genetic studies of drought tolerance in cotton l.) by...
TRANSCRIPT
GENETIC STUDIES OF DROUGHT TOLERANCE IN COTTON
(Gossypium hirsutum L.)
By
Muhammad Sarwar
M.Sc. (Hons.) Agri.(Plant Breeding and Genetics) Reg. No. 87-ag-1304
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 & GENETICS
FACULTY OF AGRICULTURE UNIVERSITY OF AGRICULTURE,
FAISALABAD PAKISTAN
2013
To, The Controller of Examination University of Agriculture Faisalabad
We, the supervisory committee, certify that the contents and form of thesis
submitted by Mr. Muhammad Sarwar, Reg. No. 87-ag-1304 have been found
satisfactory and recommend that it be processed for evaluation by external examiner(s)
for award of degree.
Supervisory committee:
1. Chairman ________________________ (Dr. Iftikhar Ahmed Khan)
2. Member _________________________ (Dr. Faqir Muhammad Azhar)
3. Member _________________________ (Dr. Asghar Ali)
DEDICATED
TO
LOVING PARENTS
AND
FAMILY MEMBERS
ACKNOWLEDGEMENT
Praise to the almighty Allah for the magnificent blessings who has blessed
me with the caliber to bring this task into this shape. I revere and adore to Hazrat
Muhammad (S.A.W), who is paragon of knowledge and prodigy of truthfulness.
I would like to laud the tireless efforts of my supervisor, Prof. Dr. Iftikhar
Ahmed Khan, Department of Plant Breeding and Genetics, who tackled the whole
process and provided help round the clock for the completion of my thesis.
I offer my cordial gratitude to members of my supervisory committee, the
respected Prof. Dr. Faqir Muhammad Azhar, Department of Plant breeding and
Genetics,, and Prof. Dr. Asghar Ali, Department of Agronomy, for their kindness
and commitment in all areas.
I am pleased to note that all respected teachers and my friends have dilated
my vision with their avidness and enthusiasm.
I am unfeignedly grateful to all members of my family for their heart
warming and delving support. Specially, I would like to mention the patience of
my children
(Muhammad Bilal Sarwar, Muhammad Saad Sarwar and Muhammad Ahmad
Sarwar), who spared me for the completion of my work.
Finally, the scholarship awarded by Higher Education Commission, Government
of Pakistan is also thankfully acknowledged.
(MUHAMMAD SARWAR)
CONTENTS
Chapter No. Description Page
1 INTRODUCTION 1
2 REVIEW OF LITERATURE 5
3 MATERIALS AND METHODS 64
4 RESULTS AND DISCUSSION 75
5 SUMMARY 143
LITERATURE CITED 146
LIST OF TABLES
Table
#
Title Page
#3.1 List of crosses and backcrosses 68 3.2 Coefficients of genetic effects for the weighted least squares analysis of generation
means Mather and Jinks (1982) the mean (m), additive (d), dominance (h), additive × additive (i), additive × dominance (j) and dominance × dominance (l) parameters.
71
3.3 Coefficients of the genetic variance for the weighted least squares analysis of generation variances Mather and Jinks (1982 ).
72
4.1 Mean squares for seedling traits in cotton under normal and drough conditions. 75
4.2 List of varieties/genotypes selected after screening 76
4.3 Similarity matrix for Nei’s and Li’s coefficient of 12 cotton varieties. 81
4.4 Generation Means of various morphological and physiological traits of Cross-11(NIAB-78 × CIM-446) and Cross-2 (CIM-482 × FH-1000) under normal (N)conditions.
84
4.5 Generation Means of various morphological and physiological traits of Cross-11(NIAB-78 × CIM-446) and Cross-2 (CIM-482 × FH-1000) under drought (D)conditions.
85
4.6 Best model fit estimates for generation means parameters (± standard error) by weighted least squares analysis of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under normal conditions
86
4.7 Best model fit estimates for generation means parameters (± standard error) by weighted least squares analysis of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under drought conditions
87
4.8 Components of variance, D (additive),H (dominance), F(additive× dominance), E(environmental) and narrow sense heritability and genetic advance estimates of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under normal (N))conditions.
97
4.9 Components of variance, D (additive),H (dominance), F(additive× dominance), E(environmental) and narrow sense heritability and genetic advance estimates of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under drought (D))conditions.
98
4.10 Genotypic (upper value) and phenotypic (lower value) correlations for different plant traits in cross-1 (NIAB-78 x CIM 446) of cotton under normal conditions.
132
4.11 Genotypic (upper value) and phenotypic (lower value) correlations for for different plant traits in cross-2 (CIM 482x FH-1000) of cotton under normal conditions.
133
4.12 Genotypic (upper value) and phenotypic (lower value) correlations for different plant traits in cross-1 (NIAB-78 x CIM 446) of cotton under drought conditions.
134
4.13 Genotypic (upper value) and phenotypic (lower value) correlations for differentplant traits in cross-2 (CIM 482x FH-1000) of cotton under drought conditions.
135
LIST OF FIGURES
Figure Description Page
4.1 Frequency distribution of the F2 for plant height of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
103
4.2 Frequency distribution of the F2 for monopodial branches of cross-1 (NIAB-78×CIM-446) of Cotton under ( a ) normal and ( b ) drought conditions
104
4.3 Frequency distribution of the F2 for sympodial branches of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
105
4.4 Frequency distribution of the F2 for Bolls/plant of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
106
4.5 Frequency distribution of the F2 for Seed cotton yield of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
107
4.6 Frequency distribution of the F2 for boll weight of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
108
4.7 Frequency distribution of the F2 for Fibre length of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
109
4.8 Frequency distribution of the F2 for Fibre strength of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
110
4.9 Frequency distribution of the F2 for Fibre fineness of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
111
4.10 Frequency distribution of the F2 for Ginning out turn of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
112
4.11
Frequency distribution of the F2 for Relative water content of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
113
4.12 Frequency distribution of the F2 for Excised leaf water loss of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
114
4.13 Frequency distribution of the F2 for Leaf temperature of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
115
4.14 Frequency distribution of the F2 for Leaf area of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
116
4.15 Frequency distribution of the F2 for plant height of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
117
4.16 Frequency distribution of the F2 for monopodial branches of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions
118
4.17 Frequency distribution of the F2 for sympodial branches of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
119
4.18 Frequency distribution of the F2 for bolls/plant of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
120
4.19 Frequency distribution of the F2 for Seed cotton yield of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
121
4.20 Frequency distribution of the F2 for Boll weight of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
122
4.21 Frequency distribution of the F2 for Fibre length of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
123
4.22 Frequency distribution of the F2 for Fibre strength of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
124
4.23 Frequency distribution of the F2 for Fibre fineness of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
125
4.24 Frequency distribution of the F2 for Ginning out turn of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
126
4.25 Frequency distribution of the F2 for Relative water content of cross (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
127
4.26 Frequency distribution of the F2 for ELWL of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions. 128
4.27 Frequency distribution of the F2 for Leaf temperature of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
129
4.28 Frequency distribution of the F2 for Leaf area of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and (b) drought conditions.
130
LIST OF APPENDICES
Appendix Description Page
1. Comparison of Means for shoot length and root length under normal and drought
174
2. Comparison of Means for Lateral root number and lateral root density under normal and drought
175
3. List of 30 SSR primers used in study 176
4. Meteorological data recorded at University of Agriculture, Faisalabad, during the cotton crop season 2009.
177
ABSTRACT
Fifty lines of Gossypium hirsutum L. were screened at seedling stage in glasshouse for drought tolerance. From the germplasm two drought tolerant and two susceptible lines showing genetic divergence will be identified and crossed to obtain hybrid seed. The hybrid seed was planted to develop F1 generations. Some of the plants from F1 generation were selfed for F2 and some back crossed to both the parents (P1 and P2) to develop seed for back crosses (B1 and B2). All the six generations, P1, P2, F1, F2, B1 and B2 were studied in field under normal and water stressed conditions using completete block design with three replications. During the crop season, water stress will be developed by supplying 50% less irrigations than the normal. Data was recorded on different plant traits related to drought tolerance, yield and fiber quality. The inheritance pattern of various traits was studied using generation means analysis technique. Estimates of narrow sense heritability and nature of correlation among various traits was examined. There were significant differences among six generations (P1, P2, F1, F2, B1, B2) of two crosses for all the studied plant traits of crosses NIAB-78 × CIM-446 and CIM-482 × FH 1000 under both normal all drought conditions. Generation means analysis indicated additive, dominance and epistatic genetic effects played role in the inheritance of all the traits under both normal and drought condition. Two parameter model [md] provided best fit of observed to the expected generation means for number of bolls per plant under normal conditions in cross NIAB-78 × CIM-446 and for number of monopodial branches of the same cross under drought conditions. In case of cross CIM-482 × FH-1000 two parameter model [md] was found fit for Fiber fineness under normal conditions. The dominace or dominace × dominance effects were observed for some traits in both the crosses under both normal and drought conditions. Some plant traits showed [i], [j] and [l] type of interactions together which indicated complex inheritance of these traits. In the generation variance analysis only additive effects were involved in the inheritance of most studied plant traits but generation means analysis showed that additive, dominance and epistatic effects were involved in the inheritance of these traits. The narrow sense heritability estimates of infinity generation (F∞) were consistently higher than F2 generation. High narrow sense heritability estimates 0.67, 0.66 and 0.65 were observed for number of sympodial branches, number of bolls per plant and seed cotton yield, respectively for cross-1 (NIAB-78 × CIM-446) under normal conditions and narrow sense heritability estimates 0.79, 0.69 and 0.58 were observed for boll weight, seed cotton yield and relative leaf water content respectively under drought conditions for cross-1. Seed cotton yield had positive significant correlation with boll weight, fibre length, fibre strength, lint percentage and relative water content except fibre fineness, exised leaf water loss, leaf temperature and leaf area in cross-1 (NIAB-78 × CIM-446) under normal and drought conditions and in cross-2 (CIM-482 × FH-1000) under normal conditions. The information derived from these studies will provide guideline to cotton breeders in breeding of drought tolerant cotton cultivars.
1
CHAPTER 1
INTRODUCTION
Like many other developing countries, the economy of Pakistan entirely depends
upon Agriculture. Despite substantial progress made in diversified resources, Agriculture still
plays the most important role in Pakistan’s economy. It contributes 21% to GDP and
provides livelihood to 45% of the work force (Anonymous, 2009-10). Cotton commonly
known as white gold of Pakistan accounts for 8.6 % of the value addition in agriculture and
1.8% to GDP (Anonymous, 2009-10). Pakistan ranked fourth in cotton production after
Peoples Republic of China, USA and India. It is very important source of fibre and vegetable
oil in Pakistan. Linters provide cellulose for plastics and explosives. The meal and hull are
used as livestock, poultry and fish feed and its sticks are used as fuel in the villages. It is a
leading exporting commodity of Pakistan and earns a substantial amount of foreign exchange
through the export of raw cotton and its finished products, in view of its contribution, it is
rightly called the back bone of Agrarian economy of the country.
Cotton grown in Pakistan belongs to the species Gossypium hirsutum L. The genus
Gossypium is very large, containing 50 species with basic chromosome number of 13. There
are two diploid and two tetraploid species of Gossypium which have spinable seed fibers
called lint. Diploid species of Gossypium include G. herbaceum and G. arboreum with
chromosome number (2n = 2x = 26) while tetraploid species of Gossypium include G.
hirsutum and G. barbadense (2n = 4x =52). G. hirsutum which is also known as upland
cotton is the principal cultivated cotton and accounts for about 90% of the world’ cotton
production (Poehlman & Sleper, 1995). Cotton was grown over a vast area in Pakistan and
during the year 2009-10 area under cotton crop was 3.106 million hectares with a production
of 12.7 million bales (Anonymous, 2009-010).
Growth and productivity of crop plants is adversely affected by various biotic as well
as abiotic stresses such as cold, salinity, drought, heat and heavy metal toxicity. All these
stresses are a menace for crop plants and prevent them from attaining their full genetic
potential. Water stress restricts crops yields in arid and semiarid zones of the world (Jafar et
al., 2004). In Pakistan, drought is one of them which is seriously affecting whole of the
agriculture system and expected to be more and more serious with ever increasing shortage
of irrigation water in the country.
2
Drought stress is a meteorological event which comprises of the lack of rain fall for a
period of time causing moisture deficit in soil with a decrease of water potential in plant
tissues (Kramer, 1980). In agriculture its definition would be the shortage of water
availability, including rainfall and soil moisture storage ability, in amount and distribution
during the life cycle of a crop plants, which restricts, the expression of full genetic potential
of the plant (Sinha, 1986). Drought stress develops when the amount of water depleted from
the plant body is more as compared to water taken inside the plant. This stress leads to
physiological changes in plants like loss of turgor, closing of stomata, reduction in cell
enlargement and reduced leaf area. All these factors ultimately decrease photosynthesis and
respiration (Human and Toit, 1990; Hall et al., 1990).
Drought resistance mechanisms can be grouped in to three categories viz. drought
escape, drought avoidance and drought tolerance (Levitt, 1972). Drought escape is defined as
the capability of a plant to complete its life cycle before severe soil and plant water deficits
occurs and thus never faces water shortage. This mechanism involves quick phenological
development and developmental flexibility (Turner, 1979). Drought avoidance is the ability
of a plant to maintain comparatively high tissue water potential, in spite of a deficiency of
soil-moisture. Drought tolerance is the ability of a plant to survive water-deficit, with low
tissue water potential. Drought tolerance means those varieties or species of plants that are
capable to grow and yield adequately in areas which are liable to periodic drought.
The population of the world is increasing at an alarming rate and it is now
approaching 6 billion which is expected to reach 8 billion by the year 2025. Therefore, in
order to meet food and fibre needs of large number of people we need to minimize losses due
to various stresses in agricultural crops like Wheat, Cotton, Maize and rice. According to
estimation up to 45% of the world agriculture lands are subjected to continuous or frequent
drought (Bot et al., 2000).
Total area of Pakistan is 79.61 mha and out of which 4.40 mha is drought affected
(Economic Survey of Pakistan, 2000), which is a major problem in increasing production of
crops. The availability of irrigation water in Pakistan is fastly going down and down day by
day. According to Asian Water Development Outlook 2007 (report of the Asian
Development Bank) Per capita available water in Pakistan reduced from 2,961 m3 per year in
3
2000 to 1, 420 m3 per year in 2005 and just a little over 1,000 m3 per year in 2006-07,
fractionally over the scarcity threshold.
The production potential of cotton varieties in Pakistan is faced with different types of
biotic as well as abiotic stresses. Among abiotic stresses drought is the one which is not only a
serious problem that limits the cotton production in Pakistan, is also a threat to agriculture like
in many regions of the world. Saranga et al, (2001) and Le Houerou (1996) emphasized
drought as major factor of crop productivity reduction. They reported that it is expected to
increase with the spread of arid lands and global warming. However, Christiansen and Lewis
(1982) suggested to overcome the problem either by providing irrigation to the crop or by
developing varieties which can produce higher and stable yield in water limiting areas. Thus,
the development of drought tolerant cotton genotypes is a practical solution to lessen the
negative effects of drought on crop productivity.
Drought is widely considered to be the most important abiotic factor that restricts
agricultural crop production (Nemeth et al., 2002; Lea et al., 2004). As a result overall
production of crop is decreased. Cotton plant has good potential for water stress tolerance
because it has well-developed root system and ability to stand well against temporary wilting.
The yield is severely affected when drought stress occurs during reproductive stage of the crop
(Selote and chopra, 2004). Moisture stress reduces growth and photosynthesis, increases fruit
shedding and affects other physiological processes, resulting in marked decrease in cotton
yield. When drought stress occurs during fibre elongation period causes decreased fibre
length, and drought stress after fibre elongation period results in fibre immaturity and low
micronair.
Drought resistance strategies vary with climatic or soil conditions. A plant that is capable
of acquiring more water or that has higher water use efficiency will have greater resistance to
drought. Some plants possess adaptations such as C4 and CAM modes of metabolism that allow
them to exploit more arid environments. In addition, plants possess acclimation mechanism that
is activated in response to water stress (Tiaz and Zeiger, 1991).
For successful cotton breeding programme knowledge of genetic information about
physiological and agronomic traits is necessary to breed cotton for drought tolerance. The gene
action of the traits also provides information necessary in the choice of a selection strategy in
breeding cotton. Information about the correlation of the traits is necessary to obtain the
4
expected response of other traits. The information derived from these studies will provide
guideline to cotton breeders in breeding of drought tolerant cotton cultivars.
Objectives of the study
1. To investigate the genetics of physiological and agronomic traits for drought tolerance in
cotton.
2. To measure correlation among different traits studied.
5
CHAPTER 2
REVIEW OF LITERATURE
2.1 Effect of water stress on cotton and other crop plants
All physiological processes in plants depend on water which accounts for 80–95 % of
the biomass of non-woody plants (Hirt and Shinozaki, 2004). Both biotic and abiotic stresses
adversely affect plant development and cause significant reduction in yield and quality of
crops worldwide (Boyer, 1982). Seki et al. (2002) reported that moisture deficit affects plant
growth significantly if the quantity or quality of water supplied is insufficient to meet the
basic needs of plants. Drought causes significant losses in growth and productivity by
affecting morphological, physiological, biochemical and molecular processes in plants
throughout their life cycle (Farooq et al., 2009). Lee, (1984) reported that adequate soil
moisture supplied at appropriate time through artificial irrigation system or through
precipitation is essential for good crop harvest. Cotton is cultivated during summer season in
arid and semi arid areas of Pakistan and like other agricultural crops, its growth and
development is adversely affected by water stress which has adverse effect on its yield and
quality. Therefore, such varieties of cotton are needed that can either grow successfully in
drought stress conditions with very little or with out any loss in crop productivity and quality
in dry land areas or give more yield by using less quantity of water in irrigated areas.
Therefore, an understanding, of the reaction of cotton plants to moisture stress is imperative
in order to estimate irrigation needs and breed drought resistant cotton cultivars (Pace et al.,
1999).
Basically cotton is a drought tolerant crop as compared to other crops because of
various mechanisms including osmotic adjustment, very deep tap root system and choosy
fruit shedding. Response of cotton plant to moisture stress vary depending upon the severity
of stress, stage of crop growth and the length of time for which stress is imposed on the crop
(Pettigrew, 2004 ). If the stress occurs prior to bloom, it can lessen the number of fruiting
branches. Drought after bloom has maximum effect on yield of cotton and quality of lint. As
more and more bolls are produced, cotton plant’s requirement for water increase
significantly. Drought stress not only slows down plant growth, the plants also shed small
bolls and squares due to increased requirement for water. Drought amplifies the effects of
6
high temperature. Under drought stress, growth and development is badly affected due to rise
in temperature. Availability of knowledge about particular traits that determine performance
of crop under water deficit conditions, and the possibility of them either through conventional
breeding or genetic transformation approaches, could help cotton breeders to produce drought
tolerant varieties (Turner, 1997). Ball et al. (1994) studied the differential growth response of
roots and shoots to water stress and reported that root elongation of field plants was less
sensitive to drought than leaves. It was also observed that small roots were more sensitive to
drought than medium sized. Commonly tips of small roots stopped growing several days
before as compared to medium roots. Therefore it was concluded that medium roots are more
important for continuing growth in moisture stress conditions.
Mcmichal and Quisenberry (1991) reported that terminal drought decreased the
shoot/root ratio. Keriege (1997) reported that drought stress reduced crop growth rate
through the reduction in size and number of leaves produced and by decrease in
photosynthesis. Water supply was most critical for cotton from the first square stage until the
first flower. Drought decreases the number of sympodial branches on cotton plant (Krieg and
Snug, 1986). During moisture stress growth and development inhibition are well documented
(Boyer, 1970). If moisture stress occurs during the vegetative stage of the crop it results in
the formation of smaller leaves, a reduced leaf area index (LAI) at maturity and less light is
intercepted by the crop. Boll production and leaf area development is also intrinsically linked
to leaf area (Mauney1986; Jackson and Gerik 1990; Morrow and Keriege 1990).
The most apparent adverse affect of moisture deficit response was found on stature of
cotton plant. Plants with shorter height were produced under moisture stress, because plants
under moisture stress produce less main-stem nodes resulting in plants with shorter height.
The leaf area under water stress conditions is also reduced. Therefore over all vegetative
growth of cotton plants was reduced under moisture stress condition. Studies showed that
under moisture stress conditions cotton plants had higher blooming rates, early in the
growing season, than the plants in the irrigated condition. In cotton early flowering had been
reported under water stress (Guinn and Mauney, 1984a). Pettigrew, (2004 a) reported that
irrigated plants maintain their vegetative growth longer after the initiation of reproductive
growth than the plants under water stress. Lint yield in cotton is primarily reduced due to
drop in the number of bolls in moisture stress and irrigation resulted in more number of bolls
7
per square meter. These additional bolls produced under irrigated condition were primarily
located at higher plant nodes and more distal position on sympodial branches. Lint yield was
decreased with increase in soil moisture deficit, (Kimball and Mauney, 1993; Gerik et
al.,1996; Saranga et al.,1998).
The fibre quality response to irrigation was inconsistent. In cotton fibre length was
shortened generally in response to water deficit stress. Plants under well irrigation conditions
produce more monopodial branches than plants under soil moisture deficit conditions. The
distribution of bolls on plants significantly affected by irrigation (Pettigrew, 2004 a).
2.2 Impact of water stress on physiological traits
2.2.1 Photosynthesis
When plants are grown under moisture shortage conditions leaf photosynthesis is
reduced because of a combination of stomatal and non-stomatal limitations (Mc Michael and
Hesketh, 1982; Turner et al., 1986). The effect of moisture deficit stress on photosynthetic
rates in cotton was studied and it was found that photosynthesis rates were reduced after only
five days of soil drying, and there was uniform displacement of the diurnal cycle of leaf
water potential and corresponding reduction in transpiration and CO2 uptake. Siddique et al.
(1999) studied the effects of drought stress on photosynthetic rate and leaf gas exchange
characteristics of four wheat varieties under semi-controlled conditions. Cultivars, Kanchan,
Kalyansona, Sonalika, and C306 were grown in pots subjected to four levels of water stress.
They found that the cultivar Kalyansona showed the highest photosynthetic rates at
vegetative and anthesis stages. Lee et al. (1974) reported the effect of drought stress on
water relations and net photosynthesis in pea seedlings and found that drought resistant
genotypes had higher net photosynthesis than drought susceptible ones under drought stress
conditions. Ennahli and Earl (2005) found that moisture stress cause reduction in leaf net
photosynthetic carbon assimilation (AN), both through stomatal effects, which reduced the
leaf internal CO2 concentration (Ci), and non stomatal effects, which resulted in reduced AN
at a given level of Ci. Physiological limitations to photosynthesis in leaves of moisture
stressed cotton (G. hirsutum L.) plants revealed that combined leaf gas exchange/chlorophyll
fluorescence measurements differentiate the treatments more effectively than gas exchange
measurements only. Leidi et al. (1993) studied cotton genotypes under drought and observed
that net photosynthesis, transpiration rate and stomatal conductance decreased as water stress
8
was imposed. Gent and Kiyomoto (1992) found higher net photosynthesis in resistant
genotypes than susceptible ones in winter wheat.
Levi et al. (2009) characterized the photosynthetic activity of two selected near-
isogenic lines (NILs) i. e, G. barbadense (GB) cv. F-177 and G. hirsutum (GH) cv. Siv’on
and their recipient parents under drought and irrigated field conditions. The G. barbadense
NIL expressed a stable net rate of CO2 assimilation (A) across a wide variety of leaf water
potentials and showed a notable advantage over its recipient parent, F-177 under severe
drought. The G. hirsutum NIL exhibited greater mesophyll conductance under drought
conditions than its recipient parent, Siv’on, but these cultivars did not differ in A. But both
NILs did not vary from their recipient parents, in yield. Ullah et al. (2008) assessed
genotypic variation for drought tolerance in cotton cultivars by physiological traits as
selection criteria and found the relationship of physiological traits with productivity traits.
They investigated the association of photosynthetic rate (Pn) with productivity traits both
under well watered (W1) and water limited (W2) regimes. Photosynthetic rate (Pn) was
significantly reduced with moisture stress. They found that association of photosynthetic rate
with productivity was helpful under water limited environments and may be valuable as a
selection criterion for screening germplasm. Rekika (1995) et al. measured CO2 assimilation
rate in 6 barley and 5 durum wheat genotypes subjected to increasing water stress during
seedling stage. Genetic differences were present even under moderate drought. The results
suggested that gas exchange parameters could be used as predictive criteria for drought
resistance in durum wheat and barley.
Xue et al. (1992) subjected wheat cultivar, Shaanhe-6 (drought resistant) and
Zhenyin- 1 (drought sensitive) to water stress. Under mild water stress, net photosynthesis
was reduced to about half in Zhenyin-1 but was less affected in Shaanhe-6. They concluded
that the drought resistant cultivar had higher net photosynthesis under drought compared to
the drought susceptible cultivar. Kaul (1974) measured net photosynthesis in flag leaves of
severely drought stressed wheat cultivars and observed positive correlation between net
photosynthesis and grain yield under drought stress. Gupta and Berkowitz (1987) reported
the inhibition of photosynthesis due to moisture stress in different wheat genotypes. They
observed that the genotypes were different for net photosynthesis under drought conditions.
Patel et al. (1996) subjected wheat cultivars, WH-283 and WH-331 to water stress by
9
withholding water until wilting occurred. Photosynthesis decreased in both cultivars.
However, the proportional reduction in yield of WH-331 was lower than WH-283 under
stress indicating that WH-331 was more tolerant to water stress. The results showed that the
genotype with high net photosynthesis under stress produced high yield suggesting that net
photosynthesis could be used as selection criterion for drought resistance. Ritchie et al
(1990) compared gas exchange parameters between drought resistant winter wheat genotypes
and drought susceptible genotype to determine if these parameters contribute to drought
resistance. Photosynthesis was significantly reduced in drought susceptible genotypes.
Photosynthesis declined by 74% and 84% in TAM W-101 (drought resistant) and Sturdy
(drought susceptible), respectively at the early vegetative stage.
The threshold leaf water potential required for initiating stomatal closure became
increasingly more negative when cotton plant was subjected to a series of moisture stress
cycles. The shift in the threshold water potential needed for induction of stomatal closure was
reported to be dependent on the number of previous, stress cycles and leaf age (Ackerson,
1980). Studies on the adaptive response of cotton to various irrigation levels in terms of
transpiration, stomatal role in transpiration, leaf temperature (TL) and CO2 assimilation rate
(AN) showed that stomatal area decreased significantly in response to water stress in cotton.
It was concluded that cotton is adapted to water stress by maintaining higher transpiration
rate (Isoda and Inamullah, 2005). The effect of water stress on cotton varieties studied at
early reproductive stage (40 days after planting) revealed that leaf water potential (LWP)
decreased under water stress. Stressed plants had higher stomatal resistance than control.
More bolls were retained by un-stressed than stressed plants and this retention decreased with
increase in water stress (Biswas et al., 1986).
2.2.2 Leaf area
Singh et al. (1990) conducted an experiment in which 6 parents and 15 F1s which
were crossed in a diallel fashion were grown under three irrigation regimes viz. 1. Normal
irrigation 2. Irrigation only on wilting and 3. No irrigation. Decrease in soil moisture content
resulted in a decrease in leaf area and increase in specific leaf weight. It was concluded that
varieties with small thick leaves are usually drought tolerant
10
2.2.3 Leaf Relative Water Content
Drought stress resulted in lowering the leaf relative water content in cotton.
Rajeshwari (1995) studied 30 cotton genotypes for drought tolerance under rainfed
conditions. Three genotypes were identified as drought tolerant with high yield potential,
and it was revealed that relative water content, earliness and photosynthesis were associated
with drought tolerance. Chun-yan et al. (2007) observed that transpiration ability decreased
while the leaf temperature increased under water stress condition. They further reported that
although relative leaf water contents decreasesed with the increase in water stress but cotton
has the ability for maintaining water in leaves.
Dhanda and Sethi (2002) studied thirty wheat genotypes for differences in morpho-
physiological traits in response to drought stress at anthesis and maturity. It was observed
that genotypes differed in their reaction to drought at both stages of plant growth for relative
water content under drought stress, and there were significant genotype x environment inter-
actions. Mu-XiuLing and Bao-Xiao (2003) measured the relative water content of leaves in a
pot experiment on cotton to determine the effect of different levels of drought stress on plant
growth. Soil water content was kept at 70-75% (control), 60-65% (light water stress), 50-
55% (medium water stress) and 40-45% (heavy water stress) from 20th April till 30th may
when samples were taken. Under different water stresses, the water regime in cotton leaves
changed markedly. With the aggravation of drought, the relative water content in leaves
decreased. Dedio (1975) studied five cultivars of wheat to evaluate relative water content
under different levels of soil moisture stress. He found that water retaining ability of leaf was
under the control of dominant genes and concluded that drought resistant cultivars maintained
higher leaf water content under drought.
Joshi et al. (2005) evaluated 9 lines of pearl millet including 4 male sterile and 5
inbred lines for relative water content under drought stress conditions and found that the male
sterile line 95444B and the inbred line J 2340 showed the highest relative water contents
(74.2 and 77.8%) along with better grain yield (628.9 and 806.7 kg/ha), which were
considered drought resistant lines. Singh et al. (1996) studied 10 genotypes of upland cotton
(G. hirsutum), 5 of tree cotton (G. arboreum) and 4 of levant cotton (G. herbaceum) for
relative water content (RWC) under drought conditions in pots and observed that RWC was
more in upland cotton than in tree and levant cotton. Therefore, material showed wide
11
variation for drought tolerance. Ritchie et al. (1990) evaluated a drought resistant (TAM W-
101) and a drought susceptible wheat genotype (Sturdy) for relative water content (RWC) of
leaf to determine their contribution for drought resistance. Plants were grown under well-
watered conditions in growth chambers and drought stress was imposed by limited watering
of plants at anthesis and vegetative stages.
Munjal and Dhanda (2005) evaluated 30 wheat genotypes for relative water content
(RWC) under rainfed and irrigated conditions and found that genotypes exhibited high RWC
values, indicating the criterion to distinguish drought resistant from drought susceptible
genotypes. Malik and Wright (1998) conducted field and pot experiments under drought
stress to evaluate relative water content as screening criterion in six drought resistant and six
susceptible spring wheat genotypes and found that relative water content was higher in
drought resistant genotypes. Golabadi et al. (2005) evaluated 151 F3 families of durum wheat
for relative water content under drought and irrigated conditions and observed that relative
water content decreased under moisture stress, indicating the selection criterion for drought
tolerance. Lobato et al. (2008) evaluated the effects of the progressive water deficit on
soybean (Glycine max cv. Sambaiba) for relative water content with two water regimes,
stress and control and found that there was decrease in the leaf relative water content in
plants under water deficit.
Singh et al. (2006) evaluated 15 genotypes of cotton for relative water content which
were developed at Nagpur and Sirsa under irrigated and drought conditions. On the basis of
relative water content genotypes DC 274, CNH30, CNH 36, CNH40, DTS 2, LRA 5166 and
TOM 16 x BN were found to be drought tolerant. Kumari et al. (2006) evaluated 20
genotypes of American cotton at Regional Agricultural Research Station, Lam, Gunture for
yield and drought tolerance traits. It was found that the genotypes GShv 97/612 have the
highest mean value for relative water content in leaves. Pirdashti et al. (2009), Conducted an
experiment at Rice Research Institute of Iran – Deputy of Mazandaran (Amol) in glasshouse
condition during 2006. Drought stress was applied in four levels 1.continuous irrigation or no
water stress as a control 2.Drought stress at vegetative stage 3.Drought stress at flowering 4.
Drought stress at grain filling stage and cultivars Tarom, Khazar, Fajr and Nemat were used as
treatments. They found that drought stress decreased relative water content (RWC) in different
cultivars.
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2.2.4 Excised Leaf Water Loss
It has been observed that the species which are better adapted to dry environment
have low rate of water loss through leaf cuticle which is cited as an important drought
survival mechanism and rate of excised leaves water loss has been linked to drought
resistance in wheat (Salim et al, 1969). Basal et al. (2005) studied CRS, TAM 94L-25 and
LK 142 lines of cotton (G. hirsutum L.) for excised leaf water loss (ELWL), under moisture
stress and non stressed conditions and suggested that excised leaf water loss could be used as
dependable selection criterion for drought tolerance in cotton. Jaradat and Kozak (1983)
observed that excised leaf water retention was positively correlated with yield in wheat.
Clarke and Townley (1986) also concluded that low rate of water loss (high leaf water
retention) was associated with high grain yield potential under drought. Similarly Winter et
al. (1988) studied several screening techniques to differentiate drought resistant genotypes in
wheat. They used five cultivars (Scout 66, Sturdy, TAM W-101, TAM-105 and TAMj-108)
which differed in drought resistance and yield. Higher water loss from excised leaves
correlated with drought susceptibility.
Randhawa et al. (1988) air dried leaves of 10 wheat varieties and one triticale variety
for 48 hours and found varietal differences in leaf water content and leaf water retention.
They concluded that high leaf water retention (low rate of water loss) may be used as an
indicator of drought tolerance. It has been further suggested that genotypic differences in rate
of water loss, which is presumably an estimate of cuticular transpiration rate, can be used for
screening wheat genotypes for drought resistance (Clark and McCaig, 1982). McCaig and
Romagosa (1989) used eight different genotypes of wheat to evaluate excised leaf water loss
as a screening technique for drought resistance in Triticum turgidum and Triticum
aestivum wheat. Among 8 genotypes, those adapted to dry land exhibited low rate of water
loss suggesting that the trait might be used as a screening criterion for drought resistance.
Araghi and Assad (1998) evaluated the rate of water loss (RWL) from excised-leaves as
drought resistance indicator in six wheat genotypes under normal and water deficit conditions
and RWL was recognized as useful drought resistance indicator. Dhanda and Sethi (2002)
evaluated 30 wheat genotypes for excised leaf water loss at anthesis and maturity to examine
their differences under drought and well-watered conditions and observed that genotypes
differed in their response at both stages of plant growth under drought stress but under well-
13
watered conditions differences in the genotypes were not clear. Munjal and Dhanda (2005)
evaluated 30 wheat genotypes for excised leaf water loss (ELWL) under rainfed and irrigated
conditions and found that genotypes exhibited low ELWL values, indicating the criterion to
distinguish drought resistant from drought susceptible genotypes. Malik and Wright (1998)
conducted field and pot experiments under drought stress to evaluate excised leaf water loss
as screening criterion in six drought resistant and six susceptible spring wheat genotypes and
observed that excised leaf water loss was higher in drought susceptible genotypes. Kumar
and Singh (1998) studied twenty four genotypes of four Brassica species (B. campestris, B.
juncea, B. napus B. carinata) for water loss from excised leaves (WLL) under field
conditions as a selection criterion for drought tolerance and found that all the entries were
significantly different for the trait studied. The genotypes which had lower values of WLL
under drought stress produced comparatively better seed yield. Hence, under field conditions
the parameter, WLL, could be exploited to screen large number of germplasm lines of
Brassica species for moisture stress tolerance.
2.2.5 Leaf Water Potential
Biswas et al., (1986) studied effect of water stress on cotton varieties at early
reproductive stage (40 days after planting) and found that leaf water potential (LWP)
decreased under moisture stress. In several plant species, leaf water potential is reduced under
water deficit conditions, but cotton has the capacity to maintain a higher leaf turgor potential
through osmotic adjustment (Turner et al., 1986 and Nepomuceno et al., 1998).
2.3 Impact of water stress on agronomic traits
2.3.1 Plant height
Plant height is the height of plant from ground level to the top of the main stem. It has
been studied by various researchers under normal and moisture stress conditions. El-Moneim
and Belal (1997) in a study evaluated 119 durum wheat genotypes for plant height during
four successive growing seasons under low rainfed conditions (121-180 mm/year) in El-
Arish, North Sinai, Egypt and it was found that Cham 1 was the most promising genotypes
for plant height under drought conditions. Ali et al. (2005) conducted an experiment to
evaluate thirteen genotypes of rice at three water regimes by supplying 12, 8 and 4 irrigations
at different stages in Faisalabad, Pakistan, for plant height and observed that plant height
decreased with decreasing number of irrigations. They concluded that at least 12 irrigations
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were necessary to achieve reasonable plant height in rice. Effect of drought stress on 7 maize
cultivars/lines studied by Guang Mei and Zheng (2003) in a greenhouse experiment by
exposing plants to drough stress, with soil water at 45% of maximum field capacity at
Central Guizhou, China and revealed that S 9922 and Jinyinyushi among the cultivars
showed satisfactory resistance to drought for plant height. Siddiqui et al. (2007) conducted
an experiment to examine the effect of 3 irrigation regimes viz. 3, 5 and 7 against three
cotton cultivars i.e. TH-41/83, TH224/87 and Niab-78 (control). The results for plant
height were highly significant due to different cultivars and irrigation regimes. The average
values were highest for plant height (105.56 cm) in case of seven irrigations. The results
revealed that cultivars TH-41/83 and TH224/87 are tall growing genetically while Niab-78
was a relatively dwarf in height. The cotton crop irrigated five times give economical
performance as compared to 7 or 3 irrigation.
