Response in Physiological Traits and Antioxidant Capacity of Two Cotton Cultivars under Water Limitations

Abstract

Deficit irrigation water (DW) is one of the main stress factors that negatively affect cotton cultivation. Hence, the identification of cotton cultivars tolerant to DW and sandy soil conditions is particularly needed. Understanding the response of cultivars to DW is essential for estimating water needs. Besides, by understanding the physiological and antioxidant status, reflecting distinct growth, yield, and fiber quality traits under DW, the cultivar tolerant to DW can be identified in the early stage of plant growth. Therefore, two cotton cultivars (Giza 86 and Giza 92, selected for their suitability to the climatic conditions of the study area) were evaluated in this study under two DW regimes (80% or 60% of crop evapotranspiration; ETc) vs. complete irrigation water (CW; 100% of ETc as a control). These regimes amounted to 1228 or 922 vs. 1536 mm season⁻¹, respectively, for field trials conducted during the 2019 and 2020 summer seasons. DW (80% or 60% of ETc) significantly decreased relative water content, membrane stability index, chlorophyll content, plant height, yield components, and fiber quality traits. Otherwise, phenolic compounds, proline contents, as well as antioxidant enzyme activities increased in concomitance with an increase in electrolyte leakage and malondialdehyde content. The harmful effects of the higher DW (60% of ETc) were more pronounced in both cultivars. However, compared to Giza 86, Giza 92 showed higher performance under both CW and DW regimes, accounting for higher values for all studied traits in the blooming stage. The correlation coefficient showed that most of the physiological traits and antioxidants under study were effective criteria in identifying a high-yielding cultivar under DW in the cotton blooming stage and therefore can be used to select the cotton cultivar more suitable for the conditions of the study area. Biplot analysis was used to study the relationship between DW and all evaluated traits, as it was found that the most prominent traits were elongation (%) with Giza 92 + 100% ETc, yellowness degree with Giza 86 + 100% ETc, and SOD with Giza 92 + 60% ETc.

1. Introduction

In arid and semi-arid regions, plants during different stages of growth and up to harvest are exposed to various environmental stresses, including drought [1,2], salinity [3,4,5,6,7], and nutrient deficiency, especially in sandy soils [8,9]. To withstand these adverse conditions, plants must have effective enzymatic and non-enzymatic antioxidant activities and efficient physiological traits. Therefore, in dry areas, it is necessary to screen and select cultivars with a physiological efficiency and antioxidant system that enable them to be productive under stress conditions.

Deficit irrigation water (DW) causes osmotic stress and increases reactive oxygen species (ROS), such as superoxide (O₂^•−), hydrogen peroxide (H₂O₂), and hydroxyl radicals (OH⁻), in plant cell organelles (e.g., chloroplasts, mitochondria, and peroxisome), thus impairing plant growth and productivity [2,10]. The accumulation of ROS leads to the degradation of chlorophyll, destabilization of cellular membranes, disruption of secondary metabolites, and loss of redox homeostasis. It also causes oxidative damage to different components of plant cells, such as lipids, proteins, and DNA [2,10,11].

Water plays an indispensable role in the biochemical and enzymatic reactions, cell expansion, transpiration, and phytonutrient transport. Therefore, DW stress leads to undesirable changes in plant anatomy and morphology, as well as in the processes related to plant physio-biochemistry and productivity [1,11]. Prolonged DW can cause plant cell death due to maximized ROS production, thus inhibiting the scavenging function of the antioxidant system machinery [1,2]. DW adversely affects plant physiological traits, such as relative water content (RWC), membrane stability index (MSI), electrolyte leakage (EL), and chlorophyll content. RWC measures the water status of plant tissues, where an increase in tissue RWC indicates that the plant’s performance is maintained under DW conditions [2]. MSI exhibits a reverse trend of cell EL, and both are physiological indices widely used to evaluate DW tolerance. Cellular MSI is higher in tolerant genotypes than susceptible genotypes under stress conditions. DW stress leads to increased EL in plant leaves and decreased chlorophyll content. These undesirable outcomes lead to a decrease in photosynthesis, which leads to reduced crop growth and productivity [2,12].

Plants adapt, develop, and upregulate adaptation mechanisms to attenuate oxidative damage. Among the adaptation mechanisms are ROS-scavenger enzymatic and non-enzymatic antioxidants. The enzymatic antioxidants include ascorbate peroxidase (APX), catalase (CAT), peroxidase (GR), and superoxide dismutase. Besides, the non-enzymatic antioxidants include phenolic compounds, proline, glutathione, and ascorbic acid. Another essential adaptation mechanism, the accumulation of osmotic compounds (soluble sugars, proline, etc.), contributes to cell turgor maintenance through osmotic modulation [1,2,10,11]. For physiological and biochemical processes to occur successfully and antioxidants to function sufficiently, plant tissues need a water content of 70–90% of their fresh weight [1,2,10]. This water content reaches by increasing the plant’s osmotic compounds in DW-tolerant cultivars.

For cotton, growth, yield, physiology, and metabolism are greatly affected under DW conditions [13,14,15]. During the sensitive growth stages (blooming, flowering, and fruit-setting), DW can adversely affect growth, yield components, and fiber quality properties, including fiber length, uniformity index, fiber strength, and micronaire readings, which measure fineness and maturity [13,16,17,18,19]. However, DW-tolerant cotton cultivars tend to optimize their water requirements to reach a balance between vegetative growth and reproductive growth [19,20,21] through increasing their contents of osmotic compounds and antioxidant capacity. Relative to the DW stress-susceptible cotton cultivars, the DW stress-tolerant cultivars show increased antioxidant activities, which combat increased levels of ROS [18,22,23,24]. The selection of cotton cultivars that confer higher yields in dry environments has an essential objective in cotton plant production and breeding.

