Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar

Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar | 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 Article Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar Haider Hayat Khan, Ghulam Mujtaba, Nasir Mehmood Khan, Nanak Khan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5336733/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 Cotton production has been drastically affected by the climate change through its effect on agronomic practices such as sowing time and adoption of genotypes to various agro-ecological conditions. Interaction of genotypes and sowing date is an important strategy to analyze cotton yield and quality in a rainfed environment. Various morphological, and yield parameters including plant height, boll weight, crop growth rate, lint yield, seed cotton yield, ginning out turn (GOT), fiber fineness, fiber length and fiber strength was recorded. Results of the study revealed that N-878 performed best on an average during all the growing seasons followed by IUB-2013, SS-32 and BS-15 respectively. The best yield and quality were achieved when the cotton cultivars were sown on 22nd April followed by 8th April and 6th June respectively. This study will be helpful to look into performance of genotypes and possibilities of cotton cultivation under rainfed conditions of Pothwar. Also, sowing window of studied cotton genotypes will be explored for cotton cultivation under rainfed conditions of Pothwar. In addition, the present piece of work will provide future direction for research on cotton in the Pothwar region. Biological sciences/Plant sciences Biological sciences/Plant sciences/Plant development Biological sciences/Plant sciences/Plant physiology Biological sciences/Plant sciences/Plant stress responses Gossypium hirsutum sowing dates water stress rainfed cotton Pothwar region. PCA analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cotton is the world’s major industrial crop (Tuttolomondo et al., 2020 ) that supports the world's largest textile industries, worth $ 600 billion annually (Shuli et al., 2018 ). It is back bone of Pakistan economy with 55% contribution to export earnings. In Pakistan, cotton is cultivated on an area of 2.079 mha with production of 7.064 million bales. The crop accounts for 3.1% of total value addition in agriculture and 0.6% of gross domestic product (GDP) of Pakistan (Pakistan Economic Survey, 2020–2021). In spite of being largest traded commodity, there is a steady decline in the area and production of cotton in the country over the last eight years due to various biotic, abiotic constraints and mushroom growth of sugar mills in the core area of cotton cultivation. Cotton production had dropped 22.8% to 7.064 million bales, down from 9.148 million bales the previous year (Pakistan Economic Survey 2020–2021). Similarly, area under cotton cultivation is declined by 17.4% to 2.79 mha against area of 2.517 mha. High temperatures, rainfall, water availability, sowing and harvesting dates, land adaptability, and rainfall pattern are among the other major constraints that affected cotton production (Balathandayutham & Mayilswami. 2015; Sharif et al., 2020 ; Sankaranarayanan et al., 2020 ). Climate change has influenced cotton production system in a number of ways, and the consequences are likelsy to have an impact on economy of the country. The seed cotton production is determined by the climatic conditions that prevail during the squaring, flowering, and boll development stages. In Pakistan high temperature during flowering is a major cause for reduced cotton yield. Climate conditions and agronomic techniques like plant density, sowing timing, irrigation, and fertilization can all affect fiber and seed yield (Guzman et al., 2019 ; Khan et al., 2017 ; Chen et al., 2019 ). For better growth and development, cotton requires 28.5–35°C temperature (Shuli et al., 2018 ). In Pakistan, cotton is mainly cultivated in Sindh and Punjab. Climate of these provinces is very dry and hot where mercury level in summer rises to 41–47°C and sometimes 50°C. The fluctuating climatic conditions require re-standardizing and adjusting the cotton sowing window (Sankaranarayanan et al., 2020 ) for various genotypes. 