GENETIC ASSESSMENT OF CHLOROPHYLL A AND B, CAROTENOIDS AND STOMATAL CONDUCTANCE IN LEAF TISSUE OF UPLAND COTTON IN WATER STRESS CONDITIONS
A. Rehman1 and M. T. Azhar1, 2*
1School of Agricultural Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
2Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad (Pakistan)
*Corresponding author’s email address: tehseenazhar@gmail.com
ABSTRACT
Water stress is a major threat to crop production in climate change scenario. Leaf physiological traits play an important role in maintenance of plant physiological processes under water stress. Therefore, the present study was conducted to characterize the leaf physiological traits of cotton under water stress as well as investigate the genetics of their inheritance. Sixty cotton hybrids along with parents were sown in field conditions in triplicate following split plot design under normal and water stress conditions. Data were collected for leaf physiological traits i.e. Chlorophyll a and b, carotenoids, stomatal conductance and transpiration rate. Crosses namely, CIM-482 × NIAB Karishma, MNH-93 × S-12, CRIS-134 × ACALA-1517-C, CP-15/2 × FH-1000 and CRIS-134 × CIM-506 exhibited higher estimates of heritability, heterosis and heterobeltiosis suggested the presence of potential for genetic improvement through breeding and selection. Analysis of variance revealed the presence of significant differences among the genotypes for all the studied traits. Beside the lines i.e. FH-900, MNH-93 and NIAB-111 and testers CIM-506, NIAB Karishma and MNH-129 were found to be good general combiner under water stress conditions. The preponderance of non-additive gene action for the traits mentioned in this manuscript depict that superior combinations in current study can be used in drought tolerant cotton varieties with early generation selection.
Key words: Chlorophyll contents; Cotton; Genetic analysis; Stomatal conductance
INTRODUCTION
Cotton is the most important fiber crop around the globe to fulfill the demand of textile industry based on natural fiber. Upland cotton accounts for more than 90% of the world’s cotton production (Cai et al., 2017) and 2nd important oil seed crop in the world after soybean (Mahawar et al., 2017). In climate change scenario, water stress is the major threat to the world food security. Extent of water stress depends upon several factors i.e. moisture storing capacity of soils, evaporative demands and distribution of rainfall (Brauman et al., 2013). Continues appraisals are performed from two decades to understand genetic mechanisms involved in water stress tolerance in crop plants (Osakabe et al., 2014). For the development of water stress tolerance, understanding of physiological mechanisms and genetic control is most important. Likewise, water stress results in reduction of photosynthetic activity due to decrease in leaf expansion and impairs photosynthetic apparatus (Kalaji et al., 2016). Chlorophyll is the main light absorbing pigment. In higher plants, there are two types, chlorophyll a, the main pigment, and chlorophyll b that together with carotenoids compounds the accessory pigments. Chlorophylls are packed in protein complexes forming what is called the photosynthetic complexes. These are constituted by an antenna complex which absorbs the majority of light energy and the photosystems, where the light is transformed by a photochemical reaction in chemical energy (Björn et al., 2009). A considerable reduction in contents of chlorophyll, net photosynthesis and carotenoids was observed in wheat (Abid et al., 2018). Carotenes are very responsive to oxidative damage. In green plants, Β-carotene plays a vital protective role by quenching triplet chlorophyll, hence prevents the production of singlet oxygen and secure the plant from oxidative damages (Chourasia, 2017). They are separated into the hydrocarbon carotenes, such as lycopene and β-carotene or xanthophylls, typified by lutein (Jaleel et al., 2007). Oxidative damage caused by drought stress in the plant tissue is eased by an intensive action of both enzymatic and non-enzymatic antioxidant systems. These include β-carotenes, ascorbate, α-tocopherol, reduced glutathione and enzymes including superoxide dismutase, peroxidase, ascorbate peroxidase, catalase, polyphenol oxidase and glutathione reductase (Prochazkova et al., 2001). Water stress leads to reduced stomatal conductance which ultimately results in reduced rate of photosynthesis (Miner et al., 2017). Likewise, damage to oxidative system of plant tissues is increased due to water stress (Li et al., 2017). Closure of stomata under water stress conditions lead to reduced photosynthesis, and significant reduction in photosynthetic rate, CO2 uptake and transpiration rate (Karimi et al., 2015) and genotypes with stable performance of these traits may tolerate harsh conditions of water stress with minimal economic loss (Luo et al., 2016; Ullah and Zafar 2006; Fang and Xiong, 2015). Maqsood et al. (2018) reported additive gene action for stomatal conductance where as Ozdemir and Sade, (2019) observed non-additive gene action for stomatal conductance, transpiration rate and chlorophyll contents. Parveen et al. (2019) reported similar findings for chlorophyll and carotenoids contents. The combining ability and heterosis are important genetic parameters for determining breeding values of parents (Sarwarkar et al., 2015).
There are two pre-requisites for the development of water stress tolerant germplasm. Firstly, the presence of genetic variability for certain traits of interest, and secondly the identified traits must be genetically controlled. Under these circumstances, major objective of the study was to investigate the genetic variability and inheritance pattern of leaf physiological traits under normal and water stress conditions.
MATERIALS AND METHODS
Experimental site and location: The current study was carried out in the experimental area of University of Agriculture, (latitude 31°25΄N, longitude 73°09΄E and altitude 184.4 m from sea level) Faisalabad, Pakistan whilst average temperature 35 °C with 40% humidity during crop season.
Plant material: Germplasm used herein was collected from Cotton Research Station (CRS), Multan; Central Cotton Research Institute (CCRI), Multan; Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad; Cotton Research Institute (CRI), Faisalabad and Cotton Research Station (CRS), Bahawalpur-Pakistan. Fifty genotypes were screened in glass house with 80, 60, 40 an 20% of field capacity (Rehman et al., 2017). After screening, ten water stress tolerant cotton lines namely CP-15/2, NIAB-111, CIM-1100, BH-160, CRIS-134, FH-900, CIM-446, CIM-482, MNH-93, and CIM-707 and six water stress susceptible testers i.e. NIAB Karishma, CIM-506, FH-1000, MNH-129, Acala-1517C and S-12 were field planted during growing season of cotton 2014-15. Agronomic and other measures were taken to obtain healthy and better plant growth. Selected 16 cultivars then hybridized according to Line × Tester mating design at the time of flowering. At maturity, hybrid seeds from all the crosses stored till next crop season for plantation.
Experimental design and treatment: Sixty hybrids along with sixteen parents were planted in next cotton season 2015-16 with three replications following split plot design in two regimes i.e. control and water stress. From each genotype ten plants were planted in a row by maintaining row to row 75cm and plant to plant distance of 30cm. Water stressed plants irrigated four times while eight irrigations designated as control (Kirda et al., 2005). In water stress, 1st irrigation on germination stage, 2nd on seedling establishment, 3rd before squaring, while 4th on peak flowering stage while normal plants were treated with 8 irrigations. All recommended production practices and plant protection measures were adopted during the experiment. Data were recorded on seven plants from each family on biochemical and physiological parameters in field as well as in laboratory conditions at maturity stage. The detailed protocol of each parameter is mentioned below,
Gas exchange parameters: Stomatal conductance and photosynthesis rate per plant were recorded three times from leaves of seven randomly selected plants from control and water stress conditions with the help of portable infrared gas analyzer (340 Bioscientific Ltd., UK). The data were recorded on fully expanded youngest leaves for gas exchange characteristics from 10:00 AM 12:00 PM.
Determination of chlorophyll contents and carotenes: 20 mg fresh sample of young leaf tissue from each genotype was dipped in falcon tubes then pour 4 ml of 100% methanol and incubated at 4˚C for one hour after wrapping the racks with aluminum foil to avoid light. At 4˚C, centrifuged the samples at 14,000 rpm for 10 min. Almost 3ml of sample was taken in cuvette and absorbance was measured at 470 nm (max. absorbance of carotenoids), 653 nm (max. absorbance of Chlorophyll b) and 666 nm (max. absorbance of Chlorophyll a) on a double beam spectrophotometer (Shimdtzu-190). According to Costache et al. (2012) the concentrations of chlorophyll a and b and carotenoids were calculated.
Chlorophyll a = 15.65 A666 – 7.340 A653
Chlorophyll b = 27.05 A653 – 11.21 A666
Carotene = 1000 A470 – 2.860 Chl a – 129.2 Chl b/245
Statistical analysis: Data were analyzed by different techniques followed by Steel and Dickey (1997) to verify the existence of genotypic differences for all the studied parameters. Same data were utilized to determine genetic components and combining ability by using Line × Tester analysis (Kempthorne, 1957) by using the statistical software Minitab 17.