2.3.2 Yield and its components
The effect of water stress on yield of seed cotton and its components has been
observed by various workers as reported in the literature. Pettigrew (2004) in an experiment
evaluated eight diverse genotypes of cotton to study the effects of moisture deficit stress on
lint yield under dry land and irrigated conditions and found that irrigations produced more
vegetative growth and delayed maturity compared to dry land plants while water deficits
reduced 25% lint yield of dry land plants. Kar et al. (2005) assessed the response of 5 hybrid
varieties of cotton to water stress under field conditions for their tolerance to drought. It was
found that yield and yield contributing traits decreased clearly in all the varieties in response
to water stress imposed at flowering stage. On the basis of yield performance two cotton
hybrids, PKV Hy-4 and PKV Hy-2 were found to be relatively more drought tolerant than
others. Flowering stage in cotton was more critical to moisture stress, than vegetative and
ripening stages. Kumari et al. (2005) evaluated 20 cotton genotypes (CPD 731, CPD 446,
RAH 30, SCS 37, NH 545, L 762, L 760, GSHV 97/612, GBHV 139, KH 134, AKH 8363,
CCH 526613, VIKAS, VA 29, LH 1968, H 1250, PUSA 8-62, F 1945, RS 810 and TCH
1599) in a field experiment for drought tolerance under rainfed conditions in Andhra
Pradesh, India and observed that among the genotypes, L 762, GHSV 97/612 and RAH 30
showed higher seed cotton yield per plant indicating the best drought tolerance.
15
Mert (2005) investigated the effects of water stress on six cotton cultivars for seed
cotton yield under irrigated and non-irrigated conditions in Hatay, Turkey during the 2001
and 2002 cotton growing seasons and observed that medium (Stoneville 453 and Deltapine
5690) and late (Maras 92 and GW Teks) cultivars were more affected by water stress
compared to the early cultivars (Deltapine 20 and Nazilli 143). Mahmood et al. (2006),
studied drought tolerance of eight cotton varieties (BH-118, CIM-446, FH-900, FH-901,
MNH-93, MNH-552, MNH-554 & NIAB Krishma) using various growth and yield related
traits. These varieties were subjected to two and four water deficit cycles in which they showed
distinctive responses with respect to moisture deficit conditions. Certain growth and yield traits
provided some signs of drought resistance in these varieties. The MNH-93 and BH-118
appeared to be more drought tolerant for growth and yield parameters as compared to other
varieties under evaluation. MNH-552, MNH-554, CIM-446, FH-900 and NIAB-Krishma
exhibited some potential to withstand drought intensities, although an affirmative relationship
for growth and yield attributes can not be established in these varieties. Siddiqui et al. (2007)
conducted an experiment to examine the effect of 3 irrigation regimes viz. 3, 5 and 7
against three cultivars i.e. TH-41/83, TH224/87 and Niab-78 (control). It was observed that
the cotton crop which was irrigated five times produced more yield as compared to 3 or 7
irrigations. Niab-78 gave highest yield as compared to TH-41/83 and TH224/87. Gerik et
al. (1996) evaluated two short season cotton cultivars (HQ 95 and GP 74) during 1990 and
1991 under normal and drought conditions at Temple, TX and found that HQ 95 produced
more bolls than GP 74 under normal as well as water deficit conditions in each of the two
years, indicating their drought tolerance.
Cook and El-Zik (1993) evaluated six cotton cultivars (CD 3H, SP 37H, CABUCS,
MACAOS, DPL 41 and PAY 303) for two years in the field under irrigated and non-irrigated
(dry land) conditions to find out the effect of water deficit stress on boll production and
observed that genotypic variability existed among cotton germplasm sources for boll
production. Kumari et al. (2005) conducted a study to evaluate 20 cotton genotypes (CPD
731, CPD 446, RAH 30, SCS 37, NH 545, L 762, L 760, GSHV 97/612, GBHV 139, KH
134, AKH 8363, CCH 526613, VIKAS, VA 29, LH 1968, H 1250, PUSA 8-62, F 1945, RS
810 and TCH 1599) in a field experiment for drought tolerance under rainfed conditions in
16
Andhra Pradesh, India and revealed that among the genotypes, L 762, GHSV 97/612 and
RAH 30 showed higher number of bolls per plant indicating the best drought tolerance.
Similarly, the effect of water stress on yield has been reported in other crops. In
barley Nezar H. (2005) conducted a glasshouse experiment to study the effect of moisture
deficit stress on growth and grain yield of barley. Three drought treatments were applied to
plants at the beginning of grain filling: (1) 100% field capacity (well-watered), (2) 60% field
capacity (mild drought stress), and (3) 20% field capacity ( severe drought stress ) until grain
maturity. It was found that water stress treatments reduced grain yield by reduction in the
number of tillers, spikes, grains per plant and individual grain weight. It was concluded that
drought stress before anthesis was damaging to grain yield regardless severity of the stress.
In wheat, Mirbahar et al. (2009) conducted an experiment to study the effect of different
water stresses applied at different crop development stages on the yield and yield
components of twenty five wheat varieties. The five water stress treatments applied were T1
(control), T2 (post flowering drought), T3 (Pre-flowering drought), T4 (Tillering stage
drought) and T5 (terminal drought). Water stress significantly decreased the plant height,
spike length, spikelets per spike, grains per spike and 1000grain weight of all wheat varieties.
The highest reduction in all parameters was observed in T5. The two varieties Sarsabz and
Kiran-95 showed considerably good performance than other wheat varieties in control as
well as at terminal drought stress. In sunflower Tahir et al. (2002) evaluated twenty five
inbred lines of sunflower under normal and water stress conditions in the field. All the
characters were reduced under water stress conditions. The maximum reduction was observed
in yield per plant. In rice Gulzar et al. (2010) studied twenty five hill rice genotyps under
water stress ( E1) and irrigated ( E2) conditions for drought tolerance and found that water
stress resulted in reduced grain yield due to reduction in number of panicles, panicle length,
grains per panicle, spike lets per panicle and harvest index. In ground nut Vurayai et al.
(2011) evaluated the response of bambara ground nut for agronomic traits to water stress
imposed at different growth stages. The treatments were watering plants to 100 % plant
available water (PAW), withholding water to 30 % PAW at vegetative, flowering and pod
filling stage and irrigation was applied after 21 days of each stress treatment. Seed yield in all
stressed plants was reduced by moisture stress due to reduction in number of pods per plant,
number of seeds per pod and seed weight.
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2.3.3 Fibre quality traits
The effect of water stress on fibre quality traits of cotton has been studied by various
research workers as reported in the literature. Pettigrew (2004) in an experiment evaluated
eight diverse genotypes of cotton to study the effects of moisture deficit stress on fibre length
under dry land and irrigated conditions and reported that irrigations not only caused to
produce more vegetative growth and delayed maturity but also produced approximately 2%
longer fibre than the dry land plants. Mert (2005) conducted an experiment to investigate the
effects of water stress on six cotton cultivars for fibre length, fibre strength and fibre fineness
under irrigated and non-irrigated conditions in Hatay, Turkey during 2001 and 2002 cotton
growing seasons and observed that growing cotton under non-irrigated conditions resulted in
the production of shorter and weaker fibres with reduced micronair. Marani (1973) evaluated
the effects of moisture stress during different stages of cotton development and observed that
fibre fineness was affected adversely by stress during boll development stage.
2.4 Genetic diversity studies
The genetic diversity of crop plants is broadly defined as the extent of dissimilarity
among them. For the improvement and breeding of new varieties of any crop this information
is very helpful for the efficient selection of parental lines for new crosses. Measurement of
genetic diversity is also essential for planning genetic conservation programs and use of
conserved biodiversity for further breeding programs in crop improvement.
Khan et al. (2009) assessed the genetic diversity of 40 representative cotton cultivars
released from 1914 to 2005 in Pakistan with Simple sequence repeat (SSR) markers. Thirty-
four of the 57 SSR primer pairs were found polymorphic and 122 of the 204 SSR bands
detected by these polymorphic primer pairs were polymorphic across the cultivars. The
frequencies of these polymorphic bands ranged from 0.02 to 0.98 with an average 0.27.
Clustering 40 cultivars resulted in three major clusters mixed with cultivars released from
various breeding periods at different research stations. The average dissimilarity (AD) of a
cultivar ranged from 0.191 to 0.365 with the mean AD of 0.248, and genetically distinct
cultivars were identified.
Akter et al. (2008) generated DNA fingerprints of ten jute cultivars from two
Corchorus species (C. olitorius and C. capsularis) by using jute specific SSR markers to
estimate the genetic relatedness among jute cultivars. By using 23 primer pairs 106 alleles
18
were identified among the ten genotypes with an average of 4.61 ± 1.92 alleles per locus, and
estimated a mean genetic diversity 0.68±0.16. Jute cultivars were grouped into two major
clusters, with UPGMA analysis. Asif et al. (2009) verified parentage of F1 hybrids of cotton
by using random amplified polymorphic DNA (RAPD) and microsattelite (SSR) assays.
Between two cotton parents FH-883 and FH-631S, 3 random and 3 EST based SSR primers
out of 500 primers were found polymorphic. These primers differentiated the parent
genotypes and also confirmed the parentage of their F1 hybrids. It was concluded that RAPD
and SSR molecular markers are excellent genomic tools for confirmation of parentage and
determining hybridity.
Dodig et al. (2010) conducted a study to evaluate drought tolerance and determined
regional-based patterns of genetic diversity of bread wheat accessions and tried to find out
new sources of diversity. Genetic diversity was evaluated by simple sequence repeats
(SSR) markers and it was compared with diversity assessed by using 19 phenotypic traits
averaged over irrigated and water deficit stress field conditions. Thirty six SSR primer pairs
were used to profile 96 wheat cultivars. Total 46 loci and 366 alleles were found with a
range of 3 to 21 alleles per locus. The polymorphic information content was found to be
0.61. The genetic distance for all possible 4560 pairs of genotypes ranged from 0.06 to 0.91
with an average of 0.65. Genotypes were grouped according to their region of origin and
drought tolerance (high, medium, low). Analysis of molecular variance represented that
over 96% of the total variation could be due to variance within the drought tolerance and
geographical groups. It was found that, genetic diversity amongst the high drought
tolerance cultivars was noticeably higher than that among low drought tolerance cultivars.
On phenotypic and molecular analyses basis two dendrograms were constructed using the
Unweighted Pair Group Method with Arithmetic Mean method and these were found to be
topologically different. Genotypes characterized as highly drought tolerant were distributed
among all SSR-based cluster groups. This shows that the genetic basis of drought tolerance
in these genotypes was different, thereby enabling wheat breeders to combine these diverse
sources of genetic variability to improve drought tolerance in their breeding programs.
Juan et al. (2009) assessed genetic diversity by RAPD and SSR markers in 61 tomato
varieties from different species (Solanum lycopersicum L., hirsutum. Humb L.,
pimpinellifolium Miller L., chilense Dun. L.,chmielenskii L., peruvianum Miller L.,
19
parvuflorum Miller L.). 2062 clear fragments were amplified by RAPD and 869 by SSR,
respectively. On the other hand, more polymorphic products were found by SSR as compared
to RAPD, i.e., 100 and 43.84%, respectively. In addition, a higher value of the average
similarity coefficient and lower PIC value were reflected in RAPD (0.79, 0.407) compared to
SSR (0.56, 0.687). It can be inferred that SSR was more effective marker than RAPD to assess
genetic diversity in tomato accessions. Similarly, the genetic base of tomato varieties in
Chinese market was narrow. It is suggested that wild tomato varieties should be used to enrich
the genetic base of the cultivated tomato varieties. Bertini et al. (2006) estimated the genetic
distance between fifty three cotton cultivars and to choose a set of SSR primers which are able
to distinguish between the fifty three cotton cultivars under study. DNA was extracted from the
53 cotton cultivars and 31 pairs of SSR primers were used for characterization of cultivars. In
total 66 alleles with an average of 2.13 alleles per locus were identified. Polymorphism
information content (PIC) varying from 0.18 to 0.62 and dissimilarity coefficient varying from
zero to 0.41. Statistical analysis using the unweighted pair-group method using arithmetic
average (UPGMA) revealed 7 subgroups which were dependable with the genealogical
information available for some of the cultivars. Genetic diversity study of cotton cultivars with
SSR markers showed that there is need to introduce new alleles into the gene pool of the
breeding cultivars. Zhang et al. (2005) conducted a study to evaluate the genetic
improvement of Acala 1517 cultivars and lines released over the past 75 years. Genetic
divergence of these lines was also estimated by simple sequence repeat (SSR) markers.
Genetic distance ranged from 0.06 to 0.38 among Acala 1517 genotypes, with an average of
0.18 on the basis of 189 SSR marker alleles, showing a substantial genetic diversity among
Acala 1517 cotton germplasm. Introgression of divergent germplasm in the program has
contributed to genetic diversity of Acala cotton germplasm. Guo et al. (2006) assessed the
genetic diversity of 109 accessions with sixty cotton microsatellite markers. These
included 106 G. arboreum landraces, which were collected from 18 provinces of China. A
total of 128 alleles were identified with an average of 2.13 alleles per locus. The
largest number of alleles and also the maximum number of polymorphic loci was
detected in the A03 linkage group. The polymorphism information content for the 22
polymorphic microsatellite loci varied from 0.52 to 0.98, with an average of 0.89. Genetic
diversity analysis revealed that the landraces in the Southern region had more genetic
20
variability than those from the other two regions, and no significant difference was detected
between landraces in the Yangtze and the Yellow River Valley regions. Erkling and Karaca.
(2005) collected seeds of 36 cotton varieties from State Research Institutes, private sectors
and universities. This study was conducted to determine the genetic purity of cotton varieties
using Simple Sequence Repeat Length Polymorphisms (SSRLPs) and identify the varieties
that are cross-contaminated or segregating for specific trait or traits. The varieties were
grown in the field of West Akdeniz Agricultural Research Institute during 2003. Before and
after leaf sample collection for DNA extraction, plants were visually inspected for different
plant characteristics. For extraction of DNA, 10 leaves from randomly selected 10 plants
were used. A total of 25 SSRLP primer pairs resulted in 32 amplified bands. Five primer
pairs; BNL-3408, BNL-3563, BNL-1679, BNL-3895 and BNL-2496 produced 2, BNL-1053
produced 3 amplified products while rest of the primer pairs produced only one amplicon.
Using the plant characteristics and SSRLP technique they found that Turkish cotton varieties
have very narrow genetic base and existence of physical or genetic mixture in some varieties.
Dubey et al. (2009) studied, a set of 24 tropical maize lines with differential
responses to drought stress, including 16 lines from CIMMYT (Mexico) and eight lines from
India. These lines were characterized by using 37 polymorphic SSR markers, including 29
SSRs tagging specific candidate genes involved in drought stress tolerance in maize. These
genes, distributed on nine of the ten maize chromosomes, also co localized with 17
'consensus QTLs' for various morpho-physiological traits associated with drought tolerance at
flowering stage. The analysis using these 37 candidate gene-specific and drought 'anchor'
markers tagging consensus QTLs led to unambiguous differentiation of the genotypes as well
as assessment of genetic diversity in these important genetic resources. A total of 119 SSR
alleles with a mean of 3.22 alleles per locus were identified. Polymorphism Information
Content (PIC) of the 37 SSR loci ranged from 0.09 (umc1627) to 0.78 (umc1056 and
bnlg1866), with a mean PIC of 0.56. The study resulted in identification of eleven highly
informative markers with PIC values ≥0.65, as well as five unique SSR alleles in DTPW-C9-
F55-2-3, DTPW-C9-F115-1-4, DTPY-C9-F142-1-2, K64R and CML537. Pair-wise genetic
similarity (GS) values, estimated using Jaccard's coefficient, ranged between 0.14 (HKI1025-
K64R; HKI1025-CML247) and 0.74 (HKI-335-HKI-209), with a mean GS of 0.31,
indicating high level of genetic divergence among the genotypes selected for the study.
21
Cluster analysis revealed clear genetic differentiation of the DTP (drought tolerant
population) lines developed at CIMMYT (Mexico) from those developed and identified in
India (e.g. CM140). Principal Component Analysis (PCA) aided in further elucidation of the
genetic relationships as well as differentiation of genotypes largely based on their phenotypic
responses to drought stress.
2.5 Genetic variability for drought tolerance
There must be a considerable amount of genetic variability in the gene pool for the
success of a breeding program for drought tolerance. The fundamental approach for
developing drought tolerant cultivars is to choose locally adapted germplasm containing
genetic variability for high yield potential and drought adaptive traits (Beck et al., 1990;
Vasal et al., 1997).
Exploitation of root morphology is considered important for screening morph-
physiological traits of various crop plants under water stress. Vigorous seedlings provide
basis for good crop establishment and productivity (Mock and Mc Neill, 1979; Koscielniak
and Dubert, 1985). Therefore, assessment of seedlings under moisture deficit conditions is a
significant aspect of crop breeding to evolve drought tolerant varieties. Moisture deficit
condition at seedling stage was achieved by watering the plants with quantity of water 50%
of normal condition (Khan et al. 2004). Various morpho-physiological seedling characters
have been potentially utilized around the globe for screening genotypes of different crops
against moisture deficit conditions (Turner, 1986; Ludlow and Muchow, 1990; Takele, 2000;
Matsui and Singh 2003; Dhanda et al., 2004; Kashiwagi et al., 2004; Pathan et al., 2004;
Tabassum, 2004; Taiz and Zeiger, 2006 and Hussain, 2009). Irum et al. (2011) studied the
effect of seedling traits on seed cotton yield and found that root length was positively and
significantly correlated with seed cotton yield at genotypic and phenotypic levels. Ali et al.
(2009) studied the correlation among the different morpho-physiological traits in sorghum
and revealed that root length showed significant positive association with flag leaf area and
grain yield.
Pace et al. (1999) studied the response of shoot and root growth of cotton cultivars
after a short drought and subsequent recovery period under controlled conditions and 36 days
after planting, plants under stressed treatment were subjected to 10 days water stress by with
22
holding water followed by a recovery period of 10 days. Plants under control condition were
irrigated normally. Various parameters were analyzed and it was observed that all characters
were reduced in water stressed plants as compared to control. However, root growth was not
reduced in the stressed plants than the control. It was found that tap root length was greater in
the stressed plants than in the plants under controlled conditions and it was concluded that
tap root length after water stress may be a common response in cotton and may allow cotton
plants to endure drought by accessing water from deeper in the soil profile.
Quisenberry et al. (1981) evaluated fifteen exotic strains and one commercial cultivar
under irrigated and drought stress conditions. Significant variability was found among the
entries for water use efficiency, shoot dry matter accumulation and shoot and root growth.
They suggested that root morphology and root growth potential were important traits in the
adaptation of cotton to conditions where inadequate water availability is a major constraint of
plant growth both for irrigated and water stress conditions. Longenberger et al. (2006)
conducted an experiment to assess a screening process for drought tolerance in cotton (G.
hirsutum L.) seedlings. In this study 21 converted race stocks (CRS) and 2 cultivars were
assessed for seedling drought tolerance (SDT) on individual plant basis. Genotypes were
evaluated under glasshouse conditions. Three sequential cycles of drought were applied to
seedlings 15 days after planting. It was found that genotypes have variation in their percent
survival following three successive drought cycles. Drought cycles two and three did not
have any role in the separation of genotypes. DP 491 was found to be the most drought
tolerant genotype. Thind and Pinky (2008) evaluated 10 genotypes of cotton viz. LD-694,
LD-784, LD-805, LD-861, LD-866, LD-875, LD-876, LD-900, LD-902 and LD-908 for
moisture deficit stress. After the imposition of moisture stress, shoot length was adversely
affected in all genotypes. When water deficit level was increased, the adverse effect was also
increased. There was significant variation in root length under moisture stress. The genotypes
LD-875, LD-876 and LD-908 are able to withstand severe moisture stress conditions and
these are selected as drought tolerant. Iqball et al. (2011) examined responses of 80
genotypes/lines of G. hirsutum at seedling stage under two irrigation regimes, water
stressed and non-stressed, under glasshouse conditions. Plant growth was measured as
longest root and shoot after 45 days. Genotypic differences for indices of drought tolerance
were statistically significant.
23
Kll et al. (2005) conducted an experiment to evaluate 7 cotton genotypes in the field
to determine genetic and environmental variability and broad sense heritability for seed
cotton yield under well watered conditions in Turkey and found that broad sense heritability
was high for seed cotton yield (91.80%). Quisenberry and Micheal (1991) reported genetic
variation in yield and water use efficiency for cotton subjected to water deficit. Dhanda and
Sethi (2002) studied differences in some morpho-physiological traits amongst wheat
genotypes in response to drought stress at anthesis and maturity stages. It was revealed that
genotypes differed in their response to drought at both stages of plant growth for grain yield,
days to heading, relative water content, excised leaf water loss and leaf membrane stability
under drought stress and there were significant genotype x environment inter-actions. It was
found that terminal drought stress resulted in reduced mean values and variability for all the
traits examined.
Reddy and Kumari (2004) evaluated 39 genotypes of American cotton (G.
hirsutum), grown in Lam, Andhra Pradesh, India, during 1997-98, for the estimation of
genetic variability for yield and yield components in addition to drought tolerance
parameters. The drought tolerance parameters, specific leaf area and specific leaf weight
showed considerable variation. The genetic coefficient of variation and phenotypic
coefficient of variation were high for specific leaf area and specific leaf weight, representing
that these characters were not much affected by the environment. Alishah et al. (2009)
studied five improved cotton varieties for some plant traits under drought stress were studied
in a split plot design with three replications. On the basis of combined variance analysis
significant differences were revealed among the genotypes for boll number, boll weight,
yield, fibre length and number of sympodial and monopodial branches. Drought stress
resulted in reduction of boll number, boll weight, yield and induced earliness. Ullah et al.
(2008) assessed genotypic variability for drought tolerance in cotton using physiological
traits with yield attributes under well watered and water stress regimes in field experiment. It
was observed that seed cotton yield was distinctly affected under water stress conditions in
all cultivars studied. Substantial genotypic variations were found for physiological traits like
gas exchange. Pereira et al. (1998) evaluated medium fibre1 cotton cultivars for seed
germination under moisture stress and it was found that that there were differences in seed
germination between genotypes under different moisture stress levels. Burke (2007) in an
24
experiment identified varietal differences in cotton both under field and green-house
conditions in response to available soil water. It was found that varieties differed in their
response for the physiological traits both under irrigated and moisture stress treatments.
2.6 Genetics of drought tolerance
Basnayake et al. (1995) conducted an experiment to study the inheritance of osmotic
adjustment owing to water stress using generations resulting from three possible bi-parental
crosses between two inbred sorghum lines with a high capability for osmotic adjustment
(Tx2813 and TAM422) and one with low capability (QL27). Broad sense heritability was
found to be high. Analysis of segregation ratios by the mixture method of clustering
recognized two independent major genes for high osmotic adjustment. The line Tx2813 had a
recessive gene with the symbol oal; the line TAM422 had an additive gene with the symbol
OA2. Populations of recombinant inbred lines were produced and characterized for osmotic
adjustment. These were used to know about the contribution of high osmotic adjustment to
the grain yield of sorghum under a variety of water stress conditions. Dhanda and Sethi
(1998) conducted an experiment on the half-diallel set of crosses involving two drought
tolerant, two moderately tolerant and two sensitive varieties of wheat to study the inheritance
of relative water content and excised leaf water loss. The experiment was conducted under
glasshouse and field conditions at tillering and anthesis stages of plant development. It was
found that additive gene action, in general, played a major role in determining the inheritance
of these traits. General combining ability (GCA) was the main source of genetic variation
among crosses, while specific combining ability (SCA) was negligible. It was revealed that
selection for relative water content may be more effective at anthesis, while for excised leaf
water loss at both stages of plant growth.
Yue et al. (2006) studied the genetic basis of drought tolerance and drought
avoidance at reproductive stages in rice using a recombinant inbred line population from a
cross between an Indica lowland and a tropical Japonica upland cultivar. The plants were
grown independently in Polyvinyle chloride (PVC) pipes and two cycles of drought stress
were applied to individual plants with unstressed plants as control. A total of 21 characters
were investigated. Little correlation of relative yield traits with potential yield, plant size, and
root traits was detected. It was found that drought tolerance and drought avoidance were well
separated in the experiment. Bhutta et al. (2006) investigated six wheat varieties/lines and six
25
derived F2 hybrids to determine and compare heritability and genetic advance for flag leaf
water potential, flag leaf osmotic pressure, flag leaf venation, flag leaf area and flag leaf
thickness. Broad sense heritability for flag leaf osmotic pressure ranged from 22.11 to
74.16% for the cross AS-2002 x SARC-5 and SH-2002 x SARC-5, respectively. The highest
genetic advance value (1.16) was obtained from the cross SH-2002 x SARC-5, whereas the
lowest genetic advance (0.27) was obtained from the cross AS-2002 x SARC-5. It was
suggested that these traits deserve better attention in future breeding projects for evolving
better wheat for stress environments.
Liu et al. (1998) while evaluating cotton germplasm for abiotic stress reported that
resistance was under genetic control and suggested that germplasm possessing drought
resistance can be used effectively to extend the growing area in the arid and saline
conditions. The work of Singh (1995) indicated that heritability of drought tolerance in
common beans, varied from low to high and expected gain from selection ranged from 10 to
48 %. Iqball et al. (2011) crossed four tolerant i.e., 149F, B-557, DPL-26, BOU 1724-3 and
4 susceptible namely FH-1000, NF-801-2, CIM-446 and H-499 genotypes/lines in diallel
fashion. The responses of 64 families were examined under water stress and non-stressed
(control) conditions at seedling stage. The results revealed that both additive and non-
additive genes affected variation for drought tolerance, but the influence of additive gene
was more pronounced. High estimates of h2ns, (0.82) and mode of gene action suggest that it
is possible to improve drought tolerance in G. hirsuitum by single plant selection in later
segregating generations.
2.7 Gene action studies
The importance of information regarding gene action involved in the inheritance
pattern of different plant traits of cotton as well as other crops has been emphasized by a
large number of researchers and reported in the literature.
2.7.1 Plant height
Gene action for plant height has been studied by various research workers. Mukhtar et
al. (2000a) studied four cotton (G. hirsutum) genotypes (CIM 1100, CIM 443, VH 57 and
CIM 444) and their 12 F1 hybrids to estimate the type of gene action responsible for plant
height under well watered conditions at Faisalabad, Pakistan and indicated additive type of
gene action with partial dominance for the trait under study while epistatic effects were non
26
significant in the manifestation of the trait. They stated that the situation was quite helpful for
a plant breeder to improve the plant height through simple selection procedure. Subhani and
Chowdhry (2000) studied 6x6 diallel cross analysis in wheat to estimate type of gene action
for plant height under both irrigated and drought stress conditions. Their results exhibited
partial dominance along with additive type of gene action for plant height under both
environments. Therefore, they concluded that the presence of additive type of gene action
along with partial dominance for plant height would suggest the selection in early
segregating generations. Abro (2003) carried out a complete diallel cross experiment for
assessing gene action for plant height. His results indicated that plant height was governed by
over dominance type of gene action. Saravanan et al. (2003) evaluated a diallel set of seven
cotton cultivars (MCU 12, Paiyur 1, SVPR 2, Anjali, Maruthi, MCU 5 and Suvin) to study
the gene action and components of variation for plant height under well watered conditions at
Coimbatore, Tamil Nadu, India and indicated that additive (D) component was non-
significant while dominance (H) component of genetic variation was significant for plant
height suggesting the predominance of dominant factors involved in the trait. The degree of
dominance was more than unity which indicated the existence of over dominance for the
trait. The high estimates of narrow sense heritability for plant height indicated the presence
of additive gene effects for this trait.
Ahuja et al. (2004) evaluated 51 single plant selections of different colour linted
genotypes of cotton in the field under well watered conditions in Sirsa, Haryana, India and
found high genotypic coefficient of variation, heritability and genetic advance for plant
height indicating involvement of additive type of gene action and thus selection would be
effective for improvement of this trait. Ahmed et al. (2006) evaluated six cotton genotypes
(Chandi 95, Sohni, NIA 76, NIAB 98, NIAB 801 and LRA 5166) along with their nine F1
hybrids for plant height to estimate heritability and genetic advance under well watered
conditions at Tandojam, Pakistan and found that plant height showed moderate to high
heritability estimates and genetic advance, indicating additive with partial dominance type of
gene action suggesting the feasibility of selection in the early segregating generations.
Chandra et al. (2004) evaluated 50 F5 bulk lines of five wheat crosses viz. (1)
Kanchan x DSN 34, (2) Kanchan x YC 17, (3) Kanchan x YC 16, (4) Kanchan x BW 115
and (5) Kanchan x Ad. 119 to study heritability and genetic advance for plant height under
27
normal conditions at Mymensingh, Bangladesh and found very high heritability and
moderate to high genetic advance for the trait suggesting the predominance of additive
genetic variation. Inamullah et al. (2005) studied the gene action of bread wheat (Triticum
aestivum) using 8x8 diallel analysis under normal conditions at Peshawar, Pakistan and
found that for plant height the additive component was significant. Ahmed et al. (2007)
evaluated six wheat genotypes (LU 26S, WL 59, WL 60, WL 61, WL 62, and WL 63) along
with their five F2 progenies (WL 59 × LU 26S, WL 60 × LU 26S, WL 61 × LU 26S, WL 62
× LU 26S, and WL 63 × LU 26S) to estimate heritability and genetic advance for plant
height under natural drought conditions at Faisalabad, Pakistan and found high heritability
estimates for plant height (75.19 to 90.93%) along with moderate genetic advance values
ranging from 11.13 to 14.09 for all crosses, indicating additive type of gene action for the
trait. Memon et al. (2007) evaluated seven F3 progenies of spring wheat and their 8 parental
lines for plant height to calculate heritability estimates under normal conditions at Tandojam,
Pakistan and obtained highest broad sense heritability (92.4%) with high genetic advance for
plant height in progeny Khirman x RWM-9313, indicating better chance of selection for this
trait. Kumar et al. (2005) estimated nature of gene action in 7 maize inbred lines (HKI 1344,
HKI 1345, HKI 1347, HKI 1350, HKI 1351, HKI 1352 and HKI 1353) using 7x7 diallel
design for plant height under normal conditions and revealed that there was over-dominance
for the trait under study, indicating the importance of non-additive gene action in its
expression.
Murugan and Ganesan (2006) estimated gene action in six generations (P1, P2,
F1, F2, BC1 and BC2) of five rice crosses (IR 58025 x IR72, IR 58025 x IR24, IR 58025 x
Daunsan, IR 58025 x ARC 11353 and IR 58025 x IR 547442-22-19-3) for plant height under
normal conditions in Tamil Nadu, India and additive, dominance and interaction effects were
observed for the trait. Patra et al. (2006) evaluated 20 rice genotypes for plant height to
estimate heritability and genetic advance under normal conditions at Cuttack, Orissa, India
and found high heritability in broad sense coupled with high genetic advance for plant height
indicating the role of additive gene action providing ample scope for effective selection.
Prakash and Verma (2006) studied the six generations (P1, P2, F1, F2, BC1 and BC2) of two
barley crosses (BL-2 x RD 2433 and RD 2407 x RD 2433) to estimate the gene actions for
plant height under normal conditions and observed overdominance and partial dominance
28
type of gene action for the trait. Rahman et al. (2006) studied 21 barley (Hordeum vulgare)
genotypes including six cultivars (BARI barley 2, BBL 9402-43-1, BBL 9402-12-2, K 163,
K 351 and IBYT/97-4) and their 15 non-reciprocal F1 hybrids to estimate gene action for
plant height under normal conditions and observed that dominance effect was involved for
the character under study. Gohil et al. (2006) studied the broad-sense heritability and genetic
advance for plant height in 55 diverse soyabean (Glycine max) genotypes under normal
conditions in Gujarat, India and observed high heritability along with high genetic advance
values for plant height indicating the control of additive gene action for the trait which could
be improved through simple selection procedure. Sarwar et al. (2011) conducted a diallel
cross experiment involving three exotic lines that is DPL-775, 71-821 Bulk-OP and one local
line that is MNH-53, for genetic analysis of plant height in coton. Additive gene action with
partial dominance was found to control plant height, which could be improved through simple
selection procedure.
2.7.2 Number of monopodial branches
Singh et al. (1971) studied the genetics of number of monopodial branches in 8
cotton varieties and found that Additive and dominance genetic variances was significant
for this trait along with the genetic interactions. Abro (2003) carried out a complete diallel
cross experiment for assessing gene action for number of monopodia. His results indicated
that number of monopodia was governed by partial dominance type of gene action. Abbas et
al. (2008) studied five cotton varieties namely CIM-443, NIAB Krishma, Cris-420, RH-112
and coker-207 to evaluate genetic effects involved in the inheritance number of monopodial
branches. Additive type of gene action along with partial dominance was observed for this
trait.
2.7.3 Number of sympodial branches
Singh et al. (1971) studied the genetics of number of sympodial branches in 8
cotton varieties and reported that Additive and dominance genetic variances was
significant for this trait along with the genetic interactions. Silva and Alves (1983) studied
gene action in cotton (G. hirsutum L.) and reported that for number of fruiting branches
(sympodial branches) additive and dominance as well as epistasis was involved in the
inheritance. Iqbal and Nadeem (2003) estimated genetic effects for number of
sympodial branches per plant from two Upland cotton crosses through generation mean
29
analysis from six populations (P1, P2, F1, F2, BC1 and BC2). The generation mean
analysis indicated the presence of additive gene action in crosses i.e., S-12 x S-14, 5-
12 x Albacala (69)11, LRA-5166 x S-12 and LRA-5166 x S-14 for number of
sympodial branches per plant. Punitha et al. (1999) observed non-additive type of gene
action for sympodial branches in cotton.
Sarwar et al. (2011) conducted a diallel cross experiment involving three exotic lines
that is DPL-775, 71-821 Bulk-OP and one local line that is MNH-53, for genetic analysis of
number of sympodial branches, to evaluate gene action for this trait in upland cotton.
Additive gene action with partial dominance was found to control number of sympodial
branches which could be improved through simple selection procedure.
2.7.4 Number of bolls per plant
Gene action for number of bolls per plant has been studied by many research workers.
Pathak and Singh (1970) investigated the inheritance of number of bolls in cotton (G.
hirsutum L.) and found that additive and epistatic genetic effect were important for this
trait in all the crosses. Singh et al. (1971) studied the genetics of number of boll in 8 cotton
varieties. Additive and dominance genetic variance was significant for this character along
with the genetic interaction. Silva and Alves (1983) studied gene action in cotton (G.
hirsutum L.) and reported that for number of bolls per plant additive gene action was
predominant, while dominance affected bolls per plant to a minor extent. Randhawa et al.
(1986) revealed the presence of epistasis for number of bolls and found that additive genetic
variance was predominant for boll number. Kalsy and Garg (1988) evaluated two cotton
crosses (F 414 x A 2063 and F 286 x B 55-53) along with their parents (P1, P2), F2 and back
crosses (BC1, BC2) to find out information on the nature and magnitude of various types of
gene effects under well watered conditions at Faridkot, India and observed that the additive
component was responsible for the inheritance of boll number in both the crosses, suggesting
the possibility of exploiting this component for the isolation of desirable segregants by
simple selection technique. Shah et al. (1993) analyzed a 4 x 4 diallel cross experiment to
have genetic information about number of bolls per plant. The results indicated that the trait
bolls per plant were controlled by additive gene action. Saeed et al (1996) made intra specific
crosses in a diallel design to study the gene action for number of bolls. The result showed
that number of bolls was under the control of additive type of gene action. Ahmad et al.
