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Global Research journal of Natural Science

& Technology (GRJNST)

Volume: 04 - Issue 2 (2026), 2078

ISSN P: 2790-7643 ISSN E: 2790-7651

www.grjnst.net

https://doi.org/10.53762/grjnst.04.02.29

Evaluation of Genetic Parameters and Combining Ability for Drought

Tolerance in Cotton (Gossypium hirsutum)

Received: 27 February 2026. Accepted: 20 March 2026. Published: 31 March 2026

Muhammad Ihsan Ullah

Cotton Research Institute, Multan-60000, Pakistan

Saeed Ahmed

Cotton Research Institute, Multan-60000, Pakistan

Shoaib Liaqat(Corresponding Author)

Cotton Research Institute, Multan-60000, Pakistan

shoaibliaqat87@gmail.com

Zaib-un-Nisa

Cotton Research Institute, Multan-60000, Pakistan

Wajad Nazeer(Corresponding Author)

Departmnt of Plant Breeding and Genetics, Ghazi University, Dera Ghazi Khan-32200

wajidpbg@yahoo.com

Amna Bibi

Cotton Research Institute, Multan-60000, Pakistan

Sadia Hakeem

Cotton Research Institute, Multan-60000, Pakistan

Hammad Hussnain

Cotton Research Institute, Multan-60000, Pakistan

Javed Iqbal

Cotton Research Institute, Multan-60000, Pakistan

Nadia Hussain Ahmad

Cotton Research Institute, Multan-60000, Pakistan

Sadia Kanwal

Cotton Research Institute, Multan-60000, Pakistan

Muhammad Luqman

Cotton Research Institute, Multan-60000, Pakistan

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Article ID: 2078

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Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use and distribution

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Abstract

Global climate change especially water shortage and heat waves have drastically

reduced cotton production. It is necessary to develop climate resistant coupled

with high yielder cotton varieties suitable for various environments. For this

purpose, ten cotton verities with different morphological, yield parameters, and

drought tolerance/susceptibility were selected and hybridized using line × tester

mating design. To study the inheritance pattern, twenty-five combinations along

with their parents were grown in the field following augmented design with two

different treatments i-e normal and water-deficit. Under stress environment, the

GCA variance (σ2 gca) was higher than the SCA variance (σ2 sca) for CLCuD,

number of bolls, boll weight, seed index, fiber length, and micronaiere value that

shows additive type of gene action of investigated traits. Line L5 (VH-327),

tester T5 (FH-Lalazar) and crosse 18 (L4 x T3) and cross 21 (L5 x T1)

performed best for yield and yield contributing traits under water deficit

condition environment. Cross 18 (L4 x T3), 21 (L5 x T1) and 22 (L5 x T2)

had the higher SCA for the bolls number and cotton yield (15-20% higher

from parents) both under normal and water deficit condition. Both under

normal and water deficit condition, the best-performing genotypes and

combinations (15 to 20% increase in seed cotton yield) may be used in the

cotton breeding program to enahance the seed cotton yield and drought

tolerance.

Keywords: Hybridization, GCA effects, SCA effects, drought, cotton

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Introduction

Global climate change due to frequent and intense weather conditions, has led to

the occurrence of abiotic and biotic stressors which have a detrimental impact on plant

growth, yield and quality (Zafar et al. 2022). Abiotic stresses, specifically, impede the

physiological and metabolic activities of plants at different growth stages. Both human

activities and climatic factors such as heat, drought, humidity and erratic rainfall patterns

have influenced crop productivity (Zhu et al. 2023). Cotton (Gossypium spp.) is an

important crop that is grown commercially in China, USA, India, Pakistan, Brazil,

Australia and Uzbekistan for its fiber, fuel and feed making (Kashif et al. 2023).

Probably, cotton covers more than 33 million hectares of land in more than 100

countries and fulfills the 31% demand for fiber all over the world (Ijaz et al. 2024).

Cotton contributes almost 0.8% of GDP along with 5.5% of agricultural value addition

and is cultivated in the Southern region of Punjab and some districts of the Sindh

province (Mahmood et al. 2021).

At different plant developmental and reproduction phases, water shortage and

high day and night temperatures have vulnerable effects on cotton plants which

emphasizes the crucial importance for developing drought-tolerant cultivars. During the

year, 2021, about 35% loss in cotton production was reported in Pakistan due to water

deficit condition. Deficiency in irrigation water has negative effects on morphological,

physiological and biochemical processes at different growth stages and results in yield

reduction (Bozorov et al. 2018). A rise in both maximum and minimum temperatures

gives rise to water evaporation and results in salt stress that induces water

shortage/availability to the plants (Saleem et al. 2021). At the cell level, water deficit

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condition resulted in a higher concentration of reactive oxygen species (ROS) that lead

to oxidative stress due to the rupture of the cell membrane (Jans et al. 2021). Further, it

causes a reduction in cell division, leaf surface area, metabolic adaptation, root and stem

cell proliferation. These mechanisms resulted in limited access to resources and

ultimately inhibited plant growth and reproduction. Even at the boll development stage,

water deficiency for a short period could result in maximum loss in seed cotton yield

(Naz et al. 2022). It has also been reported that prolonged exposure to water deficit

condition lowers plant growth and productivity and ultimately causes plant death (Ullah

et al. 2019). Management practices such as more irrigation might be used to control the

water deficit condition but in the current situation, there is a dire need to boost the

cotton yield potential through the development of drought-resistant varieties with

limited water resources (Rehman et al. 2022).

To overcome this problem, various techniques have been used to develop climate-

resilient cotton germplasm for effective and long-lasting solutions (Rahman et al. 2020).

Exploitation of genetic variability in existing germplasm and screening based on

morpho-physiological and biochemical characters could lead to the development of

cotton genotypes with significant tolerance against abiotic stresses (Farooq et al. 2023).

For plant breeders, combining ability estimates are an important factor that can help to

achieve the desired targets through hybridization and selection schemes. As reported,

for the selection of desired parents, combining ability techniques has been best utilized

(Liaqat et al. 2023). Significant genetic variability and heterosis are the main

components of a successful breeding program. Parent genotypes could be identified

from the F1 population through selection and then their performance-based evaluation.