RESULTS
Genetic analysis of plant material: Analysis of variance following line × tester technique was analyzed to reveal the presence of genetic variations among plant material under normal and water stress field conditions. Biometrical analysis following line × tester technique to evaluate the genetic variation in cotton plants under normal and water stress conditions revealed that significant differences (P ≤ 0.01) were observed among 76 genotypes for all the traits studied i.e. stomatal conductance, transpiration rate, chlorophyll a, chlorophyll b and carotenoids (Table 1). Means square due to GCA effects (parents) and specific combining ability (crosses) were differed highly significant for all parameters in both conditions. Highly significant differences were observed for interaction due to parents vs crosses whereas non-significant differences (P ≥ 0.05) were noted for chlorophyll b while highly significant for all of traits in water stress conditions. Significant differences were observed in lines and testers for physiological and leaf gas exchange traits except stomatal conductance in testers almost similar trend was found in water stress conditions except chlorophyll a and transpiration rate. Highly significant differences were observed in interaction of lines and testers for the traits included in this study.
Estimation of genetic components and heritability: SCA variance (σ2sca) in normal and water stress conditions is higher as compared to the GCA variance (σ2gca) for all studied traits, indicating the presence of non-additive gene action in inheritance (Table 2). Positive sign gives us the information about the direction of dominance towards superior parent, whereas negative sign indicate that dominance was directed towards lower parent. The ratio of general to specific combining ability variance (σ2gca/σ2sca) was recorded below unity, exhibited that studied traits are inherited by non-additive gene action in normal water conditions while similar results were found in water stress conditions. This table also showed that all parameters had higher narrow sense heritability values in both conditions. A high heritability index of 0.993, 0.961, 0.943 indicated that selection breeding might be progressive for genetic improvement. Moreover, high narrow heritability indicating the possibility of progress from selection.
General combining ability: The numerical values assigned to ten lines and six testers under normal and water stress conditions indicated their general combining ability (GCA) for the traits (Table 3). In normal water conditions, estimates comparison showed that NIAB-111 showed positive value for stomatal conductance (0.10), chlorophyll a (0.01) and carotenoids (0.01) exhibited good GCA for these characters while poor combiner for transpiration rate (-1.18) and chlorophyll b (-0.04) while under waters stress conditions NIAB-111 displayed positive values and good GCA effects for stomatal conductance (0.16), transpiration rate (0.04) and carotenoids (0.02). Due to greater magnitude for stomatal conductance (0.45) and carotenoids (0.01), CP-15/2 displayed its superiority for GCA whilst poor combiner for transpiration rate (-0.31), chlorophyll a (-0.01) and chlorophyll b (-0.02) whilst in water stress exhibited highest positive values for transpiration rate (0.50) only. Line BH-160 was found superior general combiner for stomatal conductance (0.34), chlorophyll a (0.001) and chlorophyll b (0.02) while exhibited poor combiner for remaining traits whilst under water stress showed good GCA only for transpiration rate (0.79) and chlorophyll b (0.03). CIM-1100 possessed good general combining ability for stomatal conductance (0.18), transpiration rate (0.02) and carotenoids (0.03) in normal conditions whilst under water stress conditions only transpiration rate and carotenoids exhibited positive estimates while stomatal conductance was found to be -0.02. CRIS-134 was identified as good combiner for transpiration rate (0.07) whilst under stress displayed opposite results. FH-900 showed good GCA for transpiration rate, chlorophyll a, chlorophyll b and carotenoids with estimates of 1.14, 0.001, 0.07 and 0.01 respectively while in water stress maximum positive GCA effects showed for chlorophyll a (0.09), chlorophyll b (0.06) and stomatal conductance (0.06). CIM-482 showed highest estimates of transpiration rate (0.28), chlorophyll a (0.03), chlorophyll b (0.01) and carotenoids (0.001) and opposite results was found in water stress conditions. Among testers, CIM-506 displayed positive GCA for stomatal conductance (0.001), chlorophyll b (0.06) and carotenoids (0.01) while in stress conditions similar results were found. NIAB Karishma identified as good general combiner for transpiration rate and CHL a content under control conditions whereas transpiration rate, CHL a contents and carotenoids also exhibited positives values under water stress conditions. Tester MNH-129 displayed negative GCA for all the traits except stomatal conductance (0.43) whilst in water stress conditions, higher values of GCA effects exhibited by all of traits except chlorophyll a (-0.09) and carotenoids (-0.02). FH-1000 showed good general combining abilities for transpiration rate (0.74), chlorophyll b (0.001) and carotenoids (0.02) and almost similar results were showed in stress conditions i.e. attained highest positive coefficient for stomatal conductance (0.05), chlorophyll a (0.04) and chlorophyll b (0.001). Tester, Acala-1517-C exhibited good general combining ability for transpiration rate, chlorophyll b and carotenoids with high GCA values i.e. 0.47, 0.04 and 0.03 respectively. While in water stress conditions, Acala-1517-C expressed its better GCA for chlorophyll b (0.02) than other characters.
Estimation of specific combining ability (SCA): Under normal water conditions, higher SCA effects for stomatal conductance was exhibited by BH-160 × CIM-506, MNH-93 × S-12, CIM-1100 × CIM-506, CP-15/2 × CIM-506 and CIM-1100 × MNH-129 (Table 4) whilst under water stress conditions combinations of CIM-482 × NIAB Karishma, CIM-1100 × S-12, CRIS-134 × MNH-129 and CIM-707 × S-12 scored significant and highest coefficients of SCA, and appeared to be best specific combinations (Table 5). For transpiration rate, positive and significant SCA effects were found for FH-900 × CIM-506, CIM-707 × MNH-129, CIM-446 × ACALA-1517-C, CIM-482 × S-12, CRIS-134 × NIAB Karishma and MNH-93 x MNH-129 under normal conditions whilst in water stress conditions, best specific combining ability was displayed by MNH-93 × S-12, NIAB-111 × NIAB Karishma, CP-15/2 × CIM-506 and CRIS-134 × FH-1000 due to having highest values. For Chlorophyll a, CIM-446 × NIAB Karishma, CIM-482 × CIM-506, FH-900 × NIAB Karishma, FH-900 × CIM-506, CIM-446 x S-12 and CIM-707 x ACALA -1517-C expressed the best SCA estimates under normal water conditions, while the combinations of CRIS-134 × ACALA-1517-C, CIM-446 × S-12, FH-900 × FH-1000 and CIM-482 × CIM-506 presented significant and positive SCA effects under water stress. For Chlorophyll b, combinations of CP-15/2 × FH-1000, NIAB-111 × MNH-129, FH-900 × NIAB Karishma, MNH-93 × CIM-506 and NIAB 111 x ACALA 1517-C exhibited significant positive SCA values under normal water conditions. Whilst under water stress conditions higher SCA effects was exhibited by BH-160 x MNH-129, CP-15/2 × FH-1000, NIAB-111 × ACALA-1517-C, NIAB-111 × MNH-129 and FH-900 × NIAB Karishma. For carotenoids, significant and positive SCA effects were found for MNH-93 × NIAB Karishma, CIM-482 × NIAB Karishma, CIM-707 × FH-1000 and CP-15/2 × MNH-129 under water stress.
Estimation of heterosis and heterobeltiosis: Higher heterosis effects for stomatal conductance was showed by BH-160 × CIM-506, CP-15/2 x MNH-129 and CIM-1100 x MNH-129 under normal conditions and CIM-482 × NIAB Karishma under normal water conditions (Table 6 and Table 7). For transpiration rate, significant and positive heterosis were found for CRIS-134 × NIAB Karishma whilst under water stress conditions NIAB-111 × NIAB Karishma showed maximum heterotic estimates. For Chlorophyll a, combination of NIAB-111 × S-12 expressed the highest heterosis while CRIS-134 × ACALA-1517C exhibited highest estimates under normal and water stress conditions respectively. For Chlorophyll b, combinations of CIM-446 × ACALA-1517-C exhibited significant positive heterotic effects under both normal and water stress conditions. Whilst cross combination of CIM-446 × FH-1000 exhibited the highest estimates of heterosis while CRIS-134 × CIM-506 presented highest estimates.
Under water stress conditions, for stomatal conductance, combination of CIM-482 × NIAB Karishma scored highest and significant coefficients of heterobeltiosis (Table 7). Combination MNH-93 × S-12 showed highest estimates for transpiration rate in normal conditions while CRIS-134 × NIAB Karishma in water stress conditions. NIAB-111 × S-12 showed positive estimates for chlorophyll a while the combination of CRIS-134 × ACALA-1517-C presented highest, significant and positive heterobeltiosis effects in water stress conditions. Combination CIM-446 × ACALA-1517-C exhibited highest estimates of heterobeltiotic for chlorophyll b under both conditions. For carotenoids, maximum heterosis and heterobeltiosis estimates were exhibited by CRIS-134 × CIM-506.
