Estimation of Drought Tolerance Indices in Upland Cotton under Water Deficit Conditions

Abstract

Cotton is a precious commodity that offers raw material to the textile industry. This crop is grown in tropical and sub-tropical regions of the world. Abiotic stresses exerts negative impact on cotton production, but water scarcity has the worst impact. It is rising due to current situation, in which global warming is producing a decrease in precipitation while an increase in evapo-transpiration is causing an agricultural drought. Thus, it is a difficult task for cotton breeders to identify cotton lines that can respond to areas with limited water supplies without lowering yields and might be utilized as suitable parents in a breeding program to produce drought-tolerant indices. The primary objective of this research was the estimation of drought tolerance indices in upland cotton under water deficit conditions. For this purpose, fifty accessions of upland cotton were assessed for their ability to tolerate the water stress under three conditions, namely control, 50% FC, and 75% FC. These genotypes showed significant variations based on morpho-physiological and biochemical characteristics. At control conditions, these genotypes exhibited enhanced growth and better performance. Whereas, the behavior of some indices under 75% FC showed less growth as compared to control, while under the 50% FC highly significant reductions were observed among genotypes. The genotypes that are resistant to drought and those that are susceptible were found using the K-means cluster and biplot analysis. In terms of performance, the genotypes namely Cyto-515, CIM-595, FH-142, and IR-3701 exhibited relatively better in all the treatments and low excised leaf water loss, high relative water contents, chlorophyll contents, free proline contents, and peroxidase activity were observed to be useful selection criteria for drought resistance. These identified genotypes namely, Cyto-515, CIM-595, FH-142, and IR-3701 may be grown in water deficit areas of the country to assess their potential, could be used in breeding programs for development of germplasm suitable for water stress conditions.

1. Introduction

Pakistan is transitioning from a country with ample water to one with limited water. It was recently predicted that a disastrous fall of ~9.6% (11.69 million acre- feet) could reach 30 MAF by 2025 [1]. The most significant emerging abiotic stress on all living things particularly on plant development and crop productivity is water scarcity. Under drought stress, major agricultural harvests have been reported to be reduced by 50% on average over the world [2]. Cotton is a precious commodity that offers raw materials to the textile industry. It is a major source of fiber, which provides 35% of the world’s total fiber requirements, and is classified as the second largest oilseed crop in the world after soybeans [3]. It contributes 5.2% to agriculture and 1.0% to GDP [4]. In terms of cotton production, Pakistan has fourth rank worldwide. It is grown all over the world in a wide variety of environments. Majority of abiotic factors, particularly the water shortage have a negative impact on cotton yield. Approximately 35% reduction in cotton yield was reported in 2021 due to water shortage [5]. This makes it difficult for cotton breeders to identify and choose cotton varieites that can survive in environments with limited water resources [6]. Various plant characteristics may be the most important predictors of how plants respond to drought stress [7]. In case of genetic variability, cultivars may respond to drought in two ways i.e., increased root length and decreased root length [8]. It has been proposed that relative water contents (RWC) in leaves are the main cause of drop in photosynthesis [9]. Plant roots serve as sensors that detect osmotic stress, which may change the physiological and water potential condition. Consequently, they can contribute in the mechanism for development of tolerate drought [10]. Plants have been found to have a variety of root systems. Deep-rooted plants are more tolerant to drought and are able to absorb water and nitrogen. Under drought stress, long roots minimize axial roots and lateral branching, which lowers the metabolic cost and sustains plant metabolism [11]. In cotton, root morphology is strongly associated with drought resistance throughout the seedling growth stages [12,13]. It is reported that cotton seedlings under drought stress had significantly longer and thicker roots than control plant seedlings after the cotton response to drought. As stomatal closure is adjusted and more water is accumulated in the leaf tissue to support photosynthetic activities, the increase in chlorophyll contents and RWC also contribute to these processes [14]. Positive relationship exists between physiological characteristics such as relative water content, excised leaf water loss, cell membrane stability, bolls per plant, boll weight, and seed cotton production. Therefore, these characteristics could be exploited to detect water deficit stress in cotton germplasm [15]. Water stress leads to water loss from leaf tissue, which significantly deteriorates membrane integrity and function [16]. The primary target of stresses is the cell membrane [17]. Drought tolerance in plants is determined by their ability to protect membrane integrity [18]. According to reports, relative water content (RWC) of leaves may serve as a direct indicator of water status in plant during shortage of water [19,20]. It is reported that during drought stress, the rate of transpiration reduced due to insufficient moisture while leaf temperature increased. However, the relative water content decreases under water stress circumstances but resistant genotypes exhibit a smaller reduction. To test plant germplasm for drought tolerance, relative water content under normal and water shortage conditions could be compared [21,22]. Wheat grain yield was observed to significantly correlate with lower excised leaf water loss and water retention [23,24]. Thirty genotypes of wheat were examined for excised leaf water loss (ELWL) under stressed and non-stressed watering conditions and it was found that tolerant genotypes showed low ELWL values. Plants can respond to stressful situations according to physiological and biochemical factors. Plants frequently store proline to prevent cell damage and membrane leakage under stress [25]. It is documented in the literature that proline is essential for cell signaling to control mitochondrial functioning and plant stress response [26]. Proline concentrations are higher in tolerant plants than sensitive plants when they are grown in water-deficit conditions [27,28]. Plants under drought stress experience a storm of reactive oxygen species (ROS) that causes oxidative damage and lead to cell death [29]. This results from an imbalance between the amount of light that is captured and how it is used by plant systems, which lead the production of superoxide anion, hydroxyl radicals, singlet oxygen, and H₂O₂ [30]. ROS can damage cell components by oxidizing photosynthetic pigments and degrading the lipids, proteins, and nucleic acids, have important role in synthesis of cell membranes [31,32]. The assessment of malondialdehyde (MDA) level can be utilized to evaluate membrane damage due to environmental stress and increased ROS production [33]. Antioxidant molecules i.e., peroxidases, catalases, reductases, and mutases that scavenge ROS, plants can protect themselves against drought stress and oxidative damage. In addition, plants also undergo osmotic alteration in their cells [34]. Proline is one of the most prevalent osmoregulatory solute found in plants when experiencing drought stress. It enhances water flux in plants by decreasing the cell osmotic potential to sustain turgor and cell development [35,36,37]. Due to current condition of global warming, water supplies are falling while water demand is rising that resulting in a decrease in precipitation (meteorological drought), while rise in evapo-transpiration is noted during agricultural drought [38]. Therefore, it is need of the hour to develop tolerant genotypes that can survive severe drought with little loss in yield and fiber quality. The findings from this study will be used to assemble features in cotton plant that could enhance the ability of cotton yield to grow in water stress conditions.

