Comparative assessment of okra and non-okra leaf cotton (Gossypium hirsutum L.) genotypes for drought tolerance

doi:10.21203/rs.3.rs-3357052/v1

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Comparative assessment of okra and non-okra leaf cotton (Gossypium hirsutum L.) genotypes for drought tolerance

https://doi.org/10.21203/rs.3.rs-3357052/v1

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Cotton production worldwide is adversely affected by drought stress, and the variation in drought tolerance among genotypes can be attributed to differences in their physiological, morphological, and root-related parameters. In upland cotton, there are two major leaf types: okra and non-okra. To investigate genetic variations related to drought tolerance, a study was conducted using two cotton genotypes from each leaf type (IUB 14052, IUB 14053, isolines of IUB-13, and MM-58 for okra leaves; IUB-13 and MM-58 for non-okra leaves) under both normal and drought conditions for two seasons. The experiment followed a factorial layout with a completely randomized design. Cotton genotypes were examined for root, morphological, physiological, and fibre-related variables using genotype × treatment × year interactions. Traits such as transpiration rate, stomatal conductance, chlorophyll contents, leaf area, seed cotton yield, number of nodes, fibre strength and ginning out-turn were higher in okra leaf type genotypes as compared to non-okra leaf type genotypes. As a result, genotypes of okra showed less reduction during drought in all parameters indicated above. Furthermore, under drought conditions, root parameters such as primary root length, lateral root numbers, root fresh weight, and root dry weight increased in okra leaf type accessions, indicating that okra leaf cotton genotypes have a greater drought tolerance capacity. Correlation studies demonstrated a negative correlation between stomatal conductance and leaf area in okra leaf genotypes. This correlation ultimately led to an increase in boll weight and seed cotton yield for okra leaf genotypes. This relationship underscores the role of resource allocation and stomatal conductance in enhancing the overall drought tolerance and productivity of okra leaf cotton genotypes.

Cotton

drought tolerance

okra-leaf genotypes

Cotton, one of the most significant commercial crops globally, serves as a well-known and vital source of fiber and is cultivated worldwide, in regions both arid and humid. Water availability stands as a pivotal factor influencing plant growth and crop productivity. The persistent threat of water deficits poses a constant challenge to achieving sustainability in cotton production.

In vascular plants, leaves serve as the primary photosynthetic organs. Okra (Abelmoschus esculentus (L.) Moench), a close relative of cotton belonging to the Malvaceae or mallow family, exhibits deeply incised leaves known as 'okra leaves.(Mishra et al., 2021). In tetraploid cottons, there are two principal leaf types: normal leaves, commonly referred to as broad leaves, which predominate in major cultivated cotton varieties, and okra leaves, characterized by their deeply cut leaf edges (as illustrated in Fig. 1). This trait offers several agronomic advantages, primarily stemming from its reduced leaf area, resulting in less water loss by the plant. When compared to normal-leaf cotton isolines, Okra leaf cotton types are said to have more photosynthetic capacity per unit leaf area and consequently improved water usage efficiency (Smith, 2021). Previous studies have demonstrated that Okra-type cotton enhances drought tolerance by promoting early maturity and increasing photosynthesis through a reduction in leaf area in cotton (Meshram et al., 2022). Okra-leaf varieties of cotton may be potentially drought-tolerant due to these traits (Wells et al., 1986) (Andres et al., 2016) (Karami et al., 1980) (Levitt, 1980). An okra-leaf cotton breeding programme was started with an eye on the significance of the plant's developing canopy and its okra-like leaf form at the department PBG, IUB, Pakistan. This effort has so far produced a variety of okra-leaf germplasm and improved upland cotton cultivars. Two of the okra-leaf accessions produced by the program were included in the current study and assessed for their potential drought tolerance. In cotton, extensive research has focused on developing cultivars with higher yields and good staple length. However, when these cultivars are grown in the field, they must contend with numerous unpredictable biotic and abiotic stresses. Modifying the leaf shape of currently cultivated varieties to the okra shape may help mitigate yield losses caused by drought and pest infestations Plant height reduction, leaf wilting, and changes in the number and size of leaves are the most visible signs of water shortage throughout the vegetative period Severe drought conditions lead to a significant reduction in plant height, and this reduction shows an inverse correlation with cell enlargement and leaf senescence. Under conditions resembling drought stress, the primary factors influencing a plant's height are decreased cell expansion, increased leaf loss, and impaired mitosis. Leaves, being the primary organs responsible for plant respiration and assimilation, act as indicators of the extent of water deprivation. In response to drought, plant leaves often exhibit characteristics such as smaller leaf areas, increased leaf thickness, and higher leaf tissue density (Werner et al., 1999) One noticeable effect of drought stress on plant leaves is the change in leaf area, which directly impacts photosynthesis and overall yield (Taiz et al., 2015). Drought leads to a decrease in the rate of photosynthesis and leaf turgor pressure, resulting in a reduction in leaf area (Rucker et al., 1995) The initial plant functions impacted by drought are cell elongation and division. Photosynthesis is also affected by drought, primarily due to stomatal closure in the early stages of stress. However, as drought stress intensifies, photosynthesis is further hindered for metabolic reasons. It's important to note that these reductions in photosynthesis occur after the initial effects on cell elongation and division have already taken place ²⁸

As a result, the goal of this study was to determine which drought-related features are relevant for drought tolerance in okra leaf type cotton genotype.s vs non-okra (normal) leaf type cotton genotypes. So that the found drought tolerance indicators may be employed to improve the drought tolerance of okra type plants in Gossypium hirsutum genotypes.