Table 1. Mean squares of various quantitative traits of cotton grown under normal and water stress conditions.
Normal water conditions
|
Water stress conditions
|
SOV
|
DF
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
Replications
|
2
|
0.03
|
0.20
|
0.01
|
0.001
|
0.001
|
0.06
|
0.01
|
0.01
|
0.001
|
0.001
|
Genotypes
|
75
|
1.99**
|
9.50**
|
0.06**
|
0.04**
|
0.01**
|
0.36**
|
2.16**
|
0.07**
|
0.03**
|
0.01**
|
Parents
|
15
|
1.12**
|
10.99**
|
0.05**
|
0.03**
|
0.02**
|
0.36**
|
0.66**
|
0.07**
|
0.02**
|
0.01**
|
Crosses
|
59
|
1.60**
|
8.83**
|
0.05**
|
0.04**
|
0.01**
|
0.31**
|
1.93**
|
0.06**
|
0.02**
|
0.01**
|
Parents Vs Crosses
|
1
|
38.32**
|
27.13**
|
0.71**
|
0.001
|
0.04**
|
3.27**
|
38.45**
|
0.58**
|
0.34**
|
0.00
|
Lines
|
9
|
0.35**
|
4.37**
|
0.06**
|
0.03**
|
0.03**
|
0.32**
|
0.35**
|
0.08**
|
0.01**
|
0.02**
|
Testers
|
5
|
0.15
|
0.44**
|
0.01*
|
0.02**
|
0.02**
|
0.25**
|
0.25
|
0.01
|
0.05**
|
0.01**
|
Lines × Testers
|
45
|
2.01**
|
10.65**
|
0.05**
|
0.04**
|
0.01**
|
0.32**
|
2.43**
|
0.07**
|
0.02**
|
0.01**
|
Error
|
150
|
0.08
|
0.07
|
0.01
|
0.003
|
0.008
|
0.05
|
0.11
|
0.01
|
0.003
|
0.001
|
Where, DF stands for degree of freedom, SC- stomatal conductance, TR- transpiration rate, CHL a- chlorophyll a, CHL b- chlorophyll b and CAR- carotenoids.
Table 2. Genetic components of variation of various quantitative traits of cotton grown under normal and water stress conditions.
Plant traits
|
Normal water conditions
|
Water stress conditions
|
|
σ2gca
|
σ2sca
|
σ2A
|
σ2D
|
σ2gca/
σ2sca
|
Heritability
|
σ2gca
|
σ2sca
|
σ2A
|
σ2D
|
σ2gca/
σ2sca
|
Heritability
|
SC
|
-0.007
|
-165.65
|
-0.004
|
-165.65
|
0.00004
|
0.908
|
-0.0008
|
-20.62
|
-0.0004
|
-20.62
|
0.00004
|
0.854
|
TR
|
-0.04
|
-127.76
|
-0.02
|
-127.7
|
0.00031
|
0.899
|
-0.009
|
-112.6
|
-0.004
|
-112.6
|
0.00008
|
0.948
|
CHL a
|
-0.0002
|
-43.15
|
-0.0001
|
-43.15
|
0.00001
|
0.943
|
-0.0002
|
-13.6
|
-0.0001
|
-13.6
|
0.00001
|
0.801
|
CHL b
|
-0.0001
|
-0.11
|
-0.00006
|
-0.11
|
0.00091
|
0.961
|
-0.00001
|
-34.73
|
-0.00001
|
-34.73
|
0.00001
|
0.875
|
CAR
|
0.01
|
-14.79
|
0.01
|
-14.79
|
0.0006
|
0.993
|
-0.00004
|
-0.91
|
-0.00002
|
-0.91
|
0.00004
|
0.888
|
Where, σ2gca = estimate of gca variance, σ2sca = estimate of sca variance, σ2A = Additive variance, σ2D= Dominance variance, σ2gca/σ2sca = Variance ratio
Table 3. General combining abilities of various quantitative traits of cotton grown under normal and water stress conditions.
|
Normal water conditions
|
Water stress conditions
|
PARENTS
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
LINES
|
NIAB-111
|
0.10
|
-1.18
|
0.01
|
-0.04
|
0.01
|
0.16
|
0.04
|
-0.04
|
-0.06
|
0.02
|
CP-15/2
|
0.45
|
-0.31
|
-0.01
|
-0.02
|
0.01
|
-0.21
|
0.50
|
-0.04
|
-0.04
|
0.001
|
BH-160
|
0.34
|
-0.54
|
0.001
|
0.02
|
-0.03
|
-0.23
|
0.79
|
-0.03
|
0.03
|
-0.03
|
CIM-1100
|
0.18
|
0.02
|
-0.05
|
-0.03
|
0.03
|
-0.02
|
0.21
|
-0.06
|
-0.01
|
0.001
|
CRIS-134
|
-0.03
|
0.07
|
-0.01
|
-0.05
|
-0.02
|
0.14
|
-0.30
|
0.03
|
-0.04
|
0.03
|
CIM-446
|
-0.10
|
-0.06
|
-0.11
|
0.07
|
-0.03
|
-0.04
|
0.07
|
-0.06
|
0.06
|
0.001
|
FH-900
|
-0.09
|
1.14
|
0.001
|
0.07
|
0.01
|
0.06
|
-0.28
|
0.09
|
0.06
|
0.001
|
MNH-93
|
-0.35
|
0.16
|
0.10
|
-0.02
|
0.02
|
0.14
|
-0.09
|
0.08
|
0.001
|
0.001
|
CIM-707
|
-0.24
|
0.41
|
0.03
|
0.01
|
0.01
|
-0.17
|
-0.35
|
0.001
|
0.001
|
-0.01
|
CIM-482
|
-0.25
|
0.28
|
0.03
|
0.01
|
0.001
|
0.18
|
-0.58
|
0.03
|
-0.01
|
-0.01
|
Standard Error
|
28.38
|
26.62
|
14.69
|
0.81
|
8.6
|
10.18
|
23.81
|
8.25
|
13.18
|
2.14
|
TESTERS
|
|
|
|
|
|
|
|
|
|
|
CIM-506
|
0.001
|
-1.5
|
-0.04
|
0.06
|
0.01
|
0.12
|
0.09
|
-0.06
|
-0.01
|
0.06
|
NIAB Karishma
|
-0.11
|
0.45
|
0.1
|
-0.04
|
-0.01
|
-0.05
|
0.07
|
0.06
|
-0.02
|
0.03
|
MNH-129
|
0.43
|
-0.46
|
-0.05
|
-0.06
|
-0.02
|
0.05
|
0.41
|
-0.09
|
0.001
|
-0.02
|
FH-1000
|
-0.23
|
0.74
|
-0.03
|
0.001
|
0.02
|
0.05
|
-0.27
|
0.04
|
0.001
|
-0.01
|
S-12
|
0.19
|
0.35
|
0.07
|
0.01
|
-0.03
|
-0.02
|
-0.20
|
0.06
|
0.02
|
-0.05
|
ACALA-1517-C
|
-0.28
|
0.47
|
-0.04
|
0.04
|
0.03
|
-0.14
|
-0.10
|
0.001
|
0.02
|
0.001
|
Standard Error
|
40.78
|
36.27
|
20.77
|
1.14
|
12.16
|
14.39
|
33.67
|
11.67
|
18.63
|
3.03
|
Table 4. Specific combining ability estimates of various quantitative traits of cotton grown under normal water conditions.