Among the agricultural lands, sandy soils suffer from rapid loss of nutrients and water. These adverse conditions of sandy soils lead to nutrient deficiency and DW stresses [8,9]. However, drip irrigation for 4 h every three days confers the highest average growth, yield, yield component, and fiber properties under these adverse conditions [18,23,24]. Given this, DW-tolerant cotton cultivars should be selected for cultivation under these adverse conditions. In the study area that hosted this research, only two cultivars of cotton (Giza 86 and Giza 92) are intended for cultivation due to their suitability to the prevailing climatic conditions and stress factors.

Therefore, the main objective of the present study was to evaluate the physiological traits and antioxidants in two cotton cultivars (Giza 86 and Giza 92) to identify the high-performing, productive, and adaptable cultivar under DW (80% and 60% of crop evapotranspiration) on sandy soil conditions.

2. Materials and Methods

2.1. Plant Material and Experimental Site

Two experiments were conducted on 15 April 2019 and 2020, on sandy soil in a private farm in Shebin El-Kanater (30°20′53.7″ N, 31°17′41.5″ E), Qalyubia Governorate, Egypt. The geographic features of the experimental site are in Figure S1. In this studied area, only two cultivars (Giza 86 and Giza 92) of cotton (Gossypium barbadense L.) were used for cultivation. Therefore, sterilized, pure seeds of these two cultivars were provided by the Cotton Research Institute, Agriculture Research Center, Egypt, for the two trials.

The main initial physicochemical characteristics of the soil [25,26] are in

Table 1. Average physicochemical soil parameters of the experimental site in both 2019 and 2020 seasons.

Soil Depth (cm)	Sand (%)		Silt (%)		Clay (%)		Soil Texture
0–30	88.2		10.3		1.5		Sandy
30–60	91.4		8.3		0.3		Sandy
Soil depth (cm)	pH		O.M		EC (dS m−1)		CaCO3
0–30	7.41		0.43		0.48		0.28
30–60	7.40		0.41		0.35		0.25
Soil depth (cm)	Soluble cations (meq L−1)					Soluble anions (meq L−1)
	Ca2+	Mg2+	K+	Na+		HCO3−	SO42−	Cl−
0–30	1.83	1.02	0.38	0.80		2.60	0.17	0.93
30–60	1.91	0.80	0.21	0.86		2.90	0.19	0.96

. The climatic data of the area of study were obtained from the Meteorological Station of Shebin El-Kanater, Qalyubia Governorate, Egypt, and they are presented in Table S1.

2.2. Irrigation Water Treatments, Experimental Design, and Sampling for Measurements

Three different irrigation water treatments (100%, 80%, and 60% of the crop evapotranspiration; ETc) were applied, i.e., 1536, 1228, and 922 mm season⁻¹, respectively, and computed according to the Penman–Montieth method [27], which gives more consistently accurate ETo estimates than other methods of ETo. A drip irrigation system was applied. The drip lateral had emitters spaced 30 cm apart with a nominal discharge of 4 L h⁻¹. Different irrigation water treatments were applied using an electric timer with appropriate run times.

The experimental treatments were arranged in a split-plot design. The distribution of treatments is described in Figure S2. Therefore, the total was 480 plants per cultivar in each treatment. Other agricultural practices were applied as recommended by Cotton Research Institute, Agriculture Research Center, Egypt.

Fifty plants were randomly taken at the blooming stage (60 days after sowing) from each replicate of each irrigation water treatment to determine physiological attributes and antioxidants. Assessments of growth, yield, and fiber quality were performed using the remaining plants (400 per cultivar per treatment) in samples taken after 165 days of planting.

2.3. Physiological Traits and Antioxidant Capacity

Relative water content (RWC, %) was determined in 60-day-old plant leafy samples [28]. Electrolyte leakage (EL, %) and membrane stability index (MSI, %) were measured in 60-day-old leaf samples as detailed in Rady [29]. The content of the total chlorophyll (mg g⁻¹ leaf fresh mass; FW) was determined as reported in [30].

Lipid peroxidation (nmol g⁻¹ FW), phenolics, and proline contents (mg g⁻¹ FW) were evaluated in 60-day-old plant leaf samples based on the procedures in [31,32,33], respectively. The activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) were assayed in similar leaf samples in units mg⁻¹ protein following the procedures of [34,35,36,37], respectively. SOD activity was noticed by a photochemical reduction inhibition of nitro-blue tetrazolium (NBT) in a solution that contained 50 μM NBT, 1.3 μM riboflavin, 13 mM methionine, 75 nM EDTA, 50 mM P-buffer (pH 7.8), and 50 μL enzyme extract (EE). One unit of SOD was defined as an enzyme amount required to cause 50% NBT reduction inhibition at 560 nm. POD activity was assayed in a mixture (3 mL) that contained 0.1 mL EE, 50 mM P-buffer (pH 7.0), 20 mM guaiacol, and 40 mM H₂O₂. One unit of activity was defined as an absorbance change of 0.01 unit min⁻¹ at 470 nm. CAT activity was assayed in a solution (3 mL) contained 50 mM P-buffer (pH 7.8), 0.1 mL EE, and 5.9 mM H₂O₂ to initiate the reaction. The activity was noted as the decrease in absorbance at 240 nm. APX activity was assayed in a mixture (1600 µL) that contained 50 mM PK-buffer (pH 7.0), 0.5 mM ascorbic acid, 0.1 mM H₂O₂, and 400 µL EE. The absorbance of the mixture was read at 290 nm, and the enzyme activity was expressed in units mg⁻¹ protein.