30
(1997) evaluated four upland cotton (G. hirsutum) varieties (S 12, BP 52-63, Okra and Bar.
8) along with their 12 F1 hybrids for diallel analysis under well watered conditions at
Faisalabad, Pakistan and found additive and partial dominance gene action along with
epistatic effects for number of bolls per plant.
Yingxin and Xiangming (1998) designed a complete diallel cross test to study the
combining ability and inheritance of bolls per plant in 7 upland cotton (G. hirsutum L.)
varities. The results indicated that bolls per plant were controlled by additive and non
additive gene effects. Gomaa (1997) studied three crosses (Giza 45 x Giza 75, Giza 45 x Giza
77 and Giza 45 x Family 10/87) of cotton along with their parents, F2 and backcrosses for
generation means analysis under well watered conditions and dominance variance was
observed for number of bolls/plant in crosses II and III. Ali et al. (1998) evaluated four
cotton varieties (1517-75, D 2-L-9-68, M 4 and CIM 240) along with their reciprocal crosses
to estimate heritability for number of bolls per plant under well watered conditions at
Faisalabad, Pakistan and revealed that broad sense heritability estimates were 15.19 to 60.45
% for boll number, indicating additive and non-additive type of gene action for the trait.
Gomaa et al. (1999) evaluated two cotton crosses (Family 8/87 x S 6037 and Giza 80 x S
6037), their parents (P1, P2), F2 and F3 families to estimate the type of gene action for number
of bolls/plant under well watered conditions and observed additive variance in the first cross
and dominance variance in the second cross for bolls /plant. Mukhtar et al. (2000a) studied
four cotton (G. hirsutum) genotypes (CIM 1100, CIM 443, VH 57 and CIM 444) and their
12 F1 hybrids to estimate the type of gene action responsible for number of bolls per plant
under well watered conditions at Faisalabad, Pakistan and indicated additive type of gene
action with partial dominance for the trait under study while epistatic effects were non
significant in the manifestation of the trait.
Ahmad et al. (2001) studied the inheritance of bolls per plant in a 4 x 4 diallel cross
experiment under well watered conditions at Faisalabad, Pakistan and found additive type of
gene action with partial dominance for the trait under study. Bertini et al. (2001) studied the
genetics of two parental lines and their F1, F2, RC1 and RC2 generations in cotton under well
watered conditions and observed dominance type of gene action for number of bolls per
plant. Saravanan et al. (2003) evaluated a diallel set of seven cotton cultivars (MCU 12,
Paiyur 1, SVPR 2, Anjali, Maruthi, MCU 5 and Suvin) to study the gene action and
31
components of variation for number of bolls per plant under well watered conditions at
Coimbatore, Tamil Nadu, India and indicated that dominance (H) component was significant
but additive (D) component of genetic variation was non-significant for number of bolls per
plant. The degree of dominance was more than unity which indicated the existence of over
dominance for the trait. Abro (2003) carried out a complete diallel cross experiment for
assessing gene action for boll number. His results indicated that boll number was governed
by over dominance type of gene action. Ahuja et al. (2004) evaluated 51 single plant
selections of different colour linted genotypes of cotton in the field under well watered
conditions in Sirsa, Haryana, India and found high genotypic coefficient of variation,
heritability and genetic advance for number of bolls per plant indicating involvement of
additive type of gene action and thus selection would be effective for the trait. Number of
bolls per plant also exhibited positive and high direct effects on seed cotton yield. Therefore,
they concluded that number of bolls per plant was the most important trait for selection of
genotypes with high potential of seed cotton yield.
Murtaza (2005) studied a complete diallel cross experiment including eight cotton (G.
hirsutum) genotypes (Laokra 5-5, DPL 7340-424, Fregobract, Glandless 4195-220, SA 100,
Stoneville 857, S 14 and B 557), their 56 F1 and 56 F2 generations to estimate gene action for
boll number per plant under well watered conditions at Multan, Pakistan and estimated that
boll number had low narrow sense heritability (0.37) in F1 and high (0.75) in F2, indicating
that boll number had over dominance type of gene action in F1 and additive in F2. Therefore,
it was suggested that additive variation in the character under study should be exploited
through selection in early segregating generations while those with over dominance should
be delayed. Prasad et al. (2005) studied 42 F1 cotton progenies along with their parents in the
field to estimate the magnitude of heritability and genetic advance for number of bolls per
plant under well watered conditions in Andhra Pradesh, India and showed moderate
estimates of heritability and genetic advance for the trait under study. Ahmed et al. (2006)
evaluated six cotton genotypes (Chandi 95, Sohni, NIA 76, NIAB 98, NIAB 801 and LRA
5166) along with their nine F1 hybrids for number of bolls per plant to estimate heritability
and genetic advance under well watered conditions at Tandojam, Pakistan and found
moderate to high heritability along with low genetic advance values for the trait under study,
which indicated over dominance type of gene action thereby revealing that delayed selection
32
might be useful. Esmail (2007) evaluated six generations (P1, P2, F1, F2, BC1 and BC2) of two
cotton crosses (Mc-Naire 235 x Nazilli-m55 and Giza 70 x Uzbek) under well watered
conditions to estimate the gene action for boll number per plant and revealed that this trait
was under the control of additive, dominance and epistatic gene effects. Desalegn et al.
(2009) studied 15 F1 cotton hybrids in the field to estimate heritability for boll number under
well watered conditions in Ethiopia and found high heritability (59 %) in broad sense,
indicating additive type of gene action for the trait.
2.7.5 Boll weight per plant
The nature and magnitude for the inheritance of boll weight has been studied by many
research workers. Pathak and Singh (1970) investigated the inheritance of boll weight in
cotton (G. hirsutum L.) and found that additive and epistatic genetic effects were
important for this character in all the crosses. Singh et al. (1971) studied the genetics of
boll weight in 8 cotton varieties. Additive and dominance genetic variance was significant
for this character along with the genetic interactions. Kaseem et al. (1984) reported
additive, dominance and epistatic gene effects in the inheritance of boll weight. Tyagi (1988)
reported that boll weight was controlled by dominant gene action. Kalsy and Garg (1988)
evaluated two cotton crosses (F 414 x A 2063 and F 286 x B 55-53) along with their parents
(P1, P2), F2 and back crosses (BC1, BC2) to find out information on the nature and magnitude
of various types of gene effects under well watered conditions at Faridkot, India and
observed that additive, dominance and epistatic gene effects were involved in the inheritance
of boll weight. The contribution of dominance and epistatic gene effects was greater than the
additive effects. Therefore, reciprocal recurrent selection may be the most suitable breeding
procedure for the improvement of boll weight to exploit additive and non-additive gene
effects. Ahmad et al. (1997) evaluated four upland cotton (G. hirsutum) varieties (S 12, BP
52-63, Okra and Bar. 8) along with their 12 F1 hybrids for diallel analysis under well watered
conditions at Faisalabad, Pakistan and found additive gene action along with partial
dominance for boll weight. Gomaa (1997) studied three crosses (Giza 45 x Giza 75, Giza 45
x Giza 77 and Giza 45 x Family 10/87) of cotton along with their parents, F2 and backcrosses
for generation means analysis under well watered conditions and additive variance was
observed for boll weight in crosses I and II.
33
Ali et al. (1998) evaluated four cotton varieties (1517-75, D 2-L-9-68, M 4 and CIM
240) along with their reciprocal crosses to estimate heritability for boll weight under well
watered conditions at Faisalabad, Pakistan and revealed that broad sense heritability
estimates were prominent for boll weight (44.22 to 85.77 %), suggesting the improvement for
this trait through selection. Gomaa et al. (1999) evaluated two cotton crosses (Family 8/87 x
S 6037 and Giza 80 x S 6037), their parents (P1, P2), F2 and F3 families to estimate the type
of gene action for boll weight under well watered conditions and observed that additive and
dominant genetic variances were controlling boll weight in both the crosses. Mukhtar et al.
(2000a) studied four cotton (G. hirsutum) genotypes (CIM 1100, CIM 443, VH 57 and CIM
444) and their 12 F1 hybrids to estimate the type of gene action responsible for boll weight at
Faisalabad, Pakistan and indicated additive type of gene action with partial dominance for the
trait under study while epistatic effects were non significant in the manifestation of the trait.
Ahmad et al. (2001) studied the inheritance of boll weight in a 4 x 4 diallele cross experiment
under well watered conditions at Faisalabad, Pakistan and found additive type of gene action
with partial dominance for the trait under study. Bertini et al. (2001) studied the genetics of
two parental lines and their F1, F2, RC1 and RC2 generations in cotton under well watered
conditions and dominance type of gene action was observed for boll weight. Saravanan et al.
(2003) evaluated a diallel set of seven cotton cultivars (MCU 12, Paiyur 1, SVPR 2, Anjali,
Maruthi, MCU 5 and Suvin) to study the gene action and components of variation for boll
weight under well watered conditions at Coimbatore, Tamil Nadu, India and indicated that
dominance (H) component was significant but additive (D) component of genetic variation
was non-significant for boll weight. The degree of dominance was more than unity which
indicated the existence of over dominance for the trait.
Reddy and Kumari (2004) evaluated 39 genotypes of G. hirsutum, to estimate
heritability and genetic advance for boll weight under normal conditions during 1997-98 in
Lam, Andhra Pradesh, India. High heritability along with high genetic advance was observed
for boll weight, indicating the operation of additive gene action for this trait. Murtaza (2005)
studied a complete diallel cross experiment including eight cotton (G. hirsutum) genotypes
(Laokra 5-5, Fregobract,DPL 7340-424, Glandless 4195-220, Stoneville 857, SA 100, S 14
and B 557), their 56 F1 and 56 F2 generations to estimate gene action for boll weight under
well watered conditions at Multan, Pakistan and found that boll weight had low narrow sense
34
heritability (0.24 F1, 0.23 F2), representing the involvement of non-additive type of gene
action for the trait under study. Prasad et al. (2005) studied 42 F1 cotton progenies along with
their parents in the field to estimate the magnitude of heritability and genetic advance for boll
weight under well watered conditions in Andhra Pradesh, India and showed moderate
estimates of heritability and genetic advance for the trait under study. Ahmed et al. (2006)
evaluated six cotton genotypes (Chandi 95, Sohni, NIA 76, NIAB 98, NIAB 801 and LRA
5166) along with their nine F1 hybrids for boll weight to estimate heritability and genetic
advance under well watered conditions at Tandojam, Pakistan and exhibited moderate to high
heritability along with low genetic advance values for the trait under study, which indicated
over dominance type of gene action thereby revealing that delayed selection might be useful.
Esmail (2007) evaluated six generations (P1, P2, F1, F2, BC1 and BC2) of two cotton crosses
(Mc-Naire 235 x Nazilli-m55 and Giza 70 x Uzbek) to estimate the gene action for boll
weight and revealed that the character studied was under the control of additive, dominance
and epistatic gene effects. Desalegn et al. (2009) studied 15 F1 cotton hybrids in the field to
estimate heritability for boll weight under well watered conditions in Ethiopia and found high
heritability (62 %) in broad sense, indicating additive type of gene action for the trait. Singh
et al. (2010) analyzed 8 x 8 diallel mating design for cotton lines and observed that additive
type of gene action was involved in the inheritance of boll weight trait.
2.7.6 Yield
Gene action for yield has been studied by many research workers under drought and
normal conditions. Pathak and Singh (1970) investigated the inheritance of seed cotton
yield in cotton (G.hirsutum L.) and found that additive and epistatic genetic effect were
important for the seed cotton yield trait in all the crosses. Kaseem et al. (1984) reported
additive, dominance and epistatic gene effects in the inheritance of seed cotton yield.
Randhawa et al. (1986) revealed the presence of epistasis for seed cotton yield. They
concluded that additive genetic variance was predominant for seed cotton yield. Kalsy and
Garg (1988) performed generation means analysis for yield of seed cotton per plant. Their
results showed that additive, dominance and epistasis gene action were important for
inheritance of seed cotton yield. Sayal and Sulemani (1996) studied the genetics of 56 F1
hybrids along with their 8 parents for seed cotton yield under irrigated conditions and found
additive gene action for this trait. Ahmad et al. (1997) evaluated four upland cotton (G.
35
hirsutum) varieties (S 12, BP 52-63, Okra and Bar. 8) along with their 12 F1 hybrids to
estimate the gene action of seed-cotton yield under well watered conditions at Faisalabad,
Pakistan and found additive and partial dominance gene action along with epistatic effects
for the trait under study. El-Seidy (1997) assessed the genetic variability for grain yield per
plant in 15 F1 and 15 F2 populations of barley under water stressed and non-stressed
conditions, in Tanta, Egypt and found that additive and dominance gene effects significantly
influenced the trait in F1 populations under both conditions, while additive gene effects were
higher than dominance gene effects in the F2 generation under both environments. Drought
reduced the yield in both populations. All hybrids were relatively drought tolerant. Therefore,
selection and pedigree breeding based on grain yield was recommended to improve barley
productivity under drought environments.
Gomaa (1997) studied three crosses (Giza 45 x Giza 75, Giza 45 x Giza 77 and Giza
45 x Family 10/87) of cotton along with their parents, F2 and backcrosses under well watered
conditions to estimate the gene action of seed cotton yield and found additive variance in
cross III, while dominance variance was observed in cross II for seed cotton yield/plant. Ali
et al. (1998) evaluated four cotton varieties (1517-75, D 2-L-9-68, M 4 and CIM 240) along
with their reciprocal crosses to estimate heritability for seed cotton yield per plant under well
watered conditions at Faisalabad, Pakistan and revealed that broad sense heritability
estimates were prominent for seed cotton yield (9.89 to 62.69 %), suggesting the
improvement for this trait through selection. Gomaa et al. (1999) evaluated two cotton
crosses (Family 8/87 x S 6037 and Giza 80 x S 6037), their P1, P2, F2 and F3 families to
estimate the type of gene action for seed cotton yield/plant under well watered conditions and
observed additive variance in the first cross and dominance variance in the second cross for
seed cotton yield/plant. Mukhtar et al. (2000a) studied four cotton (G. hirsutum) genotypes
(CIM 1100, CIM 443, VH 57 and CIM 444) and their 12 F1 hybrids to estimate the type of
gene action responsible for seed cotton yield per plant at Faisalabad, Pakistan and indicated
additive type of gene action with partial dominance for the trait under study while epistatic
effects were non significant in the manifestation of the trait. They stated that the situation
was quite helpful to a plant breeder to improve the yield through simple selection procedure.
Subhani and Chowdhry (2000) evaluated 36 wheat genotypes including six parent
varieties and their thirty F1 generations to estimate type of gene action for grain yield per
36
plant under both irrigated and drought stress conditions. Their results exhibited that over
dominance type of gene action changed into partial dominance or vice versa with the change
of environment for grain yield per plant. Ahmad et al. (2001) studied the inheritance of seed
cotton yield in a 4 x 4 diallele cross experiment under well watered conditions at Faisalabad,
Pakistan and found additive type of gene action with partial dominance for the trait under
study. Saravanan et al. (2003) evaluated a diallel set of seven cotton cultivars (MCU 12,
Paiyur 1, SVPR 2, Anjali, Maruthi, MCU 5 and Suvin) to study the gene action and
components of variation for seed cotton yield per plant under well watered conditions at
Coimbatore, Tamil Nadu, India and indicated that additive (D) and dominance (H)
components of genetic variation were significant for seed cotton yield per plant. The degree
of dominance was more than unity which indicated the existence of over dominance for the
trait. Iqbal and Nadeem (2003) estimated genetic effects for yield of seed cotton from
two Upland cotton crosses through generation mean analysis from six populations (P1,
P2, F1, F2, BC1 and BC2). The scaling test revealed involvement of epistasis in all the
crosses, except S-14 x LRA-5166 for yield of seed cotton per plant. The rest of the
crosses were predominately under non additive genetic control except S-14 x LRA5166
for yield of seed cotton per plant, hence delayed selection would be fruitful in these
crosses. Ahuja et al. (2004) evaluated 51 single plant selections of different colour linted
genotypes of cotton in the field under well watered conditions in Sirsa, Haryana, India and
found high genotypic coefficient of variation, heritability and genetic advance for seed cotton
yield per plant indicating involvement of additive type of gene action and thus selection
would be effective for the trait under study. Azhar et al. (2004) evaluated a diallel cross
experiment of 5 cotton varieties [CIM 726 (white cotton), Dark brown, Light brown, Dark
green and Light green] to study the broad sense heritability under well watered conditions at
Faisalabad, Pakistan and estimated 33 % broad sense heritability for seed cotton yield
indicating involvement of interaction type of gene action for the trait. Chandra et al. (2004)
evaluated 50 F5 bulk lines of five wheat crosses viz. (1) Kanchan x DSN 34, (2) Kanchan x
YC 17, (3) Kanchan x YC 16, (4) Kanchan x BW 115 and (5) Kanchan x Ad. 119 to study
heritability and genetic advance for grain yield per plant under normal conditions at
Mymensingh, Bangladesh and found very high heritability and moderate to high genetic
advance for the trait, suggesting the predominance of additive genetic variation.
37
Afarinesh et al. (2005) evaluated 21 maize genotypes to find out gene action for grain
yield under normal and drought conditions and revealed that dominance variance was
responsible for controlling grain yield under both irrigated and drought conditions, while
additive and dominance variances were involved under irrigated conditions. Inamullah et al.
(2005) studied the gene action of bread wheat (Triticum aestivum) using 8x8 diallel analysis
under normal conditions at Peshawar, Pakistan and indicated that the dominance component
was significant for grain yield per plant. Karad et al. (2005) estimated the variability,
heritability and genetic advance for plant height in 16 soybean genotypes under normal
conditions at Kolhapur, Maharashtra, India and found that genotypic coefficients of variation,
heritability and genetic advance were high for grain yield per plant representing the presence
of additive gene action for this trait, which had scope for improvement through selection. Kll
et al. (2005) evaluated 7 cotton genotypes in the field to estimate genetic and environmental
variability and broad sense heritability for seed cotton yield under irrigated conditions in
Turkey and found that broad sense heritability was high for seed cotton yield (91.80%),
which could be easily improved through selection.
Kumar et al. (2005) estimated nature of gene action in 7 maize inbred lines (HKI
1344, HKI 1345, HKI 1347, HKI 1350, HKI 1351, HKI 1352 and HKI 1353) using 7x7
diallel design for grain yield per plant under normal conditions and revealed that there was
over-dominance for the trait under study, representing the importance of non-additive gene
action in its expression. Prakash et al. (2005) estimated the gene action in six generations (P1,
P2, F1, F2, BC1 and BC2) of two barley crosses (BL 2 x RD 2433 and RD 2407 x RD 2433)
for grain yield per plant under well watered conditions at Jaipur, Rajasthan, India and
observed that additive, dominance and epistatic effects were involved in the inheritance of
the trait under study. Prasad et al. (2005) studied 42 F1 cotton progenies along with their
parents in the field to estimate the magnitude of heritability and genetic advance for seed
cotton yield per plant under well watered conditions in Andhra Pradesh, India and found high
estimates of heritability and genetic advance for the trait under study indicating the role of
additive gene action, providing ample scope for effective selection. Reddy and
Satyanarayana (2005) estimated heritability and genetic advance for seed cotton yield in 55
cotton genotypes under four different environments [normal sowing- irrigated (E1); normal
sowing- rainfed (E2); delayed sowing- irrigated (E3) and delayed sowing- rainfed (E4)] in
38
Andhra Pradesh, India and found high heritability estimates along with high genetic advance
for seed cotton yield, indicating the operation of additive gene action in the inheritance of the
trait, which could be easily improved through simple selection procedure.
Ahmed et al. (2006) evaluated six cotton genotypes (Chandi 95, Sohni, NIA 76,
NIAB 98, NIAB 801 and LRA 5166) along with their nine F1 hybrids for seed cotton yield
per plant to estimate heritability and genetic advance under well watered conditions at
Tandojam, Pakistan and found that seed cotton yield per plant showed moderate to high
heritability estimates and genetic advance, indicating additive with partial dominance type of
gene action suggesting the feasibility of selection in the early segregating generations.
Ashour et al. (2006) studied the genetic basis of grain yield by a generation means analysis in
five crosses of winter wheat (Triticum aestivum) cultivars (Roshan, Mahdavi, Inia, Atila and
Goscoyin) along with their F1, F2, BC1 and BC2 populations under normal conditions and
found that grain yield per plant had additive type of gene action and heritability estimates of
broad sense and narrow sense were from 28.5% to 58.6% and 24.0% to 48.5% for the five
crosses, respectively indicating that early selection will be fruitful for this trait.
Gohil et al. (2006) studied the broad-sense heritability and genetic advance for seed
yield per plant in 55 diverse soyabean (Glycine max) genotypes under normal conditions in
Gujarat, India and observed high heritability along with high genetic advance values for seed
yield per plant indicating the control of additive gene action for the trait which could be
improved through simple selection procedure. Murugan and Ganesan (2006) estimated gene
action in six generations (P1, P2, F1, F2, BC1 and BC2) of five rice crosses (IR 58025 x IR72,
IR 58025 x IR24, IR 58025 x Daunsan, IR 58025 x ARC 11353 and IR 58025 x IR 547442-
22-19-3) for grain yield under normal conditions in Tamil Nadu, India and found additive
and dominant gene action for the trait. Patra et al. (2006) evaluated 20 rice genotypes for
grain yield per hill to estimate heritability and genetic advance under normal conditions at
Cuttack, Orissa, India and found high heritability in broad sense coupled with high genetic
advance for the character under study, indicating the role of additive gene action providing
ample scope for effective selection. Prakash and Verma (2006) studied the six generations
(P1, P2, F1, F2, BC1 and BC2) of two barley crosses (BL-2 x RD 2433 and RD 2407 x RD
2433) to estimate the gene action for grain yield per plant under normal conditions and
observed overdominance type of gene action for the trait.
39
Rahman et al. (2006) studied 21 barley (Hordeum vulgare) genotypes including six
cultivars (BARI barley 2, BBL 9402-43-1, BBL 9402-12-2, K 163, K 351 and IBYT/97-4)
and their 15 non-reciprocal F1 hybrids to estimate gene action for grain yield per plant under
normal conditions and observed that dominance effect was involved for grain yield per plant.
Ahmed et al. (2007) evaluated six wheat genotypes (LU 26S, WL 59, WL 60, WL 61, WL
62, and WL 63) along with their five F2 progenies (WL 59 × LU 26S, WL 60 × LU 26S, WL
61 × LU 26S, WL 62 × LU 26S, and WL 63 × LU 26S) to estimate heritability and genetic
advance for grain yield per plant under natural drought conditions at Faisalabad, Pakistan and
found high heritability estimates for grain yield per plant (87.11 to 97.38%) along with
moderate to high genetic advance values ranged from 13.69 to 23.34 for all crosses,
indicating additive type of gene action for the trait. Esmail (2007) evaluated six generations
(P1, P2, F1, F2, BC1 and BC2) of two cotton crosses (Mc-Naire 235 x Nazilli-m55 and Giza
70 x Uzbek) under well watered conditions to estimate the gene action for seed cotton yield
and revealed that the character studied was under the control of additive, dominance and
epistatic gene effects.
Memon et al. (2007) evaluated seven F3 progenies of spring wheat and their 8
parental lines for grain yield per plant to calculate heritability estimates under normal
conditions at Tandojam, Pakistan and obtained highest broad sense heritability (86.5%) with
high genetic advance (22.0) for grain yield per plant in progeny Marvi 2000 x Soghat 90,
indicating better chance of selection for the trait. Munir et al. (2007) evaluated two wheat
crosses [Kohistan 97 (high yielding) x Inqlab 91 (medium yielding) and Kohistan 97 (high
yielding) x Chakwal 86 (low yielding)] along with their parents (P1, P2), F2 and back crosses
(BC1, BC2) to estimate the gene action for grain yield per plant under drought conditions and
revealed that additive, dominance and epistatic effects were involved in the inheritance of the
trait. Therefore, selection in later segregating generations was suggested to obtain drought
tolerant and high yielding lines. Abbas et al. (2008) studied five cotton varieties namely
CIM-443, NIAB Krishma, Cris-420, RH-112 and coker-207 to evaluate genetic effects
involved in the inheritance of seed cotton yield. Additive type of gene action along with
partial dominance was observed for this trait. Selection breeding may be helpful in improving
yield of seed cotton due to presence of high narrow sense herirtability and additive type of
gene action. Ashokkumar and Ravikesavan (2008) studied twenty eight hybrid including four
40
cultivated varieties as female (lines) and the seven G. hirsutum accessions as males (testers)
in line × Tester experiment to evaluate the inheritance of seed cotton yield. It was observed
that seed cotton yield was controlled mainly by non-additive type of gene action. The present
study revealed that heterosis breeding may be helpful in developing hybrid with better seed
cotton yield. Farshadfar et al. (2008) studied gene action of 22 durum genotypes for grain
yield under rainfed and irrigated conditions at Kermanshah, Iran and found highly significant
additive components in addition to significant epistatic effects for the trait under study.
Waqar-ul-haq et al. (2008) evaluated 10 wheat genotypes (Chakwal 86, Iqbal 2000, Uqab
2000, GA 2002, 00FJ03, IC 001, IC 002, NR 234, 3C061 and 3C062) for grain yield per
plant to estimate heritability and genetic advance under rainfed conditions at Rawalpindi,
Pakistan and revealed that grain yield per plant showed high values of heritability coupled
with high genetic advance, indicating additive type of gene action for the trait. Desalegn et
al. (2009) studied 15 F1 cotton hybrids in the field to estimate heritability for seed cotton
yield under well watered conditions in Ethiopia and found moderate heritability (44 %) in
broad sense, indicating additive and non-additive type of gene action for the trait. Singh et al.
(2010) analyzed 8 x 8 diallel mating design for cotton lines and observed that additive type of
gene action was involved in the inheritance of seed cotton yield. Sarwar et al. (2011)
conducted a diallel cross experiment involving three exotic lines that is DPL-775, 71-821
Bulk-OP and one local line that is MNH-53, for genetic analysis of yield, to evaluate gene
action for this trait in upland cotton. Additive gene action with partial dominance was found to
control yield of seed cotton which could be improved through simple selection procedure.
2.7.7 Ginning out-turn (GOT)
The nature and magnitude for the inheritance of lint percentage has been studied by
many research scientists. Sayal and Sulemani (1996) studied the genetics of 56 F1 hybrids
along with their 8 parents for lint percentage under well watered conditions and found
dominance gene action for the trait. Gomaa (1997) studied three crosses (Giza 45 x Giza 75,
Giza 45 x Giza 77 and Giza 45 x Family 10/87) of cotton along with their parents, F2 and
backcrosses under well watered conditions to estimate the gene action for lint percentage and
found additive variance in cross I and III, while dominance variance was observed in cross II
for the trait under study. Gomaa et al. (1999) evaluated two cotton crosses (Family 8/87 x S
6037 and Giza 80 x S 6037), their P1, P2, F2 and F3 families to estimate the type of gene
41
action for lint percentage under well watered conditions and observed that both additive and
dominant genetic variances were controlling lint percentage in both the crosses. Pavasia et al.
(1999) conducted an 8×8 diallel analysis in cotton under well watered conditions and found
additive types of gene action for ginning out-turn. Singh and Yadavendra (2002) reported
additive, dominance and epistatic effects for the inheritance of ginning out-turn. Mert et al.
(2003) used generation means analysis on 6 generations (P1, P2, F1, F2, BC1, BC2) of a cross
in cotton to find the inheritance of ginning out-turn (GOT) under well watered conditions and
reported that additive, dominance and epistatic genetic effects were responsible for GOT.
Nimbalkar et al. (2004) conducted an 8×8 diallel experiment in desi cotton (G.
arboreum and G. herbaceum) and concluded that GOT was controlled by both additive and
non additive genetic effects. Prasad et al. (2005) studied 42 F1 cotton progenies along with
their parents in the field to estimate the magnitude of heritability for lint percentage under
well watered conditions in Andhra Pradesh, India and found moderate estimates of
heritability, indicating additive and non-additive type of gene action for the trait. Reddy and
Satyanarayana (2005) estimated heritability and genetic advance for lint percentage in 55
cotton genotypes under four different environments [normal sowing- irrigated (E1); normal
sowing- rainfed (E2); delayed sowing- irrigated (E3) and delayed sowing- rainfed (E4)] in
Andhra Pradesh, India and found high heritability estimates along with high genetic advance
for lint percentage, indicating the operation of additive gene action in the inheritance of the
trait, which could be easily improved through simple selection procedure. Singh and Chahal
(2005) studied 34 progenies of upland cotton along with their parents in the field to estimate
gene action for lint percentage under well watered conditions at Bathinda, India and indicated
the presence of additive and dominance genetic components along with epistasis for the trait
studied. Therefore, they concluded that the trait under study was not simply inherited and its
selection in later segregating generations was recommended for population improvement.
Esmail (2007) evaluated six generations (P1, P2, F1, F2, BC1 and BC2) of two cotton crosses
(Mc-Naire 235 x Nazilli-m55 and Giza 70 x Uzbek) under well watered conditions to
estimate the gene action for lint percentage and revealed that the character studied was under
the control of additive, dominance and epistatic gene effects. Khan et al. (2009) conducted a
6×6 diallel cross to work out nature of gene action in Upland cotton for lint percentage and to
estimate combining ability of parents and their crosses. They reported highly significant
42
mean squares for general combining ability and specific combining ability in both
generations for this trait. They reported that lint percentage in both generations was
controlled by additive type of gene action. Ali and Awan (2009) studied the gene action of
GOT in upland cotton using Mather and Jinks approach. Significant differences among
parental genotypes were observed for all the parametes. GOT revealed the significance of
additive component of variation (D). The genetic analysis suggested that GOT could be
upgraded through full-or half-sib family, pedigree and progeny selection. Desalegn et al.
(2009) studied 15 F1 cotton hybrids in the field to estimate heritability for lint percentage
under well watered conditions in Ethiopia and found very high heritability (97 %) in broad
sense, indicating additive and dominance type of gene action for the trait.
2.7.8 Fibre length
Gene action for fibre length has been studied by many research workers under
drought and normal conditions. Singh et al. (1983) estimated gene action for fibre quality
characters from F1, F2 and backcross generations of a cross in G. hirsutum. Epistasis was
observed for the trait fibre length. Lin and Zhao (1988) in a study of three inter varietial
crosses of G. hirsutum L. estimated genetic effects of fibre length. The effects of dominance
and epistasis varied significantly in different years and different hybrids but additive effects
were relatively stable for this trait. Sayal and Sulemani (1996) studied the genetics of 56 F1
hybrids along with their 8 parents for fibre length under well watered conditions and found
dominance gene action for the trait. Gomaa (1997) studied three crosses (Giza 45 x Giza 75,
Giza 45 x Giza 77 and Giza 45 x Family 10/87) of cotton along with their parents, F2 and
backcrosses under well watered conditions to estimate the gene action for fibre length and
found additive variance in cross III, while dominance variance was observed in cross I for the
trait under study. Nistor and Nistor (1999) studied the genetics of 10 cotton genotypes and
their F1 hybrids under well watered conditions and concluded from their study that additive
and dominance effects were involved in the inheritance of fibre length.
Hendawy et al. (1999) evaluated 10 cotton varieties using diallel analysis to study the
inheritance of fibre length under well watered conditions and according to Hayman approach
(1954) it was revealed that additive genetic variance was highly significant, whereas
according to Griffing approach (1956) additive and additive × additive type of gene actions
43
were of greater importance for the trait. Mukhtar et al. (2000b) studied four cotton (G.
hirsutum) genotypes (CIM 1100, CIM 443, VH 57 and CIM 444) and their 12 F1 hybrids to
estimate the type of gene action responsible for fibre length under well watered conditions at
Faisalabad, Pakistan and indicated additive type of gene action with partial dominance for the
trait under study. Bertini et al. (2001) studied the genetics of two parental lines and their F1,
F2, RC1 and RC2 generations in cotton under well watered conditions and observed that
additive effects were present in the inheritance of fibre length. Singh and Yadavendra (2002)
reported additive, dominance, additive × additive and additive × dominance genetic effects
for staple length.
Azhar et al. (2004) evaluated a diallel cross experiment of 5 cotton varieties [CIM
726 (white cotton), Dark brown, Light brown, Dark green and Light green] to study the broad
sense heritability under well watered conditions at Faisalabad, Pakistan and estimated 51 %
broad sense heritability for fibre length, indicating involvement of additive and non-additive
type of gene action for the trait. Nimbalkar et al. (2004) conducted a 8×8 diallel experiment
in desi cotton (G. arboreum and G. herbaceum) under well watered conditions and
concluded that staple length was only controlled by additive type of gene action. Kll et al.
(2005) evaluated 7 cotton genotypes in the field to estimate genetic and environmental
variability and broad sense heritability for fibre length under well watered conditions in
Turkey and found that broad sense heritability value was much higher for fibre length
(94.58%), which could be easily improved through selection.. Ahmed et al. (2006) evaluated
six cotton genotypes (Chandi 95, Sohni, NIA 76, NIAB 98, NIAB 801 and LRA 5166) along
with their nine F1 hybrids for fibre length to estimate heritability and genetic advance under
well watered conditions at Tandojam, Pakistan and found that fibre length exhibited
moderate to high heritability along with low genetic advance values, which indicated over
dominance type of gene action thereby revealing that delayed selection might be useful. Ali
et al. (2008) studied the genetic basis of fiber quality traits in upland cotton. They followed
Mather and Jinks approach to assess genetics of fibre length. They observed that fibre length
was controlled by additive gene action. They recommended full sib or half sib family
selection, pedigree and progeny test to achieve genetic progress for fibre length. Desalegn et
al. (2009) studied 15 F1 cotton hybrids in the field to estimate heritability for fibre length
44
under well watered conditions in Ethiopia and found high heritability (86 %) in broad sense,
indicating additive type of gene action for the trait.
2.7.9 Fibre strength
Gene action for fibre strength has been studied by many research workers. Pathak
(1975) used six populations (P1, P2, F1, F2, B1 and B2) of five upland cotton (G. hirsutum
L.) crosses to evaluate genetic effects for fibre properties in cotton by the analysis of
generation means and indicated that fibre strength was additive gene action. Singh et al.
(1983) estimated gene action for six yield related and fibre quality characters from F1, F2 and
backcross generations of a cross in G. hirsutum. Epistasis was observed for fibre strength
trait. Lin and Zhao (1988) in a study of three inter varietial crosses of G. hirsutum L.
estimated genetic effects of fibre strength. The effects of dominance and epistasis varied
significantly in different years and different hybrids but additive effects were relatively stable
for this character. Gomaa (1997) studied three crosses (Giza 45 x Giza 75, Giza 45 x Giza 77
and Giza 45 x Family 10/87) of cotton along with their parents, F2 and backcrosses under
well watered conditions to estimate the gene action for fibre strength and found dominance
variance in cross II for the trait under study. Hendawy et al. (1999) evaluated 10 cotton
varieties using diallel analysis to study the inheritance of fibre strength under well watered
conditions and according to Hayman approach (1954) it was revealed that additive genetic
variance was highly significant, whereas according to Griffing approach (1956) additive and
additive × additive type of gene actions were of greater importance for the trait. Mukhtar et
al. (2000b) studied four cotton (G. hirsutum) genotypes (CIM 1100, CIM 443, VH 57 and
CIM 444) and their 12 F1 hybrids to estimate the type of gene action responsible for fibre
strength under well watered conditions at Faisalabad, Pakistan and indicated over dominance
type of gene action for the trait under study. Bertini et al. (2001) studied the genetics of two
parental lines and their F1, F2, RC1 and RC2 generations in cotton under well watered
conditions and observed that additive effects were present in the inheritance of fibre strength.
Azhar et al. (2004) evaluated a diallel cross experiment of 5 cotton varieties [CIM 726 (white
cotton), Dark brown, Light brown, Dark green and Light green] to study the broad sense
heritability under well watered conditions at Faisalabad, Pakistan and estimated 28 % broad
sense heritability for fibre strength, indicating involvement of interaction type of gene action
45
for the trait. Kll et al. (2005) evaluated 7 cotton genotypes in the field to estimate genetic and
environmental variability and broad sense heritability for fibre strength under well watered
conditions in Turkey and found that broad sense heritability value was much high for fibre
strength (94.60%), which could be easily improved through selection.
