the inbred lines are omitted
from the analysis (Griffing, 1956).
On the assumption of no genotype × environment interaction and absence of
differences between reciprocal crosses the mean squares for c, d and block interactions
are all estimates of the environmental component of variation. If reciprocal crosses
differ, c detects the average maternal effects of each parental line and d the reciprocal
differences not ascribed to c. if genotype × environment interactions are present they
will be detected as a difference between the block interaction for the a and b items if
the additive and dominance variation are influenced to different extent by the
environment.
3.6.3. Gene action
Gene action was determined following Hayman (1954a, b), Jinks (1954) and
Whitehouse et al. (1958). There are certain conditions prior to the application of
diallel analysis.
The main assumptions were as follow:
Homozygous parents
Normal diploid segregation
No reciprocal difference (No maternal effect)
Independent distribution of genes among the parents
Absence of multiple allelism
Independent action of non-allelic genes
In the present study most of conditions were assumed to be fulfilled and
reciprocal differences avoided by taking the means to both arrays reciprocal
differences we avoided to fulfill these crosses were arranged in array and the F1 values
were set out in the diallel table from each diallel tables. The statistics like variance
(Vr) of the family means with an array and co-variance (Wr) of these means with non-
recurrent parents were calculated.
The regression line was calculated from the mean variance (Vr) and mean co-
variance (Wr). The regression line was calculated. The slope and position of the
regression line was fitted to the array points. If the line of a unit slope (b=1) passes
28
through the origin, complete dominance is revealed. The movement of line upward
and downward in graphs indicated decreasing and increasing dominance, respectively.
If it intercepts the axis below the origin, it discloses over-dominance and if it is almost
tangent to the parabola, additive type of gene action is showed. The position of the
array points on the regression lines shows the distribution of the dominant and
recessive genes among the common parents of the array.
3.6.4. Limiting parabola
The limiting parabola was out lined on the basis of the formula
Wr2 = Vr.Vp, i.e., by plotting Vr, (Wr x Vp) points.
The corresponding values for Wr for all obtained values were calculated as Vr.Vp
Where: Vp = parental variance Vr = Genotypic variance
As their limiting points different arrays were fitted within the limits of parabola using
the gingival variance and co-variance. Array nearest to the point of origin told most
dominant genes, while the array far from the point of origin indicated most recessive
genes, while the intermediate position reveals the presence of both dominant and
recessive genes in the array.
3.6.5. Test for the validity of diallel assumption
Hayman (1954b) suggested assumption of diall analysis were assured while
conducting these studies. Tow scaling tests were employed to fulfill the assumptions
of absence of epistasis, absence multiple allelism and independent gene distribution.
The first test was an analysis of regression coefficient. Variance (of each array) and
covariance (arrays with its parental values) were calculated from the mean diallel
table. Then the regression of covariance on the variance was estimated. The expected
regression coefficient is to be significantly different from zero but not from unity.
Failure of this test indicates that non-allelic interaction (epistasis) is present or genes
are not independent it heir action, or show non random association among parents.
Analysis of variance of Wr+Vr and Wr-Vr was done as a second test for the adequacy
of the additive dominance model. If dominance (or certain types of non-allelic
interaction) is present Wr+Vr must vary from array to array. Similarly, if there exists
epistasis, Wr-Vr will change between arrays.
29
Failure of these tests completely invalidates the additive dominance model.
However, the additive-dominance model was considered partially adequate and
analyzed further if one of them fulfils the assumptions. Components of variance
models were used by various scientists for such type partially adequate models
(Johnson and Askel, 1964; Wilson et al. 1978; Azhar and McNeilly, 1988 and
Mahmood, 1998).
3.6.6. Genetic components of variation
The methods for calculating genetic components of variance following a diallel
analysis Hayman (1954b) and Mather and Jinks (1982) outlined. These genetic
parameters were calculated by using the formula as given by Singh and Chaudhry
(1985).
3.6.6.1. Additive variation (D)
D = Vp-E
Where: Vp = variance of the parents
3.6.6.2. Variation due to dominant effect of genes (H1)
En
nVrWrVpH
23441
Where, Vr = mean of they array variance
Wr = mean of the coverainces between parents and arrays and
N = number of the parents
3.6.6.3. Variation due to dominant effect of genes correlated for gene
distribution (H2)
EVmVrH 2442
Where, Vm = variance of the mean of arrays
3.6.6.4. Relative frequency of dominant and recessive alleles
In the presence of gene frequencies, the sign and magnitude of F determines
the relative frequency of dominant and recessive alleles in the parental population and
the variation in the dominance level over loci. F is positive whenever the dominant
alleles are more than the recessive ones, irrespective of whether these are increasing or
decreasing in their effect. It was calculated as under:
30
En
nWrVpF
)2(242
3.6.6.5. Overall dominance effect of heterozygous loci
En
nMMh LL 2
201
2 )1(4)(4
22
01 }] )/.{(/1[)( valueParentalnTGnMM LL
Where, G.T. = Grand total of all the observations
3.6.6.6. Environmental variation
pofNopdfErrordf
pSSErrorSSE Re.
Re
Re
Where, Error SS = error sum of square and
Rep. SS = replication sum of square in the analysis of variance
3.6.6.7. Average degree of dominance
DH /1
3.6.6.8 Proportion of genes with positive and negative effects of parents.
H2/4 H1
3.6.6.9. Proportion of dominant and recessive genes in the parents
3.6.6.10. Heritability narrow sense
EFHHD
FHHD
5.025.05.05.0
5.05.05.05.0
21
21
3.7. COMBINING ABILITY ANALYSIS
Using method I Model II (Griffing, 1956), combining ability studies were conducted.
The genetic variability in the material was partitioned into components or general and
specific combining ability, reciprocal effects and error. Sums of squares for these
components were calculated as under:
√4DH1 + F
√4DH1 - F
31
SS due to GCA = 222 )/2()()2/1( YnYYn ji
SS due to SCA = 222 )/1()()2/1)()2/1( xYnYiYjnYjiYijYijxx
SS due to reciprocals = 2))2/1( YjiYij
Where,
Yi & Yj = total of the ith and jth arrays in the mean table
Y = grand total of the mean table
Yij = mean value of the cross of ith parent with jth parent
Yji = men values of the cross of jth parent with ith parent
(reciprocal cross)
N = number of parents
3.7.1. SS due to error
The mean sum of squares obtained in the ANOVA due to error was
used after dividing with number of replications because mean values are used there,
Thus, SS due to error =SS (error) in ANOVA/r
While, r =number of replications
Using the values ANOVA for combining ability in method I model II was prepared as
under.
SOV df SS MS F-value Expected (MS)
GCA (p-1) Sg Mg Ng/Ms gnsnne 222 )/2)1(2
SCA P(p-1)/2 Ss Ms Ms/Me’ snnne 2222 )/)1(2
Reciprocal P(p-1)/2 Sr Mr Mr/Me’ re 22 2
Error (r-1)(p2-1) Se Me’ e2
The component estimation of variation was carried out as under:
1
)1('
2
12
2
nn
MsnnMeMg
ng
32
)'()1(2 2
22 MeMs
nn
ns
)'(2
12 MeMrr
'2 Mee
Where 2g, are the estimates of variance due to general combining ability, 2s
specific combining ability, 2r reciprocal effects and 2e environment, respectively.
General combing ability effects were calculated using the expression
..1
)..(2
12
Yn
iYYin
gi
Specific combining ability effects were calculated using the expression:
..1
)....(2
1)(
12
Yn
jYYjiYYin
YjiYijY
Sij
By using the expression reciprocal effects were calculated
)(2
1YjiYijrij
Variance were calculated as under:
en
ngVar i
222
)1()(
en
nsVar ij
22
2
2
)1()(
erVar ij2
2
1)(
By taking the square root of the respective variance standard errors were calculated as
under:
)().(. ii gVargES
)().(. ijij sVarsES
)().(. ijij rVarrES
33
CHAPTER IV
RESULTS
4.1. Preliminary Assessment Phase
4.1.1. Experiment 1
Mean survival rate of thirty cotton genotypes are given in Table 2. Thirty
cotton genotypes are managed from the lowest to the highest survival rate. FH-113
revealed the highest survival rate (80 %) and (94%) under moisture levels 50 % and 75 %
of field capacity respectively. While CIM-506 showed 8 % and 16 % under 50 % and 75
% of field capacity respectively. All other genotypes were between these two extremes.
Eight cotton genotypes with the highest survival rate under the lowest moisture level and
a set of seven cotton genotypes with the lowest survival rate were selected for further
evaluation.
4.2. Screening experiment 2
Analysis of variance for seedling traits under normal and water stress
conditions in the green house is presented in Table 5. Variability was found in the
material which was indicated by the presence of highly significant genotypic differences
for all the characters viz., fresh root weight, fresh shoot weight, fresh root length, fresh
shoot length, dry root weight and dry shoot weight.
Mean performance of cotton genotypes in Table 6 and Table 7 revealed
that FH-113 had maximum fresh root weight (0.145) and (0.155) under normal and water
stress condition respectively. While CIM-506 had minimum fresh root weight (0.034)
and (0.049) under normal and water stress conditions respectively. After FH-113, PB-899
and MNH-789 had maximum fresh root weight (0.140) and (0.120) under normal
conditions, (0.150) and (0.135) under water stress conditions respectively. After CIM-
506, FH-901 and CRIS- 466 had minimum fresh root weight (0.050) and (0.060) under
normal conditions, (0.055) and (0.070) under water stress conditions respectively.
34
Table 2: Mean survival rate (%), 35 days after sowing of 30 cotton genotypes grown in three moisture levels.
Sr. No. Genotypes 50% of FC 75% of FC 100% of FC
1 CIM-506 8 16 100
2 FH-901 10 18 100
3 CRIS-466 12 20 100
4 FH-167 14 23 100
5 CIM-707 17 26 100
6 CIM-496 20 30 100
7 CIM-541 22 33 100
8 FH-127 25 36 100
9 CIM-446 26 37 100
10 CRIS-134 30 38 100
11 NIAB-846 34 43 100
12 CRIS-342 37 45 100
13 NIBGE-4 39 45 100
14 CIM-538 40 47 100
15 CIM-534 40 49 100
16 NIAB-824 41 50 100
17 TH-84/99 42 52 100
18 CRIS-9 45 54 100
19 SLH-284 48 57 100
20 TH-35/99 50 61 100
21 MNH-732 50 60 100
22 MNH-786 54 65 100
23 BH-160 58 68 100
24 FH-1000 60 71 100
25 NIAB-111 65 75 100
26 MARVI 67 81 100
27 CIM-554 70 84 100
28 MNH-789 74 86 100
29 PB-899 78 88 100
30 FH-113 80 94 100
SE 12.152 12.868
35
Table 3: Ranking of different genotypes on the basis of their tolerance level under waters stress conditions.
Table 4: Six drought tolerant and susceptible parents selected.
Sr. No. Genotypes Status
1 CIM-506 Most susceptible
2 FH-901 Susceptible
3 CRIS-466 Susceptible
4 FH-167 Moderately susceptible
5 CIM-707 Moderately susceptible
6 CIM-496 Moderately susceptible
7 CIM-541 Moderately susceptible
8 BH-160 Moderately tolerant
9 FH-1000 Moderately tolerant
10 N-111 Moderately tolerant
11 MARVI Moderately tolerant
12 CIM-554 Moderately tolerant
13 MNH-789 Tolerant
14 PB-899 Tolerant
15 FH-113 Most tolerant
Sr. No. Genotypes Status
1 FH-113 Tolerant
2 PB-899 Tolerant
3 MNH-789 Tolerant
4 CRIS-466 Susceptible
5 FH-901 Susceptible
6 CIM-506 Susceptible
36
Table 5: F-value and coefficient of variation (CV %) of 15 cotton genotypes for various seedling traits under normal and water stress conditions.
Trait Level F-value Error C.V%
Root length (cm) Normal 3.022** 0.5422 11.14
Water stress 3.526** 0.5871 10.58
Shoot length (cm) Normal 20.707** 1.0240 7.04
Water stress 16.344** 0.8525 7.11
Root weight (g) Normal 128.677** 0.0181 5.48
Water stress 142.837** 0.0187 4.81
Shoot weight (g) Normal 9.107** 0.0494 12.84
Water stress 3.466** 0.0370 15.23
Dry root weight (g) Normal 264.676** 0.0027 3.01
Water stress 124.480** 0.0031 4.35
Dry shoot weight (g) Normal 36.465** 0.0109 9.59
Water stress 33.566** 0.0080 7.16
37
Table 6: Mean and statistical significance of 52 days old seedling of cotton genotypes under normal and water stress conditions.
Root length (cm) Shoot length (cm) Root weight (g)
Genotypes Normal Water
stress Normal
Water
stress Normal
Water
stress
CIM-506 4.71 c 5.01 c 6.72 e 6.01 d 0.034 o 0.049 m
FH-901 5.75 bc 6.51 b 8.53 cd 7.22 c 0.050 n 0.055 l
CRIS-466 6.31 b 6.52 b 8.01 d 8.02 bc 0.060 m 0.070 k
FH-167 5.92 bc 7.01 b 8.51 cd 8.11 bc 0.065 l 0.070 k
CIM-707 6.41 b 7.21 ab 8.52 cd 8.01 bc 0.070 k 0.075 j
CIM-496 6.51 b 7.22 ab 8.71 cd 8.51 b 0.075 j 0.080 i
CIM-541 6.72 ab 7.31 ab 8.81 cd 8.41 b 0.080 i 0.090 h
BH-160 6.91 ab 7.21 ab 8.62 cd 8.01 bc 0.085 h 0.100 g
FH-1000 6.82 ab 7.01 b 8.51 cd 8.22 bc 0.090 g 0.115 e
N-111 6.74 ab 7.11 ab 9.01 cd 8.81 b 0.10 f 0.110 f
MARVI 6.83 ab 7.22 ab 9.51 bc 9.01 b 0.105 e 0.110 f
CIM-554 6.92 ab 7.51 ab 10.21 b 9.02 b 0.110 d 0.120d
MNH-789 7.01 ab 8.51 a 10.51 b 8.01 bc 0.120 c 0.135 c
PB-899 7.02 ab 7.52 ab 13.01 a 11.02 a 0.140 b 0.150 b
FH-113 8.02 a 8.51 a 13.02 a 12.01 a 0.145 a 0.155 a
LSD 1.222 1.262 1.097 1.016 0.00288 0.00288
Means sharing same letters are similar at P ≤0.05.
38
Table 7: Mean and statistical significance of 52 days old seedling of cotton genotypes under normal and water stress conditions.
Shoot weight (g) Dry root weight (g) Dry shoot weight (g)
Genotypes Normal Water
stress Normal
Water
stress Normal
Water
stress
CIM-506 0.27 ef 0.26 c 0.007 f 0.012 f 0.030 h 0.030 h
FH-901 0.30 de 0.27 c 0.010 e 0.015 e 0.080 a 0.080 a
CRIS-466 0.21 f 0.31 c 0.014 d 0.016 e 0.037 g 0.038 g
FH-167 0.27 ef 0.27 c 0.014 d 0.016 e 0.040 f 0.038 g
CIM-707 0.29 def 0.27 c 0.013 d 0.015 e 0.060 e 0.058 e
CIM-496 0.30 de 0.28 c 0.014 d 0.015 e 0.061 e 0.060 e
CIM-541 0.35 cde 0.30 c 0.015 d 0.016 e 0.060 e 0.058 e
BH-160 0.36 cd 0.31 c 0.018 c 0.020 d 0.062 de 0.060 e
FH-1000 0.36 cd 0.31 c 0.019 bc 0.022 c 0.060 e 0.058 e
N-111 0.37 cd 0.32 c 0.021 b 0.025 b 0.065 d 0.065 d
MARVI 0.40 bc 0.35 bc 0.020 bc 0.022 c 0.070 c 0.065 d
CIM-554 0.40 bc 0.32 c 0.018 c 0.020 d 0.065 d 0.060 e
MNH-789 0.43 abc 0.30 c 0.018 c 0.020 d 0.063 de 0.050 f
PB-899 0.50 a 0.40 ab 0.026 a 0.030 a 0.010 i 0.070 c
FH-113 0.46 ab 0.45 a 0.019 bc 0.030 a 0.076 b 0.075 b
LSD 0.07457 0.0745 0.002358 0.00167 0.00288 0.00289
Means sharing same letters are similar at P ≤0.05.
39
It was noted that there was increase in root weight under water stress conditions.
As regarding root length, FH-113 had maximum root length (8.02 cm) and (8.51 cm)
under normal and water stress conditions respectively. CIM-506 had minimum root
length (4.71 cm) and (5.01 cm) under normal and water stress conditions respectively.
After FH-11, PB-899 and MNH-789 had maximum root length (7.02 cm) and (7.01 cm)
under normal conditions, (7.52 cm) and (8.51 cm) under water stress conditions
respectively. After CIM-506, FH-901 and CRIS-466 had minimum root length (5.75 cm)
and (6.31 cm) under normal conditions, (6.51 cm) and (6.52 cm) under water stress
conditions respectively. It was noted that there was increase in root length under water
stress conditions. As regarding shoot length FH-113, had maximum shoot length (13.02
cm) and (12.01 cm) under normal and water stress conditions respectively. CIM-506 had
minimum shoot length (6.72 cm) and (6.01 cm) under normal and water stress conditions
respectively. After FH-113, PB-899 and MNH-789 had maximum shoot length (13.01
cm) and (10.51 cm) under normal conditions, (11.02 cm) and (8.01 cm) under water
stress conditions respectively. After CIM-506, FH-901 and CRIS-466 had minimum
shoot length (8.53 cm) and (8.01 cm) under normal conditions, (7.22 cm) and (8.02 cm)
under waters stress conditions respectively. It was noted that there was decrease in shoot
length under water stress conditions.
As regarding dry root weight, PB-899 had maximum dry root weight
(0.026 g) and (0.030 g) under normal and water stress conditions respectively. CIM-506
had minimum dry root weight (0.007 g) and (0.012 g) under normal and water stress
conditions respectively. After PB-899, FH-113 and MNH-789 had maximum dry root
weight (0.019 g) and (0.018 g) under normal, (0.030 g) and (0.020 g) under water stress
conditions respectively. It was noted that there was increase in dry root weight under
water stress conditions. It was also observed that root/shoot ratio was increased under
water stress conditions.
In the seedling screening experiment, simultaneous determination of chlorophyll
and carotenoids of fifteen cotton accessions were carried out and polyphenols were also
found in order to investigate the responses of cotton accessions to drought. It was found
that genotypes FH-113, PB-899 and MNH-789 had maximum amount of chlorophyll a
(mg/100 mL), (2.98), (2.89) and (2.81) under normal conditions, (2.89), (2.80) and (2.79)
40
under water stress conditions respectively (Fig. 1). CIM-506, FH-901 and CRIS-466 had
minimum amount of chlorophyll (2.39), (2.42) and (2.44) under normal conditions,
(1.97), (1.99) and (2.01) under water stress conditions respectively. FH-113, PB-899 and
MNH-789 had minimum amount of chlorophyll b (mg/100 mL), (1.38), (1.37) and (1.37)
under normal conditions and more amount of chlorophyll b (1.18), (1.19) and (1.20)
under water stress conditions respectively. CIM-506, FH-901 and CRIS-466 had
maximum amount of chlorophyll b, (1.69), (1.67) and (1.65) under normal conditions and
low value of chlorophyll b (0.99), (0.98) and (0.98) under water stress conditions
respectively.
As regarding carotenoids (mg/100 mL) (Fig. 2) FH-113, PB-899 and
CRIS-466 had maximum amount of (0.79), (0.69) and (0.66) under normal conditions,
(0.77), (0.67) and (0.64) under water stress conditions respectively. CIM-506, FH-901
and CRIS-466 had minimum amount of carotenoids, (0.29), (0.34) and (0.39) under
normal conditions, (0.18), (0.24) and (0.26) under water stress conditions respectively. It
was noted that chlorophyll and carotenoids were reduced under water stress conditions.
As regarding a/b ratio (Fig. 3) FH-113, PB-899 and MNH-789 had
maximum a/b ratio (2.15), (2.10) and (2.05) under normal conditions, (2.44), (2.35) and
(2.32) under water stress conditions respectively. CIM-506, FH-901 and CRIS-466 had
minimum a/b ratio (1.41), (1.44) and (1.47) under normal conditions, (1.98), (2.03) and
(2.05) under water stress conditions respectively. It was noted that a/b ratio was increased
under water stress conditions.
As regarding polyphenols (µg/g of leaf ) (Fig. 4), it was indicated that FH-
113, PB-899 and MNH-789 had maximum amount of polyphenols (0.014), (0.012) and
(0.011) under normal conditions, (0.016), (0.014) and (0.013) under water stress
conditions respectively. CIM-506, FH-901 and CRIS-466 had minimum amount of
(0.005), (0.006) and (0.007) under normal conditions, (0.006), (0.008) and (0.009) under
water stress conditions respectively. It was noted that polyphenols were increased under
water stress conditions.
41
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
CIM-506
FH-901 CRIS-466
FH-167 CIM-707
CIM-496
CIM-541
BH-160
FH-1000
N-111 MARVI CIM-554
MNH-789
PB-899
FH-113
Cotton genotypes
Ch
loro
ph
yll (
mg
/ 100
ml)
a under normal a under stress b under normal b under stress
Fig. 1. Chlorophyll a, b determination under normal and water stress conditions.
Fig. 1. Chlorophyll a and b determination under normal and water stress conditions.
42
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
CIM-5
06FH-9
01CRIS
-466
FH-167
CIM-7
07CIM
-496
CIM-5
41BH-1
60FH-1
000
N-111
MARVI
CIM-5
54M
NH-789
PB-899
FH-113
Cotton genotypes
To
tal β
car
ote
no
ids
(mg
/ 100
ml)
ß under normal ß under stress
Fig. 2. Total β-carotenoids determination under normal and water stress conditions.
43
0.000
0.500
1.000
1.500
2.000
2.500
3.000
CIM-5
06FH-9
01CRIS
-466
FH-167
CIM-7
07CIM
-496
CIM-5
41BH-1
60FH-1
000
N-111
MARVI
CIM-5
54M
NH-789
PB-899
FH-113
Cotton genotypes
Ch
loro
ph
yll a
/b
a/b ratio under normal a/b ratio under stress Fig. 3. Chlorophyll a/b ratio determination under normal and water stress conditions.
44
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
CIM-5
06FH-9
01CRIS
-466
FH-167
CIM-7
07CIM
-496
CIM-5
41BH-1
60FH-1
000
N-111
MARVI
CIM-5
54M
NH-789
PB-899
FH-113
Cotton genotypes
Po
lyp
he
no
ls (μg
/ g o
f le
af
tis
su
e)
Polyphenol under normal Polyphenol under stress
Fig. 4. Polyphenols determination under normal and water stress conditions.
45
Fig. 5. Yield (g) of six drought tolerant and susceptible parents under normal and water stress
conditions.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
FH-113
PB-899
MNH-7
89
CRIS-4
66
FH-901
CIM-5
06
Cotton genotypes
Yie
ld (
g)
Yield under normal Yield under stress
46
0.00
20.00
40.00
60.00
80.00
100.00
120.00
FH-113
PB-899
MNH-7
89
CRIS-4
66
FH-901
CIM-5
06
Cotton genotypes
RC
I (%
)RCI under normal RCI under stress
Fig. 6. Relative cell injury (%) of six drought tolerant and susceptible parents under normal and water stress conditions.
47
Table 8: Analysis of variance of six cotton genotypes and their 30 crosses for various characters
** = P≤0.01
Sr. No. Characters Mean square of genotypes
Normal Water stress
1 Plant height 113.183** 62.913**
2 No. of monopodial branches 3.098** 5.157**
3 No. of sympodial branches 10.640** 12.618**
4 No. of bolls per plant 86.828** 97.259**
5 Boll weight 37.112** 39.047**
6 Yield 228.438** 375.226**
7 Staple length 2.789** 4.621**
8 Staples fineness 6.483** 17.767**
9 Staple strength 7.980** 4.853**
10 GOT 6.613** 4.192**
11 Seed index 21.791** 48.195**
12 Lint index 8.898** 12.351**
13 Relative water contents 32.446** 35.456**
14 Leaf temperature 7.890** 8.833**
15 Relative cell injury 56.921** 34.367**
48
4.3. Adequacy of additive-dominance model:
Adequacy of additive-dominance model for various plant traits of cotton under
normal and water stress conditions, and validity of some of the assumptions underlying
genetic model were tested by joint regression analysis and analysis of variance of (Wr
+Vr) and (Wr - Vr). The results of two tests under normal and water stress environment
are presented in Table 9 and Table 10. The regression coefficient ‘b’ for all the characters
departed significantly form zero but not deviated from unity. This property of the
regression line indicated the presence of intra-allelic interaction, independent distribution
of the genes among the parents for the traits, genes were independent in their action. The
unit slope of regression lines for all the plant traits studied, suggested that all the
assumptions underlying the additive-dominance model were met (Mather and Jinks,
1982).
The mean squares of analysis of variance of (Wr +Vr) and (Wr -Vr) showed that
significant differences between the arrays (Wr +Vr) and non-significant differences
within the arrays (Wr -Vr) for plant height, sympodial branches, boll weight, yield, staple
length, staple fineness and leaf temperature normal conditions and for traits like boll
weight, staple fineness, seed index and leaf temperature under water stress conditions
indicated that dominance was present and epistasis was absent. Thus the results of both
the tests proposed that the simple genetic model was fully adequate for these traits.
However, non-significant differences between the arrays (Wr +Vr) for traits like
monopodial branches, number of bolls, staple strength, GOT, seed index, lint index,
relative water content and relative cell injury under normal conditions and traits like plant
height, monopodial branches, sympodial branches, number of bolls, yield, staple length,
staple strength, GOT, lint index, relative water content and relative cell injury under
water stress conditions showed the absence of dominant effects and presence of epistasis.
Non-significant differences within the arrays (Wr -Vr) for all the traits under normal
conditions except plant height, number of bolls and yield under water stress conditions
indicated the absence of epistasis. Thus, based upon the results of two tests simple
genetic model was partially adequate for analyzing the data set for plant traits like
monopodial branches, number of bolls, staple strength, GOT, seed index, lint index,
relative water content and relative cell injury under normal conditions, and the traits like
49
plant height, monopodial branches, sympodial branches, number of bolls, yield, staple
length, staple strength, GOT, lint index, relative water content and relative cell injury
under water stress conditions.
50
Table 9: Scaling tests for adequacy of additive-dominance model for various plant traits under normal conditions of cotton (Gossypium hirsutum L.)
Traits Regression slope Mean squares
Remarks b0 b1 Wr + Vr Wr - Vr
Plant height 22.56** -1.66 NS 9464.27** 34.43 NS Model is fully adequate
No. of monopodial
branches 2.85* 0.004 NS 0.292 NS 0.02 NS
Model is partially adequate
No. of sympodial
branches 5.10** 0.19 NS 37.13* 2.63 NS
Model is fully adequate
No. of bolls per plant 14.80** 1.19 NS 36.52 NS 6.11 NS Model is partially adequate
Boll weight 15.15** -1.72 NS 0.05** 0.0003 NS Model is fully adequate
Yield 23.32** 0.63 NS 21766.83** 270.67 NS Model is fully adequate
Staple length 3.07* 0.05 NS 19.78** 1.23 NS Model is fully adequate
Staple fineness 8.88** -0.19 NS 0.01** 0.0003 NS Model is fully adequate
Staple strength 7.73* 1.00 NS 36.80 NS 1.23 NS Model is partially adequate
GOT (%) 5.93** -0.91 NS 14.06 NS 0.25 NS Model is partially adequate
Seed index 6.02* 1.46 NS 0.01 NS 0.001 NS Model is partially adequate
Lint index 7.55** -0.03 NS 0.006 NS 0.0001 NS Model is partially adequate
Relative water content 3.68* 0.95 NS 228.54 NS 23.64 NS Model is partially adequate
Leaf temperature 5.11** 0.04NS 162.82* 8.15 NS Model is fully adequate
Relative cell injury % 19.16** 0.68 NS 170271.5 NS 3737.6 NS Model is partially adequate
* = P≤0.05 ** = P≤0.01 NS = Non significant
51
Table 10: Scaling tests for adequacy of additive-dominance model for various plant traits under water stress conditions of cotton (Gossypium hirsutum L.)
Traits Regression slope Mean squares
Remarks b0 b1 Wr + Vr Wr - Vr
Plant height 9.75** 0.51NS 3374.89** 62.87* Model is partially adequate
No. of monopodial
branches 4.26* 1.10 NS 0.15 NS 0.008 NS
Model is partially adequate
No. of sympodial
branches 9.98** -0.15 NS 29.00 NS 0.51 NS
Model is partially adequate
No. of bolls per plant 32.70** 1.64 NS 1378.47** 17.80** Model is partially adequate
Boll weight 18.33** 0.76 NS 0.02** 0.0005 NS Model is fully adequate
Yield 10.22** 0.50 NS 53231.46** 1091.71** Model is partially adequate
Staple length 4.45* 0.62 NS 17.34 NS 2.09 NS Model is partially adequate
Staple fineness 13.54** -0.04 NS 0.01** 0.0001 NS Model is fully adequate
Staple strength 6.05** 0.06 NS 26.16 NS 0.34 NS Model is partially adequate
GOT (%) 3.42** 1.18 NS 26.57 NS 1.64 NS Model is partially adequate
Seed index 5.59** 1.17 NS 0.03** 0.002 NS Model is fully adequate
Lint index 12.73** -0.74 NS 0.02 NS 0.0001 NS Model is partially adequate
Relative water content 4.36* 2.51 NS 265.54 NS 31.91 NS Model is partially adequate
Leaf temperature 4.76** -0.19 NS 247.88** 10.05 NS Model is fully adequate
Relative cell injury % 27.71** -0.82 NS 211455.92 NS 5863.29 NS Model is partially adequate
* = P≤0.05 ** = P≤0.01 NS = Non significant
52
4.4. Results of gene action studies under normal and water stress
condition
4.4.1. Plant height under normal conditions
The analysis of variance of diallel (Table 11) showed that ‘a’ item was highly
significant for plant height under normal conditions indicated the presence of additive
gene effects. The ‘b’ item was also highly significant indicating the importance of
dominant genetic effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found to be non-significant thus suggesting
the unimportant role of directional dominance for the control of this trait. The ‘b2’
component which gives the information about asymmetrical distribution of genes was
found to be non-significant thus indicating absence of asymmetrical distribution of genes
among the parents. The ‘b3’ component which showed presence of the part of dominance
deviation unique to each F1 was found to be significant showing presence of domination
deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were found to be
non-significant for the genotypes and the character under study, hence the retesting of ‘a’
and ‘b’ against ‘c’ and ‘d’ was useless and the previous significance of ‘a’ and ‘b’ stood
valid.