2.4. Growth, Yield, and Fiber Quality Traits

Plant height, number of fruiting branches, number of total bolls, number of open bolls, open bolls (%), average boll weight (g), seed cotton yield (kg feddan⁻¹) (feddan = 4200 m² = 0.42 hectare), lint yield (kg feddan⁻¹), lint (%), and seed index were determined.

Fiber quality was evaluated at the Cotton Technology Lab., Cotton Research Institute, Agriculture Research Center, Egypt. Fiber length (expressed as upper half mean length in mm) and uniformity index were measured utilizing a digital fibrograph [38]. Fiber strength (expressed in g tex⁻¹) was measured using a stelometer at 1/8 inch gauge length [39]. Micronaire value (Mic.), which measures fineness and maturity, was measured by a micronaire instrument [40]. Besides, elongation (%), yellowness degree, and reflection (%) were evaluated.

2.5. Statistical Analysis

Statistical analysis was performed using tow-way ANOVA through the GLM procedure of Gen STAT (version 11) (VSN International Ltd., Oxford, UK) after testing for homogeneity of error variances [41]. A total of 50 plants were used for physiological traits and antioxidants, while 400 plants were used to measure yield components and fiber quality. Differences between means were tested using the least significant difference (LSD) test [42] at the 1% and 5% probability levels (p ≤ 0.01 and p ≤ 0.05). Correlation analysis was also performed [43]. Principal component analysis (PCA) was performed using R statistical software version 3.6.1. on the averages of the assessed physiological traits, different antioxidants, yield components, and fiber quality traits to determine the relationships among them.

3. Results

Since all tested data obtained from the 2019 season matched the corresponding data from the 2020 season, the average of the two seasons has been treated. The two tested cotton cultivars (Giza 86 and 92), grown under stress conditions due to deficit irrigation water (DW; 80 or 60% vs. 100% ETc as a control) and sandy soil, were subjected to testing for physiological traits, non-enzymatic and enzymatic antioxidants, yield components, and fiber quality.

3.1. Physiological Traits

Regarding the cultivars, Giza 92 significantly outperformed Giza 86 in physiological traits (i.e., RWC by 3.54%, MSI by 6.92%, and Chls by 7.22%), while EL was lower in Giza 92 by 8.19% than Giza 86 (

Table 2. Physiological traits of cotton (cv. Giza 86 and 92) under different water regimes (WRs) on sandy soil. All values are the average of the 2019 and 2020 seasons.

Source of Variation	RWC	MSI	EL	Chls (mg g−1 FW)
	(%)
Cultivar (Cv)	*	*	*	*
Giza 86	48.0 b ± 0.92	31.8 b ± 0.71	34.2 a ± 0.91	0.97 b ± 0.040
Giza 92	49.7 a ± 1.05	34.0 a ± 0.92	31.4 b ± 0.81	1.04 a ± 0.048
WRs	**	*	**	*
100% ETc	69.3 a ± 1.40	49.6 a ± 1.16	11.6 c ± 0.47	1.35 a ± 0.061
80% ETc	45.4 b ± 0.93	32.5 b ± 0.77	33.8 b ± 0.86	0.95 b ± 0.042
60% ETc	31.8 c ± 0.63	16.6 c ± 0.56	53.0 a ± 1.26	0.72 c ± 0.030
Cv × WRs	*	*	*	*
G 86 × 100% ETc	68.0 b ± 1.32	48.4 b ± 1.01	11.9 e ± 0.42	1.31 a ± 0.051
G 86 × 80% ETc	44.9 c ± 0.83	31.4 c ± 0.72	35.3 c ± 0.90	0.92 c ± 0.041
G 86 × 60% ETc	31.0 d ± 0.62	15.5 e ± 0.50	55.3 a ± 1.42	0.68 e ± 0.029
G 92 × 100% ETc	70.5 a ± 1.48	50.7 a ± 1.32	11.3 e ± 0.52	1.39 a ± 0.071
G 92 × 80% ETc	45.9 c ± 1.03	33.5 c ± 0.82	32.3 d ± 0.82	0.98 b ± 0.043
G 92 × 60% ETc	32.6 d ± 0.64	17.7 d ± 0.62	50.6 b ± 1.10	0.75 d ± 0.031

Values are means (±SE). * and ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01 probability levels, respectively. Means followed by different lowercase letters in each column are significantly different according to the LSD test (p ≤ 0.05). ETc = crop evapotranspiration, RWC = relative water content, MSI = membrane stability index, EL = electrolyte leakage, and Chls = total chlorophylls.

).

Concerning the DW, Table 2 shows that RWC, MSI, and Chls decreased significantly gradually, while EL showed a significant progressive increase, with the gradual decrease in the water regime from 100% to 60% ETc. The reductions in RWC, MSI, and Chls were 34.5% and 54.1%, 34.5% and 66.5%, and 29.6% and 46.7% under both 80% and 60% ETc, respectively. On the other hand, EL increased by 191.4% and 356.9% under both 80% and 60% ETc, respectively.

For the combinations, Giza 92 × 100% ETc was the best combined treatment that recorded the highest values for RWC, MSI, and Chls and the lowest values for EL. Giza 86 × 100% ETc was the combined treatment that ranked second, followed by Giza 92 × 80% ETc (Table 2).

3.2. Antioxidant Capacity

For the cultivars, Giza 92 significantly exceeded Giza 86 in the contents of non-enzymatic antioxidants and enzymatic antioxidant activities (i.e., phenolic content by 6.92%, proline content by 2.94%, POD activity by 7.05%, and APX activity by 5.71%), while MDA was lower in Giza 92 by 8.66% than Giza 86. On the other hand, SOD and CAT activities did not register a significant difference in both cultivars (

Table 3. Non-enzymatic antioxidant contents and enzymatic antioxidant activities, as well as lipid peroxidation level (determined as malondialdehyde; MDA) of cotton (cv. Giza 86 and 92) under different water regimes (WRs) on sandy soil. All values are the average of the 2019 and 2020 seasons.