2 Adjusting sowing times has proven to be an effective management strategy for increasing seed cotton yield (Sankaranarayanan et al., 2020 ). Sowing time determines not only the growth and production components, but also the fiber quality of cotton (Shah et al., 2017 ; Khan et al., 2017 ). Similarly, a crucial management strategy for limiting the effects of abiotic variables such as drought and heat stress is planting of cotton at optimum sowing time. Selection of best cultivar and optimum sowing date accounts for a considerable improvement in yield and quality parameters of cotton (Deho et al., 2012 ). Growth and yield response of cotton genotypes is dependent on interaction of genotypes and sowing dates. Cultivar selection and management of optimum sowing dates are essential elements that have a great impact on yield and quality parameters of cotton (Zeng et al., 2014). Optimum sowing dates for different cultivars may differ according to environmental conditions of the region. (Usman et al., 2016). Best adopted cultivars ensure higher yield, fiber quality, tolerance against adverse conditions and early maturity. By sowing cotton cultivars at different times like early sowing, normal sowing and late sowing, we can appraise best cultivar for higher yield and quality (Usman et al., 2016) for diverse climatic conditions. The cotton production can be increased by expanding area under its cultivation and by increasing per acre yield. The increase in area seems impracticable due to sugarcane adoption by the farmers in core area of cotton cultivation due to more profitability and risk of failure. However, cotton production can be increased by exploring new pockets for its cultivation. The determination of genotype-by-environment interaction at various planting times could be a technique for achieving optimum cotton seed and lint yields under new agro-ecological conditions (Tuttolomondo et al., 2020 ). Keeping in view the aforementioned challenges, the current study had been designed to explore the influence of different sowing dates on the growth and yield of various cotton genotypes under rainfed conditions in Pothwar region. The given piece of work was carried out with objective of: (a) Exploring possibilities of cotton cultivation under rainfed conditions of Pothwar, (b) Evaluating best genotypes of cotton for cultivation under rainfed conditions of Pothwar and (c) Determining best sowing time for cotton cultivation in Pothwar region under rainfed conditions. Methods The study was conducted at University Research Farm Koont, Rawalpindi (PMAS-AAUR) in the growing year 2021 to determine the best growing cultivar and optimum sowing date of cotton crop in agro-climatic conditions of Pothwar. After the harvesting of wheat crop, rotavator was used to plow the soil. The land was prepared by ploughing with planking after it rains to make the soil fine and leveled. The study was comprised of two factors viz., genotypes and sowing dates. Four genotypes of cotton (IUB-2013, N-878, SS-32, BS-15) was planted on three different sowing dates depending on rainfall. RCBD factorial design was used with four replicates. Sowing date was kept in main plots and cotton cultivars was sown in subplots. The crop was flat sown manually by dibbling method, planting four seeds per hole. The plant-plant distance of 25 cm and row-row distance of 75cm was maintained Thinning was done to one plant per hole when the seedlings established. Each treatment will consist of five lines. The recommended dose of N, P, K at120, 90, 80 kg ha − 1 , respectively, was applied. Nitrogen fertilizer was applied in three splits viz. 1/3 at basal, 1/3 at flowering stage and 1/3 at boll formation stage. Pest scouting was done on weekly basis to monitor the pest infestation and for suitable measures. Climate description Pakistan Meteorological Department (PMD) data on climatic variables, such as daily minimum and maximum temperatures (◦C) and rainfall (mm), were gathered. However, the FAO approach was used to compute solar radiation (Mj m − 2 day − 1 ). The average annual temperature of URFK Chakwal is 22.4°C, placing it in the medium rainfall zone (van Ogtrop et al., 2014). Weather conditions prevailed during the study season at URF Chakwal have been depicted in Fig. 4 . Data collection Plant height (cm) was measured from the base of the stem to terminal bud of 20 tagged plants in cm. Plant height was recorded at maturity stage by using measuring rod. Boll Weight of seed cotton per boll was determined by dividing total weight of bolls by total numbers of collected bolls from 20 representative tagged plants of each treatment. Crop Growth Rate (gm − 2 day − 1 ) of each genotype in interaction with each sowing date was calculated. Dry weight of selected plants was recorded at two different stages. CGR was calculated by using a formula: $$\:CGR=\:\frac{W2-W1}{P\:(t1-t2)}$$ Where; P = ground area W1 = Dry weight of plants recorded at time t1 W2 = Dry weight