2. Materials and Methods

2.1. Collection of Germplasm

For this research work, the germplasm consisted of 50 accessions upland cotton were collected from Cotton Research Station (CRS) Bahawalpur-Pakistan, Central Cotton Research Institute (CCRI) Multan, Nuclear Institute for Agriculture and Biology (NIAB) Faisalabad, Cotton Research Institutes (CRI) Multan, Cotton Research Station, Faisalabad and the University of Agriculture, Faisalabad (UAF) Pakistan (

Table 1. List of cotton genotypes used to assess the potential in water deficit conditions.

Code	Genotypes	Code	Genotypes	Code	Genotypes	Code	Genotypes	Code	Genotypes
G1	SB-149	G11	IUB-13	G21	VH-363	G31	Kehkshan	G41	KZ-189
G2	FH-452	G12	FH-114	G22	IR-3701	G32	Mubarak	G42	FH-172
G3	KZ-181	G13	Cyto-178	G23	FH-170	G33	Bahar-2017	G43	BS-80
G4	KZ-191	G14	CRS-2007	G24	Cyto-124	G34	FH-215	G44	NS-121
G5	VH-341	G15	S-9	G25	AGC-2	G35	VH-228	G45	FH-118
G6	AA-703	G16	VH-259	G26	Ghouri	G36	FH-142	G46	FH-169
G7	Tipo-1	G17	Debal	G27	VH-339	G37	NIAB-777	G47	AGC-501
G8	MNH-992	G18	FH-154	G28	MNH-888	G38	VH-330	G48	NIAB-820
G9	Tarzan	G19	Cyto-179	G29	FH-458	G39	CIM-595	G49	FH-490
G10	CRS-2	G20	VH-377	G30	AA-802	G40	Cyto-608	G50	Cyto-515

Where G = Genotype.

). The study was carried out at the Institute of Molecular Biology and Biotechnology Bahauddin Zakariya University Multan, Pakistan (latitude 30.270172° N, longitude 71.505625° E).

2.2. Experimental Design and Drought Treatment

The experiment was conducted in a greenhouse to reduce the probability of error. The internal temperature of greenhouse was maintained at 30 °C during the day and ±20 °C during night with humidity level of 44–49% and light cycle of 14 h. The experiment was carried out in plastic pots filled with sand, and field capacity of the pots was determined using the following calculations,

FC = Saturated soil weight − Dry soil weight

Saturated soil weight

The experiment was conducted under complete block design with two factorial arrangements. Three water levels including control (normal irrigation), 50% of pot capacity, and 75% of pot capacity were maintained during experiment. Each accession was reproduced by three times in each group, resulting in a total size of 150 pots for each set. Four seeds were placed in each pot (Figure 1A) and once the seeds had germinated the seedlings were thinned out to to keep one healthy seedling per pot. All the pots were watered regularly till the emergence of first true leaf (Figure 1D).

At the time of emergence of first true leaf, 50% and 75% water stress were applied after every two days, and stress was maintained for up to 60 days (Figure 2).

The effect of drought was monitored using soil moisture meter (HH2 Theta Probe Type, Delta-T device, Cambridge, England). Data based on morpho-physiological characteristics were recorded according to prescribed protocols. Clark and Townley-Smith’s (1986) method was used to determine relative water content as under:

Relative water content (%) = Fresh weight − Dry weight
Turgid weight − Dry weight

The Clarke and McCaig (1982) method was used to calculate the water loss from excised leaves.

Excised leaf water loss (%) = Fresh weight − Wilted weight
Dry weight

The Blum and Ebercon (1981) method was used to calculate the stability of cell membranes.

Cell membrane stability % = [{1 − (T1/T2)]/[1 − (C1/C2)}] × 100

To determine the 100% FC, 75%, and 50% FC, the FC formula was multiplied by 100 in the control conditions while in the 75% (FC) water stress treatment, the FC formula was multiplied by 75, and in the last but not the least the formula of FC was multiplied with 50 in 50% (FC) water stress treatment (Figure 2).

2.3. Analysis of Biochemical Parameters

2.3.1. Hydrogen Peroxide

The quantity of hydrogen peroxide H₂O₂ was measured by using the [39]. At the time of picking the crop, tissues of leaves were stored at −80 °C in the freezer to use for the evaluation. For this estimation, 0.1 g leaf tissue was taken and crushed by adding 5 mL pre-chilled acetone. Later the solution was centrifuged at 3200× g at 4 °C for 9 min which was done by using normal speed microcentrifuge and SCILOGEX D2012. For further evaluation 1 mm supernatant was mixed with 0.1 mL of 95% (v/v) hydrochloric acid (HCl), 0.3 mL of ammonia, and 30% (v/v) titanium tetrachloride which was then centrifuged at 11,000× g at 4 °C for 9 min. Afterward, the sediments were washed again and again by using cold acetone. Again, centrifugation was performed at 12,000 rpm for 9 min. In last take 2 mL of 1 M H₂SO₄ has dissolved in it also. To find out absorbance at 410 nm Nano Drop Spectrophotometer (Model No. ND-8000 Thermo Scientific USA) was used and a standard curve was used, based on the known concentration, to evaluate H₂O₂ concentration.

