Deciphering Host Plant Resistance in Upland Cotton (G. hirsutum) against Amrasca biguttula biguttula (Ishida) pest in North Indian Zone

Deciphering Host Plant Resistance in Upland Cotton (G. hirsutum) against Amrasca biguttula biguttula (Ishida) pest in North Indian Zone | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deciphering Host Plant Resistance in Upland Cotton (G. hirsutum) against Amrasca biguttula biguttula (Ishida) pest in North Indian Zone Om Prakash Yadav, Minakshi Jattan, Sandeep Kumar, Anil Jakhar, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7242070/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cotton leafhopper or jassid has become a major pest of cotton and inflicts substantial yield losses. Developing insect-resistant cultivars is a key economic strategy for mitigating this damage. Host plant resistance (HPR) through morpho-physiological and biochemical traits reveals a crucial role in plant defence against sucking pests. In this study, 127 Gossypium hirsutum accessions were evaluated during Kharif 2021 and 2022 for various morpho-physiological and biochemical traits/parameters to determine their impact on leafhopper population. Results Leafhopper population showed significant positive correlations with jassid injury grade (r = 0.77), total sugars (r = 0.62), stomatal conductance (r = 0.45), transpiration rate (r = 0.33) and leaf water content (r = 0.24). Conversely, significant negative correlations were observed with gossypol content (r=-0.77), trichome density (r=-0.68), phenol content (r=-0.67), tannin content (r=-0.66), seed cotton yield (r=-0.39), trichome length (r=-0.26) and photosynthetic rate (r=-0.23). Regression analysis indicated that morpho-physiological and biochemical traits collectively explained 75.88% impact on leafhopper population, with trichome density alone had 45.33% impact. On the basis of two-year field evaluations for jassid injury grade studies, four genotypes viz ., H 1652, HS 45, H 1655 and DL1 were found to be resistant. Three genotypes (B 61-2128, HS 1652, SHAHD 50) significantly out yielded the tolerant check RS 2013 (100.37 g/plant), producing seed cotton yield of 118.65, 115.03 and 111.97 g/plant, respectively. Traits such as leaf trichome density, trichome length, and the contents of sugar, phenol, tannin, and gossypol showed both high heritability and high genetic advance, suggesting the predominance of additive gene action and implying that these traits can be effectively improved through direct selection. Conclusions The present investigation identified the major role of leaf trichome density and phenol content on leafhopper population in cotton. Four genotypes viz. , H 1652, HS 45, H 1655 and DL1 were identified as resistant against leafhopper and these can be effectively exploited in future breeding programmes against leafhopper resistance. Three genotypes viz. , B 61-2128, HS 1652 and SHAHD 50 (moderately resistant/resistant against leafhopper) showed significantly higher seed cotton yield (118.65, 115.03 and 111.97 g/plant, respectively) than the tolerant check RS 2013 (100.37 g/plant). These can be utilized for the simultaneous improvement of yield and leafhopper resistance. Biochemical germplasm jassid injury grade leafhopper morpho-physiological Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Cotton ( Gossypium spp.) is one of the most economically significant fiber and oilseed crops cultivated globally, playing a crucial role in the socio-economic development of cotton-growing nations. This widely recognized crop thrives in tropical and subtropical climates and is extensively grown across continents (Komala et al., 2018 ). Among the cultivated species, Gossypium hirsutum (AD₁ genome), commonly known as upland or New World cotton, dominates global cotton cultivation, accounting for over 90% of the total cotton-growing area worldwide. According to the ICAR-AICRP on Cotton Annual Report (2024–25), China leads global cotton production with a 26.25% share, followed by India (20.67%), Brazil (14.05%), and the USA (11.92%). Although India ranks high in terms of total cotton production, the yield per unit area remains a concern due to the persistent challenges posed by various abiotic and biotic stresses. Cotton cultivation is frequently threatened by a broad spectrum of insect pests that attack the crop at different stages of its growth cycle. In India alone, over 160 insect species are known to infest cotton, of which about 15 are considered economically important pests (Matre et al., 2023 ). These pests are mainly classified into two categories: sucking pests and bollworms. Sucking insects such as jassid (leafhopper), whitefly, aphid, and thrips contribute significantly to yield reduction. Among them, the cotton leafhopper ( Amrasca biguttula biguttula Ishida), also known as the Indian jassid, is of particular concern due to its widespread occurrence and potential to cause up to 40% yield loss (Mawblei et al., 2024 ). Leafhopper damage is evident through symptoms like yellowing, reddening, curling, and eventual drying of leaves. These visible changes result from phytotoxemia—commonly termed ‘hopperburn’—a physiological disorder that negatively impacts plant growth and fiber quality. Managing such pests requires a deeper understanding of plant-insect interactions and host plant resistance mechanisms. The foundation for understanding plant resistance to insect pests was laid by Painter ( 1958 ), who categorized resistance into three mechanisms: non-preference (antixenosis), antibiosis, and tolerance. Non-preference refers to plant characteristics that discourage insect feeding or egg-laying. Antibiosis involves plant traits that negatively affect pest development or survival. Tolerance allows the plant to sustain pest damage without severe yield or quality reduction. Resistance to pests like leafhopper is influenced by a combination of morphological, physiological, and biochemical plant traits. Morpho-physiological features such as trichome density and length, leaf angle, lamina thickness, phloem characteristics, and pigmentation play a significant role in deterring pest infestation (Manivannan et al., 2017 ). Likewise, biochemical components such as phenols, tannins, gossypol, and flavonoids may act as deterrents or disrupt pest physiology by interfering with digestion, growth, or reproduction. Some of these compounds may also attract pests by acting as feeding stimulants, highlighting the need for trait-specific investigations. Additionally, physiological parameters like chlorophyll content, stomatal conductance, and transpiration rate have been linked to pest susceptibility. Higher chlorophyll content, for instance, may enhance photosynthetic activity, inadvertently making plants more attractive to insect pests (Chen et al., 2018 ). Despite the importance of these resistance-associated traits, there is limited literature on a comprehensive germplasm panel evaluated specifically for leafhopper resistance using a combined morpho-physiological and biochemical trait approach. Identifying and utilizing these host plant traits could serve as a sustainable strategy to breed leafhopper-resistant cotton cultivars. Therefore, the present study was undertaken to evaluate a diverse set of cotton germplasm accessions to identify key morpho-physiological and biochemical traits that confer resistance to leafhopper. This approach aims to facilitate the development of improved cotton genotypes with enhanced pest resilience, contributing to sustainable cotton production and improved yield stability. Materials and methods Plant Material The experimental material consisted of 127 genotypes of upland cotton ( G. hirsutum L.) (Supplementary Table S1 ). The experiment was raised in randomized block design with three replications under unprotected conditions during Kharif 2021 and 2022 at the research farm of Cotton Section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, Haryana (29°10'N latitude, 75°46'E longitude with an altitude of 215.2 m above mean sea level). An infector row of okra ( Abelmoschus esculentus ) was planted between the two genotypes to build a sufficient leafhopper population for infestation. Standard cultural practices as recommended by the University package of practices were performed during both the growing seasons. The pooled weather parameters for Kharif 2021 and 2022 season have been depicted in Supplementary Fig. S1 . Estimation of the leafhopper nymphal population and jassid injury grade Five competitive plants were selected randomly from each germplasm accession in each replication for observations. The nymphal population was observed on the abaxial surface of three leaves (one each from the top, middle and bottom leaf canopy) of each tagged plant. The nymphal population and jassid injury grade were recorded at 30, 45, 60, 75, 90, 105, 120 and 135 days after sowing (DAS). The okra plants were grown as infector rows and uprooted after 45 DAS. However, uprooted plants were kept in field to promote the migration of the leafhopper from okra to cotton and build the pest population on the cotton plants. Jassid damage was assessed by leaf burning (hopperburn), drying and shedding of young plant leaves and scoring was done by standard protocol as suggested by Kranthi et al. ( 2009 ). The genotypes were categorized on the basis of grading scale i.e. Resistant (0.1 -1.0); moderately resistant (1.1-2.0); susceptible (2.1-3.0) and highly susceptible (3.1–4.1). Observations on morpho-physiological and yield related traits Leaf samples were collected from five tagged plants of each genotype in each replication during the peak infestation period (60–90 DAS) of the leafhopper. Three leaf discs of one cm 2 from each of the top, middle and bottom leaves were used to measure trichome density and trichome length on the abaxial leaf surface using a stereomicroscope (Carl Zeiss Microscopy GmbH) at 0.63X