Singh and Chahal (2005) studied 34 progenies of upland cotton along with their
parents in the field to estimate gene action for fibre strength under well watered conditions at
Bathinda, India and indicated the presence of additive and dominance genetic components
along with epistasis for the trait studied. Therefore, they concluded that the trait under study
was not simply inherited and its selection in later segregating generations was recommended
for population improvement. Minhas et al. (2008) crossed five American cotton (G. hirsutum
L.) varieties namely Stoneville, coker-4601, MNH-552, S-14 and Allepo-41 in all possible
combination in a randomized complete block design to determine nature of gene action and
combining ability effects for fibre strength, Additive gene action along with partial
dominance was observed for fibre strenght. Desalegn et al. (2009) studied 15 F1 cotton
hybrids in the field to estimate heritability for fibre strength under well watered conditions in
Ethiopia and found low heritability (33 %) in broad sense, indicating interaction type of gene
action for the trait.
2.7.10 Fibre fineness
Gene action for fibre fineness has been studied by many research workers. Gad et al.
(1974) observed that dominance effect was significant for fibre fineness. Lin and Zhao
(1988) in a study of three inter varietial crosses of G. hirsutum L. estimated genetic effects
of fibre fineness. The effects of dominance and epistasis varied significantly in different
years and different hybrids but additive effects were relatively stable for this trait. Gomaa
(1997) studied three crosses (Giza 45 x Giza 75, Giza 45 x Giza 77 and Giza 45 x Family
10/87) of cotton along with their parents, F2 and backcrosses under well watered conditions
to estimate the gene action for fibre fineness and found additive variance in cross II for the
trait under study. Pavasia et al. (1999) conducted an 8×8 diallel analysis in cotton under well
watered conditions and found additive types of gene action for fibre fineness. Hendawy et al.
(1999) evaluated 10 cotton varieties using diallel analysis to study the inheritance of fibre
fineness under well watered conditions and according to Hayman approach (1954) it was
46
revealed that additive genetic variance was highly significant, whereas according to Griffing
approach (1956) additive and additive × additive type of gene actions were of greater
importance for the trait. Mukhtar et al. (2000b) studied four cotton (G. hirsutum) genotypes
(CIM 1100, CIM 443, VH 57 and CIM 444) and their 12 F1 hybrids to estimate the type of
gene action responsible for fibre fineness under well watered conditions at Faisalabad,
Pakistan and indicated additive type of gene action with partial dominance for the trait under
study. Bertini et al. (2001) studied the genetics of two parental lines and their F1, F2, RC1 and
RC2 generations in cotton under well watered conditions and observed that additive effects
were present in the inheritance of fibre fineness. Azhar et al. (2004) evaluated a diallel cross
experiment of 5 cotton varieties [CIM 726 (white cotton), Dark brown, Light brown, Dark
green and Light green] to study the broad sense heritability under well watered conditions at
Faisalabad, Pakistan and estimated 41 % broad sense heritability for fibre fineness, indicating
involvement of interaction type of gene action for the trait. Prasad et al. (2005) studied 42 F1
cotton progenies along with their parents in the field to estimate the magnitude of heritability
for fibre fineness under well watered conditions in Andhra Pradesh, India and found
moderate estimate of heritability, indicating additive and non-additive type of gene action for
the trait.
Singh and Chahal (2005) studied 34 progenies of upland cotton along with
their parents in the field to estimate gene action for fibre fineness under well watered
conditions at Bathinda, India and indicated the presence of additive and dominance genetic
components for the trait studied. Magnitude of additive genetic component was greater than
dominance component, which showed the involvement of partial dominance in the
inheritance of the trait. Ahmed et al. (2006) evaluated six cotton genotypes (Chandi 95,
Sohni, NIA 76, NIAB 98, NIAB 801 and LRA 5166) along with their nine F1 hybrids for
fibre fineness to estimate heritability and genetic advance under well watered conditions at
Tandojam, Pakistan and exhibited moderate to high heritability along with low genetic
advance values, which indicated over dominance type of gene action thereby revealing that
delayed selection might be useful. Minhas et al. (2008) crossed five American cotton (G.
hirsutum L.) varieties namely Stoneville, coker-4601, MNH-552, S-14 and Allepo-41 in all
possible combination in a randomized complete block design to determine nature of gene
action and combining ability effects for fibre fineness. Additive gene action along with
47
partial dominance was observed for this trait. Akhtar et al. (2008) studied eight diverse
cotton varieties to investigate the genetic mechanism controlling variation in fibre fineness in
(G. hirsutum L.) and found simple additive-dominance model adequate for this trait. They
also found partial degree of dominance for fibre fineness. Desalegn et al. (2009) studied 15
F1 cotton hybrids in the field to estimate heritability for fibre fineness under well watered
conditions in Ethiopia and found high heritability (60 %) in broad sense, indicating additive
type of gene action for the trait. Therefore, it was recommended that selection in early
segregating generations may be fruitful for the trait.
2.7.11 Leaf area
Hussain et al, (2008) studied genetic mechanisms controlling inheritance pattern of
leaf area by examining the six generations of cotton, through generation means analysis. He
reported that leaf area in cotton was governed by additive [d], additive × additive [i], additive
× dominance [j] and dominance × dominance [l] genetic effects.
2.7.12 Excised leaf water loss (ELWL)
Malik and Wright (1995) conducted generation means analysis to estimate inheritance
of relative water content under moisture deficit conditions in wheat and found that additive
and dominance gene action control this trait. Ahmed et al. (2000) evaluated the parental, F2
and backcross generations of two wheat crosses (Fsd. 85 x Pak. 81 and Fsd. 85 x Rohtas 90)
to estimate the type of gene action for physiological trait excised leaf water loss under
drought conditions. Generation means analysis was used to study gene action of this trait.
They found that additive, dominance and additive x dominance gene effects were significant
for excised leaf water loss. Majeed et al. (2001) studied Parents, F1, F2 and backcross
generations of a barley cross (Jau 83 x B 96039) to find out the gene action of excised leaf
water loss and found that dominance and epistatic effects controlled the inheritance of ELWL
under drought conditions in barley. Kumar and Sharma (2007) studied the genetic effects on
twelve wheat populations including two parents (P1 and P2), F1, F2, first backcross generations
(BC1 and BC2), second back cross generations (BC11, BC12, BC21, BC22) and backcross selfed
generations (BC1s and BC2s) of four crosses involving three drought tolerant and three
drought susceptible cultivars to determine nature of gene action for excised-leaf water loss
(ELWL) and found that additive, dominance and epistatic effects were predominant for the
inheritance of this trait.
48
2.7.13 Relative water content (RWC)
Malik and Wright (1995) conducted generation means analysis to estimate inheritance
of relative water content under moisture deficit conditions in wheat and found that additive
and dominance along with additive x dominance interaction were responsible in the
inheritance of this trait. Ahmed et al. (2000) evaluated the parental, F2 and backcross
generations of two wheat crosses (Fsd. 85 x Pak. 81 and Fsd. 85 x Rohtas 90) to estimate the
type of gene action for physiological trait relative water content. Generation means analysis
was used to study gene action of this trait. They found that additive, dominance and additive
x dominance gene effects were significant for relative water content. Majeed et al. (2001)
studied Parents, F1, F2 and backcross generations of a barley cross (Jau 83 x B 96039) to find
out the gene action of relative water content and found that additive type of gene action
controlled the inheritance of relative water content under drought conditions in barley.
Therefore, they suggested that selection for relative water content would be effective in early
segregating generations. Kumar and Sharma (2007) studied the genetic effects on twelve
wheat populations including two parents (P1 and P2), F1, F2, first backcross generations (BC1
and BC2), second back cross generations (BC11, BC12, BC21, BC22) and backcross selfed
generations (BC1s and BC2s) of four crosses involving three drought tolerant and three
drought susceptible cultivars to determine nature of gene action for relative water content and
found that additive, dominance and epistatic effects were responsible for the inheritance of
this trait.
2.8 Correlation studies
2.8.1 Plant height
Correlation of plant height with other agronomic traits has been studied by many
research workers. Arshad et al. (1993) evaluated four upland cotton varieties (CIM 70,
MNH 129, NIAB 78 and MNH 93) under well watered conditions to calculate correlation
coefficient of plant height with other agronomic traits and found that plant height was
correlated positively with number of bolls per plant and seed cotton yield. Carvalho et al.
(1994) evaluated 6 cotton varieties and their 30 hybrids from a diallel set of crosses under
irrigated conditions for correlation analysis and found that plant height had positive
correlation with seed cotton yield. Amutha et al. (1996) studied fifteen cotton genotypes
under well watered conditions and found positive correlation of plant height with boll weight
49
and number of bolls per plant. El-Moneim and Belal (1997) evaluated 119 durum wheat
genotypes during four successive growing seasons under low rainfed conditions (121-180
mm/year) in El-Arish, North Sinai, Egypt and found that Cham 1 was the most promising
genotype for plant height under drought conditions. They also found that plant height was
significantly and positively correlated with grain yield. Younis and Shalaby (1997) evaluated
ten genotypes of Egyptian cotton (G. barbadense) under well water conditions for correlation
analysis and found that plant height had positive correlation with lint yield per plant. Murthy
(1999) studied 10 cotton varieties along with 45 crosses under well watered conditions and
found that plant height had positive correlation with seed cotton yield.
Hussian et al. (2000) revealed positive correlation of plant height with seed cotton
yield, number of sympodial branches, number of bolls per plant and GOT. Singh et al. (2000)
evaluated five kinds of populations in maize derived from different composites [8551 and
85134 (most tolerant), 8527 and 85164 (tolerant), 8557, Ageti 76 and DRC 8601 (moderately
tolerant) and A 68 (most susceptible)] under three moisture regimes [optimum moisture (four
irrigations, I4), moderate moisture stress (two irrigations, I2) and high moisture stress
(completely rainfed without irrigation, I0)] and observed moderate reduction (15-30%) for
plant height which was also highly heritable and positively correlated with grain yield per
plot. Ahmed et al. (2001) evaluated the parents, F2 and backcross populations from two
wheat crosses (Fsd. 85 x Pak. 81 and Fsd. 85 x Rohtas 90) involving drought susceptible and
resistant genotypes for correlation analysis under rainfed conditions at Faisalabad, Pakistan
and found that plant height was positively and significantly correlated with 100 grain weight,
which revealed that height of plant contributed to higher yield under drought conditions.
Ahuja et al. (2004) evaluated 51 single plant selections of different colour linted genotypes
of cotton in the field to find out association of plant height with other traits under well
watered conditions in Sirsa, Haryana, India and found that plant height had significant
positive association with seed cotton yield per plant. Chandra et al. (2004) studied 50 F5 bulk
lines of five wheat crosses viz. (1) Kanchan x DSN 34, (2) Kanchan x YC 17, (3) Kanchan x
YC 16, (4) Kanchan x BW 115 and (5) Kanchan x Ad. 119 for correlation analysis under
normal conditions at Mymensingh, Bangladesh and found that plant height showed
significant positive correlation with grain yield per plant in most of the crosses. Rauf et al.
(2004) studied a diallel cross experiment of 5 cotton varieties (NIAB 999, CIM 473, ACALA
50
1517/C, CRIS 420, FVH 57) for correlation analysis under well watered conditions at
Faisalabad, Pakistan and found that plant height had negative correlation with seed cotton
yield. Karami et al. (2005) evaluated 26 barley genotypes for correlation analysis under
drought and irrigated conditions in Tehran, Iran and observed that drought stress caused a
decrease in plant height. They estimated that plant height had high and positive correlation
with grain yield under both conditions.
Kaushik et al. (2005) studied 10 strains of cotton (G. hirsutum) along with their 45 F1
hybrids in the field for correlation analysis under well watered conditions at Sriganganagar,
Rajasthan, India and showed that plant height had positive correlations with seed cotton yield
per plant. Kll et al. (2005) evaluated 7 cotton genotypes in the field for correlation analysis
under well watered conditions in Turkey and found that plant height was positively
correlated with seed cotton yield. Murthy et al. (2005) evaluated 12 G. herbaceum cotton
cultivars in the field for correlatioin analysis under saline soil condition at two locations in
Prakasam, Andhra Pradesh, India and found that plant height had positive correlation with
boll number and seed index. Ganapathy et al. (2006) evaluated 43 genotypes of upland cotton
in the field for correlation analysis under well watered conditions in Hisar, Haryana, India
and found that plant height showed significant positive correlation with seed cotton yield per
plant. Muthuswamy and Kumar (2006) evaluated 22 drought-resistant rice cultivars for
correlation analysis under aerobic conditions in Tamil Nadu, India and found that plant
height had positively significant correlation with yield per plant. They also observed that
plant height had highly positive direct effect on yield, which indicated that selection based on
plant height will improve the yield in drought resistant cultivars.
Saravanan et al. (2006) evaluated six genotypes (PA 402, PA 255, PA 314, PA 398, PA
405 and PA 304) of Desi cotton (G. arboreum) along with their F1 generations in the field
for correlation analysis under well watered conditions in Tamil Nadu, India and their results
revealed that plant height had positive correlation with seed cotton yield. Karademir et al.
(2009) evaluated 20 genotypes, including 2 cultivars and 18 advanced cotton lines under
induced drought stress conditions. They reported that plant height had positive and
significant correlation with number of bolls per plant, number of sympodial branches and
boll weight in cotton under drought stress conditions.
51
2.8.2 Number of monopodial branches
Murthy (1999) studied ten parents and 45 crosses and found that number of
monopodial branches, had positive correlation with seed cotton yield, while negative with
ginning %age. Hussian et al. (2000) revealed positive correlation of seed cotton yield with
monopodial branches. Gite et al. (2006) observed that seed cotton yield had positive
genotypic and phenotypic correlations with number of monopodial branches per plant.
2.8.3 Number of sympodial branches
Kyei (1968) found positive association between number of bolls and number of
fruiting branches. Singh et al. (1968) reported that number of sympodial branches per
plant had a strong association with number of bolls per plant. Channa and Ahmad (1982)
concluded that number of sympodial branches per plant was positively correlated with seed
cotton yield per plant.
Karademir et al. (2009) evaluated 20 genotypes, including 2 cultivars and 18
advanced cotton lines under induced drought stress conditions. They found that number of
sympodial branches had positive correlation with number of bolls per plant in cotton under
drought stress conditions.
2.8.4 Number of bolls per plant
Alam and Islam (1991) evaluated 20 diverse cotton genotypes for correlation analysis
under well watered condition and found that boll number had significantly positive
correlation with seed cotton yield per plant. Path coefficient analysis showed that the number
of bolls per plant had the maximum positive direct effect on seed cotton yield per plant.
Baloch et al. (1992) found stronger and positive phenotypic correlation coefficients
between number of bolls and seed cotton yield, seed index and boll weight. Number of
bolls had major and direct effect on seed cotton yield. Tomar et al. (1992) evaluated the
parental and F1 generations of a 20 line X 3 tester cross of desi cotton for correlation analysis
under well watered conditions and found that boll number positively and significantly
correlated with seed cotton yield. Arshad et al. (1993) evaluated four upland cotton varieties
(CIM 70, MNH 129, NIAB 78 and MNH 93) for correlation analysis under well watered
conditions and found that number of bolls per plant positively correlated with plant height
and seed cotton yield. Carvalho et al. (1994) evaluated six cotton varieties and their 30
52
hybrids from a diallel set of crosses under well watered conditions for correlation analysis
and found that boll number had positive correlation with seed cotton yield.
Tyagi (1994) evaluated progenies of a cross (J 34 x IC 1926) in cotton for correlation
analysis under well watered conditions and found that boll number had significantly positive
association with seed cotton yield. Amutha et al. (1996) studied fifteen cotton genotypes
under well watered conditions and found positive correlation of boll number with plant
height and boll weight. Rao and Mary (1996) studied ten upland cotton (G. hirsutum)
genotypes and their 45 F1 hybrids for correlation analysis under well watered
conditions and found positive correlation between boll number and seed cotton yield.
Path analysis showed that boll number had the highest direct effects on seed cotton
yield. Younis and Shalaby (1997) evaluated ten genotypes of Egyptian cotton (G.
barbadense) under well water conditions for correlation analysis and found that boll number
had positive correlation with lint yield per plant. Gomaa et al. (1999) evaluated two cotton
crosses (Family 8/87 x S 6037 and Giza 80 x S 6037), their P1, P2, F2 and F3 families to
estimate correlation coefficients under well watered conditions and found that boll number
had positive genotypic correlation with seed cotton yield/plant. Murthy (1999) studied 10
cotton varieties along with 45 crosses under well watered conditions and found that number
of bolls per plant had positive correlation with seed cotton yield, while negative with lint
percentage.
Sultan et al. (1999) studied 20 diverse genotypes of upland cotton (G. hirsutum) to
calculate correlation coefficients under well watered conditions at Jessor, Bangladesh and
found significant positive correlations of boll number with fibre yield at both the genotypic
and phenotypic levels, while negative correlation with boll weight. Path coefficient analysis
showed that boll number had strong direct effect on fibre yield. Satange et al. (2000)
evaluated 30 genotypes of American cotton (G. hirsutum) to study correlation coefficients
under well watered conditions and found that number of bolls per plant had positive
significant correlation with seed cotton yield/plant both at genotypic and phenotypic levels.
Ahuja et al. (2004) evaluated 51 single plant selections of different colour linted genotypes
of cotton in the field for correlation analysis under well watered conditions in Sirsa, Haryana,
India and found that boll number had significant positive association with seed cotton yield
per plant. Number of bolls per plant also exhibited positive and high direct effect on seed
53
cotton yield. Therefore, they concluded that number of bolls per plant was the most important
trait for selection of genotypes with high potential of seed cotton yield. Rauf et al. (2004)
studied a diallel cross experiment of 5 cotton varieties (NIAB 999, CIM 473, ACALA
1517/C, CRIS 420, FVH 57) for correlation analysis under well watered conditions at
Faisalabad, Pakistan and estimated that boll number per plant had positive correlation with
seed cotton yield. They concluded that the best influence on seed cotton yield was of number
of bolls per plant.
Kaushik et al. (2005) studied 10 strains of cotton (G. hirsutum) along with their 45 F1
hybrids in the field for correlation analysis under well watered conditions at Sriganganagar,
Rajasthan, India and indicated that boll number per plant had positive correlation with seed
cotton yield per plant. They also revealed that boll number per plant had positive direct effect
on seed cotton yield per plant. Therefore, selection based on this character might contribute
considerable to improvement in seed cotton yield. Ganapathy et al. (2006) evaluated 43
genotypes of upland cotton in the field to estimate correlation coefficients under well watered
conditions in Hisar, Haryana, India and found that bolls per plant showed significant positive
correlation with seed cotton yield per plant. Path coefficient analysis revealed that bolls per
plant showed very high positive direct effect on seed cotton yield. Therefore, selection on the
basis of bolls per plant will increase the seed cotton yield automatically. Iqbal et al. (2006)
conducted a field experiment on cotton for correlation analysis under well watered conditions
at Multan, Pakistan and indicated that boll number positively and significantly correlated
with seed cotton yield. Path coefficient analysis showed that boll number had maximum
direct positive effect on seed cotton yield. Saravanan et al. (2006) evaluated six genotypes
(PA 402, PA 255, PA 314, PA 398, PA 405 and PA 304) of Desi cotton (G. arboreum) along
with their F1 generations in the field for correlation analysis under well watered conditions in
Tamil Nadu, India and revealed that boll number had positive correlation with plant height,
fibre fineness and seed cotton yield. Desalegn et al. (2009) studied 15 F1 cotton hybrids in
the field for correlation analysis under well watered conditions in Ethiopia and observed that
boll number had positive correlation with seed cotton yield.
2.8.5 Boll weight per plant
Correlation of boll weight with other agronomic traits has been studied by many
research workers. Sanyasi (1981) found that boll weight was negatively correlated with fibre
54
length, seed index and lint index. Alam and Islam (1991) evaluated 20 diverse cotton
genotypes for correlation analysis under well watered condition and found that boll weight
had significantly positive correlation with seed cotton yield per plant. Baloch et al. (1992)
found positive correlation between boll weight and number of bolls per plant. Carvalho et al.
(1994) evaluated six cotton varieties and their 30 hybrids from a diallel set of crosses under
well watered conditions for correlation analysis and found that boll weight had positive
correlation with seed cotton yield. Tyagi (1994) evaluated progenies of a cross (J 34 x IC
1926) in cotton for correlation analysis under well watered conditions and found that boll
weight had significantly positive association with seed cotton yield. Bhatnagar (1995) studied
5 entries of cotton [H 777 x Del. Cot (F2), H 777 x Texas I (F2), HS 168 x BR 181 (F2), a
stable strain HS 6 and variety H 777] for correlation analysis under well watered conditions
and found positive correlation of boll weight with seed cotton yield. Amutha et al. (1996)
studied fifteen cotton genotypes under well watered conditions and found positive correlation
of boll weight with plant height and number of bolls per plant.
Rao and Mary (1996) studied ten upland cotton (G. hirsutum) genotypes and
their 45 F1 hybrids for correlation analysis under well watered conditions and found
positive correlation between boll weight and seed cotton yield. Path analysis showed
that boll weight had the highest direct effects on seed cotton yield. Younis and Shalaby
(1997) evaluated ten genotypes of Egyptian cotton (G. barbadense) under well water
conditions for correlation analysis and negative correlations for boll weight were recorded
with lint yield. Gomaa et al. (1999) evaluated two cotton crosses (Family 8/87 x S 6037 and
Giza 80 x S 6037), their P1, P2, F2 and F3 families to estimate correlation coefficients under
well watered conditions and found that in cross 1 boll weight had positive genotypic
correlation with seed cotton yield/plant. Sultan et al. (1999) studied 20 diverse genotypes of
upland cotton (G. hirsutum) to calculate correlation coefficients under well watered
conditions at Jessor, Bangladesh and found significant positive correlations of boll weight
with fibre yield at both the genotypic and phenotypic levels, while negative correlation with
boll number. Path coefficient analysis showed that boll weight had strong direct effect on
fibre yield. Hassan et al. (1999) conducted correlation studies in cotton and found that boll
weight was positively correlated with yield of seed cotton.
55
Khan and Azhar (2000) conducted correlation studies in cotton. They found positive
correlation of seed cotton yield with number of bolls per plant, boll weight and staple length.
Staple length also had positive correlation with number of bolls. Lint index and seed index
positively correlated. Satange et al. (2000) evaluated 30 genotypes of American cotton (G.
hirsutum) to study correlation coefficients under well watered conditions and found that boll
weight had positive significant correlation with seed cotton yield/plant both at genotypic and
phenotypic levels. Rauf et al. (2004) studied a diallel cross experiment of 5 cotton varieties
(NIAB 999, CIM 473, ACALA 1517/C, CRIS 420, FVH 57) for correlation analysis under
well watered conditions at Faisalabad, Pakistan and estimated that boll weight had negative
correlation with seed cotton yield. Iqbal et al. (2006) conducted a field experiment on cotton
for correlation analysis under well watered conditions at Multan, Pakistan and indicated that
boll weight positively and significantly correlated with seed cotton yield. Path coefficient
analysis showed that boll weight had maximum direct positive effect on seed cotton yield.
Desalegn et al. (2009) studied 15 F1 cotton hybrids in the field for correlation analysis under
well watered conditions in Ethiopia and observed that boll weight had positive correlation
with seed cotton yield.
2.8.6 Yield
Correlation of yield with other agronomic traits has been studied by many research
workers. Alam and Islam (1991) evaluated 20 diverse cotton genotypes for correlation
analysis under well watered condition and found that seed cotton yield per plant
significantly and positively cogrrelated with number of bolls per plant and boll weight.
Tomar et al. (1992) evaluated the parental and F1 generations of a 20 line X 3 tester cross of
desi cotton to estimate relationship of seed cotton yield with other agronomic traits under
well watered conditions and found that seed cotton yield positively and significantly
correlated with lint yield, number of bolls per plant and ginning percentage. Arshad et al.
(1993) evaluated four upland cotton varieties (CIM 70, MNH 129, NIAB 78 and MNH 93)
for correlation analysis under well watered conditions and found that seed cotton yield had
positive correlation with plant height and number of bolls per plant. Carvalho et al. (1994)
evaluated six cotton varieties and their 30 hybrids from a diallel set of crosses under well
watered conditions for correlation analysis and found that seed cotton yield correlated
56
positively with number of bolls per plant, boll weight and plant height, while seed cotton
yield had a negative correlation with fibre strength. Tyagi (1994) evaluated progenies of a
cross (J 34 x IC 1926) in cotton for correlation analysis under well watered conditions and
found that seed cotton yield significantly and positively associated with number of
bolls/plant, boll weight, ginning out turn and fibre fineness. While seed cotton yield was
significantly and negatively correlated with fibre length. Bhatnagar (1995) studied 5
genotypes of cotton [H 777 x Del. Cot (F2), H 777 x Texas I (F2), HS 168 x BR 181 (F2), a
stable strain HS 6 and variety H 777] for correlation analysis under well watered conditions
and found positive correlation of seed cotton yield with boll weight. Rao and Mary (1996)
studied ten upland cotton (G. hirsutum) genotypes and their 45 F1 hybrids for
correlation analysis under well watered conditions and found positive correlation of
seed cotton yield with boll number, boll weight and fibre fineness.
Hussain et al. (1998) evaluated 12 upland cotton (G. hirsutum) genotypes for
correlation analysis under well watered conditions and found that seed cotton yield had
positive correlation with staple length. Gomaa et al. (1999) evaluated two cotton crosses
(Family 8/87 x S 6037 and Giza 80 x S 6037), their P1, P2, F2 and F3 families to estimate
correlation coefficients under well watered conditions and found that seed cotton yield/plant
had positive genotypic correlation with bolls/plant and boll weight. Hassan et al. (1999)
reported that superiority of yield was associated with number of bolls rather than the boll
weight. They also found that number of bolls per plant, boll weight and 100 seed weight
were positively correlated with yield of seed cotton. Murthy (1999) studied 10 cotton
varieties along with 45 crosses under well watered conditions and found that seed cotton
yield had positive correlation with number of bolls per plant and plant height. Hussian et al.
(2000) revealed positive correlation of seed cotton yield with plant height, monopodial
branches and number of bolls per plant. Satange et al. (2000) evaluated 30 genotypes of
American cotton (G. hirsutum) to study correlation coefficients under well watered
conditions and found that seed cotton yield/plant had positive and significant correlations
with number of bolls per plant and boll weight both at genotypic and phenotypic levels.
57
Afiah and Ghoneim (2000) conducted an experiment to investigate phenotypic and
genotypic correlation and path analysis. They observed that seed cotton yield was highly and
positively correlated with number of sympoidal branches, number of bolls per plant, boll
weight, ginning out-turn. Baloch et al. (2001) reported that seed cotton yield had positive
phenotypic correlation with number of bolls per plant and lint percentage while seed cotton
yield had negative relationship with boll weight. Multiple correlation coefficients revealed
that about 91.8% of total variation in yield was dependent on variables number of bolls per
plant, boll weight and lint percentage. Ahuja et al. (2004) evaluated 51 single plant
selections of different colour linted genotypes of cotton in the field to find out association of
seed cotton yield with other traits under well watered conditions in Sirsa, Haryana, India and
found that seed cotton yield per plant had significant positive association with plant height
and boll numbers per plant. Azhar et al. (2004) evaluated a diallel cross experiment of 5
cotton varieties [CIM 726 (white cotton), Light brown, Dark brown, Light green and Dark
green] to study correlation coefficients under well watered conditions at Faisalabad,
Pakistan and revealed that seed cotton yield positively correlated with fiber fineness (rp =
0.59, rg = 0.65) and fiber strength (rp = 0.28, rg = 0.54), while it negatively associated with
fiber length (rp = -0.45, rg = -0.82). Chandra et al. (2004) studied 50 F5 bulk lines of five
wheat crosses viz. (1) Kanchan x DSN 34, (2) Kanchan x YC 17, (3) Kanchan x YC 16, (4)
Kanchan x BW 115 and (5) Kanchan x Ad. 119 for correlation analysis under normal
conditions at Mymensingh, Bangladesh and found that grain yield per plant showed
significant positive correlation with plant height in most of the crosses. Rauf et al. (2004)
studied a diallel cross experiment of 5 cotton varieties (NIAB 999, CIM 473, ACALA
1517/C, CRIS 420, FVH 57) for correlation analysis under well watered conditions at
Faisalabad, Pakistan and found that seed cotton yield had positive correlation with boll
number per plant, while negative correlation with plant height and boll weight per plant.
Karami et al. (2005) evaluated 26 barley genotypes under drought and irrigated conditions
in Tehran, Iran, to find out the relationship of grain yield with other traits and estimated that
grain yield had high and positive correlation with plant height and 1000 grain weight under
both conditions, indicating the possibility of yield improvement though these characters.
58
Kaushik et al. (2005) studied 10 strains of cotton (G. hirsutum) along with their 45
F1 hybrids in the field for correlation analysis under well watered conditions at
Sriganganagar, Rajasthan, India and indicated that seed cotton yield per plant had positive
correlations with plant height and boll number per plant, while a negative correlation with
lint percentage. Kll et al. (2005) evaluated 7 cotton genotypes in the field for correlation
analysis under well watered conditions in Turkey and found that seed cotton yield positively
correlated with plant height, 100-seed weight and fibre length. Gite et al. (2006) observed
that seed cotton yield had positive genotypic and phenotypic correlations with number of
bolls per plant, number of sympodial branches per plant, boll weight plant height and
number of monopodial branches per plant. Ganapathy et al. (2006) evaluated 43 genotypes
of upland cotton in the field to estimate association of yield with other traits under well
watered conditions in Hisar, Haryana, India and found that seed cotton yield per plant
showed significant positive correlation with plant height and bolls per plant.
Iqbal et al. (2006) conducted a field experiment on cotton for correlation analysis
under well watered conditions at Multan, Pakistan and indicated that seed cotton yield had
positive and significant correlation with boll number and boll weight. Kulkarni and Nanda
(2006) studied 29 upland cotton genotypes in the field to estimate relationship of seed cotton
yield with other traits under well watered conditions in Raipur, Chhattisgarh, India and
indicated that seed cotton yield per plant had significant and positive correlation with plant
height, seed index and boll weight. Rasheed et al. (2009) studied genetic potential of 15
cotton (G. hirsutum L.) genotypes by analyzing genotypic, phenotypic correlation, path co-
efficient analysis. They reported positive and highly significant association of number of
bolls per plant and boll weight with seed cotton yield. Muthuswamy and Kumar (2006)
evaluated 22 drought-resistant rice cultivars for correlation analysis under aerobic conditions
in Tamil Nadu, India and found that yield per plant had positively significant correlation with
plant height and 100 seed weight. Saravanan et al. (2006) evaluated six genotypes (PA 402,
PA 255, PA 314, PA 398, PA 405 and PA 304) of Desi cotton (G. arboreum) along with their
F1 generations in the field for correlation analysis under well watered conditions in Tamil
Nadu, India and revealed that seed cotton yield had positive correlation with plant height,
boll number and fibre fineness. Desalegn et al. (2009) studied 15 F1 cotton hybrids in the
59
field for correlation analysis under well watered conditions in Ethiopia and observed that
seed cotton yield had positive correlation with boll number, boll weight and lint percentage,
while negative correlation with fibre length and strength. Karademir et al. (2009) evaluated
20 genotypes, including 2 cultivars and 18 advanced cotton lines under induced drought
stress conditions. They reported that seed cotton yield had positive and significant correlation
with ginning out turn in cotton under drought stress conditions. Salahuddin et al. (2010)
evaluated fifteen genotypes (six parents, nine crosses) of American upland cotton (G.
hirsutum L.) for Phenotypic correlation and path coefficient analysis of some important
characters and found that Sympodial branches, bolls per plant, boll weight, G.O.T (%) and
lint index were positively correlated with yield per plant in all the genotypes at 1.0 percent
level of probability. Further partitioning of correlation coefficients into direct and indirect
path ways of influences showed that the characters having most influence on seed cotton
yield were bolls per plant and boll weight, which should be taken care of while selecting for
higher yields in further breeding programme.
2.8.7 Ginning out-turn (GOT)
Tyagi (1987) found negative correlation of fibre length with GOT and fibre
fineness. Khan et al. (1991) found that lint percentage was negatively correlated with
staple length and seed lint index. Tomar et al. (1992) evaluated the parental and F1
generations of a 20 line X 3 tester cross of desi cotton to estimate relationship of ginning
percentage with other agronomic traits under well watered conditions and found that ginning
percentage positively and significantly correlated with seed cotton yield. Tyagi (1994)
evaluated progenies of a cross (J 34 x IC 1926) in cotton for correlation analysis under well
watered conditions and found that ginning out turn significantly and positively associated
with seed cotton yield. Bhatnagar (1995) studied 5 entries of cotton [H 777 x Del. Cot (F2),
H 777 x Texas I (F2), HS 168 x BR 181 (F2), a stable strain HS 6 and variety H 777] for
correlation analysis under well watered conditions and found positive correlation of ginning
percentage with boll weight.
Younis and Shalaby (1997) evaluated ten genotypes of Egyptian cotton (G.
barbadense) under well water conditions for correlation analysis and found positive
phenotypic correlation coefficients between lint percentage and lint yield. Sultan et al. (1999)
studied 20 diverse genotypes of upland cotton (G. hirsutum) to calculate correlation
60
coefficients under well watered conditions at Jessor, Bangladesh and found significant
positive correlations of ginning percentage with fibre yield at both the genotypic and
phenotypic levels. Larik et al. (1999) studied the correlation of lint percentage with other
agronomic traits in cotton (G. hirsutum) under well watered conditions and indicated that
ginning out-turn had positive correlation with fibre strength. Murthy (1999) studied 10 cotton
varieties along with 45 crosses under well watered conditions and found that lint percentage
had negative correlation with number of bolls per plant and plant height. Badr and Aziz
(2000) found that staple length had negative correlation with ginning out-turn. Kaushik et al.
(2005) studied 10 strains of cotton (G. hirsutum) along with their 45 F1 hybrids in the field
for correlation analysis under well watered conditions at Sriganganagar, Rajasthan, India and
indicated that lint percentage had a negative correlation with seed cotton yield per plant.
Iqbal et al. (2006) conducted a field experiment on cotton for correlation analysis under well
watered conditions at Multan, Pakistan and indicated that lint percentage had negative
correlation with seed cotton yield. Desalegn et al. (2009) studied 15 F1 cotton hybrids in the
field for correlation analysis under well watered conditions in Ethiopia and observed that lint
percentage had positive correlation with seed cotton yield.
2.8.8 Fibre length
Correlation of fibre length with other agronomic traits has been studied by many
research workers. Aguilar et al. (1980) reported correlation between fiber length and fiber
strength. They concluded that the associations between fibre percentage and fibre length
might be attributed to linkage or pleiotropy. Bocharova (1980) reported positive correlation
between fibre length and fibre fineness, whereas, negative correlation was observed between
fibre length, strength and fineness. Tyagi (1987) found negative correlation of fibre length
with GOT and fibre fineness. Carvalho et al. (1994) evaluated six cotton varieties and their
30 hybrids from a diallel set of crosses under well watered conditions for correlation analysis
and found that correlation between fibre length and fibre fineness was negative. Tyagi (1994)
evaluated progenies of a cross (J 34 x IC 1926) in cotton for correlation analysis under well
watered conditions and found that fibre length was significantly and negatively correlated
with seed cotton yield. Rao and Mary (1996) studied ten upland cotton (G. hirsutum)
genotypes and their 45 F1 hybrids for correlation analysis under well watered
61
conditions and found negative correlation of fibre length with fibre fineness and seed
cotton yield. Hussain et al. (1998) evaluated 12 upland cotton (G. hirsutum) genotypes for
correlation analysis under well watered conditions and found that staple length positively
correlated with seed cotton yield.
Larik et al. (1999) studied the correlation of fibre length with other agronomic traits in
cotton (G. hirsutum) under well watered conditions and indicated that fibre length had
positive correlation with fibre strength and negative with fibre fineness. Badr and Aziz
(2000). They also found that staple length had positive correlation with fibre fineness and
negative correlation with seed index and ginning out-turn.Azhar et al. (2004) evaluated a
diallel cross experiment of 5 cotton varieties [CIM 726 (white cotton), Dark brown, Light
brown, Dark green and Light green] to study correlation coefficients under well watered
conditions at Faisalabad, Pakistan and revealed that fibre length had negative association (rp
= -0.45, rg = -0.82) with seed cotton yield. Kll et al. (2005) evaluated 7 cotton genotypes in
the field for correlation analysis under well watered conditions in Turkey and found that fibre
length had positive correlation with seed cotton yield. Iqbal et al. (2006) conducted a field
experiment on cotton for correlation analysis under well watered conditions at Multan,
Pakistan and indicated that fibre length had negative correlation with seed cotton yield.