Crosses
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
NIAB-111 × CIM-506
|
0.26
|
1.00
|
0.03
|
0.06
|
-0.05
|
CP-15/2 × CIM-506
|
0.98
|
0.93
|
0.08
|
0.03
|
-0.01
|
BH-160 × CIM-506
|
1.55
|
-1.84
|
-0.15
|
-0.06
|
0.07
|
CIM-1100 × CIM-506
|
1.04
|
-1.73
|
0.06
|
-0.06
|
0.05
|
CRIS-134 × CIM-506
|
-0.91
|
-0.82
|
-0.18
|
-0.02
|
0.01
|
CIM-446 × CIM-506
|
-0.97
|
-0.02
|
-0.13
|
-0.04
|
-0.07
|
FH-900 × CIM-506
|
-0.21
|
2.88
|
0.11
|
0.03
|
0.03
|
MNH-93 × CIM-506
|
-0.62
|
0.22
|
-0.02
|
0.13
|
0.07
|
CIM-707 × CIM-506
|
-0.44
|
-1.52
|
0.08
|
0.05
|
0.01
|
CIM-482 × CIM-506
|
-0.69
|
0.90
|
0.14
|
-0.12
|
-0.11
|
NIAB-111 × NIAB Karishma
|
0.30
|
1.49
|
-0.19
|
-0.09
|
-0.01
|
CP-15/2 × NIAB Karishma
|
0.27
|
1.35
|
0.01
|
-0.13
|
-0.03
|
BH-160 × NIAB Karishma
|
-1.19
|
0.88
|
-0.13
|
-0.09
|
-0.06
|
CIM-1100 × NIAB Karishma
|
-0.87
|
0.92
|
0.09
|
-0.03
|
-0.04
|
CRIS-134 × NIAB Karishma
|
0.18
|
1.77
|
-0.02
|
0.01
|
0.03
|
CIM-446 × NIAB Karishma
|
0.09
|
-2.67
|
0.15
|
0.07
|
-0.05
|
FH-900 × NIAB Karishma
|
0.08
|
-0.13
|
0.12
|
0.15
|
0.05
|
MNH-93 × NIAB Karishma
|
0.01
|
-3.88
|
0.06
|
-0.05
|
0.09
|
CIM-707 × NIAB Karishma
|
0.36
|
1.40
|
0.07
|
0.09
|
-0.07
|
CIM-482 × NIAB Karishma
|
0.77
|
-1.14
|
-0.17
|
0.08
|
0.09
|
NIAB-111 × MNH-129
|
0.06
|
0.94
|
0.06
|
0.17
|
0.05
|
CP-15/2 × MNH-129
|
0.55
|
-0.66
|
0.04
|
-0.04
|
0.09
|
BH-160 × MNH-129
|
0.56
|
-1.13
|
0.06
|
0.04
|
0.02
|
CIM-1100 × MNH-129
|
0.88
|
-2.09
|
0.07
|
0.08
|
-0.02
|
CRIS-134 × MNH-129
|
0.5
|
-1.48
|
0.10
|
-0.01
|
-0.08
|
CIM-446 × MNH-129
|
-0.36
|
1.02
|
-0.01
|
0.01
|
0.01
|
FH-900 × MNH-129
|
-0.97
|
0.29
|
0.04
|
-0.13
|
-0.02
|
MNH-93 × MNH-129
|
-0.38
|
1.77
|
-0.16
|
0.04
|
-0.1
|
CIM-707 × MNH-129
|
0.04
|
2.38
|
-0.23
|
-0.12
|
0.04
|
CIM-482 × MNH-129
|
-0.88
|
-1.02
|
0.03
|
0.05
|
0.02
|
NIAB-111 × FH-1000
|
-0.50
|
-1.70
|
0.04
|
-0.07
|
0.03
|
CP-15/2 × FH-1000
|
-0.51
|
1.26
|
0.03
|
0.19
|
-0.06
|
BH-160 × FH-1000
|
-0.40
|
0.45
|
0.10
|
0.12
|
0.04
|
CIM-1100 × FH-1000
|
-0.51
|
0.70
|
-0.08
|
0.08
|
-0.04
|
CRIS-134 × FH-1000
|
0.27
|
-0.23
|
0.02
|
-0.10
|
-0.06
|
CIM-446 × FH-1000
|
0.71
|
-0.54
|
-0.06
|
-0.03
|
0.01
|
FH-900 × FH-1000
|
0.53
|
-1.07
|
-0.21
|
0.06
|
-0.03
|
MNH-93 × FH-1000
|
0.26
|
1.45
|
0.04
|
0.02
|
0.01
|
CIM-707 × FH-1000
|
-0.12
|
0.32
|
0.04
|
-0.09
|
0.09
|
CIM-482 × FH-1000
|
0.26
|
-0.64
|
0.10
|
-0.19
|
0.06
|
NIAB-111 × S-12
|
-0.86
|
-0.51
|
0.05
|
-0.20
|
0.02
|
CP-15/2 × S-12
|
-1.34
|
-1.45
|
-0.15
|
-0.11
|
-0.05
|
BH-160 × S-12
|
-1.03
|
0.82
|
0.04
|
0.07
|
-0.09
|
CIM-1100 × S-12
|
-0.44
|
0.99
|
-0.04
|
0.06
|
0.06
|
CRIS-134 × S-12
|
0.14
|
0.14
|
0.08
|
0.13
|
0.03
|
CIM-446 × S-12
|
0.48
|
0.20
|
0.11
|
-0.02
|
0.05
|
FH-900 × S-12
|
0.70
|
-0.08
|
0.06
|
-0.12
|
0.02
|
MNH-93 × S-12
|
1.13
|
0.98
|
0.06
|
0.08
|
-0.02
|
CIM-707 × S-12
|
0.54
|
-3.00
|
-0.07
|
0.07
|
0.04
|
CIM-482 × S-12
|
0.69
|
1.89
|
-0.13
|
0.12
|
-0.01
|
NIAB-111 × ACALA-1517-C
|
0.75
|
-1.23
|
0.05
|
0.13
|
-0.02
|
CP-15/2 × ACALA-1517-C
|
0.04
|
-1.43
|
0.01
|
0.07
|
0.07
|
BH-160 × ACALA-1517-C
|
0.51
|
0.83
|
0.08
|
-0.01
|
0.07
|
CIM-1100 × ACALA-1517-C
|
-0.10
|
1.21
|
-0.09
|
-0.04
|
-0.01
|
CRIS-134 × ACALA-1517-C
|
-0.18
|
0.62
|
0.04
|
-0.01
|
0.06
|
CIM-446 × ACALA-1517-C
|
0.05
|
2.01
|
-0.06
|
0.01
|
0.04
|
FH-900 × ACALA-1517-C
|
-0.12
|
-1.89
|
-0.13
|
0.01
|
-0.05
|
MNH-93 × ACALA-1517-C
|
-0.40
|
-0.54
|
0.01
|
-0.21
|
-0.05
|
CIM-707 × ACALA-1517-C
|
-0.38
|
0.41
|
0.11
|
0.01
|
-0.07
|
CIM-482 × ACALA-1517-C
|
-0.16
|
0.01
|
0.03
|
0.05
|
-0.04
|
Standard Error
|
12.89
|
11.45
|
6.57
|
0.36
|
3.84
|
Table 5. Specific combining ability estimates of various quantitative traits of cotton grown under water stress conditions.