In 2020 and 2021, the experiment was carried out in the Department of PBG, The Islamia University of Bahawalpur, Pakistan. In this experiment, polyvinyl chloride (PVC) pipes were employed to prevent moisture infiltration from the surrounding environment, protect against the effects of rainfall, and facilitate accurate data collection of root parameters by safely uprooting the plants from the pipes. Each polyvinyl chloride pipe contained 7 kilogrammes of a sand and organic-rich field soil mixture (1:1), and the cotton was grown in them. The pipes had a 20 cm diameter and a 105 cm total column height. Within a row plants were spaced 60cm apart while distance between rows was 90 cm. The pipes used for drought stress treatment were covered with plastic sheets to shield them from rain. Total 4 cotton genotypes 2 from each, i.e. okra (IUB-14052 and IUB-14053, isolines of IUB-13 and MM-58) and from non-okra (IUB-13 and MM-58) were used for experimentation. With three replications (10 pipes in each replication of each genotype under both non-stressed and water stressed conditions), the experiment was set up as a fully randomised design (CRD) factorial design with two components (2 moisture levels and 4 genotypes). In the field, there were 240 pipes vertically buried. Each genotype was housed in a pipe. In each pipe, 4 seeds for each genotype were directly sown. Only one seedling was kept in each pipe after germination. The seedlings were grown for 30 days under optimal moisture conditions. At the end of this 30-day period following germination, half of the pipes were supplied with water as a control group, while the other half received no water, inducing water stress.. Moisture level was measured by using Pro Check soil moisture sensor connected to GS3 soil moisture, temperature, and EC sensor (Decagon Inc., Pullman, WA, USA) (Hassan-Esfahani et al., 2015); During the season, control plants were watered when moisture levels dropped to 80%, whereas drought stressed plants were irrigated when moisture levels dropped to 30%. Watering thresholds (80 and 30% of pipe capacity were identified from a pilot experiment (Chaturvedi et al., 2019; Iqbal et al., 2019)that provided enough growth differences. Before planting, a dosage of basal fertiliser was applied in the 10–20 cm layer (2.76 g of N, 9.36 g of P₂O₅, 6.38 g of K₂O per pipe). Until maturity, an extra dosage of 18 g/pipe of N were administered in three split doses at 45, 75 and 105 days of planting.

2.1. Data collection

The pipes were carefully removed on the 140th day after sowing and soaked in water overnight to soften the soil. The plants were gently removed from the pipes the next day without injuring the roots. Each pipe was meticulously excavated and broken down into 20 cm pieces from top to bottom to measure root features. The segments were submerged in water for 1/2 hour, and the roots were cleaned, gently removed, and rinsed with tap water. Debris, weeds, and dead roots were manually sorted from active roots using the procedure described by (Gwenzi et al., 2011). Live roots were uniformly distributed in a plastic tray containing deionized water from each pipe. The following characters were studied as;

2.1.1. Primary root length (PRL)

A metre rod was used to measure PRL (cm) for each plant.

2.1.2. Lateral root numbers (LRN)

LRN were manually counted for each plant root.

2.1.3. Root fresh weight (RFW)

During this period, the Root Fresh Weight (RFW) was measured by recording the weight in grams. To achieve this, the roots were rinsed with tap water, followed by distilled water, and then gently blotted with soft paper to absorb any excess water from the root surface. This process ensured that no water residue was left on the roots while preserving their natural moisture content

2.1.4. Root dry weight (RDW)

Fresh roots were cut into tiny pieces and oven dried at 80°C for three days in a fan-forced oven to determine RDW.

2.1.5. Leaf area (LA)

The plant was divided into three sections based on the number of nodes, specifically the upper section (located at the 24th node from the surface), the middle section (at the 16th node), and the lower section (at the 8th node). The leaf area was estimated by summing the individual leaf areas on the upper and middle sections of the plant, each separately to calculate LA (cm²). A portable Leaf Area Meter CL-202, manufactured by CID Bio-Science, was employed to measure the area of each leaflet (Mehraj et al., 2013).

2.1.6. Chlorophyll contents (CC)

The measurements taken at the end of the experiment, Early in the morning, between 7:00 and 9:00 a.m., a portable CL-01 chlorophyll content meter (Hansatech instruments Ltd) plant analyser was used to measure the CC (mol/m²) of fully expanded leaves at 6th and 9th node.