Source of Variation	Phenolics	Proline	MDA (nmol g−1 FW)	SOD	CAT	POD	APX
	(mg g−1 FW)			(Unit mg−1 Protein)
Cultivar (Cv)	*	*	*	ns	ns	*	*
Giza 86	13.0 b ± 0.17	0.34 b ± 0.014	56.6 a ± 1.10	266 ± 5.4	1013 ± 14.9	893 b ± 12.2	105 b ± 2.1
Giza 92	13.9 a ± 0.15	0.35 a ± 0.015	51.7 b ± 0.87	264 ± 5.4	1037 ± 15.9	956 a ± 13.1	111 a ± 2.2
WRs	*	*	**	**	*	*	**
100% ETc	8.3 c ± 0.12	0.28 c ± 0.011	35.6 c ± 0.74	145 c ± 3.2	605 c ± 10.4	518 c ± 8.6	65 c ± 1.3
80% ETc	13.4 b ± 0.15	0.34 b ± 0.012	53.6 b ± 0.90	239 b ± 5.0	1062 b ± 15.9	979 b ± 12.7	106 b ± 2.2
60% ETc	18.7 a ± 0.21	0.42 a ± 0.021	73.3 a ± 1.31	412 a ± 8.1	1409 a ± 20.1	1277 a ± 16.7	154 a ± 3.0
Cv × WRs	*	*	*	*	*	*	*
G 86 × 100% ETc	7.9 d ± 0.13	0.27 c ± 0.011	36.9 e ± 0.90	143 c ± 3.1	603 d ± 10.2	505 e ± 8.3	63 c ± 1.2
G 86 × 80% ETc	13.1 c ± 0.17	0.34 b ± 0.012	56.2 c ± 0.89	237 b ± 5.2	1032 c ± 15.4	945 d ± 12.1	103 b ± 2.1
G 86 × 60% ETc	18.1 b ± 0.21	0.41 a ± 0.020	76.8 a ± 1.51	419 a ± 8.0	1404 a ± 19.2	1229 b ± 16.2	150 a ± 3.0
G 92 × 100% ETc	8.7 d ± 0.11	0.28 c ± 0.011	34.3 e ± 0.58	146 c ± 3.2	606 d ± 10.5	531 e ± 8.9	66 c ± 1.4
G 92 × 80% ETc	13.7 c ± 0.13	0.34 b ± 0.012	51.0 d ± 0.91	240 b ± 4.8	1091 b ± 16.3	1012 c ± 13.3	109 b ± 2.2
G 92 ×60% ETc	19.3 a ± 0.21	0.42 a ± 0.022	69.8 b ± 1.11	405 a ± 8.2	1414 a ± 21.0	1325 a ± 17.2	158 a ± 3.0

Values are means (±SE). * and ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01 probability levels, respectively, and ns indicates non-significant differences. Means followed by different lowercase letters in each column are significantly different according to the LSD test (p ≤ 0.05). ETc = crop evapotranspiration, SOD = superoxide dismutase, CAT = catalase, POD = peroxidase, and APX = ascorbate peroxidase.

).

Regarding the DW, Table 3 showed that the contents of phenolics, proline, and MDA, as well as the activities of SOD, CAT, POD, and APX increased significantly progressively with the gradual decrease in the water regime from 100 to 60% ETc. The increases were 62.0% and 126.1%, 21.4% and 50.0%, 50.6% and 105.9%, 64.8% and 184.1%, 75.5% and 132.9%, 89.0% and 146.5%, and 63.1% and 136.9% under both 80% and 60% ETc, respectively.

Concerning the combinations, Giza 92 × 60% ETc was the best combined treatment that recorded the highest values for phenolics and proline contents, as well as SOD, CAT, POD, and APX activities and the lowest values for MDA content. Giza 86 × 60% ETc was the combined treatment that ranked second, followed by followed by Giza 92 × 80% ETc (Table 3).

3.3. Growth, and Yield Attributes

Concerning the cultivars, Giza 92 significantly outperformed Giza 86 in growth and yield attributes (i.e., plant height by 1.56%, No. of fruiting branches by 3.94%, No. of toal bolls by 3.61%, No. of open bolls by 7.41%, open bolls percentage by 3.10%, average boll weight by 4.66%, seed cotton yield by 3.55%, lint cotton yield by 4.27%, and seed index by 2.30%). On the other hand, lint percentage did not register a significant difference between both cultivars (

Table 4. Growth, yield, and yield attributes of cotton (cv. Giza 86 and 92) under different water regimes (WRs) on sandy soil. All values are the average of the 2019 and 2020 seasons.