4.4.2. Plant height under water stress conditions
Mean squares presented in Table 12, showed that ‘a’ item was highly significant
for plant height under water stress conditions which indicated the presence of additive
gene effects. The item ‘b’ was found to be highly significant indicating the involvement
of dominant effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found to be non-significant thus suggesting
the insignificant role of directional dominance for the control of this trait. The ‘b2’
component was found significant thus indicating dissimilar gene distribution among the
parents. The ‘b3’ was highly significant thus confirming the presence of the part of
dominance deviation unique to each F1. Maternal effects ‘c’ and reciprocal effects ‘d’
were found non-significant for the genotypes and the character under study, hence their
retesting is needless against ‘a’ and ‘b’. So previous significance of ‘a’ and ‘b’ stood
valid.
53
Table 11: Mean squares of components of variation of plant height under normal
conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares
Retesting against
c d
a additive effects 5 521.67**
b general dominance effects 15 5.15**
b1 directional dominance effects 1 0.22NS
b2 effects due to unequal distribution of
dominance 5 1.34NS
b3 effects due to dominance deviation unique to
F1s 9 7.81**
c maternal effects 5 0.44NS
d non-maternal reciprocal differences 10 1.14NS
Blocks 2 1.17
B×a 10 0.59
B×b 30 0.54
B×b1 2 0.06
B×b2 10 0.60
B×b3 18 0.55
B×c 10 0.71
B×d 20 0.93
Block interaction 70 0.68
Total 107
** = P≤0.01 NS = Non significant
54
Table 12: Mean squares of components of variation of plant height under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 311.21**
b general dominance effects 15 6.09**
b1 directional dominance effects 1 4.62NS
b2 effects due to unequal distribution of dominance
5 7.11*
b3 effects due to dominance deviation unique to F1s
9 5.69**
c maternal effects 5 0.87NS
d non-maternal reciprocal differences 10 0.78NS
Blocks 2 1.95
B×a 10 0.64
B×b 30 0.83
B×b1 2 0.78
B×b2 10 1.13
B×b3 18 0.68
B×c 10 1.23
B×d 20 0.45
Block interaction 70 0.75
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
55
4.4.3. Monopodial branches under normal conditions
The results of analysis of variance of diallel (Table 13) showed the item ‘a’ was
highly significant for monopodial branches under normal conditions which indicated the
presence of additive gene effects. The item ‘b’ was found to be non-significant so
indicating the absence of dominant effects for the inheritance of monopodial branches
under normal condition. The ‘b1’ was found to be non-significant thus suggesting the
unimportant role of directional dominance for the control of monopodial branches under
normal conditions. The ‘b2’ component which gives information about asymmetrical
distribution of genes was found to be non-significant thus indicating the absence of
asymmetrical distribution of genes among the parents. The ‘b3’ component was found to
be non-significant showing the absence of domination deviation unique to F1. Maternal
effects ‘c’ and reciprocal effects ‘d’ were found to be non-significant, hence their
retesting is needless against ‘a’ and ‘b’. So previous significance for ‘a’ and ‘b’ stood
valid.
4.4.4. Monopodial branches under water stress conditions
The results of analysis of variance of diallel (Table 14) showed that the item ‘a’
was highly significant for monopodial branches under water stress conditions which
indicated the character was controlled by additive gene effects. The general dominance
effect ‘b’ was found to be highly significant indicating the presence of dominance effects.
The item ‘b1’ was found to be non-significant thus suggesting the insignificant role of
directional dominance for the control of monopodial branches under stress. The ‘b2’
component was found to be significant thus indicating the presence of asymmetrical
distribution of genes among the parents. The ‘b3’ component was found to be non-
significant showing the absence of domination deviation unique to F1. Maternal effects
‘c’ and reciprocal effects ‘d’ were found to be non-significant, hence their retesting is
needless against ‘a’ and ‘b’. So previous significance for ‘a’ and ‘b’ stood valid.
56
Table 13: Mean squares of components of variation of monopodial per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 13.00**
b general dominance effects 15 0.15NS
b1 directional dominance effects 1 0.13NS
b2 effects due to unequal distribution of dominance
5 0.13NS
b3 effects due to dominance deviation unique to F1s
9 0.15NS
c maternal effects 5 0.21NS
d non-maternal reciprocal differences 10 0.17NS
Blocks 2 0.12
B×a 10 0.09
B×b 30 0.08
B×b1 2 0.02
B×b2 10 0.06
B×b3 18 0.10
B×c 10 0.16
B×d 20 0.11
Block interaction 70 0.10
Total 107
** = P≤0.01 NS = Non significant
57
Table 14: Mean squares of components of variation of monopodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 1.16**
b general dominance effects 15 0.08**
b1 directional dominance effects 1 0.07NS
b2 effects due to unequal distribution of dominance
5 0.13*
b3 effects due to dominance deviation unique to F1s
9 0.06NS
c maternal effects 5 0.06NS
d non-maternal reciprocal differences 10 0.04NS
Blocks 2 0.03
B×a 10 0.05
B×b 30 0.03
B×b1 2 0.06
B×b2 10 0.03
B×b3 18 0.03
B×c 10 0.05
B×d 20 0.05
Block interaction 70 0.04
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
58
4.4.5. Sympodial branches under normal conditions
Sympodial branches under normal conditions were found to be under the control
of additive genetic effects as indicated from the significance of ‘a’ item (Table 15). The
item ‘b’ was found to be non-significant indicating the absence of dominance effects for
the control of this trait. The non-significant ‘b1’ component exhibited unimportant role of
directional dominance for the control of this trait. The ‘b2’ component which gives
information about asymmetrical distribution of genes was found non significant thus
indicating the absence of asymmetrical distribution of genes among the parents. The ‘b3’
component which showed the presence of the part of dominance deviation unique to each
F1 was found to be non-significant showing absence of domination deviation unique to
F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were also found to be non-significant
for the genotypes and the character under study, hence the retesting of ‘a’ and ‘b’ against
‘c’ and ‘d’ was useless and the previous significance of ‘a’ and ‘b’ stood valid.
4.4.6. Sympodial branches under water stress conditions
Highly significant ‘a’ item for sympodial branches under water stress conditions
was indicative of the presence of additive gene effects (Table 16). The item ‘b’ was found
to be highly significant indicating the presence of dominance effects for the control of
this trait. The ‘b1’ component which gives information about directional dominance was
found to be non-significant thus suggesting the unimportant role of directional dominance
for the control of this trait. The ‘b2’ component which gives information about
asymmetrical distribution of genes was found non significant thus indicating the absence
of asymmetrical distribution of genes among the parents. The ‘b3’ component which
showed the presence of the part of dominance deviation unique to each F1 was found to
be significant showing presence of domination deviation unique to F1. Maternal effects
‘c’ and reciprocal effects ‘d’ were also found to be non-significant for the genotypes and
the character under study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was
useless and the previous significance stood valid.
59
Table 15: Mean squares of components of variation of sympodial branches per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 39.84**
b general dominance effects 15 1.21 NS
b1 directional dominance effects 1 0.00NS
b2 effects due to unequal distribution of dominance
5 1.22NS
b3 effects due to dominance deviation unique to F1s
9 1.34 NS
c maternal effects 5 0.99NS
d non-maternal reciprocal differences 10 0.42NS
Blocks 2 1.29
B×a 10 0.25
B×b 30 0.65
B×b1 2 0.27
B×b2 10 0.78
B×b3 18 0.62
B×c 10 0.44
B×d 20 0.81
Block interaction 70 0.61
Total 107
** = P≤0.01 NS = Non significant
60
Table 16: Mean squares of components of variation of sympodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 28.88**
b general dominance effects 15 1.13**
b1 directional dominance effects 1 0.63NS
b2 effects due to unequal distribution of dominance
5 0.85NS
b3 effects due to dominance deviation unique to F1s
9 1.34*
c maternal effects 5 0.36NS
d non-maternal reciprocal differences 10 0.57NS
Blocks 2 0.03
B×a 10 0.19
B×b 30 0.39
B×b1 2 0.11
B×b2 10 0.36
B×b3 18 0.42
B×c 10 0.44
B×d 20 0.44
Block interaction 70 0.38
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
61
4.4.7. Bolls per plant under normal conditions
Item ‘a’ was highly significant for bolls per plant under normal conditions and
was indicative of the presence of additive gene effects (Table 17). Item ‘b’ was found to
be highly significant indicating the presence of dominance effects for the control of this
trait. The ‘b1’ component which gives information about directional dominance was
found to be non-significant, thus suggesting the unimportant role of directional
dominance for the control of this trait. The ‘b2’ component which gives information about
asymmetrical distribution of genes was found non significant, thus indicating absence of
asymmetrical distribution of genes among parents. The component ‘b3’ which showed the
presence of the part of dominance deviation unique to each F1 was found to be significant
showing presence of domination deviation unique to F1. Maternal effects ‘c’ and
reciprocal effects ‘d’ were found to be non-significant for the genotypes and the character
under study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and the
previous significance stood valid.
4.4.8. Bolls per plant under water stress conditions
The item ‘a’ was highly significant for bolls per plant under water stress
conditions and was indicative of the presence of additive gene effects (Table 18). The
general dominance effects ‘b’ was found to be significant indicating the presence of
dominance effects. The directional dominance effects ‘b1’ component was found to be
non-significant, thus suggesting the insignificant role of directional dominance for the
control of bolls per plant under water stress conditions. The ‘b2’ component was found to
be non-significant, thus indicating the absence of asymmetrical distribution of genes
among the parents. The ‘b3’ component which showed the presence of the part of
dominance deviation unique to each F1 was found to be significant showing presence of
domination deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were
found to be significant for the genotypes and the character under study, hence the
retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was necessary. After retesting of ‘c’ and ‘d’
against ‘a’ and ‘b’ concluded that the item ‘a’ remained significant while for ‘d’ the item
‘b’ and components reduced to non-significant.
62
Table 17: Mean squares of components of variation of bolls per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 278.22**
b general dominance effects 15 1.65**
b1 directional dominance effects 1 0.63NS
b2 effects due to unequal distribution of dominance
5 0.84NS
b3 effects due to dominance deviation unique to F1s
9 2.21**
c maternal effects 5 0.74NS
d non-maternal reciprocal differences 10 0.44NS
Blocks 2 0.18
B×a 10 0.37
B×b 30 0.23
B×b1 2 0.09
B×b2 10 0.33
B×b3 18 0.19
B×c 10 0.58
B×d 20 0.83
Block interaction 70 0.47
Total 107
** = P≤0.01 NS = Non significant
63
Table 18: Mean squares of components of variation of bolls per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 236.95** 139.38**
b general dominance effects 15 3.21** 1.93NS
b1 directional dominance effects 1 0.01NS 0.004 NS
b2 effects due to unequal distribution of dominance
5 0.88NS 0.53 NS
b3 effects due to dominance deviation unique to F1s
9 4.86** 2.92 NS
c maternal effects 5 1.70*
d non-maternal reciprocal differences 10 1.67**
Blocks 2 0.39
B×a 10 0.53
B×b 30 0.24
B×b1 2 0.24
B×b2 10 0.27
B×b3 18 0.23
B×c 10 0.47
B×d 20 0.43
Block interaction 70 0.37
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
64
4.4.9. Boll weight under normal conditions
The analysis of variance of diallel (Table 19) showed that item ‘a’ was highly
significant that character was controlled by additive gene effects. The general dominance
effect ‘b’ was found to be significant indicating the presence of dominance effects. The
directional dominance effects ‘b1’ component was found to be non-significant thus
suggesting the insignificant role of directional dominance for the control of boll weight
under normal conditions. The ‘b2’ component was found to be significant thus indicating
presence of asymmetrical distribution of genes among the parents. The ‘b3’ component
which showed presence of the part of dominance deviation unique to each F1 was found
to be significant showing presence of domination deviation unique to F1. Maternal effects
‘c’ was found significant so its retesting against ‘a’ is necessary and reciprocal effects
‘d’ was found to be non-significant for the genotypes and the character under study,
hence the retesting of ‘b’ against ‘d’ was useless and the previous significance of ‘b’
stood valid. After retesting ‘c’ the ‘a’ component remained significant, thus confirming
the presence of maternal effects. And maternal effects did not influence these
components.
4.4.10. Boll weight under water stress conditions
The results of analysis of variance of diallel (Table 20) showed that the ‘a’ item
was highly significant for boll weight under water stress conditions which indicated the
presence of additive gene effects. The item ‘b’ was found to be significant indicating the
presence of dominance effects for the control of this trait. The ‘b1’ component which
gives information about directional dominance was found to be non-significant thus
suggesting the unimportant role of directional dominance for the control of this trait. The
‘b2’ component which gives information about asymmetrical distribution of genes was
found to be non-significant, thus indicating the absence of asymmetrical distribution of
genes among the parents. The ‘b3’ component which showed presence of the part of
dominance deviation unique to each F1 was found to be significant showing presence of
domination deviation unique to F1. Maternal effects ‘c’ was found non-significant,
however, reciprocal effects ‘d’ were found to be significant for the genotypes and the
character under study hence the retesting of ‘b’ against ‘d’ was necessary. The item ‘b’,
‘b1’, ‘b2’ and ‘b3 reduced to non-significant.
65
Table 19: Mean squares of components of variation of boll weight under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d
a additive effects 5
1.153** 49.67*
b general dominance effects 15 0.03**
b1 directional dominance effects 1 0.008NS
b2 effects due to unequal distribution of dominance
5 0.02*
b3 effects due to dominance deviation unique to F1s
9 0.03**
c maternal effects 5 0.02**
d non-maternal reciprocal differences 10 0.002NS
Blocks 2 0.01
B×a 10 0.004
B×b 30 0.004
B×b1 2 0.003
B×b2 10 0.005
B×b3 18 0.004
B×c 10 0.005
B×d 20 0.005
Block interaction 70 0.004
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
66
Table 20: Mean squares of components of variation of boll weight under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 1.02**
b general dominance effects 15 0.01* 0.91 NS
b1 directional dominance effects 1 0.003NS 0.29 NS
b2 effects due to unequal distribution of dominance
5 0.003NS 0.24 NS
b3 effects due to dominance deviation unique to F1s
9 0.02* 1.35 NS
c maternal effects 5 0.006NS
d non-maternal reciprocal differences 10 0.01**
Blocks 2 0.001
B×a 10 0.002
B×b 30 0.005
B×b1 2 0.005
B×b2 10 0.006
B×b3 18 0.005
B×c 10 0..004
B×d 20 0.003
Block interaction 70 0.003
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
67
4.4.11. Yield under normal conditions
The results of analysis of variance of diallel (Table 21) showed that the item ‘a’
was highly significant for yield under normal conditions which indicated the presence of
additive gene effects. The general dominance effects ‘b’ was found to be significant
indicating the presence of dominance effects. The directional dominance effects ‘b1’
component was found to be non-significant, thus suggesting the insignificant role of
directional dominance for the control of yield under normal conditions. The ‘b2’
component was found significant thus indicating presence of asymmetrical distribution of
genes among the parents. The ‘b3’ component which showed presence of the part of
dominance deviation unique to each F1 was found to be significant showing presence of
domination deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were
found non-significant for the genotypes and the character under study, hence the retesting
of ‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and the previous significance of ‘a’ and ‘b’
stood valid.
4.4.12. Yield under water stress conditions
The results of analysis of variance of diallel (Table 22) showed that the ‘a’ item
was highly significant indicating that additive effects were controlling yield under water
stress conditions. The item ‘b’ was found to be significant indicating the presence of
dominance effects for the control of this trait. The ‘b1’ component which provides
information about directional dominance was significant thus suggesting the important
role of directional dominance for the control of this trait. The ‘b2’ component which gives
information about asymmetrical distribution of genes was found significant, thus
indicating presence of asymmetrical distribution of genes among the parents. The ‘b3’
component which showed presence of the part of dominance deviation unique to each F1
was found to be significant showing presence of domination deviation unique to F1.
Maternal effects ‘c’ and reciprocal effects‘d’ were found significant indicating the
presence of maternal effects and reciprocal differences for the genotype and the character
under study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and‘d’ was conducted. After
retesting the ‘a’ remained unchanged (significant) thus indicating the presence of
maternal effects and maternal effects did not influence these components. Similarly after
retesting the ‘b’ item against ‘d’ reduced to non-significant.
68
Table 21: Mean squares of components of variation of yield under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c D a additive effects 5 2051.84**
b general dominance effects 15 5.67**
b1 directional dominance effects 1 3.54NS
b2 effects due to unequal distribution of dominance
5 4.07**
b3 effects due to dominance deviation unique to F1s
9 6.62**
c maternal effects 5 1.51NS
d non-maternal reciprocal differences 10 1.04NS
Blocks 2 1.17
B×a 10 0.06
B×b 30 1.42
B×b1 2 2.54
B×b2 10 0.71
B×b3 18 0.78
B×c 10 1.90
B×d 20 1.56
Block interaction 70 1.30
Total 107
** = P≤0.01 NS = Non significant
69
Table 22: Mean squares of components of variation of yield under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 1844.03** 197.62**
b general dominance effects 15 13.49** 1.69NS
b1 directional dominance effects 1 19.37** 2.46 NS
b2 effects due to unequal distribution of dominance
5 11.34** 1.41 NS
b3 effects due to dominance deviation unique to F1s
9 14.00** 1.75 NS
c maternal effects 5 9.33**
d non-maternal reciprocal differences 10 8.00**
Blocks 2 0.02
B×a 10 0.41
B×b 30 0.64
B×b1 2 0.07
B×b2 10 0.63
B×b3 18 0.72
B×c 10 0.66
B×d 20 1.04
Block interaction 70 0.73
Total 107
** = P≤0.01 NS = Non significant
70
4.4.13. Staple length under normal conditions
Analysis of variance (Table 23) for staple length under normal conditions
indicated that the ‘a’ item was highly significant which was responsible for the presence
of additive gene effects. Highly significant item ‘b’ indicated the presence of general
dominance effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found to be non-significant thus suggesting
the unimportant role of directional dominance for the control this trait. The ‘b2’
component which gives information about asymmetrical distribution of genes was found
to be significant thus indicating presence of asymmetrical distribution of genes among the
parents. The ‘b3’ component which showed presence of the part of dominance deviation
unique to each F1 was found to be non-significant showing absence of domination
deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were also found to
be non-significant for the genotypes and the character under study, hence the retesting of
‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and the previous significance of ‘a’ and ‘b’
stood valid.
4.4.14. Staple length under water stress conditions
Mean squares presented in Table 24, showed that ‘a’ item was highly significant
for staple length under water stress conditions which indicated presence of additive gene
effects. The item ‘b’ was found to be highly significant indicating the involvement of
dominance effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found non-significant, thus suggesting the
insignificant role of directional dominance for the control of this trait. The ‘b2’
component was found non-significant, thus indicating the absence of asymmetrical
distribution of genes among the parents. The ‘b3’ was significant thus confirming the
presence of part of dominance deviation unique to each F1. Maternal effects ‘c’ and
reciprocal effects ‘d’ were found non-significant for the genotypes and the character
under study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was unnecessary and the
previous significance of ‘a’ and ‘b’ stood valid.
71
Table 23: Mean squares of components of variation of staple length under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 6.19**
b general dominance effects 15 1.38**
b1 directional dominance effects 1 0.67NS
b2 effects due to unequal distribution of dominance
5 2.13**
b3 effects due to dominance deviation unique to F1s
9 1.04NS
c maternal effects 5 0.92NS
d non-maternal reciprocal differences 10 0.63NS
Blocks 2 0.34
B×a 10 0.37
B×b 30 0.39
B×b1 2 0.17
B×b2 10 0.33
B×b3 18 0.46
B×c 10 1.33
B×d 20 0.79
Block interaction 70 0.64
Total 107
** = P≤0.01 NS = Non significant
72
Table 24: Mean squares of components of variation of staple length under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 5.97**
b general dominance effects 15 1.10**
b1 directional dominance effects 1 2.20NS
b2 effects due to unequal distribution of dominance
5 0.67NS
b3 effects due to dominance deviation unique to F1s
9 1.22*
c maternal effects 5 0.47NS
d non-maternal reciprocal differences 10 0.58NS
Blocks 2 1.14
B×a 10 0.32
B×b 30 0.40
B×b1 2 0.55
B×b2 10 0.42
B×b3 18 0.36
B×c 10 0.20
B×d 20 0.32
Block interaction 70 0.34
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
73
4.4.15. Staple fineness under normal conditions
Mean squares presented in Table 25, showed that ‘a’ item was highly significant
for staple fineness under normal conditions which indicated the presence of additive gene
effects. The item ‘b’ was found to be highly significant indicating the importance of
dominant genetic effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found non-significant, thus suggesting the
unimportant role of directional dominance for the control of this trait. The ‘b2’
component which gives information about asymmetrical distribution of genes was found
to be significant, thus indicating the presence of asymmetrical distribution of genes
among the parents. The ‘b3’ component which showed presence of part of dominance
deviation unique to each F1 was found to be significant showing presence of domination
deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were found non-
significant for the genotypes and the character under study, hence the retesting of ‘a’ and
‘b’ against ‘c’ and ‘d’ was unnecessary and the previous significance of ‘a’ and ‘b’ stood
valid.
4.4.16. Staple fineness under water stress conditions
Mean squares presented in Table 26, showed that ‘a’ item was highly significant
for staple fineness under water stress conditions which indicated the presence of additive
gene effects. The item ‘b’ was found to be highly significant indicating the importance of
dominant genetic effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found significant thus suggesting the
important role of directional dominance for the control of this trait. The ‘b2’ component
which gives information about asymmetrical distribution of genes was found to be non-
significant, thus indicating the absence of asymmetrical distribution of genes among the
parents. The ‘b3’ component was found to be significant showing presence of the part of
dominance deviation unique to each F1. However, maternal effects ‘c’ and reciprocal
effects ‘d’ were also found non-significant for the genotypes and the character under
study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and the previous
significance of ‘a’ and ‘b’ stood valid.
74
Table 25: Mean squares of components of variation of staple fineness under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 0.28**
b general dominance effects 15 0.02**
b1 directional dominance effects 1 0.06NS
b2 effects due to unequal distribution of dominance
5 0.01*
b3 effects due to dominance deviation unique to F1s
9 0.02**
c maternal effects 5 0.03NS
d non-maternal reciprocal differences 10 0.01NS
Blocks 2 0.01
B×a 10 0.01
B×b 30 0.004
B×b1 2 0..01
B×b2 10 0.002
B×b3 18 0.004
B×c 10 0.02
B×d 20 0.01
Block interaction 70 0.01
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
75
Table 26: Mean squares of components of variation of staple fineness under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 0.29**
b general dominance effects 15 0.02**
b1 directional dominance effects 1 0.04**
b2 effects due to unequal distribution of dominance
5 0.002NS
b3 effects due to dominance deviation unique to F1s
9 0.03**
c maternal effects 5 0.01NS
d non-maternal reciprocal differences 10 0.004NS
Blocks 2 0.01
B×a 10 0.002
B×b 30 0.002
B×b1 2 0.0001
B×b2 10 0.003
B×b3 18 0.002
B×c 10 0.003
B×d 20 0.004
Block interaction 70 0.003
Total 107
** = P≤0.01 NS = Non significant
76
4.4.17. Staple strength under normal conditions
Mean squares presented in Table 27, showed that ‘a’ item was highly significant
for staple strength under normal conditions which indicated the presence of additive gene
effects. The item ‘b’ was found to be non-significant indicating the absence of dominant
effects for this trait. The ‘b1’ component which gives information about directional
dominance was found non-significant, thus suggesting the unimportant role of directional
dominance for the control of this trait. The ‘b2’ component which gives information about
asymmetrical distribution of genes was found to be non-significant, thus indicating the
absence of asymmetrical distribution of genes among the parents. The ‘b3’ component
which showed presence of part of dominance deviation unique to each F1 was found to be
non-significant showing absence of domination deviation unique to F1. Maternal effects
‘c’, found to be non-significant. However, reciprocal effects ‘d’ were found to be
significant which indicated retesting of ‘d’ against item ‘b’. After retesting the item ‘b’
and its components reduced to non-significant.
4.4.18. Staple strength under water stress conditions
The results of analysis of diallel in Table 28, showed that ‘a’ item was highly
significant for staple strength under water stress conditions which indicated the presence
of additive gene effects. The item ‘b’ was found to be non-significant so indicating the
absence of dominant effects for the inheritance of staple strength under water stress
conditions. The ‘b1’ component was found non-significant, thus suggesting the
unimportant role of directional dominance for the control of this trait. The ‘b2’
component which gives information about asymmetrical distribution of genes was found
to be non-significant thus indicating the absence of asymmetrical distribution of genes
among the parents. The ‘b3’ component was found to be non-significant showing absence
of domination deviation unique to F1. Maternal effects ‘c’ and reciprocal effects ‘d’ were
found to be non-significant, hence their retesting is useless, so previous significance for
‘a’ and ‘b’ stood valid.
77
Table 27: Mean squares of components of variation of staple strength under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 28.86**
b general dominance effects 15 1.22NS 0.62 NS
b1 directional dominance effects 1 2.4NS 1.21 NS
b2 effects due to unequal distribution of dominance
5 0.69NS 0.35 NS
b3 effects due to dominance deviation unique to F1s
9 1.40NS 0.71 NS
c maternal effects 5 0.72NS
d non-maternal reciprocal differences 10 1.97**
Blocks 2 0.03
B×a 10 0.61
B×b 30 0.76
B×b1 2 1.84
B×b2 10 0.32
B×b3 18 0.89
B×c 10 0.82
B×d 20 0.47
Block interaction 70 0.67
Total 107
** = P≤0.01 NS = Non significant
78
Table 28: Mean squares of components of variation of staple strength under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 21.71**
b general dominance effects 15 1.55NS
b1 directional dominance effects 1 5.60NS
b2 effects due to unequal distribution of dominance
5 1.38NS
b3 effects due to dominance deviation unique to F1s
9 1.20NS
c maternal effects 5 1.50NS
d non-maternal reciprocal differences 10 0.67NS
Blocks 2 0.23
B×a 10 0.92
B×b 30 0.96
B×b1 2 0.74
B×b2 10 0.56
B×b3 18 1.19
B×c 10 0.83
B×d 20 0.70
Block interaction 70 0.86
Total 107
** = P≤0.01 NS = Non significant
79
4.4.19. GOT (%) under normal conditions
The mean squares of GOT% in diallel crosses under normal conditions are
presented in Table 29. The significance of ‘a’ item in the table indicated the presence of
additive gene effects. The item ‘b’ was also highly significant indicating the importance
of general dominance and the ‘b1’ item was also significant indicating the important role
of directional dominance for the control of GOT% under normal conditions. The ‘b2’
item was significant which gives information about asymmetrical distribution of genes,
thus indicating the presence of asymmetrical distribution of genes among the parents. The
item ‘b3’ was also significant indicating the importance of specific gene effects. Maternal
effects ‘c’ and reciprocal effects‘d’ were found to be non-significant thus suggesting the
absence of these effects, so their further retesting is useless.
4.4.20. GOT (%) under water stress conditions
The results of analysis of variance of diallel (Table 30) showed that the ‘a’ item
highly significant for GOT% under water stress conditions which indicated the presence
of additive gene effects. The item ‘b’ was found to be highly significant indicating the
presence of general dominance effects for the control of this trait. The ‘b1’ component
which gives information about directional dominance was found to be significant, thus
suggesting the important role of directional dominance for the control of this trait. The
‘b2’ component which gives information about asymmetrical distribution of genes was
found to be non-significant, thus indicating the absence of asymmetrical distribution of
genes among the parents. The ‘b3’ component was found to be significant showing the
presence of the part of dominance deviation unique to each F1. However, the maternal
effects ‘c’ and reciprocal effects ‘d’ were found to be non-significant for the genotypes
and the character under study, hence retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was
useless and previous significance of ‘a’ and ‘b’ stood valid.