For selection, estimation of combining ability and possible gene action of parental lines,

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Line × tester analysis, proved to be the wisely adopted method in different breeding

programs (Ammar et al. 2023). It not only provides the estimation of specific

combining (SCA) and general combining ability (GCA) but also the major genetic

components.

In the current study, line × tester mating design was utilized for the

hybridization of different cotton genotypes with contrasting traits to investigate the

inheritance pattern and enhance the drought tolerance and seed cotton yield. The main

objectives of this study were to develop the breeding material of cotton, undertaking the

morphological traits contributing to drought tolerance and inheritance pattern and

association in different combinations under different water level conditions.

Materials and Methods

2.1 Plant material and design of experimental

Ten commercial cotton varieties were selected for hybridization purposes. Line ×

tester mating design viz five lines and five testers was employed, and 25 crosses were

developed (Kempthorne, 1957). The characteristics of the parent varieties along with

passport information is presented in Table 01. The experimental was conducted at

Cotton Research Institute Multan (30°08′55″ N, 71°26′30″ E) during the cotton

growing season 2022-23. The material was sown following augmented design under

normal (irrigation was applied at 8-10 days intervals till 165 days after sowing) and

water deficit conditions (one irrigation was skipped compared to Normal, after the

seedling establishment). Weather parameters including maximum and minimum

temperature (°C), rainfall (mm), and relative humidity (%), were recorded using the

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automatic weather station with data logger CR1000X (Campbell Scientific Inc., UT,

USA) installed at ~750 m distance from the experimental area.

2.2 Data recording for investigated traits

Cotton leaf curl disease (ClCuD) was scored for ten plants (avoiding border

plants) per replication (30 plants per entry) following the severity grade 0

(Asymptomatic), 1 (slight thickening of primary veins), 2 (secondary and tertiary veins

thickening), 3 (vein thickening, enation or leaf curling or both), 4 (stunting of growth

along with leaf enation and curling). The disease index (%) was calculated according to

the formula by Liaqat et al. (2023).

Disease index = (Sum of all disease ratings/ total plants) × (100/maximum grade)

Plant height (PH; cm), and yield components including number of bolls (NB), boll

weight (BW; g), seed index (SI; g), ginning out turn (GOT; %), seed cotton yield (SCY;

g), and quality traits related to fiber i.e., fiber length (FL; cm), strength of fiber (FS;

g/tax), micronaire (Mic; μg/inch) were recorded. Seed index was computed by counting

the one hundred seeds, followed by weighing using analytical weight balance. Fiber

quality traits were recorded using Uster High Volume Instrumnet Spectrum 1 (Uster

Technologies AG (HQ), Switzerland).

Table 1: Passport of lines and testers used for hybridization

Lines

Varieties

VH-259

VH-282

Characteristics

1

2

Tall, low input, medium boll size.

Compact plant growth, early maturing, long staple length

(>30mm)

3

VH-363

Heat tolerant, single stem, short stature, early maturing.

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4

5

VH-369

VH-327

Tall, broad leaf, late maturing

High tolerant against sucking insect/pests, CLCuD and lodging

tolerant, creamy pollen.

Tester

1

2

3

MNH-886 Compact plant shape, early maturing,

MNH-814 Tall, Bold boll size, spreading growth habit

MNH-

1016

Tall, semi-compact, low input, good boll opening.

4

5

FH-142

Bold boll size, high yielder, CLCuD tolerant.

FH-Lalazar Heat tolerant, spreading growth habit, big boll size.

2.3 Statistical analysis

Variability among lines, testers and F1 hybrids was estimated through analysis of

variance using package “agricolea” at a significance level P < 0.05 (Mendiburu and de

Mendiburu, 2019). Genotype-trait-environement (GGE) biplot was used to evaluate the

performance of genotypes under normal and stress conditions. For the estimation of

genetic components and combining ability, line × tester analysis was performed. All the

statistics were performed using R software version 3.6.3.

Results

3.1 Weather conditions

Temperature, rainfall, and humidity are important parameters while studying

drought tolerance. During cotton growing season, range of maximum temperature was

recorded form 21°C to 48°C while range for minimum was 9°C to 32°C. Maximum

rainfall was observed during July i.e. 57 mm (Figure 1). Maximum temperature (48°C)

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was recorded during May, while high humidity (up to 90%) was observed during June

due to continuous rainfall (~30 mm).

Figure 1: Daily average weather parameters during cotton growing season, 2022-23.

3.2 Evaluation of parents and F1 material

3.2.1 Analysis of variance

Sufficient variations among treatments, parents vs crosses, parents, crosses, and

their interaction were observed for all the traits under observation (Table 2) except few

non-sinficant trends. For instance, lines showed non-significant differences for boll

weight, and testers varied non-significantly for PH, GOT, FL, and Mic, under normal

conditions. While under water deficit conditions, all sources of variations varied

significantly (Table 3).

Table 2: Sum of squares for morphological and quality related traits of ten

parents and 25 F

1

crosses under normal condition.

D

F

CLCu

SC

GO

T

Source

D

PH

NB BW

Y

SI

FL

FS

Mic.

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D

F

CLCu

D

SC

Y

GO

T

Source

PH

NB BW

SI

FL

FS

Mic.

205.9 1.50 1.48 8.49 5.15 122. 1.48 1.48 1.48 3.88

N.S

Rep. (R) 02

2 ^N.S

1.30 1.37 1.64 6.18

5.99

*

5.52

*

Trt. (T) 34 728.3 *

*

2.46*

3.11*

2.06*

5.97*

6.78*

1.02*

1.15*

3.61*

2.68*

3.91*

5.04*

1.12*

0.30*

0.31*

0.30*

0.25*

0.60*

Parents

1.47 3.89 1.44 3.39

9.62

*

7.44

*

(P)

09 517.2 *

24 812.4 *

01 612.7 *

*

Crosses

(C)

1.29 1.76 1.47 6.82

4.87

*

5.03

*

5.86 9.52 7.68 1.59

2.75

*

1.61

*

P vs C

*

1233.2 2.48 4.19 0.78 2.42

N.S

1.45

*

1.58

*

Lines (L) 04

Tester

*

1400.2 7.09 1.72 2.86 7.09

N.S

1.86 1.39 9.35 0.11

N.S

(T)

04

*

1.14 1.16 1.30 2.40

3.20

*

1.25

*

L × T

Error

16 560.2 *

68 0.22

*

2.71*

0.27*

8.82 9.10 0.01 2.48 0.04 9.10 9.10 9.10 0.04

*= Significant at p ≤0.05, N.S = Non-significant

Table 3: Sum of squares for morphological, yield, and fiber traits of ten parents

and 25 F

1

crosses under water deficit condition.