Source of Variation	Plant Height (cm)	No. of Fruiting Branches	No. of Total Bolls	No. of Open Bolls	Open Bolls (%)	Average Boll Weight (g)	Seed Cotton Yield	Lint Cotton Yield	Lint (%)	Seed Index
							(Kantar fad−1)
Cultivar (Cv)	*	*	*	*	*	*	*	*	ns	*
Giza 86	128 b ± 10.2	12.7 b ± 1.01	36.0 b ± 2.86	18.9 b ± 1.699	51.6 b ± 4.1	2.36 b ± 0.15	8.7 b ± 0.70	3.51 b ± 0.20	41.7 a ± 3.22	9.1 b ± 0.70
Giza 92	130 a ± 11.1	13.2 a ± 1.10	37.3 a ± 3.13	20.3 a ± 1.72	53.2 a ± 4.6	2.47 a ± 0.19	9.0 a ± 0.71	3.66 a ± 0.20	41.9 a ± 3.30	9.3 a ± 0.75
WRs	*	*	*	**	*	*	*	*	ns	*
100% ETc	137 a ± 11.6	15.7 a ± 1.3	47.8 a ± 3.58	27.0 a ± 2.36	56.4 b ± 4.6	2.95 a ± 0.23	11.7 a ± 0.87	4.71 a ± 0.26	41.7 a ± 3.10	11.5 a ± 0.85
80% ETc	133 a ± 10.8	13.2 b ± 1.0	39.4 b ± 3.23	22.7 b ± 1.97	60.7 a ± 5.3	2.38 b ± 0.19	9.6 b ± 0.76	3.87 b ± 0.21	41.5 a ± 3.17	9.4 b ± 0.79
60% ETc	118 b ± 9.6	10.1 c ± 0.9	22.9 c ± 2.17	9.2 c ± 0.78	40.1 c ± 3.3	1.93 c ± 0.11	5.3 c ± 0.50	2.19 c ± 0.15	42.3 a ± 3.51	6.8 c ± 0.54
Cv × WRs	*	*	*	*	*	*	*	*	*	*
G 86 × 100% ETc	137 a ± 11.1	15.4 a ± 1.21	46.7 b ± 3.12	25.9 b ± 2.31	55.5 c ± 4.2	2.86 b ± 0.21	11.4 b ± 0.91	4.59 b ± 0.25	42.0 a ± 3.12	11.3 a ± 0.80
G 86 × 80% ETc	132 b ± 10.3	12.7 c ± 1.01	38.7 d ± 3.04	21.9 d ± 2.02	59.5 b ± 5.1	2.33 c ± 0.17	9.7 c ± 0.71	3.82 c ± 0.21	40.8 a ± 3.02	9.4 b ± 0.75
G 86 × 60% ETc	115 c ± 9.2	10.1 d ± 0.82	22.6 e ± 2.42	9.0 e ± 0.73	39.8 d ± 3.1	1.89 d ± 0.08	5.1 d ± 0.48	2.12 d ± 0.15	42.3 a ± 3.52	6.7 c ± 0.55
G 92 × 100% ETc	137 a ± 12.0	15.9 a ± 1.31	48.9 a ± 4.04	28.0 a ± 2.41	57.3 c ± 5.0	3.01 a ± 0.24	12.0 a ± 0.82	4.82 a ± 0.27	41.3 a ± 3.08	11.7 a ± 0.90
G 92 × 80% ETc	133 b ± 11.3	13.7 b ± 1.04	40.0 c ± 3.42	23.5 c ± 1.92	61.8 a ± 5.4	2.42 c ± 0.21	9.6 c ± 0.80	3.91 c ± 0.20	42.1 a ± 3.32	9.5 b ± 0.83
G 92 × 60% ETc	120 c ± 10.0	10.1 d ± 0.95	23.2 e ± 1.92	9.4 e ± 0.83	40.4 d ± 3.4	1.97 d ± 0.13	5.5 d ± 0.51	2.26 d ± 0.14	42.3 a ± 3.50	6.8 c ± 0.52

Values are means (±SE). * and ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01 probability levels, respectively, and ns indicates non-significant differences. Means followed by different lowercase letters in each column are significantly different according to the LSD test (p ≤ 0.05). ETc = crop evapotranspiration and fad (feddan) = 4200 m².

).

For the DW, Table 4 showed that plant height, No. of fruiting branches, No. of toal bolls, No. of open bolls, average boll weight, seed cotton yield, lint cotton yield, and seed index decreased significantly progressively with the gradual decrease in the water regime from 100% to 60% ETc. On the other hand, open bolls percentage increased by 7.62% and 28.9% under both 80% and 60% ETc, respectively, while lint percentage did not register a significant difference in both cultivars.

Regarding the combinations, in general, Giza 92 × 100% ETc was the best combined treatment that recorded the highest values for growth and yield attributes. In general, Giza 86 × 100% ETc was the combined treatment that ranked second, followed by Giza 92 × 80% ETc (Table 4).

3.4. Fiber Quality Properties

For the cultivars, Giza 92 significantly exceeded Giza 86 in fiber length (UHM) (by 1.23%), uniformity ratio (by 2.89%), fiber strength (by 1.82%), yellowness degree (by 7.36%), and reflection (by 2.56%). On the other hand, micronaire value, maturity ratio, and elongation percentage did not register significant differences between both cultivars (

Table 5. Fiber quality properties of cotton (cv. Giza 86 and 92) under different water regimes (WRs) on sandy soil. All values are the average of the 2019 and 2020 seasons.