of plants recorded at time t2 Ginning Out Turn (%) (GOT) is the ratio of ginned lint to seed cotton yield. GOT was calculated by a formula: $$\:GOT=\:\frac{Lint\:Weight\:\left(g\right)}{SeedCotton\:Yield\:\left(g\right)}\:x\:100$$ Lint Yield (kg/ha), after ginning seed cotton of selected plants, lint yield of each treatment was calculated by using electric balance. The assessment of quality parameter (fiber length and fiber strength) was carried out from CCRI Multan. One of the key factors that directly affects yarn quality and can lead to expensive disruptions or breakage in the yarn manufacturing process is the length of the cotton fiber, also known as the staple length. The irregularity of the yarn is directly influenced by the length of the fibers; yarn with longer fibers has a stronger tenacity. The majority of spinning mills currently use the High-Volume Instrument (HVI) and the Advanced Fiber Information System (AFIS) to measure the quality of cotton fiber. Based on a fiber bundle testing method, the HVI system aids spinning specialists in managing bale lay down in warehouses and is utilized for fiber classification. We have chosen to use the HVI equipment, which measures light attenuation through a combed beard of fibers to determine fiber length. Samples are placed in the HVI baskets, where cotton fibers (lint) are captured by a comb. Fiber beards are created by combed fibers that have been clamped such that each fiber is parallel to the others. After that, light is passed through these beards by the device, and sensors analyze the difference in light attenuation across the fiber beard to determine how long the cotton fibers are. The sowing dates can have an impact on the fiber fineness of different cotton genotypes. Fiber fineness refers to the thickness or diameter of the individual cotton fiber which is an important characteristic in determining the quality of cotton. Micronaire values are typically used to express the fineness of cotton fiber. After being weighed, the fibers are compressed to a certain volume in a chamber. Micronaire values (Table 1) are calculated by passing a regulated airflow through the prepared sample based on the permeability through the fibers. Lord ( 1956 ) demonstrated that the fineness and maturity of cotton fiber were correlated with resistance to airflow. M × H = 3.86 × Mic 2 + 18.16 × Mic + 13 where M is maturity ratio, H is linear density, and mic is micronaire. The classification standard of cotton fiber micronaire values is summarized in Table 1. Table.1. Classification standard of cotton fiber micronaire value (Saville., 1999), Micornaire Value (µg/inch) Ratings Below 3 Very fine 3.1–3.9 Fine 4.0–4.9 Average 5.0–5.9 Coarse Over 6 Very coarse The seed cotton yield (kg/ha) from each treatment was taken by adding seed cotton yield of first picking and second picking and expressed in kg/ha. Data analysis The statistical analysis was performed by the analysis of variance using SPSS. The treatment means was compared by Least Significant Difference test (α = 0.05). Principle component analysis (PCA) was performed with Origin Pro v 2024b. The correlations between the quantitative variables were determined using Pearson correlation coefficient formula. The PCA was carried-out based on the biplot method in Origin Pro v 2024b in order to investigate how sowing dates (SDs) and Genotypes (Vs) altered cotton parameters. The PCA is a multiple-variate tool that examines the data using a variety of related, measured dependent variables in order to extract the essential information from the data, designate it as a collection of novel orthogonal variables called principal components (PC), and demonstrate the relationships between the observations and variables (Mishra et al., 2017 ). The variables thought to best describe the system qualities were those with high factor loading and main components with high eigenvalues. As a result, only PCs with eigenvalues of ≥ 1 (Kaiser, 1960 ) is taken into consideration. Component scores, often referred to as factor scores and loadings, provide an explanation of the obtained findings of a PCA (Wold et al., 1987 ). Results Plant height (cm) Plant height is a main parameter to investigate the crop growth of the all crops. Plant height varied on different sowing dates. An experiment was conducted to investigate the variation of plant height of the four different cultivars of cotton under rainfed condition on three different sowing dates in Pothwar region. Results of our study showed that an average plant height of cultivar N-878 (92.233 cm) was significantly higher results than others