At the maturity of the experiment, chlorophyll content measurements were conducted early in the morning, typically between 7:00 and 9:00 a.m. A portable CL-01 chlorophyll content meter manufactured by Hansatech Instruments Ltd was employed. This device was used to measure the Chlorophyll Content (CC) in units of mol/m² in fully expanded leaves situated at 4th main-stem leaf from the plant’s apex.

Stomatal conductance (SC) & Transpiration Rate (TR)

The plant was segmented into three sections, determined by the number of nodes present. These sections included the upper portion (positioned at the 24th node from the surface), the middle section (at the 16th node), and the lower portion (at the 8th node). To measure stomatal conductance, readings were taken from the upper, middle, and lower leaves of each plant. This was achieved using an SC-1 Porometer, manufactured by Meter Group, Inc. in the USA. The relative value of the transpiration rate (mmol m^− 2 s^− 1) was derived by dividing the transpiration value from the porometer by 10,000 and multiplying by 1000 (Munawar et al., 2021).

2.1.7. Plant height (PH)

For each chosen plant, the measurement between the plant's tallest point and the ground during harvest was taken using a measuring tape and recorded in centimetres. .

2.1.8. Number of nodes (NON)

Nodes on the main stem of each genotype were counted manually and their average was computed.

2.1.9. Number of Bolls (NB)

Boll weight refers to the whole boll biomass (fiber, seed and bur). Fully opened bolls of each plant were counted manually and their average was computed for each genotype.

2.1.10. Boll weight (BW)

To calculate the BW (Boll Weight) for each genotype, the total weight of the seed cotton harvested from the plant was divided by the total number of open bolls

2.1.11. Seed cotton yield (SCY)

Each plant's SCY was manually removed, and each plant's weight in grams, was recorded.

Ginning out turn (%):

Seed cotton of each plant was ginned on roller ginner machine and ginning out turn was calculated in percentage by the following formula:

$$\text{G}\text{O}\text{T} \left(\text{%}\right) =\frac{\text{W}\text{e}\text{i}\text{g}\text{h}\text{t} \text{o}\text{f} \text{l}\text{i}\text{n}\text{t} }{\text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{o}\text{f} \text{s}\text{e}\text{e}\text{d} \text{c}\text{o}\text{t}\text{t}\text{o}\text{n} }\times 100$$

2.1.12. Fibre traits

By placing the ginned samples in a humidifier in a blow room (65% humidity and 18–20°C temperature), the samples were reconditioned for fibre trait analysis. The Cotton Research Institute (CRI), Multan, Pakistan, used a High Volume Instrument (HVI-900-SA; Zellweger Ltd., Switzerland) to measure fibre quality traits like staple length (mm), fibre strength (g/tex), and fibre fineness (micronaire values). .

2.2. Statistical analyses

The responses of four cotton genotypes to two moisture levels were studied in a four-by-two factorial experiment with three replications using a CRD framework. The Statistix 8.1 computer application was used to run an analysis of variance on the data. At the 5% and 1% levels of probability, statistical significance is stated. Because the differences between cotton varieties and studied treatment were not significant, the data were averaged across two years and utilised for more study. Tukey's Test was used to make mean comparisons (Statistix 8.1 computer application). Pearson correlation among morphological, physiological, roots and fibre quality traits was computed in both non-stressed and water stressed conditions by using XLSTAT 2014. The following method was used to determine the reduction/change in all above mentioned studied attributes in both conditions(Iqbal et al., 2019);

Reduction (%) in stressed conditions = {1 – (Performance under stressed /Performance under non-stressed)} × 100.

3.1. Meteorological data for the cotton growing seasons (2020 and 2021)

The average monthly temperature, relative humidity, and rainfall are shown in meteorological data collected throughout the crop-growing season for both years (Fig. 2a & b).. In the months of May and October of the 2020 growing season, the mean highest and lowest temperatures were measured as 44°C and 25.64°C, respectively.. In contrast, the mean highest and lowest temperatures in 2021 were determined to be 40°C and 21.1°C, respectively, in the months of June and October.. The highest relative humidity in 2020 October (85%) and August (79%) and 2021 was recorded in October (58.1%) and August (59.1%) respectively. Total rainfall through the season was 222.9 mm in 2020 and 134.72mm in 2021. The highest rainfall (126.9 and 41.76mm) was observed in August (2020 and 2021) respectively.

3.2 Assessment of the genetic variation in okra and non-okra genotypes under normal and water deficit conditions

The ANOVA findings in Table 1 (a & b) revealed significant differences between examined genotypes and moisture treatments at both P-values (P-0.05 and P-0.01) for significance, showing that there may be potential for drought resistance across genotypes for all studied characters. For all attributes, the interactions of Genotype × Year (G×Y) and Genotype × Year × Treatment (G×Y×T) were non-significant, implying that genotypes performed rather consistently across the 2 years. As a result, mean values for the 2 years were used for biometrical analysis and discussed.