Source

D CLCu

PH

NB

GO

T

F

D

BW SCY SI

FL

FS

Mic.

Rep. (R) 02 236.7

N.S

195.

8^N.S

195.

8^N.S

5.60 112.

8.15 272. 172.

N.S

138.

6^N.S

9.72

N.S

1^N.S

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Trt. (T)

34 589.0* 639.

8*

109.

6*

0.59 112.

7*

0.71 2.66 2.61* 26.7* 0.10

*

Parents

(P)

09 558.8* 612.

1*

973.

4*

0.52 670.

9*

0.88 3.31 2.62* 22.6* 0.06

*

Crosses

(C)

24 549.5* 675.

4*

968.

5*

0.68 989.

0*

0.70 3.01 4.05* 29.0

4*

0.15

*

P vs C

01 180.7* 33.8* 528.

1*

0.79 855.

1*

0.47 11.1 44.1* 8.53* 0.86

*

Lines (L) 04 117.3* 582.

1*

940.

4*

0.81 184.

3*

1.39 9.85 17.8* 31.6* 0.49

*

Tester

(T)

04 260.0* 124.

6*

305.

7*

2.86 188.

8*

1.38 5.49 6.91* 13.0

9*

0.17

*

L × T

16 143.7* 611.

5*

453.

3.2.3 *

1*

0.10 551.

0.36 0.67 0.65* 2.91* 0.07

5*

*

Error

68 20.5

12.2

11.0

4

0.36 17.8

0.80 2.65 2.28

7.64

0.11

*= Significant p ≤0.05, N.S = Non-significant

3.2.2. Estimates of genetic components, heritability and mid parent heterosis

Under normal conditions, the GCA was less than SCA variance of all traits that

represent the non-additive type of gene action under normal water condition. Also, the

ratio for GCA variance to SCA variance was lower than 1, indicating higher contribution

of dominance genetic variance (Table 4). Comparatively, under water deficit condition

condition, the values of GCA variance for CLCuD index, NB, BW, SI, FL, and mic.

were observed higher than the SCA variance, representing the influence of additive type

of gene action for these traits under water deficit environment (Table 5). Ratio of the

GCA variance to SCA variance was lower than 1 for all the studied traits except

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micronaiere value. Broad sense heritability (H²) and narrow sense heritability (h²)

showed a significant variaton among the two different environments (Table 4 & 5).

Under normal codition, BW had maximum (0.99) while FS possessed minimum (0.77)

H²value. In case of h², BW, PH and FS had maximum value with 0.75. 0.74 and 0.64,

respectively. Under water deficit condition, FS (0.88) and BW (0.79) possessed

maximum H²value while in terms of h², BW had maximumvalue of 0.79 also.

Table 4: Genetic components of the morphological, yield and fiber traits under

normal condition.

Genetic

components CLCuD PH

NB

BW SCY SI

GOT FL

FS

Mic.

Cov. H.S.

(lines)

-

44.8

56.0

6.3

89.4

202.4

145.7 0.37

0.03

0.75 0.56

9.7

0.02

Cov. H.S.

(tester)

-

-28.8 372.1 0.10 313.1

-0.08 -0.08 5.40 -0.01

0.008

Cov. H.S.

(average)

3.78

149.5 0.04 110.6 0.02

0.04 0.02

2.18 1.69

0.94 0.07

29.3 0.10

Cov.

F.S.

354.7

481.9 786.5 0.54 375.1 0.97

(average)

6.3

3.75

149.6 0.04 110.6 0.02

0.04 0.025 0.9

0.005

0.08

σ ²gca

σ ²sca

186.6

380.8 385.8 0.42 800.6 0.36

1.06 0.90

4.1

2

σ gca/ σ

2

sca

0.03

25.2

0.009 0.38

0.09 0.13

0.05

0.03 0.02

0.21 0.06

F=0

Additive

genetic

variance

15.1

589.1 0.01 442.5 0.09

0.16 0.11

3.7

0.02

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Genetic

components CLCuD PH

NB

BW SCY SI

GOT FL

FS

Mic.

F=1

Additive

genetic

12.6

7.5

299.2 0.08 221.2 0.04

0.08 0.05

1.8

0.01

variance

F=0

Variance

due

dominance

373.3

186.6

761.7 775.9 0.85 160.1 0.73

380.8 385.8 0.42 800.6 0.36

2.12 1.80

1.06 0.90

8.3

4.1

0.17

0.08

to

F=1

Variance

due

to

dominance

H²

h²

0.99

0.21

0.84

0.74

0.97

0.24

0.99 0.89

0.75 0.53

0.91

0.33

0.95 0.94

0.24 0.21

0.77 0.99

0.64 0.61

Table 5: Genetic components of the morphological, yield and fiber traits under

water deficit condition.

Genetic

Components CLCuD PH

NB

BW SCY

SI

GOT FL

FS

Mic.

Cov

(lines)

H.S.

-1.75

164.0

10.1

-1.95 32.4

0.04 860.9

0.07

0.61

0.32

1.14

1.01 0.02

8.53 0.06

0.65 0.02

15.8 0.04

Cov

(tester)

27.5

1.59

173.6 0.18 888.7

0.06

0.41

0.09

2.05

Cov

(average)

12.8

0.01 109.3

0.008 0.05

Cov

(average)

F.S.