magnification and the mean values were computed. The physiological parameters such as the relative water content ( RWC) were estimated as described by Barrs and Weatherley ( 1962 ). The leaf chlorophyll content was measured through chlorophyll meter (Model No. Minolta SPAD-502 Plus) and the photosynthetic rate, transpiration rate and stomata conductance were measured by an infrared gas analyzer (IRGALCi_SD, ADC Biosciences) during the peak infestation period (60–90 DAS) of the leafhopper. The leaf shape, colour and leaf nectaries of the fourth leaf on the main stem from the top of the plant were recorded at 50% flowering stage. The mean seed cotton yield/plant was calculated and ginning outturn (%) was worked out after harvesting. Observations on biochemical parameters To estimate the biochemical parameters, young leaves at the third or fourth node from the top of the plant with similar age and size were taken. The leaf samples were sun dried for 2–3 days and then oven dried at 60 0 C. The total sugar content (mg/g) was estimated using the method as suggested by Dubois et al. ( 1956 ), phenol content (mg/g) using the method as described by Bray and Thorpe ( 1954 ), the tannin content (mg/g) as suggested by Porter et al. ( 1986 ) and gossypol content (mg/g) as given by Bell ( 1967 ). Statistical analysis The pooled mean data of Kharif 2021 and 2022 was used for analysis. The data analysis for all traits was carried out via R studio (RStudio, 2020) software. Analysis of variance (ANOVA) was performed for all the traits. The phenotypic correlation coefficients (r p ) among leafhopper population, jassid injury grade, morpho-physiological and biochemical characters were estimated (Al-Jibouri et al. 1958 ). Regression analysis was used to define the relative contribution of component traits towards the leafhopper population as recommended by Snedecor and Cochran ( 1981 ). Results One hundred and twenty seven cotton genotypes were evaluated for three qualitative traits (leaf shape, colour and leaf nectaries) and 16 quantitative traits (morpho-physiological, biochemical, leafhopper population, jassid injury grade and yield related traits). Some morphological traits of cotton plants such as leaf shape, leaf colour and leaf nectaries favour or disfavour leafhopper infestation. Hence, cotton genotypes were also characterized and classified into various categories on the basis of leaf morphological traits (qualitative) following the guidelines of the Protection of Plant Varieties and Farmers’ Right Act (2001). In the present study, five genotypes viz . Surab 72, NHBBR 9 − 1, PIL 43, B 61-2128 and B 59-1679 had okra type leaves and in two genotypes (B 61-2128 and B 59-1679) leaf nectarines were absent. These genotypes were found to be moderately resistant to leafhopper, except for Surab 72, which was found to be susceptible. Among these five genotypes, two genotypes i.e., PIL43 and B 61-2128 had light green leaves which further favoured resistance to leafhopper as light green leaves are less preferred by the pest. Analysis of variance (ANOVA) was used to analyze the components of variance for different quantitative traits among 127 genotypes of upland cotton. The mean sum of squares due to genotypes was highly significant for all the traits (Table 1 ). The results revealed considerable genetic variability in the cotton germplasm for all the traits under investigation. Table 1 Analysis of variance for 16 different quantitative traits Source of Variation Mean sum of squares Replication Treatment Error Degrees of freedom 2 126 252 Leafhopper population 0.08 2.75** 0.08 Jassid injury grade 0.25 0.54** 0.03 Plant height 198.65 402.76** 25.91 Leaf trichome density 298.11 25060.59** 103.67 Trichome length 65.7 404.68** 7.61 Relative water content 18.58 86.73** 7.76 Chlorophyll content 4.82 24.92** 3.96 Photosynthetic rate 4.63 19.69** 1.03 Transpiration rate 0.19 1.14** 0.09 Stomatal conductance 0.002 0.010** 0.001 Total sugar 5.03 223.93** 2.72 Phenol content 1.43 12.07** 0.28 Tannin content 0.88 6.07** 0.11 Gossypol content 0.06 3.93** 0.04 Seed cotton yield 244.94 850.65** 39.21 Ginning outturn 0.22 16.66** 1.65 ** Significant at 1% The violin graph and box plot (Fig. 1 ) depicted huge variations for 16 different quantitative traits. The large variations for leaf trichome density, trichome length, plant height, seed cotton yield, total sugar content, tannin content and gossypol content were observed in this study. Genetic parameters of variability among cotton genotypes The estimates of genetic parameters of variability are essential in plant breeding because these help in identification of the type of gene action operative in the control of quantitative traits. The PCV and GCV were high for leaf trichome density (72.30, 71.85), trichome length (36.16, 35.17), gossypol (32.20, 31.70), total sugar (31.87, 31.30), seed cotton yield (27.58, 25.77), photosynthetic rate (26.93, 24.99), tannin (24.50, 23.87), stomatal conductance (24.24, 20.12), transpiration rate (23.30, 20.64), leafhopper population (22.24, 21.21) and jassid injury grade (22.03, 20.70); moderate for total phenol (17.84, 17.23) and low for chlorophyll content (8.76, 7.00), plant height (8.31, 7.56), relative leaf water content (8.13, 7.15) and ginning outturn (6.69, 5.80) (Table 2 & Fig. 2 ). The high heritability along with high genetic advance as percent of mean was observed for leaf trichome density (98.77), gossypol (96.87), total sugar (96.44), tannin content (94.85), trichome length (94.56), seed cotton yield (87.34), total phenol (93.23), leafhopper per 3 leaves (90.90), jassid injury grade (88.23), photosynthetic rate (86.13), transpiration rate (78.50) and stomatal conductance (68.84) (Table 2 & Fig. 3 ). It showed the presence of additive gene action behind the traits. Hence direct selection will be rewarding for these traits. Table 2 Genetic parameters of variability for various traits/parameters among 127 cotton genotypes Trait /Parameter Coefficient of Variation (%) Heritability (%) GA GA as percent of mean GCV PCV H 2 Leaf hopper population 21.21 22.24 90.90 1.85 41.65 Jassid injury grade 20.70 22.03 88.23 0.80 40.05 Plant height 7.56 8.31 82.90 21.02 14.19 Leaf trichome density 71.85 72.30 98.77 186.73 147.10 Trichome length 35.17 36.16 94.56 23.05 70.45 Relative leaf water content 7.15 8.13 77.23 9.29 12.94 Chlorophyll content 7.00 8.76 63.77 4.35 11.51 Photosynthetic rate 24.99 26.93 86.13 4.77 47.78 Transpiration rate 20.64 23.30 78.50 1.08 37.68 Stomatal conductance 20.12 24.24 68.84 0.04 34.38 Total sugar 31.30 31.87 96.44 17.37 63.33 Phenol 17.23 17.84 93.23 3.94 34.27 Tannin content 23.87 24.50 94.85 2.83 47.88 Gossypol content 31.70 32.20 96.87 2.31 64.27 Ginning outturn 5.80 6.69 75.19 4.00 10.36 Seed cotton yield 25.77 27.58 87.34 31.66 49.62 Leafhopper infestation and Jassid injury grade The leafhopper infestation among 127 upland cotton genotypes during Kharif 2021 and 2022 and their pooled data for the two seasons revealed a significant increase in the nymphal population after 45 DAS. The peak infestation period for the nymphal population was recorded between 60 to 90 DAS during both seasons. The lowest mean nymphal population of leafhopper was recorded in the genotype SHAHD 50 (2.70 per three leaves/plant), followed by HS 45 (2.76 per three leaves/plant) and DL 1 (3.00 per three leaves/plant), whereas the highest mean nymphal population was recorded in Auburn-M (8.02 per three leaves/plant), followed by the T 219-6-SV 6 A (7.26 per three leaves/plant) and H 1661 (6.64 per three leaves/plant) (Fig. 4 ). Observations on jassid damage were recorded to determine the degree of jassid injury according to different signs of leaf damage in the genotypes. A significant sign of jassid injury grade was recorded at 90 DAS for both the seasons. The pooled data of two seasons revealed that the jassid injury grade was highest at 105 DAS. The lowest mean jassid injury grade of 1.00 was recorded in H 1652, HS 45, H 1655 and DL 1, whereas the highest mean jassid injury grade (3.19) was recorded in N 27, followed by Auburn-M (3.14) and PD 695-1 (3.10) (Fig. 5 ). This evaluation revealed that out of 127 upland cotton genotypes; four genotypes viz ., H 1652, HS 45, H 1655 and DL1 were resistant; 62 genotypes were moderately resistant; and 56 genotypes were susceptible and five genotypes viz ., G Cot H 8 P2, T 219-6-SV 6 A, PD 695-1, Auburn-M and N 27were highly susceptible. Morpho-physiological and yield related traits in cotton genotypes The morpho-physiological traits were highly significant in upland cotton genotypes. The cotton genotypes were distinguished based on leaf trichome density on abaxial surface and rating scale as shown in Supplementary Fig. S2. The leaf trichome density ranged widely from 10.17 (T 219-6-SV 6 A) to 639.10 (HS 45) trichomes/cm 2 with a mean value of 126.94 trichomes/cm 2 . The highest leaf trichome density on leaf abaxial surface was observed for the genotype HS 45 (639.10 trichomes/cm 2 ) followed by H 1655 (413.92 trichomes/cm 2 ) and 1301 DDP3 (408.55 trichomes/cm 2 ). The maximum trichome length was observed for the genotype NHBBR 9 − 1 (88.88 µm) and minimum trichome length was observed for H 1640 (13.44 µm) (Table 3 ). The relative water content was highest in genotype PIL 43 (87.49%) and lowest in H 1659 (55.49%. The maximum chlorophyll content (SPAD value) was observed in genotype H 1643 (46.80) and the minimum chlorophyll content was detected in L 147 (29.88). The photosynthetic rate was maximum in genotype USA 47A (15.00 µmol CO 2 m − 2 s − 1 ) and lowest in H 1640 (2.59 µmol CO 2 m − 2 s − 1 ). The maximum transpiration rate was observed in genotype H 1640 (4.30 mmol H 2 O m − 2 s − 1 ) and the minimum in Tamcot CAMDE (1.24 mmol H 2 O m − 2 s − 1 ). The stomatal conductance ranged from 0.06 (H 1639) to 0.20 mol H 2 O m − 2 s − 1 (B 57–562) (Table 3 ). The highest seed cotton yield/plant