Desalegn et al. (2009) studied 15 F1 cotton hybrids in the field for correlation analysis under
well watered conditions in Ethiopia and observed that fibre length had negative correlation
with seed cotton yield.
2.8.9 Fibre strength
Bocharova (1980) observed negative correlation between fibre length, strength and
fineness. Carvalho et al. (1994) evaluated six cotton varieties and their 30 hybrids from a
diallel set of crosses under well watered conditions forcorrelation analysis and found that
fibre strength had a negative correlation with seed cotton yield. Younis and Shalaby (1997)
evaluated ten genotypes of Egyptian cotton (G. barbadense) under well water conditions for
correlation analysis and found negative correlation between fibre strength and lint yield.
Larik et al. (1999) studied the correlation of fibre strength with other agronomic traits in
cotton (G. hirsutum) under well watered conditions and indicated that fibre strength had
positive correlation with fibre length, fibre fineness and ginning outturn percentage.
Echekwu (2001) evaluated F3 generation for two years. He found negative correlation
62
between fibre strength and fineness. Azhar et al. (2004) evaluated a diallel cross
experiment of 5 cotton varieties [CIM 726 (white cotton), Dark brown, Light brown, Dark
green and Light green] to study correlation coefficients under well watered conditions at
Faisalabad, Pakistan and revealed that fibre strength had positive correlation (rp = 0.28, rg =
0.54) with seed cotton yield. Desalegn et al. (2009) studied 15 F1 cotton hybrids in the field
for correlation analysis under well watered conditions in Ethiopia and observed that fibre
strength had negative correlation with seed cotton yield.
2.8.10 Fibre fineness
Correlation of fibre fineness with other agronomic traits has been studied by many
research workers. Carvalho et al. (1994) evaluated six cotton varieties and their 30 hybrids
from a diallel set of crosses under well watered conditions for correlation analysis and found
that correlation between fibre fineness and fibre length was negative. Tyagi (1994) evaluated
progenies of a cross (J 34 x IC 1926) in cotton for correlation analysis under well watered
conditions and found that fibre fineness was significantly and positively associated with seed
cotton yield. Rao and Mary (1996) studied ten upland cotton (G. hirsutum) genotypes
and their 45 F1 hybrids for correlation analysis under well watered conditions and
found that fibre fineness had positive correlation with seed cotton yield, wlile
negative with fibre length. Larik et al. (1999) studied the correlation of fibre fineness with
other agronomic traits in cotton (G. hirsutum) under well watered conditions and found that
fibre fineness showed positive correlation with fibre strength and negative with staple length.
Azhar et al. (2004) evaluated a diallel cross experiment of 5 cotton varieties [CIM 726 (white
cotton), Dark brown, Light brown, Dark green and Light green] to study correlation
coefficients under well watered conditions at Faisalabad, Pakistan and revealed that fibre
fineness had positive corelation (rp = 0.59, rg = 0.65) with seed cotton yield. Saravanan et al.
(2006) evaluated six genotypes (PA 402, PA 255, PA 314, PA 398, PA 405 and PA 304) of
Desi cotton (G. arboreum) along with their F1 generations in the field for correlation analysis
under well watered conditions in Tamil Nadu, India and revealed that fibre fineness had
positive correlation with plant height, boll number and seed cotton yield.
63
2.8.11 Relative water content (RWC)
Malik et al. (2006) studied genetic linkage among drought tolerant and agronomic
traits and found that relative water content showed positive correlation with boll weight and
negative with fibre length and gonning out-turn, while it had no correlation with other
agronomic traits. From the above review, it is evident that identification and use of cotton
genotypes with better genetic potential is a continous prerequisite for synthesis of genetically
superior genotypes showing promise for increased production per unit area under water
limited conditions.
64
CHAPTER-3
MATERIALS AND METHODS
The research work reported in this dissertation was carried out in the experimental
area of the Department of Plant Breeding and Genetics and Centre of Agricultural
Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan.
3.1 Collection of plant material
Fifty lines/varieties of cotton as listed below were collected from different sources
such as, Cotton Research Institute, Ayub Agricultural Research Institute (AARI), Faisalabad,
Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Central Cotton Research
Institute (CCRI), Multan, Cotton Research Station (CRS) Multan and Department of Plant
Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan.
1. CIM-534 2. CIM-496 3. CIM-473 4. CIM-446 5. CIM-499
6. CIM-707 7. CIM-482 8. CIM-1100 9. MNH-6070 10. MNH-786
11. PB-765 12. Glandless-Rex 13. BH-116 14. PB-899 15.LA-85-52-1
16. Acala-63-75 17. DPL-61 19. FH-113 19. A-637-33 20. NIAB-78
21. MNH-552 22. NIAB-86 23. SLH-41 24. U-C-D-581 25. NIAB-999
26. BH-160 27. FH-1000 28. VH-54 29. FH-900 30. NIAB-766
31. BH-123 32. Gregg-25 V 33. FH-925 34. NIAB-111 35. BH-124
36. NIAB Krishma37. BH-95 38. BH-36 39. SHL-1 40. VH-142
41. PB-630 42. BH-147 43. PB-622 44. BH-118 45. BH-162
46. MNH-93 47. Acala-1517-C 48. FH-901 49. VH-59 50. VH-55
3.2 Screening of plant material
The 50 lines were screened out in glass house under irrigated and drought conditions
at seedling stage. Seeds of lines/varieties were sown in polyethylene bags measuring
25x15cm filled with soil and sand in a 2:1 ratio (Taiz and Zeiger 2006; Hussain, 2009 Iqbal
65
et al., 2010). Sowing in glass house was done on 25-2-2008. Four seeds of each genotype
were sown per bag and later on thinned to one plant. Fifteen bags of each entry were
arranged in completely randomized design. The maximum and minimum temperature of the
glass house throughout the experiment remained between 36 and31oC. All plants were
watered regularly to keep the soil at field capacity and watering was continued till the
development of first true leaf and subsequently the treatment bags were divided in to two
groups i.e. control and stress groups. One group to be treated as control was watered at
regular intervals and other group was subjected to two consecutive drought cycles. Drought
stress was initiated by withholding water when plants reached first true leaf stage. Plants
subjected to stress were watered to field capacity 10 hours after visual signs of wilting, and
were again subjected to water stress till the appearance of wilting. After 2nd drought cycle,
data for four seedling parameters were collected from each group as follows:
3.2.1 Root length (cm)
Seedlings were uprooted gently avoiding breakage, and roots were separated by cutting at the
junction of root and shoot. Roots were washed with water to make them free of soil. Root
length was determined by direct measurement of fresh tap roots with measuring scale in cm.
3.2.2 Shoot length (cm) Shoot length was measured in cm with measuring scale.
3.2.3 Lateral Root Number
Lateral root number ( LRN) was determined by direct count of lateral roots before
drying. 3.2.4 Lateral Root Density Lateral root density (LRD) was determined by dividing lateral root number (LRN) by root length ( RL ).
3.3 Assessment of genetic diversity in screened genotypes by SSR
marker analysis
3.3.1 Plant material
The selected twelve genotypes were analyzed for genetic distance among them. The young
leaves were collected from these genotypes and stored at -70oC for extraction of genomic
DNA.
66
3.3.2 DNA Extraction
The genomic DNA of the twelve cotton genotypes was extracted by following the
miniprep DNA extraction method as described by Khan et al., 2004. Samples of 0.2-0.3 gm
of stored leaf tissues of twelve cotton genotypes were taken and immediately transferred in to
zippered plastic bags (size 6 × 12 cm) containing 1.5- ml CTAB. Air was removed carefully
from the plastic bags containing the leaf samples and all were double sealed with an impulse
sealer. Each plastic bag was then put in to another plastic bag and double sealed with the
sealer. Plant material in the double plastic bags placed on a smooth hard surface and ground
with a hand roller until a homogenized mixture was formed. These homogenized leaf
samples were incubated in a water bath at 65oC for 30 minutes and after incubation
homogenized leaf tissues were transferred in to two 1.5-ml Eppendorf tubes. 0.75ml of
chloroform: isoamylalcohol was added and tubes were vertically inverted 5-10 times
followed by spinning at 13,000rpm for 10 minutes in a centrifuge. After centrifugation 800
µl of supernatant was transferred from both tubes in to another 1.5-ml Eppendorf tube. After
that approximately 700 µl of isopropanol was added in the supernatant and mixed by
inverting the tube about 10 times. The DNA was pelleted by centrifugation at 1300rpm for 10
minutes and supernatant was discarded. The DNA pellet was washed with 70% ethanol, air
dried and resuspended in 200 µl of 0.1×TE.
3.3.3 Estimation of DNA concentration
The concentration of the extracted genomic DNA was measured by
spectrophotometer ( CECIL CE 2021 2000series ) by measuring the OD at the 260nm
wavelength. Extracted DNA quality was checked by running 5 µl DNA on 0.5 % agarose gel
prepared in 0.5X TBE buffer. The DNA samples giving smear in the gel were rejected.
3.3.4 SSR (Polymerase chain reaction)
The SSR (PCR) protocol for cotton germplasm was optimized and 30 polymorphic
SSR Primers were used for this purpose. The SSR fragments generated were separated
through Agarose gel electrophoresis. DNA amplification reaction was performed in a thermal
cycler (Eppendorf AG No.5333).
67
Reagents 1X
dd H2O 5.4µl
10X buffer 4.4 µl
MgCl2 2 µl
dNTPs 4 µl
Taq polymerase 0.2 µl
DNA 2 µl
Primer 1+1 µl
Total Volume 20 µl
Table: SSR (PCR) Reagents
3.3.5 SSR data analysis
The PCR amplification profiles for all the 12 cotton lines/varieties were compared with each
other and presence of DNA fragments were scored as present (1) or absent (0). The data for
all the 30 primers were used to estimate the similarity on the basis of the number of shared
amplification products (Nei and Li, 1979). Similarity coefficients were utilized to generate a
dendrogram by means of unweighted pair group method of arithmetic mean (UPGMA).
3.4 DEVELOPMENT OF GENERATIONS.
On the basis of seedling characters and molecular studies two drought tolerant (NIAB-78,
CIM-482) and two susceptible (CIM-446, FH-1000) varieties/lines were selected for the
development of plant material for genetic studies. The selected four parents were field
planted during May, 2008 and crosses were made at the time of flowering between selected
drought tolerant and susceptible lines to develop seed for F1 generation. The F1 generation
and the parents were grown in green house during November, 2008. The F1 plants were
backcrossed to both the parents to develop BC1 and BC2 generations. Some of the F1 plants
were selfed to develop seed for F2 generation. The parents were selfed to have seed for
further studies. For genetic analysis of the traits generation means analysis technique (Mather
and Jinks, 1982) was used. List of crosses produced is given in Table-3.1.
68
Table 3.1: List of crosses and backcrosses
S. No. Generations Parents
1 F1, F2 NIAB-78 x CIM 446
B1 (NIAB-78x CIM 446) x NIAB-78
B2 (NIAB-78x CIM 446) x CIM 446
2 F1, F2 CIM-482 x FH-1000
B1 (CIM-482 x FH-1000) x CIM-482
B2 (CIM-482 x FH-1000) x FH-1000
3.5 Assessment of genetic material under field conditions
In this experiment, F1, F2, B1, B2 and both the parents of respective crosses were
sown under drought as well as normal conditions separately during May, 2009 using a
randomized complete block design with three replications. Each entry was planted in rows
keeping 75 cm row to row and 30 cm plant to plant distance. The row length was kept 4.5
meters accommodating 15 plants in each. Ten plants from the middle of each row were
considered as experimental plants. A single row for each parent and F1 generation, three
rows for each back cross generation and five for F2 generation in each replication were
planted. In this way fifty plants per replication for each F2 generation, 30 plants per
replication for each backcross and 10 plants per replication for each F1 and parental
generations were tagged as experimental plants. All agronomic and cultural practices were
kept same in both the experiments except irrigation. During the crop season, water stress
was imposed by supplying 50% less irrigations in the drought treatment. Data were
recorded for the following physiological, agronomical and morphological parameters:
3.5.1 Leaf area
Leaf area of the three fully expanded leaves at 5 th, 10 th and 15th sympodial nodes of main
stem of all the selected plants was measured with the help of portable leaf area meter
(model CI 203, CID, Inc. USA).
3.5.2 Leaf temperature
Leaf temperature of tagged plants was observed from fully exposed leaves to sunlight
at 13.00-15.00. Data were recorded from three leaves of each tagged plant with
infrared thermometer (RAPRM 30 CFRJ, RAYTEK, USA).
69
3.5.3 Excised leaf water loss (ELWL)
Three fully developed leaf samples were taken from each of the selected plants grown under
well-watered and drought conditions during the last week of September. The samples were
covered with polythene bags soon after excision and fresh weight was recorded using
electronic balance. The leaf samples were left on laboratory bench at room temperature for
twenty four hours. The weight of the wilted leaf samples was then recorded. After that the
leaf samples were oven dried at 70°C for 72 hours for recording oven dry weight. Excised
leaf water loss was calculated following Clarke and McCaig (1982a) as follows:
ELWL = (Fresh weight – wilted weight) / Dry weight
3.5.4 Relative water content (RWC)
Three fully developed leaf samples were taken from each of the selected plants grown under
both well-watered and drought conditions during the last week of September. The 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. After recording the turgid weight, the leaf samples were dried under room
temperature for about one hour. Then the samples were oven dried at 70°C for 72 hours for
recording dry weight and RWC was calculated following Barrs and weatherly (1962) as
follows:
RWC = [(Fresh weight–Dry weight) / (Turgid weight–Dry weight)] x 100
3.5.5 Plant Height (cm)
When apical bud of the main stem ceased to grow, the height of each selected plant was
measured in cm with the help of meter rod. The height was recorded from the 1st
cotyledonary node to the apical bud in cm.
3.5.6 Number of Monopodial Branches per Plant
The monpodial branches are vegetative branches in a cotton plant. At maturity, the
monopodial branches per plant were counted on all the selected plants.
70
3.5.7 Number of Sympodial Branches per Plant
The sympodial branches are the fruit bearing branches i.e. bearing the bolls. At
maturity, the sympodial branches on each selected plant were counted.
3.5.8 Number of Bolls per Plant
The number of bolls picked at each picking was recorded from individual plants.
When final picking was over, picking record was summed up to calculate the total number of
bolls per plant.
3.5.9 Boll Weight
It is the average weight of seed cotton in a mature boll. Average boll weight was
calculated by dividing the total weight of seed cotton from a plant with its number of picked
bolls.
3.5.10 Seed cotton yield per plant
Three pickings of seed cotton were performed at regular intervals of three weeks.
Seed cotton of each plant was picked separately and put in kraft paper bags. After completion
of three pickings, the total produce of each plant was cleaned and weighed using electrical
balance.
3.5.11 Ginning Out-turn (GOT)
It is also referred to as lint percentage and is the weight of lint that can be obtained
from a given weight of seed cotton expressed as percentage. Dry samples of seed cotton
harvested from individual plants were weighed and ginned separately with a single roller
electrical gin in the laboratory. Electronic balance was used to weigh the seed cotton and lint.
GOT was calculated as %age of lint in seed cotton.
3.5.12 Fibre Traits
Fibre length, fibre strength and fibre fineness were measured by using Spinlab HVI-
900 in the Department of Fibre Technology, University of Agriculture Faisalabad.
3.6. STATISTICAL ANALYSIS
The data were subjected to analysis of variance (Steel et al., 1997) to determine
significance of genetic differences among generations used in the experiment under both
normal and drought conditions.
71
3.6 .1 Generation Means Analysis
Generation means analysis was performed following Mather and Jinks (1982) using a
computer programme provided by Dr. H.S. Pooni, School of Biological Sciences, University
of Birmingham. Means and variances of the two parents, B'1, B2, F1 and F2 generations used in
the analysis were calculated from individual plant basis pooled over replications. The
coefficients of the genetic components of generation means are shown in the Table 3.2. A
weighted least square analysis was performed on the generation means commencing with the
simplest model using parameter m only. Further models of increasing complexity (md, mdh,
etc.) were fitted if the chi-squared value was significant. The best fit model was chosen as the
one which had significant estimates of all parameters along with non-significant chi-squared
value. For each trait the higher value parent was taken as P1 in the model fitting.
Table 3.2: Coefficients of genetic effects for the weighted least square analysis of
generation means (Mather and Jinks (1982). The mean (m), additive
(d), dominance (h), additive × additive (i), additive × dominance (j)
and dominance × dominance (l) parameters
Generations
Components of genetic effects
M [d] [h] [i] [j] [l]
P1 1 1.0 0.0 1.00 0.00 0.00
P2 1 -1.0 0.0 1.00 0.00 0.00
F1 1 0.0 1.0 0.00 0.00 1.00
F2 1 0.0 0.5 0.00 0.00 0.25
B1 1 0.5 0.5 0.25 0.25 0.25
B2 1 -0.5 0.5 0.25 -0.25 0.25
72
3.6.2 Analysis of Components of Genetic Variance
A weighted least squares analysis of variance based on the method as described by
Mather and Jinks (1982) using a computer programme provided by Dr. H.S. Pooni, School of
Biological Sciences, University of Birmingham was performed on the data of the experiment
containing six generations (Parents, F1, F2, BC1 and BC2). The coefficients of additive (D),
dominance (H), cross product of dominant and additive effects (F) and environmental
variation (E) are shown in Table 3.3. Model fitting was started using the E parameter only, D,
H and F parameters were successively included until a satisfactory fit was obtained. The best
fit model was chosen as the one with all significant parameters and non-significant chi-
squared value.
Table 3.3: Coefficients for the genetic variance for the weighted least squares
analysis of generation variances (Mather and Jinks, 1982)
Generation Components of variation
D H F E
P1 0.00 0.00 0.00 1
P2 0.00 0.00 0.00 1
F1 0.00 0.00 0.00 1
F2 0.50 0.25 0.00 1
B1 0.25 0.25 -0.5 1
B2 0.25 0.25 0.50 1
73
3.7 Heritability Estimates
Estimation of narrow sense heritability (h2ns) in F2 (Warner, 1952) and F infinity
(F∞) generations (Mather and Jinks, 1982) from the components of variance from the best fit
model of weighted least squares analysis by using the formula:
a) h2ns(F2) = (0.5D/VF2) ×100
b) h2 (F∞) = D/(D+E)
3.8 Genetic advance
Expected genetic advance in the next generation was computed by the following formula
(Falconer and Mackay, 1996).
G.A. = K .6p . h2
Where
G.A = genetic advance
K = selection differential, being 2.06 at 5% selection intensity
6p = standard deviation of the phenotypic variance of the population
under selection
h2 = heritability estimate, in fraction of the trait under study
3.9 CORRELATIONS
The phenotypic and genotypic correlation coefficients between pairs of plant traits
were calculated using the individual plant data of the F2 populations.
3.9.1 Phenotypic correlations
The phenotypic correlations (rp) between two traits x and y were calculated by using
the following formula:
rp = COVP (x,y)/ (VPX . VPY)1/2
Where,
COVp(x, y) is the mean phenotypic covariance of x and y traits.
Vp (x) and Vp(y) are the phenotypic variance of the same traits respectively.
74
3.9.2 Genotypic correlations
The genotypic correlations (rg) between two characters, x and y, were computed by
using the following formula:
rg = COVg(x,y)/(Vg(x) . Vg(y)) 1/2
Where,
COVg(x,y) = COV(x,y) F2 – COV(x,y)E
COV(x,y)E = (1/4)[COV(x,y)P1 + COV(x,y)P2 + 2COV(x,y)F1]
COVg(x,y), COV(x,y)E, COV(x,y)P1, COV(x,y)P2, COV(x,y)F1 and COV(x,y) F2 are
covariances of x and y associated with genetic effects, non-genetic effects, P1, P2, F1 and F2
generations respectively and Vg (x) and Vg (y) are genetic variances of x and y traits
respectively.
3.10. Chi-square analysis
The segregating ratios of plants in F2 and back crosses for all the traits were tested for
their fitness to a theoretical ratio through chi-square test (Harris, 1912)
75
CHAPTER-4
RESULTS AND DISCUSSIONS
4.1 Screening on the basis of seedling traits for drought tolerance
The mean values of shoot length, root length, lateral root number and lateral root density
of all the fifty cotton lines are given in appendices 1 and 2. Analysis of variance indicated highly
significant differences for all the characters among the genotypes (Table.4.1) Highly significant
differences were also noted in respect of irrigation treatments (T) as well as interaction of
genotype with treatments (G × T).
Table. 4.1. Mean squares for seedling traits in cotton under normal and drought conditions. Source of variation
DF
Shoot Length
Root Length
Lateral root number
Lateral root density
Treatments(T)
1 495.111** 1056.563** 6769.70** 10.3268**
Genotypes(G)
49 17.059** 12.746** 75.23** 0.2181**
G x T
49 6.428** 7.203** 46.63** 0.1832**
Error
200 1.271 0.487 1.77 0.0258
** = Highly significant (P < 0.01)
Out of 50 lines 6 were identified as tolerant and six susceptible on the basis of above mentioned
four seedling traits as suggested by Pace et al., 1999; Basal et al., 2003; Basal et al., 2005;
Quisenberry et al.,1981; Cook and El-Zik., 1992. Ball et al., 1994; Ludlow and Muchow 1999,
and Iqball et al., 2010. In the present studies the lines showing higher shoot length, root length,
lateral root number and lateral root density under drought were classified as tolerant, whereas the
ones having lower values for these parameters were consided as susceptible. The tolerant and
susceptible lines thus selected are given in Table 4.2.
76
Table. 4.2. List of varieties/genotypes selected after screening S.No Drought tolerant genotypes S.No Drought susceptible genotypes
1 NIAB-78 1 CIM-446
2 CIM-482 2 FH-1000
3 CIM-473 3 BH-160
4 CIM-1100 4 FH-901
5 NIAB-111 5 FH-900
6 CIM-707 6 VH-142
Out of these two groups 2 tolerant (NIAB-78 and CIM-482) and 2 susceptible genotypes (CIM-
446 and FH-1000) were selected to be used as experimental material for crossing and further
studies.
4.2 Genetic variation of 12 lines at molecular level using SSR marker
Above mentioned 12 lines (6 tolerant and 6 susceptible) were studied for genetic
variation at moleculer level using simple sequence repeat (SSR) markers. Total 240 bands were
amplified by 30 specific primer pairs, out of these 240 bands 115 were found polymorphic and
showing 48% genetic diversity. Distinct genetic variation was found among the twelve cotton
genotypes. Dendrogram showed a maximum range of similarity i.e. from 41 % to 97 %.
Minimum similarity (40 %) was observed among the line NIAB-78 and VH-142, whereas,
maximum (97 %) was observed between the lines CIM-446 and FH-900. On the basis of
similarity percentage, the dendrogram was divided into three main groups, i.e. group A, group B
and group C. Group A comprised of six genotypes namely CIM-446, FH-900, FH-1000, NIAB-
78, NIAB-111 and BH-160. Which included four susceptible and two tolerant to drought, as
identified on the basis of seedling traits reported above.
CIM-446 and FH-900 were susceptible lines with 97 % similarity to each other. Both
genotypes made a cluster with each other and showed minimum diversity. CIM-446 was taken
from CCRI, Multan, while FH-900 from Faisalabad. A third susceptible genotype FH-1000 was
taken from Faisalabad also which showed 90% and 94% similarity with both susceptible lines
CIM-446 and FH- 900, respectively. In group A tolerant variety NIAB-78 was found different
77
from other susceptible lines on the basis of genetic similarity. However, this line was 85 %
similar to NIAB-111, a tolerant line. This tolerant line made clustered with a susceptible line
BH-160, and showed 88 % similarity. These combinations revealed two sub-groups in group A.
In group B, there were five genotypes comprising of four tolerant and one susceptible.
This group also contained two sub-groups. The first sub-group consisted of two tolerant lines
having (96%) genetic resemblance with each other. The second subgroup comprised of two
tolerant (CIM-1100 & CIM-707) and one susceptible (FH-901). Both tolerant have 92 %
similarity and were collected from CCRI, Multan, whereas FH-901 was collected from
Faisalabad.
The third group C consisted of only one susceptible line (VH-142) collected from Vehari.
This line was dissimilar with rest of the lines. In comparison with all the tolerant lines, VH-142
was found only 40% similar with NIAB-78, 50% with NIAB 111, 58 % with CIM-482, 50%
with CIM-473, 62% with CIM-1100 and 62% similar with CIM-707. Among the susceptible,
VH-142 showed 56%, 64%, 55%, 41%, and 68% similarity with CIM-446,FH-900, FH-1000,
BH-160 and FH-901 respectively.
M 1 2 3 4 5 6 7 8 9 10 11 12
SSR (PCR) of twelve cotton genotypes with primer JESPR 285. M is a 1Kb ladder. 1.CIM-446, 2.CIM-482,
3.NIAB-78, 4.FH-1000, 5.NIAB-111, 6.CIM-1100 7.FH-900, 8.VH142, 9.CIM-707, 10.CIM-473, 11.FH-901,
12.BH-160
78
SSR (PCR) of twelve cotton genotypes with primer BNL-3031. M is a 1Kb ladder. 1.CIM-446,
2.CIM-482, 3.NIAB-78, 4.FH-1000, 5.NIAB-111, 6.CIM-1100 7.FH-900, 8.VH142, 9.CIM-707,
10.CIM-473, 11.FH-901, 12.BH-160
M 1 2 3 4 5 6 7 8 9 10 11 12 M
SSR (PCR) of twelve cotton genotypes with primer BNL-3474. M is a 1Kb ladder. 1.CIM-446, 2.CIM-482,
3.NIAB-78, 4.FH-1000, 5.NIAB-111, 6.CIM-1100 7.FH-900, 8.VH142, 9.CIM-707, 10.CIM-473, 11.FH-901,
12.BH-160
M 1 2 3 4 5 6 7 8 9 10 11 12
79
M 1 2 3 4 5 6 7 8 9 10 11 12
SSR (PCR) of twelve cotton genotypes with primer BNL-3383 M is a 1Kb ladder. 1.CIM-446, 2.CIM-482, 3.NIAB-78, 4.FH-1000, 5.NIAB-111, 6.CIM-1100 7.FH-900, 8.VH142, 9.CIM-707, 10.CIM-473, 11.FH-901, 12.BH-160
80
Dendrogram
81
Table. 4.3. Similarity matrix for Nei’s and Li’s coefficient of 12 cotton
varieties.
Var. CIM-
446
CIM-
482
NIAB-
78
FH-
1000
NIAB-
111
CIM-
1100
FH-
900
VH-
142
CIM-
707
CIM-
473
FH-
901
BH-
160
CIM-
446
**** 0.9037 0.9354 0.9095 0.9354 0.9014 0.9701 0.5669 0.8321 0.8686 0.8686 0.9014
CIM-
482
**** 0.8281 0.8767 0.8281 0.9309 0.8767 0.5855 0.8593 0.9661 0.8667 0.7877
NIAB-
78
**** 0.9075 0.8571 0.8154 0.9075 0.4041 0.7412 0.8571 0.8281 0.8895
FH-
1000
**** 0.8427 0.8072 0.9412 0.5500 0.8072 0.9075 0.8767 0.8745
NIAB-
111
**** 0.8154 0.9075 0.5051 0.7412 0.7857 0.8281 0.8895
CIM-
1100
**** 0.8745 0.6290 0.9231 0.8895 0.9309 0.7692
FH
900
**** 0.6417 0.8745 0.8427 0.9393 0.9393
VH-
142
**** 0.6290 0.5051 0.5051 0.4193
CIM-
707
**** 0.8154 0.9309 0.6923
CIM-
473
**** 0.8281 0.8154
FH901 **** 0.7877
BH-
160
****
82
Significant differences were observed among generations of two crosses for plant
traits i.e. leaf area, leaf temperature, excised leaf water loss, relative water content, plant height,
monopodial branches, sympodial branches, number of bolls per plant, boll weight, seed cotton
yield, lint percentage, fibre length, fibre strength and fibre fineness under normal as well as
drought conditions. Generation means, population effects and LSD values to compare the
generation means are shown in the Table 4.4 and 4.5
4.3 Generation Means Analysis
In quantitative traits, gene action is described as additive, dominance and epistatic
(additive x additive, additive x dominance and dominance x dominance). Additive effect is
normally the average effect of genes from both parents; dominance is the interaction of allelic
genes and epistasis is the interaction of non-allelic genes affecting a particular trait. Gene action
may be studied using different biometrical techniques like diallel analysis as described by
Hayman (1954) and Jinks (1954) or by using generation means and variance of different
populations (parents, F1, segregating and backcross populations) as suggested by Mather and
Jinks (1982). Latter approach as applied in cotton by Pathak, 1975; Dhillon and Singh, 1980;
Singh and Sandhu, 1985; Kalsy and Garg, 1988 and in wheat by Malik and Wright, 1997; Munir
et al. 2007, has been used in present studies. The results of generation means analysis showing
the values of different parameters like mean [m], additive [d], dominance [h], additive × additive
[i], additive × dominance [j] and dominance × dominance [l] along with their X2 values for
different plant traits in two crosses under normal and droughtful conditions are given in Table
4.4 and4.5 respectively. The additive variance [d] refers to average effect of genes on all
segregating loci. It is fixable and therefore, selection for traits governed by such variance is very
effective. The dominance [h] variance refers to deviation from mean value due to intra-allelic
interaction. Dominance variance is the chief cause of heterosis. It is not fixable and therefore,
selection for traits controlled by such variance is not effective. The epistatic variance (additive ×
additive [i], additive × dominance [j] and dominance × dominance [l]) refers to deviation from
mean value due to non-allelic interaction. The epistatic variance (additive × additive [i], refers to
interaction between two or more loci each exhibiting lack of dominance individually. It is
fixable. The epistatic variance additive × dominance [j] refers to interaction between two or more
loci one exhibiting lack of dominance and other dominance individually. It is non fixable. The
83
epistatic variance dominance × dominance [l] refers to interaction of two or more loci each
exhibiting dominance individually.Trait wise results are described as under:
4.3.1. Leaf Area
Under normal conditions four parameters [mdhj] in cross-1 and five parameters model
[mdhij] in cross-2 provided best fit of observed to the expected generation means (Table 4.6).
The dominance genetic effects [h]were found greater than the additive effects in cross-1 and 2
which indicated heterosis either due to overdominance or dispersion of dominant genes in the
parents. The detection of epistatic effects in the inheritance of leaf area in cross-1 and 2 further
complicates the situation as far as selection in F2 generation is concerned.
Under drought conditions five parameters [mdhil] in cross-1 and four parameters [mdhi]
model in cross-2 were found fit of the observed to expected generation means (Table 4.4). The
negative dominance effects for leaf area in cross-1 under droughtful conditions indicated that the
decrease was dominant over increase. Additive and additive × additive components are also
negative. Leaf area is one of the important factors in determining drought resistance in cotton
cultivars. Lesser the leaf area more the resistance to drought. Many workers like, (Bhatt and
Andal, 1979; Singh et al. 1990; Singh and Narayanan, 1993) have observed while working on
cotton that small and thick leaves with thick layer of palisade tissue are associated with drought
resistance. However reduction in leaf area may lead to reduction in total photosynthates per
plant. The present results, therefore, indicated the possibility of decreasing leaf area through
selection but in the F∞ generation. Opposite signs of h and l indicated duplicate type of epistasis
which further complicates the situation. In cross-2 dominance genetic effects were greater than
the additive effects thus indicating non additive and non fixable effects through selection.
However, it could be useful information if the exploitation of heterosis is the objective. The
presence of epistatic effects in the inheritance of leaf area in cross-1 and 2 further complicates
the situation as far as selection in F2 generation is concerned.
Hussain et al, (2008) also reported the inheritance of leaf area in cotton to be governed by
additive [d], additive × additive [i], additive × dominance [j] and dominance × dominance [l]
genetic effects.
84
Table.4.4.Generation Means of various morphological and physiological traits of Cross-1 (NIAB-78 × CIM-446) and Cross-2 (CIM-482 × FH-1000) under normal conditions.
Traits Cross (C)
Generations Pop. Effects
LSD (0.05) P1 P2 F1 F2 B1 B2
Leaf Area
C1 177.10 d 191.47 b 193.03 b 188.64 c 179.01 d 197.43 a ** 2.76
C2 174.83 c 190.63 a
193.37 a
181.34 b
178.38 b
191.51 a ** 3.19
Leaf Temp C1 29.83 b 30.66 a 30.566 29.79 b 29.63 b 30.95 a ** 0.56
C2 28.56 c
29.63 ab
29.43 ab
28.44 c
29.04 bc
30.05 a ** 0.69
ELWL C1 2.51 d 3.57 a 2.62 d 2.84 c 2.36 e 3.31 b ** 0.12
C2 2.54 d
3.54 a
2.79 c
2.80 c 2.44 e
3.38 b ** 0.08
RWC C1 88.09 a 80.20 c 88.02 a 84.14 b 87.88 a 82.01 c ** 2.02
C2 85.66 a
78.88 d
84.43 a
81.62 bc
83.86 ab
80.54 cd ** 2.65
Plant Height(cm)
C1 130.50 a 115.83 c 124.30 b 117.15 c 124.57 b 116.68 c ** 4.55
C2 126.97 a 108.63 d 124.03 a 112.57 c 117.84 b 116.22 bc
** 3.84
Monopodial Branches
C1 1.06 b 1.20 b 1.80 a 1.40 ab 1.08 b 1.13 b * 0.55
C2 1.00 c 1.66667 1.86 a 1.38 b 1.60 ab 1.67ab ** 0.36
Sympodial Branches
C1 25.33 a 21.50 b 24.90 a 20.90 b 24.47 a 21.53 b ** 1.46
C2 21.73 a 17.06 c 20.80 ab 18.09c 20.077 b 17.77 c ** 1.11
No of bolls
C1 35.03 a 30.40 c 32.13 bc 31.14 c 34.14 ab 30.85 c ** 2.26
C2 32.06 a 22.70 c 31.36 a 27.63 b 31.52 a 24.15 c ** 2.56
Boll Weight (gm)
C1 4.21 a 3.42 c 4.08 ab 3.183 4.04 ab 3.60 c ** 0.22
C2 4.05 a 3.39 c 3.91 ab 3.42 c 3.81 b 3.33 c ** 0.21
Seed Cotton Yield (gm)
C1 119.66 a 110.53 c 117.60 b 117.38 b 118.13 ab 112.09 c ** 1.90
C2 118.67 a 90.37 e 113.77 b 106.57 c 118.34 a 95.43 d ** 3.41
GOT C1 36.99 a 35.68 bc 37.50 a 36.04 b 37.44 a 35.22 c ** 0.76
C2 38.83 ab 37.59 c
39.05 a
37.10 c
38.26 b
37.07 c ** 0.64
Fibre length (mm)
C1 29.91 a 28.40 b 29.05 ab 28.59 b 28.22 b 27.84 b * 1.25
C2 28.80 a 27.24 bc 29.09 a 27.43 b 27.73 b 26.64 c ** 0.70
Fibre Strength
(g/tex)
C1 28.54 a 26.93 c 27.89 ab 27.36 bc 27.97 ab 26.73 c ** 0.79
C2 27.91 b 25.49 d 28.79 a 26.77 c 27.79 b 25.95 d ** 0.70
Fibre Fineness
(mic)
C1 3.89 d 5.01 a 3.94 d 4.22 c 4.16 c 4.80 b ** 0.17
C2 4.03 d
4.69 a
4.26 bc
4.32 b
4.07 cd
4.36 b ** 0.19
*, P < (0.05); **, P < (0.01), ns = non-significant Mean separation is by row and is based on pair wise comparison test for generations means
85
Table.4.5. Generation Means of various morphological and physiological traits of Cross-1 (NIAB-78 × CIM-446) and Cross-2 (CIM-482 × FH-1000) under drought conditions.