80
Table 29: Mean squares of components of variation of GOT (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 15.55**
b general dominance effects 15 1.62**
b1 directional dominance effects 1 5.50*
b2 effects due to unequal distribution of dominance
5 0.56*
b3 effects due to dominance deviation unique to F1s
9 1.78**
c maternal effects 5 0.97NS
d non-maternal reciprocal differences 10 0.40NS
Blocks 2 0.16
B×a 10 0.16
B×b 30 0.31
B×b1 2 0.14
B×b2 10 0.15
B×b3 18 0.41
B×c 10 1.16
B×d 20 0.55
Block interaction 70 0.48
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
81
Table 30: Mean squares of components of variation of GOT (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 9.47**
b general dominance effects 15 1.89**
b1 directional dominance effects 1 9.87**
b2 effects due to unequal distribution of dominance
5 0.78NS
b3 effects due to dominance deviation unique to F1s
9 1.61*
c maternal effects 5 0.81NS
d non-maternal reciprocal differences 10 0.55NS
Blocks 2 0.02
B×a 10 0.27
B×b 30 0.57
B×b1 2 0.03
B×b2 10 0.82
B×b3 18 0.49
B×c 10 0.93
B×d 20 0.58
Block interaction 70 0.58
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
82
4.4.21. Seed index under normal conditions
The results of analysis of variance of diallel (Table 31) showed that the ‘a’ item
was highly significant indicating that additive effects were controlling seed index under
normal conditions. The ‘b’ component was found to be significant, indicating the
presence of dominance effects of genes. The component ‘b1’ was found to be non-
significant, thus suggesting the insignificant role of directional dominance for the control
of seed index under normal conditions. The ‘b2’ component was found to be significant
thus indicating the presence of asymmetrical distribution of genes among the parents. The
component ‘b3’ was found to be significant, indicating that the specific gene interaction
was present in the inheritance of the character under normal conditions. Maternal effects
‘c’ and reciprocal effects ‘d’ were found to be non-significant, indicating that these
effects were absent, so their further retesting is useless.
4.4.22. Seed index under water stress conditions
Mean squares presented in Table 32, showed that the item ‘a’ was found to be
highly significant for seed index under water stress conditions, indicating the presence of
additive gene effects. The ‘b’ item was also highly significant, indicating the importance
of dominant genetic effects for the control of this trait. The ‘b1’ component which gives
information about directional dominance was found to be non-significant thus suggesting
the unimportant role of directional dominance for the control of this trait. The ‘b2’
component which gives information about asymmetrical distribution of genes was found
to be non-significant, thus indicating the absence of asymmetrical distribution of genes
among the parents. The ‘b3’ component was found to be significant which indicated that
specific gene interaction was present in the inheritance of the character under water stress
conditions. Maternal effects ‘c’ and reciprocal effects‘d’ were found to be non-
significant, indicating the absence of these effects. So their further retesting is useless.
83
Table 31: Mean squares of components of variation of seed index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 1.35**
b general dominance effects 15 0.03**
b1 directional dominance effects 1 0.03NS
b2 effects due to unequal distribution of dominance
5 0.01*
b3 effects due to dominance deviation unique to F1s
9 0.03**
c maternal effects 5 0.01NS
d non-maternal reciprocal differences 10 0.01NS
Blocks 2 0.000092
B×a 10 0.01
B×b 30 0.01
B×b1 2 0.01
B×b2 10 0.003
B×b3 18 0.008
B×c 10 0.016
B×d 20 0.009
Block interaction 70 0.009
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
84
Table 32: Mean squares of components of variation of seed index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 1.33**
b general dominance effects 15 0.03**
b1 directional dominance effects 1 0.07NS
b2 effects due to unequal distribution of dominance
5 0.01NS
b3 effects due to dominance deviation unique to F1s
9 0.03**
c maternal effects 5 0.01NS
d non-maternal reciprocal differences 10 0.01 NS
Blocks 2 0.001
B×a 10 0.004
B×b 30 0.003
B×b1 2 0.005
B×b2 10 0.003
B×b3 18 0.003
B×c 10 0.008
B×d 20 0.004
Block interaction 70 0.004
Total 107
** = P≤0.01 NS = Non significant
85
4.4.23. Lint index under normal conditions
Analysis of variance for lint index under normal conditions (Table 33) indicated
that the item ‘a’ was highly significant which is responsible for the presence of additive
gene effects. The item ‘b’ was found to be highly significant, indicating the presence of
general dominance effects for the control of this trait. The item ‘b1’ was found to be non-
significant and exhibited unimportant role of directional dominance for the control of this
trait. The ‘b2’ component which gives information about asymmetrical distribution of
genes was found to be significant, thus indicating the presence of asymmetrical
distribution of genes among the parents. The ‘b3’ component which showed the presence
of the part of dominance deviation unique to each F1 was found to be non-significant
showing absence of domination deviation unique to each F1. Maternal effects ‘c’ and
reciprocal effects ‘d’ were found to be non-significant for the genotypes and the character
under study, hence retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was unnecessary and
previous significance of ‘a’ and ‘b’ stood valid.
4.4.24. Lint index under water stress conditions
The results of analysis of variance of diallel (Table 34) showed that the item ‘a’
was highly significant for lint index under water stress conditions which indicated the
presence of additive gene effects. The item ‘b’ was found to be highly significant,
indicating the presence of general dominance effects for the control of this trait. The ‘b1’
component which gives information about directional dominance was found to be
significant, thus suggesting the important role of directional dominance for the control of
this trait. The ‘b2’ component which gives information about asymmetrical distribution of
genes was found to be non-significant, thus indicating the absence of asymmetrical
distribution of genes among the parents. The ‘b3’ component which showed presence of
the part of dominance deviation unique to each F1 was found to be non-significant
showing absence of domination deviation unique to each F1. Maternal effects ‘c’ and
reciprocal effects‘d’ were found to be non-significant for the genotypes and the character
under study, hence indicating the absence of these effects. So their further retesting is
useless.
86
Table 33: Mean squares of components of variation of lint index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 0.50**
b general dominance effects 15 0.01**
b1 directional dominance effects 1 0.06NS
b2 effects due to unequal distribution of dominance
5 0.01*
b3 effects due to dominance deviation unique to F1s
9 0.01NS
c maternal effects 5 0.01NS
d non-maternal reciprocal differences 10 0.02NS
Blocks 2 0.05
B×a 10 0.01
B×b 30 0.004
B×b1 2 0.02
B×b2 10 0.002
B×b3 18 0.005
B×c 10 0.02
B×d 20 0.01
Block interaction 70 0.01
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
87
Table 34: Mean squares of components of variation of lint index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 0.54**
b general dominance effects 15 0.02**
b1 directional dominance effects 1 0.18*
b2 effects due to unequal distribution of dominance
5 0.009NS
b3 effects due to dominance deviation unique to F1s
9 0.01 NS
c maternal effects 5 0.004 NS
d non-maternal reciprocal differences 10 0.01 NS
Blocks 2 0.01
B×a 10 0.004
B×b 30 0.007
B×b1 2 0.009
B×b2 10 0.005
B×b3 18 0.008
B×c 10 0.01
B×d 20 0.008
Block interaction 70 0.007
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
88
4.4.25. Relative water contents (%) under normal conditions
Analysis of variance for relative water contents under normal conditions
(Table 35) indicated that the item ‘a’ was highly significant which is responsible for the
presence of additive gene effects. The item ‘b’ was found to be highly significant,
indicating the presence of dominance effects for the control of this trait. The ‘b1’
component which gives information about directional dominance was found to be non-
significant thus suggesting the unimportant role of directional dominance for the control
of this trait. The ‘b2’ component which gives information about asymmetrical distribution
of genes was found to be significant, thus indicating the presence of asymmetrical
distribution of genes among the parents. The ‘b3’ component was found to be significant
indicating the importance of specific gene effects. Maternal effects ‘c’ and reciprocal
effects ‘d’ were found to be non-significant for the genotypes and the character under
study, hence retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and previous
significance of ‘a’ and ‘b’ stood valid.
4.4.26. Relative water contents (%) under water stress conditions
The mean squares of relative water contents under water stress conditions
presented in Table 36, showed that the ‘a’ item was highly significant which indicated the
presence of additive genetic effects. The item ‘b’ was also found to be highly significant,
indicating the importance of general dominance effects for the control of this trait. The
‘b1’ component which gives information about directional dominance was found to be
significant, thus suggesting the important role of directional dominance for the control of
this trait. The ‘b2’ component was found to be significant showing asymmetrical
distribution of genes among the parents. The ‘b3’ component was found to be significant
showing presence of the part of dominance deviation unique to each F1. However,
maternal effects ‘c’ and reciprocal effects ‘d’ were found to be non-significant for the
genotypes and the character under study, hence the retesting of ‘a’ and ‘b’ against ‘c’ and
‘d’ was useless and the previous significance stood valid.
89
Table 35: Mean squares of components of variation of relative water contents (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 203.69**
b general dominance effects 15 2.81**
b1 directional dominance effects 1 7.12NS
b2 effects due to unequal distribution of dominance
5 2.75*
b3 effects due to dominance deviation unique to F1s
9 2.37*
c maternal effects 5 0.89NS
d non-maternal reciprocal differences 10 1.07NS
Blocks 2 0.16
B×a 10 1.02
B×b 30 0.79
B×b1 2 0.89
B×b2 10 0.59
B×b3 18 0.89
B×c 10 1.32
B×d 20 0.96
Block interaction 70 0.95
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
90
Table 36: Mean squares of components of variation of relative water contents (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 195.52**
b general dominance effects 15 3.95**
b1 directional dominance effects 1 6.23*
b2 effects due to unequal distribution of dominance
5 2.76*
b3 effects due to dominance deviation unique to F1s
9 4.35*
c maternal effects 5 0.61NS
d non-maternal reciprocal differences 10 1.36NS
Blocks 2 1.28
B×a 10 0.24
B×b 30 0.75
B×b1 2 0.19
B×b2 10 0.57
B×b3 18 0.92
B×c 10 0.87
B×d 20 1.28
Block interaction 70 0.85
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
91
4.4.27. Leaf temperature under normal conditions
The results of analysis of variance of diallel Table 37, showed that the ‘a’ item
was highly significant for leaf temperature under normal conditions which indicated the
presence of additive genetic effects. The item ‘b’ was found to be significant, indicating
the presence of dominance effects for the control of this trait. The ‘b1’ component which
gives information about directional dominance was found to be non-significant, thus
suggesting the unimportant role of directional dominance for the control of this trait. The
‘b2’ component was found to be significant thus indicating the presence of asymmetrical
distribution of genes among the parents. The ‘b3’ component which showed the presence
of the part of dominance deviation unique to each F1 was found significant showing
presence of domination deviation unique to F1. Maternal effects ‘c’ and reciprocal effects
‘d’ were found to be non-significant for the genotypes and the character under study,
hence retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was useless and previous significance of
‘a’ and ‘b’ stood valid.
4.4.28. Leaf temperature under water stress conditions
The results of analysis of variance of diallel Table 38, showed that the item ‘a’ was
highly significant for leaf temperature under water stress conditions which indicated the
presence of additive genetic effects. The item ‘b’ was found to be highly significant,
indicating the involvement of dominance effects for the control of this trait. The ‘b1’
component which gives information about directional dominance was found to be non-
significant, thus suggesting the insignificant role of directional dominance for the control
of this trait. The ‘b2’ component was found to be non-significant, thus indicating the
absence of asymmetrical distribution of genes among the parents. The ‘b3’ component
was found to be significant indicating the importance of specific gene effects for the
character under study. Maternal effects ‘c’ and reciprocal effects ‘d’ were found non-
significant for the genotypes and the character under study, hence the retesting of ‘a’ and
‘b’ against ‘c’ and ‘d’ was useless and the previous significance of ‘a’ and ‘b’ stood
valid..
92
Table 37: Mean squares of components of variation of leaf temperature under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 30.08**
b general dominance effects 15 2.05**
b1 directional dominance effects 1 0.31NS
b2 effects due to unequal distribution of dominance
5 1.08*
b3 effects due to dominance deviation unique to F1s
9 2.79*
c maternal effects 5 2.11NS
d non-maternal reciprocal differences 10 0.34NS
Blocks 2 0.25
B×a 10 0.45
B×b 30 0.61
B×b1 2 0.47
B×b2 10 0.28
B×b3 18 0.79
B×c 10 1.03
B×d 20 0.84
Block interaction 70 0.71
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
93
Table 38: Mean squares of components of variation of leaf temperature under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 34.26**
b general dominance effects 15 2.33**
b1 directional dominance effects 1 0.82NS
b2 effects due to unequal distribution of dominance
5 0.81NS
b3 effects due to dominance deviation unique to F1s
9 3.34**
c maternal effects 5 1.13 NS
d non-maternal reciprocal differences 10 0.50 NS
Blocks 2 0.44
B×a 10 0.58
B×b 30 0.70
B×b1 2 1.09
B×b2 10 1.07
B×b3 18 0.45
B×c 10 0.70
B×d 20 0.77
Block interaction 70 0.70
Total 107
** = P≤0.01 NS = Non significant
94
4.4.29. Relative cell injury (%) under normal conditions
Analysis of variance (Table 39) for relative cell injury under normal conditions
indicated that the item ‘a’ was highly significant which is responsible for the presence of
additive genetic effects. Highly significant item ‘b’ indicating the presence of general
dominance effects for this trait. The component ‘b1’ was found to be significant
indicating the important role of directional dominance for the control of this trait. The
‘b2’ component which gives information about asymmetrical distribution of genes was
found to be significant, thus indicating the presence of asymmetrical distribution of genes
among the parents. The ‘b3’ component which was found to be significant indicated the
importance of specific gene effects in the inheritance of the character. Maternal effects
‘c’ and reciprocal effects‘d’ were found to be non-significant. Hence, their retesting is
useless. So, their previous significance of ‘a’ and ‘b’ stood valid.
4.4.30. Relative cell injury (%) under water stress conditions
Analysis of variance (Table 40) for relative cell injury (%) under water stress
conditions indicated that the item ‘a’ was highly significant which indicated the presence
of additive genetic effects. The item ‘b’ was found to be non-significant, indicating the
absence of dominance effects for the control of this trait. The ‘b1’ component was found
to be significant thus suggesting the important role of directional dominance for the
control of this trait. The ‘b2’ component which gives information about asymmetrical
distribution of genes was found to be non-significant, thus indicating the absence of
asymmetrical distribution of genes among the parents. The ‘b3’ component was found to
be non-significant showing absence of dominance deviation unique to F1. Maternal
effects ‘c’ and reciprocal effects ‘d’ were found to be non-significant for the genotypes
and the character under study, hence retesting of ‘a’ and ‘b’ against ‘c’ and ‘d’ was
useless and the previous significance of ‘a’ and ‘b’ stood valid.
95
Table 39: Mean squares of components of variation of relative cell injury (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 6022.85**
b general dominance effects 15 43.37*
b1 directional dominance effects 1 6.49*
b2 effects due to unequal distribution of dominance
5 38.84*
b3 effects due to dominance deviation unique to F1s
9 49.99*
c maternal effects 5 14.62NS
d non-maternal reciprocal differences 10 24.25NS
Blocks 2 35.08
B×a 10 5.61
B×b 30 14.48
B×b1 2 0.31
B×b2 10 7.77
B×b3 18 19.78
B×c 10 16.38
B×d 20 21.89
Block interaction 70 15.60
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
96
Table 40: Mean squares of components of variation of relative cell injury (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components of variation df Mean
squares Retesting against
c d a additive effects 5 6283.52**
b general dominance effects 15 31.73NS
b1 directional dominance effects 1 47.57*
b2 effects due to unequal distribution of dominance
5 79.15NS
b3 effects due to dominance deviation unique to F1s
9 3.62NS
c maternal effects 5 4.33NS
d non-maternal reciprocal differences 10 44.23NS
Blocks 2 17.23
B×a 10 15.40
B×b 30 31.21
B×b1 2 5.97
B×b2 10 31.62
B×b3 18 33.78
B×c 10 8.84
B×d 20 35.22
Block interaction 70 26.90
Total 107
* = P≤0.05 ** = P≤0.01 NS = Non significant
97
4.5. Results of Genetic Components and Graphical Presentation
4.5.1. Plant height under normal conditions
Genetic components of variation given in Table 41 showed that significant value
of ‘D’ for plant height under normal conditions indicated the importance of additive
genetic effects in controlling plant height. Significant H components (H1 and H2)
revealed the importance of dominant variation.
The extent of H1 and H2 was less than ‘D’ indicating that genes showing
dominance effects were less important than additive genes for plant height. A significant
and positive value of ‘F’ indicated the important role of positive dominant genes. The
negative value of h^2 was noted. It showed less effect of dominant genes towards the
parents. The degree of dominance was less than one, suggesting the presence of partial
dominance in F1 hybrids which was supported by the regression slope in Fig. 7. The
estimate of narrow sense heritability was 96% which is high and trait is more heritable
and selection of this trait is effective. Influence of environment is less if heritability is
high. Fig. 7 showed that PB-899 and MNH-789 had maximum number of dominant
genes and CRIS-466 followed by CIM-506 had maximum recessive genes for plant
height under normal conditions.
4.5.2. Plant height under water stress conditions
The estimation of genetic components of variation given in Table 42 for plant
height under water stress conditions showed that value of ‘D’ was positive and significant
that indicated the importance of additive genetic effects in controlling plant height under
water stress conditions.
Significant H components (H1 and H2) indicated the importance of dominant
variation. The components H1 and H2 were less than ‘D’ indicating that genes showing
dominance effects were less important than additive genes. Unequal magnitude of H1 and
H2 revealed that unequal dominant gene distribution in the parents. Significant and
positive value of ‘F’ indicated the important role of positive dominant genes. The non-
significant value of h^2 indicated unimportant effect of heterozygous loci for this trait.
Component ‘E’ was significant indicating that environment play significant role in the
expression of this trait. The degree of dominance was less than one, suggesting the
additive gene action with partial dominance for this trait under water stress conditions.
98
Table 41: Estimates of components of variation for plant height under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 64.76 ±0.48
H1 = dominance variance 3.12 ± 1.22
H2 = proportion of positive and negative genes in the parents 2.97 ± 1.09
F = Relative frequency of dominant and recessive alleles in the parents 7.02 ± 1.18
h^2 = dominance effect (over all loci in heterozygous phase) -0.08 ± 0.74
E = environmental variance 0.23 ± 0.18
√H1/D = mean degree of dominance 0.22
Heritability (n.s) 0.96
Fig. 7 : Wr/Vr graph for plant height under normal conditions.
99
Table 42: Estimates of components of variation for plant height under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 38.86±0.59
H1 = dominance variance 4.94 ± 1.50
H2 = proportion of positive and negative genes in the parents 3.53 ± 1.34
F = Relative frequency of dominant and recessive alleles in the parents 5.78 ± 1.45
h^2 = dominance effect (over all loci in heterozygous phase) 0.71 ± 0.90
E = environmental variance 0.26 ± 0.22
√H1/D = mean degree of dominance 0.35
Heritability (n.s) 0.93
Fig. 8: Wr/Vr graph for plant height under water stress conditions.
100
which was supported by the regression slope in Fig. 8. The estimate of narrow sense
heritability was 93%. Fig. 8 showed that MNH-789 and FH-113 had maximum number
of dominant genes and CIM-506 followed by FH-901 had maximum recessive genes for
plant height under water stress conditions.
4.5.3. Monopodial branches under normal conditions
Genetic components of variation given in Table 43 revealed that significant value
of ‘D’ for monopodial branches under normal conditions confirmed the importance of
additive genetic effects in controlling the monopodial branches.
The extent of H1 and H2 was less than ‘D’ indicating that genes showing
dominance effects were less important than additive genes for monopodial branches.
Positive value of ‘F’ indicated the important role of positive dominant genes. The non-
significant value of h^2 indicated unimportant effect of heterozygous loci for this trait.
‘E’ component was significant indicating that environment played significant role in the
expression of this trait. The degree of dominance was less than one, suggesting the
additive gene action with partial dominance for this trait under normal conditions which
was supported by the regression slope (Fig. 9). The estimate of narrow sense heritability
was 60%. The graphical presentation indicated that CRIS-466 and MNH-789 possessed
the maximum dominant genes and PB-899 possessed the maximum recessive genes for
monopodial branches under normal conditions.
4.5.4. Monopodial branches under water stress conditions
The estimation of genetic components of variation given in Table 44 showed that
value of ‘D’ was positive and significant which indicated the importance of additive
genetic effects in controlling monopodial branches under water stress conditions.
The extent of H1 and H2 was less than ‘D’ indicating that genes showing dominance
effects were less important than additive genes for monopodial branches under water
stress conditions. Negative value of ‘F’ indicated the unimportant role of dominant genes
or more contribution of recessive genes for this trait. Non-significant value of h^2
indicated unimportant effect of heterozygous loci for this trait. Component ‘E’ was
significant, that showed important effects of environment for this trait. The estimate of
narrow sense heritability was 74%. The degree of dominance was
101
Table 43: Estimates of components of variation for number of monopodial branches under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.14 ±0.02
H1 = dominance variance 0.03 ± 0.04
H2 = proportion of positive and negative genes in the parents 0.03± 0.04
F = Relative frequency of dominant and recessive alleles in the parents 0.02 ± 0.04
h^2 = dominance effect (over all loci in heterozygous phase) 0.005 ± 0.03
E = environmental variance 0.03 ± 0.01
√H1/D = mean degree of dominance 0.49
Heritability (n.s) 0.60
Fig. 9: Wr/Vr graph for monopodial branches per plant under normal conditions.
102
Table 44: Estimates of components of variation for monopodial per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.051±0.008
H1 = dominance variance 0.04 ± 0.02
H2 = proportion of positive and negative genes in the parents 0.02 ± 0.01
F = Relative frequency of dominant and recessive alleles in the parents -0.05 ± 0.02
h^2 = dominance effect (over all loci in heterozygous phase) 0.006 ± 0.01
E = environmental variance 0.01 ± 0.003
√H1/D = mean degree of dominance 0.95
Heritability (n.s) 0.74
Fig. 10: Wr/Vr graph for monopodial branches per plant under water stress
conditions.
103
less than one, but near to one (0.95), so suggesting additive gene action with partial
dominance for monopodial branches under water stress conditions which was supported
by graphical presentation in Fig. 10. The graphical presentation showed that FH-113 and
FH-901 had maximum number of dominant genes as near to origin and CIM-506
followed by PB-899 had maximum recessive genes as which were away from the origin.
4.5.5. Sympodial branches under normal conditions
Genetic components of variation given in Table 45 showed that significant value
of ‘D’ confirmed the importance of additive genetic effects in controlling the sympodial
branches under normal conditions. The extent of H1 and H2 was less than ‘D’ indicating
that genes showing dominance effects were less important than additive genes for
sympodial branches under normal conditions. Unequal magnitude of H1 and H2 showed
that unequal dominant gene distribution in the parents. Significant and positive value of
‘F’ indicated the important role of positive dominant genes. Negative value of h^2
indicated less effect of dominant genes towards the parents. ‘E’ component was
significant showing important effects of environment for this trait. The degree of
dominance was less than one, suggesting the additive gene action with partial dominance
for this trait under normal conditions which was supported by the regression slope (Fig.
11). The estimate of narrow sense heritability was 87%. The graphical presentation
showed that FH-113 and FH-901 possessed the maximum number of dominant genes due
to near to origin and MNH-789 followed by PB-899 possessed the maximum number of
recessive genes being away from the origin.
4.5.6. Sympodial branches under water stress conditions
The estimation of genetic components of variation given in Table 46 revealed that
significant and higher value of ‘D’ confirmed the importance of additive genetic effects
in controlling sympodial branches under water stress conditions.
The extent of H1 and H2 was less than ‘D’ indicating that genes showing dominance
effects were less important than additive genes for sympodial branches under water stress
conditions. Significant H components (H1 and H2) indicated the importance of dominant
variation. Unequal magnitude of H1 and H2 showed that unequal dominant gene
distribution in the parents. Significant and positive value of ‘F’ indicated the important
role of positive dominant genes. Non-significant value of h^2 indicated unimportant
104
Table 45: Estimates of components of variation for number of sympodial branches under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 4.77 ±0.11
H1 = dominance variance 0.52 ± 0.28
H2 = proportion of positive and negative genes in the parents 0.38± 0.25
F = Relative frequency of dominant and recessive alleles in the parents 0.55 ± 0.27
h^2 = dominance effect (over all loci in heterozygous phase) -0.12 ± 0.17
E = environmental variance 0.21 ± 0.04
√H1/D = mean degree of dominance 0.33
Heritability (n.s) 0.87
Fig. 11: Wr/Vr graph for sympodial branches per plant under normal conditions.
105
Table 46: Estimates of components of variation for sympodial branches per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 4.45± 0.05
H1 = dominance variance 0.61 ± 0.13
H2 = proportion of positive and negative genes in the parents 0.50 ± 0.12
F = Relative frequency of dominant and recessive alleles in the parents 1.38 ± 0.13
h^2 = dominance effect (over all loci in heterozygous phase) 0.04 ± 0.08
E = environmental variance 0.12 ± 0.02
√H1/D = mean degree of dominance 0.37
Heritability (n.s) 0.86
Fig. 12: Wr/Vr graph for sympodial branches per plant under water stress
conditions.
106
effect of heterozygous loci for this trait. Component ‘E’ was significant, indicating that
environment played significant role in the expression of this trait. The degree of
dominance was less than one, suggesting the additive gene action with partial dominance
for this trait under water stress conditions which was supported by the regression line in
Fig. 12 .The estimates of narrow sense heritability was 86%. Fig. 12 showed that CRIS-
466 and FH-901 had maximum number of dominant genes and MNH-789 followed by
PB-899 had maximum recessive genes for sympodial branches under water stress
conditions.
4.5.7. Number of bolls per plant under normal conditions
Genetic components of variation given in Table 47 showed that higher value of
‘D’ confirmed the importance of additive genetic effects in controlling the number of
bolls per plant under normal conditions. The extent of H1 and H2 was less than ‘D’
indicating that genes showing dominance effects were less important than additive genes
for number of bolls per plant under normal conditions. Unequal magnitude of H1 and H2
revealed the unequal dominant gene distribution in the parents. Negative value of ‘F’
indicated the unimportant role of dominant genes or more contribution of recessive genes
for this trait. Non-significant value of h^2 indicated unimportant effect of heterozygous
loci for this trait. ‘E’ component was significant that showed important effects of
environment for this trait. The degree of dominance was less than one (0.17). So, additive
effects with partial dominance for number of bolls per plant under normal conditions
which was supported by the graphical presentation in Fig. 13 which showed that MNH-
789 possessed the maximum number of dominant genes near to origin and CIM-506
possessed the maximum number of recessive genes as away from the origin.
The estimate of narrow sense heritability was 97% which was high and influence
of environment was less. If heritabilityns is high, the selection of this trait is effective and
trait is more heritable.
4.5.8. Number of bolls per plant under water stress conditions
The estimation of genetic components of variation given in Table 48 revealed that higher
value of ‘D’ showed the importance of additive genetic effects. ‘D’ is greater than H1 and
H2 then it revealed that additive gene effects were more important in the inheritance of
the character. The extent of H1 and H2 was less than ‘D’ indicating that genes showing
107
dominance effects were less important than additive genes. Unequal magnitude of H1 and
H2 revealed that unequal dominant gene distribution was in the parents. Significant and
positive value of ‘F’ indicated the important role of positive dominant genes. Negative
value of h^2 indicated the less effect of dominant genes towards the parents. ‘E’
component was significant which showed that important effects of environment for this
trait. The degree of dominance was less than one, suggesting the additive gene action
with partial dominance for this trait under water stress conditions which was supported
by regression slope in Fig. 14 .The estimates of narrow sense heritability was 95% which
was high then influence of environment was less thus selection of trait was effective.
Graphical presentation showed that MNH-789 and CRIS-466 had maximum number of
dominant genes and FH-113 followed by PB-899 had maximum number of recessive
genes as away from the origin for number of bolls per plant under water stress conditions.
108
Table 47: Estimates of components of variation for number of bolls per plant under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 29.47 ±0.14
H1 = dominance variance 0.88 ± 0.35
H2 = proportion of positive and negative genes in the parents 0.79± 0.31
F = Relative frequency of dominant and recessive alleles in the parents -1.30 ± 0.33
h^2 = dominance effect (over all loci in heterozygous phase) 0.03 ± 0.21
E = environmental variance 0.15 ± 0.05
√H1/D = mean degree of dominance 0.17
Heritability (n.s) 0.97
Fig. 13: Wr/Vr graph for No. of bolls per plant under normal conditions.
109
Table 48: Estimates of components of variation for number of bolls per plant under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 27.85± 0.14
H1 = dominance variance 2.00 ± 0.36
H2 = proportion of positive and negative genes in the parents 1.89 ± 0.32
F = Relative frequency of dominant and recessive alleles in the parents 1.67 ± 0.35
h^2 = dominance effect (over all loci in heterozygous phase) -0.06 ± 0.21
E = environmental variance 0.12 ± 0.05
√H1/D = mean degree of dominance 0.26
Heritability (n.s) 0.95
Fig. 14: Wr/Vr graph for No. of bolls per plant under water stress conditions.
110
4.5.9. Boll weight under normal conditions
Components of variation given in Table 49 showed that ‘D’ was significant and
higher value of ‘D’ confirmed the importance of additive genetic effects. Significant H
components (H1 and H2) indicated the importance of dominant variation. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Unequal magnitude of H1 and H2 revealed that unequal
dominant gene distribution was in the parents. Significant and positive value of ‘F’
indicated the important role of positive dominant genes. Non-significant value of h^2
indicated unimportant effect of heterozygous loci for this trait. ‘E’ component was
significant that showed important effects of environment for this trait. The degree of
dominance was less than one (0.34). So, additive effects with partial dominance for boll
weight under normal conditions which was supported by the graphical presentation (Fig.
15) and showed that PB-899 possessed the maximum number of dominant genes and
MNH-789 followed by FH-901 possessed the maximum number of recessive genes as
away from the origin. The estimate of narrow sense heritability was 92% for boll weight
under normal conditions which was high so, selection of this trait was effective.
4.5.10. Boll weight under water stress conditions
Components of variation given in Table 50 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. Significant H components (H1 and
H2) indicated the importance of dominant variation. The extent of H1 and H2 was less
than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Unequal magnitude of H1 and H2 showed that unequal dominant gene
distribution was in the parents. Significant and positive value of ‘F’ indicated the
important role of positive dominant genes. Negative value of h^2 indicated the less effect
of dominant genes towards the parents. ‘E’ component was significant which showed that
environment played significant role in the expression of this trait. The degree of
dominance was less than one. So, additive effects were with partial dominance for boll
weight under water stress conditions which was supported by regression slope in Fig. 16.