Crosses
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
NIAB-111 × CIM-506
|
-0.18
|
0.80
|
0.03
|
-0.14
|
-0.04
|
CP-15/2 × CIM-506
|
0.06
|
1.04
|
0.10
|
0.03
|
0.01
|
BH-160 × CIM-506
|
0.14
|
0.91
|
-0.13
|
-0.05
|
-0.03
|
CIM-1100 × CIM-506
|
-0.19
|
0.69
|
0.06
|
-0.04
|
-0.01
|
CRIS-134 × CIM-506
|
-0.09
|
-0.69
|
-0.24
|
0.01
|
0.18
|
CIM-446 × CIM-506
|
0.25
|
-1.56
|
-0.13
|
0.02
|
-0.09
|
FH-900 × CIM-506
|
-0.02
|
-0.25
|
0.06
|
0.07
|
0.02
|
MNH-93 × CIM-506
|
-0.03
|
-0.20
|
-0.06
|
0.05
|
0.01
|
CIM-707 × CIM-506
|
0.19
|
0.06
|
0.12
|
0.09
|
0.02
|
CIM-482 × CIM-506
|
-0.13
|
-0.79
|
0.19
|
-0.05
|
-0.08
|
NIAB-111 × NIAB Karishma
|
0.13
|
1.16
|
-0.14
|
0.76
|
-0.05
|
CP-15/2 × NIAB Karishma
|
-0.84
|
0.77
|
0.06
|
-0.14
|
-0.05
|
BH-160 × NIAB Karishma
|
-0.42
|
0.74
|
-0.05
|
-0.05
|
-0.05
|
CIM-1100 × NIAB Karishma
|
-0.16
|
-1.12
|
-0.04
|
0.54
|
0.03
|
CRIS-134 × NIAB Karishma
|
0.04
|
-0.70
|
-0.05
|
0.04
|
0.05
|
CIM-446 × NIAB Karishma
|
0.19
|
-0.24
|
0.10
|
0.03
|
0.14
|
FH-900 × NIAB Karishma
|
0.12
|
0.01
|
0.05
|
0.11
|
0.01
|
MNH-93 × NIAB Karishma
|
-0.12
|
-0.11
|
0.10
|
-0.02
|
0.02
|
CIM-707 × NIAB Karishma
|
0.26
|
-0.05
|
0.12
|
0.01
|
-0.06
|
CIM-482 × NIAB Karishma
|
0.78
|
-0.46
|
-0.15
|
0.04
|
0.03
|
NIAB-111 × MNH-129
|
0.19
|
-1.52
|
0.10
|
0.12
|
0.04
|
CP-15/2 × MNH-129
|
0.22
|
0.22
|
0.08
|
-0.02
|
0.08
|
BH-160 × MNH-129
|
0.11
|
-0.17
|
0.09
|
1.23
|
0.03
|
CIM-1100 × MNH-129
|
-0.13
|
0.50
|
0.08
|
0.05
|
-0.04
|
CRIS-134 × MNH-129
|
0.34
|
0.79
|
0.06
|
-0.04
|
-0.07
|
CIM-446 × MNH-129
|
0.15
|
0.58
|
-0.07
|
0.04
|
-0.05
|
FH-900 × MNH-129
|
-0.18
|
0.23
|
-0.04
|
-0.12
|
-0.03
|
MNH-93 × MNH-129
|
0.21
|
-0.56
|
-0.15
|
0.04
|
-0.01
|
CIM-707 × MNH-129
|
-0.58
|
-0.06
|
-0.19
|
-0.11
|
0.04
|
CIM-482 × MNH-129
|
-0.36
|
-0.01
|
0.03
|
0.03
|
0.04
|
NIAB-111 × FH-1000
|
-0.08
|
-0.86
|
-0.04
|
0.02
|
0.01
|
CP-15/2 × FH-1000
|
0.25
|
-1.05
|
-0.07
|
0.19
|
-0.04
|
BH-160 × FH-1000
|
0.31
|
-0.48
|
0.02
|
0.08
|
0.02
|
CIM-1100 × FH-1000
|
-0.06
|
0.10
|
-0.11
|
0.07
|
-0.04
|
CRIS-134 × FH-1000
|
-0.13
|
1.01
|
-0.13
|
-0.12
|
-0.09
|
CIM-446 × FH-1000
|
-0.12
|
0.61
|
0.02
|
-0.08
|
-0.01
|
FH-900 × FH-1000
|
0.32
|
0.59
|
0.26
|
-0.01
|
-0.03
|
MNH-93 × FH-1000
|
-0.26
|
-0.13
|
0.10
|
0.10
|
0.03
|
CIM-707 × FH-1000
|
0.02
|
-0.34
|
-0.04
|
-0.04
|
0.09
|
CIM-482 × FH-1000
|
-0.26
|
0.55
|
-0.01
|
-0.11
|
0.07
|
NIAB-111 × S-12
|
-0.10
|
-0.30
|
0.04
|
-0.15
|
0.03
|
CP-15/2 × S-12
|
0.03
|
-0.89
|
-0.14
|
-0.10
|
-0.03
|
BH-160 × S-12
|
0.12
|
-0.99
|
0.05
|
0.06
|
0.03
|
CIM-1100 × S-12
|
0.42
|
0.02
|
-0.05
|
0.01
|
0.05
|
CRIS-134 × S-12
|
-0.08
|
-0.43
|
0.02
|
0.08
|
-0.02
|
CIM-446 × S-12
|
-0.64
|
0.80
|
0.28
|
-0.03
|
0.01
|
FH-900 × S-12
|
0.10
|
-0.49
|
-0.04
|
-0.04
|
0.01
|
MNH-93 × S-12
|
-0.01
|
1.36
|
0.06
|
0.03
|
0.05
|
CIM-707 × S-12
|
0.33
|
0.46
|
-0.06
|
0.05
|
-0.01
|
CIM-482 × S-12
|
-0.18
|
0.45
|
-0.15
|
0.10
|
-0.01
|
NIAB-111 × ACALA-1517-C
|
0.03
|
0.74
|
0.01
|
0.15
|
0.03
|
CP-15/2 × ACALA-1517-C
|
0.27
|
-0.09
|
-0.02
|
0.04
|
0.02
|
BH-160 × ACALA-1517-C
|
-0.28
|
-0.01
|
0.03
|
-0.04
|
0.05
|
CIM-1100 × ACALA-1517-C
|
0.12
|
-0.20
|
0.06
|
-0.08
|
0.06
|
CRIS-134 × ACALA-1517-C
|
-0.08
|
0.01
|
0.33
|
0.02
|
0.01
|
CIM-446 × ACALA-1517-C
|
0.16
|
-0.19
|
-0.20
|
0.02
|
0.02
|
FH-900 × ACALA-1517-C
|
-0.34
|
-0.08
|
-0.29
|
-0.01
|
0.02
|
MNH-93 × ACALA-1517-C
|
0.19
|
-0.36
|
-0.05
|
-0.09
|
-0.05
|
CIM-707 × ACALA-1517-C
|
-0.23
|
-0.07
|
0.06
|
0.01
|
-0.09
|
CIM-482 × ACALA-1517-C
|
0.15
|
0.25
|
0.09
|
-0.01
|
0.02
|
Standard Error
|
4.55
|
10.64
|
3.69
|
5.89
|
0.95
|
Table 6. Heterosis and Heterobeltiosis estimates of various quantitative traits of cotton grown under normal water conditions.
Crosses
|
Heterosis
|
Heterobeltiosis
|
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
NIAB-111 × CIM-506
|
-14.39
|
-3.09
|
-1.85
|
-3.68
|
-8.99
|
-23.27
|
-15.32
|
-2.62
|
-4.27
|
-16.06
|
CP-15/2 × CIM-506
|
8.83
|
0.71
|
-6.2
|
-2.82
|
5.14
|
-1.91
|
-16.86
|
-11.88
|
-5.49
|
3.57
|
BH-160 × CIM-506
|
12.37
|
-45.54
|
-12.59
|
-4.43
|
2.21
|
-2.89
|
-56.74
|
-16.41
|
-7.93
|
-7.04
|
CIM-1100 × CIM-506
|
3.14
|
-33.64
|
-2.3
|
-5.4
|
21.34
|
-8.07
|
-45.8
|
-2.36
|
-10.98
|
20.61
|
CRIS-134 × CIM-506
|
-41.75
|
-6.81
|
-10.54
|
-5.35
|
14.1
|
-47.8
|
-14.93
|
-10.72
|
-10.16
|
6.75
|
CIM-446 × CIM-506
|
-41.04
|
-19.92
|
-16.55
|
5.92
|
6.23
|
-44.37
|
-38.59
|
-20.38
|
-3.66
|
-11.04
|
FH-900 × CIM-506
|
-24.16
|
68.82
|
0.85
|
8.27
|
23.18
|
-28.67
|
52.43
|
0.13
|
0.57
|
14.11
|
MNH-93 × CIM-506
|
-38.81
|
2.14
|
-5.04
|
7.45
|
28.85
|
-42.25
|
-12.85
|
-9.7
|
1.02
|
23.31
|
CIM-707 × CIM-506
|
-33.33
|
-24.35
|
-0.32
|
9.01
|
4.45
|
-38.1
|
-37.74
|
-2.27
|
-1.63
|
1.15
|
CIM-482 × CIM-506
|
-39.64
|
0.66
|
-1.37
|
-8.61
|
-20.22
|
-44.3
|
-20.76
|
-6.16
|
-12.6
|
-26.42
|
NIAB-111 × NIAB KARISHMA
|
-16.72
|
44.11
|
-5.82
|
-17.6
|
-6.84
|
-24.84
|
18.47
|
-8.54
|
-19.55
|
-14.56
|
CP-15/2 × NIAB KARISHMA
|
-9.47
|
43.22
|
-5.23
|
-16.59
|