Source of Variation	Micronaire Value	Maturity Ratio	Fiber Length (mm)	Uniformity Ratio (%)	Elongation (%)	Fiber Strength (g tex−1)	Yellowness Degree	Reflection (%)
Cultivar (Cv)	ns	ns	*	*	ns	*	*	*
Giza 86	4.13 a ± 0.070	93.3 a ± 0.345	32.4 b ± 0.510	83.1 b ± 0.31	7.55 a ± 0.040	38.4 b ± 0.38	10.5 a ± 0.23	74.2 b ± 1.11
Giza 92	4.25 a ± 0.100	93.9 a ± 0.432	32.8 a ± 0.571	85.5 a ± 0.41	7.54 a ± 0.051	39.1 a ± 0.41	9.8 b ± 0.24	76.1 a ± 1.45
WRs	*	ns	*	*	*	*	*	*
100% ETc	4.38 a ± 0.096	92.4 a ± 0.358	31.0 b ± 0.523	86.7 a ± 0.38	8.25 a ± 0.056	36.7 b ± 0.31	12.4 a ± 0.30	68.1 b ± 1.08
80% ETc	4.14 b ± 0.080	94.3 a ± 0.396	33.6 a ± 0.552	83.9 b ± 0.36	7.28 b ± 0.044	39.9 a ± 0.45	8.8 c ± 0.19	79.2 a ± 1.41
60% ETc	4.05 b ± 0.079	94.2 a ± 0.412	33.2 a ± 0.547	82.4 b ± 0.36	7.12 b ± 0.038	39.6 a ± 0.42	9.2 b ± 0.22	78.3 a ± 1.36
Cv × WRs	*	ns	*	*	*	*	*	*
G 86 × 100% ETc	4.35 a ± 0.081	92.4 a ± 0.314	30.9 b ± 0.484	85.7 b ± 0.34	8.18 a ± 0.051	36.5 b ± 0.29	12.4 a ± 0.29	67.9 d ± 0.92
G 86 × 80% ETc	4.07 b ± 0.065	93.8 a ± 0.371	33.5 a ± 0.523	82.5 cd ± 0.30	7.33 b ± 0.036	39.5 a ± 0.44	9.2 c ± 0.18	77.9 bc ± 1.20
G 86 × 60% ETc	3.97 c ± 0.064	93.8 a ± 0.351	32.7 a ± 0.523	81.1 d ± 0.30	7.15 c ± 0.033	39.1 a ± 0.40	9.9 b ± 0.21	76.9 c ± 1.21
G 92 × 100% ETc	4.41 a ± 0.111	92.3 a ± 0.402	31.1 b ± 0.562	87.7 a ± 0.42	8.31 a ± 0.060	36.8 b ± 0.32	12.4 a ± 0.31	68.2 d ± 1.23
G 92 × 80% ETc	4.21 b ± 0.095	94.8 a ± 0.421	33.7 a ± 0.580	85.2 b ± 0.41	7.23 b ± 0.051	40.3 a ± 0.46	8.4 d ± 0.19	80.5 a ± 1.62
G 92 × 60% ETc	4.13 c ± 0.094	94.6 a ± 0.473	33.7 a ± 0.571	83.6 c ± 0.41	7.08 c ± 0.042	40.1 a ± 0.44	8.5 d ± 0.22	79.7 ab ± 1.51

Values are means (±SE). * indicates significant differences at p ≤ 0.05 probability level, and ns indicates non-significant differences. Means followed by different lowercase letters in each column are significantly different according to the LSD test (p ≤ 0.05). ETc = crop evapotranspiration.

).

Regarding the DW, Table 5 showed that micronaire value, uniformity ratio, elongation percentage, and yellowness degree decreased significantly progressively with the gradual decrease in the water regime from 100% to 60% ETc. The decreases were 5.48% and 7.53%, 3.23% and 4.96%, 11.8% and 13.7%, and 28.8% and 25.5% under both 80% and 60% ETc, respectively. On the other hand, fiber length, fiber strength, and reflection percentage increased by 8.39% and 7.10%, 8.72% and 7.90%, and 16.3% and 15.0% under both 80% and 60% ETc, respectively, while maturity ratio did not register a significant difference between both cultivars.

Concerning the combinations, in general, Giza 92 × 100% ETc was the best combined treatment that recorded the highest values for fiber quality properties. In general, Giza 86 × 100% ETc was the combined treatment that ranked second, followed by Giza 92 × 80% ETc (Table 5).

3.5. Correlation Study among Seed Cotton Yield, Physiological, and Antioxidant Capacity

To identify the most desirable physiological attributes and antioxidant capacity as screening criteria, indicating drought tolerance, a correlation analysis among seed cotton yield, physiological traits, and antioxidant capacity was conducted (

Table 6. Correlation coeffecients and p-values among seed cotton yield, physiological and antioxidant traits under different water regimes (WRs) on sandy soil conditions.

Traits	SCY	EL	MSI	Chls	Phenols	MDA	ProC	SOD	CAT	POD
EL	−0.955 **
MSI	0.966 **	−0.991 **
TChC	0.930 **	−0.976 **	0.980 **
PhC	−0.872 **	0.901 **	−0.898 **	−0.895 **
MDA	−0.875 **	0.895 **	−0.895 **	−0.892 **	0.993 **
ProC	−0.842 **	0.870 **	−0.861 **	−0.859 **	0.982 **	0.983 **
SOD	−0.906 **	0.897 **	−0.901 **	−0.885 **	0.964 **	0.969 **	0.957 **
CAT	−0.853 **	0.895 **	−0.894 **	−0.904 **	0.968 **	0.969 **	0.958 **	0.936 **
POD	−0.858 **	0.905 **	−0.907 **	−0.918 **	0.960 **	0.962 **	0.956 **	0.930 **	0.984 **
APX	−0.885 **	0.903 **	−0.907 **	−0.903 **	0.973 **	0.976 **	0.975 **	0.960 **	0.969 **	0.982 **

** denotes significance at 0.01 level of probability. SCY, EL, MSI, Chls, Phenols, MDA, ProC, SOD, CAT, POD, and APX mean seed cotton yield, electrolyte leakage (%), membrane stability index (%), total chlorophyll content, phenolic content, lipid peroxidation, proline content, superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase, respectively.

).

Under different water regimes (80% and 60% ETc vs. 100% ETc as a control), correlation analyses revealed that the yield showed a positive correlation with membrane stability index and total chlorophyll. Contrariwise, it showed a negative correlation with electrolyte leakage, phenolic content, lipid peroxidation, proline content, superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase.