was recorded in genotype B 61-2128 (118.65 g), followed by H 1652 (115.03 g), SHAHD-50 (111.97 g), HS 45 (109.29 g), USA 57/7 (109.28 g) and V 78–156 (107.27 g). The minimum seed cotton yield was recorded for the H 1640 genotypes (36.27 g). Three genotypes viz ., B 61-2128 (moderately resistant), HS 1652 (resistant) and SHAHD 50 (moderately resistant) had significantly higher seed cotton yield (118.65, 115.03 and 111.97 g, respectively) than the tolerant check RS 2013 (100.37 g) (Table 3 ). Hence, these can be utilized further in the breeding programme for yield improvement along with leafhopper resistance. Biochemical parameters in cotton genotypes The 127 cotton genotypes showed a significant difference for all the biochemical parameters i.e. total sugar, phenol (mg/g), tannin (mg/g) and gossypol (mg/g). The total sugar content ranged from 6.57 (HS 45) to 43.17 mg/g (TH 16). The phenol content was recorded highest in genotype PIL 43 (15.53 mg/g) and lowest in T 219-6-SV 6A (3.91 mg/g). The tannin content was highest in the Ganganagar Ageti (8.04 mg/g) followed by H 1645 (8.03 mg/g), Texas 377 (7.99 mg/g) and Tamcot CAMDE-2 (7.95 mg/g). The minimum tannin content was recorded in genotype G Cot H8P2 (2.14 mg/g). The gossypol content was highest in genotype CB 2482 (5.53 mg/g) and lowest in T 219-6-SV 6 A (1.01 mg/g). The other genotypes with relatively high gossypol content were G Cot H 8 P2 (1.15 mg/g), Auburn-M (1.31 mg/g), PD 695-1 (1.40 mg/g) and N 27 (1.41 mg/g) (Table 3 ). Table 3 Phenotypic correlation coefficients and range of the leafhopper population, jassid injury grade, morpho-physiological and biochemical traits Parameters/ traits Correlation (r p ) with leafhopper population Range Mean ± SE(m) Minimum Maximum Leafhopper population (nymphs per 3 leaves/plant) - 2.74 7.85 4.44 ± 0.16 Jassid injury grade 0.77** 1.00 3.19 2.01 ± 0.09 Plant height (cm) -0.05 117.83 186.83 148.2 ± 2.72 Leaf trichome density (number/cm 2 ) -0.68** 10.17 639.1 126.41 ± 5.88 Trichome length (µm) -0.26** 13.44 88.88 32.65 ± 1.39 Leaf water content (%) 0.24** 55.49 87.49 71.8 ± 1.68 Photosynthetic rate (µmol CO 2 m − 2 s − 1 ) -0.23** 2.59 15 9.99 ± 0.58 Chlorophyll content (SPAD value) -0.09 29.88 46.8 37.78 ± 1.15 Transpiration rate (mmol H 2 O m − 2 s − 1 ) 0.33** 1.24 4.3 2.86 ± 0.18 Stomatal conductance (mol H 2 O m − 2 s − 1 ) 0.45** 0.06 0.2 0.13 ± 0.01 Total sugar (mg/g) 0.62** 6.57 43.17 27.43 ± 0.95 Total phenol (mg/g) -0.67** 3.91 15.53 11.51 ± 0.31 Tannin content (mg/g) -0.66** 2.14 8.03 5.89 ± 0.19 Gossypol content (mg/g) -0.77** 1.01 5.53 3.59 ± 0.12 Ginning outturn (%) -0.07 33.24 41.96 38.58 ± 0.74 Seed cotton yield per plant (g) -0.39** 36.27 118.65 63.82 ± 3.62 ** Significant at 1% Association between the leafhopper population and other parameters/traits The correlations between three qualitative traits related to leaves (leaves colour, shape and nectaries) and the leafhopper population were not statistically significant. However, out of five genotypes with okra type leaves four genotypes had relatively less infestation of leafhopper. The correlations among the remaining traits and the leafhopper population were analyzed. The results of correlation coefficient analysis of various traits among the cotton genotypes are presented in Table 3 & Fig. 6 . The leafhopper population was significant positively correlated with jassid injury grade (r = 0.77), total sugar content (r = 0.62), stomatal conductance (r = 0.45), transpiration rate (r = 0.33) and relative water content (r = 0.24). It was significant negatively correlated with the gossypol content (r=-0.77), trichome density (r=-0.68), phenol content (r=-0.67), tannin content (r=-0.66), seed cotton yield (r=-0.39), trichome length (r=-0.26) and photosynthetic rate (r=-0.23). Impact of morpho-physiological and biochemical traits on the jassid population The regression model was fitted among the leafhopper population and its distinct morpho-physiological and biochemical traits in 127 cotton genotypes. The model was a good fit, and the coefficients of regression (b) are presented in Supplementary Table S2. The coefficient of determination (R 2 ) of the multiple linear regression model revealed that morpho-physiological and biochemical traits explained 75.88% impact on the leafhopper population in cotton genotypes. The leaf trichome density had a significant maximum impact (45.33%) on the leafhopper population. Other traits, such as total phenol content (7.70%), stomatal conductance (5.4%), transpiration rate (3.89%), jassid injury grade (3.67), gossypol content (3.38%) and tannin content (2.67%) also significantly impacted the leafhopper population. Multiple linear regression analysis of variance revealed that all the traits were well fitted. Discussion Germplasm screening for leafhopper resistance The peak period of leafhopper infestation was observed between 60–90 DAS during the year 2021 and 2022. This investigation revealed that there was a change in the leafhopper population 70 days after sowing, possibly because of changes in weather conditions. According to Muhammad et al. ( 2021 ), twelve cotton genotypes were screened for jassid tolerance in which significant signs of jassid injury grade were observed at 90 DAS. On the basis of jassid injury grade; four genotypes viz., H 1652, HS 45, H 1655 and DL1 were found resistant. Sixty two genotypes were moderately resistant, 56 genotypes as susceptible and five genotypes viz ., G Cot H 8 P2, T 219-6-SV 6 A, PD 695-1, Auburn-Mand N 27 as highly susceptible. Variability among cotton genotypes with respect to different traits The phenotypic coefficient of variation (PCV) was slightly greater than the genotypic coefficient of variation (GCV) which indicated the minimal role of environmental factors in the expression of different traits for cotton genotypes. The leaf trichome density, trichome length, leafhopper population and jassid injury grade had high estimates of PCV and GCV. Sivasubramanian et al. ( 1991 ) also observed high PCV and GCV for trichome density and trichome length in cotton germplasm. Trichome density and trichome length was greater in plants which confer resistance to leafhopper infestation. Similarly, Subhashini et al. ( 2022 ) and Mawblei et al. ( 2024 ) observed a high variation in cotton leaf trichome density and advocated its role in leafhopper resistance. High heritability and genetic advance was seen for leaf trichome density and trichome length which indicated the presence of additive gene effect and direct selection will be effective for these traits. Similar findings were reported by Nawab et al. ( 2011 ) in cotton. Hence, leaf trichome density and length may be regarded as a key criterion for leafhopper resistance and selection for these traits would be effective for developing resistant cotton genotypes. In the present investigation, a high level of relative leaf water content was detected in susceptible genotypes, whereas a lower level was observed in resistant genotypes. The results were in agreement with the results as reported by Muregsan and Kavitha (2010). The leafhopper resistant cotton genotypes had relatively high photosynthetic rate, whereas the photosynthetic rate was low in the susceptible genotypes. These results indicated that leafhopper infestation in susceptible genotypes reduced the photosynthetic rate. Few studies related to the role of physiological traits in leafhopper tolerance have been documented in cotton. However, results documented by Gomez et al. ( 2006 ) in cotton; Macedo et al. ( 2003 ) in wheat against aphids were in concordance with the present results. The transpiration rate and stomatal conductance were relatively high in susceptible genotypes and relatively low in resistant genotypes. Similar results were reported by Shannag (1998) in cotton and Shannag ( 2007 ) in black bean against aphids. The increased rate of transpiration and stomatal conductance might be attributed to the presence of metabolically active substances produced due to sucking by insect, which interfered with the regulatory process in the host plant. The plant biochemical components have a significant impact on insect resistance. These components function as feeding stimulants as well as nutrient deficit inducers and physiological inhibitors; thereby interfering with the development and metabolism of insects (Rizwan et al. 2021). The high variability was observed for the biochemical parameters studied across the cotton genotypes and results were supported by the findings of Bhoge et al. ( 2019 ) and Mawblei et al. ( 2024 ). In resistant cotton genotypes, low total sugar content was observed, whereas in susceptible cotton genotypes, it was greater. These results were supported by the findings of Rohini et al. ( 2011 ). The phenol, tannin and gossypol act as a repellent to leafhopper and protect against further damage. Higher levels of phenol, tannin and gossypol were detected in resistant cotton genotypes, whereas these levels were lower in susceptible genotypes. Rohini et al. ( 2011 ) reported ample amount of phenol and gossypol in resistant cotton genotypes, whereas low levels were reported in susceptible genotypes. The tannin content was greater in resistant genotypes than in susceptible genotypes. Similar results were reported by Rohini et al. ( 2011 ) and Deb et al. ( 2015 ) in cotton. Hence, these biochemical differences can be exploited not only as selection parameters but also as potential biochemical indices to accelerate breeding pipelines for leafhopper resistance (Zulqurnain et al. 2023 ). Furthermore, integration of biochemical trait screening with genomic tools, such as QTL mapping and genome-wide association studies, may help identify candidate loci underlying resistance traits (Malik et al. 2014 ; Noormohammadi et al. 2018 ). This can reduce the reliance on conventional field screening, which is resource-intensive and environment-dependent (Miyazaki et al. 2013 ). Combining these biochemical parameters with morphological traits like trichome density can enhance selection accuracy in resistance breeding (Grover et al. 2016 ). Such integrated approaches could also help in developing multi-pest resistant varieties, since several of these traits confer cross-protection against other sucking pests like aphids and whiteflies (Chu et al. 2002 ; Saleem et al. 2013 ). The leafhopper population was significant positively correlated with the jassid injury grade. Similar findings have been reported by Gangavati and Maralappanavar ( 2022 ) in cotton genotypes. The leafhopper population and jassid injury grade were significant negatively correlated with leaf trichome density and trichome length. Similar results were reported by Syed et al. ( 2003 ) and Murugesan and Kavitha ( 2010 ). The leafhopper population and jassid injury grade were significant positively correlated with the total sugar content but significant negatively correlated with total phenol, tannin and gossypol content. Keerthivarman et al. ( 2022 ) reported similar results for total sugar and leafhopper population. Rushpam and Ravikesavan ( 2019 ) and Muhammad et al. ( 2021 ) observed similar correlations of leafhopper population with phenol, tannin and gossypol content. The multiple linear regression (R 2 ) model revealed that 75.88% of variance in the leafhopper population was explained by independent variables (morpho-physiological and biochemical components). Leaf trichome density had a highly significant negative effect and maximum impact (45.33%) on the leafhopper population. The results reported by Aslam et al. ( 2004 ) and Bhatti et al. ( 2015 ) also revealed a significant negative impact of leaf trichome density on leafhopper population. Stomatal conductance (5.4%) and transpiration rate (3.89%) also significantly affected the leafhopper population. The other traits, such as total phenol (7.7%), gossypol (3.38%) and tannin (2.67%) significant negatively affected the leafhopper population. These results suggest that phenolic compounds play a role in plant defence against sucking pests. Conclusion The host plant resistance traits can be deployed in cotton plant defence mechanism as an alternative management strategy to control leafhopper. In the present investigation; leaf trichome density had the major negative impact on leafhopper population followed by total phenol in cotton genotypes. Hence, these traits can be included as important morphological and biochemical traits to evaluate for leafhopper resistance in cotton. Four genotypes viz. , H 1652, HS 45, H 1655 and DL1 were identified as resistant against leafhopper based on higher phenol, tannin, gossypol content, trichome density/trichome length and a lower content of soluble sugar. These can be effectively utilized in future breeding programmes against leafhopper resistance. Three genotypes viz ., B 61-2128 (moderately resistant), HS 1652 (resistant) and SHAHD 50 (moderately resistant) had significantly higher seed cotton yield (118.65, 115.03 and 111.97 g/plant, respectively) than the tolerant check RS 2013 (100.37 g/plant). These can be utilized further in the improvement of both yield and leafhopper resistance. Declarations Ethics approval and consent to participate: This study did not involve any human or animal subjects and hence ethical approval and consent to participate were not required. Consent for publication: All authors have read and approved the final manuscript and consent to its publication Availability of data and materials: All data relevant to the manuscript are provided within the text and in supplementary file. Competing interests : The authors declare that they have no competing interests. Funding: This research received no external funding. Author contributions Om Prakash Yadav performed the experiments and wrote the original draft. Minakshi Jattan performed conceptualization of the study, supervision and manuscript editing. Sandeep Kumar assisted in statistical analysis. Anil Jakhar assisted with methodology and data curation. Shiwani Mandhania assisted with methodology. Anil Kumar Saini assisted in data interpretation. Subhash Chandra reviewed the manuscript. Amit Sharma edited the manuscript. Rukoo Chawla assisted in statistical analysis. 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4","display":"","copyAsset":false,"role":"figure","size":121826,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"FiguresFinal4a.png","url":"https://assets-eu.researchsquare.com/files/rs-7242070/v1/0e4e25143b9319957a7e78f5.png"},{"id":92439347,"identity":"77966884-8781-405d-a7cf-4a4dfb1890df","added_by":"auto","created_at":"2025-09-29 17:59:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87343,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"FiguresFinal5a.png","url":"https://assets-eu.researchsquare.com/files/rs-7242070/v1/b373b2e049cfe7469621ee9d.png"},{"id":92440288,"identity":"e3ae9d1c-f98d-46f0-93f6-546208b1314d","added_by":"auto","created_at":"2025-09-29 18:15:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1215687,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"FiguresFinal6.png","url":"https://assets-eu.researchsquare.com/files/rs-7242070/v1/05a9d5de88d29c147141f668.png"},{"id":104779764,"identity":"e7940c65-42f5-441e-8987-13a26f2fb2db","added_by":"auto","created_at":"2026-03-17 07:45:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3248862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7242070/v1/c68f1a52-5f62-4d4a-aca6-a4b230243840.pdf"},{"id":92440296,"identity":"a5df99af-9b85-4181-8cfe-38e426135db4","added_by":"auto","created_at":"2025-09-29 18:15:53","extension":"doc","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3634688,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-7242070/v1/d68019df0e6005c18bc7b54d.doc"}],"financialInterests":"","formattedTitle":"Deciphering Host Plant Resistance in Upland Cotton (G. hirsutum) against Amrasca biguttula biguttula (Ishida) pest in North Indian Zone","fulltext":[{"header":"Background","content":"\u003cp\u003eCotton (\u003cem\u003eGossypium\u003c/em\u003e spp.) is one of the most economically significant fiber and oilseed crops cultivated globally, playing a crucial role in the socio-economic development of cotton-growing nations. This widely recognized crop thrives in tropical and subtropical climates and is extensively grown across continents (Komala et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Among the cultivated species, \u003cem\u003eGossypium hirsutum\u003c/em\u003e (AD₁ genome), commonly known as upland or New World cotton, dominates global cotton cultivation, accounting for over 90% of the total cotton-growing area worldwide.\u003c/p\u003e\u003cp\u003eAccording to the ICAR-AICRP on Cotton Annual Report (2024\u0026ndash;25), China leads global cotton production with a 26.25% share, followed by India (20.67%), Brazil (14.05%), and the USA (11.92%). Although India ranks high in terms of total cotton production, the yield per unit area remains a concern due to the persistent challenges posed by various abiotic and biotic stresses.\u003c/p\u003e\u003cp\u003eCotton cultivation is frequently threatened by a broad spectrum of insect pests that attack the crop at different stages of its growth cycle. In India alone, over 160 insect species are known to infest cotton, of which about 15 are considered economically important pests (Matre et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These pests are mainly classified into two categories: sucking pests and bollworms. Sucking insects such as jassid (leafhopper), whitefly, aphid, and thrips contribute significantly to yield reduction. Among them, the cotton leafhopper (\u003cem\u003eAmrasca biguttula biguttula\u003c/em\u003e Ishida), also known as the Indian jassid, is of particular concern due to its widespread occurrence and potential to cause up to 40% yield loss (Mawblei et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLeafhopper damage is evident through symptoms like yellowing, reddening, curling, and eventual drying of leaves. These visible changes result from phytotoxemia\u0026mdash;commonly termed \u0026lsquo;hopperburn\u0026rsquo;\u0026mdash;a physiological disorder that negatively impacts plant growth and fiber quality. Managing such pests requires a deeper understanding of plant-insect interactions and host plant resistance mechanisms.\u003c/p\u003e\u003cp\u003eThe foundation for understanding plant resistance to insect pests was laid by Painter (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1958\u003c/span\u003e), who categorized resistance into three mechanisms: non-preference (antixenosis), antibiosis, and tolerance. Non-preference refers to plant characteristics that discourage insect feeding or egg-laying. Antibiosis involves plant traits that negatively affect pest development or survival. Tolerance allows the plant to sustain pest damage without severe yield or quality reduction.\u003c/p\u003e\u003cp\u003eResistance to pests like leafhopper is influenced by a combination of morphological, physiological, and biochemical plant traits. Morpho-physiological features such as trichome density and length, leaf angle, lamina thickness, phloem characteristics, and pigmentation play a significant role in deterring pest infestation (Manivannan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Likewise, biochemical components such as phenols, tannins, gossypol, and flavonoids may act as deterrents or disrupt pest physiology by interfering with digestion, growth, or reproduction. Some of these compounds may also attract pests by acting as feeding stimulants, highlighting the need for trait-specific investigations. Additionally, physiological parameters like chlorophyll content, stomatal conductance, and transpiration rate have been linked to pest susceptibility. Higher chlorophyll content, for instance, may enhance photosynthetic activity, inadvertently making plants more attractive to insect pests (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the importance of these resistance-associated traits, there is limited literature on a comprehensive germplasm panel evaluated specifically for leafhopper resistance using a combined morpho-physiological and biochemical trait approach. Identifying and utilizing these host plant traits could serve as a sustainable strategy to breed leafhopper-resistant cotton cultivars. Therefore, the present study was undertaken to evaluate a diverse set of cotton germplasm accessions to identify key morpho-physiological and biochemical traits that confer resistance to leafhopper. This approach aims to facilitate the development of improved cotton genotypes with enhanced pest resilience, contributing to sustainable cotton production and improved yield stability.