The estimate of narrow sense heritability was 95% which is high then influence of
environment is less thus the selection of trait is effective. Graphical presentation showed
that PB-899 and CIM-506 had maximum dominant genes as near to origin and MNH-789
111
Table 49: Estimates of components of variation for boll weight under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.17 ±0.002
H1 = dominance variance 0.02 ± 0.01
H2 = proportion of positive and negative genes in the parents 0.01± 0.004
F = Relative frequency of dominant and recessive alleles in the parents 0.05 ± 0.01
h^2 = dominance effect (over all loci in heterozygous phase) 0.001 ± 0.003
E = environmental variance 0.002 ± 0.001
√H1/D = mean degree of dominance 0.34
Heritability (n.s) 0.92
Fig. 15: Wr/Vr graph for bolls weight under normal conditions.
112
Table 50: Estimates of components of variation for boll weight under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.12± 0.0009
H1 = dominance variance 0.004 ± 0.002
H2 = proportion of positive and negative genes in the parents 0.005 ± 0.002
F = Relative frequency of dominant and recessive alleles in the parents 0.01 ± 0.002
h^2 = dominance effect (over all loci in heterozygous phase) -0.000048 ± 0.001
E = environmental variance 0.001 ± 0.0003
√H1/D = mean degree of dominance 0.19
Heritability (n.s) 0.95
Fig. 16: Wr/Vr graph for bolls weight under water stress conditions.
113
followed by FH-901 had maximum recessive genes as away from the origin for boll
weight under water stress conditions.
4.5.11. Yield under normal conditions
The estimation of genetic components of variation given in Table 51 showed that
‘D’ was significant and higher value of ‘D’ confirmed the importance of additive genetic
effects. Significant H components (H1 and H2) indicated the importance of dominant
variation. The extent of H1 and H2 was less than ‘D’ indicating that genes showing
dominance effects were less important than additive genes. Unequal magnitude of H1 and
H2 revealed that unequal dominant gene distribution was in the parents. Negative value of
‘F’ indicated the unimportant role of dominant genes or more contribution of recessive
genes for this trait. Non-significant value of h^2 indicated unimportant effect of
heterozygous loci for this trait. ‘E’ component was non-significant indicating negligible
effects of environment in determination of this trait. The degree of dominance was less
than one (0.13). So, additive gene action with partial dominance was there for yield under
normal conditions which was supported by regression slope (Fig. 17). The estimate of
narrow sense heritability was 99% which was high, so influence environment was less
and selection of trait was effective. Fig. 17 showed that MNH-789 and FH-113 possessed
the maximum number of dominant genes as near to the origin and CIM-506 followed by
FH-901 possessed the maximum number of recessive genes as away from the origin for
yield under normal conditions.
4.5.12. Yield under water stress conditions
Components of variation given in Table 52 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. H1 and H2 are non-significant so,
additive genes were more important in this trait than non-additive genes. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Unequal magnitude of H1 and H2 showed that unequal
dominant gene distribution was in the parents. Negative value of ‘F’ indicated the
unimportant role of dominant genes or more contribution of recessive genes for this trait.
Positive value of h^2 indicated that dominance effect of genes is considerable and
towards the parents. ‘E’ component was non-significant indicating negligible effects of
environment in determination of this trait. The degree of dominance was less than one
114
Table 51: Estimates of components of variation for yield under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 208.57 ±0.65
H1 = dominance variance 3.49 ± 1.66
H2 = proportion of positive and negative genes in the parents 2.87± 1.48
F = Relative frequency of dominant and recessive alleles in the parents -18.65 ± 1.59
h^2 = dominance effect (over all loci in heterozygous phase) 0.42 ± 0.99
E = environmental variance 0.42 ± 0.25
√H1/D = mean degree of dominance 0.13
Heritability (n.s) 0.99
Fig. 17: Wr/Vr graph for yield under normal conditions.
115
Table 52: Estimates of components of variation for yield under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 89.66 ± 2.25
H1 = dominance variance 10.88 ± 5.73
H2 = proportion of positive and negative genes in the parents 8.52 ± 5.12
F = Relative frequency of dominant and recessive alleles in the parents -12.78 ± 5.51
h^2 = dominance effect (over all loci in heterozygous phase) 3.52 ± 3.44
E = environmental variance 0.23 ± 0.86
√H1/D = mean degree of dominance 0.23
Heritability (n.s) 0.97
Fig. 18: Wr/Vr graph for yield under water stress conditions.
116
(0.23). So, additive gene action with partial dominance was there for yield under water
stress conditions which was supported by the regression slope in Fig. 18. The estimate of
narrow sense heritability was 97% which was high so influence of environment was less.
Fig. 18 showed that FH-113 and MNH-789 had maximum number of dominant genes and
FH-901 followed by CIM-506 had maximum recessive genes as away from the origin for
yield under water stress conditions.
4.5.13. Staple length under normal conditions
The estimation of genetic components of variation given in Table 53 showed that
‘D’ was significant and higher value of ‘D’ confirmed the importance of additive genetic
effects. The extent of H1 and H2 was less than ‘D’ indicating that genes showing
dominance effects were less important than additive genes. Unequal magnitude of H1 and
H2 showed that unequal dominant gene distribution was in the parents. Positive value of
‘F’ indicated the important role of positive dominant genes or more number of dominant
genes than recessive genes. Non-significant value of h^2 indicated unimportant effect of
heterozygous loci for this trait. ‘E’ component was significant that showed important
effects of environment for this trait. The degree of dominance was less than one. So,
additive gene action with partial dominance was there which was supported by graphical
presentation in Fig. 19. The estimate of narrow sense heritability was 47%. Fig. 19
showed that CRIS-466 and CIM-506 had the maximum number of dominant genes as
near to the origin and MNH-789 followed by FH-113 had the maximum number of
recessive genes as away from the origin for staple length under normal conditions.
4.5.14. Staple length under water stress conditions
Components of variation given in Table 54 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. Significant H components (H1 and
H2) indicated the importance of dominant variation. The extent of H1 and H2 was less
than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Unequal magnitude of H1 and H2 revealed that unequal dominant gene
distribution was in the parents. Positive value of ‘F’ indicated the important role of
positive dominant genes or more number of dominant genes than recessive genes.
Significant value of h^2 component indicated the important effects of heterozygous loci
among parents. ‘E’ component was significant and showed important effects of
117
Table 53: Estimates of components of variation for staple length under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 1.26 ±0.13
H1 = dominance variance 0.83 ± 0.32
H2 = proportion of positive and negative genes in the parents 0.49 ± 0.29
F = Relative frequency of dominant and recessive alleles in the parents 0.98 ± 0.31
h^2 = dominance effect (over all loci in heterozygous phase) 0.01 ± 0.19
E = environmental variance 0.21 ± 0.05
√H1/D = mean degree of dominance 0.81
Heritability (n.s) 0.47
Fig. 19: Wr/Vr graph for staple length under normal conditions.
118
Table 54: Estimates of components of variation for staple length under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.99 ± 0.06
H1 = dominance variance 0.56 ± 0.15
H2 = proportion of positive and negative genes in the parents 0.49 ± 0.14
F = Relative frequency of dominant and recessive alleles in the parents 0.44 ± 0.15
h^2 = dominance effect (over all loci in heterozygous phase) 0.34 ± 0.09
E = environmental variance 0.11 ± 0.02
√H1/D = mean degree of dominance 0.75
Heritability (n.s) 0.56
Fig. 20: Wr/Vr graph for staple length under water stress conditions.
119
environment for this trait. The degree of dominance was less than one. So, additive gene
action with partial dominance was there which was supported by graphical presentation
in Fig. 20. The estimate of narrow sense heritability was 56%. Fig. 20 showed that CIM-
506 and FH-901 had maximum number of dominant genes as near to origin and PB-899
followed by MNH-789 had maximum number of recessive genes as away from the origin
for staple length under water stress conditions.
4.5.15. Staple fineness under normal conditions
Components of variation given in Table 55 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. Significant H components (H1 and
H2) indicated the importance of dominant variation. The extent of H1 and H2 was less
than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Unequal magnitude of H1 and H2 revealed that unequal dominant gene
distribution was in the parents. Positive value of ‘F’ indicated that more number of
dominant genes than recessive genes. Significant value of h^2 component indicated the
important effects of heterozygous loci among the parents. ‘E’ component was significant
which showed that environment played significant role in the expression of this trait. The
degree of dominance was less than one. So, additive gene action with partial dominance
was there which was supported by regression slope in Fig. 21. The estimate of narrow
sense heritability was 76%. Fig. 21 showed that PB-899 and FH-113 had the maximum
number of dominant genes as near to the origin and FH-901 and CRIS-466 had the
maximum number of recessive genes as away from the origin for staple fineness under
normal conditions.
4.5.16. Staple fineness under water stress conditions
Components of variation given in Table 56 revealed that ‘D’ was significant and
indicated the importance of additive genetic effects. Significant H components (H1 and
H2) indicated the importance of dominant variation. The extent of H1 and H2 was less
than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Unequal magnitude of H1 and H2 revealed that unequal dominant gene
distribution was in the parents. Positive value of ‘F’ indicated that more number of
dominant genes than recessive genes. Significant value of h^2 component indicated the
important effects of heterozygous loci among parents. ‘E’ component was significant
120
Table 55: Estimates of components of variation for staple fineness under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.04 ±0.001
H1 = dominance variance 0.01 ± 0.003
H2 = proportion of positive and negative genes in the parents 0.01 ± 0.003
F = Relative frequency of dominant and recessive alleles in the parents 0.01 ± 0.003
h^2 = dominance effect (over all loci in heterozygous phase) 0.01 ± 0.002
E = environmental variance 0.003 ± 0.001
√H1/D = mean degree of dominance 0.43
Heritability (n.s) 0.76
Fig. 21: Wr/Vr graph for staple fineness under normal conditions.
121
Table 56: Estimates of components of variation for staple fineness under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.035 ± 0.0008
H1 = dominance variance 0.011 ± 0.002
H2 = proportion of positive and negative genes in the parents 0.01 ± 0.002
F = Relative frequency of dominant and recessive alleles in the parents 0.003 ± 0.002
h^2 = dominance effect (over all loci in heterozygous phase) 0.006 ± 0.001
E = environmental variance 0.001 ± 0.0003
√H1/D = mean degree of dominance 0.57
Heritability (n.s) 0.79
Fig. 22: Wr/Vr graph for staple fineness under water stress conditions.
122
and showed important effects of environment for this trait. The degree of dominance was
less than one. So, additive gene action with partial dominance was there which was
supported by regression slope in Fig. 22. The estimate of narrow sense heritability was
79%. Fig. 22 showed that PB-899 and CIM-506 had maximum number of dominant
genes as near to origin and CRIS-466 followed by FH-901 had maximum number of
recessive genes as away from the origin for staple fineness under water stress conditions.
4.5.17. Staple strength under normal conditions
Components of variation given in Table 57 revealed that ‘D’ was significant and
higher value of ‘D’ confirmed the importance of additive genetic effects. Significant H
components (H1 and H2) indicated the importance of dominant variation. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Positive value of ‘F’ indicated the important role of
positive dominant genes or more number of dominant genes than recessive genes.
Significant value of h^2 component indicated the important effects of heterozygous loci
among the parents. ‘E’ component was significant which showed the important effects of
environment for this trait. The degree of dominance was less than one. So, additive gene
action with partial dominance was there which was supported by regression slope in Fig.
23. The estimate of narrow sense heritability was 83%. Fig. 23 showed that CRIS-466
and CIM-506 had the maximum number of dominant genes and FH-901 followed by PB-
899 had the maximum number of recessive genes as away from the origin for staple
strength under normal conditions.
4.5.18. Staple strength under water stress conditions
The estimation of genetic components of variation given in Table 58 showed that
‘D’ was significant and indicated the importance of additive genetic effects. Significant H
components (H1 and H2) indicated the importance of dominant variation. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Unequal magnitude of H1 and H2 revealed that unequal
dominant gene distribution was in the parents. Positive value of ‘F’ indicated more
number of dominant genes than recessive genes. Significant value of h^2 component
indicated the important effects of heterozygous loci among parents. ‘E’ component was
significant and showed that environment played significant role in the expression of
123
Table 57: Estimates of components of variation for staple strength under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 3.32 ±0.08
H1 = dominance variance 0.39 ± 0.19
H2 = proportion of positive and negative genes in the parents 0.39 ± 0.17
F = Relative frequency of dominant and recessive alleles in the parents 0.20 ± 0.18
h^2 = dominance effect (over all loci in heterozygous phase) 0.32 ± 0.11
E = environmental variance 0.22 ± 0.03
√H1/D = mean degree of dominance 0.34
Heritability (n.s) 0.83
Fig. 23: Wr/Vr graph for staple strength under normal conditions.
124
Table 58: Estimates of components of variation for staple strength under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 1 2.59 ± 0.09
H1 = dominance variance 0.59 ± 0.24
H2 = proportion of positive and negative genes in the parents 0.47 ± 0.21
F = Relative frequency of dominant and recessive alleles in the parents 0.39 ± 0.23
h^2 = dominance effect (over all loci in heterozygous phase) 0.88 ± 0.14
E = environmental variance 0.28 ± 0.03
√H1/D = mean degree of dominance 0.47
Heritability (n.s) 0.74
Fig. 24: Wr/Vr graph for staple strength under water stress conditions.
125
this trait. The degree of dominance was less than one. So, additive gene action with
partial dominance was there which was supported by regression slope in Fig. 24. The
estimate of narrow sense heritability was 74%. Fig. 24 showed that CRIS-466 and FH-
113 had maximum number of dominant genes as near to origin and MNH-789 followed
by FH-901 had maximum number of recessive genes as away from the origin for staple
strength under water stress conditions.
4.5.19. GOT (%) under normal conditions
The estimation of genetic components of variation given in Table 59 showed that
‘D’ was significant indicating the presence of additive effects in controlling GOT under
normal conditions. Higher value of ‘D’ confirmed the importance of additive genetic
effects. The components H1 and H2 were less than ‘D’ indicating that gene showing
dominance effects for GOT were less important than additive genes. Unequal magnitude
of H1 and H2 showed that different dominant gene distribution was in the parents.
Positive value of ‘F’ indicated that more number of dominant genes than recessive genes.
Significant value of h^2 indicated the important effects of heterozygous loci among the
parents. ‘E’ component was significant which showed the important effects of
environment for this trait. The degree of dominance was less than one. So, additive gene
action with partial dominance was there which was supported by regression slope in Fig.
25. The estimate of narrow sense heritability was 70%. Fig. 25 showed that FH-901 and
FH-113 had the maximum number of dominant genes as near to origin and MNH-789
followed by PB-899 had the maximum number of recessive genes as away from the
origin for GOT under normal conditions.
4.5.20. GOT (%) under water stress conditions
Components of variation given in Table 60 revealed that ‘D’ was significant indicating
the presence of additive effects in controlling GOT under water stress conditions.
Higher value of ‘D’ confirmed the importance of additive genetic effects. Significant H
components (H1 and H2) indicated the importance of dominant variation. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Unequal magnitude of H1 and H2 showed that unequal
dominant gene distribution was in the parents. Positive value of ‘F’ indicated more
126
Table 59: Estimates of components of variation for GOT (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 2.17 ±0.07
H1 = dominance variance 0.79 ± 0.18
H2 = proportion of positive and negative genes in the parents 0.77 ± 0.16
F = Relative frequency of dominant and recessive alleles in the parents 0.51 ± 0.17
h^2 = dominance effect (over all loci in heterozygous phase) 0.93 ± 0.11
E = environmental variance 0.16 ± 0.03
√H1/D = mean degree of dominance 0.60
Heritability (n.s) 0.70
Fig. 25: Wr/Vr graph for GOT % under normal conditions.
127
Table 60: Estimates of components of variation for GOT (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 1.38 ± 0.10
H1 = dominance variance 0.92 ± 0.25
H2 = proportion of positive and negative genes in the parents 0.88 ± 0.22
F = Relative frequency of dominant and recessive alleles in the parents 0.44 ± 0.24
h^2 = dominance effect (over all loci in heterozygous phase) 1.72 ± 0.15
E = environmental variance 0.18 ± 0.03
√H1/D = mean degree of dominance 0.81
Heritability (n.s) 0.54
Fig. 26: Wr/Vr graph for GOT % under water stress conditions.
128
number of dominant genes than recessive genes. Significant value of h^2 component
indicated the important effects of heterozygous loci among parents. ‘E’ component was
significant and showed that environment played significant role in the expression of this
trait. The degree of dominance was less than one. So, additive gene action with partial
dominance was there which was supported by regression slope in Fig. 26. The estimate of
narrow sense heritability was 54%. If heritability was high then influence of environment
was less. Fig. 26 showed that FH-901 and MNH-789 had maximum number of dominant
genes as near to origin and FH-113 followed by PB-899 had maximum number of
recessive genes as away from the origin for GOT under water stress conditions.
4.5.21. Seed index under normal conditions
The estimation of genetic components of variation given in Table 61 for seed
index under normal conditions showed that value of ‘D’ was positive and significant
indicating the presence of additive genetic effects in controlling seed index under normal
conditions. The extent of H1 and H2 was less than ‘D’ indicating that gene showing
dominance effects were less important than additive genes. Negative value of ‘F’ showed
unimportant role of dominant genes or more contribution of recessive genes for this trait.
Positive value of h^2 indicated that dominance effect of gene is considerable and towards
the parents. ‘E’ component was significant which showed the important effects of
environment for this trait. The degree of dominance was less than one. So, additive gene
action with partial dominance was there which was supported by regression slope in Fig.
27. The estimate of narrow sense heritability was 92%. Fig. 27 showed that CRIS-466
and CIM-506 had the maximum number of dominant genes as near to origin and MNH-
789 followed by FH-113 had the maximum number of recessive genes as away from the
origin for seed index under normal conditions.
4.5.22. Seed index under water stress conditions
The estimation of genetic components of variation given in Table 62 showed that
‘D’ was positive and significant indicating the presence of additive effects in controlling
seed index under water stress conditions. The components H1 and H2 were more than ‘D’
indicating the importance of dominance effects for this trait. Unequal magnitude of H1
and H2 revealed that unequal dominant gene distribution was in the parents. Negative
value of ‘F’ indicated the unimportant role of dominant genes or more contribution of
129
Table 61: Estimates of components of variation for seed index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.15±0.002
H1 = dominance variance 0.01 ± 0.005
H2 = proportion of positive and negative genes in the parents 0.01 ± 0.006
F = Relative frequency of dominant and recessive alleles in the parents -0.001 ± 0.005
h^2 = dominance effect (over all loci in heterozygous phase) 0.004 ± 0.003
E = environmental variance 0.003 ± 0.0009
√H1/D = mean degree of dominance 0.29
Heritability (n.s) 0.92
Fig. 27: Wr/Vr graph for seed index under normal conditions.
130
Table 62: Estimates of components of variation for seed index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.14 ± 0.003
H1 = dominance variance 0.017 ± 0.008
H2 = proportion of positive and negative genes in the parents 0.016 ± 0.007
F = Relative frequency of dominant and recessive alleles in the parents -0.003± 0.008
h^2 = dominance effect (over all loci in heterozygous phase) 0.01 ± 0.005
E = environmental variance 0.001 ± 0.001
√H1/D = mean degree of dominance 0.35
Heritability (n.s) 0.93
Fig. 28: Wr/Vr graph for seed index under water stress conditions.
131
recessive genes for this trait. Significant value of h^2 component indicated the important
effects of heterozygous loci among parents. ‘E’ component was non-significant
indicating negligible effects of environment in determination of this trait. The degree of
dominance was less than one. So, additive gene action with partial dominance was there
for seed index under water stress conditions which was supported by regression slope in
Fig. 28. The estimate of narrow sense heritability was 93%. Fig. 28 showed that CRIS-
466 and CIM-506 had maximum number of dominant genes as near to origin and FH-113
followed by MNH-789 had maximum number of recessive genes as away from the origin
for seed index under water stress conditions.
4.5.23. Lint index under normal conditions
The estimation of genetic components of variation given in Table 63 showed that
‘D’ was positive and significant indicated the importance of additive genetic effects. H1
and H2 were non-significant. So, additive genes were more important in this trait than
non-additive genes. The extent of H1 and H2 was less than ‘D’ indicating that genes
showing dominance effects were less important than additive genes. Unequal magnitude
of H1 and H2 showed that unequal dominant gene distribution was in the parents. Positive
value of ‘F’ indicated the important role of positive dominant genes or more number of
dominant genes than recessive genes. Significant value of h^2 indicated the important
effects of heterozygous loci among parents. ‘E’ component was significant which showed
the important effects of environment for this trait. The degree of dominance was less than
one. So, additive gene action with partial dominance was there which was supported by
graphical presentation in Fig. 29. The estimate of narrow sense heritability was 87%. Fig.
29 showed that PB-899 and CRIS-466 had the maximum number of dominant genes as
near to origin and MNH-789 followed by FH-113 had the maximum number of recessive
genes as away from the origin for lint index under normal conditions.
4.5.24. Lint index under water stress conditions
Components of variation given in Table 64 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. Significant H components (H1 and
H2) indicated the importance of dominant variation. The extent of H1 and H2 was less
than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Positive value of ‘F’ indicated the important role of positive dominant
132
Table 63: Estimates of components of variation for lint index under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.05±0.001
H1 = dominance variance 0.0005 ±0.002
H2 = proportion of positive and negative genes in the parents 0.001 ± 0.002
F = Relative frequency of dominant and recessive alleles in the parents 0.003 ± 0.002
h^2 = dominance effect (over all loci in heterozygous phase) 0.008 ± 0.001
E = environmental variance 0.003 ± 0.0004
√H1/D = mean degree of dominance 0.09
Heritability (n.s) 0.87
Fig. 29: Wr/Vr graph for lint index under normal conditions.
133
Table 64: Estimates of components of variation for lint index under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 0.06 ± 0.001
H1 = dominance variance 0.01 ± 0.003
H2 = proportion of positive and negative genes in the parents 0.01 ± 0.002
F = Relative frequency of dominant and recessive alleles in the parents 0.008± 0.003
h^2 = dominance effect (over all loci in heterozygous phase) 0.03 ± 0.002
E = environmental variance 0.002 ± 0.0005
√H1/D = mean degree of dominance 0.41
Heritability (n.s) 0.84
Fig. 30: Wr/Vr graph for lint index under water stress conditions.
134
genes or more number of dominant genes than recessive genes. Significant value of h^2
component indicated the important effects of heterozygous loci among parents. ‘E’
component was significant which showed the important effects of environment for this
trait. The degree of dominance was less than one. So, additive gene action with partial
dominance was there which was supported by regression slope in Fig. 30. The estimate of
narrow sense heritability was 84%. Fig. 30 showed that PB-899 and CRIS-466 had the
maximum number of dominant genes as near to origin and FH-113 followed by MNH-
789 had the maximum number of recessive genes as away from the origin for lint index
under water stress conditions.
4.5.25. Relative water content (%) under normal conditions
The estimation of genetic components of variation given in Table 65 showed that
‘D’ was significant indicated the presence of additive genetic effects in controlling
relative water content under normal conditions. Higher value of ‘D’ confirmed the
importance of additive genetic effects. H1 and H2 are non-significant. So, additive genes
were more important in this trait than non-additive genes. The extent of H1 and H2 was
less than ‘D’ indicating that genes showing dominance effects were less important than
additive genes. Unequal magnitude of H1 and H2 revealed that unequal dominant gene
distribution was in the parents. Positive value of ‘F’ indicated the important role of
positive dominant genes or more number of dominant genes than recessive genes.
Significant value of h^2 component indicated the important effects of heterozygous loci
among parents. ‘E’ component was significant which showed the important effects of
environment for this trait. The degree of dominance was less than one. So, additive gene
action with partial dominance was there which was supported by regression slope shown
in Fig. 31. The estimate of narrow sense heritability was 94%. Fig. 31 showed that PB-
899 and CRIS-466 had the maximum number of dominant genes as near to origin and
FH-113 followed by MNH-789 had the maximum number of recessive genes as away
from the origin for relative water content under normal conditions.
4.5.26. Relative water content (%) under water stress conditions
Components of variation given in Table 66 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. The extent of H1 and H2 was less
than ‘D’ indicated that genes showing dominance effects were less important than
135
Table 65: Estimates of components of variation for relative water content (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 26.33±0.36
H1 = dominance variance 1.66 ± 0.91
H2 = proportion of positive and negative genes in the parents 1.25 ± 0.81
F = Relative frequency of dominant and recessive alleles in the parents 4.20± 0.87
h^2 = dominance effect (over all loci in heterozygous phase) 1.15 ± 0.54
E = environmental variance 0.31 ± 0.13
√H1/D = mean degree of dominance 0.25
Heritability (n.s) 0.94 _____________________________________________________________
Fig. 31: Wr/Vr graph for relative water content under normal conditions.
136
Table 66: Estimates of components of variation for relative water content under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 26.55 ± 0.47
H1 = dominance variance 2.48 ± 1.19
H2 = proportion of positive and negative genes in the parents 2.05 ± 1.07
F = Relative frequency of dominant and recessive alleles in the parents 5.35 ± 1.15
h^2 = dominance effect (over all loci in heterozygous phase) 0.99 ± 0.72
E = environmental variance 0.28 ± 0.18
√H1/D = mean degree of dominance 0.30
Heritability (n.s) 0.93
Fig. 32: Wr/Vr graph for relative water content under water stress conditions.
137
additive genes. Unequal magnitude of H1 and H2 showed the unequal dominant gene
distribution was in the parents. Positive value of ‘F’ indicated more number of dominant
genes than recessive genes. Positive value of h^2 indicated that dominance effect of gene
is considerable towards the parents. Non-significant value of ‘E’ component indicated the
negligible effects of environment in determination of this trait. The degree of dominance
was less than one. So, additive gene action with partial dominance was there which was
supported by regression slope in Fig. 32. The estimate of narrow sense heritability was
93%. Fig. 32 showed that PB-899 and CIM-506 had the maximum number of dominant
genes as near to origin and MNH-789 had the maximum number of recessive genes as
away from the origin for relative water content under water stress conditions.
4.5.27. Leaf temperature under normal conditions
Components of variation given in Table 67 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. H1 and H2 are non-significant so
additive genes were more important in this trait than non-additive genes. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
important than additive genes. Unequal magnitude of H1 and H2 revealed that unequal
dominant gene distribution was in the parents. Positive value of ‘F’ indicated the more
number of dominant genes than recessive genes. Negative value of h^2 component
showed less effect of dominant genes towards the parents. ‘E’ component was significant
which showed the important effects of environment for this trait. The degree of
dominance was less than one. So, additive gene action with partial dominance was there
which was supported by regression slope shown in Fig. 33. The estimate of narrow sense
heritability was 78%. Fig. 33 showed that FH-901 and CIM-506 had the maximum
number of dominant genes as near to origin and FH-113 had the maximum number of
recessive genes as away from the origin for leaf temperature under normal conditions.
4.5.28. Leaf temperature under water stress conditions
Components of variation given in Table 68 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. H1 and H2 are non-significant so
additive genes were more important in this trait than non-additive genes. The extent of H1
and H2 was less than ‘D’ indicating that genes showing dominance effects were less
138
Table 67: Estimates of components of variation for leaf temperature under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 3.96±0.24
H1 = dominance variance 0.99 ± 0.61
H2 = proportion of positive and negative genes in the parents 0.90 ± 0.54
F = Relative frequency of dominant and recessive alleles in the parents 0.78 ± 0.59
h^2 = dominance effect (over all loci in heterozygous phase) -0.07 ± 0.36
E = environmental variance 0.23 ± 0.09
√H1/D = mean degree of dominance 0.50
Heritability (n.s) 0.78
Fig. 33: Wr/Vr graph for leaf temperature under normal conditions.
139
Table 68: Estimates of components of variation for leaf temperature under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 4.46 ± 0.29
H1 = dominance variance 1.11 ± 0.74
H2 = proportion of positive and negative genes in the parents 1.09 ± 0.66
F = Relative frequency of dominant and recessive alleles in the parents 0.76 ± 0.71
h^2 = dominance effect (over all loci in heterozygous phase) 0.02 ± 0.44
E = environmental variance 0.23 ± 0.11
√H1/D = mean degree of dominance 0.50
Heritability (n.s) 0.78
Fig. 34: Wr/Vr graph for leaf temperature under water stress conditions.
140
important than additive genes. Unequal magnitude of H1 and H2 showed the unequal
dominant gene distribution was in the parents. Positive value of ‘F’ indicated more
number of dominant genes than recessive genes. Non-significant value of h^2 component
indicated unimportant effect of heterozygous loci for this trait. ‘E’ component was
significant which showed the important effects of environment for this trait. The degree
of dominance was less than one. So, additive gene action with partial dominance was
there which was supported by regression slope in Fig. 34. The estimate of narrow sense
heritability was 78%. Fig. 34 showed that CIM-506 and FH-901 had the maximum
number of dominant genes as near to origin and FH-113 followed by PB-899 had the
maximum number of recessive genes as away from the origin for leaf temperature under
water stress conditions.
4.5.29. Relative cell injury (%) under normal conditions
Genetic components of variation given in Table 69 showed that ‘D’ was
significant indicating the presence of additive genetic effects in controlling relative cell
injury under normal conditions. Higher value of ‘D’ confirmed the importance of additive
genetic effects. The components of H1 and H2 were less than ‘D’ indicating that genes
showing dominance effects were less important than additive genes. Unequal magnitude
of H1 and H2 showed that unequal dominant gene distribution was in the parents.
Negative value of ‘F’ indicated unimportant role of dominant genes or more contribution
of recessive genes for this trait. Negative value of h^2 component indicated less effect of
dominant genes towards the parents. ‘E’ component was significant which showed that
environment played significant role in the expression of this trait. The degree of
dominance was less than one. So, additive gene action with partial dominance was there
which was supported by regression slope shown in Fig. 35. The estimate of narrow sense
heritability was 97%. Fig. 35 showed that CRIS-466 and MNH-789 had the maximum
number of dominant genes as near to origin and FH-113 followed by PB-899 had the
maximum number of recessive genes as away from the origin for relative cell injury
under normal conditions.