3.6. Princial Components Analysis

Biplot analysis was used to study the relationship between DW treatments and the evaluated traits (Figure 1). The tested DW treatments and cotton cultivars (Giza 92 and Giza 86) were divided into three groups. The first group included 100% ETc and the two cultivars, the second group contained 60% ETc and the two cultivars, while the third group contained 80% ETc and the two cultivars. The first group was more influential with respect to micronaire value, Chls, RWS, elongation (%), and yellowness degree. The most prominent trait was the elongation (%) with Giza 92 + 100% ETc and the yellowness degree with Giza 86 + 100% ETc. The second group was more influential with respect to phenolics, EL, APX, proline, MDA, SOD, and lint (%). The most prominent trait was SOD with Giza 92 + 60% ETc. The third group was more influential with respect to fiber length, fiber strength, reflection (%), maturity ratio, POD, and CAT. However, the treatments did not significantly affect open bolls, plant height, No. of open bolls, lint cotton yield, seed cotton yield, No. of total bolls, No. of fruiting branches, seed index, uniformity ratio, MSI, and boll weight.

4. Discussion

In arid and semi-arid regions, including Egypt, crop productivity is threatened because these regions have a hot and dry climate with a high risk of exposure to freshwater shortage. Besides, the agricultural lands are poor in terms of fertility and productivity [9,44]. This problem may be exacerbated with cultivation on sandy soils because they cannot retain water and nutrients, as well as lack of fertility. Therefore, identifying cultivars that are resistant and adapted to arid and semi-arid environmental conditions should be an important priority for producers of crop cultivars to avoid loss of yield and quality as much as possible [45].

The study area is located in a semi-arid environment and is characterized by low soil fertility (Table 1) and limited irrigation water. In this region, two cotton cultivars; Giza 92 and Giza 86, are the most commonly used. Therefore, under DW and sandy soil conditions, Giza 92 and Giza 86 were tested for physiological attributes and antioxidant capacity that reflect cultivar yield and fiber quality. Giza 92 cultivar outperformed Giza 86 in enduring stress conditions of DW on sandy soil. It displayed obvious superiority in most physiological attributes and antioxidants, which reflected the best yield components and fiber quality (Table 2, Table 3, Table 4, Table 5 and Table 6).

Like chlorophyll as a physiological indicator, RWC, MSI, and EL are related to biological membranes in plant cells, and are thus considered as physiological indicators (Table 2) to identify the cotton cultivar most suitable for sandy soil conditions and DW in the study area (Table 1 and Table S1, Figure S1). Biological membranes are the first target of many abiotic stresses. The maintenance of the integrity and stability of cell membranes under DW stress is a major component of drought tolerance in plants [46]. MSI is reciprocal to cell EL, and both are physiological indices widely used for evaluating drought tolerance [47]. Compared to Giza 86, Giza 92 had a noticeable lower EL and a marked higher MSI under different DW regimes (Table 2), which are utilized as indicators to evaluate cell membrane integrity and permeability, resulting in leakage of intracellular contents. There is considerable genotypic variation for EL and MSI between both cultivars, measured as the percentage of stressed-leaf tissue injury of cotton genotypes, so they can be used to screen for the stress conditions of DW and sandy soil. A similar trend has been recorded for our data previously [2,12,18,46,48,49]. Under DW stress conditions, high RWC is required to maintain water balance in plant tissues. So, lower leaf water loss and higher leaf water content can be a selection criterion to breed plants against DW stress [12]. Sairam and Saxena [50] and Sanchez-Blanco et al. [51] reported that increasing the duration and severity of DW stress decreases the plant RWC. Besides, Abdelkader et al. [18] and Parida et al. [52] observed a significant difference in RWC between cotton cultivars grown under normal and DW treatments. The drought-tolerant cultivars collect a higher leaf RWC than drought-susceptible cultivars [18,52]. In line with these results, this study displayed that Giza 96 cultivar had a higher leaf RWC than the Giza 86 (Table 2). This cultivar also showed a higher leaf chlorophyll content than Giza 86. A similar trend has been previously reported by [18,53]. Chlorophyll content plays a vital role in photosynthesis, which ultimately increases crop growth and productivity [2]. Since DW stress is one of the adverse factors affecting chlorophyll content [54,55], these parameters can be used to select a suitable cultivar for stress conditions in DW and sandy soils. Since the results of previous reports are in close line with those of our report, the tested physiological attributes (e.g., RWC, MSI, EL, and chlorophyll content; Table 2) can be successfully applied to screen the most suitable cultivar with the greatest tolerance to stress conditions of DW and sandy soil. This finding is because the physiological qualities of the plant are known to reflect in its high antioxidant capacity and productivity (Table 2, Table 3, Table 4, Table 5 and Table 6).

Enzymatic and non-enzymatic antioxidant (e.g., phenolics, free proline, and malondialdehyde (lipid peroxidation)) levels, as well as superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) were used to select the cotton cultivar most suitable for the conditions of the study area (Table 1 and Table S1, Figure S1). All these antioxidants (Table 3) exhibited significant differences between both cultivars (Giza 92 and Giza 86) tested under the stress conditions of DW and sandy soil. Compared to Giza 86, Giza 92 recorded higher enzymatic and non-enzymatic antioxidant capacity, except for a slight fluctuation, under different DW regimes. Moreover, these antioxidants recorded higher activity under 60% ETc compared to 80% ETc, which in turn led to a noticeable increase in the capacity of different antioxidants examined compared to complete irrigation water (100% ETc).

The maintenance of membrane integrity and function accompanied by minimized lipid peroxidation under stress are indicators of drought tolerance in plants. Lipid peroxidation was measured as malondialdehyde (MDA) content. It increased with the accumulation of reactive oxygen species (ROS; H₂O₂ and O₂^•−), which directly attack membrane lipids and destroy cell metabolism due to oxidative stress under stress conditions [56,57]. Lower MDA indicates higher antioxidant capacity and higher resistance to drought stress. In addition, a low level of MDA is a feature of drought tolerance in cotton plants [57]. Thus, the MDA level could be a breeding criterion for optimizing cotton genotype under drought stress.