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003ePlant Material\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe experimental material consisted of 127 genotypes of upland cotton (\u003cem\u003eG. hirsutum\u003c/em\u003e L.) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The experiment was raised in randomized block design with three replications under unprotected conditions during \u003cem\u003eKharif\u003c/em\u003e 2021 and 2022 at the research farm of Cotton Section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, Haryana (29\u0026deg;10'N latitude, 75\u0026deg;46'E longitude with an altitude of 215.2 m above mean sea level). An infector row of okra (\u003cem\u003eAbelmoschus esculentus\u003c/em\u003e) was planted between the two genotypes to build a sufficient leafhopper population for infestation. Standard cultural practices as recommended by the University package of practices were performed during both the growing seasons. The pooled weather parameters for \u003cem\u003eKharif\u003c/em\u003e 2021 and 2022 season have been depicted in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of the leafhopper nymphal population and jassid injury grade\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFive competitive plants were selected randomly from each germplasm accession in each replication for observations. The nymphal population was observed on the abaxial surface of three leaves (one each from the top, middle and bottom leaf canopy) of each tagged plant. The nymphal population and jassid injury grade were recorded at 30, 45, 60, 75, 90, 105, 120 and 135 days after sowing (DAS). The okra plants were grown as infector rows and uprooted after 45 DAS. However, uprooted plants were kept in field to promote the migration of the leafhopper from okra to cotton and build the pest population on the cotton plants. Jassid damage was assessed by leaf burning (hopperburn), drying and shedding of young plant leaves and scoring was done by standard protocol as suggested by Kranthi et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The genotypes were categorized on the basis of grading scale i.e. Resistant (0.1 -1.0); moderately resistant (1.1-2.0); susceptible (2.1-3.0) and highly susceptible (3.1\u0026ndash;4.1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eObservations on morpho-physiological and yield related traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLeaf samples were collected from five tagged plants of each genotype in each replication during the peak infestation period (60\u0026ndash;90 DAS) of the leafhopper. Three leaf discs of one cm\u003csup\u003e2\u003c/sup\u003e from each of the top, middle and bottom leaves were used to measure trichome density and trichome length on the abaxial leaf surface using a stereomicroscope (Carl Zeiss Microscopy GmbH) at 0.63X magnification and the mean values were computed. The physiological parameters such as the relative water content \u003cb\u003e(\u003c/b\u003eRWC) were estimated as described by Barrs and Weatherley (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). The leaf chlorophyll content was measured through chlorophyll meter (Model No. Minolta SPAD-502 Plus) and the photosynthetic rate, transpiration rate and stomata conductance were measured by an infrared gas analyzer (IRGALCi_SD, ADC Biosciences) during the peak infestation period (60\u0026ndash;90 DAS) of the leafhopper. The leaf shape, colour and leaf nectaries of the fourth leaf on the main stem from the top of the plant were recorded at 50% flowering stage. The mean seed cotton yield/plant was calculated and ginning outturn (%) was worked out after harvesting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObservations on biochemical parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo estimate the biochemical parameters, young leaves at the third or fourth node from the top of the plant with similar age and size were taken. The leaf samples were sun dried for 2\u0026ndash;3 days and then oven dried at 60\u003csup\u003e0\u003c/sup\u003eC. The total sugar content (mg/g) was estimated using the method as suggested by Dubois et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), phenol content (mg/g) using the method as described by Bray and Thorpe (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1954\u003c/span\u003e), the tannin content (mg/g) as suggested by Porter et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and gossypol content (mg/g) as given by Bell (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1967\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe pooled mean data of \u003cem\u003eKharif\u003c/em\u003e 2021 and 2022 was used for analysis. The data analysis for all traits was carried out via R studio (RStudio, 2020) software. Analysis of variance (ANOVA) was performed for all the traits. The phenotypic correlation coefficients (r\u003csub\u003ep\u003c/sub\u003e) among leafhopper population, jassid injury grade, morpho-physiological and biochemical characters were estimated (Al-Jibouri et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Regression analysis was used to define the relative contribution of component traits towards the leafhopper population as recommended by Snedecor and Cochran (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOne hundred and twenty seven cotton genotypes were evaluated for three qualitative traits (leaf shape, colour and leaf nectaries) and 16 quantitative traits (morpho-physiological, biochemical, leafhopper population, jassid injury grade and yield related traits). Some morphological traits of cotton plants such as leaf shape, leaf colour and leaf nectaries favour or disfavour leafhopper infestation. Hence, cotton genotypes were also characterized and classified into various categories on the basis of leaf morphological traits (qualitative) following the guidelines of the Protection of Plant Varieties and Farmers\u0026rsquo; Right Act (2001). In the present study, five genotypes \u003cem\u003eviz\u003c/em\u003e. Surab 72, NHBBR 9\u0026thinsp;\u0026minus;\u0026thinsp;1, PIL 43, B 61-2128 and B 59-1679 had okra type leaves and in two genotypes (B 61-2128 and B 59-1679) leaf nectarines were absent. These genotypes were found to be moderately resistant to leafhopper, except for Surab 72, which was found to be susceptible. Among these five genotypes, two genotypes i.e., PIL43 and B 61-2128 had light green leaves which further favoured resistance to leafhopper as light green leaves are less preferred by the pest.\u003c/p\u003e\u003cp\u003eAnalysis of variance (ANOVA) was used to analyze the components of variance for different quantitative traits among 127 genotypes of upland cotton. The mean sum of squares due to genotypes was highly significant for all the traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results revealed considerable genetic variability in the cotton germplasm for all the traits under investigation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of variance for 16 different quantitative traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSource of Variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMean sum of squares\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReplication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegrees of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeafhopper population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.75**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJassid injury grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402.76**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf trichome density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25060.59**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrichome length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e404.68**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative water content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.73**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.92**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhotosynthetic rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.69**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranspiration rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomatal conductance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223.93**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhenol content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTannin content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGossypol content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.93**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed cotton yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e244.