2.3.2. Proline

The level of Proline was determined based on the protocol suggested by [40]. One-gram crushed leaves were homogenized with 5 mL of 3% sulfosalicylic acid. The extracted solution was centrifuged at 11,000× g at 4 °C for 10 min and the supernatant was removed from eppendorf tubes. Then three percent Ninhydrin solution was prepared that contained an equal quantity of 6 M orthophosphoric acid and glacial acetic acid. Then collect 1 mL of each component that is glacial acetic acid, the ninhydrin solution, and the supernatant of the leaf extract have been poured into cuvettes and incubated at 100 degree Celsius for 50 min. Subsequently, the reaction mixture was chilled in an ice bath and 0.5 mL of toluene was added prior to vortex for six minutes to obtain the organic layer while the aqueous layer was disposed of. At that time an organic layer was poured into the ELISA plate and absorption was saved at 520 nm by using toluene as a blank for the standard curve.

2.3.3. Peroxidase Activity

The peroxidase activity was evaluated using a methodology described by [41]. The leaf tissue was crushed with a pestle and mortar with 0.05 M buffer of sodium phosphate and centrifuged at 10,000× g for 20 min and the supernatant was transferred to the eppendorf tube. Next, 3 mL of the reaction mixture was prepared by blending an equal quantity of guaiacol and H₂O₂ ultimately poured into the enzymatic extract. The absorption was measured at 470 nm by using the NanoDrop Spectrophotometer (Model No. ND-8000 Thermo Scientific Model USA).

2.3.4. Catalase

Catalase activity has been tested by using the [42]. Leaf tissue was crushed with a buffer of sodium phosphate to obtain an enzyme extract of 0.1 mL. The CAT reaction solution was subsequently obtained using a phosphate buffer of 40 mM, 15 mM H₂O_2, and 0.1 mL of enzymatic extract. The absorption was recorded at 240 nm with the NanoDrop Spectrophotometer (Model No. ND-8000 Thermo Scientific USA) after every 20 s.

2.4. Statistical Analysis

Genetic differences among 50 cotton germplasm were assessed from these above-recorded traits by using factorial design analysis of variance [43]. While the K-means cluster analysis and biplot analysis were performed by using different statistical software tools, i.e., SPSS v. 19 and STATISTICA v. 5.0 to evaluate the response of different upland cotton accessions under control and various stress levels of drought.

3. Results

3.1. Effect of Drought on Morphological Traits

Root length (cm):

In terms of root length, Cyto-515 performed best among all cotton genotypes under both normal and water-scarce conditions while VH-363 exhibited the shortest root length among all genotypes, demonstrating its vulnerability to drought stress (

Table 2. Maximum and minimum values and representative genotypes for the parameters of cotton grown under normal and drought stress conditions.

	Normal			75% (FC) Water Stress			50% (FC) Water Stress
Traits	Genotypes	Coding	Max. Value Min. Value	Genotypes	Coding	Max. Value Min. Value	Genotypes	Coding	Max. Value Min. Value
SL	Cyto-515	G50	25	Cyto-515	G50	20	CIM-595	G39	10
	VH-363	G27	9	VH-363	G21	4	VH-363	G21	1
RL	Cyto-515	G50	18	FH-142	G36	17	Cyto-515	G50	13
	FH-114	G12	5	VH-363	G21	6.67	VH-363	G21	0.96
FSW	Cyto-515	G50	5	Cyto-515	G50	3.45	Cyto-515	G50	3.76
	KZ-181	G3	2	KZ-181	G3	1.12	FH-114	G13	0.9
FRW	Cyto-515	G50	3	CIM-595	G39	1.38	IR-3701	G22	0.9
	FH-114	G32	1	KZ-181	G3	1.01	AA-802	G30	0.03
DSW	CIM-595	G39	3	Cyto-515	G50	2.42	FH-142	G36	1.2
	VH-363	G27	0.75	FH-114	G12	0.88	FH-114	G12	0.62
DRW	Cyto-515	G38	1.18	AA-802	G30	0.12	Kehkshan	G31	0.15
	FH-114	G12	0.02	FH-172	G42	34	VH-363	G21	0.06
Chlr	R-3701	G22	48	FH-142	G36	43	Cyto-178	G13	35.12
	KZ-181	G3	20	CRS-2	G10	17.03	FH-118	G45	14.23
ELWL	AA-802	G30	0.7	FH-114	G12	3.16	CIM-595	G39	2.51
	CIM-595	G39	0.35	VH-363	G21	3.97	FH-114	G50	4.01
RLWL	Cyto-608	G-49	83	IR-3701	G22	75	IR-3701	G22	67
	FH-490	G35	10.67	FH-172	G42	25	KZ-181	G3	24
CMS	Cyto-178	G13	69.35	Kehkshan	G31	58.67	FH-142	G12	58
	KZ-181	G3	21	KZ-181	G21	21	FH-114	G36	29
H2O2	Debal	G17	1.40	CIM-595	G39	0.17	Cyto-515	G39	0.27
	AA-802	G30	0.22	VH-363	G21	0.18	FH-114	G12	0.054
Proln	FH-458	G29	0.59	FH-142	G36	0.39	IR-3701	G22	0.72
	FH-114	G32	0.07	FH-114	G12	0.087	VH-363	G21	0.016
POD	Cyto-515	G50	15	Cyto-515	G50	18.67	CIM-595	G39	60
	FH-114	G12	6	CRS-2	G10	3.11	KZ-181	G3	18
CAT	IR-3701	G22	16	CIM-595	G39	31	CIM-595	G39	45
	VH-363	G21	8	KZ-181	G3	11	VH-363	G21	12

Where G = Genotype; SL = Shoot length (cm); RL = Root length (cm); FSW = Fresh shoot weight (g), FRW = Fresh root weight (g); DSW = Dry shoot weight (g); DRW = Dry root weight (g); ELWL = Excised leaf water loss, RLWL = Relative leaf water loss, CMT = Cell membrane stability, Chlr = Chlorophyll content [CCI]; Proline = Proline content [µmol g⁻¹ (FW)]; POD = Peroxidase [U mg⁻¹ protein]; H₂O₂ = Hydrogen peroxide [µmol g⁻¹ (FW)]; CAT = catalase activity [U mg⁻¹ protein].