Traits Cross (C)
Generations Pop. Effects
LSD (0.05) P1 P2 F1 F2 B1 B2
Leaf Area
C1 158.93 e 193.63 a 187.13 b 176.66 d 161.27 e 180.68 c ** 3.36
C2 156.73 d
181.30 a 182.43 a 172.79 b
166.52 c
180.69 a ** 5.03
Leaf Temp C1 30.90 d 32.46 ab 32.53 a 31.94 c 31.78 c 31.98 bc ** 0.49
C2 31.46 b
32.76 a
30.06 c
31.49 b
31.04 bc
31.75 b ** 0.99
ELWL C1 2.04 d 3.03 a 2.18 c 2.18 c 1.85 e 2.74 b ** 0.08
C2 2.47 a
2.14 b
2.20 b
2.03 c
2.40 a
2.23 b ** 0.09
RWC C1 83.25 a 75.19 c 82.64 a 78.69 b 83.13 a 76.18 c ** 1.73
C2 81.31 a
72.55 c
80.46 a
77.15 b
80.22 a
75.92 b ** 1.66
Plant Height(cm)
C1 118.93 a 107.73 c 119.60 a 117.020 113.41 b 109.23 c ** 3.51
C2 114.47 a
87.40 d 113.27 a 104.45 b
112.17 a
95.42 c ** 3.21
Monopodial
Branches
C1 2.00 b 2.76 a 2.26 b 2.36 ab 2.00 b 2.24 b * 0.47
C2 1.40 c
2.33 a
2.46 a
2.17 ab
1.91 b
2.13 ab ** 0.42
Sympodial Branches
C1 19.06 a 16.66 b 19.93 a 17.16 b 19.16 a 17.11 b * 1.90
C2 17.50 a
14.86 b
17.57 a
16.90 a
17.57 a
16.25 a * 1.48
No of bolls
C1 26.30 a 22.06 c 21.86 c 24.04 b 25.67 a 22.31 c ** 1.25
C2 23.03 a
16.56 e
21.86 b
20.72 c
21.94 b
18.68 d ** 0.92
Boll Weight
(gm)
C1 3.68 a 3.05 bc 3.32 b 2.93 c 3.35 b 2.99 c ** 0.30
C2 3.26 a
2.54 e
2.93 c
2.75 d
3.11 b
2.48 e ** 0.13
Seed Cotton
Yield (gm)
C1 101.77 a 93.23 b 102.30 a 95.85 b 101.03 a 95.06 b ** 4.24
C2 86.60 b
66.53 d
84.86 b
82.50 c
94.744 a 68.45 d ** 2.31
GOT C1 36.05 a 35.18 c 36.20 a 35.45 bc 35.75 ab 35.25 bc ** 0.55
C2 37.27 b
36.09 e
37.88 a
36.83 cd
37.02 bc
36.46 de ** 0.44
Fibre length (mm)
C1 26.43 a 25.07 bcd 25.56 b 24.81 cd 25.17 bc 24.42 d ** 0.66
C2 25.95 a
23.83 bc
26.35 a
22.97 c
24.59 b 23.10 c ** 1.08
Fibre Strength (g/tex)
C1 24.26 a 22.26 c 23.31 b 22.45 c 24.95 a 20.84 d ** 0.76
C2 26.06 ab
20.22 d
26.61 a 23.21 c
25.30 b
21.02 d ** 0.92
Fibre Fineness
(mic)
C1 3.29 c 4.10 a 2.98 d 3.72 b 3.39 c 3.80 b ** 0.17
C2 4.36 ab
3.53 d
4.15 bc
4.04 c
4.60 a
3.98 c ** 0.27
*, P < (0.05); **, P < (0.01), ns = non-significat Mean separation is by row and is based on pair wise comparison test for generations means
86
Table 4.6. Best model fit estimates for generation means parameters (± standard error) by weighted least squares analysis of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM- 482×FH-1000) under normal conditions
Traits Cross(C) [m] [d] [h] [i] [j] [l] X2
(df) Probability
Leaf area C1 184.17 ± 0.50 -7.16 ± 0.55 8.61 ± 0.95 -11.30 ± 1.12 0.464 2 0.7929
C2 169.46±1.39 -7.89±0.57 24.00±1.96 13.32±1.53 -5.23±1.08 0.179 1 0.6722 Leaf temperature
C1 29.15 ± 0.23 -0.41 ± 0.09 1.48 ± 0.32 1.13 ± 0.25 -0.91 ±0.18 3.45 1 0.0633
C2 24.65±0.70 -0.68±0.121 10.37±1.74 4.45±0.68 -5.59±1.12 3.40 1 0.0652 ELW Loss C1 3.045 ± 0.017 -0.535 ± 0.019 -0.418 ± 0.034 -0.417 ± 0.04 0.102 2 0.9503
C2 2.82±0.015 -0.50±0.023 0.22±0.029 -0.43±0.044 4.43 2 0.1092 R.W.C C1 80.14 ± 0.64 3.94 ± 0.23 7.81 ± 0.89 3.97 ± 0.70 1.89 ± 0.49 0.330 1 0.5557
C2 78.70±0.83 3.37±0.30 5.57±1.23 3.48±0.92 0.634 2 0.7283 Plant height (cm)
C1 110.12 ± 1.48 7.46 ± 0.48 14.25 ± 2.10 13.05 ± 1.60 0.333 2 0.8446
C2 101.36 ±1.84 9.14 ±0.62 22.72 ± 2.37 16.46 ± 1.99 -7.51 ± 1.48 0.115 1 0.7345 Monopodial branches
C1 1.07 ± 0.06 0.69 ± 0.16 7.590 4 0.1078
C2 1.29 ±0.08 -0.26 ±0.07 0.51 ±0.15 6.97 3 0.0729 Sympodial branches
C1 17.20 ± 0.58 1.90 ± 0.19 7.89 ± 0.82 6.28 ± 0.62 0.95 ± 0.42 3.662 1 0.0557
C2 15.29 ±0.78 2.32 ±0.25 5.43 ±1.11 4.07 ±0.85 0.258 2 0.8790 Number of Bolls/plant
C1 32.22 ± 0.20 2.54 ± 0.32 9.42 4 1.0000
C2 27.03 ±0.26 4.67 ±0.34 2.65 ±0.76 4.09 ±0.59 2.584 2 0.2747 Bolls weight (gm)
C1 3.60 ± 0.09 0.40 ± 0.03 0.45 ± 0.12 0.19 ± 0.09 5.35 2 0.0689
C2 2.90 ±0.11 0.36 ±0.03 0.98 ±0.15 0.81 ±0.12 4.102 2 0.1286 SCY (gm) C1 124.40 ± 2.78 4.94 ± 0.41 -21.28 ± 6.77 -9.31 ± 2.73 14.48 ± 4.23 2.408 1 0.1207
C2 99.14 ±1.58 14.15 ±0.74 14.05 ±2.38 5.07 ±1.76 8.68 ±1.25 2.097 1 0.1476 GOT % C1 34.51 ± 0.27 0.65 ± 0.10 2.93±0.40 1.79 ± 0.30 1.56 ± 0.21 1.45 1 0.2285
C2 35.07±0.26 0.62±0.11 3.87±0.37 3.08±0.28 0.58±0.20 3.309 1 0.0689
Fibre length (mm)
C1 31.41 ± 0.66 0.65 ± 0.10 -8.95 ± 1.63 -2.23 ± 0.65 6.59 ± 1.03 2.57 1 0.1089
C2 28.03±0.14 0.86±0.11 -4.02±0.58 5.08±0.60 3.27 2 0.1950
Fibre strength (g/tex)
C1 26.69 ± 0.36 0.91 ± 0.11 1.09 ± 0.50 0.99 ± 0.39 4.89 2 0.0867
C2 26.37±0.11 1.20±0.18 0.63±0.30 2.27±0.26 5.18 2 0.0750
Fibre fineness C1 3.39 ± 0.22 -0.58 ± 0.03 2.77 ± 0.54 1.05 ± 0.21 -2.22 ± 0.34 1.18 1 0.2774
C2 4.29±0.024 -0.34±0.036 6.48 4 0.1661
87
Table.4.7.Best model fit estimates for generation means parameters (± standard error) by weighted least squares analysis of various morphological and physiological traits for cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under drought conditions.
Traits Cross (C)
[m] [d] [h] [i] [j] [l] X2 (df) Probability
Leaf area C1 199.32 ±3.87 -17.90 ±0.60 -78.47 ±9.54 -22.97 ±3.81 66.27 ±6.06 2.252 1 0.1334
C2 162.80±1.49 -12.77±0.51 19.30±2.15 6.14±1.63 3.855 2 0.1455 Leaf temperature
C1 31.60 ±0.102 -.0.78 ±0.115 0.75 ±0.196 0.59 ±0.220 2.59 2 0.2739
C2 32.27±0.12 0.66±0.12 -1.88±0.24 5.47 3 0.1404 ELW Loss C1 2.19 ± 0.01 - 0.49 ±0.01 0.34 ±0.02 -0.39 ±0.039 1.831 2 0.4003
C2 1.17 ± 0.11 0.16 ±0.01 2.40 ±0.29 1.13 ±0.11 -1.38±0.18 0.065 1 0.7988 R.W.C C1 74.65 ± 0.75 4.034 ± 0.29 7.91 ± 1.07 4.53 ± 0.82 2.92 ± 0.57 0.247 1 0.6192
C2 35.60 ±0.30 0.57 ±0.08 2.13 ±0.43 1.04 ±0.32 5.83 2 0.0542 Plant height (cm)
C1 136.13 ± 2.87 5.228 ± 0.43 -59.92 ± 7.01 -22.75 ± 2.82 43.39 ± 4.39 2.06 1 0.1512
C2 100.94±0.33 13.53±0.47 3.19±0.93 12.32±0.80 0.956 2 0.6200 Monopodial branches
C1 2.26 ± 0.05 -0.34 ± 0.09 5.425 4 0.2464
C2 1.83±0.07 -0.40±0.07 0.55±0.13 4.97 3 0.1740 Sympodial branches
C1 14.42 ± 0.65 1.41 ± 0.21 5.55 ± 0.95 3.43 ± 0.70 3.156 2 0.2064
C2 16.19±0.18 1.31±0.18 1.39±0.35 0.025 3 0.9990 Number of Bolls/plant
C1 26.41 ± 0.56 2.12 ± 0.24 -4.33 ± 0.82 -2.11 ± 0.62 1.21 ± 0.45 3.49 1 0.0618
C2 19.69±0.20 3.23±0.20 1.94±0.37 2.475 3 0.4798 Bolls weight (gm)
C1 2.58 ± 0.08 0.31 ± 0.02 0.74 ± 0.107 0.78 ± 0.088 1.175 2 0.5557
C2 2.53 ±0.071 0.36 ±0.028 0.39 ±0.092 0.36 ±0.07 0.28 ±0.05 1.174 1 0.2786 SCY (gm) C1 89.56 ± 1.29 4.64 ± 0.41 12.80 ± 1.76 7.95 ± 1.40 3.048 2 0.2178
C2 76.56 ±0.31 10.03 ±0.31 13.03 ±1.55 16.34 ±0.77 -4.73 ±1.58 2.154 1 0.1422 GOT % C1 35.41 ± 0.08 0.458 ± 0.10 0.64 ± 0.219 5.096 3 0.1649
C2 36.58 ±0.072 0.55 ±0.081 1.17 ±0.195 3.64 3 0.3031
Fibre length (mm)
C1 25.75 ± 0.11 0.70 ± 0.095 -3.60 ± 0.475 3.40 ± 0.495 0.132 2 0.9361
C2 21.92 ±0.16 1.17 ±0.11 2.99 ±0.23 4.44 ±0.31 3.188 2 0.2031 Fibre strength (g/tex)
C1 21.59 ± 0.34 1.00 ± 0.14 1.73 ± 0.488 1.67 ± 0.37 3.118 ± 0.26 0.034 1 0.8537
C2 23.144 ±0.19 2.92 ±0.19 -3.31 ±0.78 1.35 ±0.35 6.77 ±0.79 0.041 1 0.8395 Fibre fineness C1 4.50 ± 0.090 -0.40 ± 0.033 -1.49 ± 0.12 -0.78 ± 0.10 2.902 2 0.2343
C2 16.19 ±0.18 1.31 ±0.18 1.39 ±0.35 0.025 3 0.9990
88
4. 3.2. Leaf Temperature
Under normal conditions five parameters [mdhij] in cross-1 and also five parameters model
[mdhil] in cross-2 were found fit of the observed to expected generation means (Table 4.6). In
cross-1 and 2 dominance genetic effects [h] were greater than the additive effects, which indicated
heterosis either due to overdominance or dispersion of genes in the parents. In cross-1 and 2
presence of interaction showed that inheritance of this trait was not simple. Therefore, selection in
advanced segregating generations may be useful to breed cotton for this trait.
Under drought conditions four parameters [mdhj] in cross-1 and three parameters [mdh]
model in cross-2 were found fit of the observed to the expected generation means (Table 4.7). In
cross-2 dominance genetic effects were found greater than the additive effects, which indicated
heterosis either due to overdominance or dispersion of genes in the parents. The negative
dominance effects for leaf temperature indicated that decrease was dominant over increase in cross-
2. In cross-1 presence of interaction showed that inheritance of this trait was not simple. Therefore,
selection in advanced generations may be fruitful to breed cotton for this trait. In cross-2 three
parameters model [m, d, h] was best fit indicating that inheritance of this trait was relatively simple.
Therefore, selection in early segregating generations would be useful.
4. 3.3. Excised leaf water loss (ELWL)
Models of four parameter m, d, h and j in cross-1 and m, d, i and j in cross-2 were adequate
under normal conditions for ELWL; whereas, under drought four parameter [mdij] in cross-1 and
five parameter [mdhil] models in cross-2 provided best fit of the observed to the expected
generation means For excised leaf water loss, under normal conditions, four parameter [mdhj]
model in cross-1 while in cross-2 four parameter model [mdij] provided a best fit of the observed to
the expected generation means respectively for this trait.
Both additive and nonaddditive alongwith epistatic effects were noted in the expression of
ELWL in both the crosses under both the environmental regimes. Therefore, both the crosses did
not show any promise as a breeding material for improvement of this trait through selection in early
generations.
Malik and Wright (1995) estimated additive and dominance gene action of ELWL from their
studies under drought conditions in wheat. Ahmed et al. (2000) reported that dominance along with
additive x dominance interaction controlled the inheritance of ELWL under drought conditions in
wheat. Majeed et al. (2001) observed dominance and epistatic effects in the inheritance of ELWL
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under drought conditions in barley. Kumar and Sharma (2007) applied generation means analysis to
estimate inheritance of ELWL under drought conditions in wheat and reported that additive,
dominance and epistatic effects were responsible for the inheritance of this trait.
4. 3.4 Relative water content (RWC)
For RWC, five parameters [mdhij] in cross-1 and four parameter [mdhi] models in cross-2
were fit under normal conditions. Similarly, under drought, five parameter [mdhij] in cross-1 and
four parameter [mdhi] models in cross-2 were adequate for the trait.
Although both the crosses showed their consistant behavior over the change in irrigation levels, the
genetic control of RWC Cross-1 involved non-fixable epistatic effects of the type [j] which
indicated the possibility of improvement of this trait in latter segregating generations. However,
Cross-2, which was free of non-fixable epistatic effects and involved additive type of gene action
alongwith additive × additive (fixable) epistasis for genetic controle of inheritance of RWC could
be focused upon for its improvement through selection.
Malik and Wright (1995) conducted generation means analysis to estimate inheritance of
relative water content under moisture deficit conditions in wheat and found that additive and
dominance along with additive x dominance interaction were responsible in the inheritance of this
trait. Ahmed et al. (2000) estimated additive and additive x dominance interaction for the
inheritance of RWC under drought conditions in wheat. Majeed et al. (2001) reported that only
additive type of gene action controlled the inheritance of relative water content under drought
conditions in barley. Kumar and Sharma (2007) estimated inheritance of relative water content
under drought conditions in wheat and found that additive, dominance and epistatic effects
governed the inheritance of this trait.
4. 3.5. Plant height
Under normal conditions four parameters model [mdhi] in cross-1 and five parameters
model [mdhij] in cross-2 were found fit of the observed to the expected generation means (Table
4.6). In cross-1 and 2 dominance effects [h] were greater than additive showing thereby presence of
heterosis which may either be due to overdominance or dispersion of dominant genes among the
parents. But the presence of epistatic effects particularly in cross-2 reveals ineffectiveness of
selection for improvement of plant height.
Under droughtful conditions five parameters model [mdhil] in cross-1 and four parameter
[mdjl] model in cross-2 were found adequate for plant height (Table 4.7). Both additive and non
additive gene actions along with different epistatic effects were observed to be involved in the
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inheritance of plant height under drought conditions. Opposite signs of h and l indicate that
duplicate type of gene interaction prevailed in cross-1.
The results are in accordance with Singh et al. 1983 and Randhawa et al. 1986, Mukhtar et
al. (2000a), Subhani and Chowdhry (2000), Ahuja et al. (2004), Ahmed et al. (2006), Murugan and
Ganesan (2006), Patra et al. (2006) who showed additive type of gene action for plant height trait.
However overdominance and epistatic type of gene action were reported by Singh et al. 1983,
Randhawa et al. 1986 and Saravanan et al. (2003).
4. 3.6. Number of monopodial branches
For number of monopodial branches per plant a two parameter [ml] and three parameters
models [mdh] appeared to be the best fit in cross-1 and 2 respectively under normal conditions
(Table 4.6). Greater value of h in cross-2 indicated the presence of heterosis and the inheritance of
this character was free of any epistatic effects.
Similarly, under drought, two parameter [md] in cross-1 and three parameter [mdh] model in
cross-2 indicated the best fitness of the observed to expected generation means for number of
monopodial branches (Table 4.7).
In cross-2, the situation remained unchanged with the change in irrigation levels.
Greater values of h under both the regimes indicated the presence of heterosis. Singh et al. (1971)
found additive and dominance genetic variances with the genetic interactions in the inheritance of
monopodial branches. Abro (2003) repoted that number of monopodia was governed by partial
dominance type of gene action. Abbas et al. (2008) observed Additive type of gene action along
with partial dominance for number of monopodial branches.
4. 3.7. Number of sympodial branches
Five parameter [mdhij] in cross-1 and four parameters [mdhi] models in cross-2 were best fit
for sympodial branches under normal conditions (Table 4.6).
Similarly, under droughtful conditions four parameters [mdhi] in cross-1 and three
parameter [mdh] model in cross-2 were adequate (Table 4.7) for this plant trait.
Both additive and non additive gene actions with greater dominance effects than additive
ones were operative in the inheritance of this trait. Epistatic effects were also evident except in
cross-2 under drought where h is almost equal to d. Sympodial branches in cotton plant is a
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desirable character and selection may be effective for its improvement in cross-2 under drought
conditions.
Similar results have been reported by various workers, e.g. Singh et al. (1971) they
studied the genetics of the number of sympodial branches in cotton and revealed significant
additive and dominance genetic variance along with interactions for the character. Silva and Alves
(1983) reported that for number of fruiting branches (sympodial branches) additive and dominance
as well as epistasis was involved in the inheritance. Iqball and Nadeem (2003) studied inheritance
of sympodial branches through generation mean analysis and advocates the presence of additive
gene action for number of sympodial branches. Punitha et al. (1999) observed non-additive type of
gene action for sympodial branches in cotton. Sarwar et al. (2011) found additive gene action with
partial dominance for number of sympodial branches.
4. 3.8. Number of bolls per plant
For number of bolls per plant, two parameters [md] in cross-1 and four parameters [mdjl]
model in cross-2 appeared to be adequate under normal conditions (Table 4.6).
Under drought, 5 parameters [mdhij] model in cross-1 and 3 parameter [mdh] model in
cross-2 showed best fitness of the observed to the expected generation means for the trait (Table
4.7).
Under normal conditions significant additive component in cross-1 revealed that additive
variances are pronounced for this trait and there existed a scope for its genetic improvement.
However, in cross-2 epistatic effects of the type j and l are unfixable, therefore, heterosis breeding
may be rewarding for this trait.
As far as the situation under drought conditions is concerned, both additive and non-additive
gene actions indicated their involvement in the inheritance pattern of this trait in both the crosses.
Higher value of h than d indicated the presence of heterosis for number of bolls but negative sign of
h showed the trend of heterosis towards decreasing side. Further epistatic effects were also
pronounced. However, in cross-2 higher magnitude of d than h without any complication due to
epistatic effects revealed the scope of its fixation through selection. Kalsy and Garg (1988), Ahmad
et al. (2001) and Desalegn et al. (2009) reported additive gene action for the inheritance of this trait.
However, Pathak and Singh (1970) and Esmail (2007) also studied the inheritance of number of
bolls per plant in cotton and reported additive, dominance and epistatic effects for this trait.
Similarly Singh et al. (1971) studied genetics of number of bolls per plant in cotton and found
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additive and dominance genetic variances along with the interactions for this trait. Silva and Alves
(1983) studied the gene action in cotton (G. hirsutum) and reported additive and dominance affects
for number of bolls per plant. Randhawa et al. (1986) estimated genetic effects in cotton and found
additive genetic variance as well as epistasis for number of bolls per plant. Difference of gene
action in the crosses of present study and in the studies reported by the above workers might be due
to different genetic back ground of the varieties used.
4. 3.9. Boll weight
Under normal conditions 4 parameter [mdhi] model showed its adequacy to the data set for
boll weight in both the crosses (Table 4.6). Whereas under droughtful conditions, 4 parameter
[mdhi] in cross-1 and 5 parameter [mdhij] model in cross-2 appeared adequate (Table 4.7). Both the
crosses behaved almost consistent over the stress regimes with positive values of all the parameters
involved in the inheritance of boll weight. Dominance component is there but almost of equal
magnitude in cross-1 under normal and in cross 2 under drought. Overall, both the crosses seemed
convincing to be considered as far as improvement in boll weight, an important component of yield
of seed cotton is concerned.
Different types of gene actions involved in the inheritance of boll weight in cotton have been
reported in the literature by the researchers like, Pathak and Singh (1970) reported additive and
epistatic effects for this trait Singh et al. (1971), Kaseem et al. (1984) and Kalsy and Garg (1988)
observed additive and dominance genetic variance along with the epistatic effects and Tyagi (1988)
and Esmail (2007) observed additive and dominance variance.
4. 3.10. Seed cotton yield
Under normal conditions, 5 parameter models i.e., m, d, h, i and l in cross-1 and m, d, h, i
and j in cross-2 were indicated to be adequate for seed cotton yield (Table 4.6). Whereas, under
drought, 4 parameters [mdhi] in cross-1 and 5 parameter [mdhjl) model in cross-2 provided the best
fit for this trait (Table 4.7). Although additive component is greater than dominance under normal
condition in cross-1, but the presence of epistatic effects like dominance × dominance [l]
complicated the situation. Opposite signs of h and l indicated the presence of duplicated type of
gene interaction. Similarly, in cross-2, d and i are there with h almost equal to d indicating the
possibility of improving the trait through selection as well as use of heterosis breeding but in later
generations because of the presence of the epistatic effects due to additive × dominance interaction.
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Under drought (in cross-1) both additive and dominance effects were present the genes
showing non-additive influence appeared to be more important than the additive genes. The additive
× additive [i] interaction, however, indicated that fixation of additive alleles is possible in the later
segregating generations as suggested by Singh and Narayanan (2000).
In cross-2, again additive × non-additive gene actions with epistatic effects were operative for the
expression of yield of seed cotton. Greater h than d and presence of j and l indicated the unfixability
of the character and therefore, hetrosis breeding may be rewarding in this case. Opposite signs of h
and l indicated the presence of duplicated type of epistasis. The results are in agreement with Pathak
and Singh (1970), Kaseem et al. (1984), Kalsy and Garg (1988) and Esmail (2007) who studied the
inheritance of seed cotton yield per plant in cotton and reported additive, dominance and epistatic
gene effects for this trait. Similarly Randhawa et al. (1986) reported the presence of additive and
epistatic effects in the inheritance of this trait.
4. 3.11. Ginning out-turn (GOT)
Under normal irrigation regime five parameter [mdhij] models gave the best fitness in both
the crosses. Similarly, three parameter [mdl] models provided good fit for ginning out turn
percentage in both the crosses under drought conditions.
Both additive and nonadditive genes alongwith their epistatic effects were evident to be
involved in the inheritance of this trait under normal conditions in both the crosses. Greater values
of h than those of d indicated the presence of heterosis. Positive signs showed the effect of
favourable or increasing alleles for GOT but the presence of non-additive genetic and epistatic
effects do not favour the effectiveness of selection. However, heterosis breeding may be exploited.
Under droughtful regime both the crosses again showed the same genetic picture. Three
parameter (mdl) model was fit in both the crosses. Additive component was there but complicated
by epistatic effects due to dominance × dominance.
Additive, dominance and interactions were reported to be responsible for the inheritance of
lint percentage by Dhillon and Singh (1980), Singh and Yadavendra (2002) and Mert et al. (2003)
while analyzing generation means in cotton. However Pavasia et al. (1999) reported additive type of
gene action in the inheritance of lint percentage in cotton.
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4. 3.12. Fibre Length
Under no stress of water five parameter [mdhil] in cross 1 and 4 parameter [mdhl] model in
cross 2 were found best fit of the observed to the expected generation means for staple length (Table
4.6). Whereas, under drought condition 4 parameter [mdhl] in cross 1 and 4 parameter [mdil] model
in cross 2 was fit (Table 4.7).
Both additive and non-additive genes were acting and interacting in the inheritance of staple
length in both crosses under both the environmental regimes. Further, the dominance component
with reducing genes was more prominent. Staple length is one of important fibre property traits and
reduction in its expression is not a desirable characteristics, both the crosses, therefore did not
represent a suitable genetic material as for as improvement in this trait is concerned.
Singh et al. (1983) and Lin and Zhao (1988) studied gene action in cotton for this trait and
recorded additive, dominance and epistatic effects. Nadarajan and Rangasamy (1990) found that the
trait was controlled by simple additive gene action, while Singh and Yadavendra (2002) concluded
that fibre length in cotton was governed by additive and dominance genetic effects along with
involvement of interactions. Nimbalkar et al. (2004) observed in desi cotton (Gossypium arboreum
and Gossypium herbaceum) that only additive type of gene action was responsible for the
inheritance of fibre length. Murtaza et al. (2004) estimated gene action in cotton and found that
epistatic effects were responsible for the inheritance of fibre length.
4. 3.13. Fibre Strength
Four parameters, m, d, h and i model in cross-1 and m,d, j and l in cross-2 provided the best
fitness of the observed to the expected generation means for fibre strength under normal conditions
(Table 4.6). Similarly, five parameters, m, d, h, i and j model in cross-1 and m,d,h, j and l in cross-2
was adequare under drought experiment (Table 4.7). Fibre strength is another desirable and
important trait of cotton. Both additive and nonadditive components alongwith epistatic effects were
observed to be involved in the inheritance of this trait in both the crosses under both the
environments except the cross-1 which seemed a promising material as for the possibility of
improving this trait under normal conditions. In this cross prominence of d and i components are
fixable.
Pathak (1975) and Hendawy et al. (1999) observed that fibre strength in cotton had additive
and dominance genetic effects as well as additive x additive interaction. Singh et al. (1983) as well
as Lin and Zhao (1988) concluded that additive, dominance and epistatic genetic variances were
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involved in the inheritance of fibre strength in cotton. Murtaza et al. (2004) observed that fibre
strength in cotton was controlled by additive and dominance genetic effects.
4. 3.14. Fibre Fineness
In the case of fibre fineness five parameters [mdhil] in cross-1 and two parameters [md]
model in cross-2 two were adequate under normal conditions; whereas, under drought conditions
four parameters [mdhi] in cross-1 and three parameters [mdh] model in cross-2 were found fit for
this trait. Measuring units of fibre fineness in cotton are the micronairs which means “ rate of air
flow through fibre mass”. In other words higher the micronair value coarser is the fibre and the vice
versa.The breeder therefore have to be careful during the process of selection from the breeding
material for fineness.
In the present studies both the crosses indicated significant negative values of additive
effects indicating thereby desirable situation for the improvement of fibre fineness under normal
conditions but only in cross-2 because cross-1 showed the presence of non additive and epistatic
effects in the expression of this character. Under drought conditions negative values of h, d and i in
cross-1 indicated the dominance of decreasing genes and thus seemed promising material as far as
the improvement of fibre fineness is concerned for drought tolerance. Cross-2, under drought
conditions, however proved reverse as far as the improvement of fibre fineness is concerned. Both
additive and non additive genetic effects in the phenotypic manifestation of fibre fineness have been
reported in the literature. Gad et al. (1974) estimated that additive and dominance variances were
involved in the inheritance of fibre fineness. Ma et al. (1983) evaluated six generations of cotton for
the inheritance of fibre fineness and found dominance effects for this trait. Lin and Zhao (1988)
reported from their studies in cotton that fibre fineness was governed by additive, dominance and
epistatic genetic effects. Nadarajan and Rangasamy (1990) found that fibre fineness was controlled
by additive gene action in cotton.
4.4. Generation Variance analysis
Differences in morphological and physiological traits are due to genetic and environmental
variation. Generation variance analysis has widely been used by plant breeders for partitioning the
total variance into genetic and environmental components. The partitioning of phenotypic variance
into its genotypic and environmental components is not sufficient to study the genetic properties of
a breeding material, so genotypic variance is further partitioned into additive (D), dominance (H)
and interaction (F). Genetic and environmental variance can be measured from an experiment which
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includes some non segregating (e.g. pure lines, inbred lines, F1 etc.) and segregating populations
(e.g. backcrosses, F2 etc.). In the present studies a model incorporating DE (additive and
environmental) components gave the best fit for all the traits in both the crosses, both under normal
and drought conditions except for number of sympodial branches in cross-1 under normal
conditions where model DFE gave the best fit (Table 4.8).
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Table.4.8 Components of variance, D (additive), F(additive× dominance), E(environmental), narrow sense heritability and genetic advance estimates of various morphological and physiological traits in cross-1 (Niab-78×CIM-446) and cross-2 (CIM- 482×FH-1000) under normal conditions.
Traits Cross(C) [D] [F] [E] [X2] [d.f] h2 (F2) h2 (F∞) G.A Leaf area C1 88.64±13.89 19.69±2.84 1.98 4 0.59 0.78 9.90
C2 72.359 ± 12.670 19.481 ± 2.793 0.44 4 0.60 0.77 9.75 Leaf temperature
C1 2.53±0.37 0.49±0.07 0.56 4 0.59 0.80 0.67
C2 2.940 ± 0.703 1.276 ± 0.179 0.27 4 0.48 0.68 0.62 ELW Loss C1 0.14±0.02 0.025±0.003 1.53 4 0.65 0.82 0.64
C2 0.108 ± 0.021 0.033 ± 0.005 0.53 4 0.50 0.72 0.46 R.L.W.C C1 19.69±2.74 3.50±0.50 3.17 4 0.63 0.82 4.48
C2 18.409 ± 4.484 8.149 ± 1.149 0.21 4 0.49 0.67 2.61 Plant height (cm)
C1 95.33±15.99 23.88±3.43 1.29 4 0.49 0.75 5.92
C2 223.349 ± 24.603 22.483 ± 3.314 9.38 4 0.79 0.89 9.82 Monopodial branches
C1 1.024±0.31 0.62±0.08 1.23 4 0.36 0.58 2.59
C2 1.74 ± 0.324 0.518 ± 0.074 4.114 4 0.17 0.45 0.11 Sympodial branches
C1 15.61±2.16 -2.77±1.35 2.70±0.39 0.98 3 0.67 0.83 2.77 C2 23.186 ± 3.848 5.700 ± 0.820 0.41 4 0.69 0.77 2.28
Number of Bolls/plant
C1 15.62±2.15 2.707±0.39 5.50 4 0.66 0.85 2.59 C2 49.517 ± 6.297 7.224 ± 1.057 1.26 4 0.64 0.84 5.44
Bolls weight (gm)
C1 0.45±0.06 0.09±0.013 6.59 4 0.60 0.83 0.37
C2 0.647 ± 0.085 0.102 ± 0.015 6.04 4 0.66 0.84 0.42 SCY (gm) C1 67.06±9.72 12.88±1.87 1.80 4 0.65 0.82 4.94
C2 46.669 ± 16.165 33.301 ± 4.559 0.41 4 0.37 0.56 9.27 GOT % C1 2.76±0.48 0.74±0.10 0.93 4 0.537 0.74 1.06
C2 1.949 ± 0.432 0.757 ± 0.107 1.07 4 0.50 0.69 0.90 Fibre length (mm)
C1 3.53±0.57 0.84±0.12 1.96 4 0.593 0.78 0.89 C2 2.756 ± 0.696 1.291 ± 0.180 2.27 4 0.38 0.61 0.75
Fibre strength (g/tex)
C1 5.30±0.82 1.15±0.100 0.60 4 0.591 0.78 0.83
C2 2.809 ± 0.907 1.832 ± 0.252 1.58 4 0.40 0.60 1.05 Fibre fineness C1 0.38± 0.06 0.09±0.01 5.19 4 0.533 0.74 0.50
C2 0.737 ± 0.098 0.119 ± 0.017 5.50 4 0.29 0.67 0.14
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Table 4.9. Components of variance, D (additive), F(additive× dominance), E(environmental), narrow sense heritability and genetic advance estimates of various morphological and physiological traits in cross-1 (Niab-78×CIM-446) and cross-2 (CIM-482×FH-1000) under drought conditions.
Traits Cross-2 [D] [F] [E] [X2] [d.f] h2 (F2) h2 (F∞) G.A
Leaf area C1 115.621 ± 20.011 30.529 ± 4.380 1.31 4 0.54 0.75 15.52
C2 77.629 ± 14.217 22.497 ± 3.215 1.27 4 0.51 0.73 10.77 Leaf temperature C1 2.731 ± 0.521 0.844 ± 0.120 0.55 4 0.49 0.71 0.60
C2 2.819 ± 0.722 1.347 ± 0.188 0.69 4 0.34 0.58 0.63 ELW Loss C1 0.132 ± 0.016 0.017 ± 0.003 3.47 4 0.40 0.78 0.37
C2 0.823 ± 0.019 0.033 ± 0.005 0.80 4 0.59 0.79 0.20 R.W.C C1 20.952 ± 3.595 5.451 ± 0.783 0.31 4 0.57 0.76 4.33
C2 18.131 ± 4.252 7.649 ± 1.075 0.81 4 0.42 0.65 2.93 Plant height (cm) C1 69.291±10.477 14.413 ±2.087 0.94 4 0.63 0.81 6.62
C2 59.746 ± 9.499 13.622 ± 1.966 0.61 4 0.61 0.79 13.90 Monopodial branches
C1 2.103 ± 0.418 0.694 ± 0.099 0.51 4 0.51 0.72 0.29
C2 1.775 ± 0.317 0.493 ± .071 0.53 4 0.36 0.67 0.28 Sympodial branches C1 14.598 ±2.608 4.063 ± 0.582 2.02 4 0.51 0.73 1.44
C2 14.598 ±2.608 4.063 ± 0.582 2.02 4 0.51 0.73 1.44 Number of Bolls/plant
C1 9.855 ±2.149 3.739 ±0.528 0.82 4 0.43 0.66 1.72
C2 37.461 ± 3.860 3.037 ± 0.449 0.29 4 0.76 0.91 3.82 Bolls weight (gm) C1 0.453 ± 0.048 0.039 ± 0.006 0.54 4 0.78 0.91 0.45
C2 0.306 ± 0.038 0.043 ± 0.006 4.21 4 0.72 0.87 0.47 SCY (gm) C1 84.745 ± 11.119 13.254 ± 1.936 0.57 4 0.69 0.85 5.62
C2 65.131 ± 6.926 5.877 ± 0.868 2.10 4 0.79 0.91 17.88 GOT % C1 0.274 ± 0.0555 0.093 ±0.013 1.16 4 0.42 0.63 0.36
C2 4.269 ± 0.552 0.647 ± 0.095 7.69 4 0.58 0.81 0.75 Fibre length (mm) C1 2.802 ± 0.505 0.792 ± 0.113 0.44 4 0.54 0.74 0.78
C2 4.018 ± 0.744 1.184 ± 0.169 0.33 4 0.52 0.73 1.55 Fibre strength (g/tex)
C1 3.815 ± 0.747 1.229 ± 0.175 0.29 4 0.52 0.72 1.61
C2 5.258 ± 1.266 2.303 ± 0.323 1.04 4 0.35 0.60 1.94 Fibre fineness C1 0.274 ± 0.555 0.093±0.013 1.16 4 0.54 0.73 0.44
C2 0.549 ± 0.099 0.155 ± 0.022 4.09 4 0.16 0.51 0.12
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Additive and dominance genetic variance of various traits in cotton has been reported by
Gad et al. (1974), Tyagi (1988), May and Green (1994), Nistor and Nistor (1999),
Mukhtar et al. (2000), Bertini et al. (2001), Khan et al. (2001).