-

182.6

126.4 0.29 -119.5 0.08

0.89

162.2

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10.1

1.595 12.85 0.01 109.35 0.005 0.055 0.095 0.65 0.02

σ ²gca

-

σ ²sca

-87.9

204.9

411.1

-0.14 0.65

-0.54 1.57 0.01

217.2 0.08

2

σ gca/ σ

-

sca

-0.11

40.5

0.07

6.39

-0.05 0.12 0.26

-0.03 0.08

-0.17 0.41 2.0

F=0

Additive

genetic

variance

51.5

0.05 437.4

0.03

0.01

0.23

0.11

0.39

0.19

2.61 0.08

1.30 0.04

F=1

Additive

genetic

variance

20.2

3.19

-

25.7

-

0.02 218.7

F=0

Variance

due

dominance

-

175.8

-87.9

-822.3 -0.29 -1.31 -1.08

-411.1 -0.14 -0.65 -0.54

to

409.8 434.1 0.17

3.15 0.03

F=1

Variance

due

-

204.9 217.2 0.08

1.57 0.01

dominance

H²

h²

0.73

0.28

0.18

0.59

0.50

0.79 0.63

0.79 0.48

0.51

0.40

0.52

0.47

0.71

0.45

0.88 0.20

0.64 0.20

CLCuD; Cotton leaf curl disease (%), PH; Plant height (cm), NB; Number of bolls,

BW; Boll weight (g), SCY; Seed cotton yield (g), SI; Seed index (g), GOT; Ginning out

turn (%), FL; Fiber length (cm), Fiber strength (g/tax), Mic; Micronaire (μg/inch), σ²

GCA, GCA variance; σ²SCA, SCA variance; H², broad heritability; h², narrow

heritability

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3.2.3 Estimates of GCA of parents

GCA effects both under both conditions i.e., the normal & water deficit, have

been presented in the Table 7 and 8, respectively. CLcuD had negative and lower values

for L5 under normal conditions, L2 under water deficit conditions, and T5, T3 in both

conditions. Under normal conditions, L5 had higher and positive GCA effects for NB,

SCY, FL, and FS , while L4 for PH, BW, and GOT. Comparatively, T5 showed

positive and higher GCA effects for boll weight, GOT%, fiber length, strength, T3 for

the SCY, T4 had higher GCA for mic (Table 6). Under water deficit condition

condition, L2 had highest positive GCA values for the NB, SCY, and SI, L5 for the

BW, GOT, FL, and mic. Among testers, T5 showed significantly positive GCA for BW,

SI, FL, and FS, and T3 for NB and SCY (Table 7).

Table 6: General combining ability estimates for lines and tester under normal

condition.

GCA

Effects CLCuD PH

NB

BW SCY

SI

GOT FL

FS

Mic.

Lines

L1

8.59

-0.6

-41.1 -

0.11

-130.1 -

-0.01 -1.1 -

1.08

0.19

0.38

L2

L3

3.59

3.54

- 7.6

-23.7 0.12 -45.1

1.17 -1.5

0.3

-4.3 0.19

-16.5

-27.1 -

0.30

-88.4

-

0.5

-0.6 -0.9 -0.01

0.35

L4

L5

-0.60

15.4

1.4

0.29 92.5

-

1.05

0.5

2.4

-0.11

-0.27

0.39

-15.13

9.5

90.5

-

171.2 -

-0.02 0.8

3.9

0.01

0.05

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GCA

Effects CLCuD PH

NB

BW SCY

SI

GOT FL

FS

Mic.

S.E.

0.12

0.02

0.03

0.03 0.04

0.04 0.02

0.03 0.02 0.01

Testers

T1

T2

T3

T4

0.71

-1.6

-7.6

9.4

47.6

-

4.2

0.17 -0.20 -

0.31

-2.2 -0.07

-2.8 -0.03

0.30

-0.42

-6.42

15.57

-18.2 -

0.08

-28.8

-

0.10

-

0.06

0.01

23.4

-

117.2 -

-0.42 -

0.05

0.2

2.2

-0.05

0.14

0.02

0.38

0.26

4.4

-21.2 0.05 -40.8

-

0.001 -

0.11

0.20

T5

-9.42

-4.6

-31.6 0.71 -51.8

0.35 0.52

0.05 0.02

0.50 2.5

S.E.

0.12

0.02

0.0.3 0.03

0.02 0.02 0.02

Table 7: General combining ability estimates for lines and tester under water

deficit condition.

GCA

Effects CLCuD PH

NB

BW SCY

SI

GOT FL

FS

Mic.

Lines

L1

2.29

10.6

-2.5

-

-7.3

-0.24 -0.03 0.18 -

0.10

0.05

0.07

L2

L3

-3.97

-2.5

-4.9

9.3

0.02 37.1

0.39

-0.17 0.40 1.27 -0.08

3.02

-10.5 -

0.38

-53.4 -0.3

-0.91 -

1.60

-2.4

-0.14

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GCA

Effects CLCuD PH

NB

BW SCY

SI

GOT FL

FS

Mic.

L4

-0.97

-3.1

-2.4

0.18 -1.2

0.11

-0.19 -

0.64 -0.11

0.35

L5

-0.37

3.2

0.04

1.3

6.2

2.5

0.19 24.9

0.15 3.4

0.10

0.20

1.31

1.36 0.63 0.29

0.30 0.71 0.08

S.E.

0.42

Testers

T1

T2

T3

T4

4.33

3.0

-7.4

-6.66 -

0.09

-10.8 0.07

-25.1 0.07

-0.30 -

0.64

-3.8

-

-0.07

-10.1 -8.40 -

0.04

-0.49 -

0.06

0.45 2.18

-12.0

18.33

8.5

25.02 -

62.1

-0.29 -0.41 -

0.09

0.96 -0.07

0.54

5.2

-1.46 -

0.07

-19.2 -0.28 0.25

0.09 1.80 0.14

T5

-13.66

3.8

1.2

-8.48 0.68 -6.8

1.8 0.15 3.3

0.43

0.94

1.09 3.30 -0.12

0.30 0.56 0.08

S.E.

2.7

0.20

0.35

3.2.4. Specific combining ability (SCA) effects for hybrids

Under normal condition, cross L2 × T4, showed the highest positive SCA value

for NB, SCY, and mic values. Cross L2 × T3 exhibited the higher SCA value for GOT

while cross L2 × T5 for FS. Cross L4 × T1 had the highes negative values for CLCuD

(Table 8). Under water deficit stress, L3 × T5 showed highest positive SCA values for

NB, SCY, SI, and FL (Table 9). Cross L2 × T1 showed the higher SCA value for BW.