cultivars on all sowing dates especially on 22nd of the April as followed by other two dates of 8th April and 6th of June respectively. Plant height of cultivar IUB-2013 (89.667 cm) also performed best at second sowing date after cultivar N-878 under same conditions, (Table 2 ). After IUB-2013, SS-32 showed decent results on an average followed by BS-15. Anova showed that plant height and different sowing dates had no significant relationship (Table 2 ). Results of our study are in line with Jamro et al. ( 2017 ) who also found the 2nd half of the April month showed great results of cotton crop yield in the rainfed area as compared to the other sowing dates. Table 2 Effect of sowing dates on Plant Height (cm) of various cotton genotypes under rainfed conditions of Pothwar Sowing date Variety V1 V2 V3 V4 Mean S1 83.267 81.367 86.233 80.067 82.733B S2 89.667 88 92.233 85.500 89.025A S3 78.8 76.50 81 73.66 77.092C Mean 83.911AB 81.956BC 86.722A 79.744C LSD values: varieties (4.0223); sowing date (3.4834); S ×V (6.9668) Values sharing common letters did not differ significantly Boll weight (g) Average boll weight is the major factor that affect cotton yield. The difference in the average boll weight of four different cotton cultivars is shown in Table 3 . The interaction of sowing data and genotype was non-significant for average boll weight. The results demonstrated that the sowing date of 22nd April showed significantly better (0.31g) than the other two sowing dates on an average boll weight. There was no significant difference in boll weight for sowing on 1st (0.28g) and 3rd sowing (0.71g). The average boll weight of the cotton genotypes sown on 8th April (0.28g) was higher than the average boll weight of the cotton genotypes sown on 6th June (0.71g) but the difference was not significant. The N-878 had shown significantly better results than other three varieties in all the three sowing dates followed by the IUB-2013 which shows better results than SS-32 and BS-15 although, these differences were significant when the varieties were sown on 8th April as compared to other two sowing dates where the difference was not significant. This could be due to the better water and nutrient storage capacity of the N-878 and IUB-2013 as compared to the other two varieties which made them more favorable to increase their boll weight. Our results are in line with (Iqbal et al., 2018 ) who also found that the cotton genotypes performed better when sowed in the mid of April. Table 3 Effect of sowing dates on boll weight (g) of various cotton genotypes under rainfed conditions of Pothwar Variety V1 V2 V3 V4 Mean Sowing date S1 0.29 0.26 0.33 0.25 0.28B S2 0.31 0.30 0.36 0.27 0.31A S3 0.27 0.26 0.31 0. 25 0.271B Mean 0.2922B 0.2722C 0.3333A 0.2567C LSD values: varieties (0.44); sowing date (0.0087) Values sharing common letters did not differ significantly (α = .05) Crop growth rate (gm day) Dynamics of crop growth rate of the four different cotton cultivars as affected by the three different sowing dates is shown in Table 4 . The Results of our study showed that there was no significant difference between sowing dates and cotton genotypes in terms of crop growth rate. CGR was higher when the cotton cultivars were sown on 8th April as compared to the other two sowing dates on an average. Although, this difference was significant when the cultivars were sown on 6th June but the difference was not significant when the crops were sown on 22nd April. The N-878 showed the best results on all the three sowing dates followed by IUB-2013 during the sowing dates of 8th and 22nd April but during the sowing of 6th June SS32 showed best results than IUB. Table 4 Effect of sowing dates on CGR (gm − 2 day − 1 ) of various cotton genotypes under rainfed conditions of Pothwar Variety Sowing date V1 V2 V3 V4 Mean S1 0.317 0.30 0.37 0.297 0.3225B S2 0.35 0.34 0.39 0.32 0.3517A S3 0.29 0.297 0.32 0.26 0.2925C Mean 0.319B 0.31BC 0.36A 0.29C LSD values: varieties (0.0248); sowing date (0.0215); S ×V (0.0429) Values sharing common letters did not differ significantly (α = .05) Fiber fineness (µg/inch) Fiber fineness is the crucial parameter for evaluating quality of cotton. Fiber fineness is the thickness or the diameter of individual cotton fibers. Effect of different sowing dates on the fiber fineness of four different genotypes of cotton has been shown in the Table 5. The results showed that Fiber finesses of all the cultivars when they were sown on 22nd April were significantly better than the sowing dates of 8th April and 6th June. The Study revealed that cotton genotype N-878 showed significantly