Table 1

a. Mean square values for various morphological, physiological and fibre quality traits of two Okra and Non Okra genotypes evaluated for 2 years under two treatments (normal and drought stress conditions)
SOV	df	PH	NON	NB	SCW	BW	LA-1	LA-2	CC-1	CC-2	TR
Genotype	3	528.77**	33.98**	226.86**	2580.68**	73.70**	6224.12**	5137.32**	40.53**	33.16**	16.51**
Treatment	1	14608.0**	861.48**	6807.73**	66533.9**	0.78	2579.95**	2747.42**	115.121**	88.94**	66.56**
Year	1	8.93	3.98	1.045	2.516	2.53	2.64	1.03	6.4	1.63	1.23
Genotype × Treat	3	396.24**	73.35**	521.86**	4106.12**	111.39**	392.2**	392.47**	28.03**	17.98**	11.34**
Genotype × Year	3	9.23	5.66	3.87	2.30	9.04	1.04	1.50	1.98	1.10	2.45
Years × Treat	1	3.98	2.02	2.40	3.19	1.16	9.02	2.54	8.29	8.19	1.98
Geno × Years × Treat	3	1.13	1.58	3.86	1.35	3.82	2.67	6.034	5.98	9.94	2.06
Error	32	96.1	39.3	20.26	69.6	2.03	3.06	0.15	0.09	0.07	1.22
SOV, source of variations; DF, degree of freedom; PH, plant height; NON, number of nodes per plant; NB, number of bolls per plant; SCY, seed cotton yield; BW, boll weight; LA-1, upper leaf area; LA-2, middle leaf area; CC-1, upper leaf chlorophyll content; CC-2, middle leaf chlorophyll content; TR, transpiration rate.

Table 1

b. Mean square values for various morphological, physiological and fibre quality traits of two Okra and Non Okra genotypes evaluated for 2 years under two treatments (normal and drought stress conditions)
SOV	df	SC-1	SC-2	SC-3	FL	FS	FF	LRN	PRL	RDW	RFW
Genotype	3	653.22**	1892.39**	8412.88**	20.46**	32.6659**	39.67290**	668.35**	1748.47**	107.71**	821.92**
treatment	1	12652.2**	6453.31**	2560.55**	21.95**	65.85**	19.05**	1032.86**	10147.8**	390.222**	1349.59**
Year	1	1.35	3.98	2.13	3.62	3.52	2.04	1.59	3.32	3.39	1.68
Genotype × Treatment	3	1670.08**	748.56**	170.97**	41.97**	62.23**	69.17**	73.52**	392.13**	27.01**	99.33**
Genotype × Year	3	8.24	6.55	3.37	3.20	8.15	2.11	2.07	2.46	1.41	2.24
Years × Treat	1	2.92	2.92	3.03	2.99	1.45	7.20	2.71	6.23	9.20	3.31
Geno × Years × Treat	3	2.21	2.58	2.36	1.53	2.71	3.76	4.43	3.78	6.14	5.61
Error	32	66.1	93.3	26.20	96.9	3.02	2.86	0.75	1.31	1.78	0.09
SOV, source of variations; DF, degree of freedom; SC-1, upper leaf stomatal conductance; SC-2, middle leaf stomatal conductance; SC-3, lower leaf stomatal conductance; FL, fiber length; FS, fiber strength; FF, fiber fineness; LRN, lateral root number; PRL, Primary root length; RDW, root dry weight; RFW, root fresh weight

Under drought, root characteristics associated to drought tolerance, such as LRN, PRL, RDW, and RFW, were significantly enriched in okra genotypes compared to non-okra genotypes (Table 2). In the case of the LRN trait, the decline (percentage) during drought was much lower in okra genotypes (–43.0 and − 39.0%) in IUB-14052 and IUB-14053 respectively. While in non-okra genotypes, IUB-13 and MM-58 it was − 24.70 and − 16.90% respectively. Under drought, regarding PRL, the average reduction % in okra genotypes IUB-14052 and IUB-14053 was − 45.40% and − 48.70%, respectively. While, it was − 28.40% (IUB-13) and − 24.90% (MM-58) in non-okra genotypes. Regarding RDW and RFW, the average reduction (%) in non-okra genotypes was − 24.40% in (IUB-13), − 22.70% (MM-58) for RDW, while − 17.20% (IUB-13) and − 18.30% (MM-58) for RFW. Likewise, for okra genotypes it was − 52.40% (IUB-14052), − 55.40% (IUB-14053) for RDW and for RFW it was − 38.30 and − 40.80% in IUB-14052 and IUB-14053 respectively.