Phenolic compounds are potent scavengers that can minimize oxidative stress. Thus, a drought-tolerant genotype keeps phenolic contents higher than sensitive ones [57,58]. Phenolic compounds act as signaling molecules and protect plants from environmental stress-induced ROS generation and thus stimulate stress tolerance. The accumulation of these compounds confers the plant greater tolerance to drought stress by stimulating various mechanisms. Thereafter, plants have the ability to enhance phenol biosynthesis under stress conditions. Phenolic compounds are able to reduce cell membrane peroxidation (MDA) by detoxifying ROS and attenuating oxidative stress [59,60]. Besides, proline is the principal solute that may allow plants to overcome the effects of drought through osmotic adjustment. It also serves as nitrogen and carbon storage forms for future use under stress conditions [61,62]. The increase in proline content is an adaptive mechanism for plants to withstand drought stress [2,18,63]. Therefore, more proline accumulation in drought-stressed tissues was observed in drought-resistant genotypes/cultivars and maintained this higher accumulated proline even after stress alleviation [18,52]. Increased plant enzymatic defenses against stress are associated with elevated enzymatic activities, which are catalyzed by synthesizing more levels of these enzymes to minimize cellular damage caused by H₂O₂ and O₂^•− [2]. Therefore, with increased enzymatic potency/activity and non-enzymatic antioxidant levels, including proline and phenolic compounds, plants can withstand stress efficiently in Giza 92 compared to Giza 86 in this study (Table 3). Similar results are reported in [18,64,65]. In these reports, plant tolerance to drought stress is due to increased antioxidant activities that counteract increased levels of ROS, while the drought-sensitive genotypes exhibit decreased enzymatic antioxidant activities. In confirmation of these results, Daud et al. [22] and Sairam and Saxena [49] determined that there is a relationship between antioxidant activities and tolerance in cotton genotypes, as they found that the activities of antioxidant enzymes under drought stress are higher in tolerant genotypes compared to sensitive genotypes [22,49].

As shown in Table 4, cotton plant height was adversely affected by DW, and the maximum reduction in plant height was observed under 60% ETc. Furthermore, Giza 86 was more negatively affected than Giza 92. Similar results were previously obtained [18,23,24]. The data in Table 4 also display that the components of the cotton yield (number of fruiting branches, number of total bolls, number of open bolls, average boll weight, seed cotton yield, lint cotton yield, lint percentage, and seed index) were negatively affected by DW, and the highest decreases in the yield components were under the highest DW. Under all water treatments applied in this study, Giza 92 had a lower negative influence than Giza 86. Similar trends were observed in [18,23,24]. Since the yield is the consequence of the integration of metabolic reactions in plants, any factor influencing metabolic activity at any stage of plant growth can adversely affect the yield. Drought stress can reduce crop production by affecting agronomic traits such as seed yield, lint yield, lint percentage, and seed index [18,23,24]. DW during peak flowering had the most detrimental effects on cotton yield [66]. Cotton yield is reduced by reducing boll production and by increasing boll abortion when DW stress is severe during reproductive growth [13,18]. Moreover, a positive correlation was observed between irrigation water level and yield and its components such as the number of bolls, boll weight, seed cotton yield, lint yield, and lint percentage [18,23,24]. As physiology and metabolism, cotton plant growth and yield are greatly affected under adverse DW conditions. Insufficient water content in the soil during sensitive growth stages, including blooming, flowering, and fruit-setting, can harm plant growth, yield, and yield components [18,23,24].

Information about the impacts of DW on the fiber properties of cotton genotypes may assist researchers in selecting a cultivar of high fiber quality that is tolerant to DW stress [20]. Therefore, many previous studies reported variations in fiber properties under DW stress conditions. High fiber quality is important to the textile industry because of its direct impact on processing performance, yarn quality, and textile marketing [15]. The water treatments and the severity of DW caused significant differences in the fiber quality properties between the two cultivars (Giza 92 and Giza 86) tested in this study (Table 5). With Giza 92 outperforming Giza 86, maturity ratio, fiber length and strength, and reflection were promoted, while micronaire value, uniformity ratio, elongation, and yellowness degree were negatively affected under DW (80% and 60% ETc). Micronaire value, maturity ratio, and elongation showed non-significant differences between the two cotton cultivars. Similar results were reported in [18,23,24].

Correlation study among seed cotton yield, physiological traits, and antioxidant capacity shown in Table 6 revealed that some physiological attributes, including MSI and chlorophylls, contributed efficiently to the cotton yield in both tested cultivars.

The plants of the two tested cotton cultivars have higher enzymatic and non-enzymatic antioxidant levels (Table 3). They consume the plant nutritional resources to develop their own defense systems. However, the plants of these cultivars were able to exhibit greater yields (Table 4 and Table 5). These findings are due to the fact that different antioxidants protect various components of the cells from damage and play different essential roles in plant growth and development by modulating cellular and sub-cellular processes such as mitosis, cell differentiation, growth, division, and elongation as well as regulation senescence. Besides, they are involved in a wide range of processes, such as the detoxification of xenobiotics, conjugation of metabolites, synthesis of proteins and nucleotides, phytochelatins, and expression of stress-responsive genes. These antioxidants also protect the unsaturated membrane lipids, nucleic acids, and other cellular structures from the negative impacts of free radicals [67].

5. Conclusions

In the current study, compared to Giza 86 cultivar, Giza 92 showed higher physiological traits, enzymatic and non-enzymatic antioxidants, yield components, and fiber quality under normal and stress conditions. Therefore, Giza 92 must be taken into account for its high productivity under DW on sandy soils for breeding programs. These programs provide an opportunity to focus the analysis on the resilience of this cultivar with increasing environmental stress to improve, ensure, and sustain its yield and fiber quality.