Both generation means and generation variance analyses indicated presence of
additive and dominance variance for various traits, but epistatic effects were not detected
in the generation variance analysis. This discrepancy may be due to differences in the
estimation precision of the two analyses. However Malik et al (1999) reported that
generation means analysis is relatively more reliable compared to generation variance
analysis. The results of generation variance analysis and narrow sense heritability (F2)
and F (infinity) and genetic advance are given in Table 4.7 and 4.8.
4.5. Heritability and genetic advance for various plant traits
The narrow sense heritability estimates for all the plant traits in F2 generation of
cross-1(NAIB 78×CM446 ) ranged between 0.67 to 0.37 under normal and 0.79 to 0.41
under drought conditions.. Johnson et. al. (1955a), categorized the heritability values as
low (less than 30 %), moderate (30-60 %) and high (more than 60 %). High narrow sense
heritability estimates 0.67, 0.66 and 0.65 were observed for number of sympodial
branches, number of bolls per plant and seed cotton yield, respectively under normal
conditions and 0.79, 0.69 and 0.58 for boll weight, seed cotton yield and relative leaf
water content respectively under drought conditions in cross-1. These high heritability
estimates were due to additive gene effects which suggested that these traits can be
improved by selection during successive generations.
The narrow sense heritability estimates of infinity generation (F∞) were
consistently higher than in F2 generation and ranged between 0.85 to 0.58 under normal
and 0.91 to 0.63 under drought conditions in the cross-1. In the cross-2, narrow sense
heritability estimates in F2 generation ranged from 0.69 to 0.17 under normal and 0.79 to
0.16 under drought conditions. In this cross high heritability estimates 0.69, 0.66 and 0.64
were observed for plant height, boll weight and number of bolls per plant respectively
under normal and 0.79, 0.76 and 0.72 for seed cotton yield , bolls per plant and boll
weight respectively under drought conditions. High heritability estimates suggested the
possibility of genetic improvement for these traits through selection in segregating
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populations. For F infinity generation heritability estimates were ranging between 0.89 to
0.45 under normal and 0.91 to 0.51 under drought conditions in the cross-2.
Based upon the estimates of narrow sense heritability, the extent of genetic
advance for all the characters was calculated in both the crosses under normal (4.8) as
well as drought (4.9) conditions.
Under normal conditions, cross-1 revealed higher value (9.90) of genetic advance
for leaf area and moderate for plant height (5.92 ) and seed cotton yield (4.94). Whereas,
the estimates remained lesser ranging from 0.37 to 4.48 for all other traits.
Similarly, cross-2, under normal conditions indicated higher estimates of genetic
advance for plant height (9.82), leaf area (9.75) and seed cotton yield (9.27) and other
traits remained with in the range of 0.14 to 5.44 ( Table 4.8).
` Under drought, cross-1 revealed higher genetic advance (15.52) for leaf area,
moderate (6.62) for plant height and lower for other traits which remained within the
range of 0.29 and 5.62.
Similarly, the cross-2, showed higher values of seed cotton yield (17.88), plant
height (13.90) and leaf area (10.77) whereas, all other traits remained with in the range of
0.12 and 3.82 (Table 4.9). Moderate to high narrow sense heritability and genetic
advance for various plant traits inculuding plant height, seed cotton yield, number of
bolls, lint percentage, fibre length, leaf area, monopodial branches and boll weight by
Ahmed et al.(2006), Baloch et al. (2004), Kumari and Chamundeswari (2005), Singh and
Singh (1981), Gupta (1987), Ulloa (2006). However, low estimates of narrow sense
heritability for different plant traits have been observed by Murtaza (2005) and Esmail
(2007).
In the present studies the breeding material analysed genetically consisted of two
crosses. The cross NIAB-78 x CIM-446 was cross-1 and CIM-482 x FH-1000 was cross-
2. Both the crosses were studied under normal as well as drought conditions. Our main
focus was to look for the possibility of improvement of future cotton varities studied
under droughtfull conditions.The materials were suitable for the plant traits including
physiological, agronomical as well as fibre quality traits. Generation means analysis
revealed the involvement of both additive and non additive gene actions alongwith some
epistatic effects in the phenotypic manifestation of the trait.
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Similarly, the narrow sense heritability and genetic advance estimates ranged
from low to high for the traits in both the crosses. Overall, the cross-1 proved to be
promising for improvement in the plant traits like, monopodial and sympodial branches,
boll weight, seed cotton yield and fibre fineness. In all theses traits additive type of gene
action was predominantly involed in their interitance and narrow sense heritability
estimates were high.
Similarly, cross-2 indicated to be a promising breeding material for the
improvement of leaf area, leaf temperature, relative water content, monopodia, sympodia,
number of bolls and fibre fineness because additive type of gene action was prominantly
involved in their inheritance and the heritability estimates were generally moderate.
Although the extent of genetic advance was generally low in all the traits
however, selection may yield improvement with slow progress but one has to be careful
while making selection, particularly, for the trait like, leaf area, leaf temperature, excised
leaf water loss and fibre fineness where lower or negative values will be desireable. At
the same time one has to keep an eye on the association of these plant traits with others
during the process of selection. The results of correlation studies are presented in table
4.10 to 4.13.
4.6 . Frequency distribution of F2 population
The frequency distribution, of physiological, agronomic and fibre quality traits in
F2 populations are given in Figures 4.1 to 4.28. The graphs for all the traits for crosses
NAIB-78×CIM-446 and CIM 482 ×FH 1000 under both normal and drought conditions
show near normal distribution in F2. The appearance of transgressive segregants in F2
generation is the function of the following favourable genetic situations associated with
the parents involved:
1. The character must be polygenically controlled.
2. The parents should be completely homozygous.
3. Parents should be complementary to each other for the (+v) and (-V) genes
conditioning the trait in point.
4. There should be no linkage.
102
The distribution showed continuous variation representing the polygenic nature of these
traits. In all the traits some F2 plants excelled their parents exhibiting transgressive
segregation.
In case of cross-1 (NAIB 78×CM446) under normal conditions Figures 4.1- 4.14a
F1 means fall outside the parental range for leaf area, monopodial branches and lint %
age, while the remaining plant traits like, leaf temperature, excised leaf water loss,
relative water content, plant height, sympodial branches, number of bolls per plant, boll
weight, seed cotton yield, fibre length, fibre strength and fibre fineness fell inside the
parental range.
In cross-1 (NAIB-78×CIM-446) under drought conditions Figures 4.1- 4.14b F1
means were found outside the parental range for leaf temperature, plant height, sympodial
branches, seed cotton yield, lint percentage, and fibre fineness whereas the other
indicated plant traits fell inside the parental range.
In case of cross-2 (CIM 482×FH-1000) under normal conditions Figures 4.15-
4.28a F1 means fall outside the parental range and showed heterosis for monopodial
branches, lint % age, leaf area, fibre length, fibre strength, while F1 means for remaining
plant traits fell inside the parental range.
In case of cross CIM 482 ×FH 1000 under drought conditions Figures 4.15-4.28b
the heterosis was greatly pronounced for monopodial branches, sympodial branches, fibre
length, fibre strength, lint percentage leaf temperature and leaf area, whereas other plant
traits fell inside the parental range.
103
CROSS-1
(a) Normal
BC2
BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
50
98 102 106 110 114 118 122 126 130 134
Nu
mb
er o
f p
lan
ts
Plant Height (cm)
(b) Drought
P1
P2
F1
F2
B1
B2
0
5
10
15
20
25
30
35
96 99 102 105 108 111 114 117 120 123 126 129 132
Nu
mb
er o
f p
lan
ts
Plant Height (cm)
Fig-4.1. Frequency distribution of the F2 for plant height of cross-1 (NIAB-78×CIM-446) of Cotton under ( a ) normal and ( b ) drought conditions.
104
(a) Normal
B2
B1
F2
F1
P2
P1
10
20
30
40
50
60
-1 0 1 2 3 4 5
Nu
mb
er o
f p
lan
ts
(b) Drought
P1
P2
F1F2
B1B2
10
20
30
40
50
60
-1 0 1 2 3 4 5 6 7
Nu
mb
er o
f p
lan
ts
Monopodial branches Fig-4.2. Frequency distribution of the F2 for monopodial branches of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions
105
(a) Normal
B2
B1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
50
12 14 16 18 20 22 24 26 28 30
Nu
mb
er o
f p
lan
ts
Sympodial branches
(b) Drought
P1
P2
F1
F2
B1
B2
0
5
10
15
20
25
30
35
40
45
8 10 12 14 16 18 20 22 24 26
Nu
mb
er o
f p
lan
ts
Sympodial branches Fig-4.3. Frequency distribution of the F2 for sympodial branches of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
106
(a) Normal
P1
P2F1F2
B1
B2
0
5
10
15
20
25
18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
Nu
mb
er o
f p
lan
ts
Drought
P1
P2
F1
B1
B2
0
5
10
15
20
25
30
35
40
45
50
16 18 20 22 24 26 28 30 32
Nu
mb
er o
f p
lan
ts
Number of bolls
Fig-4.4. Frequency distribution of the F2 for Bolls/plant of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
107
(a) Normal
B2
B1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
50
96 100 104 108 112 116 120 124 128 132 136
Nu
mb
er o
f p
lan
ts
Seed cotton yield
(b) Drought
P1
P2
F1
F2B1
B2
0
5
10
15
20
25
30
35
40
45
70 75 80 85 90 95 100 105 110 115 120
Nu
mb
er
of
pla
nts
Seed cotton yield
Fig-4.5. Frequency distribution of the F2 for Seed cotton yield of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
108
(a) Normal
B2
B1F2 F1
P2
P1
0
5
10
15
20
25
30
35
40
45
2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5
Nu
mb
er o
f p
lan
ts
Boll weight
(b) Drought
P1
P2
F1
F2
B1
B2
0
5
10
15
20
25
30
1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2
Nu
mb
er o
f p
lan
ts
Boll weight
Fig-4.6. Frequency distribution of the F2 for boll weight of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
109
(a) Normal
B2
B1
F2
F1
P2
P1
0
5
10
15
20
25
30
25 25.5 26 26.5 27 27.5 28 28.5 29 29.5 30 30.5 31 31.5 32
Nu
mb
er o
f p
lan
ts
Fibre length
(b) Drought
P1
P2F1
F2
B1
B2
0
5
10
15
20
25
30
35
22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 27.5 28
Nu
mb
er o
f p
lan
ts
Fibre length Fig-.4.7. Frequency distribution of the F2 for Fibre length of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and (b) drought conditions.
110
(a) Normal
B2
B1F2 F1
P2
P1
0
5
10
15
20
25
30
35
40
22 23 24 25 26 27 28 29 30 31 32
Nu
mb
er o
f p
lan
ts
Fibre strength
(b) Drought
P1
P2
F1
F2
B1
B2
0
5
10
15
20
25
30
35
40
45
18 19 20 21 22 23 24 25 26 27 28
Nu
mb
er o
f p
lan
ts
Fibre strength
Fig-4.8. Frequency distribution of the F2 for Fibre strength of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
111
(a) Normal
B2
B1F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4
Nu
mb
er o
f p
lan
ts
Fibre fineness
(b) Drought
P1
P2
F1
F2
B1
B2
0
5
10
15
20
25
30
35
40
45
2.4 2.7 3 3.3 3.6 3.9 4.2 4.5 4.8 5.1
Nu
mb
er o
f p
lan
ts
Fibre fineness Fig-4.9. Frequency distribution of the F2 for Fibre fineness of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
112
(a) Normal
B2
B1F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
32 33 34 35 36 37 38 39 40 41
Nu
mb
er o
f p
lan
ts
Ginning out-turn
(b) Drought
P1
P2
F1
F2 B1B2
0
5
10
15
20
25
30
32 33 34 35 36 37 38 39
Nu
mb
er o
f p
lan
ts
Ginning out-turn
Fig-4.10. Frequency distribution of the F2 for Ginning out-turn of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
113
(a) Normal
B2
B1
F2
F1P2
P1
0
5
10
15
20
25
30
35
40
74 76 78 80 82 84 86 88 90 92
Nu
mb
er o
f p
lan
ts
Relative water content
(b) Drought
P1
P2
F1
F2 B1
B2
0
5
10
15
20
25
30
35
40
45
50
68 70 72 74 76 78 80 82 84 86 88
Nu
mb
er o
f p
lan
ts
Relative water content Fig-4.11. Frequency distribution of the F2 for Relative water content of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
114
(a) Normal
B2
B1
F2F1
P2
P1
0
5
10
15
20
25
30
35
40
1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8
Nu
mb
er o
f p
lan
ts
Excised leaf water loss
(b) Drought
P1
P2
F1F2
B1 B2
0
10
20
30
40
50
60
1.4 1.8 2.2 2.6 3 3.4
Nu
mb
er o
f p
lan
ts
Excised leaf water loss Fig-4.12. Frequency distribution of the F2 for Excised leaf water loss of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
115
(a) Normal
B2
B1F2
F1P2
P1
0
10
20
30
40
50
60
25 26 27 28 29 30 31 32 33 34
Nu
mb
er o
f p
lan
ts
Leaf temperature
(b) Drought
P1
P2F1F2
B1B2
0
5
10
15
20
25
30
35
40
45
50
27 29 31 33 35 37
Nu
mb
er o
f p
lan
ts
Leaf temperature Fig-4.13. Frequency distribution of the F2 for Leaf temperature of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
116
(a) Normal
B2
B1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
165 170 175 180 185 190 195 200 205 210
Nu
mb
er o
f p
lan
ts
Leaf area
(b) Drought
P1 P2
F1
F2
B1
B2
0
5
10
15
20
25
30
35
155 165 175 185 195 205
Nu
mb
er o
f p
lan
ts
Leaf area
Fig-4.14. Frequency distribution of the F2 for Leaf area of cross-1 (NIAB-78×CIM-446) of Cotton under (a) normal and ( b ) drought conditions.
117
CROSS-2 (a) Normal
BC2BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
80 85 90 95 100 105 110 115 120 125 130 135 140 145
Nu
mb
er o
f p
lan
ts
Plant height
(b) Drought
P1P2F1
F2BC1
BC2
0
5
10
15
20
25
30
35
40
45
50
80 85 90 95 100 105 110 115 120 125 130
Nu
mb
er o
f p
lan
ts
Plant height Fig-4.15 Frequency distribution of the F2 for plant height of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
118
(a) Normal
P1
P2
F1
F2
BC1
BC2
10
20
30
40
50
60
70
-1 0 1 2 3 4 5
Nu
mb
er o
f p
lan
ts
Monopodial branches
(b) Drought
P1
P2
F1F2
BC1BC2
10
20
30
40
50
60
-1 0 1 2 3 4 5 6
Nu
mb
er o
f p
lan
ts
Monopodial branches
Fig-4.16. Frequency distribution of the F2 for monopodial branches of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
119
(a) Normal BC2
BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
8 10 12 14 16 18 20 22 24 26 28 30
Nu
mb
er
of
pla
nts
Sympodial branches
(b) Drought
P1
P2
F1F2
BC1
BC2
0
5
10
15
20
25
30
35
40
8 10 12 14 16 18 20 22 24 26 28
Nu
mb
er o
f p
lan
ts
Sympodial branches
Fig-4.17. Frequency distribution of the F2 for sympodial branches of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
120
(a) Normal
BC2 BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
16 18 20 22 24 26 28 30 32 34 36 38 40
Nu
mb
er o
f p
lan
ts
Bolls/plant
(b) Drought
P1
P2
F1
F2
BC1
BC2
0
5
10
15
20
25
30
35
8 10 12 14 16 18 20 22 24 26 28 30 32 34
Nu
mb
er
of
pla
nts
Bolls/plant
Fig-4.18. Frequency distribution of the F2 for bolls/plant of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
121
(a) Normal
BC2
BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
45
85 90 95 100 105 110 115 120 125 130
Nu
mb
er o
f p
lan
ts
Seed cotton yield
(b) Drought
P1
F1F2
BC1
BC2
0
5
10
15
20
25
30
35
40
67 70 73 76 79 82 85 88 91 94 97 100
Nu
mb
er o
f p
lan
ts
Seed cotton yield Fig-4.19. Frequency distribution of the F2 for Seed cotton yield of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
122
(a) Normal
BC2
BC1F2F1
P2
P1
0
5
10
15
20
25
30
35
1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8 5.2
Nu
mb
er o
f p
lan
ts
Boll weight
(b) Drought
P1
P2
F1
F2
BC1
BC2
0
5
10
15
20
25
30
1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4
Nu
mb
er o
f p
lan
ts
Boll weight
Fig-4.20. Frequency distribution of the F2 for Boll weight of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
123
(a) Normal
P1
P2F1
F2 BC1
BC2
0
5
10
15
20
25
24 25 26 27 28 29 30 31
Nu
mb
er o
f p
lan
ts
Fibre length
(b) Drought
P1
P2
F1
F2
BC1
BC2
0
5
10
15
20
25
30
35
40
18 19 20 21 22 23 24 25 26 27 28
Nu
mb
er o
f p
lan
ts
Fibre length
Fig-4.21. Frequency distribution of the F2 for Fibre length of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
124
(a) Normal
BC2
BC1
F2
F1
P2P1
0
5
10
15
20
25
30
35
40
21 22 23 24 25 26 27 28 29 30 31 32
Nu
mb
er o
f p
lan
ts
Fibre strength
(b) Drought
P1
P2
F1
F2
BC1
BC2
0
5
10
15
20
25
30
35
19 20 21 22 23 24 25 26 27 28 29 30
Nu
mb
er o
f p
lan
ts
Fibre strength
Fig-4.22.Frequency distribution of the F2 for Fibre strength of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
125
(a) Normal BC2
BC1F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
Nu
mb
er o
f p
lan
ts
Fibre fineness
(b) Drought
P1
P2
F1F2
BC1
BC2
0
5
10
15
20
25
30
35
40
2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6
Nu
mb
er o
f p
lan
ts
Fibre fineness
Fig-4.23.Frequency distribution of the F2 for Fibre fineness of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
126
(a) Normal
BC2
BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
33.6 34.2 34.8 35.4 36 36.6 37.2 37.8 38.4 39 39.6 40.2 40.8
Nu
mb
er o
f p
lan
ts
Ginning out turn
(b) Drought
P1
P2
F1F2BC1
BC2
0
5
10
15
20
25
30
35
40
32 33 34 35 36 37 38 39 40 41 42
Nu
mb
er o
f p
lan
ts
Ginning out turn
Fig-4.24. Frequency distribution of the F2 for Ginning out turn of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
127
(a) Normal
BC2
BC1
F2
F1
P2
P1
0
5
10
15
20
25
30
35
40
70 72 74 76 78 80 82 84 86 88 90 92
Nu
mb
er o
f p
lan
ts
Relative water content
(b) Drought
P1
P2
F1
F2
BC1
BC2
0
5
10
15
20
25
30
35
40
45
68 70 72 74 76 78 80 82 84 86 88
Nu
mb
er o
f p
lan
ts
Relative water content
Fig-4.25. Frequency distribution of the F2 for Relative water content of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b) drought conditions.
128
(a) Normal
BC2
BC1
F2F1
P2
P1
0
10
20
30
40
50
60
2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8
Nu
mb
er o
f p
lan
ts
(b) Drought
P1
P2F1
F2
BC1
BC2
0
10
20
30
40
50
60
70
80
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8
Nu
mb
er o
f p
lan
ts
Excised Leaf Water Loss
Fig-4.26. Frequency distribution of the F2 for ELWL of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
129
(a) Normal
BC2BC1
F2F1
P2P1
0
5
10
15
20
25
30
35
40
24 25 26 27 28 29 30 31 32
Nu
mb
er o
f p
lan
ts
(b) Drought
P1
P2
F1
F2
BC1BC2
0
5
10
15
20
25
30
35
40
27 28 29 30 31 32 33 34 35 36
Nu
mb
er
of
pla
nts
Leaf temperature
Fig-4.27. Frequency distribution of the F2 for Leaf temperature of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
130
(a) Normal
BC2
BC1 F2
F1P2
P1
0
5
10
15
20
25
30
35
40
164 168 172 176 180 184 188 192 196 200 204
Nu
mb
er o
f p
lan
ts
(b) Drought
P1
P2F1
F2
BC1
BC2
0
5
10
15
20
25
30
35
40
45
50
152 156 160 164 168 172 176 180 184 188 192 196
Nu
mb
er o
f p
lan
ts
Leaf area
Fig-4.28. Frequency distribution of the F2 for Leaf area of cross-2 (CIM-482×FH-1000) of Cotton under (a) normal and ( b ) drought conditions.
131
4.7. Correlation studies
Correlation is degree of association among the traits. To breed a high yielding
cultivar, breeder has to tailor a plant with combination of a number of desirable traits.
The estimates of correlation among traits are helpful for planning a breeding programme
to synthesize a genotype with desirable traits. Correlation was estimated among
agronomic and the traits related to drought resistance in cotton. Four large F2 populations
(150 plants from each population) involving parents with contrasting traits were used in
correlation studies. The correlation calculated in such a recombinant large population
shows linkage behavior of the genes (Malik et al. 2006). Generally, the correlations for
the pair of traits among the populations were consistent. However, in some cases
correlation was significant for a trait in one cross but non-significant in the other. This
may be due to the difference in allele combinations of the parents involved in the
populations. Correlation matrix among the traits in both the crosses is given in
Table 4.10-4.13.
4.7.1 Plant height
Plant height was positively and significantly correlated with sympodial branches,
number of bolls per plant, seed cotton yield, boll weight, fibre length, fibre strength, lint
percentage and relative water content and it had negative non significant correlation with
monopodial branches, excised leaf water loss, leaf temperature and leaf area in cross-1
under normal and drought conditions and in cross-2 under normal conditions. In cross-2
under drought conditions, plant height indicated negative but non significant correlation
with monopodial branches, leaf temperature and leaf area. Whereas, significant and
positive correlation with all others.
132
Table.4.10. Genotypic (upper value) and phenotypic (lower value) correlations for different plant traits in cross-1 (NIAB-78 x CIM 446) of cotton under normal conditions. Traits Mono Symp BN SCY BW FL FS FF GOT RWC ELWL LT LA
PH G -0.07 0.98* 0.97* 0.86* 0.91* 0.93* 0.98* -0.86 0.85* 0.96* -0.84 -0.55 -0.78 P - 0.05 0.95** 0.94** 0.81** 0.89** 0.78** 0.96** -0.82** 0.81** 0.89** -0.82** -0.52* -0.76**
Mono G 0.12 -0.39 0.16 0.17 0.22 0.95 -0.35 0.31 0.28 -0.12 0.20 0.47* P 0.10 -0.30 0.16 0.19 0.15 0.04 -0.33 0.32 0.22 -0.12 0.23 0.45
Symp G 0.98* 0.73* 0.87* 0.72* 0.99* -0.80 0.94* 0.91* -0.80 -0.39 -0.63 P 0.86** 0.70** 0.82** 0.65** 0.89** -0.76** 0.88** 0.89** -0.78** -0.36 -0.61**
BN G 0.89* 0.91* 0.70* 0.99* -0.80 0.78* 0.97* -0.92 -0.78 -0.98 P 0.81** 0.84** 0.62** 0.92** -0.75 0.75** 0.85** -0.85** -0.69** -0.89**
SCY G 0.99* 0.73* 0.98* -0.98 0.86* 0.95* -0.97 -0.85 -0.72 P 0.98** 0.66** 0.90** -0.97** 0.80** 0.93** -0.96** -0.79** -0.72**
BW G 0.79* 0.97* -0.98 0.90* 0.97* -0.99 -0.73 -0.69 P 0.69** 0.93** -0.99** 0.85** 0.98** -0.96 -0.66** -0.67**
FL G 0.98* -0.79 0.62* 0.64* -0.54 -0.51 -0.63 P 0.81** -0.70** 0.54* 0.60** -0.49* -0.39 -0.56*
FS G -0.96 0.93* 0.99* -0.95 -0.77 -0.87 P -0.90** 0.86** 0.92** -0.88** -0.69** -0.82**
FF G -0.88 -0.97 0.95 0.68* 0.60* P -0.84** -0.95** 0.93** 0.65** 0.59*
GOT G 0.99* -0.91 -0.65 -0.64 P 0.93** -0.88** -0.56* -0.62**
RLWC G -0.99 -0.66 -0.65 P -0.97** -0.61** -0.64**
ELWL G 0.81* 0.72* P 0.76* 0.71**
LT G 0.93* P 0.87**
* = P < 0.05, ** = P < 0.01 Plant traits: Plant Height (PH, cm), Monopodial Branches, Sympodial Branches, Boll Number (BN),Seed Cotton Yield (SCY, g), Boll Weight (BW, g), Fibre Length (FL, mm), Fibre Strength (FS, g/tex), Fibre Fineness (FF. Mic), Lint Percentage (LP, %), Relative Water Content (RWC, %), Excised Leaf Water Loss (ELWL, g/g), Leaf Temperature(LT) and Leaf area (LA)
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Table. 4.11. Genotypic (upper value) and phenotypic (lower value) correlations for for different plant traits in cross-2 (CIM 482x FH-1000) of cotton under normal conditions. Traits Mono Symp BN SCY BW FL FS FF GOT RWC ELWL LT LA
PH G -0.36 0.98* 0.84* 0.79* 0.91* 0.84* 0.85* -0.81 0.85* 0.99* -0.67 -0.29 -0.32 P -0.33 0.94** 0.82** 0.78** 0.89** 0.80** 0.83** -0.80** 0.81** 0.92** -0.66** -0.26 -0.31
Mono G -0.37 -0.38 -0.41 -0.34 -0.18 -0.12 0.52 -0.08 -0.51 0.46 0.89* 0.93* P -0.38 -0.32 -0.39 -0.36 -0.20 -0.08 0.48* -0.10 -0.41 0.43 0.73** 0.84**
Symp G 0.99* 0.93* 0.99* 0.90* 0.95* -0.89 0.93* 0.98* -0.84 -0.49 -0.49 P 0.94** 0.91** 0.98** 0.88** 0.91** -0.85** 0.90** 0.98** -0.82** -0.45 -0.48*
BN G 0.98* 0.98* 0.86* 0.97* -0.94 0.83* 0.99* -0.97 -0.65 -0.58 P 0.99** 0.92** 0.82** 0.95** -0.90** 0.80** 0.97** -0.95** -0.59** -0.56*-
SCY G 0.91* 0.78* 0.92* -0.95 0.77* 0.98* -0.98 -0.71 -0.66 P 0.89** 0.75** 0.91** -0.93** 0.73* 0.96** -0.98** -0.64** -0.65**
BW G 0.92* 0.92* -0.82 0.99* 0.99* -0.82 -0.48 -0.48 P 0.91** 0.89** -0.77** 0.95** 0.95** -0.79** -0.45 -0.47*
FL G 0.93* -0.58 0.97* 0.94* -0.66 -0.43 -0.23 P 0.89** -0.54* 0.94** 0.84** -0.64** -0.43 -0.22
FS G -0.80 0.90* 0.97* -0.84 -0.44 -0.29 P -0.77** 0.86** 0.93** -0.83** -0.42 -0.28
FF G -0.59 -0.99 0.94* 0.63 0.73* P -0.55* -0.91** 0.91* 0.54* 0.69**
GOT G 0.93* 0.62 -0.22 -0.22 P 0.82** -0.60** -0.23 -0.19
RLWC G -0.93 -0.62 -0.57 P -0.89** -0.53* -0.55
ELWL G 0.79* 0.74* P 0.73** 0.73** G 0.89*
* = P < 0.05, ** = P < 0.01 Plant traits: Plant Height (PH, cm), Monopodial Branches, Sympodial Branches, Boll Number (BN),Seed Cotton Yield (SCY, g), Boll Weight (BW, g), Fibre Length (FL, mm), Fibre Strength (FS, g/tex), Fibre Fineness (FF. Mic), Lint Percentage (LP, %), Relative Water Content (RWC, %), Excised Leaf Water Loss (ELWL, g/g), Leaf Temperature(LT) and Leaf area (LA)
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Table. 4.12. Genotypic (upper value) and phenotypic (lower value) correlations for different plant traits in cross-1 (NIAB-78 x CIM 446) of cotton under drought conditions. Traits Mono Symp BN SCY BW FL FS FF GOT RWC ELWL LT LA
PH G -0.65 0.78* 0.41* 0.89* 0.60* 0.69* 0.57* -0.86 0.95* 0.82* -0.80 -0.38 -0.43 P -0.55* 0.73** 0.40 0.79** 0.56* 0.65** 0.54* -0.84** 0.87** 0.80** -0.78** -0.35 -0.41
Mono G -0.86 -0.82 -0.99 -0.69 -0.56 -0.68 0.81* -0.70 -0.99 0.96* 0.91* 0.98* P -0.69** -0.72** -0.78 -0.65 -0.41 -0.58* 0.70** -0.63** -0.79 0.83** 0.69** 0.87**
Symp G 0.36* 0.97* 0.94* 0.65* 0.78* -0.99 0.99* 0.96* -0.85 -0.20 -0.46 P 0.35 0.98** 0.78** 0.66** 0.75** -0.97** 0.95** 0.94** -0.79 -0.21 -0.45
BN G 0.59* 0.71* 0.60* 0.75* -0.29 0.34 0.66* -0.76** -0.96 -0.98 P 0.50* 0.64** 0.54 0.74** -0.29 0.36 0.64** -0.74** -0.88** -0.96**
SCY G 0.93 0.78* 0.87* -0.98* 0.98* 0.99* -0.89 -0.42 -0.62 P 0.83 0.72** 0.79** -0.96** 0.96** 0.97** -0.83** -0.38 -0.59*
BW G 0.99* 0.84* -0.76 0.91* 0.89* -0.62 -0.69 -0.69 P 0.93** 0.78** -0.69** 0.80** 0.81** -0.59** -0.64** -0.65**
FL G 0.76* -0.64 0.92* 0.75* -0.51 -0.58 -0.50 P 0.70** -0.61** 0.77** 0.71** -0.49** -0.57* -0.48*
FS G -0.62 0.76* 0.89* -0.81 -0.45 -0.68 P -0.62** 0.69** 0.87** -0.79** -0.43 -0.68**
FF G -0.98 -0.93 0.78* 0.23 0.40* P -0.97** -0.90** 0.77** 0.22 0.41
GOT G 0.98* -0.81 -0.42 -0.48 P 0.92** -0.74** -0.29 -0.43
RLWC G -0.94 -0.47 -0.69 P -0.92** -0.46 -0.69**
ELWL G 0.54 0.78* P 0.51* 0.78**
LT G 0.91* P 0.88**
* = P < 0.05, ** = P < 0.01 Plant traits: Plant Height (PH, cm), Monopodial Branches, Sympodial Branches, Boll Number (BN),Seed Cotton Yield (SCY, g), Boll Weight (BW, g), Fibre Length (FL, mm), Fibre Strength (FS, g/tex), Fibre Fineness (FF. Mic), Lint Percentage (LP, %), Relative Water Content (RWC, %), Excised Leaf Water Loss (ELWL, g/g), Leaf Temperature(LT) and Leaf area (LA)
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Table. 4.13. Genotypic (upper value) and phenotypic (lower value) correlations for different plant traits in cross-2 (CIM 482x FH-1000) of cotton under drought conditions. Traits Mono Symp BN SCY BW FL FS FF GOT RWC ELWL LT LA
PH G -0.48 0.98* 0.99* 0.92* 0.91* 0.73* 0.98* 0.91* 0.92* 0.99* 0.55* -0.88 -0.62 P -0.45 0.97** 0.98** 0.91** 0.90** 0.70** 0.98** 0.88** 0.90** 0.98** 0.54* -0.83** -0.60**
Mono G -0.47 -0.59 -0.45 -0.71 -0.25 -0.39 -0.63 -0.14 -0.56 -0.84 -0.16 0.99* P -0.41 -0.55* -0.42 -0.68** -0.25 -0.36 -0.58* -0.11 -0.51* -0.79 -0.08 0.95**
Symp G 0.99* 0.99* 0.89* 0.69* 0.98* 0.98* 0.96 0.98* 0.54 -0.97 -0.64 P 0.97** 0.89** 0.80** 0.57* 0.92** 0.91** 0.87** 0.97** 0.48* -0.86** -0.54
BN G 0.91* 0.92* 0.67* 0.95* 0.92* 0.89* 0.98* 0.58* -0.80 -0.69 P 0.90** 0.90** 0.64** 0.94** 0.88** 0.85** 0.98** 0.56* -0.76** -0.68**
SCY G 0.89* 0.54* 0.89* 0.93* 0.74* 0.89* 0.52* -0.75 -0.65 P 0.89** 0.52* 0.88** 0.91** 0.72** 0.88** 0.51* -0.69** -0.64**
BW G 0.77* 0.91* 0.86* 0.72* 0.90* 0.75 -0.57 -0.82 P 0.74** 0.89** 0.82** 0.70** 0.89** 0.74** -0.53* -0.81**
FL G 0.85* 0.49 0.87* 0.77* 0.59 -0.66 -0.32 P 0.82** 0.44 0.81** 0.72** 0.58* -0.62** -0.30
FS G 0.82* 0.97* 0.97* 0.53* -0.92 -.0.52 P 0.79** 0.94** 0.96** 0.52* -0.85** -0.52*
FF G 0.67* 0.92* 0.73 -0.71 -0.71 P 0.63** 0.90** 0.69** -0.66** -0.68**
GOT G 0.92* 0.33 -0.98 -0.26 P 0.88** 0.31 -0.94** -0.24
RLWC G 0.66* -0.86 -0.62 P 0.63** -0.81** -0.62**
ELWL G -0.21 -0.74 P -0.19 -0.72**
LT G 0.07 P 0.06
* = P < 0.05, ** = P < 0.01
Plant traits: Plant Height (PH, cm), Monopodial Branches, Sympodial Branches, Boll Number (BN),Seed Cotton Yield (SCY, g), Boll Weight (BW, g), Fibre Length (FL, mm), Fibre Strength (FS, g/tex), Fibre Fineness (FF. Mic), Lint Percentage (LP, %), Relative Water Content (RWC, %), Excised Leaf Water Loss (ELWL, g/g), Leaf Temperature(LT) and Leaf area (LA)
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Amutha et al. (1996) studied 15 cotton genotypes and found that plant height had positive
correlation with boll weight and number of bolls per plant. Hussian et al. (2000) reported that
plant height showed positive correlation with number of bolls per plant. Rauf et al. (2004)
estimated positive correlation of plant height with boll weight and negative with boll number and
seed cotton yield. Karademir et al. (2009) reported that plant height had positive and significant
correlation with number of bolls per plant, number of sympodial branches and boll weight in
cotton under drought stress conditions.
Positive correlation of plant height with yield and boll number indicated that taller plants
had more bolls and seed cotton yield. Tall plant height shows higher plant vigor. So the plant
may bear more fruiting branches and hence more bolls and yield per plant. Moreover, positive
correlation of plant height with relative water content and negative with excised leaf water loss
showed that higher water content and low water loss of leaves contributed to drought resistance
of plants.
4. 7.2 Number of monopodial branches
Number of monopodial branches had positive correlation with number of sympodial
branches, seed cotton yield, boll weight, fibre length, fibre strength, lint percentage, relative
water content, leaf temperature and leaf area and it had negative correlation with number of bolls
per plant, fibre fineness and exised leaf water loss in cross-1 under normal conditions. In cross-1
under drought and in cross-2 under normal conditions number of monopodial branches had
positive correlation with fibre fineness, excised leaf water loss, leaf temperature and leaf area
and negative correlation with all other traits. In cross-2 under drought conditions number of
monopodial branches had Positive correlation with leaf area and it had negative correlation
with all other traits. Number of monopodial branches also had positive association with RWC.
This suggests that a plant with higher number of monopodial branches may maintain high
RWC. A plant with higher number of monopodial branches may have more cover of the soil in
the root zone allowing less solar radiation to reach ground and hence lower evaporation.