).

The genotype Cyto-515 (18.96 cm) had the longest root length followed by CIM-595 (13.76 cm), FH142 (14.01 cm), and IR3701 (16.01 cm) while smallest root length was observed by KZ-181 (10.51 cm), FH-114 (10 cm) and VH-363 (5 cm) under normal conditions. Under 75% stressed conditions, genotypes Cyto-515 (14.91 cm), CIM-595 (15 cm), FH142 (17.02 cm) and IR3701 (14.09 cm) had highest root length, while accessions that had minimum root length included KZ-181 (8 cm), FH-114 (7.41 cm), and VH-363 (3.29 cm). Under 50% stressed conditions, genotypes Cyto-515 (13.44 cm), CIM-595 (7.2 cm), FH-142 (11.25 cm), and IR3701 (13 cm) had highest root length, while genotypes that had shortest root length included KZ-181 (5 cm), FH-114 (4.57 cm), and VH-363 (1 cm) (Figure 3A).

Shoot length (cm):

Under the control conditions, Cyto-515 (25 cm) had the highest shoot length followed by CIM-595 (22.46 cm), FH142 (20 cm), and IR3701 (23.29 cm) while the lowest values were found in KZ-181 (14.15 cm), FH-114 (16 cm) and VH-363 (8.5 cm). Under 75% stressed conditions, the longest shoot length was found in Cyto-515 (20 cm), CIM-595 (18.49 cm), FH-142 (16 cm), and IR-3701 (18.79 cm) while genotypes that had minimum shoot length included KZ-181 (9 cm) FH-114 (12.5 cm) and VH-363 (4.84 cm). Under 50% stressed conditions, genotypes Cyto-515 (14.24 cm), CIM-595 (10 cm), FH142 (9.07 cm), and IR3701 (16 cm) had maximum shoot length, while accessions that had minimum shoot length included KZ-181(5 cm), FH-114 (5.11 cm), and VH-363 (1.07 cm) (Figure 3B and Table 2).

Fresh root weight (g):

The average genotype performance for fresh root weight (g) revealed that genotype Cyto-515 (0.68 g) had the highest fresh root weight, followed by genotype IR-3701 (0.67 g), CIM-595 (0.66 g) and FH-142 (0.64 g), while lowest weight was gained by FH-114 (0.31 g), KZ-181 (0.39 g) and VH-363 (0.34 g) under control conditions. On the other hand, CIM-595 (0.56 g) had the maximum fresh weight of root followed by Cyto-515 (0.59 g), FH-142 (0.55 g), and IR3701 (0.54 g) while accessions that had minimum fresh root weight included FH-114 (0.29 g), KZ-181(0.27 g) and VH-363 (0.26 g) under 75% drought conditions. Genotypes Cyto-515 (0.49 g), FH142 (0.32 g), CIM-595 (0.46 g), and IR3701 (0.37 g) had maximum fresh root weight under 50% drought conditions, while accessions that had the lowest fresh root weight included FH-114 (0.14 g), KZ-181(0.18 g) and VH-363 (0.11 g) (Figure 3C and Table 2).

Dry root weight (g):

The average genotype performance for fresh root weight (g) revealed that genotype Cyto-515 (0.19 g) had the highest dry root weight values followed by IR3701 (0.15 g), CIM-595 (0.18 g) and FH142 (0.16 g), while lowest dry weight values were found by FH-114 (0.09 g), KZ-181 (0.09 g) and VH-363 (0.08 g) under normal water conditions. On the other hand, under 75% drought conditions, the highest dry root weight was found for CIM-595 (0.12 g) followed by Cyto-515 (0.13 g), FH-142 (0.10 g), and IR3701 (0.11 g) had highest dry root weight, while genotypes that had lowest dry root weight included FH-114 (0.07 g), KZ-181 (0.06 g) and VH-363 (0.05 g). Under 50% drought conditions, genotypes Cyto-515 (0.03 g), FH142 (0.02 g), CIM-595 (0.06 g), and IR3701 (0.03 g) had the highest values for dry root weight, while genotypes had lowest dry root weight included FH-114 (0.005 g), KZ-181(0.01 g) and VH-363 (0.002 g) (Figure 3D and Table 2).

Fresh shoot weight (g):

In terms of fresh shoot weight genotypes Cyto-515 (1.78 g) had maximum fresh shoot weight followed by IR-3701 (1.88 g), CIM-595 (1.76 g), and FH142 (1.96 g) while the lowest weight was found by FH-114 (1.59 g), KZ-181 (1.6 g) and VH-363 (1.54 g) under normal water conditions. Under 75% drought stress Cyto-515 and CIM-515 (1.57 g) both had the same highest values for fresh shoot weight followed by IR-3701(1.51 g) and FH-142 (1.48 g) and genotypes that had minimum fresh shoot weight included FH-114 (1.26 g), KZ-181 (1.22 g) and VH-363 (1.2 g). Fresh shoot weight under the 50% drought conditions revealed that genotypes Cyto-515 (1.35 g), FH-142 (1.34 g), CIM-595 (1.38 g), and IR3701 (1.31 g) had the highest fresh shoot weight, while accessions that had lowest fresh shoot weight included FH-114 (0.42 g), KZ-181(0.49 g) and VH-363 (0.35 g) (Figure 3E and Table 2).