better results than all the other three varieties during all the three sowing dates. The statistical analysis showed that among the interaction between cotton genotypes and sowing date the variety(N-878) planted on 22 April showed best result in case of fiber fineness followed by the cotton genotypes (SS-32 and BS-15) that were both planted on 6 June respectively, genotype N-878 having The mean LSD for fiber fineness is 3.98 having the highest mean as compared to other genotypes (SS-32, BS-15 and IUB-2013) having the means of 3.784, 3.747 and 3.733 respectively. While the LSD mean for sowing date having the highest fiber fineness value of 3.967 for two sowing dates 8th April and 22nd April thus the fiber fineness has no effect on the genotypes that are sown on 8th April and 22nd April 2022, Similarly the sowing date 6 June 2022 having the lowest LSD mean of 3.622 which affected the fiber fineness of cotton. Ullah et al. ( 2019 ) demonstrated that cotton genotype grown in the mid of April showed better results of fiber fineness as compared to other sowing dates. Table.5. Effect of sowing dates on Fiber Fineness (µg/inch) of various cotton genotypes under rainfed conditions of Pothwar. Variety V1 V2 V3 V4 Mean Sowing date S1 3.71 3.51 3.87 3.40 3.6225B S2 3.90 3.82 4.29 3.86 3.9675A S3 3.59 4.02 3.78 3.98 3.8450A Mean 3.7333B 3.7844B 3.9811A 3.7478B LSD values: varieties (0.1877); sowing date (0.1626); S ×V (0.3251) Values sharing common letters did not differ significantly (α = .05) Fiber length (mm) Fiber length is an important factor affecting cotton quality. It refers to the physical length of individual fibers. Variation in fiber length of the four different genotypes of cotton which are sown on three different sowing dates in rainfed areas of the Pothwar is shown in the Table 6 . Results of the study illustrate non-significant difference between cotton genotypes and sowing dates. The LSD mean of sowing date 22nd April 2022 have the highest value of 30.14 in fiber length followed by sowing date 8th April 2022 and 6th June 2022 having the mean LSD value of 28.08 and 25.93 respectively. While in case of comparing the mean LSD for variety effect to fiber length named N-878 showed an exceptional performance on an average having the mean LSD value of 30.58 while there is no significance difference between genotypes IUB-2013, BS-15 and SS-32 in case of fiber length. The overall results showed that the best average of the cotton genotypes regarding fiber length was found during the sowing date of 22nd April. Table 6 Effect of sowing dates on Fiber Length (mm) of various cotton genotypes under rainfed conditions of Pothwar Variety V1 V2 V3 V4 Mean Sowing date S1 27.84 25.90 30.40 28.19 28.082B S2 30.14 29.10 33.15 28.18 30.142A S3 27.65 24.22 28.19 23.67 25.934C Mean 28.541B 26.408C 30.581A 26.681BC LSD values: varieties (1.9237); sowing date (1.6660); S ×V (3.3319) Values sharing common letters did not differ significantly (α = .05) Fiber strength (g tex − 1 ) Effect of different sowing dates on the fiber strength of four different cultivars of cotton has been shown in the Table 7 . Statistical analysis shows that there is no significant difference between the interaction of sowing date and cotton genotypes. Fiber finesses of all the cultivars when they were sown on 22nd April were significantly having the LSD mean of 28.75 for sowing date better than the sowing dates of 8th April having the LSD mean of 26.43 followed by 6th June having the mean LSD value of 25.07 least fiber strength was observed in the 6th June sowing date. The Study revealed that N-878 showed significantly better results having the Mean LSD value of 29.95 followed by IUB-2013 having the LSD mean value of 27.02 while there is no significant difference between the cotton genotypes SS-32 and BS-15 having the mean LSD value of 25.55 and 24.64 respectively. The results of our study are in line with (Iqbal et al., 2018 ) who also found that the cotton genotypes which were sown during the month of April give good results as compared to the late sowing dates. He observed improved fiber strength in the genotypes that were sown in the mid of April. Table 7 Effect of sowing dates on Fiber Strength (g tex − 1 ) various cotton genotypes rainfed conditions of Pothwar Variety V1 V2 V3 V4 Mean Sowing date S1 26.52 24.8 29.86 24.52 26.43B S2 29.22 27.70 32.56 26.03 28.88A S3 25.33 24.15 27.46 23.37 25.08C Mean 27.02B 25.55C 29.96A 24.64C LSD values: varieties (1.4685); sowing date (1.2718); S ×V (2.5436) Values sharing common letters did not differ significantly (α = .05) Lint yield (kg/ha) Dynamics