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e850.65**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinning outturn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.66**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e** Significant at 1%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe violin graph and box plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) depicted huge variations for 16 different quantitative traits. The large variations for leaf trichome density, trichome length, plant height, seed cotton yield, total sugar content, tannin content and gossypol content were observed in this study.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eGenetic parameters of variability among cotton genotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe estimates of genetic parameters of variability are essential in plant breeding because these help in identification of the type of gene action operative in the control of quantitative traits. The PCV and GCV were high for leaf trichome density (72.30, 71.85), trichome length (36.16, 35.17), gossypol (32.20, 31.70), total sugar (31.87, 31.30), seed cotton yield (27.58, 25.77), photosynthetic rate (26.93, 24.99), tannin (24.50, 23.87), stomatal conductance (24.24, 20.12), transpiration rate (23.30, 20.64), leafhopper population (22.24, 21.21) and jassid injury grade (22.03, 20.70); moderate for total phenol (17.84, 17.23) and low for chlorophyll content (8.76, 7.00), plant height (8.31, 7.56), relative leaf water content (8.13, 7.15) and ginning outturn (6.69, 5.80) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The high heritability along with high genetic advance as percent of mean was observed for leaf trichome density (98.77), gossypol (96.87), total sugar (96.44), tannin content (94.85), trichome length (94.56), seed cotton yield (87.34), total phenol (93.23), leafhopper per 3 leaves (90.90), jassid injury grade (88.23), photosynthetic rate (86.13), transpiration rate (78.50) and stomatal conductance (68.84) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It showed the presence of additive gene action behind the traits. Hence direct selection will be rewarding for these traits.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic parameters of variability for various traits/parameters among 127 cotton genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTrait /Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCoefficient of Variation (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeritability (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGA as percent\u003c/p\u003e\u003cp\u003eof mean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf hopper population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJassid injury grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf trichome density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e186.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e147.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrichome length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative leaf water content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhotosynthetic rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranspiration rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomatal conductance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhenol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTannin content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGossypol content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinning outturn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed cotton yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLeafhopper infestation and Jassid injury grade\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe leafhopper infestation among 127 upland cotton genotypes during \u003cem\u003eKharif\u003c/em\u003e 2021 and 2022 and their pooled data for the two seasons revealed a significant increase in the nymphal population after 45 DAS. The peak infestation period for the nymphal population was recorded between 60 to 90 DAS during both seasons. The lowest mean nymphal population of leafhopper was recorded in the genotype SHAHD 50 (2.70 per three leaves/plant), followed by HS 45 (2.76 per three leaves/plant) and DL 1 (3.00 per three leaves/plant), whereas the highest mean nymphal population was recorded in Auburn-M (8.02 per three leaves/plant), followed by the T 219-6-SV 6 A (7.26 per three leaves/plant) and H 1661 (6.64 per three leaves/plant) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eObservations on jassid damage were recorded to determine the degree of jassid injury according to different signs of leaf damage in the genotypes. A significant sign of jassid injury grade was recorded at 90 DAS for both the seasons. The pooled data of two seasons revealed that the jassid injury grade was highest at 105 DAS. The lowest mean jassid injury grade of 1.00 was recorded in H 1652, HS 45, H 1655 and DL 1, whereas the highest mean jassid injury grade (3.19) was recorded in N 27, followed by Auburn-M (3.14) and PD 695-1 (3.10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis evaluation revealed that out of 127 upland cotton genotypes; four genotypes \u003cem\u003eviz\u003c/em\u003e., H 1652, HS 45, H 1655 and DL1 were resistant; 62 genotypes were moderately resistant; and 56 genotypes were susceptible and five genotypes \u003cem\u003eviz\u003c/em\u003e., G Cot H 8 P2, T 219-6-SV 6 A, PD 695-1, Auburn-M and N 27were highly susceptible.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMorpho-physiological and yield related traits in cotton genotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe morpho-physiological traits were highly significant in upland cotton genotypes. The cotton genotypes were distinguished based on leaf trichome density on abaxial surface and rating scale as shown in Supplementary Fig. S2. The leaf trichome density ranged widely from 10.17 (T 219-6-SV 6 A) to 639.10 (HS 45) trichomes/cm\u003csup\u003e2\u003c/sup\u003e with a mean value of 126.94 trichomes/cm\u003csup\u003e2\u003c/sup\u003e. The highest leaf trichome density on leaf abaxial surface was observed for the genotype HS 45 (639.10 trichomes/cm\u003csup\u003e2\u003c/sup\u003e) followed by H 1655 (413.92 trichomes/cm\u003csup\u003e2\u003c/sup\u003e) and 1301 DDP3 (408.55 trichomes/cm\u003csup\u003e2\u003c/sup\u003e). The maximum trichome length was observed for the genotype NHBBR 9\u0026thinsp;\u0026minus;\u0026thinsp;1 (88.88 \u0026micro;m) and minimum trichome length was observed for H 1640 (13.44 \u0026micro;m) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relative water content was highest in genotype PIL 43 (87.49%) and lowest in H 1659 (55.49%. The maximum chlorophyll content (SPAD value) was observed in genotype H 1643 (46.80) and the minimum chlorophyll content was detected in L 147 (29.88). The photosynthetic rate was maximum in genotype USA 47A (15.00 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and lowest in H 1640 (2.59 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The maximum transpiration rate was observed in genotype H 1640 (4.30 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the minimum in Tamcot CAMDE (1.24 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The stomatal conductance ranged from 0.06 (H 1639) to 0.20 mol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (B 57\u0026ndash;562) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe highest seed cotton yield/plant was recorded in genotype B 61-2128 (118.65 g), followed by H 1652 (115.03 g), SHAHD-50 (111.97 g), HS 45 (109.29 g), USA 57/7 (109.28 g) and V 78\u0026ndash;156 (107.27 g). The minimum seed cotton yield was recorded for the H 1640 genotypes (36.27 g). Three genotypes \u003cem\u003eviz\u003c/em\u003e., B 61-2128 (moderately resistant), HS 1652 (resistant) and SHAHD 50 (moderately resistant) had significantly higher seed cotton yield (118.65, 115.03 and 111.97 g, respectively) than the tolerant check RS 2013 (100.37 g) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Hence, these can be utilized further in the breeding programme for yield improvement along with leafhopper resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiochemical parameters in cotton genotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 127 cotton genotypes showed a significant difference for all the biochemical parameters i.e. total sugar, phenol (mg/g), tannin (mg/g) and gossypol (mg/g). The total sugar content ranged from 6.57 (HS 45) to 43.17 mg/g (TH 16). The phenol content was recorded highest in genotype PIL 43 (15.53 mg/g) and lowest in T 219-6-SV 6A (3.91 mg/g).\u003c/p\u003e\u003cp\u003eThe tannin content was highest in the Ganganagar Ageti (8.04 mg/g) followed by H 1645 (8.03 mg/g), Texas 377 (7.99 mg/g) and Tamcot CAMDE-2 (7.95 mg/g). The minimum tannin content was recorded in genotype G Cot H8P2 (2.14 mg/g). The gossypol content was highest in genotype CB 2482 (5.53 mg/g) and lowest in T 219-6-SV 6 A (1.01 mg/g). The other genotypes with relatively high gossypol content were G Cot H 8 P2 (1.15 mg/g), Auburn-M (1.31 mg/g), PD 695-1 (1.40 mg/g) and N 27 (1.41 mg/g) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotypic correlation coefficients and range of the leafhopper population, jassid injury grade, morpho-physiological and biochemical traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters/ traits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCorrelation (r\u003csub\u003ep\u003c/sub\u003e) with leafhopper population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE(m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeafhopper population (nymphs per 3 leaves/plant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e4.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJassid injury grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e148.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf trichome density (number/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.68**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e639.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e126.