Cross L3 × T4 had the higher SCA value for FS.

Table 8: SCA estimates for lines × tester under normal condition.

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Crosses

L1×T1

CLCuD PH

NB

BW

SCY

SI

GOT FL

FS

Mic.

-

12.28

3.42

4.42

0.6

-52.8 -0.83 -54.2

0.12 -0.08

0.71

3.50 0.16

L1×T2

-

-8.4

15.0

0.34

28.8

0.02 0.02 -0.38

0.66 0.10

L1×T3

-

9.6

-5.4

3.6

-7.6

22.0

23.4

-0.45 -92.2

-0.16 0.25

0.27

1.47 0.21

L1×T4 -7.57

0.50

0.44

65.8

51.8

0.46 -0.38 0.31

0.47 0.21

-

L1×T5

-12.57

0.50 0.40 -0.10 2.23

0.16

L2×T1

-

2.28

27.6

-49.2 0.22

-14.2 0.52 -0.32 0.95

-

2.23

-

0.26

L2×T2

-

3.42

-16.4 6.6

0.10

-21.2

-1.62 0.75

0.03

1.12 0.20

L2×T3

-

4.42

-3.4

-7.0

-0.19 -87.2

1.50

0.03

0.004 3.78 0.38

L2×T4 -2.57

11.6

36.6

0.56

150.8 0.10 1.48 -0.54 0.21 0.51

-

L2×T5

-7.57

-19.4 13.0

-0.69 -28.2

-1.04 -1.16 2.47 0.33

0.55

L3×T1

-

2.86

-33.4 -61.6 0.42

-50.2

0.22 -0.54

-

0.37

0.71

1.24

L3×T2

-1.71

-12.4 9.2

-0.89 -37.2 0.02 0.42

0.69 0.23

-

0.744

L3×T3

-

4.28

15.6

16.6

-0.09 -3.2

-0.56 -0.40 1.03

0.27

0.04

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Crosses

L3×T4

CLCuD PH

NB

BW

SCY

SI

GOT FL

FS

Mic.

-

12.28

10.6

12.2

-0.13 4.8

0.06 -0.28 0.15

0.03

-

0.24

L3×T5

-

-17.71

19.6

-5.4

20.6

-6.4

8.6

23.6

0.70

85.8

0.90 0.20 1.53

1.02 -0.58 0.35

0.50 0.32

L4×T1 -18.71

-29.2 -0.77 48.8

1.79 0.17

L4×T2

-

1.23

2.42

7.6

0.30

0.70

-23.2

-0.08 0.85

0.73

0.16

L4×T3 -11.57

32.0

145.8 0.56 0.06 0.49

0.07 0.05

L4×T4

-

16.42

-10.4 -0.43

-1.02 -1.64

-0.30 -0.06

136.2 0.49

0.92 0.24

L4×T5

-

-35.2

-

11.42

-17.4 0.01

0.20

0.17

2.16

0.35

L5×T1

-

69.8

-

1.28

10.6

16.6

192.8 0.96

-38.4 0.14

0.56 -0.68 0.73

0.11

0.10

L5×T2

-

-7.57

52.8

36.8

0.72 1.26 -0.48

0.23

0.12

L5×T3 -1.57

-15.4 -34.0 0.04

0.02 -0.72 -0.34 1.21 0.15

L5×T4

-

-18.57

-25.4 -60.4 -0.49 -85.2

-1.84 1.71

0.74 -0.20

0.21

0.13

0.24

L5×T5

-

26.42

13.6

0.05

-60.0 -0.65 -74.2

0.49

2.02 0.02

S.E.

0.27

0.05

0.06

0.57

0.12 0.05 0.05

0.04 0.03

Table 9: SCA estimates for lines × tester under water deficit condition.

Crosses CLCuD PH

NB

BW

SCY

SI

GOT FL

FS

Mic.

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Crosses CLCuD PH

L1×T1

NB

BW

SCY

SI

GOT FL

FS

Mic.

-

-2.0

2.66

1.0

9.80

0.01

-0.23 -19.12 0.29 -0.13

0.02

0.03 0.86

L1×T2

L1×T3

L1×T4

L1×T5

L2×T1

L2×T2

L2×T3

L2×T4

L2×T5

L3×T1

L3×T2

-

-4.20 -3.91 0.24

-4.85

0.15 -0.68 0.11

0.05

-

0.54

-

-13.6 0.11

21.20

-20.45

0.35

0.31

0.15

0.07

0.02 0.48 0.01

-

-7.66

6.0

18.80 4.08

-0.18 -5.78

0.54 0.03

0.30

0.34

-

0.25 1.34

0.09

-3.20 13.49 0.06

50.21

31.68

0.03

-

0.15 0.75

0.14

3.66

-5.0

3.0

1.80

6.13

2.74

5.81

0.33

0.05 0.07

-

0.09 0.18

0.17

-0.02 15.28

-0.05 13.68

0.19

0.01

-

15.80 9.12

0.18 -0.11 0.13 0.76

0.04

-

0.21 -0.18 0.27

0.73

-3.38 0.04

3.34

0.10

25.86

-

2.13

-

3.33

5.66

5.33

-0.30 -63.98

-0.12 -45.78

0.02

0.03

0.24

14.30

0.44

0.65 0.96

-

-9.86

-

0.15

11.65

0.39

0.20 1.06

-

-2.20 -5.91 0.02

-14.85 0.33 -0.51

0.12

-

0.39 0.40

L3×T3 8.66

-14.2

-0.10 -57.12

0.15

-

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Crosses CLCuD PH

L3×T4

NB

BW

SCY

SI

GOT FL

FS

Mic.