of lint yield of the four different genotypes of cotton crop as affected by the three different sowing dates have been shown in the Table 8 . There is no significant difference between the sowing dates and genotypes of cotton affecting the lint yield. Results of the study illustrate that the cotton genotype named N-878 showed a very good performance on an average having the LSD mean of 261.45 while there was no significant difference between IUB-2013 and SS-32 while there is a significant difference between the N-878 and BS-15 during all the three sowing dates but its peak results were obtained during the sowing date of 22nd April (238.08). The Results showed that genotypes sown on 22nd April performed significantly better having the Mean LSD value of 4.64 as compare to all other sowing dates 8th April and 6th June in case of lint yield. The Reason behind the higher lint yield by N-878 could be its higher plant height and its good adaptation to the environment than all the other varieties. Malik et al. ( 2022 ) has also observed maximum lint yield was recorded in the cotton genotypes that were planted in the mid of April. Table 8 Effect of sowing dates on Lint Yield kg/ha of various cotton genotypes under rainfed conditions of Pothwar Variety V1 V2 V3 V4 Mean Sowing date S1 246.14 242.56 253.53 238.08 245.08AB S2 251.64 246.14 261.45 234.52 248.08A S3 240.82 237.13 241.05 230.97 237.49B Mean 246.20AB 241.94AB 252.01A 234.52B LSD values: varieties (12.01); sowing date (10.402); S ×V (20.804) Values sharing common letters did not differ significantly (α = .05) Seed cotton yield (kg/ha) Seed Cotton yield refers to the amount of raw cotton produced per unit of land area. Seed cotton yield is important parameter that indicates the production of cotton crop. Influence of different sowing dates on the seed cotton yield of four different genotypes of cotton has been shown in the Table 9 . Statistical analysis shows that there is no significant difference between the interaction of sowing date and cotton genotypes. Our study indicates that higher seed cotton yield was obtained by cotton genotype N-878 when it was sown during 22nd April (1809) followed by cotton genotypes IUB-2013 (1185) in 22nd April Sowing. Cotton genotype BS-15 showed minimum seed cotton yield in all three sowing but among all three sowing dates least seed cotton yield was obtained in 8th April (599.3). Results indicates that variety N8- 78 was best genotype under rainfed conditions of Pothwar. Similarly, sowing date 22nd April showed good results for all cotton genotypes. Our results are in line with the study of (Malik et al., 2022 ). He showed that higher seed cotton yield was obtained in 20th April sowing followed by 10th May and 20th May. Table 9 Effect of sowing dates on Seed Cotton Yield (kg/ha) of various cotton genotypes under rainfed conditions of Pothwar. Variety V1 V2 V3 V4 Mean Sowing date S1 927.8 1401.9 721.2 599.3 912.6B S2 1185.3 977.2 1809.7 839.7 1203A S3 777.4 714.1 979.2 614.1 771.2C Mean 963.5B 1031.1B 1170A 684.4C LSD values: varieties (101.51); sowing date (87.913); S ×V (175.83) Values sharing common letters did not differ significantly (α = .05) Ginning out turn (%) Variation in GOT (%) of the four different cultivars of cotton which were sown on three different sowing dates in rainfed areas of the Pakistan has been shown in the Table 10 . The statistical analysis shows that between interaction between cotton genotypes and sowing dates in case of GOT is not significant. Results of our study showed that GOT of the varieties was significantly higher when they were sown on 22nd April having the LSD mean of 38.30 as followed by other two dates of 8th April having the LSD mean of 36.46 and 6th June having the LSD mean of 35.33 there is no significant difference between these two sowing dates in case of GOT respectively. The N-878 variety showed the best results regarding GOT in all the three sowing dates as compared to all the other three varieties which might be due to the well adaptation to the environment of Pothwar region and its drought resistance capacity. Whereas, in general on comparative basis the IUB-2013 showed best results in all three sowing seasons after the N-878. After IUB-2013, SS-32 showed decent results on an average followed by BS-15. The overall results showed that the best average of the cotton varieties regarding GOT was found during the sowing date of 22nd April. Results of our research are in line with (Iqbal et al., 2018 ) in which he noticed the higher GOT in the genotypes that were sown in the mid of April. Malik et al. ( 2022 ) has also observed