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrichome length (\u0026micro;m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.26**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e32.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf water content (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.24**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhotosynthetic rate (\u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e9.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll content (SPAD value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e37.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranspiration rate (mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomatal conductance (mol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugar (mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e27.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal phenol (mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.67**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e11.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTannin content (mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.66**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGossypol content (mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.77**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinning outturn (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e38.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed cotton yield per plant (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.39**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e63.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e** Significant at 1%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between the leafhopper population and other parameters/traits\u003c/b\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe correlations between three qualitative traits related to leaves (leaves colour, shape and nectaries) and the leafhopper population were not statistically significant. However, out of five genotypes with okra type leaves four genotypes had relatively less infestation of leafhopper. The correlations among the remaining traits and the leafhopper population were analyzed. The results of correlation coefficient analysis of various traits among the cotton genotypes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The leafhopper population was significant positively correlated with jassid injury grade (r\u0026thinsp;=\u0026thinsp;0.77), total sugar content (r\u0026thinsp;=\u0026thinsp;0.62), stomatal conductance (r\u0026thinsp;=\u0026thinsp;0.45), transpiration rate (r\u0026thinsp;=\u0026thinsp;0.33) and relative water content (r\u0026thinsp;=\u0026thinsp;0.24). It was significant negatively correlated with the gossypol content (r=-0.77), trichome density (r=-0.68), phenol content (r=-0.67), tannin content (r=-0.66), seed cotton yield (r=-0.39), trichome length (r=-0.26) and photosynthetic rate (r=-0.23).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpact of morpho-physiological and biochemical traits on the jassid population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe regression model was fitted among the leafhopper population and its distinct morpho-physiological and biochemical traits in 127 cotton genotypes. The model was a good fit, and the coefficients of regression (b) are presented in Supplementary Table S2. The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) of the multiple linear regression model revealed that morpho-physiological and biochemical traits explained 75.88% impact on the leafhopper population in cotton genotypes. The leaf trichome density had a significant maximum impact (45.33%) on the leafhopper population. Other traits, such as total phenol content (7.70%), stomatal conductance (5.4%), transpiration rate (3.89%), jassid injury grade (3.67), gossypol content (3.38%) and tannin content (2.67%) also significantly impacted the leafhopper population. Multiple linear regression analysis of variance revealed that all the traits were well fitted.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eGermplasm screening for leafhopper resistance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe peak period of leafhopper infestation was observed between 60\u0026ndash;90 DAS during the year 2021 and 2022. This investigation revealed that there was a change in the leafhopper population 70 days after sowing, possibly because of changes in weather conditions. According to Muhammad et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), twelve cotton genotypes were screened for jassid tolerance in which significant signs of jassid injury grade were observed at 90 DAS. On the basis of jassid injury grade; four genotypes viz., H 1652, HS 45, H 1655 and DL1 were found resistant. Sixty two genotypes were moderately resistant, 56 genotypes as susceptible and five genotypes \u003cem\u003eviz\u003c/em\u003e., G Cot H 8 P2, T 219-6-SV 6 A, PD 695-1, Auburn-Mand N 27 as highly susceptible.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariability among cotton genotypes with respect to different traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe phenotypic coefficient of variation (PCV) was slightly greater than the genotypic coefficient of variation (GCV) which indicated the minimal role of environmental factors in the expression of different traits for cotton genotypes. The leaf trichome density, trichome length, leafhopper population and jassid injury grade had high estimates of PCV and GCV. Sivasubramanian et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) also observed high PCV and GCV for trichome density and trichome length in cotton germplasm. Trichome density and trichome length was greater in plants which confer resistance to leafhopper infestation. Similarly, Subhashini et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Mawblei et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed a high variation in cotton leaf trichome density and advocated its role in leafhopper resistance. High heritability and genetic advance was seen for leaf trichome density and trichome length which indicated the presence of additive gene effect and direct selection will be effective for these traits. Similar findings were reported by Nawab et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in cotton. Hence, leaf trichome density and length may be regarded as a key criterion for leafhopper resistance and selection for these traits would be effective for developing resistant cotton genotypes.\u003c/p\u003e\u003cp\u003eIn the present investigation, a high level of relative leaf water content was detected in susceptible genotypes, whereas a lower level was observed in resistant genotypes. The results were in agreement with the results as reported by Muregsan and Kavitha (2010). The leafhopper resistant cotton genotypes had relatively high photosynthetic rate, whereas the photosynthetic rate was low in the susceptible genotypes. These results indicated that leafhopper infestation in susceptible genotypes reduced the photosynthetic rate. Few studies related to the role of physiological traits in leafhopper tolerance have been documented in cotton. However, results documented by Gomez et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in cotton; Macedo et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) in wheat against aphids were in concordance with the present results. The transpiration rate and stomatal conductance were relatively high in susceptible genotypes and relatively low in resistant genotypes. Similar results were reported by Shannag (1998) in cotton and Shannag (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) in black bean against aphids. The increased rate of transpiration and stomatal conductance might be attributed to the presence of metabolically active substances produced due to sucking by insect, which interfered with the regulatory process in the host plant.\u003c/p\u003e\u003cp\u003eThe plant biochemical components have a significant impact on insect resistance. These components function as feeding stimulants as well as nutrient deficit inducers and physiological inhibitors; thereby interfering with the development and metabolism of insects (Rizwan et al. 2021). The high variability was observed for the biochemical parameters studied across the cotton genotypes and results were supported by the findings of Bhoge et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Mawblei et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In resistant cotton genotypes, low total sugar content was observed, whereas in susceptible cotton genotypes, it was greater. These results were supported by the findings of Rohini et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The phenol, tannin and gossypol act as a repellent to leafhopper and protect against further damage. Higher levels of phenol, tannin and gossypol were detected in resistant cotton genotypes, whereas these levels were lower in susceptible genotypes. Rohini et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported ample amount of phenol and gossypol in resistant cotton genotypes, whereas low levels were reported in susceptible genotypes. The tannin content was greater in resistant genotypes than in susceptible genotypes. Similar results were reported by Rohini et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Deb et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in cotton.