16.34

0.42

0.42 0.84 0.04

0.28 1.42 0.03

-

-10.0

-9.66

-2.0

20.80 14.08 -0.04 34.21

0.29

0.06

L3×T5

L4×T1

L4×T2

-

0.75 0.88

0.26

5.46

0.13

19.82 0.24

83.547 0.54 0.03

-

8.34

0.01

16.347 0.40 -0.26 0.23 0.61

0.08

-

2.66

-5.53 -0.58 -0.04 -3.387

-0.11 0.55 0.37

0.39

0.14

L4×T3 -7.33

19.13 13.72 0.06

51.680 0.26 0.04

-

0.41 0.72 0.02

L4×T4

-

5.66

-8.78 0.02

12.53

-15.32

0.37

-0.04

0.29

1.11

0.03

0.17

0.07

0.76 1.44

L4×T5

-

1.0

-1.20

-0.06 -49.32

0.007 16.88

-0.21 7.813

-0.01 12.21

12.70

0.19

0.43 0.27

L5×T1

-

-5.33

-1.86 0.53

0.56 0.04

0.38 0.14

0.35

0.14

L5×T2

-

-5.66

5.80

0.46

4.60

7.17

0.09

0.36

L5×T3

-

2.66

0.04 -0.43

0.08

0.09 0.16

L5×T4

-

9.0

-1.20 -5.99 0.15

-3.20 -6.31 0.06

-16.45 0.26 -0.80 0.51 0.20

0.20

L5×T5

-

-0.66

-20.45 0.14 -0.16 0.08

0.99 0.06

S.E.

1.16

2.21

1.91

0.35

2.71

0.51 0.94

0.87 1.59 0.19

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3.2.5 Mid parent hetersosis

Mid parent hetersosis of all traits under both environmental conditions was

calsulated and presented in Table 10 & 11, respectively. Combinations, L1 × T4 and

L2 × T4, revealed the positive and highly significant heterosis for NB, BW and SCY

under water defcit condition in contrast to normal condition. While, cross L3 × T4

showed positive and highly significant heterosis for NB, BW and SCY both under

normal and water deficit conditions. Maximum and highly sifnificant possitive heterosis

was observed for SCY in the cross L3 × T5, for NB in cross L2 × T1, for BW in L3

× T4 under water deficit environment. Under normal condition, the maximum and

significant posssitive heterosis for FL was observed in L4 × T3, for FS in L2 × T1, for

micronaier valu in cross L2 × T5. all sources of variations varied significantly (Table 3)

deficit condition, maximum positive and significant heterosis for FL was recorded in

cross L2 × T2, for FS in cross L1 × T5, for micronaier value in combination L2 × T4.

Table 10: Mid parent heterosis of crosses under normal condition.

Crosses CLCuD

L1 ×

PH

8.80**

9.80**

NB

4.57**

3.57**

BW

SCY

SI

GOT

FL

FS

Mic.

-

T1

4.26**

15.44** -14.07** -4.42** 0.63*

-0.31** -3.13** -6.15**

-1.31** -4.13** -5.15**

1.52NS 1.11** -0.53** 0.64** -2.08**

L1 ×

T2

-

5.26**

19.44** -17.07** -3.42** 1.63*

L1 ×

T3

-

-5.88** -5.66** 6.89**

16.21** 25**

L1 ×

T4

-6.66*

8.47**

-27.84** 2.23**

-20

-3.42** 1.0**

0.8**

10.60** 3.09**

11.94** 6.12**

L1 ×

T5

-14.2** 14.28** 10.75NS 32.18** 48.83**

0.23*

-0.12* 0.1**

L2 ×

T1

-57.14* 1.81**

0*

26.82** 28.57**

4.72**

1.59*

-0.52** 13.66** -2.08**

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L2 ×

T2

-

-12.5^NS

0^NS

18.51** 58.73**

-

21.42** 23.07**

6.32**

6.17**

6.32**

64.70**

27.81**

11.11**

16.93** 1.44*

-

5.66**

-0.78** 3.22NS

L2 ×

T3

-

4.21**

6.01**

2.59^NS

2.21** 6.83**

6.36** 3.30**

15.38** 0**

L2 ×

T4

-

0^NS

-9.67** -6.59**

18.84** -5.37**

L2 ×

T5

0**

15.38** 90.88**

-

24.13** 11.86**

27.93** 49.05**

-3.37** 7.69*

5.60** 0.51**

-

-2.85** 17.02**

L3 ×

T1

-

45.45^NS

-0.50** 1.15** -0.85** 0.689** 13.04**

L3 ×

T2

-

0^NS

-25**

-16**

7.14**

-1.44**

9.85**

20**

-

15.55**

5.88**

7.5**

1.61** 1.142^NS-6.16** 10.37**

L3 ×

T3

0**

16.41** -6.77**

18.70** 38.09**

18.21** 19.14**

38.66** 82.60**

1.84** 0**

-4.41^NS

0**

9.37**

1.54**

0.43**

L3 ×

T4

-

20**

16.09**

5.88**

4.29**

0.24** 0.35**

L3 ×

T5

-

50**

-

77.77** 7.69**

21.73** 2.36**

0.122* 1.41**

1.36**

L4 ×

T1

23.07**

18.56** 0.84** 6.9**

-2.78** -1.7**

L4 ×

T2

-

66.66** -3.22** 50.79**

6.45**

130.98** 34.2**

0.36** 5.4**

4.72**

-6**

-

L4 ×

T3

20**

6.25**

3.12**

43.75** 66.67**

78.70** 25.45^NS

1.96*

0.60** 9.09**

-0.68** 11.11**

L4 ×

T4

-50**

-8.57** 26.55**

21.91** 0.98** 6.90**

-0.64** -8.0**

-

L4 ×

T5

71.42** 13.33** -32.43** 34.22** -1.36**

1.28**

3.03** -1.58** 0.63**

10.89**

L5 ×

T1

-

71.42** 18.18** -26.22** 47.22** 50**

7.5**

0.35** 4.67**

-4.94** -3.03**

L5 ×

T2

-60**

-

84.62** 8.47**

12.28** 488.40** 28**

94.28**

71.64**

9.20**

0.24** -1.42** 3.40**

-3.37**

2.27**

-1.12**

L5 ×

T3

55.71**

9.09**

11.76** 1.70** 1.027** -2.6**

-

5.52**

L5 ×

T4

-

81.82** 10.76** 40.52**

15.85** 63.88**

2.85** 0.68**

3.7**

GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643

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L5 ×

T5

-

76.47** -5.45** 15.70**

1.92**

15.88**

-1.74** 6.09^NS

6.7**

5**

-6.66**

*= Significant, **= Highly Significant, N.S = Non-significant

Table 11: Mid parent heterosis of crosses under water deficit condition.