-4.56
|
-17.83
|
11.76
|
-9.11
|
-16.77
|
-6.55
|
BH-160 × NIAB KARISHMA
|
-45.52
|
24.14
|
-8.15
|
-10.77
|
-22.78
|
-52.6
|
-6.45
|
-10.3
|
-11.45
|
-30.15
|
CIM-1100 × NIAB KARISHMA
|
-39.79
|
39.21
|
2.27
|
-7.69
|
-0.61
|
-45.96
|
7.6
|
0.15
|
-10.58
|
-1.82
|
CRIS-134 × NIAB KARISHMA
|
-21.95
|
79.65
|
-0.58
|
-7.62
|
11.55
|
-29.56
|
53.73
|
-2.51
|
-9.72
|
4.97
|
CIM-446 × NIAB KARISHMA
|
-20.74
|
-24.23
|
-2.63
|
9.47
|
4.8
|
-24.65
|
-44.69
|
-5.13
|
2.38
|
-11.8
|
FH-900 × NIAB KARISHMA
|
-21.03
|
62.75
|
5.04
|
12.54
|
22.67
|
-25.17
|
37.86
|
3.52
|
7.56
|
14.29
|
MNH-93 × NIAB KARISHMA
|
-28.15
|
-24.87
|
1.65
|
-8.49
|
29.03
|
-31.69
|
-39.57
|
-1.3
|
-11.45
|
24.22
|
CIM-707 × NIAB KARISHMA
|
-19.27
|
54
|
2.96
|
7.8
|
-15.22
|
-24.49
|
19.84
|
2.76
|
0
|
-18.39
|
CIM-482 × NIAB KARISHMA
|
-11.19
|
5.56
|
-9.02
|
0.44
|
7.91
|
-17.45
|
-21.11
|
-11.61
|
-1.08
|
-1.04
|
NIAB-111 × MNH-129
|
-7.19
|
20.33
|
-2.14
|
0.65
|
8.33
|
-18.87
|
-1.35
|
-4.68
|
-4.53
|
-5.7
|
CP-15/2 × MNH-129
|
11.59
|
-0.76
|
-10.01
|
-10.1
|
21.54
|
-1.91
|
-22.75
|
-13.96
|
-12.9
|
12.5
|
BH-160 × MNH-129
|
3.42
|
-16.98
|
-7.26
|
-0.67
|
-6.9
|
-12.72
|
-37.59
|
-9.7
|
-2.85
|
-20
|
CIM-1100 × MNH-129
|
11.43
|
-18.81
|
-4.5
|
-4.37
|
5.84
|
-3.11
|
-37.4
|
-6.19
|
-4.59
|
-1.21
|
CRIS-134 × MNH-129
|
-0.72
|
7.29
|
-1.8
|
-7.54
|
-6.81
|
-13.21
|
-8.46
|
-3.41
|
-8.17
|
-7.13
|
CIM-446 × MNH-129
|
-15.71
|
12.58
|
-14.36
|
7.03
|
21.74
|
-22.54
|
-18.01
|
-16.81
|
2.98
|
7.69
|
FH-900 × MNH-129
|
-29.77
|
54.6
|
-4.22
|
-5.13
|
12.77
|
-35.66
|
30.58
|
-5.31
|
-6.65
|
11.19
|
MNH-93 × MNH-129
|
-21.84
|
50.66
|
-12.35
|
-0.36
|
-4.79
|
-28.17
|
20.85
|
-15.15
|
-0.69
|
-6.71
|
CIM-707 × MNH-129
|
-11.28
|
55.39
|
-14.65
|
-5.41
|
9.15
|
-19.73
|
20.62
|
-14.75
|
-9.75
|
-0.57
|
CIM-482 × MNH-129
|
-32.84
|
-5.34
|
-7.65
|
-0.57
|
-0.6
|
-39.6
|
-29.41
|
-10.55
|
-2
|
-13.47
|
NIAB-111 × FH-1000
|
-31.6
|
-3.3
|
-1.82
|
-14.96
|
24.07
|
-42.14
|
-20.72
|
-4.07
|
-15.23
|
-1.97
|
CP-15/2 × FH-1000
|
-23.6
|
46.6
|
-10.22
|
4.22
|
14.29
|
-35.03
|
14.12
|
-14.42
|
2.28
|
-4.76
|
BH-160 × FH-1000
|
-27.92
|
22.64
|
-4.5
|
3.73
|
7.4
|
-41.04
|
-7.8
|
-7.31
|
0.83
|
-16.08
|
CIM-1100 × FH-1000
|
-30.63
|
40.59
|
-9.23
|
0.76
|
25.63
|
-41.61
|
8.4
|
-10.56
|
-4.35
|
5.45
|
CRIS-134 × FH-1000
|
-17.47
|
50.44
|
-3.68
|
-13.51
|
22.83
|
-30.19
|
28.36
|
-4.96
|
-17.18
|
9.86
|
CIM-446 × FH-1000
|
-3.17
|
8.08
|
-14.95
|
3.61
|
54.96
|
-14.08
|
-21.29
|
-17.64
|
-4.97
|
53.57
|
FH-900 × FH-1000
|
-7.51
|
52.18
|
-12.51
|
6.96
|
35.46
|
-18.18
|
28.54
|
-13.23
|
0.21
|
22.3
|
MNH-93 × FH-1000
|
-19.84
|
64.99
|
-3.74
|
-2.85
|
42.53
|
-28.87
|
32.34
|
-7.1
|
-7.87
|
24.83
|
CIM-707 × FH-1000
|
-27.63
|
42.56
|
-3.1
|
-3.75
|
42.66
|
-36.73
|
10.66
|
-3.53
|
-12.42
|
17.24
|
CIM-482 × FH-1000
|
-19.69
|
16.94
|
-3.93
|
-16.31
|
28.52
|
-30.2
|
-12.8
|
-7.23
|
-19.25
|
1.55
|
NIAB-111 × S-12
|
-33.8
|
4.17
|
5.88
|
-22.22
|
-12.4
|
-40.88
|
-9.91
|
5.6
|
-22.22
|
-13.99
|
CP-15/2 × S-12
|
-36.17
|
-5.04
|
-9.73
|
-13.56
|
-17.51
|
-42.68
|
-22.35
|
-16.03
|
-15.43
|
-21.51
|
BH-160 × S-12
|
-35.57
|
16.67
|
-0.32
|
-2.97
|
-36.1
|
-44.51
|
-8.16
|
-5.63
|
-5.97
|
-38.19
|
CIM-1100 × S-12
|
-23.78
|
32.55
|
-0.73
|
0.22
|
5.98
|
-32.3
|
7.25
|
-1.83
|
-5.14
|
0
|
CRIS-134 × S-12
|
-15.49
|
41.6
|
5.52
|
2.59
|
1.22
|
-24.53
|
27.86
|
4.21
|
-2.06
|
-10.75
|
CIM-446 × S-12
|
-4.12
|
7.82
|
-2.08
|
5.51
|
12.84
|
-9.86
|
-18.01
|
-7.51
|
-3.5
|
-10.22
|
FH-900 × S-12
|
0.75
|
53.53
|
4.81
|
-3.74
|
5.23
|
-5.59
|
37.14
|
2.98
|
-10.08
|
-8.06
|
MNH-93 × S-12
|
4.87
|
43.58
|
3.58
|
2.06
|
-3.28
|
-1.41
|
21.28
|
-2.49
|
-3.5
|
-12.9
|
CIM-707 × S-12
|
-7.35
|
-17.42
|
-0.71
|
8.16
|
-10.56
|
-14.29
|
-32.68
|
-3.66
|
-1.85
|
-13.44
|
CIM-482 × S-12
|
-5.11
|
40.13
|
-6.16
|
4.39
|
-17.15
|
-12.75
|
9.34
|
-11.61
|
0.41
|
-18.65
|
NIAB-111 × ACALA-1517-C
|
-7.58
|
-4.96
|
0.27
|
6.23
|
-1.65
|
-19.5
|
-18.02
|
0
|
0
|
-7.25
|
CP-15/2 × ACALA-1517-C
|
-14.91
|
-2.88
|
-8.45
|
5.82
|
19.17
|
-25.48
|
-20.78
|
-14.42
|
1.72
|
18.13
|
BH-160 × ACALA-1517-C
|
-12.03
|
18.74
|
-3.84
|
4.18
|
3.24
|
-26.01
|
-6.74
|
-8.5
|
1.1
|
-4.02
|
CIM-1100 × ACALA-1517-C
|
-24.73
|
37.59
|
-7.98
|
1.27
|
10.71
|
-34.78
|
11.07
|
-8.52
|
0.69
|
8.77
|
CRIS-134 × ACALA-1517-C
|
-30.69
|
51.93
|
-3.09
|
0.8
|
22.68
|
-39.62
|
36.82
|
-3.79
|
-0.68
|
12.28
|
CIM-446 × ACALA-1517-C
|
-22.31
|
32.63
|
-13.87
|
15.87
|
30.25
|
-28.87
|
0.64
|
-18.24
|
12.35
|
7.02
|
FH-900 × ACALA-1517-C
|
-26.44
|
26.43
|
-8.06
|
13.28
|
7.74
|
-32.87
|
12.62
|
-9.19
|
12.35
|
-2.34
|
MNH-93 × ACALA-1517-C
|
-38.46
|
22.73
|
-3.44
|
-9.52
|
6.88
|
-43.66
|
3.4
|
-8.64
|
-9.95
|
0
|
CIM-707 × ACALA-1517-C
|
-36.6
|
33.49
|
0.97
|
12.73
|
-8.99
|
-42.86
|
8.56
|
-1.51
|
8.39
|
-9.77
|
CIM-482 × ACALA-1517-C
|
-32.58
|
16.89
|
-5.26
|
8.2
|
-7.69
|
-39.6
|
-9
|
-10.31
|
5.79
|
-12.95
|
Table 7. Heterosis and Heterobeltiosis estimates of various quantitative traits of cotton grown under water stress conditions.