Table 2

Mean values for root related traits under normal, drought stress and reduction (%) under drought
	Normal				Drought				Reduction Percentage
	Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes
	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58
LRN	32.30C	29.80D	24.20F	21.40G	46.19A	41.42B	30.18D	25.02E	-43.00	-39.00	-24.70	-16.90
PRL	84.10E	78.70F	72.10G	69.60H	122.28A	117.03B	92.58C	86.93D	-45.40	-48.70	-28.40	-24.90
RDW	14.60D	13.70E	12.80F	10.80G	22.25A	21.29B	15.92C	13.25E	-52.40	-55.40	-24.40	-22.70
RFW	39.23D	36.20E	34.40F	25.70H	54.26A	50.97B	40.32C	30.40G	-38.30	-40.80	-17.20	-18.30
LRN, lateral root number; PRL, Primary root length; RDW, root dry weight; RFW, root fresh weight

Regarding morpho-physiological traits in drought environments, less reduction was also observed in okra genotypes than those of non-okra genotypes (Table 3). For instance, for plant height attribute, the average reduction (%) was 20.0% (IUB-14052) and 23.0% (IUB-14053) while it was 39.0 and 34.50% in IUB-13 and MM-58 respectively. Regarding NON attribute, the average reduction (%) in IUB-14052 and IUB-14053 was 17.60 and 24.30% respectively. IUB-13 and MM-58 showed 46.30 and 49.70% reduction under water deficit conditions, respectively. Correspondingly, for number of bolls the mean reduction (%) was also maximum in non-okra genotypes (54.70 and 58.10%). While, it was 27.30 (IUB-14052) and 25.40% (IUB-14053) in okra genotypes. SCY was maximum (186.40g) in IUB-14053 (okra genotype) followed by 164.11g in IUB-14052 (okra genotype) under normal condition. However, under drought, reduction was much higher in non-okra than okra genotypes. BW was (3.02 and 2.89g) in IUB-14052 under normal and water deficit respectively and in IUB-14053 it was 3.43g and 3.28g (normal and drought respectively). Likewise, IUB-13 and MM-58 produced average BW as (2.70 and 2.40g) and (2.78 and 2.39g) in non-stressed and water deficit conditions respectively. Reduction was also higher in non-okra genotypes being 11.28 and 14.06% in IUB-13 and MM-58 respectively much higher than the okra genotypes. LA and CC were taken from two different positions (top and middle of the plant) and the reductions under drought in okra genotypes was much less than those of non-okra ones. Similarly SC was taken from three different locations (top, middle and bottom of the plant) in studied environments. As expected average reduction under drought was also much higher in non-okra genotypes than in okra ones. Reduction in TR was also found to be higher in non-okra than okra genotypes.

Table 3

Mean values for morphological and physiological traits under normal and drought stress conditions and reduction (%) under drought
	Normal				Drought				Reduction Percentage
	Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes
	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58
PH (cm)	119.72B	122.31A	124.97A	115.21C	95.78D	94.18D	76.23E	75.46E	20.00	23.00	39.00	34.50
NON	24.50C	22.78D	29.20A	25.20B	20.19E	17.24F	15.68G	12.68H	17.60	24.30	46.30	49.70
NB	52.00C	48.61D	69.40A	56.40B	37.80E	36.26F	31.44G	23.63H	27.30	25.40	54.70	58.10
SCY (g)	164.11B	186.40A	154.75D	158.60C	113.43F	118.82E	75.31G	56.46H	26.70	27.60	59.60	64.40
BW (g)	3.02BCD	3.43ABC	2.70D	2.78D	2.89AB	3.28A	2.40D	2.39CD	4.30	4.48	11.28	14.06
LA-1 (cm²)	54.33E	34.56G	92.65A	89.77B	48.03F	29.62H	65.60D	67.42C	11.60	14.30	29.20	24.90
LA-2 (cm²)	61.07E	39.39G	98.62A	84.38B	53.99F	33.76H	69.82C	63.37D	11.60	14.30	29.20	24.90
CC-1	20.65B	20.90B	26.46A	20.53B	19.68BC	19.17CD	19.40D	15.91E	4.70	8.30	26.70	22.50
CC-2	21.07AB	21.57A	21.27AB	19.69D	20.08BC	19.78C	15.59E	15.26E	4.70	8.30	26.70	22.50
SC-1	156.30D	169.70B	183.60A	165.70C	145.83F	152.90E	123.56G	121.13H	6.70	9.90	32.70	26.90
SC-2	117.50E	137.60A	125.50B	119.80D	109.63F	123.98C	84.46H	87.57G	6.70	9.90	32.70	26.90
SC-3	108.20B	112.20A	71.80D	69.10E	100.95C	101.09C	48.32G	50.51F	6.70	9.90	32.70	26.90
TR	8.42B	8.61B	11.30A	11.43A	6.40D	6.80CD	7.06C	6.85CD	23.99	21.02	37.52	40.07
PH, plant height; NON, number of nodes per plant; NB, number of bolls per plant; SCY, seed cotton yield; BW, boll weight; LA-1, upper leaf area; LA-2, middle leaf area; CC-1, upper leaf chlorophyll content; CC-2, middle leaf chlorophyll content; SC-1, upper leaf stomatal conductance; SC-2, middle leaf stomatal conductance; SC-3, lower leaf stomatal conductance; TR, transpiration rate.