Crosses CLCuD PH

L1 ×

NB

BW

SCY

SI

GOT

FL

FS

Mic.

T1

3.7**

10**

27.45**

-20.58** 0^NS

-5.26** 1.84^NS

-6.32** 1.06^NS

-2.59** 3.0^NS

0.61NS 0.18^NS

-1.15^NS

-

2.713** -4.8**

-6.1**

-5.88^NS

6.93^NS

L1 ×

T2

-4**

-18.18** 31.14**

8.82**

42.85**

L1 ×

T3

4.76**

-18.51** -11.76** -3.44**

-7.40**

68.75**

28**

-1.84** -0.95^NS1.96^NS

L1 ×

T4

10.34** 15.78**

36.17**

11.80**

88.75**

23.52**

15.38**

5.55**

0**

0.18^NS

-1.8^NS

8.22**

4.83**

0^NS

L1 ×

T5

-20**

12**

-25**

-40**

1.19**

17.5**

3.0**

1.29^NS

1.25^NS

-5.3**

7.1**

10.86** -2.97^NS

L2 ×

T1

100**

0^NS

-7.07^NS

-

L2 ×

T2

4.34**

-

-59.09** 34.94**

34.61**

32.25**

90**

11.36** -3.27** 4.081^NS

L2 ×

T3

26.31** -25.92** 15.315** 3.22**

7.8**

-0.18^NS

1.48^NS

-1.51** -5.05^NS

L2 ×

T4

-11.1** -36.84** 50.7**

13.88**

-7.3**

-2.13** 4.00*

-4**

14.85**

12.24**

L2 ×

T5

-44.4** -41.6**

-

18.18** -56.25** 21.495** 13.3**

40.77**

33.33**

53.84**

-2.6**

6.5**

-0.07^NS-0.54^NS

3.7**

L3 ×

T1

0.37^NS

0.58**

-7.91** 13.6**

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L3 ×

T2

20**

-22.22** -9.44**

-4.347** 65.38**

-13.33** -24.05** 9.09**

1.36^NS

2.53**

-3.87** 10.63*

L3 ×

T3

12.5**

12**

84.6**

5.0**

-0.76^NS

-2.04** -7.9**

3.15^NS

L3 ×

T4

33.33** 46.66**

51.02**

70.2**

16.66**

34.28**

29.82**

33.3**

4.054** -0.24^NS

1.50^NS

-0.72^NS1.03^NS

L3 ×

T5

-

73.33** 0^NS

213.0**

113.7**

26.79** -0.61^NS

-

9.023** -5.1**

-2.12^NS

L4 ×

T1

-

64.70** 9.80**

44.08**

40.14** 0.746^NS5.22**

-4.27** -7.69*

L4 ×

T2

20**

-

-20**

-16.53** 36.842** 38.36**

4.89**

0.24^NS

10.90** -3.54** -6.79**

L4 ×

T3

0.882

NS

45.45** -16.92** 31.60**

74.468** 128.5**

23.74**

5.01**

-3.31** -7.69^NS

L4 ×

T4

68.42** 22.44**

-51.14** 29.82**

-29.03** -4.05** 0.12^NS

-2.35** -6.57** -11.3*

L4 ×

T5

100**

-60**

-18.64** -22.22** 43.28**

52.72**

7.18**

0.48^NS

6.0**

0^NS

-4.85^NS

-1.07^NS

L5 ×

T1

23.404** 60.572** 23.94**

108.88** 11.6**

-0.37^NS3.9**

-2.7**

L5 ×

T2

-77.7** 13.72**

-

71.42** -18.03** 46.52**

56.950** 4.22**

63.17**

65.51**

19.14**

28.7**

8.7**

7.6**

0.36^NS

5.4**

-4.89** 2.17^NS

-2.74** -1.07^NS

-0.66^NS-7.4^NS

-5.19** 0^NS

L5 ×

T3

14.75**

-1.40**

-2.52^NS1.73**

L5 ×

T4

9.090** -2.2**

38.46** -5.45**

21.21**

-3.03** -6.14** 8.61**

L5 ×

T5

-

21.160** -6.17**

11.76** -1.09^NS3.44**

*= Significant, **= Highly Significant, N.S = Non-significant

3.2.6 Proportional contribution of lines, testers, and their interactions

The proportional contribution of lines, testers and their interactions under

normal and water defict conditions have been presented in Fig. 3a & 3b. Under normal

condition, lines contributed higher for the traits; SCY, SI, GOT, FL, while the

contribution of crosses was higher for CLCuD, PH, BW, and mic. Under water deficit

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condition, testers contributed significantly higher for CLCuD, NB, BW and mic value.

Lines contributed higher towards GOT, FL, and FS.

contribution to PH, SCY, and SI.

Crosses showed higher

Figure 2: Proportional contribution of lines, testers, and their interaction (line ×

tester) for the morphological, yield, and fiber quality traits under normal (a) and water

deficit (b) conditions.

3.2.7. Performance of parents and their hybrids under normal and water deficit

conditions

Under normal condition, PC1 and PC2 explained 33.8% and 20.6% of the total

variability, respectively (Fig. 3a). Overall, hybrids (H21; L5 x T1, H18; L4 x T3, H23;

L5 x T3) showed highest values for seed cotton yield and number of bolls under both

normal and water deficit condition environments. Parents had higher values for ClCuD

compared to hybrids. Cross 15 (L3 x T5) had highest boll weight, cross 6 (L2 x T1)

had highest seed index. Line 5 had highest number of bolls, while T3 showed highest

GOT%.

Under water deficit condition environment, PC1 explained the 41.8% while PC2

18.1% of the variability (Fig. 3b). Line 5 was found at the extreme of the plot for the

number of bolls. Tester, T3, was also higher for GOT% same as under normal

condition.

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Figure 3: Biplot analysis of lines, testers and hybrids for morphological, yield and fiber

traits under normal (a) and water deficit (b) conditions.