Crosses
|
Heterosis
|
Heterobeltiosis
|
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
SC
|
TR
|
CHL a
|
CHL b
|
CAR
|
NIAB-111 × CIM-506
|
-17.78
|
-2.51
|
-14.34
|
-15.82
|
-0.97
|
-22.92
|
-2.88
|
-22.46
|
-18.58
|
-0.97
|
CP-15/2 × CIM-506
|
-13.04
|
7.17
|
-12.62
|
4.51
|
25.97
|
-16.67
|
2.61
|
-22.67
|
4.06
|
10.68
|
BH-160 × CIM-506
|
-7.69
|
7.64
|
-20.16
|
5.19
|
-10.38
|
-14.29
|
0.62
|
-25.61
|
3.8
|
-12.84
|
CIM-1100 × CIM-506
|
-22.73
|
-4.83
|
-5.02
|
4.88
|
23.6
|
-26.09
|
-8
|
-6.32
|
0.58
|
6.8
|
CRIS-134 × CIM-506
|
-3.8
|
-44.9
|
-16.78
|
6.51
|
117.5
|
-9.52
|
-47.4
|
-17.32
|
2.92
|
68.93
|
CIM-446 × CIM-506
|
-10
|
-53.68
|
-21.37
|
17.25
|
-7.1
|
-15.63
|
-54.48
|
-26.42
|
12.28
|
-17.48
|
FH-900 × CIM-506
|
-6.52
|
-29.89
|
6.07
|
16.74
|
-4.13
|
-9.52
|
-32.14
|
5.84
|
16.23
|
-16.55
|
MNH-93 × CIM-506
|
-4.54
|
-24.16
|
-10.63
|
9.49
|
21.98
|
-7.38
|
-27.14
|
-17.43
|
9.33
|
7.77
|
CIM-707 × CIM-506
|
-15.73
|
-28.92
|
0.52
|
15.22
|
11.54
|
-20.21
|
-30.61
|
-2.64
|
12.87
|
10.48
|
CIM-482 × CIM-506
|
-5
|
-51.89
|
1.39
|
4.57
|
-19.21
|
-9.52
|
-53.64
|
-6.25
|
0.29
|
-20.39
|
NIAB-111 × NIAB KARISHMA
|
-4.88
|
13.62
|
-19.7
|
-8.5
|
-9.8
|
-18.75
|
5.04
|
-24.91
|
-9.6
|
-10.68
|
CP-15/2 × NIAB KARISHMA
|
-47.59
|
9.23
|
-10.95
|
-15.83
|
-1.68
|
-50.65
|
-3.27
|
-18.67
|
-19.2
|
-12.87
|
BH-160 × NIAB KARISHMA
|
-28.57
|
11.83
|
-10.96
|
-1.13
|
-24.76
|
-30.56
|
-3.11
|
-14.21
|
-6.67
|
-27.52
|
CIM-1100 × NIAB KARISHMA
|
-20
|
-38.06
|
-6.49
|
2.76
|
27.27
|
-30.43
|
-44.67
|
-8.47
|
-5.6
|
10.89
|
CRIS-134 × NIAB KARISHMA
|
5.63
|
-41.18
|
0
|
3.17
|
37.98
|
1.35
|
-48.05
|
-4.03
|
-4.53
|
7.92
|
CIM-446 × NIAB KARISHMA
|
-9.76
|
-20.15
|
-3.31
|
11.63
|
56.91
|
-22.92
|
-27.59
|
-6.42
|
2.4
|
40.59
|
FH-900 × NIAB KARISHMA
|
2.32
|
-18.07
|
9.62
|
13.33
|
-13.33
|
-4.58
|
-22.14
|
5.65
|
8.8
|
-25.18
|
MNH-93 × NIAB KARISHMA
|
-4.76
|
-15.79
|
2.59
|
-2.51
|
18.89
|
-11.39
|
-19.38
|
-2.02
|
-6.67
|
5.94
|
CIM-707 × NIAB KARISHMA
|
-11.11
|
-26.04
|
4.75
|
2.42
|
-20.39
|
-23.4
|
-33.33
|
4.44
|
-4
|
-21.9
|
CIM-482 × NIAB KARISHMA
|
36.11
|
-41.26
|
-14.23
|
5.95
|
-2.49
|
28.95
|
-47.68
|
-18.01
|
-2.67
|
-2.97
|
NIAB-111 × MNH-129
|
-5.68
|
-42.64
|
-13.44
|
7.02
|
8.6
|
-13.54
|
-45.32
|
-20.35
|
4.1
|
-1.94
|
CP-15/2 × MNH-129
|
-7.01
|
1.79
|
-17.33
|
-0.14
|
35.4
|
-8.75
|
-7.19
|
-25.67
|
-0.29
|
31.33
|
BH-160 × MNH-129
|
-9.21
|
-3.14
|
-10.65
|
9.87
|
-12.5
|
-13.75
|
-13.66
|
-15.33
|
7.8
|
-22.94
|
CIM-1100 × MNH-129
|
-20.93
|
2.9
|
-7.34
|
13.94
|
-7.6
|
-26.09
|
-5.33
|
-7.72
|
8.67
|
-12.05
|
CRIS-134 × MNH-129
|
12.99
|
-3.57
|
-1.39
|
2.26
|
2.86
|
8.75
|
-12.34
|
-3.76
|
-1.73
|
-13.25
|
CIM-446 × MNH-129
|
-13.64
|
3.32
|
-21.11
|
18.97
|
-14.11
|
-20.83
|
-3.45
|
-24.91
|
13.29
|
-15.66
|
FH-900 × MNH-129
|
-12.99
|
-7.39
|
-4.37
|
0.43
|
-32.43
|
-13.75
|
-9.16
|
-6.26
|
0.29
|
-46.04
|
MNH-93 × MNH-129
|
4.4
|
-20.78
|
-19.14
|
8.27
|
-2.47
|
3.75
|
-21.71
|
-24.04
|
7.8
|
-4.82
|
CIM-707 × MNH-129
|
-42.53
|
-20.88
|
-22.22
|
-2.08
|
-1.06
|
-46.81
|
-26.53
|
-23.33
|
-4.62
|
-11.43
|
CIM-482 × MNH-129
|
-14.1
|
-25.63
|
-11.63
|
12.12
|
-12.57
|
-16.25
|
-31.79
|
-16.91
|
6.94
|
-20
|
NIAB-111 × FH-1000
|
-3.85
|
-40.94
|
-13.71
|
-2.09
|
3.78
|
-21.88
|
-46.04
|
-20.53
|
-3.83
|
-6.8
|
CP-15/2 × FH-1000
|
8.03
|
-38.06
|
-18.15
|
17.77
|
-6.25
|
-3.9
|
-45.75
|
-26.33
|
16.43
|
-8.54
|
BH-160 × FH-1000
|
13.64
|
-21.01
|
-6.6
|
15.74
|
-15.18
|
4.17
|
-32.3
|
-11.4
|
12.46
|
-25.69
|
CIM-1100 × FH-1000
|
-7.89
|
-17.74
|
-10.79
|
14.54
|
-1.91
|
-23.91
|
-27.33
|
-11.25
|
8.22
|
-6.1
|
CRIS-134 × FH-1000
|
8.96
|
-10.04
|
-4.49
|
-4.76
|
-0.72
|
-1.35
|
-21.43
|
-6.88
|
-9.35
|
-15.85
|
CIM-446 × FH-1000
|
-12.82
|
-7.69
|
-7.92
|
8.11
|
4.94
|
-29.17
|
-17.24
|
-12.26
|
1.98
|
3.66
|
FH-900 × FH-1000
|
21.21
|
-11.38
|
23.4
|
8.88
|
-28.51
|
6.87
|
-16.79
|
20.83
|
7.65
|
-43.17
|
MNH-93 × FH-1000
|
-0.72
|
-23.77
|
3.22
|
4.89
|
19.26
|
-12.66
|
-27.91
|
-2.94
|
3.4
|
17.07
|
CIM-707 × FH-1000
|
-11.69
|
-39.69
|
-4.42
|
3.67
|
21.93
|
-27.66
|
-46.26
|
-5.68
|
0
|
8.57
|
CIM-482 × FH-1000
|
2.94
|
-25.56
|
-5.66
|
-1.05
|
16.48
|
-7.89
|
-34.44
|
-11.21
|
-6.52
|
6
|
NIAB-111 × S-12
|
-15.79
|
-29.06
|
-4.74
|
-16.91
|
-27.85
|
-25
|
-32.37
|
-15.44
|
-17.69
|
-31.9
|
CP-15/2 × S-12
|
-14.47
|
-35.48
|
-18.04
|
-8.08
|
-30.93
|
-15.58
|
-41.18
|
-28.83
|
-11.53
|
-42.24
|
BH-160 × S-12
|
-8.84
|
-33.1
|
-0.1
|
12.46
|
-42.22
|
-10.67
|
-40.37
|
-8.79
|
6.43
|
-43.97
|
CIM-1100 × S-12
|
-1.8
|
-21.01
|
-2.07
|
8.01
|
-4.71
|
-10.87
|
-27.33
|
-5.47
|
-0.54
|
-21.55
|
CRIS-134 × S-12
|
-3.36
|
-42.86
|
10.74
|
11.56
|
-12.14
|
-4
|
-48.05
|
9.04
|
3.49
|
-34.48
|
CIM-446 × S-12
|
-41.52
|
-5.54
|
12.96
|
10.5
|
-21.43
|
-47.92
|
-11.72
|
3.58
|
1.61
|
-33.62
|
FH-900 × S-12
|
-2.34
|
-38.52
|
9.98
|
5.01
|
-38.82
|
-4.58
|
-39.69
|
7.83
|
1.07
|
-43.88
|
MNH-93 × S-12
|
-3.9
|
9.8
|
6.18
|
5.87
|
-25.13
|
-6.33
|
8.53
|
-3.85
|
1.61
|
-37.07
|
CIM-707 × S-12
|
-11.24
|
-23.08
|
-0.75
|
10.41
|
-36.65
|
-20.21
|
-28.57
|
-5.88
|
3.75
|
-39.66
|
CIM-482 × S-12
|
-7.28
|
-29.24
|
-9.53
|
15.87
|
-37.96
|
-7.89
|
-35.1
|
-18.01
|
6.7
|
-42.24
|
NIAB-111 × ACALA-1517-C
|
-15.29
|
-3.4
|
-11.33
|
25.16
|
1.87
|
-25
|
-7.91
|
-20.35
|
8.74
|
-1.8
|
CP-15/2 × ACALA-1517-C
|
-9.93
|
-16.13
|
-15.56
|
21.63
|
7.94
|
-11.69
|
-23.53
|
-25.83
|
8.41
|
-8.11
|
BH-160 × ACALA-1517-C
|
-30.14
|
-10.8
|
-6.17
|
22.72
|
-8.18
|
-31.08
|
-20.5
|
-13.27
|
11.11
|
-9.01
|
CIM-1100 × ACALA-1517-C
|
-16.87
|
-23.91
|
0.32
|
18.15
|
3.33
|
-25
|
-30
|
-1.89
|
9.87
|
-13.42
|
CRIS-134 × ACALA-1517-C