Fiber characteristics were also no exception (Table 4). For FL trait, minimum reduction was recorded in okra genotypes (4.30% and 5.20%) in IUB-14052 and IUB-14053 respectively while IUB-13 and MM-58 (non-okra) exhibited maximum as 7.90 and 8.70% respectively. Like reduction trend was also detected for FS and FF traits with less reduction for okra genotypes (Table 4).

Table 4

Mean values for fibre quality traits under normal and drought stress condition and reduction (%) under drought
	Normal				Drought				Reduction Percentage
	Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes		Okra Genotypes		Non Okra Genotypes
	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58	IUB-14052	IUB-14053	IUB-13	MM-58
FL (mm)	28.20BC	28.50AB	28.90A	28.00BC	26.99C	27.02C	26.62D	25.56E	4.30	5.20	7.90	8.70
FS (g/tex)	32.00A	30.00C	30.80B	32.10A	29.70BC	27.72E	27.44F	28.67D	7.20	7.60	10.90	10.70
FF (microgram/inch)	4.20C	4.15C	4.50C	4.32C	4.56B	4.64B	5.64AB	5.37A	-8.60	-11.70	-25.30	-24.40
FL, fiber length; FS, fiber strength; FF, fiber fineness

3.3. Correlation analysis

Correlation among all the traits studies is presented in Fig. 3, which showed that PH had significant positive correlation with NON, NB, SCY, CC and SC. While with other traits its correlation was non-significant. BW had significant and positive association with CC, SC and all root related parameters under study and negatively correlated with LA. It was observed that LA is significantly and positively correlated with CC and FS while negative correlation was observed with SC and root related parameters. SC, FL and FS showed significant association with CC. SC also shown positive correlation with FL and FS, while negative with FF and root related parameters. FL was positively correlated with FS and shown negative association with FF. FS and FF were also negatively associated with each other.

Cotton plant development is largely influenced by external variables such as weather, management, genetic traits, and planting pattern. Weather is the most important factor in crop development and production in any place, from small-scale to large-scale agro-ecological zones (Manjunatha et al., 2010). One of the most damaging weather-related factors that seriously harms cotton is thought to be drought. Crop losses are influenced by the stress's duration and growth stage. A plant's ability to maintain a high water status for optimal performance and its capacity to retain metabolic operations at low water levels are both referred to as drought tolerance. (Ullah et al., 2008). By having morphological or physiological characteristics that either reduces water loss through transpiration or increase water absorption through a broad and deep root system, plants are able to maintain a favourable water balance. Both characteristics have been proposed as adaptive characteristics of plants surviving water stress, allowing plants to redirect nutrients and energy, which would otherwise be utilised for development, into protective molecules to combat stress (Zhu, 2002).

The goal of the current study was to examine the genotypes of okra and non-okra plants for their capacity to withstand drought.. A lot of things, including canopy structure, photosynthetic traits, and yield, are influenced by leaf form. Okra leaf form introduction is regarded as a key breeding approach for cotton (Zhu et al., 2008). In terms of production and fibre quality, as well as possible resilience to insect pests and drought, okra-leaf varieties of upland cotton have the potential to be competitive with those with regular leaves (Hafeez-ur-Rahman and Latif, 2005). When compared to non-okra leaf type genotypes, traits including transpiration rate, stomatal conductance, chlorophyll contents, leaf area, seed cotton yield, number of nodes, fibre strength, and ginning out-turn were greater in okra leaf type genotypes (Chaturvedi et al., 2019). Studies using isolines have shown that okra-leaf forms of cotton have lower stomatal conductance than standard types of cotton, and that they have a higher CO₂ exchange rate and water usage efficiency (Hafeez-ur-Rahman and Latif, 2005). Because of these traits, cotton varieties with okra-shaped leaves may exhibit enhanced drought tolerance in the future, potentially posing a global challenge for crop management. The increased yield associated with these varieties may result from the reduction in leaf area, a characteristic seen in okra-type genotypes. This reduction facilitates improved airflow, reduced humidity at the micro-level, greater penetration of sunlight, and enhanced photosynthesis in all leaf levels (lower, middle, and upper). These factors collectively contribute to increased yields under various conditions