Dry shoot weight (g):

The genotype Cyto-515 (1.32 g) followed by IR3701 (1.29 g), CIM-595 (1.19 g) and FH142 (1.29 g) had the highest dry shoot weight values, while minimum dry shoot weight values were found by FH-114 (0.71 g), KZ-181 (0.65 g) and VH-363 (0.73 g) under control conditions. On the other hand, under 75% drought conditions, the highest dry shoot weight was found for CIM-595 (1.02 g) followed by Cyto-515 (1.12), FH-142 (0.97 g), and IR3701 (1 g) while genotypes that had lowest dry shoot weight included FH-114 (0.42 g), KZ-181(0.39 g) and VH-363 (0.41 g). Under 50% drought conditions, genotypes Cyto-515 (0.76 g), FH142 (0.44 g), CIM-595 (0.51 g), and IR3701 (0.62 g) had the highest dry shoot weight, while genotypes that had the lowest dry shoot weight included FH-114 (0.28 g), KZ-181(0.22 g) and VH-363 (0.15 g) (Figure 3F and Table 2).

3.2. Effect of Drought on Physiological Traits

Relative water content (%):

Under control conditions, Cyto-515 (81.22) showed the highest relative water content values followed by IR3701 (78.59), CIM-595 (65), and FH 142 (62), while reduced water content was found by FH-114 (25), KZ-181 (23) and VH-363 (19) under control conditions. On the other hand, under a 75% water stress environment, the highest water content was observed in Cyto-515 (65) followed by IR3701 (57), CIM-595 (51), and FH 142 (46), while reduced water content was found by FH-114 (20), KZ-181 (18) and VH-363 (11). Under a 50% drought environment, the highest water content was maintained in Cyto-515 (48) followed by IR3701 (42), CIM-595 (37), and FH 142 (31), while the lowest water content was found by FH-114 (10), KZ-181 (7) and VH-363 (5) (Figure 4A and Table 2).

Excised leaf water loss (%)

The genotypes that showed the minimum value of excised leaf water loss under normal conditions were Cyto-515(0.35), IR3701 (0.23), CIM-595 (0.18), and FH 142 (0.65), while the highest excised leaf water loss was showed by FH-114 (1.88), KZ-181 (2.32) and VH-363 (2.64). Under 75% water stress low excised leaf water was observed in Cyto-515 (1.47) followed by IR3701 (1.42), CIM-595 (1.28), and FH 142 (2), while the highest excised leaf water was gained by FH-114 (3.16), KZ-181 (3.75) and VH-363 (3.97). Under 50% drought environment low excised leaf water loss was showed by Cyto-515 (2.46) followed by IR3701 (2.78), CIM-595 (2.51), and FH 142 (2.64) while maximum excised leaf water loss was found by FH-114 (4.01), KZ-181 (4.64) and VH-363 (4.89) (Figure 4B and Table 2).

Cell membrane stability (%):

The genotype Cyto-515 (72.08) exhibited the highest values of cell membrane stability (%) under control irrigation condition followed by IR3701 (68.48), CIM-595 (69.01), and FH 142 (68.21), while minimum value was showed by FH-114 (42), KZ-181 (39.78) and VH-363 (37.5). Under 75% water stresses, the highest cell membrane stability values were found in Cyto-515 (58.84) followed by IR3701 (56), CIM-595 (58.35), and FH 142 (54.12), while lowest cell membrane was found by FH-114 (32.02), KZ-181 (28.20) and VH-363 (26.95). Under 50% drought stress conditions the highest value of membrane stability was found in Cyto-515 (41) followed by IR3701 (44), CIM-595 (47), and FH 142 (40.09), while the lowest cell membrane stability was noted by FH-114 (19.2), KZ-181 (20) and VH-363 (2.35) (Figure 4C and Table 2).

Chlorophyll content:

The highest values of chlorophyll under normal irrigation conditions were seen in Cyto-515 (48.5), IR3701 (47), CIM-595 (44), and FH 142 (43.63), while the lowest value was found by FH-114 (22.05), KZ-181 (20) and VH-363 (24.17). Under 75% water stress conditions highest chlorophyll content values were maintained in Cyto-515 (39) followed by IR3701 (36.95), CIM-595 (38), and FH 142 (37), while minimum chlorophyll content was found by FH-114 (19), KZ-181 (16.94) and VH-363 (18.5). Under 50% drought conditions, the highest value was maintained by Cyto-515 (28.7) followed by IR3701 (27.11), CIM-595 (26), and FH 142 (21), while the lowest chlorophyll content was found by FH-114 (17.8), KZ-181 (14.5) and VH-363 (14.6) (Figure 4D and Table 2).

3.3. Effect of Drought on Biochemical Traits

Proline (umol g-1):

Under normal conditions genotype Cyto-515 (0.11) had the highest proline accumulation followed by IR3701 (0.15), CIM-595 (0.13), and FH 142 (0.12), while the lowest proline concentration was found by FH-114 (0.19), KZ-181 (0.21) and VH-363 (0.22). Under 75% water stresses the highest proline content was observed in Cyto-515 (1.45) followed by IR3701 (1.41), CIM-595 (1.35), and FH 142 (1.31), while the lowest proline content was found by FH-114 (0.9), KZ-181 (0.7) and VH-363 (0.58). Under 50% drought stress highest proline concentration was maintained in Cyto-515 (1.20) followed by IR3701 (1.18), CIM-595 (1.19), and FH 142 (1.21), while reduced proline was found by FH-114 (0.4), KZ-181 (0.32) and VH-363 (0.29) (Figure 5A and Table 2).

Catalase (U mg-1 protein):

The highest values of catalase were present in Cyto-515 (8) followed by IR3701 (7), CIM-595 (5), and FH 142 (13.45), while FH-114 (11.34), KZ-181 (9.2) and VH-363 (11.04) showed lowest values under the control conditions. Under 75% water stress high catalase values were observed in Cyto-515 (54.01) followed by IR3701 (48.31), CIM-595 (44.5), and FH 142 (42.81), while the lowest catalase values were found by FH-114 (29), KZ-181 (26) and VH-363 (24). Under a 50% drought environment, the highest catalase values were found in Cyto-515 (29.45) followed by IR3701 (26.11), CIM-595 (23.56), and FH 142 (21.5), while minimum values of proline were found by FH-114 (19.04), KZ-181 (17.1) and VH-363 (16.91) (Figure 5B and Table 2).