\u003c/p\u003e\u003cp\u003eHence, these biochemical differences can be exploited not only as selection parameters but also as potential biochemical indices to accelerate breeding pipelines for leafhopper resistance (Zulqurnain et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, integration of biochemical trait screening with genomic tools, such as QTL mapping and genome-wide association studies, may help identify candidate loci underlying resistance traits (Malik et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Noormohammadi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can reduce the reliance on conventional field screening, which is resource-intensive and environment-dependent (Miyazaki et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Combining these biochemical parameters with morphological traits like trichome density can enhance selection accuracy in resistance breeding (Grover et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such integrated approaches could also help in developing multi-pest resistant varieties, since several of these traits confer cross-protection against other sucking pests like aphids and whiteflies (Chu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Saleem et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe leafhopper population was significant positively correlated with the jassid injury grade. Similar findings have been reported by Gangavati and Maralappanavar (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in cotton genotypes. The leafhopper population and jassid injury grade were significant negatively correlated with leaf trichome density and trichome length. Similar results were reported by Syed et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Murugesan and Kavitha (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The leafhopper population and jassid injury grade were significant positively correlated with the total sugar content but significant negatively correlated with total phenol, tannin and gossypol content. Keerthivarman et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported similar results for total sugar and leafhopper population. Rushpam and Ravikesavan (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Muhammad et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed similar correlations of leafhopper population with phenol, tannin and gossypol content.\u003c/p\u003e\u003cp\u003eThe multiple linear regression (R\u003csup\u003e2\u003c/sup\u003e) model revealed that 75.88% of variance in the leafhopper population was explained by independent variables (morpho-physiological and biochemical components). Leaf trichome density had a highly significant negative effect and maximum impact (45.33%) on the leafhopper population. The results reported by Aslam et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and Bhatti et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also revealed a significant negative impact of leaf trichome density on leafhopper population. Stomatal conductance (5.4%) and transpiration rate (3.89%) also significantly affected the leafhopper population. The other traits, such as total phenol (7.7%), gossypol (3.38%) and tannin (2.67%) significant negatively affected the leafhopper population. These results suggest that phenolic compounds play a role in plant defence against sucking pests.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe host plant resistance traits can be deployed in cotton plant defence mechanism as an alternative management strategy to control leafhopper. In the present investigation; leaf trichome density had the major negative impact on leafhopper population followed by total phenol in cotton genotypes. Hence, these traits can be included as important morphological and biochemical traits to evaluate for leafhopper resistance in cotton. Four genotypes \u003cem\u003eviz.\u003c/em\u003e, H 1652, HS 45, H 1655 and DL1 were identified as resistant against leafhopper based on higher phenol, tannin, gossypol content, trichome density/trichome length and a lower content of soluble sugar. These can be effectively utilized in future breeding programmes against leafhopper resistance. Three genotypes \u003cem\u003eviz\u003c/em\u003e., B 61-2128 (moderately resistant), HS 1652 (resistant) and SHAHD 50 (moderately resistant) had significantly higher seed cotton yield (118.65, 115.03 and 111.97 g/plant, respectively) than the tolerant check RS 2013 (100.37 g/plant). These can be utilized further in the improvement of both yield and leafhopper resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study did not involve any human or animal subjects \u0026nbsp;and hence ethical approval and consent to participate were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All authors have read and approved the final manuscript and consent to its publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data relevant to the manuscript are provided within the text and in supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOm Prakash Yadav\u0026nbsp;\u003c/strong\u003eperformed the experiments and wrote the original draft.\u003cstrong\u003e\u0026nbsp;Minakshi Jattan\u003c/strong\u003e performed conceptualization of the study, supervision and manuscript editing. \u003cstrong\u003eSandeep Kumar\u0026nbsp;\u003c/strong\u003eassisted\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ein statistical analysis. \u003cstrong\u003eAnil Jakhar\u0026nbsp;\u003c/strong\u003eassisted with methodology and data curation. \u003cstrong\u003eShiwani Mandhania\u0026nbsp;\u003c/strong\u003eassisted with methodology. \u003cstrong\u003eAnil Kumar Saini\u0026nbsp;\u003c/strong\u003eassisted in data interpretation.\u003cstrong\u003e\u0026nbsp;Subhash Chandra\u003c/strong\u003e reviewed the manuscript. \u003cstrong\u003eAmit Sharma\u0026nbsp;\u003c/strong\u003eedited the manuscript. \u003cstrong\u003eRukoo Chawla\u003c/strong\u003e assisted\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ein statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Department of Genetics and Plant Breeding and Entomology, College of Agriculture, CCS Haryana Agricultural University, Hisar, Haryana, for providing the essential facilities during the investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharya VS, Singh AP. 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J Cotton Res. 2023;6(3):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s42397-023-00140-3\u003c/span\u003e\u003cspan address=\"10.1186/s42397-023-00140-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biochemical, germplasm, jassid injury grade, leafhopper, morpho-physiological","lastPublishedDoi":"10.21203/rs.3.rs-7242070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7242070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCotton leafhopper or jassid has become a major pest of cotton and inflicts substantial yield losses. Developing insect-resistant cultivars is a key economic strategy for mitigating this damage. Host plant resistance (HPR) through morpho-physiological and biochemical traits reveals a crucial role in plant defence against sucking pests. In this study, 127 \u003cem\u003eGossypium hirsutum\u003c/em\u003e accessions were evaluated during \u003cem\u003eKharif\u003c/em\u003e 2021 and 2022 for various morpho-physiological and biochemical traits/parameters to determine their impact on leafhopper population.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLeafhopper population showed significant positive correlations with jassid injury grade (r\u0026thinsp;=\u0026thinsp;0.77), total sugars (r\u0026thinsp;=\u0026thinsp;0.62), stomatal conductance (r\u0026thinsp;=\u0026thinsp;0.45), transpiration rate (r\u0026thinsp;=\u0026thinsp;0.33) and leaf water content (r\u0026thinsp;=\u0026thinsp;0.24). Conversely, significant negative correlations were observed with gossypol content (r=-0.77), trichome density (r=-0.68), phenol content (r=-0.67), tannin content (r=-0.66), seed cotton yield (r=-0.39), trichome length (r=-0.26) and photosynthetic rate (r=-0.23). Regression analysis indicated that morpho-physiological and biochemical traits collectively explained 75.88% impact on leafhopper population, with trichome density alone had 45.33% impact. On the basis of two-year field evaluations for jassid injury grade studies, four genotypes \u003cem\u003eviz\u003c/em\u003e., H 1652, HS 45, H 1655 and DL1 were found to be resistant. Three genotypes (B 61-2128, HS 1652, SHAHD 50) significantly out yielded the tolerant check RS 2013 (100.37 g/plant), producing seed cotton yield of 118.65, 115.03 and 111.97 g/plant, respectively. Traits such as leaf trichome density, trichome length, and the contents of sugar, phenol, tannin, and gossypol showed both high heritability and high genetic advance, suggesting the predominance of additive gene action and implying that these traits can be effectively improved through direct selection.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe present investigation identified the major role of leaf trichome density and phenol content on leafhopper population in cotton. Four genotypes \u003cem\u003eviz.\u003c/em\u003e, H 1652, HS 45, H 1655 and DL1 were identified as resistant against leafhopper and these can be effectively exploited in future breeding programmes against leafhopper resistance. Three genotypes \u003cem\u003eviz.\u003c/em\u003e, B 61-2128, HS 1652 and SHAHD 50 (moderately resistant/resistant against leafhopper) showed significantly higher seed cotton yield (118.65, 115.03 and 111.97 g/plant, respectively) than the tolerant check RS 2013 (100.37 g/plant). These can be utilized for the simultaneous improvement of yield and leafhopper resistance.\u003c/p\u003e","manuscriptTitle":"Deciphering Host Plant Resistance in Upland Cotton (G. hirsutum) against Amrasca biguttula biguttula (Ishida) pest in North Indian Zone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 17:59:47","doi":"10.21203/rs.3.rs-7242070/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62805238-e284-474d-9e67-e2bc5c649f5c","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T00:27:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 17:59:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7242070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7242070","identity":"rs-7242070","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}