In the current study, it was observed that the Stomatal Conductance (SC) values in the lower leaves of okra-type cotton genotypes were notably higher in comparison to non-okra genotypes under both normal and drought conditions (as shown in Table 3). This difference can be attributed to the non-shading of leaves and the heightened photosynthetic activity in okra-type genotypes. The advantages of okra-shaped leaves include early maturation, reduced boll rot, increased CO2 absorption per unit leaf area within the canopy, and higher single-leaf photosynthesis per unit leaf area, all contributing to improved crop productivity(Pettigrew, 2004).. Given these benefits, several researchers have placed a strong emphasis on breeding cotton varieties with okra-shaped leaves, as was undertaken in this experiment. Our findings align with those of previous research (Nawab et al., 2011), which also reported enhancements in yield-related traits in okra-type genotypes.. The adoption of okra-shaped leaves in cotton genotypes with various lobe structures has resulted in alterations to the plant's canopy structure and leaf area index, subsequently impacting the plant's ability to capture and utilize light efficiently .. As anticipated, in comparison to non-okra leaf genotypes, all okra leaf-oriented cotton genotypes in our study improved light penetration and increased light availability within both the middle and lower canopies.. Remarkably, the net photosynthetic rate per unit leaf area in the lower canopy of the okra-type genotype in this study was the highest, primarily due to a substantial increase in light penetration compared to non-okra leaf genotypes. Indeed, the photosynthates produced by the leaves in the middle canopy played a more significant role in contributing to the overall yield compared to those from other regions within the canopy (Zhu et al., 2008). This might be the main cause of the okra cotton genotypes' higher yield and higher-quality fibre under drought conditions in this study. Root length is an important criterion to screen the germplasm for drought tolerance. In the present study, root length was found to be more in okra type genotypes, than in non-okra cotton genotypes under both conditions (Table 2). So, these results exhibited that okra genotypes have more drought tolerance. Under drought stress conditions, roots tend to grow much deeper into the soil to find water (Taiz and Zeiger, 2002). The findings are further supported by research (Zhu et al., 2008)that revealed incorporating okra leaf parental lines in cotton breeding might increase fibre quality while maintaining a similar level of lint output Correlation coefficients define the level of association between two variables. It is quite pertinent in plant breeding since it can show a prophetic correlation that can be utilized ultimately providing information about the relations between several favoured traits (Ahmed et al., 2019). The finding of (Gumber et al., 2005; Sambamurthy et al., 2006; Yadav et al., 2000) are similar to the results, who also observed significant positive association of plant height with number of nodes and fiber strength.. Similarly, (Patil et al., 2017)reported that seed cotton yield per plant was significant and positive association with plant height, number of nodes, number of bolls per plant. This significant positive values of association of plant height, number of nodes and, number of bolls per plant exhibited that improvement in above characters will also assist to improve seed cotton yield per plant. Under dry conditions, the open canopy and resulting high light penetration, as well as the high stomatal conductance, particularly at the lowest level of the okra leaf cotton canopy, may speed up photosynthetic activity per leaf and increase lint output.

Considering the widespread utilization and consumption of cotton and its derivatives, there exists a compelling necessity to advance okra leaf-type cotton genotypes. This advancement holds the potential to significantly enhance both yield and fiber quality characteristics, especially in conditions characterized by water scarcity. The identification and selection of okra leaf drought-tolerant genotypes can be a pivotal step toward the creation of novel genotypes capable of thriving under drought-induced stress conditions. Such genotypes could prove to be invaluable resources for the development of cotton varieties that can effectively withstand and endure drought stress, thus ensuring a more resilient cotton industry

Conflict of interest: The authors have declared that no competing or conflicts of interest exist.