3.2.8 Mean performance of crosses for morphological, yield and fiber traits

The data for morphological, yield and fiber parameters under both water

environments were recorded. Mean data of crosses have been presented in Fig. 4. Cross

20 (L4 x T5) and 21 (L5 x T1) showed higher men values for boll weight. Cross 15

(L3 x T5), 16 (L4 x T1) and 22 (L5 x T2) were significantly high for seed index. In

case of CLCuD, fiber length, strength and micronaire value, cross 22 (L5 x T2), 23 (L5

x T3) and 24 (L5 x T4) were observed with maximum mean values both under normal

and water deficit condition.

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Figure 4: Mean performance of crosses under normal (N) and water deficit

(WD) conditions.

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Discussion

Global climate change threatens cotton yield (Loka et al. 2020). A 5^oC rise in

temperature enhance the risk of both floods and droughts (Soong et al. 2020). Both the

increasing global population and fiber demand have created a challenge to develop

stress-resistant cultivars. Cotton crop is exposed to various climate-driven biotic and

abiotic stresses (Haroon et al. 2023). This calls for assessment of variability and create

new genetic combinations to select drought and heat tolerant germplasm for future

breeding programs. This study found that the genetic variability was better manifested in

hybrids as they revealed significant variations for all the traits under two different

environmental conditions (Table & 3) (Kamara et al. 2021). Genetic dversity was

further portioned into different components due to SCA and GCA (Griffing, 1956)

which provides a better understanding of how these characters have been controlled

(Imtiaz et al. 2023). SCA and GCA variances were found significant under both watered

envionments that revealed the contribution of additive and non-additive gene action.

Earlier, in various studies, both type of gene actions have been reported in the

inheritance pattern of morphological, yield, and fiber traits (Abro et al. 2009; Imran et

al. 2012). It was observed that under normal irrigation condition, the GCA variance was

less than the SCA variance indicating the superiority of non-additive gene action for all

the traits (Table 4). Previously, the dominating role of the non-additive genes for SI,

fiber quality and SCY has been reported (Zare et al. 2014; Ali et al. 2016). Non-

additive gene action supports the opportunity for the selection of such genotypes or

combinations for the improvement of the specific trait in a hybrid. While under water

deficit condition condition, traits like CLCuD, NB, BW, SI, FL, and mic value exhibit a

higher GCA variance than the SCA variance which represents the additive type of gene

action (Table 5) as also supported by Javid et al. 2014. Sufficint heterosis is also needed

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for any breeding programme. Crosses, L1 × T4, L2 × T4, L3 × T4, L3 × T5, L2 ×

T1, L3 × T4 were observed with with maximum heterosis for different traits under

both treatments. These results are related with previos findings and may be selected to

design the breeding stategy for the targeted traits (Monicashree et al. 2017; Maqbool et

al. 2017; Sajjad et al. 2016).

For a successful breeding program and gene pyramiding, several crossing cycles

followed by selection of good combining parents are important (Desoky et al. 2021).

Among lines, L5 (VH-327), had the highest values for the NB, SCY, FL and FS both

under normal and water deficit condition environments (Table 6) which indicated that

this line can be used for the improvement of these traits. While, T5 (FH-Lalazar)

showed higher GCA effects for BW, GOT%, FL, FS under normal while higher values

for BW, SI, FL and FS under water deficit condition (Table 7). Previously, the

dominant role of genes regarding the genetic mechanism for these traits has been

reported (Imran et al. 2012; Shakeel et al. 2012). Specific combining ability estimates is

used to identify the crosses that perform well in specific combinations for the desired

trait (Liaqat et al. 2023). Under water deficit condition, cross L3 × T5, possessed

higher SCA value for NB, SCY, SI, and FL. Such crosses can produce transgressive

segregants, hence giving rise to new genetic combinations and significantly improving the

specific traits. Under water deficit condition, line 5 possessed a higher value for the NB,

Cross 18 (L4 x T3) and cross 21 (L5 x T1) was the best performing for SCY and

tester, T3, was also higher for GOT% and present on the extreme of biplot (Fig. 3b).

These lines, crossed and testers revealed significant potential for morphological and yield

traits under water deficit condition environment and important from breeding point of

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view. Emphasis on seed number per boll (Seed index) and boll numbers could be an

important criterion while breeding for drought-tolerant cotton genotypes (Ur Rehman

et al. 2007).

Conclusion

Changing climate scenario has become a serious threat to cotton yield and

production. This study provides valuable insights for the genetic components underlying

morphological, yield, and fiber traits of different cotton varieties and their interaction

under water deficit condition. Hybridization in different combinations resulted in

increased seed cotton yield and drought tolerance. General and specific combining

ability estimates revealed the inheritance pattern of lines, testers and their interactions

for morphological and yield traits undernormal and water stress conditions. VH-327,

FH-Lalazar and cross 18 (L4 x T3) and cross 21 (L5 x T1) were the best performing

for yield contributing traits under water deficit condition. These lines and testers have

the potential to be utilized in breeding programme specifically under water stress

environment. Cross L3 × T5 possessed the higher SCA effect and may be exploited for

the development of a hybrid for specific traits. Cross 18 (L4 x T3), 21 (L5 x T1), 22

(L5 x T2) and 23 (L5 x T3) exhibited the maximum seed cotton yield and number of

bolls under both normal and water deficit condition condition that is almost 15-20%

higher than the parental genotypes. Our investigation not only provides an

understanding of the inheritance pattern of drought tolerance but also provides the

opportunity to design the breeding strategy specifically for the drought-stress

environment.

Abbreviations: DF; Degree of freedom, CLCuD; Cotton leaf curl disease (%), PH; Plant

height (cm), NB; Number of bolls, BW; Boll weight (g), SCY; Seed cotton yield (g), SI;

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Seed index (g), GOT; Ginning out turn (%), FL; Fiber length (cm), FS; Fiber strength

(g/tax), Mic; Micronaire (μg/inch).

Declarations

Conflict of interest: We confirm that this paper has not been published anywhere.

Ethical approval: All the authors have no conflict of interest with any institution, journal

etc.

Funding: No specific funding was received for this research.

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