Peroxidase (U mg-1 protein):

Under the control conditions, the highest values of peroxidase reaction were present in Cyto-515 (7) followed by IR3701 (5), CIM-595 (6), and FH 142 (5.6), while FH-114 (10.57), KZ-181 (8) and VH-363 (9). (Figure 3) showed the lowest values. Under 75% water stress high peroxidase values were observed in Cyto-515 (39) followed by IR3701 (35.31), CIM-595 (32.5), and FH 142 (30), while the lowest peroxidase values were found by FH-114 (19.34), KZ-181 (15.06) and VH-363 (13.7). Under a 50% drought environment, the highest peroxidase values were found in Cyto-515 (28) followed by IR3701 (26), CIM-595 (23), and FH 142 (20), while the lowest values of peroxidase were shown by FH-114 (14.05), KZ-181 (11) and VH-363 (11.91) (Figure 5C and Table 2).

Hydrogen peroxidase (µmol g-1):

The genotype Cyto-515 (1.13) had the increased amount of H₂O₂ followed by IR3701 (1.11), CIM-595 (0.8), and FH 142 (0.6), while FH-114 (1.26), KZ-181 (1.19) and VH-363 (1.06) showed lowest values under normal conditions. Under 75% water stress, high H₂O₂ values were seen in Cyto-515 (12.06) followed by IR3701 (10.56), CIM-595 (8.98), and FH 142 (5), while minimum H₂O₂ values were found by FH-114 (3), KZ-181 (2.5) and VH-363 (2.16). Under a 50% drought environment, the highest H₂O₂ values were observed in Cyto-515 (8.8) followed by IR3701 (6.11), CIM-595 (4.6), and FH 142 (2.65), while reduced H₂O₂ was gained by FH-114 (1.76), KZ-181 (1.45) and VH-363 (1.21) (Figure 5D and Table 2).

3.4. Biplot Analysis for the Identification of Drought-Tolerant and Susceptible Genotypes

Multivariate analysis was used to examine the morphological, physiological, and biochemical traits that categorize the germplasm into several groups based on the capability and performance of the traits. Certain variables showed significant genetic diversity (p < 0.01) in the accessions (

Table 3. Mean squares for various quantitative traits of cotton under drought stress.

Source of Variation	DF	RL	SL	FRW	FSW	DSW	DRW	Chlr	CMS	ELWL	RWC	H2O2	POD	CAT	Proline
Drought	2	313.06 **	713.14 **	2.88 **	10.36 **	2.66 **	0.06 **	895.84 **	12426.02 **	10.35 **	81703.5 **	16.68 **	5656.29 **	2139.93 **	10.93 **
Genotypes	49	37.3 **	48.61 **	2.85 **	1.32 **	0.64 **	0.64 **	148.41 **	104.64 **	6.01 **	1091.7 **	3.63 **	25.59 **	341.15 **	24.67 **
Drought Genotypes	98	15.84 **	15.34 **	1.01 **	0.62 **	0.16 **	0.27 **	39.33 **	88.56 **	3.46 **	1482.53 **	4.05 **	19.82 **	347.41 **	23.15 **
Error	300	5.95	2.98	0.47	0.21	0.03	0.02	18.61	16.18	2.92	1240.4	3.71	16.23	324.7	23.43
Total	449

Where ** p < 0.01, RL = Root length [cm]; SL = Shoot length [cm]; FRW = Fresh root weight [g]; FSW = Fresh shoot weight [g]; DRW = Dry root weight [g]; DSW = Dry shoot weight [g]; ELWL = Excised leaf water loss, RLWL = Relative leaf water loss, CMS = Cell membrane stability, Chlr = Chlorophyll content [CCI]; Proline = Proline content [µmol g⁻¹ (FW)]; POD = Peroxidase [U mg⁻¹ protein]; H₂O₂ = Hydrogen peroxide [µmol g⁻¹ (FW)]; CAT = catalase activity [U mg⁻¹ protein].

).

The presence of genetic variability permits the researcher to move on to additional biometrical analysis, such as the biplot analysis performed here. One aspect and goal of employing biplot analysis in this work is to characterize and identify drought-resistant and susceptible lines. Furthermore, Cluster analysis of K means was used to arrange 50 accessions according to average values for specific variables i.e., five clusters were identified for some variables refer to

Table 4. K-means cluster analysis of 50 cotton genotypes grown under control conditions.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
SL	12.70	15.46	12.20	15.28	15.64
RL	8.41	8.81	7.10	10.10	10.14
FSW	0.98	1.19	0.87	1.23	1.31
FRW	0.45	0.52	0.31	0.82	0.85
DSW	0.26	0.25	0.21	0.4	0.51
DRW	0.14	0.12	0.09	0.36	0.37
Chlr	34.96	36.03	34	36.43	38.29
ELWL	1.43	1.48	1.68	1.66	1.23
RLWL	73.33	75.23	63.33	79.13	81.38
CMS	44.7	41.30	32.5	56.25	56.85
H2O2	1.34	1.36	1.24	1.37	1.41
Proline	0.19	0.13	0.11	0.20	0.34
POD	7.63	7.50	7.09	7.52	7.85
CAT	24.88	26.82	17.06	23.77	29.83

Where RL = Root length [cm]; SL = Shoot length [cm]; FRW = Fresh root weight [g]; FSW = Fresh shoot weight [g]; DRW = Dry root weight [g]; DSW = Dry shoot weight [g]; ELWL = Excised leaf water loss, RLWL = Relative leaf water loss, CMS = Cell membrane stability, Chlr = Chlorophyll content [CCI]; Proline = Proline content [µmol g⁻¹ (FW)]; POD = Peroxidase [U mg⁻¹ protein]; H₂O₂ = Hydrogen peroxide [µmol g⁻¹ (FW)]; CAT = catalase activity [U mg⁻¹ protein].