Ahmed, H. G. M.-D., Sajjad, M., Li, M., Azmat, M. A., Rizwan, M., Maqsood, R. H., and Khan, S. H. 2019. Selection criteria for drought-tolerant bread wheat genotypes at seedling stage. Sustainability 11, 2584.
Andres, R. J., Bowman, D. T., Jones, D. C., and Kuraparthy, V. 2016. Major leaf shapes of cotton: Genetics and agronomic effects in crop production. J Cotton Sci 20, 330–40.
Chaturvedi, A. K., Surendran, U., Gopinath, G., Chandran, K. M., Anjali, N., and Ct, M. F. 2019. Elucidation of stage specific physiological sensitivity of okra to drought stress through leaf gas exchange, spectral indices, growth and yield parameters. Agricultural Water Management 222, 92–104.
Gumber, R., Chahal, G., and Verma, P. 2005. Genetic evaluation of Gossypium arboreum genotypes for yield and fibre quality. Journal of Cotton Research and Development 19, 44–48.
Gwenzi, W., Veneklaas, E. J., Holmes, K. W., Bleby, T. M., Phillips, I. R., and Hinz, C. 2011. Spatial analysis of fine root distribution on a recently constructed ecosystem in a water-limited environment. Plant and Soil 344, 255–272.
Hafeez-ur-Rahman, B. A., and Latif, M. 2005. Okra-leaf accessions of the upland cotton (Gossypium hirsutum L.): genetic variability in agronomic and fibre traits. J. Appl. Genet 46, 149–155.
Hassan-Esfahani, L., Torres-Rua, A., Jensen, A., and McKee, M. 2015. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing 7, 2627–2646.
Iqbal, M., Khan, M. A., Chattha, W. S., Abdullah, K., and Majeed, A. 2019. Comparative evaluation of Gossypium arboreum L. and Gossypium hirsutum L. genotypes for drought tolerance. Plant Genetic Resources 17, 506–513.
Karami, E., Krieg, D., and Quisenberry, J. 1980. Water Relations and Carbon-14 Assimilation of Cotton with Different Leaf Morphology 1. Crop Science 20, 421–426.
Levitt, J. 1980. "Responses of plants to environmental stresses. Volume II. Water, radiation, salt, and other stresses," Academic Press.
Manjunatha, M., Halepyati, A., Koppalkar, B., and Pujari, B. 2010. Yield and yield components, uptake of nutrients, quality parameters and economics of Bt cotton (Gossypium hirsutum L.) genotypes as influenced by different plant densities. Karnataka Journal of Agricultural Sciences 23, 423–425.
Mehraj, H., Taufique, T., Ona, A., Roni, M., and Jamal Uddin, A. 2013. Effect of spraying frequency of gibberellic acid on growth and flowering in gerbera. Journal of Experimental Biosciences 4, 7–10.
Meshram, J. H., Singh, S. B., Raghavendra, K., and Waghmare, V. 2022. Drought stress tolerance in cotton: progress and perspectives. Climate Change and Crop Stress, 135–169.
Mishra, G. P., Seth, T., Karmakar, P., Sanwal, S. K., Sagar, V., Singh, P. M., and Singh, B. 2021. Breeding Strategies for Yield Gains in Okra (Abelmoschus esculentus L.). In "Advances in Plant Breeding Strategies: Vegetable Crops", pp. 205–233. Springer.
Munawar, W., Hameed, A., and Khan, M. K. R. 2021. Differential morphophysiological and biochemical responses of cotton genotypes under various salinity stress levels during early growth stage. Frontiers in Plant Science 12, 622309.
Nawab, N. N., Saeed, A., Tariq, M. S., Nadeem, K., Mahmood, K., Ul-Hassan, M., Shakil, Q., Alam, M. S., Hussain, S. I., and Khan, A. A. 2011. Inheritance of okra leaf type in different genetic backgrounds and its effects on fibre and agronomic traits in cotton. African Journal of Biotechnology 10, 16484–16490.
Patil, H., Deosarkar, D., and Arbad, S. K. 2017. Correlation and path analysis in upland cotton (Gossypium hirsutum L.). Journal of Cotton Research and Development 31, 19–23.
Pettigrew, W. 2004. Cotton genotypic variation in the photosynthetic response to irradiance. Photosynthetica 42, 567–571.
Rucker, K., Kvien, C., Holbrook, C., and Hook, J. 1995. Identification of peanut genotypes with improved drought avoidance traits. Peanut science 22, 14–18.
Sambamurthy, J., Ratna Kumari, S., and Chamundeshwari, N. 2006. Studies on variability correlation path analysis over environments (Gossypium herbaceum L.) cotton under coastal saline soils. Journal Maharashtra Agriculture University 31, 60–64.
Smith, C. A. 2021. Zinc Management and Salt Tolerance of Pecan in Arid Regions, The University of Arizona.
Taiz, L., and Zeiger, E. 2002. Photosynthesis: physiological and ecological considerations. Plant Physiol 9, 172–174.
Taiz, L., Zeiger, E., Møller, I. M., and Murphy, A. 2015. "Plant physiology and development," Sinauer Associates Incorporated.
Ullah, I., Ashraf, M., and Zafar, Y. 2008. Genotypic variation for drought tolerance in cotton (Gossypium hirsutum L.): Leaf gas exchange and productivity. Flora-Morphology, Distribution, Functional Ecology of Plants 203, 105–115.
Wells, R., Meredith Jr, W. R., and Williford, J. R. 1986. Canopy photosynthesis and its relationship to plant productivity in near-isogenic cotton lines differing in leaf morphology. Plant Physiology 82, 635–640.
Werner, C., Correia, O., and Beyschlag, W. 1999. Two different strategies of Mediterranean macchia plants to avoid photoinhibitory damage by excessive radiation levels during summer drought. Acta oecologica 20, 15–23.
Yadav, O., Lather, B., and Dahiya, B. 2000. Studies on variability, correlation and path analysis in desi cotton (Gossypium arboreum. L). J. Cotton Res. Dev 14, 143–46.
Zhu, J.-K. (2002). Salt and drought stress signal transduction in plants. Annual review of plant biology 53, 247.
Zhu, W., Liu, K., and Wang, X.-D. 2008. Heterosis in yield, fiber quality, and photosynthesis of okra leaf oriented hybrid cotton (Gossypium hirsutum L.). Euphytica 164, 283–291.

No competing interests reported.

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Comparative assessment of okra and non-okra leaf cotton (Gossypium hirsutum L.) genotypes for drought tolerance

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Data collection

2.1.1. Primary root length (PRL)

2.1.2. Lateral root numbers (LRN)

2.1.3. Root fresh weight (RFW)

2.1.4. Root dry weight (RDW)

2.1.5. Leaf area (LA)

2.1.6. Chlorophyll contents (CC)

2.1.7. Plant height (PH)

2.1.8. Number of nodes (NON)

2.1.9. Number of Bolls (NB)

2.1.10. Boll weight (BW)

2.1.11. Seed cotton yield (SCY)

2.1.12. Fibre traits

2.2. Statistical analyses

3. Results

3.1. Meteorological data for the cotton growing seasons (2020 and 2021)

3.2 Assessment of the genetic variation in okra and non-okra genotypes under normal and water deficit conditions

3.3. Correlation analysis

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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