4.5.30. Relative cell injury (%) under water stress conditions
Components of variation given in Table 70 showed that ‘D’ was significant and
indicated the importance of additive genetic effects. Higher value of ‘D’ confirmed the
141
importance of additive genetic effects. The components of H1 and H2 were less than ‘D’
indicating that genes showing dominance effects were less important than additive genes.
Unequal magnitude of H1 and H2 showed that unequal dominant gene distribution was in
the parents. Positive value of ‘F’ indicated more number of dominant genes than
recessive genes. Positive value of h^2 component indicated that dominance effect of
genes is considerable towards the parents. ‘E’ component was significant which showed
that environment played significant role in the expression of this trait. The degree of
dominance was less than one. So, additive gene action with partial dominance was there
which was supported by regression slope shown in Fig. 36. The estimate of narrow sense
heritability was 97%. Fig. 36 showed that CRIS-466 and PB-899 had the maximum
number of dominant genes as near to origin and FH-901 followed by MNH-789 had the
maximum number of recessive genes as away from the origin for relative cell injury
under water stress conditions.
142
Table 69: Estimates of components of variation for relative cell injury (%) under normal conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 615.43±2.22
H1 = dominance variance 23.19 ± 5.65
H2 = proportion of positive and negative genes in the parents 18.15 ± 5.04
F = Relative frequency of dominant and recessive alleles in the parents -46.93 ± 5.43
h^2 = dominance effect (over all loci in heterozygous phase) -1.78 ± 3.39
E = environmental variance 5.38 ± 0.84
√H1/D = mean degree of dominance 0.19
Heritability (n.s) 0.97
Fig. 35: Wr/Vr graph for relative cell injury %under normal conditions.
143
Table 70: Estimates of components of variation for relative cell injury (%) under water stress conditions in a 6×6 diallel cross of Gossypium hirsutum L.
Components Estimates
D = additive variance 750.24 ± 1.88
H1 = dominance variance 15.06 ± 4.78
H2 = proportion of positive and negative genes in the parents 3.39 ± 4.27
F = Relative frequency of dominant and recessive alleles in the parents 66.70 ± 4.60
h^2 = dominance effect (over all loci in heterozygous phase) 3.87 ± 2.87
E = environmental variance 8.87 ± 0.71
√H1/D = mean degree of dominance 0.14
Heritability (n.s) 0.97
Fig. 36: Wr/Vr graph for relative cell injury % under water stress conditions.
144
4.6. Combining Ability Analysis
4.6.1. Plant height under normal conditions
Table 71, presented analysis of variance for the characters under normal
conditions, showed the significant mean squares due to both general combining ability
(GCA) and specific combining ability (SCA) showing the significance of both additive
and non-additive genetic effects. Presence of non-significant reciprocal difference was
also indicated. Table 73, revealed greater GCA variance (σ2g) as compared to SCA
variance (σ2s) by the estimation of components of variance showing the preponderance of
additive genetic effects under normal condition for plant height. Estimation of combining
ability effects (Table 75) showed that three of the parents showed positive general
combining ability effects which was maximum for PB-899 (3.59) and considered as best
general combiner for this trait. CIM-506 showed the maximum negative (-6.46) GCA
effects and was the poorest general combiner.
As regarding crosses, positive SCA effects were revealed by ten cross
combination, out of these, most useful combinations were PB-899 x CIM-506 and PB-
899 x CRIS-466 having value (1.04) while five crosses have negative value. The least
useful combination was PB-899 x MNH-789 which has maximum negative value (-2.06).
Among reciprocal crosses, nine crosses had negative reciprocal effects and six crosses
had positive reciprocal effects. Cross with maximum reciprocal effects was shown (0.83)
by CIM-506 x MNH-789 and four cross combinations had lowest negative value (-0.33)
under normal conditions for plant height.
4.6.2. Plant height under water stress conditions
Table 72, presented the analysis of variance for plant height under water stress
condition and revealed significant mean squares due to both general combining ability
(GCA) and specific combining ability (SCA) effects for plant height, showed the additive
and non-additive (dominant) genetic effects. Presence of non-significant reciprocal
effects was also indicated. Table 74, indicated that GCA variance (σ2g) was greater than
SCA variance (σ2s) showing that additive genetic effects were more important for plant
height under water stress condition.
Three genotypes (Table 76) showed the positive general combining ability effects
and three genotypes showed the negative general combining ability effects. PB-899
145
proved to be the best general combiner for plant height having value of 3.37 under water
stress condition. CIM-506 proved to be the poorest general combiner for plant height
having value of -4.87 under water stress condition.
As regarding crosses, positive specific combining ability effects were shown by
eight cross combinations and seven cross combinations were shown by negative specific
combining ability effects. Out of these, most useful combination was MNH-789 x CIM-
506 having value of 1.42.The least useful combination was CIM-506 x FH-901 having a
negative value of -1.93.
Among reciprocal crosses, nine crosses had positive value and six crosses had
negative value. Cross with maximum value was MNH-789 x FH-113 having value of
0.83 and six crosses with the lowest value of -0.16 for plant height under water stress
condition.
146
Table 71: Mean squares attributed to general and specific combining abilities and reciprocal effects of six cotton genotypes under normal conditions
Traits GCA (df=5)
SCA ( df=15)
Reciprocal (df=15)
Error
Plant height 173.89 1.72 0.24 0.23
No of monopodial branches 0.42 0.04 0.06 0.03
No of sympodial branches 13.28 0.40 0.20 0.20
Number of bolls per plant 92.73 0.55 0.18 0.16
Boll weight 0.38 0.008 0.003 0.001
Yield 684.94 1.86 0.40 0.43
Staple length 2.06 0.46 0.24 0.21
Staple fineness 0.09 0.006 0.004 0.002
Staple strength 9.62 0.41 0.52 0.22
GOT (%) 5.18 0.54 0.20 0.16
Seed index 0.45 0.009 0.004 0.003
Lint index 0.17 0.004 0.004 0.003
Relative water content 67.90 0.94 0.34 0.32
Leaf temperature 10.02 0.68 0.31 0.24
Relative Cell injury% 2007.61 14.45 7.01 5.20
147
Table 72: Mean squares attributed to general and specific combining abilities and reciprocal effects of six cotton genotypes under water stress conditions
Traits GCA (df=5)
SCA ( df=15)
Reciprocal (df=15)
Error
Plant height 103.73 2.03 0.27 0.25
No of monopodial branches 0.39 0.03 0.01 0.01
No of sympodial branches 9.63 0.38 0.17 0.13
Number of bolls per plant 78.98 1.07 0.56 0.12
Boll weight 0.34 0.003 0.003 0.001
Yield 614.68 4.50 2.82 0.24
Staple length 1.99 0.37 0.18 0.11
Staple fineness 0.096 0.007 0.001 0.0009
Staple strength 7.24 0.52 0.31 0.29
GOT (%) 3.16 0.63 0.21 0.19
Seed index 0.44 0.009 0.002 0.001
Lint index 0.18 0.008 0.003 0.002
Relative water content 65.18 1.32 0.37 0.28
Leaf temperature 11.42 0.78 0.24 0.23
Relative Cell injury% 2094.51 10.58 10.31 8.97
148
Table 73: Estimates of variance components relative to general and specific combining ability and reciprocal effects of six cotton genotypes under normal conditions
Traits σ2g σ2s σ2r σ2e
Plant height 14.35 0.87 0.006 0.23
No of monopodial branches 0.03 0.008 0.01 0.03
No of sympodial branches 1.07 0.12 0.0004 0.20
Number of bolls per plant 7.68 0.23 0.01 0.16
Boll weight 0.031 0.004 0.0007 0.001
Yield 56.84 0.83 -0.01 0.43
Staple length 0.13 0.14 0.014 0.21
Staple fineness 0.007 0.002 0.001 0.002
Staple strength 0.77 0.11 0.14 0.22
GOT (%) 0.39 0.22 0.02 0.16
Seed index 0.036 0.003 0.0004 0.003
Lint index 0.01 0.0005 0.0009 0.003
Relative water content 5.58 0.36 0.01 0.32
Leaf temperature 0.78 0.26 0.04 0.24
Relative Cell injury% 166.12 5.37 0.91 5.20
149
Table 74: Estimates of variance components relative to general and specific combining ability and reciprocal effects of six cotton genotypes under water stress conditions
Traits σ2g σ2s σ2r σ2e
Plant height 8.48 1.03 0.009 0.25
No of monopodial branches 0.03 0.007 0.001 0.014
No of sympodial branches 0.77 0.15 0.019 0.13
Number of bolls per plant 6.50 0.55 0.22 0.12
Boll weight 0.03 0.001 0.001 0.001
Yield 50.85 2.47 1.29 0.24
Staple length 0.14 0.15 0.03 0.11
Staple fineness 0.007 0.003 0.0004 0.0009
Staple strength 0.56 0.13 0.01 0.29
GOT (%) 0.21 0.25 0.009 0.19
Seed index 0.04 0.004 0.0004 0.001
Lint index 0.014 0.003 0.0003 0.002
Relative water content 5.32 0.60 0.04 0.28
Leaf temperature 0.89 0.32 0.001 0.23
Relative Cell injury% 173.66 0.93 0.67 8.97
150
Table 75: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for plant height under normal conditions
Table 76: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for plant height under water stress conditions
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 1.981481 -0.56481 0.046296 0.990741 -0.84259 0.490741
PB-899 -0.16667 3.592593 -2.06481 1.04σ296 0.212963 1.046296
MNH-789 -0.16667 0.166667 3.148148 0.657407 0.490741 0.324074
CIM-506 0.333333 -0.33333 0.833333 -6.46296 -0.23148 -1.56481
FH-901 -0.33333 -0.33333 -0.16667 -0.16667 -1.96296 0.268519
CRIS-466 0.333333 -0.16667 -0.33333 0.5 0.166667 -0.2963
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 1.101852 -1.12963 0.12037 0.787037 0.425926 -0.18519
PB-899 0.166667 3.37963 -1.15741 0.509259 0.148148 0.203704
MNH-789 0.833333 -0.16667 2.12963 1.425926 -0.60185 0.σ2037
CIM-506 -0.16667 -0.16667 0.5 -4.87037 -1.93519 -0.0463
FH-901 -0.16667 0.166667 0.166667 -0.16667 -1.50926 -0.57407
CRIS-466 0.5 -0.16667 0.666667 0.333333 0.166667 -0.23148
151
4.6.3. Monopodial branches per plant under normal conditions
Analysis of variance for monopodial branches per plant (Table 71) revealed the
significant mean squares for GCA and non-significant for SCA and reciprocal effects
showing the additive genetic effects.
Table 73, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s),
showing additive genetic effects for monopodial branches per plant under normal
conditions. Two genotypes (Table 77) revealed positive general combining ability effects
and four genotypes indicated negative general combining ability effects. The genotype
FH-113 is the best combiner with maximum value of 0.36 and FH-901 was the poorest
general combiner with highest negative value of -0.13.
As regarding crosses, positive SCA effects were displayed by nine cross
combinations, out of these, most useful combination was FH-113 x PB-899 having value
of 0.21. And six cross combinations have negative value, the poorest combination was
FH-113 x FH-901 having value of -0.31. Among reciprocal crosses, twelve cross
combinations had negative value and three cross combinations had positive value. Cross
with maximum reciprocal effects was FH-901 x FH-113, having value of 0.33. Two cross
combinations, FH-901 x PB-899 and CIM-506 x MNH-789, had the lowest negative
value of -0.25, under normal condition for monopodial branches per plant.
4.6.4. Monopodial branches under water stress conditions
Table 72, presented the analysis of variance for monopodial branches under water
stress condition and revealed significant mean squares due to both general combining
ability (GCA) and specific combining ability (SCA) effects for monopodial branches and
showed the additive and non-additive (dominant) genetic effects. Presence of non-
significant reciprocal effects was also indicated.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing the predominance of additive genetic effects for monopodial branches under
water stress condition.
One genotype (Table 78) showed the positive general combining ability effects.
And five genotypes showed the negative general combining ability effects. FH-113
proved to be the best general combiner for monopodial branches having value of 0.35
152
under water stress condition. CRIS-466 proved to be the poorest general combiner for
monopodial branches having value of -0.13 under water stress condition.
As regarding crosses, positive specific combining ability effects were shown by
six cross combinations. Out of these, most useful combination was FH-113 x CIM-506
having value of 0.19. Nine cross combinations were shown by the negative value. The
least useful combination was PB-899 x CIM-506 having value of -0.11.
Among reciprocal crosses, nine crosses had positive value and six crosses had
negative value. Cross with maximum value was FH-901 x MNH-789 having value of
0.16. And the cross with the lowest value of -0.16 was MNH-789 x FH-113 for
monopodial branches under water stress condition.
153
Table 77: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for monopodial branches per plant under normal conditions
Table 78: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for monopodial branches per plant under water stress conditions
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.365741 0.217593 0.009259 0.134259 -0.31019 -0.07407
PB-899 -0.16667 0.00463 -0.12963 -0.00463 0.134259 0.037037
MNH-789 -0.08333 -0.08333 -0.03704 0.12037 0.092593 0.078704
CIM-506 -0.16667 -0.16667 -0.25 -0.0787 0.134259 -0.12963
FH-901 0.333333 -0.25 -0.16667 0.166667 -0.13426 -0.07407
CRIS-466 0.083333 -0.16667 -0.16667 -0.08333 -0.08333 -0.12037
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.356481 0.12963 0.108796 0.19213 -0.04398 -0.07176
PB-899 0.083333 -0.00463 0.053241 -0.11343 -0.09954 -0.04398
MNH-789 -0.16667 -0.08333 -0.06713 -0.05093 0.046296 -0.02315
CIM-506 -0.08333 0.083333 0.083333 -0.06713 0.12963 -0.02315
FH-901 0 0.083333 0.166667 -0.08333 -0.08102 -0.00926
CRIS-466 0.083333 0.083333 -0.04167 0.041667 -0.04167 -0.13657
154
4.6.5. Sympodial branches per plant under normal conditions
Analysis of variance for sympodial branches per plant (Table 71) displayed
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) for sympodial branches revealed both additive and non-additive
(dominant) genetic effects. Presence of non-significant reciprocal effects was also
indicated. Table 73, indicated that GCA variance (σ2g) was greater than SCA variance
(σ2s) showing additive genetic effects for sympodial branches. Two genotypes (Table 79)
revealed positive general combining ability effects and four genotypes revealed negative
general combining ability effects. The genotype FH-113 is the best general combiner
having value of 1.74 and genotype MNH-789 was the poorest general combiner with
maximum negative value of -1.28 for sympodial branches per plant under normal
condition.
As regarding crosses, positive SCA effects were displayed by seven cross
combinations, out of these most useful combinations were PB-899 x CIM-506 and MNH-
789 x CRIS-466 having value of 0.50 and eight cross combinations had negative value.
The poorest combination was CIM-506 x CRIS-466 having maximum negative value of
-0.85.
Among reciprocal crosses eleven cross combinations had negative value and four
cross combinations had positive value. Two crosses with maximum positive value (0.33)
were FH-901 x CIM-506 and CRIS-466 x FH-901 and two crosses with the lowest
negative value (-0.5) were MNH-789 x FH-113 and FH-901 x PB-899 under normal
conditions for sympodial branches per plant.
4.6.6. Sympodial branches under water stress conditions
Table 72, presented the analysis of variance for sympodial branches under water
stress condition and showed significant mean squares due to both general combining
ability (GCA) and specific combining ability (SCA) effects for sympodial branches,
showed the additive and non-additive (dominant) genetic effects. Presence of non-
significant reciprocal effects were also indicated.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing that additive genetic effects were more important for sympodial branches under
water stress condition.
155
Three genotypes (Table 80) showed the positive general combining ability
effects and three genotypes showed the negative general combining ability effects. FH-
113 proved to be the best general combiner for sympodial branches having value of 1.36
under water stress condition. MNH-789 proved to be the poorest general combiner for
sympodial branches having value of -1.21 under water stress condition.
As regarding crosses, positive specific combining ability effects were revealed by
four cross combinations. Out of these most useful combination was FH-113 x MNH-789
having value of 0.60. Eleven cross combinations showed negative specific combining
ability effects. The least useful combination was PB-899 x CIM-506 having value of
-0.66.
Among reciprocal crosses, eight crosses had positive value and seven crosses had
negative value. Crosses with maximum value were FH-901 x FH-113 and CRIS-466 x
FH-901 having value of 0.5 and cross with least value was CIM-506 x PB-899 having
value of -0.5 for sympodial branches under water stress condition.
156
Table 79: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for sympodial branches per plant under normal conditions.
Table 80: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for sympodial branches per plant under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 1.74537 0.171296 0.393519 -0.30093 -0.43981 0.476852
PB-899 -0.33333 0.606481 -0.30093 0.50463 -0.30093 -0.21759
MNH-789 -0.5 -0.33333 -1.28241 0.226852 -0.0787 0.50463
CIM-506 -0.33333 -0.33333 -0.16667 -0.4213 -0.10648 -0.85648
FH-901 0.166667 -0.5 -0.16667 0.333333 -0.11574 0.337963
CRIS-466 -0.33333 -0.16667 -0.33333 0.166667 0.333333 -0.53241
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 1.365741 -0.14352 0.606481 -0.50463 -0.31019 -0.00463
PB-899 0.333333 0.532407 -0.56019 0.662037 -0.31019 -0.1713
MNH-789 0.166667 0.166667 -1.21759 0.078704 -0.06019 0.578704
CIM-506 0.166667 -0.5 -0.16667 -0.10648 0.162037 -0.53241
FH-901 0.5 0.333333 -0.16667 -0.16667 0.032407 -0.00463
CRIS-466 -0.16667 0.166667 -0.16667 -0.16667 0.5 -0.60648
157
4.6.7. No. of bolls per plant under normal conditions
Analysis of variance for bolls per plant (Table 71) revealed the significant mean
squares due to both general combining ability (GCA) and specific combining ability
(SCA) for bolls per plant revealed both additive and non-additive (dominant) genetic
effects. Presence of non-significant reciprocal effects was also indicated. Table 73,
indicated that GCA variance (σ2g) was greater than SCA variance (σ2s) showing the
preponderance of additive genetic effects under normal conditions for bolls per plant.
Two genotypes (Table 81) showed the positive general combining ability effects and four
genotypes revealed the negative general combining ability effects. FH-113 genotype is
the best general combiner having value of 4.32 for bolls per plant. FH-901 is the poorest
general combiner having value of -2.64 for this trait under normal condition.
As regarding crosses, positive specific combining ability effects were revealed by
eight cross combinations, out of these most useful combination was FH-113 x PB-899
having value of 0.72 while seven cross combinations have negative value. The least
useful combination was (-0.93) PB-899 x MNH-789.
Among reciprocal crosses, nine crosses had positive value and six crosses had
negative reciprocal effects. Crosses with maximum positive value were CIM-506 x FH-
113 and FH-901 x FH-113 having value 0.5. And these cross combinations were with
lowest negative value of -0.33 under normal condition for bolls per plant.
4.6.8. No. of bolls per plant under water stress conditions
Table 72, presented the analysis of variance for bolls per plant under water stress
condition and showed significant mean squares due to both general combining ability
(GCA) and specific combining ability (SCA) effects for bolls per plant and showed the
additive and non-additive (dominant) genetic effects.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing the predominance of additive genetic effects for bolls per plant under water
stress condition. Two genotypes (Table 82) showed the positive general combining
ability effects and four genotypes showed the negative general combining ability effects.
FH-113 proved to be the best general combiner for bolls per plant having value of 3.85
158
under water stress condition. Genotype CIM-506 proved to be the poorest general
combiner for bolls per plant under water stress condition having value of -2.45.
As regarding crosses, positive specific combining ability effects were revealed by
eight cross combinations. Out of these, most useful combination was FH-113 x PB-899
having value of 1.39. Seven cross combinations showed negative specific combining
ability effects. The least useful combination was PB-899 x MNH-789 having value of
-1.43.
Among reciprocal crosses, twelve crosses had positive value and three crosses had
negative value. Crosses with maximum value were PB-899 x FH-113 and FH-901 x
CIM-506 having value of 1.16 and three crosses were with the least value of -0.5 for bolls
per plant under water stress condition.
159
Table 81: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls per plant under normal conditions.
Table 82: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls per plant under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 4.328704 0.726852 -0.30093 0.00463 -0.16204 -0.13426
PB-899 0.333333 2.300926 -0.93981 0.699074 -0.13426 0.560185
MNH-789 0.333333 0.333333 -0.33796 0.337963 0.50463 0.032407
CIM-506 0.5 -0.16667 0.166667 -2.47685 -0.18981 -0.8287
FH-901 0.5 0.166667 0.166667 -0.33333 -2.64352 0.337963
CRIS-466 -0.33333 -0.33333 -0.16667 0.166667 -0.16667 -1.1713
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 3.851852 1.398148 -0.2963 -0.32407 -0.62963 -0.85185
PB-899 1.166667 2.324074 -1.43519 0.203704 0.231481 0.175926
MNH-789 0.166667 0.5 -0.31481 1.009259 0.37037 0.314815
CIM-506 0 0 0.166667 -2.4537 -0.32407 -0.5463
FH-901 0.5 0.166667 0.333333 1.166667 -2.31481 0.648148
CRIS-466 -0.5 0 -0.5 -0.5 0.166667 -1.09259
160
4.6.9. Boll weight under normal conditions
Table 71, analysis of variance for boll weight displayed significant mean squares
due to both general combining ability (GCA) and specific combining ability (SCA) for
boll weight revealed both additive and non-additive (dominant) genetic effects. Table 73,
indicated that GCA variance (σ2g) was greater than SCA variance (σ2s) showing additive
genetic effects for boll weight under normal conditions. Four genotypes (Table 83)
showed the negative general combining ability effects. Two genotypes showed the
positive general combining ability effects. MNH-789 is the best general combiner for
boll weight having value of 0.23. CRIS-466 is the poorest general combiner for boll
weight having value of -0.2 under normal condition.
As regarding crosses, positive specific combining ability effects were revealed by
seven cross combinations. Out of these most useful combination was FH-113 x PB-899
having a value of 0.11. While eight cross combinations revealed negative value. The best
useful combination was FH-113 x FH-901 having value of -2.20.
Among reciprocal crosses, nine crosses had positive value and six crosses had
negative reciprocal effects. Cross with maximum value of 0.1 was CRIS-466 x PB-899
and cross with lowest value of -0.05 was PB-899 x FH-113 under normal condition for
boll weight.
4.6.10. Boll weight under water stress conditions
Table 72, presented the analysis of variance for boll weight under water stress
condition and showed significant mean squares due to both general combining ability
(GCA) and specific combining ability (SCA) effects for boll weight, which showed the
additive and non-additive (dominant) genetic effects.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing that additive genetic effects were more important for boll weight under water
stress condition. Two genotypes (Table 84) showed the positive general combining
ability effects and four genotypes showed the negative general combining ability effects.
MNH-789 proved to be the best general combiner for boll weight under water stress
condition having value of 0.22. CRIS-466 proved to be the poorest general combiner for
boll weight having value of -0.18 under water stress condition.
161
As regarding crosses, positive specific combining ability effects were revealed by
six cross combinations. Out of these, most useful combination was MNH-789 x FH-901
having value of 0.07. Nine cross combinations were shown by negative specific
combining ability effects. The least useful combination was FH-113 x MNH-789 having
value of -0.09.
Among reciprocal crosses, nine crosses had negative value and six crosses had
positive value. Cross with maximum value was CRIS-466 x MNH-789 having value of
0.06 and cross with the lowest value was PB-899 x FH-113 having value of -0.06 for boll
weight under water stress condition.
162
Table 83: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls weight under normal conditions.
Table 84: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for bolls weight under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.07778 0.111111 -0.09444 -0.06111 -2.2E-19 0.008333
PB-899 -0.05 -0.00278 -0.06944 -0.01944 -0.09167 0.05
MNH-789 -0.01667 0.016667 0.236111 0.008333 0.069444 -0.02222
CIM-506 0 0.05 -0.01667 -0.14722 -0.03056 0.061111
FH-901 -0.03333 0.016667 -0.01667 -0.03333 0.191667 0.022222
CRIS-466 0.05 0.1 0 0.033333 0.033333 -0.2
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.08426 0.062037 -0.09074 -0.00185 0.023148 0.003704
PB-899 -0.06667 -0.00926 -0.01574 -0.01019 -0.06852 0.012037
MNH-789 -0.01667 0.033333 0.226852 -0.01296 0.078704 -0.00741
CIM-506 0.016667 -0.01667 -0.05 -0.1287 -0.03241 0.031481
FH-901 -0.05 -0.03333 0.05 -0.05 0.17963 -0.01019
CRIS-466 0.033333 -0.01667 0.066667 0.05 -0.01667 -0.18426
163
4.6.11. Yield under normal conditions
Table 71, analysis of variance for yield revealed significant mean squares due to
both general combining ability (GCA) and specific combining ability (SCA) effects for
yield revealed both additive and non-additive (dominant) genetic effects. Table 73,
indicated that GCA variance (σ2g) was greater than SCA variance (σ2s) showing additive
genetic effects for yield under normal condition. Three genotypes (Table 85) showed the
positive general combining ability effects and three genotypes revealed the negative
general combining ability effects. FH-113 proved to be the best general combiner for
yield having value of 9.53 under normal conditions. CRIS-466 proved to be the poorest
general combiner for yield having value of -7.93 under normal condition.
As regarding crosses, positive specific combining ability effects were revealed by
eight crosses. Out of these, most useful combination was FH-113 x CIM-506 having
value of 1.57, while seven cross combinations revealed negative value. The least useful
combination was FH-113 x MNH-789 having value of -1.37.
Among reciprocal crosses, four crosses had positive value and eleven crosses had
negative value. Cross with maximum value of 1.06, was PB-899 x FH-113 and cross with
lowest value of -0.64, was CIM-506 x FH-113 under normal condition for yield.
4.6.12. Yield under water stress conditions
Table 72, presented the analysis of variance for yield under water stress
conditions and showed significant mean squares due to both general combining ability
(GCA) and specific combining ability (SCA) for yield and showed the additive and non-
additive (dominant) genetic effects. Table 74, indicated that GCA variance (σ2g) was
greater than SCA variance (σ2s) showing the preponderance of additive genetic effects
for yield under water stress condition. Three genotypes (Table 86) showed the positive
general combining ability effects and three genotypes showed the negative general
combining ability effects. FH-113 proved to be the best general combiner for yield
having value of 9.47 under water stress conditions. CRIS-466 proved to be the poorest
general combiner for yield having value of -7.54 under water stress conditions.
As regarding crosses, nine cross combinations showed the positive specific
combining ability (SCA) effects. Out of these, most useful combination was FH-113 x
FH-901 having value of 3.85. Six cross combinations were shown by negative specific
164
combining ability effects. The least useful combination was CIM-506 x FH-901 having
value of -1.43.
Among reciprocal crosses, seven crosses had positive value and eight crosses had
negative value. Cross with maximum value was FH-901 x FH-113 having value of 4.29.
Cross with the least value of -0.47 was CRIS-466 x FH-113 for yield under water stress
conditions.
165
Table 85: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for yield under normal conditions.
Table 86: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for yield under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 9.533632 1.396777 -1.3752 1.570793 0.437663 -0.28167
PB-899 1.066574 6.687933 -0.87194 0.217005 -0.03063 0.314793
MNH-789 -0.58627 -0.11332 3.678103 1.1069 0.505705 0.326153
CIM-506 -0.6464 -0.19082 -0.06251 -5.17802 -0.99111 -1.05148
FH-901 -0.56802 -0.54183 -0.09796 0.389085 -6.78814 -0.05917
CRIS-466 -0.04908 -0.01642 0.172505 0.297249 -0.38239 -7.93351
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 9.479606 0.526419 -1.06178 1.438261 3.853659 -1.12252
PB-899 -0.13868 5.92137 -0.73933 0.200074 -0.69804 0.741222
MNH-789 -0.16476 0.577736 3.340947 0.825124 0.320021 0.534957
CIM-506 0.052438 0.66237 -0.1114 -5.14059 -1.43111 -0.60662
FH-901 4.293617 -0.32673 0.081091 1.058731 -6.05753 0.087085
CRIS-466 -0.47557 0.225125 -0.14748 -0.38103 -0.4173 -7.5438
166
4.6.13. Staple length under normal conditions
Table 71, analysis of variance for staple length displayed significant mean squares
due to both general combining ability (GCA) and specific combining ability (SCA)
effects for staple length and showed additive and non-additive (dominant) genetic effects.
Presence of non-significant reciprocal effect was also indicated. Table 73, indicated that
GCA variance (σ2g) was less than SCA variance (σ2s) showing non-additive genetic
effects for staple length under normal condition. Three genotypes (Table 87) revealed
positive general combining ability effects and three genotypes showed negative general
combining ability effects. PB-899 proved to be the best general combiner for staple
length having value of 0.62 under normal conditions. CRIS-466 proved to be the poorest
general combiner for staple length having value of -0.51.
In case of crosses, positive specific combining ability effects were shown by eight
cross combinations. Out of these, most useful combination was FH-113 x CRIS-466
having value of 0.69. And seven cross combinations revealed negative specific
combining ability effects. The least useful combination was PB-899 x CIM-506 having
value of -0.86.
Among reciprocal crosses, six crosses had positive value and nine crosses had
negative value. Cross with maximum value of 0.66 was CIM-506 x PB-899 and crosses
with the lowest value were CIM-506 x MNH-789, FH-901 x CIM-506 having value of
-0.5 under normal condition for staple length.