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4. 7.3 Number of sympodial branches
The number of sympodial branches had positive significant correlation with number of
bolls per plant, seed cotton yield, boll weight, fibre length, fibre strength, lint percentage and
relative water content and it had negative correlation with fibre fineness, excised leaf water loss,
leaf temperature and leaf area in cross-1 under normal and drought conditions and in cross-2
under normal conditions. In cross-2 under drought conditions only leaf temperature and leaf area
had negative correlation with number of sympodial branches, all other traits had positive
correlation. Singh et al.(1968) reported that the number of sympodial branches per plant had a
strong association with number of bolls per plant. Similarly Kyei (1968) found positive
association between number of bolls and number of fruiting branches. Singh et al. (1983)
studied 50 genetically diverse Gossypium hirsutum L. varieties and observed positive
correlations between boll number and number of sympodial branches. Karademir et al. (2009)
reported that number of sympodial branches had positive correlation with number of bolls per
plant in cotton under drought stress conditions.The sympodial branches are flower bearing
branches so higher number of sympodial branches would result into higher cotton yield (Channa
and Ahmad, 1982; Chen and Zhao, 1991; Hussian et al., 2000).
4.7.4 Number of bolls per plant
Number of bolls per plant showed positive and significant correlation with seed cotton
yield, boll weight, fibre length, fibre strength, lint percentage and relative water content and it
had negative correlation with fibre fineness, exised leaf water loss, leaf temperature and leaf area
in cross-1 under normal and drought conditions and in cross-2 under normal conditions. In cross-
2 under drought conditions only leaf temperature and leaf area had negative correlation with
number of bolls per plant all other traits had positive correlation. Baloch et al. (1992) found
positive correlation between number of bolls per plant and seed cotton yield, and boll weight.
They observed that number of bolls had major and direct effect on seed cotton yield. Amutha et
al. (1996) estimated positive correlation of boll number per plant with boll weight and plant
height. Murthy (1999) observed that number of bolls per plant had positive correlation with seed
cotton yield, while negative with ginning percentage. Rauf et al. (2004) observed positive
correlation of boll number per plant with yield per plant in cotton. Desalegn et al. (2009)
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recorded positive correlation of boll number per plant with lint percentage and negative with boll
weight, seed index in cotton.
Positive correlation of relative water content and negative correlation of excised leaf
water loss and leaf area with boll number indicated that increase in relative water content and
decrease in ELWL and leaf area will improve the number of bolls per plant. Negative correlation
of boll number per plant with fibre fineness showed that this parameter had antagonist
relationship with number of bolls per plant. Some contradiction in correlations of traits compared
to the previously reported studies might be due to allele combination differences.
4. 7.5 Boll weight per plant
Boll weight per plant was positively and significantly correlated with number of bolls per
plant, seed cotton yield, fibre length, fibre strength lint percentage and relative water content and
it had negative correlation with fibre fineness, exised leaf water loss, leaf temperature and leaf
area in cross-1 under normal and drought conditions and in cross-2 under normal conditions. In
cross-2 under drought conditions only leaf temperature and leaf area had negative correlation
with boll weight per plant all other traits had positive correlation. Sanyasi (1981) reported that
boll weight negatively correlated with fibre length and seed index. Baloch et al. (1992) and
Amutha et al. (1996) found positive correlation between boll weight and number of bolls per
plant. Hassan et al. (1999) and Khan and Azhar (2000) reported that boll weight positively
correlated with seed cotton yield. Rauf et al. (2004) concluded that boll weight had positive
correlation with plant height, while negative with boll number per plant and seed cotton yield.
Malik et al. (2006) estimated positive correlation of boll weight with relative water content,
while negative with fibre length in cotton. Desalegn et al. (2009) found positive correlation of
boll weight with fibre length and negative correlation with fibre fineness in cotton.
Positive correlation of boll weight per plant with fibre length, fibre strength lint
percentage revealed that if boll weight in plant is higher, the other parameters may also be higher
in magnitude. Positive correlation of boll weight with relative water content and negative
correlation with excised leaf water loss and leaf area showed the plants which maintained higher
water content with a parameter of low rate of leaf water loss and less leaf area would maintain
higher boll weight.
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4. 7. 6. Seed cotton yield
Seed cotton yield had positive significant correlation with boll weight, fibre length, fibre
strength, lint percentage and relative water content and it had negative correlation with fibre
fineness, exised leaf water loss, leaf temperature and leaf area in cross-1 under normal and
drought conditions and in cross-2 under normal conditions. In cross-2 under drought conditions
only leaf temperature and leaf area had negative correlation with seed cotton yield, all other traits
had positive correlation. Hassan et al. (1999) reported that yield of seed cotton was associated
with number of bolls per plant, boll weight and 100 seed weight. Afiah and Ghoneim (2000)
reported that seed cotton yield was highly and positively correlated with number of sympoidal
branches, number of bolls per plant, boll weight and ginning out-turn. Khan and Azhar (2000)
found positive correlation of seed cotton yield with number of bolls per plant, boll weight and
staple length. Hussian et al. (2000) revealed positive correlation of seed cotton yield with plant
height and number of bolls per plant. Baloch et al. (2001) reported that seed cotton yield had
positive correlation with number of bolls per plant and lint percentage, while it showed negative
relationship with boll weight. Rauf et al. (2004) observed positive correlation of seed cotton
yield with boll number and negative with boll weight in cotton. Azhar et al. (2004) found that
seed cotton yield was positively correlated with fibre strength and fineness, while it had negative
correlation with fibre length in cotton. Kulkarni and Nanda (2006) reported that seed cotton yield
per plant had significant and positive correlation with plant height, seed index and boll weight.
Gite et al. (2006) reported that seed cotton yield had positive genotypic and phenotypic
correlations with number of bolls per plant, number of sympodial branches per plant, boll weight
plant height and number of monopodial branches per plant. Iqbal et al. (2006) found that seed
cotton yield had positive and significant correlation with boll number and boll weight. Desalegn
et al. (2009) reported that seed cotton yield had positive correlation with boll number, boll
weight and lint percentage in cotton. Rasheed et al. (2009) reported positive and highly
significant association of number of bolls per plant and boll weight with seed cotton yield.
Karademir et al. (2009) reported that seed cotton yield had positive and significant correlation
with ginning out turn in cotton under drought stress conditions. Salahuddin et al. (2010) found
that sympodial branches, bolls per plant, boll weight, G.O.T (%) and lint index were positively
correlated with yield per plant in all the genotypes.
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Positive correlation of boll weight, fibre length, fibre strength and lint percentage with
yield indicated that their improvement would increase the yield. Positive correlation of seed
cotton yield with relative water content and negative with excised leaf water loss, leaf
temperature and leaf area showed that these parameters helped plant to maintain yield under
drought conditions.
4.7.7 Lint percentage
Lint percentage showed positive and significant correlation with fibre strength, fibre
fineness and relative water content and negative correlation with fibre fineness, excised leaf
water loss, leaf temperature and leaf area in cross-1 under normal and drought conditions and in
cross-2 under normal conditions. In cross-2 under drought conditions leaf temperature and leaf
area had negative correlation with lint percentage and relative water content and excised leaf
water loss had positive correlation with lint percentage. Tyagi (1987) estimated negative
correlation of GOT with fibre length. Chen and Zhao (1991) observed that lint percentage had
positive correlation with fibre strength. Khan et al. (1991) found that lint percentage
negatively correlated with staple length. Larik et al. (1999) studied that lint percentage had
positive association with fibre strength. Badr and Aziz (2000) reported positive correlation of
GOT with fibre strength and negative correlation with staple length.
Positive correlation of lint percentage with relative water content, negative correlation
with excised leaf water loss and leaf area indicated that the plants which maintained higher
relative water content of leaves along with less leaf water loss and less leaf area produced higher
lint percentage.
4.7.8 Fibre length
Fibre Length showed significantly positive correlation with fibre strength, lint
percentage and relative water content and negative correlation with fibre fineness, excised leaf
water loss, leaf temperature and leaf area in cross-1 under normal and drought conditions and in
cross-2 under normal conditions. In cross-2 under drought conditions only leaf temperature and
leaf area had negative correlation with fibre length all other traits had positive correlation.
Bocharova (1980) and Lancon et al. (1993) reported positive correlation between fibre length
and fibre fineness. Badr and Aziz (2000) also reported similar results. Aguilar et al. (1980),
Herring et al. (2004) and Desalegn et al. (2009) observed a positive correlation between fibre
141
length and strength of fibre, while Tyagi (1987), Carvalho et al. (1994) and Azhar et al. (2004)
found negative correlation between staple length and fibre fineness.
In general positive correlation of fibre length with relative water content and negative
correlation with excised leaf water loss and leaf area indicated that plants produced higher fibre
length due to their potential of maintaining higher relative water content, low rate of water loss
and less leaf area.
4. 7. 9. Fibre strength
Fibre strength had positive and significant correlation with fibre length, lint percentage
and relative water content and negative correlation with fibre fineness, excised leaf water loss,
leaf temperature and leaf area in cross-1 under normal and drought conditions and in cross-2
under normal conditions. In cross-2 under drought conditions only leaf temperature and leaf area
had negative correlation with fibre strength all other traits had positive correlation. Bocharova
(1980) and Echekwu (2001) reported negative correlation between fibre strength and
fineness. Desalegn et al. (2009) found that fibre strength had positive correlation with fibre
length and negative correlation with fibre fineness.
In general positive correlation of fibre strength with relative water content and negative
correlation with excised leaf water loss showed that plants which developed higher fibre strength
was due to their maintenance of higher relative water content and low rate of water loss.
4. 7. 10 Fibre fineness
Fibre fineness exhibited significantly positive correlation with fibre length, fibre strength,
excised leaf water loss, leaf temperature and leaf area and negative correlation with lint
percentage and relative water content in cross-1, under normal and drought conditions and in
cross-2 under normal conditions. In cross-2 under drought conditions only leaf temperature and
leaf area had negative correlation with fibre fineness all other traits had positive correlation.
Malik et al. (2006) found that fibre fineness had no (non significant) correlation with excised leaf
water loss and relative water content. Larik et al. (1999) reported positive relationship of fibre
fineness with fibre strength, while negative with staple length. Azhar et al. (2004) showed
negative correlation between fibre fineness and fibre length. Desalegn et al. (2009) estimated
that fibre fineness had negative correlation with fibre strength.
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4.4 Correlation of traits related to drought tolerance
Negative correlation of excised leaf water loss and leaf area with plant height, seed cotton
yield, boll number, boll weight, lint percentage, fibre length, fibre strength and fibre fineness
showed that low rate of water loss from leaves would maintain higher relative water content in
plants under drought stress and hence would improve the agronomic traits. Positive correlation of
relative water content with plant height, seed cotton yield, boll number, boll weight, lint
percentage, fibre length, fibre strength and fibre fineness indicated the same. Leaf temperature
had significant and positive correlation with leaf area. Malik et al. (2006) reported that relative
water content showed positive correlation with boll weight and negative with fibre length, while it
had no correlation with other agronomic traits. They also observed that excised leaf water loss
indicated no correlation with any of the agronomic traits. Absence of correlation between traits
indicated that those traits segregate independently at the time of gamete formation. So those traits
might be selected with desired combination of characters during segregating generations. In the
present study negative correlation of excised leaf water loss with relative water content revealed
that the genes which restrict water loss of leaves may help to maintain higher relative water
content.
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CHAPTER-5
SUMMARY
Four cotton genopypes were selected on the basis of seedling traits and SSR analysis and
their six generations ((P1, P2, F1, F2, BC1, BC2) were evaluated in triplicated randomized
complete block design under both normal and drought conditions in the field. The mean of each
cross combination was analysed separately to estimate standard error (S.E) of means, and
narrow-sense heritability for F2 and F infinity (F∞) generation for various palnt characters. The
nature and magnitude of genetic effects involved in the expression of these characters was
determined. The degree and direction of association between morphological and physiological
traits was also determined in the F2 generation of each cross under both normal and droughtful
conditions.
There were significant differences among six generations (P1, P2, F1, F2, BC1, BC2) of
two crosses for all the studied plant traits of crosses NIAB-78 × CIM-446 and CIM-482 × FH
1000 under both normal all drought conditions. The F1 means for sympodial branches, boll
weight, seed cotton yield and relative water contents of cross (NIAB-78 × CIM-446) under
normal conditions were similar to the high parent means showing complete dominance and plant
height under normal and droght and relative water contenets under normal condition in cross
(CIM-482 × FH-1000) were also similar to the high parent means showing complete dominance.
Generation means analysis indicated additive, dominance and epistatic genetic effects
played role in the inheritance of all the traits under both normal and drought condition. Two
parameter model [md] provided best fit of observed to the expected generation means for number
of bolls per plant under normal conditions in cross NIAB-78 × CIM-446 and for number of
monopodial branches of the same cross under drought conditions. In case of cross CIM-482 ×
FH-1000 two parameter model [md] was found fit for Fiber fineness under normal conditions.
Leaf temperature, number of bolls per plant fibre fineness, monopodial branches,
sympodial branches in cross 2 (CIM-482 × FH-1000) under normal condtions and monopodial
and sympodial branches of the same cross under drought condtions exhibited simple inheritance
with additive dominance model. The remaining plant traits showed higher parameter model and
exhibited complex inheritance in both crosses under both environments. The dominace or
dominace × dominance effects were observed for some traits in both the corosses under both
144
normal and drought conditions. Some plant characters have opposite signs of h and l indicating
the presence of duplicate type of epistasis. Some plant traits showed [i], [j] and [l] type of
interactions together which indicated complex inheritance of these traits.
For generation variance analysis a model incorporating DE (additive and environmental)
components gave the best fit for all the traits in the crosses-1(NIAB-78 × CIM-446) and cross-2
(CIM-482 × FH-1000) under both normal and drought conditions except number of sympodial
branches in cross NIAB-78 × CIM-446 under normal conditions where model DFE gave the best
fit. In the generation variance analysis only additive effects were involved in the inheritance of
most studied plant traits but generation means analysis showed that additive, dominance and
epistatic effects were involved in the inheritance of these traits. This inconsistancy may be due to
differences in the estimation precision of the two analyses. Generation means analysis was found
relatively more reliable compared to generation variance analysis
High narrow sense heritability estimates 0.67, 0.66 and 0.65 were observed for number of
sympodial branches, number of bolls per plant and seed cotton yield, respectively for cross-1
(NIAB-78 × CIM-446) under normal conditions and narrow sense heritability estimates 0.79,
0.69 and 0.58 were observed for boll weight, seed cotton yield and relative leaf water content
respectively under drought conditions for cross-1. These high heritability estimates were due to
additive gene effects suggest that these traits can be improved by selection during successive
generations. The narrow sense heritability estimates of infinity generation (F∞) were consistently
higher than F2 generation.
The estimates of genetic correlation coefficients were found greater in value than the phenotypic
correlation coefficient for all the studied plant traits of crosses NIAB-78 × CIM-446 and CIM-
482 × FH-1000 under normal and drought conditions.
Seed cotton yield had positive significant correlation with boll weight, fibre length, fibre
strength, lint percentage and relative water content except fibre fineness, exised leaf water loss,
leaf temperature and leaf area in cross-1 (NIAB-78 × CIM-446) under normal and drought
conditions and in cross-2 (CIM-482 × FH-1000) under normal conditions. Plant height was
positively and significantly correlated with sympodial branches, number of bolls per plant, seed
cotton yield, boll weight, fibre length, fibre strength, lint percentage and relative water content
indicating that these characters can be improved with the improvement in plant height.
145
Negative correlation of relative water content with excised water loss shows that the
genes which help plant to restrict water loss perhaps help maintaining higher relative water
content in leaf. Relative water content and excised leaf water loss are easy and rapid in
measurements hence may be used in screening large segregating populations for evolving
drought resistant cotton cultivars.
On the basis of results summarized above, it is concluded that significant difference were
found among six generations of both the crosses under normal and drought conditions.
Generation means analysis indicated the existence of additive, dominance and epistatic genetic
effects in the inheritance of studied plant traits in both the crosses under both normal and drought
conditions. High narrow-sense heritability in F2 and F-infinity generations, indicating the
possibility of obtaining superior recombinant lines.
146
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Appendix 1. Comparison of Means for shoot length and root length under normal and drought
Means sharing similar letters are statistically non-significant (P>0.05) by Duncan’s New Multiple Range test
S # Genotypes Shoot Length Root Length Normal Drought Normal Drought
1 CIM-534 17.97 ± 0.23 J-N 16.87 ± 0.41 D-J 9.50 ± 0.29 B-H 10.67 ± 0.18 QR 2 CIM-496 17.93 ± 0.47 J-N 15.47 ± 0.47 H-L 9.30 ± 0.75 B-J 11.80 ± 0.20 N-O 3 CIM-473 21.53 ± 0.41BC 19.20 ± 0.50 C-I 10.40 ± 0.35ABC 15.73 ± 0.35 B-C 4 CIM-446 11.80 ± 0.31 P 10.67 ±0.29 O 7.13 ± 0.24 O 5.80± 0.12 U 5 CIM-499 17.87 ± 2.94 J-N 16.03 ± 0.80 F-J 8.97 ± 0.98 D-L 12.57 ± 0.81 LMN 6 MHN-6070 17.23 ± 0.30 LMN 16.27 ± 0.41 E-J 8.90 ± 0.38 E-M 11.00 ± 0.70 PQR 7 MHN-786 18.67 ± 0.47 F-N 15.37 ±0.49 I-L 8.33 ± 0.35 G-O 12.73 ± 0.18 K-N 8 CIM-707 21.07 ± 0.64 B-E 18.07 ± 0.35 C-J 9.80 ± 0.23 A-E 15.32 ± 35 B-E 9 CIM-482 23.07 ± 0.52 AB 20.27 ± 0.64 AB 10.53 ± 0.37 AB 16.07 ± 0.47AB 10 PB-765 20.60 ± 0.53 C-H 17.13 ± 0.13 C-J 9.63 ± 0.30 B-G 14.73 ± 0.81 C-F 11 Glandless-Rex 18.20 ± 0.23 I-N 17.20 ± 0.46 C-J 9.07 ± 0.37 C-K 13.20 ± 0.21 H-M 12 BH-116 20.53 ± 0.59 C-H 17.27 ± 0.07 C-J 9.00 ± 0.35 C-K 13.00 ± 0.31 I-N 13 PB-889 20.53 ± 0.47 C-H 17.93 ± 0.13 C-G 9.67 ± 0.57 B-G 14.33 ± 0.24 D-H 14 LA-85-52-1 20.93 ± 0.47 C-F 15.80 ± 0.20 G-K 9.63 ± 0.12 B-G 14.93 ± 0.29 B-F 15 Acala-63-75 (GL) 19.60 ± 1.03 C-K 17.33 ± 0.64 C-J 8.73 ± 0.29 E-N 13.67 ± 0.74 F-L 16 DPL-61 19.53 ± 0.59 C-K 17.40 ±0.50 C-I 7.93 ± 0.35 I-O 14.73 ± 0.48 C-F 17 FH-113 20.30 ± 0.44 C-I 18.87 ± 0.18 K-N 7.53 ± 0.41 MNO 10.00 ± 0.72 RS 18 A637-33 19.17 ± 0.73 D-L 17.40 ± 0.20 C-I 9.53 ± 0.47 B-G 15.20 0.35 B-E 19 NIAB-78 24.33 ± 0.58 A 21.67 ± 0.29 A 11.00 ± 0.23 A 17.07 ± 0.41 A 20 SLH-41 20.60 ± 1.14 C-H 16.17 ± 0.48 F-J 9.20 ± 0.31 B-K 12.25 ± 0.26 MNO 21 NIAB-552 14.73 ± 0.29 O 16.50 ± 0.15 E-J 9.73 ± 0.35 A-G 14.47 ±0.35 C-H 22 NIAB-86 19.67 ± 0.41 C-K 15.07 ± 0.33 J-M 8.10 ± 0.40 H-O 14.67 ± 0.58 C-G 23 UGD-581 18.63 ± 0.37 G-N 16.27 ± 0.77 E-J 9.20 ± 0.35 B-K 14.13 ± 0.29 D-J 24 NIAB-999 20.63 ± 0.41 C-G 17.07 ± 0.13 C-J 9.33 ± 0.18 B-I 12.73 ± 0.52 K-N 25 BH-160 19.67 ± 0.44 C-K 13.17 ± 0.15 M-N 9.50 ±0.51 B-H 7.67 ± 0.57 T 26 FH-1000 14.53 ± 1.75 O 12.83 ± 0.41 N 7.83 ± 0.35 K-O 6.20 ± 0.26 U 27 VH-54 18.63 ± 2.42 G-N 17.00 ± 0.31 C-J 8.07 ± 0.18 I-O 14.87 ± 0.58 B-F28 FH-900 16.87 ± 1.12 MN 13.87 ± 0.18 K-N 7.90 ± 0.29 J-O 9.90 ± 0.17 RS 29 NIAB-766 19.17 ± 1.01 D-L 16.80 ± 0.31 D-J 8.03 ± 0.27 I-O 14.47 ± 0.47 C-H 30 BH-123 20.00 ± 0.58 C-J 16.47 ± 0.18 E-J 8.53 ± 0.24 E-N 14.20 ± 0.69 D-J 31 Gregg-252 17.23 ± 0.50 LMN 17.73 ± 0.58 C-H 7.60 ± 0.12 L-O 12.67 ± 0.27 LMN 32 FH-925 19.80 ± 0.69 C-K 16.77 ± 0.15 D-J 8.80 ± 0.35 E-M 13.33 ± 0.59 G-M 33 NIAB-111 21.33 ± 0.37 G-N 18.13 ± 0.07 C-F 9.87 ± 0.24 A-E 15.33 ± 0.48 B-E 34 BH-124 20.47 ± 0.48 C-H 17.13 ± 0.24 C-J 9.27 ± 0.29 B-J 10.87 ± 0.35 PQR 35 NIAB-Krishma 17.57 ± 0.35 K-N 16.87 ± 0.41 D-J 9.33 ± 0.29 B-I 12.40 ± 0.31 L-O 36 BH-95 18.00 ± 0.23 J-N 16.87 ± 0.41 D-J 9.30 ± 0.21 B-J 10.53 ± 0.37 QR
37 BH-36 20.47 ± 1.05 C-H 16.27 ± 0.52 E-J 7.53 ± 0.44 MNO 12.33 ± 0.48 L-O 38 SLH-1 19.47 ± 0.47 C-K 17.60 ± 0.12 C-I 8.40 ± 0.35 F-O 11.13 ± 0.35 O-R 39 VH-142 20.83 ± 0.30 C-G 13.97 ± 0.81 K-N 8.73 ± 0.41 E-N 9.93 ± 0.52 RS 40 PB-630 20.63 ± 0.58 C-G 17.13 ± 0.29 C-J 7.40 ± 0.23 NO 10.20 ± 0.23 RS 41 BH-147 18.33 ± 0.73 H-N 17.53 ± 0.18 C-I 8.53 ± 0.35 E-N 12.51 ± 0.29 LMN 42 PB-622 17.70 ± 0.47 K-N 16.73 ± 0.29 D-J 7.40 ± 0.23 NO 14.27 ± 0.47 D-I 43 BH-118 20.00 ± 0.69 C-J 17.53 ± 0.44 C-I 8.67 ± 0.29 E-N 14.00 ± 0.12 E-K 44 BH-162 19.70 ± 0.32 C-K 17.33 ± 0.41 C-J 8.87 ± 0.41 E-M 12.93 ± 0.29 J-N 45 MNH-93 20.80 ± 0.35 C-G 17.20 ± 0.35 C-J 9.50 ± 0.29 B-H 12.13 ± 0.52 M-P 46 Acala15-17-c 18.80 ± 0.23 E-M 17.47 ± 0.29 C-I 9.13 ± 0.29 B-K 12.53 ± 0.29 LMN 47 FH-901 16.57 ± 0.23 N 13.47 ± 0.18 LMN 8.47 ± 0.24 E-O 9.07± 0.18 S 48 CIM-1100 21.47 ±O.75 B-C 18.50 ± 0.21 B-E 10.33 ± 0.29 A-D 15.43 ± 0.29 BCD 49 VH-59 20.83 ± 0.30 C-G 17.27 ± 0.35 C-J 9.53 ± 0.47 B-G 10.93 ± 0.52 PQR 50 VH-55 20.27 ± 0.71 C-I 16.90 ± 0.29 D-J 8.80 ± 0.23 E-M 12.47 ± 0.52 LMN
LSD value 1.815 1.124
175
Appendix 2. Comparison of Means for Lateral root number and lateral root density under normal and drought
Means sharing similar letters are statistically non-significant (P>0.05) by Duncan’s New Multiple Range test
S # Genotypes Lateral root number Lateral root density Normal Drought Normal Drought
1 CIM-534 11.53 ± 0.41 F-J 17.30 ± 0.70 ST 1.21 ± 0.007 E-M 1.62 ± 0.091 A-J 2 CIM-496 8.40 ± 0.50M-Q 20.33 ± 0.24 L-R 0.91 ± 0.036 M-P 1.72 ± 0.044 A-G 3 CIM-473 16.87 ± 0.52 B 27.13 ± 2.24 BC 1.86 ± 0.098 AB 1.89 ± 0.155 AB
4 CIM-446 8.17 ± 0.20 N-Q 7.53 ± 0.29 Y 1.15 ± 0.038 G-N 1.09 ± 0.351 M 5 CIM-499 9.47 ± 1.21 J-P 18.00 ± 1.15 QRS 1.06 ± 0.111 J-O 1.43 ± 0.007 G-L 6 MHN-6070 10.87 ± 0.35 F-M 16.87 ± 0.68 ST 1.23 ± 0.092 E-M 1.53 ± 0.035 D-J 7 MHN-786 10.40 ± 0.50 H-O 18.30 ± 1.14 P-S 1.25 ± 0.088 E-L 1.43 ± 0.068 F-L 8 CIM-707 14.67 ± 0.24 B-E 25.67 ± 0.24 CDE 1.59 ± 0.058 BCD 1.81 ± 0.053 A-E 9 CIM-482 19.60 ± 0.61 A 28.87 ± 0.29 B 1.94 ± 0.078 A 1.92 ± 0.055 AB 10 PB-765 13.47 ± 0.64 C-F 20.37 ± 2.24 BC 1.51 ± 0.051 C-F 1.73 ± 0.062 A-G 11 Glandless- Rex 11.07 ± 0.96 F-L 24.87 ± 0.35 C-F 1.21 ± 0.090 E-M 1.55 ± 0.023 C-J 12 BH-116 11.33 ± 0.41 F-J 21.53 ± 0.82 H-N 1.26 ± 0.006 E-L 1.65 ± 0.050 A-I 13 PB-889 13.13 ± 0.35 D-G 22.27 ± 0.82 G-L 1.37 ± 0.088 D-K 1.55 ± 0.029 C-J 14 LA-85-52-1 4.87 ± 0.18 R 25.00 ± 1.10 C-F 0.50 ± 0.017 R 1.68 ± 0.094 A-I 15 Acala-63-75 (GL) 6.27 ± 0.64 QR 25.33 ± 0.70 C-F 0.72 ± 0.099 PQR 1.73 ± 0.104 A-G 16 DPL-61 6.27 ± 0.44 QR 24.20 ± 0.90 D-G 0.79± 0.058 OPQ 1.64 ± 0.020 A-I 17 FH-113 7.80 0.53 OPQ 13.00 ± 0.31 UV 1.04 ± 0.123 K-O 1.31 ± 0.078 J-M 18 A637-33 9.53 ± 0.24 J-P 23.33 ± 1.21 E-I 0.97 ± 0.029 L-P 1.53 ± 0.043 D-K 19 NIAB-78 21.40 ± 0.59 A 32.73± 0.18 A 2.00 ± 0.105 A 1.94 ± 0.020 A 20 SLH-41 12.27 ± 2.08 E-I 21.60 ± 1.70 H-M 1.32 ± 0.176 D-K 1.76 ± 0.135 A-F 21 NIAB-552 9.67 ± 0.29 I-P 23.60 ± 0.95 E-H 0.94 ± 0.043 L-P 1.62 ± 0.027 A-J 22 NIAB-86 9.53 ± 0.35 J-P 22.33 ± 2.37 G-L 1.18 ± 0.020 G-N 1.67 ± 0.053 A-I 23 UGD-581 11.13± 0.24 F-K 25.65± 0.35 CDE 1.21 ± 0.022 F-M 1.62 ± 0.065 A-J 24 NIAB-999 11.47 ± 0.48 F-J 21.87± 0.52 G-L 1.23± 0.075 E-M 1.72 ± 0.062 A-G 25 BH-160 10.87 ± 0.47 F-M 10.13 ± 0.24 WX 1.13 ± 0.019 LM 1.13 ± 0.019 LM 26 FH-1000 9.47 ± 0.29 J-P 9.40 ± 0.23 XY 1.21 ± 0.020 F-M 1.13 ± 0.077 LM 27 VH-54 9.47 ± 0.41 J-P 25.20 ± 0.64 C-F 1.18 ± 0.075 G-N 1.69 ± 0.028 A-H 28 FH-900 7.67 ± 0.41 PQ 11.73 ± 0.41 VW 0.97 ± 0.043 L-P 1.36 ± 0.049 I-M 29 NIAB-766 8.47 ± 0.48 L-Q 23.07 ± 0.87 F-J 1.06 ± 0.047 J-O 1.60 ± 0.111 B-J 30 BH-123 5.13 ± 0.35 R 23.00 ± 1.06 F-J 0.61 ± 0.058 QR 1.74 ± 0.142 A-G 31 Gregg-252 8.47 ± 0.58 L-Q 19.07 ± 0.79 N-S 1.12 ± 0.086 H-N 1.50 ± 0.032 E-K 32 FH-925 12.80 ± 0.46 E-H 21.73 ± 0.37 G-L 1.45 ± 0.003 D-G 1.63 ± 0.084 A-J 33 NIAB-111 15.40 ± 0.61 BCD 26.33 ± 0.35 CD 1.62 ± 0.020 BCD 1.84 ± 0.054 A-D 34 BH-124 14.40 ± 0.35 CDE 20.40 ± 0.99 K-Q 1.38 ± 0.015 D-J 1.72 ± 0.052 A-G 35 NIAB-Krishma 12.67 ± 0.29 E-H 15.13 ± 0.93 TU 1.36 ± 0.059 D-K 1.72 ± 0.026 A-G 36 BH-95 10.67 ± 0.24 G-N 17.87 ± 1.01 RS 1.15 ± 0.000 G-N 1.69 ± 0.056 A-H
37 BH-36 8.27 ± 0.41M-Q 7.53 ± 0.29 Y 1.10 ± 0.079 I-O 1.09 ± 0.351 M 38 SLH-1 14.67 ± 0.70 B-E 12.40 ± 0.40 VW 1.45 ± 0.023 D-G 1.80 ± 0.103 A-E 39 VH-142 12.20 ± 0.87 E-I 12.33 ± 0.66 VW 1.39 ± 0.038 D-I 1.38 ± 0.356 H-M 40 PB-630 8.07 ± 0.18 N-Q 17.87± 0.75 RS 1.09 ± 0.058 I-O 1.76 ± 0.133 A-F 41 BH-147 10.60 ± 0.31 G-N 20.93 ± 0.59 I-O 1.24 ± 0.026 E-L 1.43 ± 0.232 F-L 42 PB-622 6.53 ± 0.41 QR 22.93 ± 0.59 F-K 0.88 ± 0.026 N-Q 1.60 ± 0.033 B-J 43 BH-118 9.50 ± 0.29 J-P 22.13 ± 1.23 G-L 1.09 ±0.006 I-O 1.54 ± 0.111 C-J 44 BH-162 11.20 ± 0.69 F-J 18.53 ± 0.29 O-S 1.26± 0.024 E-L 1.43 ± 0.036 F-L 45 MNH-93 8.53 ± 0.29 K-Q 20.53 ± 0.29 J-P 1.23 ± 0.317 E-M 1.53 ± 0.122 D-J 46 Acala15-17-c 7.93 ± 0.41 OPQ 17.33 ± 0.75 ST 0.87 ± 0.017 N-Q 1.39 ± 0.084 H-M 47 FH-901 13.40 ± 0.53 C-F 11.20 ± 0.52 M-S 1.44 ± 0.015 D-H 1.22 ± 0.047 KLM 48 CIM-1100 15.73 ± 0.87 BC 26.47 ± 0.98 CD 1.75 ± 0.107 ABC 1.87 ± 0.052 ABC 49 VH-59 14.20 ± 0.23 CDE 19.23 ± 2.17 M-S 1.53± 0.029 CDE 1.63 ± 0.078 A-J 50 VH-55 10.20 ± 0.53 H-P 19.13 ± 0.52 M-S 1.15 ± 0.031 G-N 1.63 ± 0.029 A-J
LSD value 2.143 0.260
176
Appendix-3: List of 30 SSR primers used in study Sr. No.
Name of primer
Forward primer Reverse primer
1 BNL530 CGTAGGATGGAAACGAAAGC GCCACACTTTTCCCTCTCAA
2 BNL830 TTCCGGGTTTTCAATAAACG GTTAATACTTTTTTTCTTTTGTGTGTG
3 BNL1030 TTTGGAGCCATTTACATGCA AAACCACTTCTGCATCTGGA
4 BNL1317 AAAAATCAGCCAAATTGGGA CGTCAACAATTGTCCCAAGA
5 BNL1414 AAAAACCCCTTTCCATCCAT GGGTGTCCTTCCCAAAAATT
6 BNL1672 TGGATTTGTCCCTCTGTGTG AACCAACTTTTCCAACACCG
7 BNL1694 CGTTTGTTTTCGTGTAACAGG TGGTGGATTCACATCCAAAG
8 BNL2449 ATCTTTCAAACAACGGCAGC CGATTCCGGACTCTTGATGT
9 BNL2572 GTCCTATTACTAAAATTGTTAATTTAGCC CGATGTTAAATCAATCAGGTCA
10 BNL2590 GAAAAACCAAAAAGGAAAATCG CTCCCTCTCTCTAACCGGCT
11 BNL3031 AGGCTGACCCTTTAAGGAGC AACCAACTTTTCCAACACCG
12 BNL3259 TTTTGAAATTCCAGCGAAGG GTCAATACCTGCTTCTCCACG
13 BNL3383 GTGTTGTCATCGGCACTGAC TGCAATGGTTCAGTGGTGAT
14 BNL3442 CATTAGCGGATTTGTCGTGA AACGAACAAAGCAAAGCGAT
15 BNL3474 AAGGTAATGCAGTGCGGTTC ATAATGGCATTGATTATAGAGTGTG
16 BNL3649 GCAAAAACGAGTTGACCCAT CCTGGTTTTCAAGCCTGTTC
17 BNL3995 ATATTTTATTCTTTTAATAGCTTTATTCCC TTGGAAAAACCCATGGTGAT
18 JESPR247 GCTTCTTCCATTTTATTCAAG CAGCGGCAACCAAAAAG
19 JESPR278 ACCCTTAAATCATAAGAGAAC CCGTAAGTTAAGGTACAAGG
20 JESPR279 GGAGTGAAAGCTAATGCCTG CGGGTCATTGGTTGTTTTTG
21 JESPR281 TGATTGATCCTAGTTCTACG GTCTCCTTACTTCGCAAC
22 JESPR282 GGAGTACAAGGACCAGCAG CATAAGCCATGGTTGTAC
23 JESPR284 CAAGATCCATCTGCTGATTAG GTATATACAAGTATAAAGTATTGG
24 JESPR285 CCCGGATATAGTACTAAGGC ATGTATGGTGTTGAGT
25 JESPR298 GATGCCCTCGTGTTAAAG GGACCTTCGGAATAATTACC
26 JESPR299 CTGAACCTGCTCCTGAATC GCCTAGGTGGAGTTCGTG
27 JESPR302 CACTCCTAGCTTCTTGGCATC CTGCGATCTTGGCACAG
28 JESPR303 CATCGGAAAACTCTGAAC GTAGCAGTACAGATGAAAGAG
29 JESPR304 GAAATGCATTCCCTCAAAAGC AGACTCTATCGAATGACCCTG
30 JESPR305 CGATCCATCAAAGGCGAC CCGCCTCAGCACCATTTAC
177
Appendix 4. Meteorological data recorded at University of Agriculture, Faisalabad, during the cotton crop season 2009. Parameter Month
May June July August September October November
Mean Max. Temp.
(oC)
40.1 40.7
38.0 33.6 36.3 32.7 25.7
Mean Min. Temp.
(oC)
24.8 27.0
27.9 27.6 24.4 17.1 10.8
Mean Relative Humidity (%)
31.4 33.6 59.0 65.8 61.0 57.9 64.7
Rain fall (mm)
9.1 9.6 43.5 116 20.6 17.5 0.7
Source: Department of Crop Physiology, University of Agriculture Faisalabad, Pakistan