.

Under control conditions, cluster 5 had maximum average values for the drought-related traits namely root length (10.14 cm), shoot length (15.64 cm), fresh root weight (0.85 g), fresh shoot weight (1.31 g), dry root weight (0.37) dry shoot weight (0.51 g), CMS (56.85%) and chlorophyll content (38.29 chlorophyll concentration index), Relative water content (81.38), catalase (29.23 U mg⁻¹ protein), proline (0.34 µmol g⁻¹ FW), POD (7.85 U mg⁻¹ protein) and H₂O₂ (1.41 µmol g⁻¹ FW) except for Excised leaf water loss % (1.23). On the contrary, cluster number 3 had smaller average values for root length (7.10 cm), shoot length (12.20 cm), fresh root weight (0.31 g), fresh shoot weight (0.87 g), dry root weight (0.09) dry shoot weight (0.21 g), CMS% (32.5) and chlorophyll content (34 chlorophyll concentration index), Relative water content (63.33), catalase (17.06 U mg⁻¹ protein), proline (0.11 µmol g⁻¹ FW), POD (7.09U mg⁻¹ protein) and H₂O₂ (1.24 µmol g⁻¹ FW) except for Excised leaf water loss (1.68). (Table 4 and Table S1). Similarly, biplot analysis showed that positive association between different parameters in control conditions. This analysis found that cluster 5 had genotypes like as, IR-3701(G22), FH-142 (G36), CIM-595 (G39), and Cyto-515 (G50) have a maximum number of drought tolerance contributing traits while cluster 3 had genotypes like KZ-181(G3), FH-114(G12), VH-363(G21) have sensitivity to drought shown in Figure 6.

Under 75% drought stress cluster analysis of K means had combined these 50 genotypes into five clusters. The genotypes in cluster number 3 showed higher average values for root length (10.14 cm), shoot length (15.64 cm), fresh root weight (0.85 g), fresh shoot weight (1.31 g), dry root weight (0.77) dry shoot weight (0.51 g), CMS% (37.04) and chlorophyll content (37.67 chlorophyll concentration index), Relative water content (56.59), catalase (59.82 U mg⁻¹ protein), proline (0.58 µmol g⁻¹ FW), POD (17.78 U mg⁻¹ protein) and H₂O₂ (5.53 µmol g⁻¹ FW) but have minimum excised leaf water loss % (2.31) had maximum drought tolerance and contain accession namely IR-3701(G22), FH-142 (G36), CIM-595 (G39), Cyto-515 (G50) (

Table 5. K-means cluster analysis of 50 cotton genotypes grown under 75% drought level.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
SL	7.81	12	13.21	10.63	11.47
RL	4.46	6.70	7.64	6.58	6.20
FSW	0.30	0.56	0.84	0.52	0.76
FRW	0.11	0.34	0.99	0.48	0.40
DSW	0.05	0.48	0.69	0.68	0.48
DRW	0.20	0.22	0.44	0.43	0.41
Chlr	11.34	37	37.67	37.47	31.79
ELWL	2.38	2.34	2.23	2.25	2.37
RLWL	41.44	54.33	56.59	49.09	51.45
CMS	24.23	37.04	37.22	36.69	29.66
H2O2	5.25	5.43	5.53	5.42	5.33
Proline	0.40	0.41	0.58	0.50	0.57
POD	16.06	17.75	17.78	15.70	16.22
CAT	52.86	58.2	59.82	59.57	58.43

Where RL = Root length [cm]; SL = Shoot length [cm]; FRW = Fresh root weight [g]; FSW = Fresh shoot weight [g]; DRW = Dry root weight [g]; DSW = Dry shoot weight [g]; ELWL = Excised leaf water loss, RLWL = Relative leaf water loss, CMS = Cell membrane stability, Chlr = Chlorophyll content [CCI]; Proline = Proline content [µmol g⁻¹ (FW)]; POD = Peroxidase [U mg⁻¹ protein]; H₂O₂ = Hydrogen peroxide [µmol g⁻¹ (FW)]; CAT = catalase activity [U mg⁻¹ protein].

).

While the cluster 1 showed the low average values for all traits but had maximum excised leaf water loss and contain accession namely KZ-181(G3), FH-114(G12), VH-363(G21) have sensitivity to drought (Table S2 and Figure 7).

Under 50% stress, the accessions in cluster number 5 showed the highest average values for root length (11.28 cm), shoot length (14.40 cm), fresh root weight (1.25 g), fresh shoot weight (1.30 g), dry root weight (0.24) dry shoot weight (0.12 g), CMS% (33.08) and chlorophyll content (37.67 chlorophyll concentration index), Relative water content (56.59), catalase (39.41 U mg⁻¹ protein), proline (0.37 µmol g⁻¹ FW), POD (12.26 U mg⁻¹ protein) and H₂O₂ (0.54 µmol g⁻¹ FW) but have minimum Excised leaf water loss % (3.34) had maximum drought tolerance and contain accession namely IR-3701(G22), FH-142 (G36), CIM-595 (G39) and Cyto-515 (G50) (

Table 6. K-means cluster analysis of 50 cotton genotypes grown under 50% drought level.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
SL	13.27	7.54	13.53	19.66	14.40
RL	8.37	4.93	7.77	7.69	11.28
FSW	0.53	0.47	0.98	1.20	1.25
FRW	0.24	0.24	0.43	0.69	1.30
DSW	0.12	0.09	0.23	0.56	0.78
DRW	0.21	0.19	0.20	0.22	0.24
Chlr	34.85	24.85	30.63	38.6	39.7
ELWL	3.77	3.88	3.75	3.63	3.34
RLWL	34.59	34.15	36.28	35.07	35.67
CMS	31.03	30.80	33.08	22.35	31.52
H2O2	0.53	0.26	0.30	0.44	0.74
Proline	0.32	0.25	0.35	0.28	0.37
POD	11.93	10.81	11.19	11.54	12.65
CAT	32.53	30.56	39.03	30.84	39.41

Where RL = Root length [cm]; SL = Shoot length [cm]; FRW = Fresh root weight [g]; FSW = Fresh shoot weight [g]; DRW = Dry root weight [g]; DSW = Dry shoot weight [g]; ELWL = Excised leaf water loss, RLWL = Relative leaf water loss, CMS% = Cell membrane stability, Chlr = Chlorophyll content [CCI]; Proln = Proline content [µmol g⁻¹ (FW)]; POD = Peroxidase [U mg⁻¹ protein]; H₂O₂ = Hydrogen peroxide [µmol g⁻¹ (FW)]; CAT = catalase activity [U mg⁻¹ protein].

and Table S3).