4.6.14. Staple length under water stress conditions
Table 72, presented the analysis of variance for staple length under water stress
conditions and showed the significant mean squares due to both general combining
ability (GCA) and specific combining ability (SCA) for staple length and showed the
additive and non-additive (dominant) genetic effects. Presence of non-significant
reciprocal effects was also indicated.
Table 74, indicated that SCA variance (σ2s) was greater than GCA variance (σ2g)
showing the non-additive genetic effects for staple length under water stress condition.
Three genotypes (Table 88) showed the positive general combining ability effects and
three genotypes showed the negative general combining ability effects. PB-899 proved to
be the best general combiner for staple length having value of 0.56 under water stress
167
conditions. Whereas FH-901 proved to be the poorest general combiner for staple length
having value of -0.47 under water stress conditions.
As regarding crosses, eight crosses showed the positive specific combining ability
effects. Out of these, most useful combination was FH-113 x PB-899 having value of
0.60. Seven crosses showed the negative specific combining ability (SCA) effects. The
least useful combination was PB-899 x CIM-506 having value of -0.75.
Among reciprocal crosses, ten crosses had negative value, and five crossed had
positive value. Cross with maximum value was CRIS-466 x FH-901 having value of 0.16
and cross with the lowest value of -0.66 was MNH-789 x FH-113 for staple length under
water stress conditions.
168
Table 87: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple length under normal conditions.
Table 88: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple length under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.223148 0.135185 -0.00093 -0.1287 0.176852 0.693519
PB-899 0.083333 0.628704 0.093519 -0.86759 -0.06204 -0.21204
MNH-789 -0.16667 0 -0.15185 0.07963 -0.28148 -0.76481
CIM-506 -0.33333 0.666667 -0.5 0.142593 0.257407 0.274074
FH-901 -0.16667 0.333333 -0.33333 -0.5 -0.32963 0.07963
CRIS-466 -0.16667 0.333333 -0.33333 -0.33333 0.333333 -0.51296
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.175 0.602778 -0.60556 -0.44444 0.144444 0.227778
PB-899 -0.41667 0.563889 0.005556 -0.75 -0.24444 -0.24444
MNH-789 -0.66667 -0.33333 -0.14444 0.291667 -0.28611 -0.28611
CIM-506 0.083333 -0.16667 -0.5 0.277778 0.208333 0.291667
FH-901 -0.25 -0.08333 0 0.083333 -0.47778 0.130556
CRIS-466 0.083333 -0.33333 -0.25 -0.25 0.166667 -0.39444
169
4.6.15. Staple fineness under normal conditions
Table 71, analysis of variance for staple fineness displayed significant mean
squares due to both general combining ability (GCA) effects and specific combining
ability (SCA) effects for staple fineness revealed both additive and non-additive genetic
effects. Presence of non-significant reciprocal effects was also indicated. Table 73,
indicated that GCA variance (σ2g) was greater than SCA variance (σ2s) showing the
preponderance of additive genetic effects under normal conditions for staple fineness.
Three genotypes (Table 89) showed the positive general combining ability effects and
three genotypes showed negative general combining ability effects. CIM-506 proved to
be the best general combiner for staple fineness under normal condition having value of
-0.10. FH-901 proved to be the poorest general combiner having value of 0.14 for staple
fineness under normal condition. Maximum negative value is desirable as compared to
maximum positive value.
As regarding crosses, positive specific combining ability effects were shown by
six crosses. Out of these, the least useful combination having value of 0.08 was FH-113 x
CIM-506. And nine cross combinations were shown by negative specific combining
ability effects. The most useful combination was PB-899 x FH-901 having value of -0.11.
Among reciprocal crosses, four crosses had positive value and eleven crosses had
negative value. Cross with maximum value of 0.08 was CRIS-466 x FH-901 and three
crosses with the lowest value of -0.06.
4.6.16. Staple fineness under water stress conditions
Table 72, presented the analysis of variance for staple fineness and displayed
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) for staple fineness revealed the additive and non-additive
(dominant) genetic effects. Table 74, indicated that GCA variance (σ2g) was greater than
SCA variance (σ2s) showing the preponderance of additive genetic effects under water
stress condition for staple fineness. Three genotypes (Table 90) showed the positive
general combining ability effects and three genotypes showed the negative general
combining ability effects. CIM-506 proved to be the best general combiner for staple
fineness under water stress condition having value of -0.10. FH-901 proved to be the
170
poorest general combiner for staple fineness under water stress condition having value of
0.14. Maximum negative value is desirable as compared to positive value.
As regarding crosses, positive specific combining ability effects were shown by
six crosses and negative specific combining ability effects were shown by nine crosses.
The most useful combination was PB-899 x FH-901 having value of -0.10. The least
useful combination was FH-113 x CIM-506 having value of 0.064.
Among reciprocal crosses, twelve crosses had negative value and three crosses
had positive value. Cross with maximum value of 0.03 was CRIS-466 x FH-901 and
cross with the lowest value of -0.06 was CIM-506 x MNH-789 for staple fineness under
water stress condition.
171
Table 89: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple fineness under normal conditions.
Table 90: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple fineness under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.07315 -0.01019 -0.0713 0.089815 -0.00463 -0.04907
PB-899 0.05 -0.03426 0.006481 0.017593 -0.11019 -0.00463
MNH-789 0.05 -0.06667 0.026852 -0.02685 0.012037 0.034259
CIM-506 -0.01667 -0.05 -0.03333 -0.10093 -0.04352 -0.03796
FH-901 -0.03333 -0.06667 -0.05 -0.03333 0.143519 0.034259
CRIS-466 0.016667 -0.03333 -0.06667 -0.03333 0.083333 0.037963
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.07315 0.000926 -0.06852 0.064815 0.050926 -0.06852
PB-899 -0.01667 -0.03704 -0.00463 0.062037 -0.10185 -0.03796
MNH-789 -0.01667 -0.01667 0.032407 -0.02407 -0.00463 0.059259
CIM-506 0.016667 -0.01667 -0.06667 -0.10093 -0.05463 -0.05741
FH-901 0.016667 -0.03333 -0.03333 -0.01667 0.146296 0.062037
CRIS-466 -0.01667 -0.05 -0.01667 -0.03333 0.033333 0.032407
172
4.6.17. Staple strength under normal conditions
Table 71, analysis of variance for staple strength showed significant mean squares
due to both general combining ability (GCA) and specific combining ability (SCA)
effects for staple strength showed the additive and non-additive genetic effects. Table 73,
indicated that GCA variance (σ2g) was greater than SCA variance (σ2s) showing the
preponderance of additive genetic effects under normal condition for staple strength. Two
genotypes (Table 91) showed the positive general combining ability effects and four
genotypes showed the negative general combining ability effects. CIM-506 proved to be
the best general combiner for staple strength under normal condition having value of
1.55. FH-901 proved to be the poorest general combiner for staple strength having value
of -1.13 under normal condition.
In case of crosses, positive specific combining ability effects were shown by five
crosses. Out of these, most useful combination having value of 0.63 was CIM-506 x FH-
901 and ten crosses displayed negative specific combining ability effects. The least useful
combination was PB-899 x FH-901 having value of -0.63.
Among reciprocal crosses, eight crosses had positive value and seven crosses had
negative value. Cross with maximum value of 0.83 was MNH-789 x PB-899. And cross
with the lowest value of -0.83 was FH-901 x MNH-789 for staple strength under normal
conditions.
4.6.18. Staple strength under water stress conditions
Table 72, presented the analysis of variance for staple strength and revealed the
significant mean squares for general combining ability (GCA) and non-significant for
specific combining ability (SCA) and reciprocal effects showing the additive genetic
effects.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing that additive genetic effects were more important for staple strength under water
stress condition. Three genotypes (Table 92) showed the positive general combining
ability effects and three genotypes showed negative general combining ability effects.
CIM-506 proved to be the best general combiner for staple strength having value of 1.28
under water stress condition while FH-901 proved to be the poorest general combiner for
staple strength having value of -1.01 under water stress condition.
173
In case of crosses, positive specific combining ability effects were shown by five
crosses. Out of these, most useful combination was FH-901 x CRIS-466 having value of
0.62. Ten crosses were shown by negative value. The least useful combination was CIM-
506 x CRIS-466 having value of -0.67.
Among reciprocal crosses, six crosses had positive value and nine crosses had
negative value. Crosses with maximum value of 0.5 were CRIS-466 x FH-113 and MNH-
789 x PB-899 and cross with the lowest value of -0.83 was FH-901 x MNH-789 for
staple strength under water stress condition.
174
Table 91: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple strength under normal conditions.
Table 92: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for staple strength under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.41667 -0.52778 0.361111 -0.08333 0.111111 -0.25
PB-899 0.666667 -0.16667 -0.05556 -0.16667 -0.63889 0.5
MNH-789 -0.66667 0.833333 0.277778 -0.11111 -0.41667 -0.44444
CIM-506 -0.5 -0.33333 0.5 1.555556 0.638889 -0.38889
FH-901 -0.33333 0.166667 -0.83333 -0.16667 -1.13889 0.472222
CRIS-466 0.333333 0.666667 -0.5 0.166667 0 -0.11111
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.40741 -0.03704 -0.2037 -0.59259 -0.28704 0.018519
PB-899 -0.33333 -0.26852 -0.17593 -0.23148 -0.42593 -0.28704
MNH-789 0.333333 0.5 0.231481 0.268519 -0.42593 0.37963
CIM-506 -0.33333 0.166667 -0.16667 1.287037 0.518519 -0.67593
FH-901 -0.33333 -0.33333 -0.83333 -0.16667 -1.01852 0.62963
CRIS-466 0.5 -0.66667 -0.16667 0.166667 0.166667 0.175926
175
4.6.19. GOT% under normal conditions
Table 71, analysis of variance for GOT showed significant mean squares due to
both general combining ability (GCA) and specific combining ability (SCA) effects for
GOT revealed the additive and non-additive (dominant) genetic effects. Presence of non-
significant reciprocal effects was also indicated. Table 73, indicated that GCA variance
(σ2g) was greater than SCA variance (σ2s) showing the preponderance of additive
genetic effects for GOT under normal conditions. Three genotypes (Table 93) showed the
positive general combining ability effects and three genotypes showed the negative
general combining ability effects. MNH-789 proved to be the best general combiner for
GOT under normal condition having value of 0.75 and CRIS-466 proved to be the
poorest general combiner for GOT having value of -1.16 under normal condition.
In case of crosses, positive specific combining ability effects were revealed by
seven cross combinations. Out of these, most useful cross combinations were FH-113 x
PB-899 and FH-901 x CRIS-466 having value of 0.49. And eight crosses displayed
negative specific combining ability effects. The least useful combination was FH-113 x
CIM-506 having value of -0.89.
Among reciprocal crosses, five crosses had positive value and ten crosses had
negative value. Two cross combinations, MNH-789 x PB-899 and CIM-506 x FH-113
had maximum value of 0.33 and three cross combinations had the lowest value of -0.5 for
GOT under normal conditions.
4.6.20. GOT % under water stress condition
Table 72, depicted the analysis of variance for GOT and showed the significant
mean squares due to both general combining ability (GCA) and specific combining
ability (SCA) for GOT which revealed the additive and non-additive (dominant) genetic
effects. Presence of non-significant reciprocal effects was also indicated. Table 74,
indicated that SCA variance (σ2s) was greater than GCA variance (σ2g) showing the non-
additive genetic effects for GOT under water stress condition. Five genotypes (Table 94)
showed positive general combining ability effects and one genotype showed the negative
general combining ability effects. MNH-789 proved to be the best general combiner for
GOT having value of 0.50 under water stress conditions. CRIS-466 proved to be the
poorest general combiner for GOT having value of -0.97 under water stress conditions.
176
In case of crosses, positive specific combining ability effects were shown by six
crosses, out of these, most useful combination was FH-113 x PB-899 having value of
0.46 under water stress conditions. Nine crosses were shown by negative specific
combining ability effects. The least useful combination was PB-899 x CIM-506 having
value of -0.80.
Among reciprocal crosses, twelve crosses had positive value and three crosses had
negative value. Four crosses had maximum value of 0.5 and two crosses FH-901 x FH-
113 and MNH-789 x PB-899, had the least value of -0.33.
177
Table 93: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for GOT (%) under normal conditions.
Table 94: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for GOT (%) under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -0.0787 0.49537 -0.19907 -0.89352 0.24537 -0.28241
PB-899 0.5 -0.10648 -0.1713 -0.36574 -0.56019 -0.4213
MNH-789 -0.16667 0.333333 0.75463 0.439815 -0.75463 0.050926
CIM-506 0.333333 0 -0.33333 0.449074 0.217593 0.189815
FH-901 0.166667 -0.5 -0.5 0.166667 0.143519 0.49537
CRIS-466 -0.16667 -0.33333 -0.33333 -0.16667 -0.16667 -1.16204
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.037037 0.462963 -0.00926 -0.63426 0.074074 -0.68981
PB-899 0.333333 0.037037 -0.00926 -0.80093 -0.59259 -0.35648
MNH-789 0.5 -0.33333 0.509259 -0.10648 -0.56481 0.337963
CIM-506 0.5 0.5 0.333333 0.300926 0.310185 0.296296
FH-901 -0.33333 -0.16667 0.333333 0 0.092593 0.25463
CRIS-466 0 0 0.5 0.083333 0 -0.97685
178
4.6.21. Seed index under normal conditions
Table 71, Analysis of variance for seed index revealed significant mean squares
due to both general combining ability (GCA) and specific combining ability (SCA)
effects for seed index which showed the additive and non-additive (dominant) genetic
effects. Presence of non-significant reciprocal effects was also shown. Table 73, indicated
that GCA variance (σ2g) was greater than SCA variance (σ2s) and revealed that additive
effects are more important for seed index under normal conditions. Three genotypes
(Table 95) showed the positive general combining ability effects and three genotypes
showed the negative general combining ability effects. FH-113 proved to be the best
general combiner having value of 0.16 for seed index under normal condition and CRIS-
466 proved to be the poorest general combiner having value of -0.35 for seed index under
normal conditions.
In case of crosses, positive specific combining ability effects were revealed by six
cross combinations. Out of these, most useful combination was FH-113 x PB-899 having
value of 0.12 and nine cross combinations revealed the negative specific combining
ability effects. The least useful combination was FH-113 x CIM-506 having value of
-0.11.
Among reciprocal crosses, ten crosses had positive effects and five crosses had
negative value. Cross combination MNH-789 x PB-899 had the maximum value of 4.34
and cross combination CRIS-466 x FH-113 had the lowest value of -0.1 for seed index
under normal conditions.
4.6.22. Seed index under water stress conditions
Table 72, presented the analysis of variance for seed index and revealed the
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) effects for seed index and displayed the additive and non-
additive (dominant) genetic effects. Presence of non-significant reciprocal effects was
also indicated. Table 74, indicated that GCA variance (σ2g) was greater than SCA
variance (σ2s) showing the preponderance of additive genetic effects under water stress
conditions for seed index.
Four genotypes (Table 96) showed the positive general combining ability effects
and two genotypes showed the negative general combining ability effects. FH-113 proved
179
to be the best general combiner for seed index having value of 0.14 under water stress
conditions. CRIS-466 proved to be the poorest general combiner for seed index having
value of -0.36 under water stress conditions.
As regarding crosses, positive specific combining ability effects were shown by
seven crosses. And eight crosses revealed the negative value. The most useful
combination was FH-113 x PB-899 having value of 0.12. The least useful combination
was FH-113 x CIM-506 having value of -0.12.
Among reciprocal crosses, nine crosses had positive value and six crosses had
negative value. Cross with maximum value of 0.05 was PB-899 x FH-113. Cross with the
lowest value of -0.12 was FH-113 x CIM-506 for seed index under water stress condition.
180
Table 95: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for seed index under normal conditions.
Table 96: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for seed index under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.168519 0.123148 0.009259 -0.11019 -0.01852 -0.0713
PB-899 0.05 -0.0037 -0.05185 -0.05463 -0.0463 0.050926
MNH-789 0 4.34 -0.03981 -0.01852 0.073148 -0.06296
CIM-506 0.05 0.066667 0 0.096296 0.037037 0.034259
FH-901 -0.01667 0.016667 -0.06667 0.033333 0.137963 -0.00741
CRIS-466 -0.1 -0.05 -0.03333 0 0.033333 -0.35926
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.147222 0.127778 0.030556 -0.12222 -0.03611 -0.10278
PB-899 0.05 0.005556 -0.04444 -0.03056 -0.04444 0.005556
MNH-789 0.016667 0 -0.03056 -0.01111 0.025 -0.05833
CIM-506 0.033333 -0.01667 0 0.105556 0.022222 0.055556
FH-901 -0.05 0 -0.03333 0 0.136111 0.008333
CRIS-466 -0.08333 0.016667 -0.05 0 -0.01667 -0.36389
181
4.6.23. Lint index under normal conditions
Table 71, presented the analysis of variance for lint index and revealed significant
mean squares for general combining ability (GCA) and non-significant for specific
combining ability (SCA) and reciprocal effects showing the additive genes controlled the
character. Table 73, indicated that GCA variance (σ2g) was greater than SCA variance
(σ2s) showing the predominance of additive genetic effects for lint index under normal
conditions. Four genotypes (Table 97) showed the positive general combining ability
effects and two genotypes showed the negative general combining ability effects. CIM-
506 proved to be the best general combiner for lint index having value of 0.11 under
normal conditions. CRIS-466 proved to be the poorest general combiner for lint index
having value of -0.20 under normal conditions.
As regarding crosses, positive specific combining ability effects were revealed by
six cross combinations. Out of these, most useful combination was PB-899 x CRIS-466
having value of 0.03. And nine cross combinations revealed the negative specific
combining ability effects. The least useful combination was PB-899 x MNH-789 having
value of -0.09. Among reciprocal crosses, eight crosses had positive effects and seven
crosses had negative effects. Cross combination CIM-506 x PB-899 had the maximum
value of 0.1 and cross combination CIM-506 x MNH-789 had the lowest value of -0.06
for lint index under normal conditions.
4.6.24. Lint index under water stress conditions
Table 72, presented the analysis of variance for lint index and revealed the
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) effects for lint index and revealed the additive and non-additive
(dominant) genetic effects. Presence of non-significant reciprocal effects was also
indicated. Table 74, indicated that GCA variance (σ2g) was greater than SCA variance
(σ2s) showing that additive genetic effects were more important for lint index under water
stress conditions.
Four genotypes (Table 98) showed the positive general combining ability effects
and two genotypes showed the negative general combining ability effects. CIM-506
proved to be the best general combiner for lint index having value of 0.10 under water
182
stress conditions. CRIS-466 proved to be the poorest general combiner for lint index
having value of -0.22 under water stress conditions.
In case of crosses, positive specific combining ability effects were shown by six
cross combinations. Out of these, most useful combination was PB-899 x CRIS-466
having value of 0.07. Nine crosses showed the negative value. The least useful
combination was PB-899 x MNH-789 having value of -0.09 for lint index under water
stress conditions.
Among reciprocal crosses, twelve crosses had negative value and three crosses
had positive value. Cross with maximum value of 0.06 was CRIS-466 x FH-113 and
three crosses were with the lowest value of -0.06 for lint index under water stress
conditions.
183
Table 97: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for lint index under normal conditions.
Table 98: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for lint index under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.043519 -0.03796 0.000926 -0.01296 -0.06852 -0.00185
PB-899 -0.05 -0.08148 -0.09074 0.012037 0.006481 0.039815
MNH-789 -0.05 0.066667 0.07963 0.017593 0.028704 -0.00463
CIM-506 -0.03333 0.1 -0.06667 0.110185 -0.00185 -0.03519
FH-901 0.016667 -0.03333 0.05 0.016667 0.049074 -0.00741
CRIS-466 0.033333 -0.01667 0.066667 -0.03333 0.033333 -0.20093
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 0.064815 -0.05926 0.012963 -0.04815 -0.05093 -0.03426
PB-899 -0.03333 -0.06296 -0.09259 0.012963 -0.03981 0.076852
MNH-789 -0.03333 -0.03333 0.064815 0.018519 0.015741 -0.03426
CIM-506 -0.01667 -0.01667 -0.01667 0.109259 0.00463 -0.04537
FH-901 0.016667 -0.06667 -0.01667 -0.01667 0.04537 -0.01481
CRIS-466 0.066667 -0.01667 0.033333 -0.06667 -0.06667 -0.2213
184
4.6.25. Relative water content under normal conditions
Table 71, revealed the analysis of variance for relative water content and
displayed significant mean squares due to both general combining ability (GCA) and
specific combining ability (SCA) for relative water content, showed the additive and non-
additive (dominant) genetic effects. Presence of non-significant reciprocal effects was
also indicated. Table 73, indicated that GCA variance (σ2g) was greater than SCA
variance (σ2s) showing that additive genetic effects were more important for relative
water content. Three genotypes (Table 99) showed the positive general combining ability
effects and three genotypes showed the negative general combining ability effects. FH-
113 proved to be the best general combiner for relative water content having value of
2.65 under normal conditions. CIM-506 and FH-901 proved to be the poorest general
combiner for relative water content having value of -2.37 under normal conditions.
As regarding crosses, positive specific combining ability effects were shown by
seven cross combinations and eight cross combinations were shown by negative specific
combining ability effects. The most useful combinations were MNH-789 x CIM-506 and
PB-899 x FH-901 having value of 0.67. The least useful combination was MNH-789 x
FH-901 having value of -1.49.
Among reciprocal crosses, four crosses had positive value and eleven crosses had
negative value. Crosses with maximum value of 0.33 were FH-901 x FH-113 and CRIS-
466 x FH-113 and cross with the lowest value of -1.0 was PB-899 x FH-113.
4.6.26. Relative water content under water stress conditions
Table 72, presented the analysis of variance for relative water content and
revealed the significant mean squares due to both general combining ability (GCA)
effects and specific combining ability (SCA) effects for relative water content which
revealed the additive and non-additive (dominant) genetic effects. Presence of non-
significant reciprocal effects was also indicated. Table 74, indicated that GCA variance
(σ2g) was greater than SCA variance (σ2s) showing that additive genetic effects were
more important for relative water content under water stress conditions. Three genotypes
(Table 100) showed positive general combining ability effects and three genotypes
showed negative general combining ability effects. FH-113 proved to be the best general
combiner for relative water content having value of 2.70 under water stress conditions.
185
CIM-506 proved to be the poorest general combiner for relative water content having
value of -2.40 under water stress conditions.
In case of crosses, positive specific combining ability effects were shown by six
crosses. Out of these, most useful combination was PB-899 x FH-901 having value of
1.04. Nine crosses showed negative value. The least useful combination was MNH-789 x
FH-901 having value of -1.98 for relative water content under water stress conditions.
Among reciprocal crosses, six crosses had positive value and nine crosses had
negative value. Cross with maximum value was MNH-789 x FH-113 having value of
0.66 and three crosses were with the lowest value of -0.66 for relative water content
under water stress conditions.
186
Table 99: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative water content under normal conditions.
Table 100: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative water content under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 2.657407 -0.18519 -0.68519 -0.32407 0.342593 -0.46296
PB-899 -1 2.490741 -0.51852 0.009259 0.675926 0.037037
MNH-789 -0.5 0.166667 1.157407 0.675926 -1.49074 0.37037
CIM-506 -0.66667 -0.16667 -0.16667 -2.37037 -0.12963 0.064815
FH-901 0.333333 0.166667 -0.33333 -0.16667 -2.37037 -0.10185
CRIS-466 0.333333 -0.33333 -0.33333 -0.16667 -0.33333 -1.56481
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 2.703704 -0.81481 -0.34259 -0.42593 0.490741 0.296296
PB-899 -0.66667 2.314815 -0.12037 -0.53704 1.046296 -0.14815
MNH-789 0.666667 -0.16667 1.175926 0.768519 -1.98148 -0.17593
CIM-506 -0.33333 0.5 -0.33333 -2.40741 0.101852 0.407407
FH-901 -0.16667 -0.66667 0.166667 -0.66667 -2.15741 -0.17593
CRIS-466 0.5 0.333333 -0.16667 0.166667 -0.16667 -1.62963
187
4.6.27. Leaf temperature under normal conditions
Table 71, presented the analysis of variance for leaf temperature and revealed
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) effects for leaf temperature, showed the additive and non-
additive (dominant) genetic effects. Presence of non-significant reciprocal effects was
also indicated. Table 73, indicated that GCA variance (σ2g) was greater than SCA
variance (σ2s) showing that additive genetic effects were more important for leaf
temperature.
Three genotypes (Table 101) showed the positive general combining ability
effects and three genotypes showed the negative general combining ability effects. FH-
113 proved to be the best general combiner for leaf temperature having maximum
negative value of -1.18. And CIM-506 proved to be the poorest general combiner for leaf
temperature having maximum positive value of 1.17 because negative value is desirable
for leaf temperature under normal conditions.
As regarding crosses, positive specific combining ability effects were shown by
eight cross combinations and seven cross combinations showed negative specific
combining ability effects. The most useful combination was CIM-506 x FH-901 having
value of -0.81. And the least useful combination having value of 1.21 was FH-113 x FH-
901.
Among reciprocal crosses, five crosses had positive value and ten crosses had
negative value. Cross with maximum negative value was FH-901 x FH-113 having value
of -0.83. Maximum negative value is desirable for leaf temperature. Three crosses were
with maximum positive value of 0.33.
4.6.28. Leaf temperature under water stress conditions
Table 72, presented the analysis of variance for leaf temperature and revealed the
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) effects for leaf temperature which revealed the additive and
non-additive (dominant) genetic effects. Presence of non-significant reciprocal effects
was also indicated.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing that additive genetic effects were more important for leaf temperature under
188
water stress conditions. Three genotypes (Table 102) showed the positive combining
ability effects and three genotypes showed the negative combining ability effects. FH-113
proved to be the best general combiner for leaf temperature having value of -1.27 under
water stress conditions because maximum negative value is desirable for leaf temperature
under water stress conditions. The poorest general combiner was CIM-506 having value
of 1.19 for leaf temperature under water stress conditions.
As regarding crosses, positive specific combining ability effects were shown by
six crosses and nine crosses showed the negative value. The most useful combination was
MNH-789 x CIM-506 having value of -3.5. And the least useful combination was FH-
113 x CIM-506 having value of 1.27.
Among reciprocal crosses, ten crosses had negative value and five crosses had
positive value. Cross with maximum negative value was MNH-789 x FH-113 having
value of -0.66 and two crosses with maximum positive value were PB-899 x FH-113 and
FH-901 x MNH-789 having value of 0.33 for leaf temperature under water stress
conditions.
189
Table 101: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for leaf temperature under normal conditions.
Table 102: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for leaf temperature under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -1.18519 -0.48148 -0.39815 0.740741 1.212963 -0.50926
PB-899 -0.16667 -0.76852 0.351852 0.157407 0.296296 -0.25926
MNH-789 -0.5 -0.33333 -0.18519 0.240741 -0.78704 0.157407
CIM-506 -0.33333 -0.5 -0.5 1.175926 -0.81481 -0.37037
FH-901 -0.83333 -0.33333 0.166667 -0.16667 0.87037 0.101852
CRIS-466 -0.33333 0.333333 0.333333 0.166667 0.333333 0.092593
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -1.27778 -0.52778 -0.69444 1.277778 0.777778 -0.41667
PB-899 0.333333 -0.83333 0.027778 0.166667 0.333333 -0.02778
MNH-789 -0.66667 0.166667 -0.33333 -3.5E-18 -0.5 0.472222
CIM-506 -0.5 -0.5 -0.16667 1.194444 -1.19444 -0.22222
FH-901 -0.33333 -0.33333 0.333333 0.166667 0.861111 -0.05556
CRIS-466 -0.33333 -0.16667 -0.16667 -0.33333 0.166667 0.388889
190
4.6.29. Relative cell injury % under normal conditions
Table 71, presented the analysis of variance for relative cell injury and revealed
significant mean squares due to both general combining ability (GCA) and specific
combining ability (SCA) effects for relative cell injury and showed the additive and non-
additive (dominant) genetic effects. Presence of non-significant reciprocal effects was
also indicated. Table 73, indicated that GCA variance (σ2g) was greater than SCA
variance (σ2s) showing the preponderance of additive genetic effects. Three genotypes
(Table 103) showed the positive general combining ability effects and three genotypes
showed the negative general combining ability effects. FH-113 proved to be the best
general combiner for relative cell injury having maximum negative value of -15.95 and
FH-901 proved to be the poorest general combiner for relative cell injury having
maximum positive value of 12.35 because negative value is desirable for relative cell
injury under normal conditions.
As regarding crosses, six cross combinations revealed the positive specific
combining ability effects and nine cross combinations were shown by negative specific
combining ability effects. Cross combination FH-113 x PB-899 was the most useful
combination having maximum negative value of -3.85. And cross combination FH-113 x
CRIS-466 was the least useful having value of 4.42.
Among reciprocal crosses, three crosses had positive value and twelve cross had
negative value. Cross CIM-506 x PB-899 was with maximum negative value of -3.66 and
cross with maximum positive value of 1.44 was CRIS-466 x FH-113.
4.6.30. Relative cell injury % under water stress conditions
Table 72, presented the analysis of variance for relative cell injury and revealed
the significant mean squares for general combining ability (GCA) and non-significant for
specific combining ability (SCA) and reciprocal effects, showing the additive genetic
effects.
Table 74, indicated that GCA variance (σ2g) was greater than SCA variance (σ2s)
showing that additive genetic effects were more important for relative cell injury under
water stress conditions. Three genotypes (Table 104) showed the positive general
combining ability effects and three genotypes showed the negative general combining
ability effects. FH-113 proved to be the best general combiner for relative cell injury
191
having value of -16.13 under water stress conditions. Because maximum negative value
is desirable for relative cell injury under water stress conditions. The poorest general
combiner for relative cell injury was CRIS-466 having value of 12.43 under water stress
conditions.