|
-8.11
|
-31.43
|
25.27
|
25.3
|
21.43
|
-8.11
|
-37.66
|
25
|
15.67
|
-8.11
|
CIM-446 × ACALA-1517-C
|
-17.65
|
-25.46
|
-21.54
|
35.51
|
-2.62
|
-27.08
|
-30.34
|
-27.17
|
26.2
|
-16.22
|
FH-900 × ACALA-1517-C
|
-23.98
|
-26.85
|
-12.04
|
26.18
|
-20
|
-26.21
|
-28.24
|
-12.61
|
12.46
|
-28.06
|
MNH-93 × ACALA-1517-C
|
-0.65
|
-28.63
|
-5.51
|
12.23
|
-18.95
|
-3.8
|
-29.46
|
-13.39
|
0.29
|
-30.63
|
CIM-707 × ACALA-1517-C
|
-35.71
|
-32.6
|
1.58
|
25.42
|
-37.96
|
-42.55
|
-37.42
|
-2.43
|
14.33
|
-39.64
|
CIM-482 × ACALA-1517-C
|
1.33
|
-31.41
|
0
|
25.34
|
-8.06
|
0
|
-37.09
|
-8.27
|
16.56
|
-12.61
|
DISCUSSION
Water stress seriously affects cotton as well as other field crops productivity around the globe and this type of stress has remained a major challenge to plant biologists and researchers (Davis et al., 2017; Khan et al., 2017). Economic survey of Pakistan (2016-17) also indicated that water stress at various critical stages is one of the reasons of low productivity in field crops, and these increasing losses warn the development of new germplasm keeping in view the adverse effects of climate changes (Govt. of Pakistan, 2016-17). Under water stress, chlorophyll contents i.e.a and b are reduced in Triticum aestivum L. (Arshad et al., 2016), likewise photosynthetic activity is also significantly affected due to water stress because this stress impaired with photosynthetic machinery. In continuation, photosynthetic pigments i.e. carotenoids are decreased in Zea mays L. and Hordeum vulgare L. (Ghahfarokhi et al., 2015; Arivalagan and Somasundaram, 2016). The contents of chlorophyll a, b and carotenoids were decreased in the leaves of water stress susceptible genotypes (El-Tayeb, 2006). Similar findings are reported from current study that under water stress, reduction in chlorophyll a, b and total carotenoids were observed. Carotenoids is considered as powerful reactive oxygen species (ROS) scavengers (Edreva 2005a and b; Leopoldidni et al., 2006) and abiotic stresses like water stress stimulates the production of different secondary metabolites (Wahid and Ghazanfar, 2006). The water stress-tolerant cultivar had higher content of chlorophyll a, b and carotene than the sensitive cultivar (Shah et al., 2011). Variability in net photosynthesis and stomatal conductance have been suggested as tools for screening of cotton germplasm (Yu et al., 2016). Because water stress leads to significant reduction in net photosynthesis in response to stomatal closure, which restricts the diffusion of CO2 into the leaf (Osório et al., 2011). Transpiration rate decreases with a decreased level of moisture. The reason behind decline in moisture stress might be because of lowered water potentials in the root zone, it triggers a signal from root to shoot (He et al., 2005).
The molecular mechanism revealed that genes during water-stress conditions are thought to function not only in protecting cells from water deficit by the production of important metabolic proteins but also in the regulation of genes for signal transduction in the water-stress response. Increase of gene expression is also positively associated with tolerance to water stress (Xiong et al., 2010). In addition, genetic analysis was partitioned variations into various genetic components. Variance due to GCA was lower than variance due to specific combing ability for transpiration rate, stomatal conductance, carotenoids chlorophyll a, and chlorophyll b which indicated the existence of non-additive genes (Javaid et al., 2014). Higher magnitude of SCA variance for studied traits is supported by Munir et al. (2018) and indicate the importance of non-additive genes in expression controlling the traits in upland cotton. The findings of Shakoor et al. (2010) also supported the role of non-additive factors in the inheritance of traits mentioned. In addition, water stress also reduces the activity of photosynthesis associated physiological markers i.e. stomatal conductance and transpiration rate (Han et al., 2016). These markers were also exploited in segregating population, and higher estimates of stomatal conductance examined in combination of CIM-482 × NIAB Karishma with low × high GCA. Cross MNH-93 × S-12 proved as good combination for transpiration rate due to hybridization of low × low general combiners. For chlorophyll a CRIS-134 × ACALA-1517-C exhibited good performance by involving high × low combiners, whilst CP-15/2 × FH-1000 identified as a good combination for chlorophyll b due to low × low parents and SCA effects for high carotenoids of CRIS-134 × CIM-506 due to high ´ high GCA estimates. Specific combining ability effects responsible for dominant gene action (Aslam et al., 2017). These estimates indicated the potential of these combinations for the development new genotypes suitable for water stress conditions.
Conclusion: This study revealed a considerable genetic diversity in studied germplasm. Identified superior hybrid combinations (CIM-482 × NIAB Karishma for stomatal conductance and carotenoids, MNH-93 × S-12 for transpiration rate, CRIS-134 × ACALA-1517-C for chlorophyll a, CP-15/2 × FH-1000 for chlorophyll b and CRIS-134 × CIM-506 for carotenoids) may be used in breeding programs to develop drought tolerant cotton variety. Non-additive type of genetic control predict that selection must be delayed till later generations for all desirable traits. As study was conducted under field conditions of subtropical semiarid regions therefore, these results may be helpful for breeders working under such climatic conditions.
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