While the cluster 2 showed the low average values for all traits but had more excised leaf water loss % (3.77) and contain accessions namely KZ-181(G3), FH-114(G12), VH-363(G21) have sensitivity to drought with the lowest value of relative water contents but have more excised leaf water loss under drought stress (Figure 8).

4. Discussion

Average cotton production in Pakistan is low in comparison to all other cotton producing countries. Various factors are responsible for this loss but scarity of water is a major issue among the others. Over the past two years, there has been a fall in cotton production. Therefore, it is essential to develop cotton that can tolerate water stress and for this aim, the presence of genetic variability is essential requirement for beginning a breeding program. [44,45]. A genotype superiority for a particular characteristic prohibits it from being credited with water stress tolerance. Due to the complexity of abiotic factors, genotypes with reduced growth and yield loss should be an indication of being less tolerant to stress. In some Cotton genotypes when 50% and 75% drought stress were applied, showed comparatively less growth, while some genotypes had better results such as accessions IR-3701 (G22), FH-142 (G36), CIM-595 (G39), CYTO-515 (G50) and AA-802 (G30) has more root length, shoot length, fresh root weight, fresh shoot weight [46]. Cell membrane stability, catalase, POD, H₂O_2, and Proline content were high except for the excised leaf water loss because tolerant genotypes have low values of excised leaf water loss, high relative water content, and high cell membrane stability [18,19]. Therefore, it might be applied as a standard for assessment of tolerance to drought stress. The genotype KZ-181 (G3), FH-114 (G12), and VH-363 (G21) have reduced root length, shoot length, fresh root and shoot weight, low relative water, catalase, POD, H₂O_2, and proline content but increased excised leaf water loss under water stress circumstances [47,48]. Azhar et al. utilized the cell membrane leakage and relative cell damage to identify the the drought susceptible and tolerant genotypes. Under environments with limited water availability, cell membrane stability helps to maintain normal plant growth and development [49]. According to the integrity of cell membrane under water deficit conditions, tolerant and susceptible cotton genotypes were characterized. Utilizing morpho-physiological traits to identify and develop the cotton cultivars that are tolerant to drought would increase the validity of the study. Sensitive genotypes were unable to obtain water due to small roots, whereas tolerant genotypes produced extended roots to obtain water from lower surface of soil. Therefore, the extended characters of root may be employed as a selection criterion for cotton drought resistance [46]. Tolerant genotypes absorb suitable solutes as a drought avoidance mechanism to grow longer roots, increase root biomass, retain higher relative water content, and decrease excised leaf water loss. When exposed to water stress, drought-avoiding plants increase their root weight and density. The results showed that relative water contents were reduced in susceptible genotypes namely KZ-181(G3), FH-114(G12), and VH-363 (G21) whereas high relative water contents were retained in tolerant genotypes IR-3701(G22), FH-142 (G36), CIM-595 (G39), Cyto-515 (G50) and AA-802 (G30) under water deficit conditions [47]. However, the relative water content of those that are not affected by drought is usually higher. Several studies have shown the association between relative water content and drought. The stability of cell membranes was positively correlated with relative water content. Therefore, it is possible to predict that genes responsible for maintaining the high relative water content may also contribute to the integrity of cell membranes. In comparison to sensitive genotypes, drought-tolerant genotypes demonstrated less excised leaf water loss. Previous research revealed that the cuticle thickness and period of stomatal closure fluctuate according to the plant species, which causes lesser excised leaf water loss [50]. Therefore, it might be advised that plant selection should be done at various plant growth phases due to the complicated nature of features. According to these findings, genes that support membrane stability and relative water content may also assist in proline accumulation for osmotic adjustment during drought conditions. Plant breeders can utilize excised leaf water loss as just a screening strategy to create resistant cotton breeding stock. However, suitable physiological and morphological traits could be coupled for the development of cotton that can stand in situation of drought stress without compromising of yield. Plants under drought stress experience the production of ROS that causes oxidative damage that progresses to cell death [29]. Plants react by launching both enzymatic and non-enzymatic antioxidant defense systems to remove ROS. Proline is an osmolyte that is produced as the initial reaction to drought stress and is a component of the non-enzymatic antioxidant defense mechanism [51,52]. According to recent studies on water stress, levels of proline and MDA are significantly increased in leaves of Chinese cotton genotypes when exposed to drought stress [53]. Catalase and peroxidase are two enzymatic ROS scavengers, which were also investigated under drought stress conditions. The activity of catalase and peroxidase enzymes was varied between genotypes in their reaction to antioxidants, whereas in the susceptible genotypes, it was either decreased or did not change significantly. Under water deficit conditions tolerant genotypes IR-3701 (G22), FH-142 (G36), CIM-595 (G39), Cyto-515 (G50), and AA-802 (G30) either exhibited increased levels of one or another antioxidant enzyme. The antioxidant enzyme catalase reduces the oxidative damage due to drought stress [54].

5. Conclusions

The new genotyeps of cotton must be introduced to farmers in current scenario of climate change where crises of water is on the top according to the reports of UNO. The genotypes namely, IR-3701(G22), FH-142(G36), CIM-595(G39), and Cyto-515(G50) were found to be drought tolerant. These accessions could be used as tolerant parental material in cotton breeding to combat the drought stress.