As regarding crosses, positive specific combining ability effects were shown by
nine crosses. And six cross combinations revealed the negative value. The most useful
combination having value of -2.00 was PB-899 x FH-901 and the least useful
combination was FH-113 x MNH-789 having value of 2.21 for relative cell injury under
water stress conditions.
Among reciprocal crosses, six crosses had negative value and nine crosses had
positive value. Cross with maximum negative value of -2.59 was CRIS-466 x PB-899
and cross with maximum positive value of 4.97 was MNH-789 x FH-113 for relative cell
injury under water stress conditions.
192
Table 103: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative cell injury (%) under normal conditions.
Table 104: Estimates of general combining ability effects (Diagonal values), specific combining ability effects (above diagonal values) and reciprocal effects (below diagonal values) for relative cell injury (%) under water stress conditions.
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -15.9545 -3.85889 3.517838 -1.84436 0.09982 4.427997
PB-899 -1.90746 -9.46304 -2.16641 1.117078 1.942052 -1.78941
MNH-789 -1.59481 -0.92473 -9.37536 -0.47653 -0.53836 -0.10254
CIM-506 1.302729 -3.66719 -1.5048 11.48984 4.095903 -0.49914
FH-901 -1.05769 0.10533 -2.27158 -2.46945 12.35247 -2.27955
CRIS-466 1.44543 -0.7911 -3.28645 -0.20234 -1.48553 10.95057
FH-113 PB-899 MNH-789 CIM-506 FH-901 CRIS-466
FH-113 -16.136 -0.55466 2.215968 0.029301 -0.86842 1.952817
PB-899 0.110408 -10.1026 0.52182 -1.80703 -2.00788 1.653906
MNH-789 4.974877 -0.42311 -9.35344 1.242801 0.290053 2.101057
CIM-506 -0.52253 0.703494 2.616976 11.71556 -0.26139 1.098377
FH-901 -2.48876 3.118142 0.192709 0.909218 11.44348 -1.15462
CRIS-466 -2.21389 -2.59241 1.184677 -1.83453 3.40739 12.43308
193
CHAPTER V
DISCUSSION 5.1. Genetic variation Genetic variation is defined as the inherent characteristic of all living organisms
that in the population provides the information necessary to choose the best selection
strategy for that population and is partitioned into three components attributable to
different causes (Meredith, 1984).
1- The additive variance is the average effect of genes. The resemblance
between parents and offspring is largely due to additive genetic effects and
is responsible for determining the response of the population to selection.
2- The dominance effect is the interaction of allelic genes. This represents the
deviation of the heterozygote from the average of the parents.
3- Non-allelic interaction or epistatic effect is the interaction of non-allelic
genes that influence a particular trait, the interaction deviation is the result
of epistatic effect (Meredith, 1984).
Genetic variation is described in statistical terms as :
The phenotypic variance of the population is a function of genotypic and
environmental variance. The breeding value of a genotype is a function of the additive
gene action. The additive genes are directly transported from the parents to the offsprings
and are responsible for the resemblance between relatives and can be used to calculate
inheritance.
Evolutionary changes depend upon two basic components:
1- There should be significant amount of variation present in the character
which is going to be selected in the breeding program.
2- The character should possess high heritability.
Both these components are necessary in effective utilization of crop resources
through selection and breeding. There are a number of biometrical techniques available
for the breeders to investigate the genetics of the character concerned. There are several
mating designs by which we can develop genetic material. Some of the common design
194
used today are: North Carolina Designs (Comstick and Robinson, 1952), combining
ability technique (Griffing, 1956), triple test cross method and diallel mating design
(Hayman, 1954a, b; Jinks, 1954, Kang, 2003).
Diallel cross is among the most commonly used biometrical technique in early
segregating generation which provides reliable information on the pattern of inheritance
of variation in the plant material. Thus it is used to study the genetic basis of variation in
various morpho-physiological traits of cotton. Diallel mating designs permit estimation of
magnitude of additive and non-additive components of heritable variance (Griffing, 1956;
Mather and Jinks, 1977). On the basis of these premises, a test for the validity of the
additive dominance model has been suggested.
The dominance additive ratio indicates the degree of dominance. Dominance:
additive ratio of less than one refer to partial dominance, near one indicates complete
dominance and greater than one indicates over dominance (Falconer, 1989).
Kapoor (1994) and Turner et al. (1976) indicated that epistasis for seed cotton
yield per plant, boll weight and ginning out turn was of duplicate type, thus additive and
dominance gene effects have been found to be important in upland cotton. However, it
varied from characteristic to characteristic.
Gad et al. (1974) and Singh (1980) reported additive genetic variation for seed
cotton yield, number of bolls, ginning out turn and lint index. Sayal and Sulemani (1996)
reported over dominance on lint percentage, seed index, lint index and staple length and
additive effects for seed cotton yield. Carvalho and De-Carvalho (1995), described fibre
percentage for incomplete dominance. Additive gene effects predominated in the control
of this trait. Ahmad et al. (1997) observed additive gene action with partial dominance
for bolls per plant, boll weight, seed cotton yield and seed index. In this study results are
according to with Gad et al. (1974), Singh and Singh (1980 ) and Ahmad et al. (1997).
Genetic markers can determine genetic variation, which makes it possible to determine
the relationships between different genotypes and to forecast which pairings can produce
new and superior gene combinations (Sharma et al., 1996). For the purpose of evaluation
a program was designed to study the genetic variability with reference to drought
tolerance among six cotton genotypes and their F1 hybrids, selected on the basis of
tolerance ability and high yield. When these genotypes and their F1 hybrids were
195
subjected to analysis of variance, they showed significant genetic variation in their
genetic behaviour for all plant traits like plant height, monopodial branches, sympodial
branches, number of bolls, boll weight, seed cotton yield, staple length, staple fineness,
staple strength, GOT, seed index, lint index, relative water content, leaf temperature and
relative cell injury. It was observed that genetic variation is an important feature in crop
plants for creating new gene combinations which have the ability to yield better under
diverse environments. According to Fehr (1978), the influence of environment and
complex inheritance are responsible for variation in quantitative characters. These traits
are affected by the environment when subjected to evaluate under varied environments.
5.2. Gene action
The preliminary analysis of variance of F1 data showed significant differences in
all the traits under study plant height, monopodial branches, sympodial branches, number
of bolls, boll weight, seed cotton yield, staple length, staple fineness, staple strength,
GOT, seed index, lint index, relative water content, leaf temperature and relative cell
injury and normal and water stress conditions showed dominance ratio less than unity,
suggesting some degree of partial dominance. Although both additive and dominance
gene effects appeared to be important in controlling the plant traits and the additive genes
seem to have more influence on the genetic control of monopodial branches, sympodial
branches and staple strength under normal conditions and staple strength and relative cell
injury under water stress conditions.
Two scaling tests showed that additive dominance model was fully adequate for
analysis of the F1 data on plant height, sympodial braches, boll weight, yield, staple
length, staple fineness and leaf temperature under normal conditions. And for boll
weight, staple fineness, seed index and leaf temperature under water stress conditions.
The remaining characters in both conditions showed partial adequacy of the genetic
model. The partial failure of the additive dominance model for these plant characters may
be due to presence of non-allelic interaction, linkage and non-independent distribution of
genes in the parents as suggested by Mather and Jinks (1982). Although, the data of the
traits did not meet the assumptions underlying the additive dominance model, thus
suggesting not to be analyzed for genetic interpretation, several partial adequacy of the
simple genetic model to the data set, for example in sorghum (Azhar and MacNeilly,
196
1988), upland cotton (Azhar et al., 1994) and wheat (Hussain, 1991). Thus in view of the
evidence present in the literature, data of all the plant characters that describe partial
adequacy to the model were also analyzed here. Many research workers have also
measured components of variance for such type of partially adequate models (Azhar and
MacNeilly, 1988; Subhani, 1997; Mahmood and Chowdhry, 1999).
Yield is an important character controlled by polygenes and is the result of
interaction between many genetic and non-genetic components (Poehlman and Sleeper,
1995). The chief objective of cotton breeding is to increase cotton production by
exploiting the available genetic material in varied environments. In F1 generation, genes
controlling yield showed partial dominance and the direction of dominance was between
the parents for higher yield. Dissimilar results were given by Azhar and MacNeilly
(1988).
Additive, dominance and epistasis are the three kinds of genetic effects which
play a major role in the inheritance of the characters under study for measuring estimates
of variations. Additive genetic effects were important for plant characters like:
monopodial branches, sympodial branches and staple strength under normal conditions
suggesting the development of a variety with considerable homozygosity for these
characters. However, plant height, number of bolls, boll weight, yield, staple length,
staple fineness, GOT, seed index, lint index, relative water content, leaf temperature and
relative cell injury showed significant additive and dominance genetic effects. Results are
agreed with Kapoor (1994) and Turner et al., (1976). It means that inheritance of these
characters is relatively simple and it is assumed those genes involved are independent of
each other in showing their effects. Similarly under water stress conditions, the characters
like: staple strength, relative cell injury showed lack of dominance and confirmed the
involvement of additive genetic effects in these characters. Involvement of additive
genetic effects confirmed homozygosity of these traits. All other characters including
plant height, monopodial branches, sympodial branches, number of bolls, boll weight,
yield, staple length, staple fineness, GOT, seed index, lint index, relative water content
and leaf temperature showed both additive and dominance properties. So, inheritance
involved in these characters was simple and genes involved are independent of each
other.
197
The Wr/Vr graph showed partial dominance for traits, plant height, monopodial
branches, sympodial branches, number of bolls, boll weight, seed cotton yield, staple
length, staple fineness, staple strength, GOT, seed index, lint index, relative water
content, leaf temperature and relative cell injury under normal and water stress
conditions.
5.3. Heritability
The heritability is defined as the ratio of variance due to hereditary differences
and genotypic variance to the total phenotypic variance (Meredith, 1984). The higher
ratio the more heritable the trait would be. If the ratio is smaller, the bigger the influence
of environment on the phenotypic expression of the trait. Thus it shows the proportion of
the total variance i.e., attributable to the average effect of genes. Heritability can be
defined in two senses:
1. Broad sense heritability includes total genetic variance (Meredith, 1984). Dudley
and Moll (1969) defined it as the ratio of total genetic variance to phenotypic
variance and it expresses the extent to which individuals phenotypes are
determined by their genotypes (Dabholkar, 1992).
2. Narrow sense heritability (n.s) is the ratio of additive genetic variance to
phenotypic variance (Dudley and Moll, 1969) and expresses to which phenotypes
are determined by the genes transmitted from parents. It is the breeding value
(additive genetic variance) of the parents which determines the genetic properties
of the progeny. Narrow sense heritability is used for determining selection
progress estimates and selection indexes and determines the degree of
resemblance between parents and offsprings (Chaudhary, 1991; Meredith, 1984).
Narrow sense heritability measures the extent of correspondence between
breeding values and phenotypic values and expresses the magnitude of genotypic
variance in the population. This is mainly responsible for changing the genetic
composition of the population via selection (Falconer, 1989). It provides a basis to
predict accuracy with which selection for genotypes could be made based on
phenotypic measurements of individuals or group of individuals (Falconer, 1989;
Dabholkar, 1992). Heritability is a property not only of the characteristic being
studied, but also of a population being sampled and the environmental conditions
198
to which individuals have been subjected (Falconer, 1989; Dabholkar, 1992).
Populations which are genetically more uniform are expected to show lower
heritability than genetically diverse populations. Since environmental variance
forms part of phenotypic variance, it affects the magnitude of heritability (Tang et
al., 1992; 1996) observed a relatively high heritability for fiber length and low
heritability for fiber fineness. But in current study, there is low heritability (h2 n.s
= 0.47) and (h2 n.s = 0.56) under both conditions for fiber length, and high
heritability for fiber fineness (h2 n.s = 0.76) under normal and (h2 n.s = 0.79)
under water stress conditions. These findings are not agreed with Tang et al.
(1992; 1996). Dedaniya and Pethani (1994) reported that yield and number of
bolls per plant had high to moderate heritability estimates. Siddiqui (1997)
observed that heritability estimates were high for yield and plant height. In current
study heritability estimates were high for yield (h2 n.s = 0.99), number of bolls (h2
n.s = 0.97), plant height (h2 n.s = 0.96) under normal conditions and for yield (h2
n.s = 0.97), number of bolls (h2 n.s = 0.95), plant height (h2 n.s = 0.93) under
water stress conditions. These results are agreed with Dedaniya and Pethani
(1994) and Siddiqui (1997). Luckett (1989) observed additive effects and high
heritability for fiber strength, GOT. In present study the high magnitude of
heritability for fiber strength (h2 n.s = 0.83) under normal and (h2 n.s = 0.74)
under water stress conditions was found, and estimates of heritability for GOT
under normal conditions was moderate to high (h2 n.s = 0.70) and (h2 n.s = 0.54)
under water stress conditions. Lancon et al. (1993) observed high heritability for
plant height, number of bolls, fineness, strength and GOT. In present study, high
heritability (h2 n.s = 0.96) for plant height, (h2 n.s = 0.97) for number of bolls, (h2
n.s = 0.76) for fineness, (h2 n.s = 0.83) for staple strength, (h2 n.s = 0.70) for GOT
under normal conditions and (h2 n.s = 0.93) for plant height, (h2 n.s = 0.95) for
number of bolls, (h2 n.s = 0.79) for fineness, (h2 n.s = 0.74) for staple strength,
and moderate magnitude of heritability (h2 n.s = 0.54) for GOT under water stress
conditions. These results are agreed with Lancon et al. (1993). Carvalho
et al. (1995) observed a low heritability estimate for yield (h2 n.s = 0.19). In
present, high estimates of heritability for yield under normal (h2 n.s = 0.99) and
199
water stress conditions (h2 n.s = 0.97). This is against the findings of Carvalho et
al., (1995). The characteristics like relative cell injury, leaf temperature and
relative water content have high estimates of heritability under normal and water
stress conditions. In present study, the range of heritability was high for most of
the plant traits, so it is assumed that genetic improvement in cotton for various
traits will be done by selection programs. High heritability values indicated that in
a future cotton breeding program for yield, yield components and fiber quality, it
is important to apply back crossing in order to concentrate traits in the genotypes
because many characteristics seem to be controlled by additive genes. Results
indicated variation in the material evaluated especially for yield components,
GOT and fiber quality. Thus improvements for these characteristics in cotton
breeding programs are possible.
5.4. Combining ability
Griffing (1956) proposed a more general procedure for diallel analysis, which
makes provision for non-allelic interaction. According to this approach, mean
measurement of a cross is partitioned into major components.
1. General combining ability (GCA) is used to designate the average performance
of the parents in hybrid combination (Sprague and Tatum, 1942). Falconer and
Muckay (1996) defined it as the mean performance of the genotypes in all
crosses, when expressed as a deviation from the mean of all crosses. GCA
consists of additive and additive epistatic variances (Matzinger, 1963).
2. Specific combining ability is used to designate those cases in which certain
combinations do relatively better or worse than would be expected on the bases
of the average performance of the genotype involved (Sprague and Tatum,
1942). It is the deviation to a greater or lesser extent from the sum of the GCA
of its two parents. SCA consists of dominance and all types of epistatic
variances are regarded as an estimate of effects on non-additive gene actions
(Falconer and Mackay, 1996). GCA and SCA effects help locate parents and
crosses that are responsible for bringing about a particular type of gene action
(Baker, 1978; Meredith, 1984). GCA and SCA effects and variances are
effective genetic parameters of direct utility to decide the next phase of the
200
breeding program (Arunachalam, 1976; Dabholkar, 1992). It helps selection of
parents for construction of synthetics, selection of suitable F1S for a multiple
crossing or composite breeding program, and the possibility of employing an
appropriate selection technique like mass selection, recurrent selection and
reciprocal selection (Dabholkar, 1992). Differences in GCA have been
attributed to additive, additive x additive and high order interactions of additive
genetic effects in the base population, while differences in SCA have been
attributed to non-additive genetic variance (Baker, 1978). In current study, PB-
899 proved the best general combiner for traits like plant height staple length for
normal and water stress conditions. FH-113 proved the best general combiner
for traits like monopodial branches, sympodial branches, bolls per plant, yield,
seed index, relative water content, leaf temperature and relative cell injury for
normal and water stress conditions. MNH-789 proved the best general combiner
for characteristics like boll weight and GOT for normal and water stress
conditions. CIM-506 proved the best general combiner for traits like staple
fineness, staple strength and lint index under normal and water stress conditions.
It is suggested that for the improvement of a particular character, utilization of
best general combiner for that very character is of great importance. In current
study, Table 73 revealed greater GCA variance (σ2g) as compared to SCA
variance (σ2s) for the traits like plant height, monopodial branches, sympodial
branches, number of bolls, boll weight, yield, staple fineness, staple strength,
GOT, seed index, lint index, relative water content, leaf temperature, relative
cell injury showing the preponderance of additive genetic effects under normal
conditions, it was also observed that SCA variance (σ2s) was greater than GCA
variance (σ2g) for the trait like staple length, showing non-additive genetic
effects for staple length under normal conditions. Table 74 showed GCA
variance (σ2g) was greater than SCA variance (σ2s) revealing additive genetic
effects for all the traits except staple length and GOT showing non-additive
effects under water stress conditions. Abdalla et al. (1999) in a study on cotton,
combining ability, genetic variance for yield and observed additive genetic
effects. In current study, yield showed preponderance of additive genetic effects
201
under normal and water stress conditions and agreed with Abdalla et al. (1999),
El-Adl and Miller (1971), Tang et al. (1993a), Lee et al. (1967) and Baloch
et al. (1996). El-Adl and Miller (1971) found preponderance of non-additive
effects for GOT. In current study it was observed non-additive effects for GOT
under water stress conditions and results are in accordance with El-Adl and
Miller (1971). Echekwu and Alaba (1995) reported non-additive genetic effects
for boll weight. These results are not agreed with the current study, where
additive genetic effects were observed for boll weight. According to Ashraf and
Ahmad (2000), high additive genetic variation for most of the cotton traits was
found and in accordance with current study, suggested a possibility of
improvement in these traits. Therefore normal breeding method such as
pedigree, back crossing or recurrent selection would be required to accumulate
the additive in order to increase seed cotton yield and fiber quality.
5.5 . Leaf pigments
Chlorophyll contents are usually reported to get reduced by water stress. Ashraf
et al., (1994) reported decrease in chlorophyll (a, b) and an increase occurred in
chlorophyll a/b ratio under water stress. A major effect of water stress is decrease in
photosynthesis which arises by impaired photosynthetic machinery. Drought stress
produced changes in photosynthetic pigments and components. (Anjum et al., 2003),
damaged photosynthetic apparatus (Fuj and Huang, 2001). The increase in a/b ratio was
smaller in tolerant genotypes than susceptible ones under water stress. In current study
the increase in a/b ratio was smaller in tolerant genotypes than susceptible ones. These
results were agreed with Ashraf et al.,(1994), Ashraf and Mehmood, (1990). Contrasting
results also have been reported in this regard. In current study chlorophyll a, b and total
carotenoids were reduced under water stress. So, results are in accordance with Ashraf et
al., (1994), Anjum et al., (2003) and Fuj and Huang (2001). In the present study water
stress reduced the chlorophyll contents in all the genotypes. Decrease in chlorophyll may
be due to slower synthesis or its faster breakdown. It is agreed with Majumdar et al.,
(1991). Current results are in agreement with some earlier studies in which it has been
observed that chlorophyll a is less affected than chlorophyll b, under water stress
conditions in maize (Garcia et al., 1987). Taiz and Zeiger, (1998) described that the
202
reduction in chlorophyll b, due to drought may have been compensated with increase in
chlorophyll a, contents. So that the light harvesting efficiency may not have affected and
the current study is in accordance with Taiz and Zeiger (1998). In current study
carotenoids were reduced under drought in all cotton genotypes. These results are agreed
with several reports earlier (Logini et al., 1999; Agastian et al., 2000).
203
CHAPTER VI
SUMMARY
Efforts have been made to improve various plant characters to get an improved
cotton plant. Breeding for yield components and the modification of the plant architecture
offer possibilities to develop more efficient breeding system for increased yield under
varied environments. The objective of the current study is to explore genes having
potential for high yield and fiber quality under drought environments in genetic material
available by crossing the genotypes in diallel fashion that may be used in future breeding
program. Gene action and combining ability were studied by analyzing diallel cross data
between six cotton varieties viz., FH-113, PB-899, MNH-789, (drought tolerant), and
CIM-506, FH-901, CRIS-466, (drought susceptible). The data obtained at maturity of
plant were analyzed. Highly significant differences among genotypes for all the traits
were found under normal and water stress conditions. A considerable reduction in almost
all parameters was shown under stress conditions.
Diallel analysis showed that characters like monopodial branches, sympodial
branches and staple strength showed additive genetic effects and traits like plant height,
number of bolls, boll weight, yield., staple length, staple fineness, GOT, seed index, lint
index, relative water content, leaf temperature and relative cell injury showed additive
and dominant genetic effects under normal conditions and water stress conditions, traits
like staple strength and relative cell injury showed additive genetic effects and traits like
plant height, monopodial branches, sympodial branches, number of bolls, boll weight,
yield, staple length, staple fineness, GOT, seed index, lint index, relative water content,
leaf temperature showed additive and dominant (non-additive) genetic effects. PB-899
proved the best general combiner for traits like plant height and staple length, FH-113
proved the best general combiner for traits like monopodial branches, sympodial
branches, number of bolls, yield , seed index, relative water content, leaf temperature and
relative cell injury, MNH-789 proved the best general combiner for traits like boll
weight, GOT and CIM-506 proved the best general combiner for staple fineness, staple
204
strength and lint index under normal and water stress conditions. Scaling tests were used
to test the adequacy of the data for analyzing additive-dominance model which showed
that additive-dominance model was fully adequate for plant traits like plant height,
sympodial branches, boll weight, yield, staple length, staple fineness and leaf temperature
under normal conditions and for characteristics like boll weight, staple fineness, seed
index, and leaf temperature under water stress conditions. All the remaining traits
revealed the partial adequacy under both conditions.
The Wr/Vr graph showed partial dominance for traits like plant height,
monopodial branches, sympodial branches, number of bolls, boll weight, yield, staple
length, staple fineness, staple strength, GOT, seed index, lint index, relative water
content, leaf temperature and relative cell injury under normal and water stress
conditions. It was also found that chlorophyll a, b and carotenoids were decreased and
polyphenols were increased under water stress conditions.
Heritability estimates for yield and yield related traits and most of traits were high
under normal and water stress conditions and had maximum ability to transfer genes to
the next generation. So, selection of desirable parents and gene combinations for high
yield on the basis of these traits under both conditions will be effective for future
breeding programs. The best combinations on the basis of mean performance of genotype
involved under normal conditions were PB-899 x CIM-506, FH-113 x PB-899, MNH-
789 x CRIS-466, FH-113 x CIM-506, FH-113 x CRIS-466, PB-899 x FH-901, CIM-506
x FH-901, PB-899 x CRIS-466, MNH-789 x CIM-506 and water stress conditions best
performing crosses were MNH-789 x CIM-506, FH-113 x CIM-506, FH-113 x MNH-
789, MNH-789 x FH-901, FH-113 x FH-901, FH-113 x PB-899, PB-899 x FH-901, FH-
901 x CRIS-466, PB-899 x CRIS-466. These crosses might be useful for transgressive
segregants in subsequent generations.
In the screening seedling experiment, it was observed that fresh root weight was
increased under water stress conditions. Roots are the key plant organ for adaptation to
drought. It may be concluded that yield and fiber quality are important selection criterion
for breeding program, it is important to apply back crossing in order to concentrate
characteristics in the genotypes because many traits seem to be controlled by additive
genes. Physio-morphological traits may contribute towards high yield and fiber quality
205
and can help the plant to perform well in normal and stress conditions. Breeders may
utilize good general combiners in breeding programs for improvements of cotton traits. It
is recommended that breeders should breed for superior combining ability aimed at
improving overall GCA for yield and fiber quality. The information obtained from these
traits during the current study may be used to evolve high yielding drought tolerant
genotypes and help the yield sustainability in those areas where drought stress is a major
threat.
206
REFERENCES
Abdalla, A.M., A.A. Aboul-El-Zahab, S.R.H. Radwan and P. Dugger. 1999. Combining
ability for yield and earliness of Pima x Egyptian cotton cultivars crosses. In: D.
Richter (Ed.), Proceedings Beltwide Cotton Conferences, pp 473-477. National
Cotton Council Memphis, TN.
Agarwal, P.K., and S.K. Sinha. 1983. Relationship between mother root and tillers as a
criterion of selection for wide or specific adaptability to drought in maize. Zacker-
Planzenb, Planzenb, 152:310-320.
Agarwal, P.K., P. Agarwal, M.K. Reddy and S.K. Sopory. 2006. Role of DREB
transcription factors in abiotic and biotic stress tolerance in plants, Plant Cell
Rep., 25: 1263–1274.
Agastian, P., S.J. Kingsley and M. Vivekanandan. 2000. Effect of salinity on
photosynthesis and biochemical characteristics in mulberry genotypes.
Photosynthetica, 38: 287–290.
Ahmad, R.T., I.A. Khan and M. Zubair, 1997. Diallel analysis for seed-cotton yield and
its contributing traits in upland cotton (Gossypium hirsutum). India Journal of
Agricultural Sciences, 67: 583-585.
Ajouri A., H. Asgedom and M. Becker. 2004. Seed priming enhances germination and
seedling growth of barley under conditions of P and Zn deficiency, J. Plant Nutr.
Soil Sc., 167: 630–636.
Anjum, F., M. Yaseen, E. Rasul, A. Wahid and S. Anjum. 2003. Water stress in barley
(Hordeum vulgare L.). I. Effect on chemical composition and chlorophyll
contents. Pakistan J. Agr. Sci., 40: 45–49.
Apel K., and H. Hirt. 2004. Reactive oxygen species: metabolism, oxidative stress, and
signal transduction, Annu. Rev. Plant Biol., 55: 373–99.
Araus, J.L., G.A. Slafer, M.P. Reynolds and C. Royo. 2002. Plant breeding and drought
in C3 cereals: what should we breed for? Ann. Bot., 89: 925–940.
Arunachalam, V. 1976. Evaluation of diallel cross by graphical and combining ability
methods. Indian Journal Genetics, 36:358-366.
207
Ashraf, M. and S. Ahmad. 2000. Genetic effects for yield components and fibre
characteristics in upland cotton (Gossypium. hirsutum L.) cultivated under
salinized (NaCl) conditions. Agronomie, 20: 917-926.
Ashraf, M. and S. Mehmood. 1990. Response of four brassica species to drought stress.
Environ. Expt. Bot., 30: 93-100.
Ashraf, M.Y., A.R. Azmi, A.H. Khan and S.S.M. Naqvi. 1994. Water relations in
different wheat (Triticum aestivum L.) genotypes under soil water deficits. Acta
Physiol. Plant., 16: 231-240.
Atlin, G.N. and H.R. Lafitte. 2002. Marker-assisted breeding versus direct selection for
drought tolerance in rice, in: Saxena N.P., O’Toole J.C. (Eds.), Field screening for
drought tolerance in crop plants with emphasis on rice, Proc. Int. Workshop on
Field Screening for Drought Tolerance in Rice. Patancheru, India, 11-14 Dec
2000,ICRISAT, Patancheru, India, and The Rockefeller Foundation, New York,
pp. 208.
Azhar, F.M. and T. McNeilly. 1988. The genetic basis of variation for salt tolerance in
Sorghum bicolor L. moench seedlings. Pl. Br., 101:114-121.
Azhar, F.M., N. Khan and S.U.K. Ajmal. 1994. Genetic basis of variation in upland
cotton. J. Agri. Res., 32(1): 9-16.
Babu, R.C., B.D. Nguyen, V.P. Chamarerk0, P. Shanmugasundaram, P. Chezhian, S.K.
Jeyaprakash, A. Ganesh, S. Palchamy, S. Sadasivam, S. Sarkarung, L.J. Wade and
H.T. Nguyen. 2003. Genetic analysis of drought resistance in rice by molecular
markers, Crop Sci., 43: 1457–1469.
Bajji, M., J. Kinet and S. Lutts. 2002. The use of the electrolyte leakage method for
assessing cell membrane stability as a water stress tolerance test in durum wheat.
Plant Growth Regul., 36: 61–70.
Baker, R.J. 1978. Issues in Diallel Analysis. Crop Science, 18: 533-536.
Baloch, M.J., H. Bhutto, R. Rind and G.H. Tunio. 1996. Combining ability estimates in
5x5 diallel intra-hirsutum crosses. Pakistan Journal of Botany, 27: 121-126.
Border, J.D., Barrier, J.W. and G.R. Lightsey. 1992. Conversion of cotton trash and other
residues to liquid fuel. In J. S. Cundiff (Eds.). Liquid fuel from renewable
resources. Proceeding of an alternative energy conference held in Nashville,
208
Tennessee, USA., St. Joseph, Michigan, USA: American Society of Agricultural
Engineers: 12-15: 198-200.
Bouchereau, A., A. Aziz, F. Larher and M. Tanguy. 1999. Polyamines and environmental
challenges: Rec. Develop, Plant Sci., 140: 103–125.
Boyer, J.S. 1982. Plant productivity and environment. Sci., 218: 443-448.
Bray, E.A., J. Bailey-Serres and E. Weretilnyk. 2000. Responses to abiotic stresses. In:
Gruissem W, Buchannan B, Jones R (eds) Biochemistry and molecular biology of
plants. American Society of Plant Physiologists, Rockville, M.D., pp 1158-1249.
Carvalho, L.P. and L.P. De-Carvalho, 1995. Genetic control of fibre percentage and boll
weight in cotton. Revista Ceres, 42: 626-636.
Cattivelli, L., F. Rizza, F.W. Badeck, E. Mazzucotelli, A.M. Mastrangelo, E. Francia, C.
Mare, A. Tondelli and A.M. Stanca. 2008. Drought tolerance improvement in
crop plants: An integrative view from breeding to genomics, Field Crop. Res.,
105: 1–14.
Chaudhary, R.C. 1991. Introduction to plant breeding pp 261. Oxford and IBH
Publishing Co. PVT. LD. New Delhi-Bombay.
Chen W.P., P.H. Li and T.H.H. Chen. 2000. Glycinebetaine increases chilling tolerance
and reduces chilling-induced lipid peroxidation in Zea mays L., Plant Cell
Environ., 23: 609–618.
Cherry, J.P. and H.R. Leffler. 1984. Seed. In: R.J. Kohel and C.F. Lewis (Eds.), Cotton.
pp 511-569. ASA, Madison, WI.
Cobley, L.S. and W.M. Steele. 1976. Vegetables fibers. In An introduction to the Botany
of Tropical Crops. 2nd Ed. Longman, London, 252-257.
Comstick, R.E. and H.F. Robinson. 1952. Estimation of the average dominance genes. Pp
494-516. In: heterosis J. W. Gowen (ed.). Iowa State College Press, Ames.
Cornic, G., Massacci, A. 1996. Leaf photosynthesis under drought stress, in: Baker N.R.,
(Ed.), Photosynthesis and the Environment, Kluwer Academic Publishers, The
Netherlands.
Dabholkar, A.R. 1992. Elements of Biometrical Genetics. Concept Publ. Camp., New
Delhi, India.
209
Dai, J.Y., W.L. Gu, X.Y. Shen, B. Zheng, H. Qi and S.F. Cai. 1990. Effect of drought on
the development and yield of maize at different growth stages. J. Shenyag, Agri.
Univ., 21: 181-185.
Davidson, E.A., L.V. Verchot, J.H. Cattanio, I.L. Ackerman and H.M. Carvalho. 2000.
Effects of soil water content on soil respiration in forests and cattle pastures of
eastern Amazonian, Biogeochemistry, 48: 53–69.
De Souza, J.G. and J.V. Da Silv. 1987. Partitioning of carbohydrates in annual and
perennial cotton (Gossypium hirsutum L.), J. Exp. Bot., 38: 1211–1218.
Dedaniya, A.D. and K.V. Pethani. 1994. Genetic variability correlations and path analysis
in deshi cotton (Gossypium arboreum L.). Indian Journal of Genetics and Plant
Breeding, 54: 229-234.
Dhanda, S.S., G.S. Sethi and R.K. Behl. 2004. Indices of drought tolerance in wheat
genotypes at early stages of plant growth, J. Agron. Crop Sci., 190: 6–12.
Dudley, J.W. and R.H. Moll. 1969. Interpretation and use of estimates of heritability and
genetic variance in plant breeding. Crop Science, 9: 257-262.
Earl, H. and R.F. Davis. 2003. Effect of drought stress on leaf and whole canopy
radiation use efficiency and yield of maize. Agron. J., 95: 688–696.
Echekwu, C.A. and S.O. Alaba. 1995. Genetic effects of yield and its components in
interspecific crosses of cotton. Discovery and innovation, 7: 395-399.
Egilla, J.N., F.T. Davies Jr. and T.W. Boutton. 2005. Drought stress influences leaf water
content, photosynthesis, and water-use efficiency of Hibiscus rosa-sinensis at
three potassium concentrations, Photosynthetica, 43: 135–140.
El-Adl, A.M. and P.A. Miller. 1971. Transgressive segregation and the nature of the gene
action for yield in an intervarietal cross of upland cotton. Crop Science, 11:381-
384.
Epstein, E. 1994. The anomaly of silicon in plant biology, Proc. NatlAcad. Sci., (USA)
91: 11–17.
Falconer, D.S. 1989. Introduction to quantitative genetics (second edition), pp 438.
Longman, New York, USA.
Falconer, D.S. and T.F.C. Mackay. 1996. Introduction to quantitative genetics, 4th (ed.)
pp 464. Longman Group Limited, England.
210
Farooq, M., S.M.A. Basra and A. Wahid. 2006. Priming of field-sown rice seed enhances
germination, seedling establishment, allometry and yield, Plant Growth Regul.,.
49: 285–294.
Farooq, M., S.M.A. Basra and N. Ahmad. 2007. Improving the performance of
transplanted rice by seed priming, Plant Growth Regul., 51: 129–137.
Farooq, M., T. Aziz, S.M.A. Basra, M.A. Cheema and H. Rehman. 2008. Chilling
tolerance in hybrid maize induced by seed priming with salicylic acid, J. Agron.
Crop Sci., 194: 161–168.
Fehr, W.R. 1978. Breeding. In: Soybean physiology, agronomy and utilization (Ed. A.G.
Norman), pp. 120-155. Academic Press, Inc. Ltd., London.
Folkert, A.H., A.G. Elena and J. Buitink. 2001. Mechanisms of plant desiccation
tolerance, Trends Plant Sci., 6: 431–438.
Foyer, C.H. and J.M. Fletcher. 2001. Plant antioxidants: colour me healthy, Biologist,
48, 115–120.
Fuj, and B. Huang. 2001. Involvement of antioxidants and lipid peroxidation in the
adaptation of two cool-season grasses to localized drought stress, Environ. Exp.
Bot., 45: 105–114.
Gad, A.M., M.A. El-RawaL, M.A. Bisher and A.A. El-Kishen. 1974. Studies on gene
action in the interspecific cross of cotton. 1. Manifestation of gene effects.
Egyptian Journal Genetics Cytology, 3: 117-124.
Garcia, A.L., A. Torrecillas, A. Lean and M.C. Ruiz-Sanchez. 1987. Biochemical
indicators of water stress in maize seedlings. Biol. Plant., 29: 45-48.
Gnanasiri, S.P., H. Saneoka and S. Ogata. 1991. Cell membrane stability and leaf water
relations as affected by potassium nutrition of waterstressedmaize. J. Exp. Bot.
42: 739–745.
GOP. 2008-09. Economic Survey of Pakistan 2008-09, Govt. of Pakistan, Finance
Division, Economic Advisor’s Wing, Islamabad.
Griffing, B. 1956. Concept of general and specific combining ability in relation to diallel
crossing systems. Australian Journal of Biological Science, 9: 463-493.
Harris, D. and M. Jones. 1997. On-farm seed priming to accelerate germination in
rainfed, dry-seeded rice. Int. Rice, Res. Notes, 22: 30.
211
Harris, D., R.S. Tripathi and A. Joshi. 2002. On-farm seed priming to improve crop
establishment and yield in dry direct-seeded rice, in: Pandey S., Mortimer M.,
Wade L., Tuong T.P., Lopes K., Hardy B. (Eds.), Direct seeding: Research
Strategies and Opportunities, International Research Institute, Manila,
Philippines, pp. 231–240.
Hasegawa, P.M., R.A. Bressan, J.K. Zhu and H.J. Bohnert. 2000. Plant cellular and
molecular responses to high salinity, Annu. Rev. Plant Phys., 51: 463–499.
Hattori, T., S. Inanaga, A. Hideki, A. Ping, M. Shigenori, L. Miroslava and A. Lux. 2005.
Application of silicon enhanced drought tolerance in Sorghum bicolor, Physiol.
Plant., 123: 459–466.
Havaux, M. 1998. Carotenoids as membrane stabilizers in chloroplasts, Trends in Plant
Sci., 3: 147–151.
Hayman, B.I. 1954a. The theory and analysis of diallel crosses. Genetics, 39: 789-809.
Hayman, B.I. 1954b. Analysis of variance of diallel crosses. Biometrics, 10: 235-245.
Hoekstra, F.A., E.A. Golovina and J. Buitink. 2001. Mechanisms of plant desiccation
tolerance, Trends Plant Sci., 6: 431–438.
Horgenboom, G., M.G. Huck and C.M. Peterson. 1987. Root growth rate of soybean as
affected by drought stress. Agron. J., 79: 607-614.
Huang, B.R. and J. Fu. 2000. Photosynthesis, respiration, and carbon allocation of two
cool-season perennial grasses in response to surface soil drying, Plant Soil, 227:
17–26.
Hussain, A. 1991. Inheritance studies on morpho-physiological and agronomic characters
in spring wheat. Euphytica, 19(1): 54-60.
Hussain, I. 2009. Genetics of drought tolerance in maize (Zea mays L.). Ph.D. thesis
Department of Plant Breeding and Genetics, University of Agriculture,
Faisalabad, Pakistan.
Hussain, M., M.A. Malik, M. Farooq, M.Y. Ashraf and M.A. Cheema. 2008. Improving
Drought tolerance by exogenous application of glycinebetaine and salicylic acid
in sunflower. J. Agron. Crop Sci., 194: 193–199.
Ingram, J. and D. Bartels. 1996. The molecular basis of dehydration tolerance in plants,
Annu. Rev. Plant Phys. Plant Mol. Biol., 47: 377–403.
212
Javot, H. and C. Maurel. 2002. The role of aquaporins in root water uptake. Ann. Bot.,
90: 301–313.
Jinks, J.L. 1954. The analysis of continuous variation in a diallel crosses of Nicotiana
mustica L. varieties. Genetics, 39: 767-788.
Johnson, L.P.V. and R. Askel. 1964. The inheritance of mating quality and agronomic
characters in diallel cross of barley. Canad. J. Genet. Cyto., 6: 178-200.
Jones, L.A. and J.H. Kersey, 2002. Cottonseed article. National cottonseed products
Association (NCPA) (USA) http://www. cottonseed.com/publication/csobro.Asp.
Kang, M.S. 2003. Handbook formulas and software for plant geneticists and breeders.
Food Products Press, New York.
Kapoor, C.J. 1994. Studies of quantitative characters in upland cotton (Gossypium
hirsutum L). Challenging the Future. Proceedings World Cotton Research
Conference, pp 297-298. Brisbane Australia.
Kavar, T., M. Maras, M. Kidric, J. Sustar-Vozlic and V. Meglic. 2007. Identification of
genes involved in the response of leaves of Phaseolus vulgaris to drought stress,
Mol. Breed., 21:159–172.
Kaya, M.D., G. Okçub, M. Ataka, Y. Çıkılı and O. Kolsarıcıa. 2006. Seed treatments to
overcome salt C drought stress during germination in sunflower (Helianthus
annuus L.), Eur. J. Agron., 24: 291–295.
Kim, H.J. and B.A. Triplett. 2001. Cotton fibre growth in planta and in vitro. Models for
plant cell elongation and cell wall biogenesis. Plant Physiology, 127: 1361-1366.
Kirda, C., S. Topeu, H. Kaman, A.C. Ulger, A. Yazici, M. Cetin and M.R. Derici. 2005.
Grain yield response and nitrogen fertilizer recovery of maize under deficit
irrigation. Photosynthetica, 19: 312-319.
Kirigwi, F.M., M. Van Ginkel, G. Brown-Guedira, B.S. Gill, G.M. Paulsen and A.K.
Fritz. 2007. Markers associated with a QTL for grain yield in wheat under
drought. Mol. Breed., 20: 401–413.
Kumar, B., D.M. Pandey, C.L. Goswami and S. Jain. 2001. Effect of growth regulators
on photosynthesis. transpiration and related parameters in water stressed cotton,
Biol. Plant. 44: 475–478.
213
Kumar, J. and S. Abbo. 2001. Genetics of flowering time in chickpea and its bearing on
productivity in the semi-arid environments, Adv. Agron., 72: 107–138.
Lambers, H., O.K. Atkin and L. Scheureater. 1996. Respiratory patterns in roots in
relation to their function, in: Waisel Y. (Ed.), Plant Roots, The Hidden Half.
Marcel Dekker, NewYork.
Lancon, J., E. Goze, G. Gawrysiak, B. Hau, B. Bachelier, J.L. Chanselme, D. Dessauw,
C. Klassou, E.N. Guessan, T.B. Nguyen and E. Ousmane. 1993. Multi-site trial of
a diallel with four elite parents bred within the cotton research African network.
Coton et Fibres Tropicales, 48: 253-282.
Lee, J.A., P.A. Miller and J.O. Rawlings. 1967. Interaction of combining ability effects
with environments in Diallel crosses of upland cotton (Gossypium hirsutum L.)
Crop Science, 7: 477- 481.
Leport, L., N.C. Turner, R.J. French, M.D. Barr, R. Duda and S.L. Davies. 2006.
Physiological responses of chickpea genotypes to terminal drought in a
Mediterranean-type environment, Eur. J. Agron., 11: 279–291.
Logini, B., A.Scartazza, E. Brugnoli and F. Navari-Izzo. 1999. Antioxidative defense
system, pigment composition, and photosynthetic efficiency in two wheat
cultivars subjected to drought. Plant Physiol., 119: 1091–1099.
Luckett, D.J. 1989. Diallel analysis of yield components, fibre quality and bacterial blight
resistance using spaced plants of cotton. Euphytica, 44: 11-20.
Ludlow, M.M. and R.C. Muchow. 1990. A critical evaluation of traits for improving crop
yields in water-limited environments, Adv. Agron., 43:107–153. Plant drought
stress: effects, mechanisms and management.
Mahmood, N. 1998. Genetic performance of bread wheat genotypes under normal and
late plantings. Ph.D. thesis, Department of Plant Breeding and Genetics, Uni.
Agri. Faisalabad, Pakistan.
Mahmood, N. and M.A. Chowdhry. 1999. Inheritance of some growth parameters in
bread wheat. Pak. J. Biol. Sci., (2):781-790.
Majumdar, S., S. Ghosh, R. Bernord nad E.B. Dumbroff. 1991. Activities of
chlorophyllase, phosphoenolpyruvate carboxylase and ribulose-1, 5-bisphosphate
214
carboxylase in the primary leaves of soybean during senescence and drought.
Pysiol. Plant, 81: 473-480.
Manikavelu, A., N. Nadarajan, S.K. Ganesh, R.P. Gnanamalar and R.C. Babu. 2006.
Drought tolerance in rice: morphological and molecular genetic consideration,
Plant Growth Regul., 50: 121–138.
Mather, K.V. and J.I. Jinks. 1977. Introduction to biometrical genetics pp 231. Chapman
and Hall, London, UK.
Mather, K.V. and J.I. Jinks. 1982. Introduction to biometrical genetics. Chapman and
Hall Ltd., London.
Matzinger, D.F. 1963. Experimental estimates of genetic parameters and their
applications in self fertilizing plants. In: W.D. Hanson and H.F. Robinson (Eds.),
Statistical genetics and plant breeding no. 982. NAS-NRC.
McWilliams, D. 2003. Drought Strategies for Cotton, Cooperative Extension Service
Circular 582, College of Agriculture and Home Economics, New Mexico State
University, USA.
Mehdi, S.S., N. Ahmad and M. Ahsan. 2001. Evaluation of maize (Zea mays L.) families
at seedling stage under drought conditions. On line J. Biol. Sci., 1: 4-6.
Meredith, W.R.Jr. and R.R. Bridge. 1984. Genetic contributions to yield changes in
upland cotton. In: W.R. Fehr (Ed.), Genetic contributions to yield changes in five
major plants pp 75-87. CSSA Spec Publ 7. Madison, WI.
Meredith, W.R.Jr. 1984. Quantitative inheritance. In: R.J. Kohel and C.F. Lewis (Eds.),
Cotton. Agronomic Monographs pp 131-150. ASA, CSSA, SSSA, South Segoe,
Madison, WI.
Monakhova, O.F. and L.I. Chernyadev. 2002. Protective role of kartolin-4 in wheat plants
exposed to soil drought. Appl. Biochem. Micro., 38: 373–380.
Moran, J.F., M. Becana, L. Iturbe-Ormaetxe, S. Frechilla, R.V. Klucas and P. Aparicio-
Trejo. 1994. Drought induces oxidative stress in pea plants. Planta, 194: 346–352.
Morgan, P.W. 1990. Effects of abiotic stresses on plant hormone systems, in: Stress
Responses in plants: adaptation and acclimation mechanisms, Wiley-Liss, Inc.,
pp. 113–146.
215
Muthukumarasamy, M., S.D. Gupta and R. Pannerselvam. 2000. Enhancement of
peroxidase, polyphenol oxidase and super oxide dismutage activities by
triadimefon in NaCl stressed Raphanus sativus L. Biol Plant, 43: 317–320.
Naidu, B.P., D.F. Cameron and S.V. Konduri. 1998. Improving drought tolerance of
cotton by glycinebetaine application and selection, in: Proceedings of the 9th
Australian agronomy conference, Wagga Wagga.
Nilsen, E.T. and D.M. Orcutte. 1996. Phytohormones and plant responses to stress, in:
Nilsen E.T., Orcutte D.M. (Eds.), Physiology of Plant under Stress: Abiotic
Factors, John Wiley and Sons, New York, pp. 183–198.
Nippon, S.K.G. 1992. J. Japan. Soc. Food Sci. Technol., 39(10): 925-928.
Nonami, H. 1998. Plant water relations and control of cell elongation at low water
potentials, J. Plant Res., 111: 373–382.
Nour, M.A. and D.E. Weibal. 1978. Evaluation of root characteristics in grain sorghum.
Agron J., 70: 217-218.
Pannu, R.K., D.P. Singh, P. Singh, B.D. Chaudhary and V.P. Singh. 1993. Evaluation of
various plant water indices for screening the genotypes of chickpea under limited
water environment, Haryana J.Agron., 9: 16–22.
Penna, S. 2003. Building stress tolerance through overproducing trehalose in transgenic
plants, Trends Plant Sci., 8: 355–357.
Pettigrew, W.T. 2004. Physiological consequences of moisture deficit stress in cotton,
Crop Sci., 44: 1265–1272.
Pillay, A.A. and G.O. Myers. 1999. Genetic diversity in cotton assessed by variation in
ribosomal RNA genes and AFLP markers. Crop Science, 39: 1881-1886.
Plaut, Z. 2003. Plant exposure to water stress during specific growth stages, Encyclopedia
of Water Science, Taylor & Francis, pp. 673– 675.
Poehlman, J.M. and D.A. Sleper. 1995. Breeding field crops. 4th ed. Iowa State
University Press. USA.
Poehlman, J.M., 1987. Breeding field crops (second edition) pp 724. Avi Publishing
Company Inc Westport Connectient.
216
Premachandra, G.S., H. Saneoka, M. Kanaya and S. Ogata. 1991. Cell membrane
stability and leaf surface wax content as affected by increasing water deficits in
maize, J. Exp. Bot., 42: 167–171.
Prentice, A. 1972. Cotton: With special reference to Africa pp 282. Longman, London.
Quan, R.D., M. Shang, H. Zhang and J. Zhang. 2004. Improved chilling tolerance by
transformation with betA gene for the enhancement of glycinebetaine synthesis in
maize, Plant Sci., 166: 141–149.
Ratnayaka, H.H., W.T. Molin and T.M. Sterling. 2003. Physiological and antioxidant
responses of cotton and spurred anoda under interference and mild drought, J.
Exp. Bot., 54: 2293–2305.
Reddy, A.R., K.V. Chaitanya and M. Vivekanandan. 2004. Drought-induced responses of
photosynthesis and antioxidant metabolism in higher plants, J. Plant Physiol., 161:
1189–1202.
Richards, R.A., H.M. Rawson and D.A. Johnson. 1986. Glaucousness in wheat: its
development, and effect on water-use efficiency, gas exchange and photosynthetic
tissue temperatures, Aust. J. Plant Physiol., 13: 465–473.
Sairam, R.K., G.C. Srivastava, S .Agarwal. and R.C.Meena. 2005. Differences in
antioxidant activity in response to salinity stress in tolerant and susceptible wheat
genotypes, Biol. Plant. 49: 85–91.
Samarah, N.H. 2005. Effects of drought stress on growth and yield of barley, Agron.
Sustain. Dev., 25: 145–149.
Sandquist, D.R. and J.R. Ehleringer. 2003. Population- and family-level variation of
brittlebush (Encelia farinosa, Asteraceae) pubescence: its relation to drought and
implications for selection in variable environments, Am. J. Bot., 90: 1481–1486.
Sayal, O.U. and M.Z. Sulemani, 1996. Comparison of gene action controlling the
qualitative traits in some early maturing cultivars of American cotton (Gossypium
hirsutum L). Sarhad Journal of Agriculture, 12: 137-145.
Schuppler, U., P.H. He, P.C.L. John and R. Munns. 1998. Effects of water stress on cell
division and cell-division-cycle-2-like cell-cycle kinase activity in wheat leaves,
Plant Physiol., 117: 667–678.
217
Senaratna, T., D. Touchell, E. Bunn and K. Dixon. 2000. Acetyl salicylic acid (aspirin)
and salicylic acid induce multiple stress tolerance in bean and tomato plants, Plant
Growth Regul., 30: 157–161.
Sharkey, TD. 1990. Water stress effects on photosynthesis, Photosynthetica, 24: 651–
661.
Sharma, S.K., M.R. Knox and T.H.N. Ellis, 1996. AFLP analysis of the diversity and
phylogeny of Lens and its comparison with RAPD analysis. Theoretical and
Applied Genetics, 93: 751-758.
Siddique, M.R.B., A. Hamid and M.S. Islam. 2001. Drought stress effects on water
relations of wheat, Bot. Bull. Acad. Sinica, 41: 35–39.
Siddiqui, M.A. 1997. A study of variability and heritability of some quantitative character
in Hirsutum cotton. Journal of Maharashtra Agricultural Universities, 21: 256-
258.
Singh, D.P. and R.B. Singh, 1980. Genetics of ginning characters in upland cotton Indian
Journal of Agriculture Science, 50: 537-540.
Singh, R.K. and B.D. Chaudhry. 1985. Biometrical methods in quantitative genetic
analysis. Kalyani Pub; Ludhiana, New Delhi, India.
Somerville, C., J. Briscoe. 2001. Genetic engineering and water, Science, 292, 2217.
Sprague, G.F. and L.A. Tatum. 1942. General versus specific combining ability in a
single cross of corn. Journal American Society Agronomy, 34: 923-932.
Steel, R.G.D. and J.H. Torrie. 1996. Principles and Procedures of Statistics: A
biometrical approach, 3rd edn. McGraw-Hill, New York.
Subbarao, G.V., C. Johansen, A.E. Slinkard, R.C.N. Rao, N.P. Saxena and Y.S. Chauhan.
1995. Strategies and scope for improving drought resistance in grain legumes,
Crit. Rev. Plant Sci., 14: 469–523.
Subhani, G.M. 1997. Genetic architecture of some morpho-physiological traits in
hexaploid wheat under stress and normal conditions. Ph.D thesis. Deptt. Pl. Br.
Genet., Univ. Agri. Faisalabad, Pakistan.
Sullivan, C.Y. 1972. Mechanism of heat and drought resistance in grain sorghum and
method of measurements. In: Rao N.G.P., P.L. R. House (eds). Stress physiology
in crop plants. Jhon Wiley & Sons, New York, pp. 263-281.
218
Taiz, L. and E. Zeiger. 1998. Plant Physiology, 2nd Ed., Sinauer Associates Inc.
Publishers, Sunderland Massachusetts.
Taiz, L. and E. Zeiger. 2006. Plant Physiology, 4th Ed., Sinauer Associates Inc.
Publishers, Massachusetts. Taylor I.B. 1991. Genetics of ABA synthesis, in:
Davies W.J., H.G., Jones (Eds.), Abscisic acid: Physiology and Biochemistry,
Bios Scientific Publishers Ltd. UK, pp. 23–38.
Tang, B., J.N. Jenkins and J.C. McCarty. 1992. Genotypic stability of cotton varieties,
resistant germplasm and their F2 hybrids. In: D.J. Herber (Ed.), Beltwide Cotton
Proceedings Research Conference pp 583-587. National Cotton Council,
Nashville, TN.
Tang, B., J.N. Jenkins, J.C. McCarty and C.E. Watson. 1993a. F2 hybrids of host plant
germplasm and cotton cultivars: Heterosis and combining ability for lint and yield
components. Crop Science, 33: 700-705.
Tang, B., J.N. Jenkins, J.C. McCarty, C.E. Watson and R.G. Creech. 1996. Evaluation of
genetic variances, heritabilities, and correlations for yield and fiber traits among
cotton F2 hybrid populations. Euphytica, 91: 315-322.
Tezara, W., V.J. Mitchell, S.D. Driscoll and D.W. Lawlor. 1999. Water stress inhibits
plant photosynthesis by decreasing coupling factor and ATP, Nature, 401: 914–
917.
Tripathy, J.N., J. Zhang, S. Robin, T.T. Nguyen and H.T. Nguyen. 2000. QTLs for cell-
membrane stability mapped in rice (Oryza sativa L.) under drought stress, Theor.
Appl. Genet., 100: 1197–1202.
Turner, H.R., H.H.Jr. Ramey and S.Jr. Worley, 1976. Relationship of yield seed quality
and fiber properties in upland cotton. Crop Science, 16: 578-580.
Turner, N.C., G.C. Wright and K.H.M. Siddique. 2001. Adaptation of grain legumes
(pulses) to water-limited environments, Adv. Agron., 71: 123–231.
Ullah, I., M.U. Rehman and Y. Zafar. 2006. Genotypic variation for drought tolerance in
cotton (Gossypium hirsutum L.): seed cotton yield responses. Pak. J. Bot., 38(5):
1679-1687.
219
Upreti, K.K., G.S.R. Murti and R.M. Bhatt. 2000. Response of pea cultivars to water
stress: changes in morpho-physiological characters, endogenous hormones and
yield, Veg. Sci., 27: 57–61.
Wahid, A. and E. Rasul. 2005. Photosynthesis in leaf, stem, flower and fruit,in:
Pessarakli M. (Ed.), Handbook of Photosynthesis, 2nd ed., CRC Press, Florida,
pp. 479–497.
Wahid, A., S. Gelani, M. Ashraf and M.R. Foolad. 2007. Heat tolerance in plants: an
overview, Environ. Exp. Bot., 61: 199–223.
Wardlaw, I.F. and J. Willenbrink. 2000. Mobilization of fructan reserves and changes in
enzyme activities in wheat stems correlate with water stress during kernel filling,
New Phytol., 148: 413–422.
Wery, J., S.N. Silim, E.J. Knights, R.S. Malhotra and R. Cousin. 1994. Screening
techniques and sources and tolerance to extremes of moisture and air temperature
in cool season food legumes, Euphytica, 73: 73–83.
Whitehouse, R.N.H, T.B. Thompson and M.A.M. Dovalle Robiero. 1958. Studies on the
breeding of self pollinated cereals. The use of diallel cross analysis in yield
prediction. Euphitica, 7: 147-169.
Wilson, N.D., D.E. Weibel and R.W. MeNew. 1978. Diallel -1 analysis of grain yield.
Percent protein yield in sorghum Crop Sci.,18: 491-495.
Xiong, L.,R. Wang, G. Mao and J.M. Koczan. 2006. Identification of drought tolerance
determinants by genetic analysis of root response to drought stress and abscisic
acid, Plant Physiol., 142: 1065–1074.
Xu, X. and W. L. Bland. 1993. Resumption of water uptake by sorghum after water
stress. Agron. J., 85 (3): 697-702.
Yokota, A., S. Kawasaki, M. Iwano, C. Nakamura, C. Miyake and K. Akashi. 2002.
Citrulline and DRIP-1 Protein (ArgE Homologue) in Drought Tolerance of Wild
Watermelon, Ann. Bot., 89, 825–832.
Zeid, I.M. and Z.A. Shedeed. 2006. Response of alfalfa to putrescine treatment under
drought stress, Biol. Plant., 50: 635–640.
Zhang, T.G.W. 2001. Present status and prospect on cotton genomic studies. Department
of Agronomy, Nanjing Agricultural University, Nanjing.
220
Zhou, Y., H.M. Lam and J. Zhang. 2007. Inhibition of photosynthesis and energy
dissipation induced by water and high light stresses in rice,J. Exp. Bot., 58: 1207–
1217.
221
WEATHER DATA 2005-2007 2005 Temperature (°C) Relative
humidity (%) Rainfall (mm)
Months Max. Min. January 23.5 4.0 67.5 32.8 February 26.0 3.5 67.7 35.1 March 33.0 12.0 56.4 48.6 April 40.5 13.0 35.0 10.8 May 41.5 19.0 31.6 18.4 June 49.0 22.0 32.5 62.5 July 41.0 25.0 65.8 88.0 August 42.5 24.0 53.9 51.6 September 42.0 21.0 51.8 84.6 October 39.0 14.0 44.2 10.0 November 32.5 7.0 50.5 0.0 December 26.5 3.0 52.0 0.0
2006 Temperature (°C) Relative
humidity (%) Rainfall (mm)
Months Max. Min. January 25.0 3.0 54.8 9.0 February 28.0 8.0 60.0 8.0 March 32.0 12.0 54.7 21.0 April 43.0 11.0 27.8 0.0 May 46.0 22.0 35.4 1.0 June 44.0 22.0 35.8 20.0 July 42.0 23.0 55.7 22.0 August 39.0 23.0 63.2 118.0 September 37.0 21.0 59.3 76.0 October 38.0 17.0 58.5 20.0 November 30.0 7.0 65.5 8.0 December 22.0 6.0 68.6 41.0
2007 Temperature (°C) Relative
humidity (%) Rainfall (mm)
Months Max. Min. January 26.0 2.0 59.6 0.0 February 26.0 8.0 71.2 28.0 March 35.0 10.0 62.0 19.0 April 44.0 14.0 39.3 0.0 May 45.0 22.0 30.4 12.0 June 48.0 24.0 47.1 11.0 July 40.0 20.0 61.7 132.0 August 39.0 26.0 59.1 2.0 September 39.0 21.0 57.1 5.0 October 36.0 14.0 45.5 3.0 November 32.0 10.0 60.6 0.0 December 25.0 4.0 56.6 5.0