improved ginning out turn (%) was 24 recorded in the cotton genotypes that were planted in the mid of April. Table 10 Effect of sowing dates on Ginning out turn (%) of various cotton genotypes under rainfed conditions of Pothwar Variety V1 V2 V3 V4 Mean Sowing date S1 37.13 35.87 37.86 35.00 36.465B S2 39.25 37.06 40.85 36.08 38.307A S3 35.87 34.75 37.66 33.07 35.338B Mean 37.416 35.894 38.787 34.718C LSD values: varieties (1.5426); sowing date (1.3359); S ×V (2.6719) Values sharing common letters did not differ significantly (α = .05) Pearson correlation matrix The Pearson correlation among the 9 numerical traits of four genotypes of American cotton under the impact of three different sowing dates is given in Fig. 1 , below. Plant height (cm) showed highly strong positive to weak positive significant association with CGR (r = 0.93), FL (r = 0.91), GOT (r = 0.91), FS (r = 0.9), LY (0.83), BW (0.82), SCY (r = 0.67) and FF (r = 0.35). A highly strong positive and intermediate positive significant association was found for the traits like FS (r = 0.98), CGR (r = 0.93), GOT (r = 0.93), LY (r = 0.9), FL (r = 0.86), SCY (r = 0.62) and FF (r = 0.58) with the boll weight. The lint yield (LY) revealed highly strong positive (r = 0.93) to weak positive (r = 0.39) significant association was found with FS (r = 0.93), GOT (r = 0.92), CGR (r = 0.91), BW (r = 0.9), PH (r = 0.83), SCY (r = 0.7) and FF (r = 0.39) (Table 6 ). The features are ranked by their correlations, and the blue bars represent lowest + ve correlations while red bar indicates highly strong positive correlations. The deeper the red color, the stronger the correlations while deeper the blue color, the weaker the correlations. Principal component analysis (PCA) Biplot One useful method for describing the relationships between genotypes and sowing dates for different cotton properties is principal component analysis. To see how the four cotton genotypes; IUB-2013, N-878, SS-32, BS-15 (V1, V2, V3 and V4) and three sowing dates (SD1, SD2 and SD3) relate to one another, the first two PCs were plotted. At each axis of differentiation, PCA shows the significance of the biggest contributor to the overall variation ((Fig. 2 , 3 a). Based on PC1 and PC2, the PCA biplot (Fig. 2 , 3 a) displayed four genotypes that differed genetically based on the scattering pattern. A good level of genetic variety was shown by the biplot's genotype dispersion. In terms of seed production, the genotypes that were closest to one another differed little or not at all. The PC1 and PC2 biplots almost validated the cluster analysis grouping. Through hybridization, distant genotypes might be used as varied parents to expand the cotton genetic base since they showed more diversity in seed output. According to Vianna et al. ( 2013 ), the majority of the genotypes found by the PCA were clustered in the same cluster, suggesting that they were comparable. Thus, it was feasible to create a second selection that focused on seed cotton output based on the homogeneity that existed in the groups. The X-axis accounts for 79.95% of the variability, the Y-axis for 9.51% of the extra original variability, and PC1 and PC2 for 89.46% of the overall variation. By redrawing the contour with estimated coefficients for the associated principal component, the two initial PCs were plotted to examine correlations between the four genotypes and three distinct sowing dates with the principal component of cotton seed yield. The plot now displays the association between the genotypes that had reasonably substantial loading on both the PCA1 and PCA2 axes, according to the correlation coefficients between the genotypes and sowing dates. The associations between the four cotton genotypes (IUB-2013, N-878, SS-32, BS-15) and the three sowing dates (April 8, April 22, and June 6) were graphically shown using the PCA biplot (Fig. 2 ). A considerable degree of genetic diversity was demonstrated by the genotypes' excellent distribution. Plotting near to one another revealed comparable traits, but plotting farther apart revealed more genetic differences. Interestingly, when sowed on April 22 (SD2), the genotype N-878 (V2) was located farthest along the positive axis of PC1, demonstrating its better performance across important characteristics such as lint yield, fiber strength, and seed cotton yield. This implies that N-878 (V2) is exceptionally adapted to rainfed environment, especially when planted on April 22 (SD2). The strongest positive correlations between genotypes and sowing dates were seen for SD3.V4 with SD3.V2, SD3.V1 with SD1.V2 and SD1.V4, and SD2.V2 with SD1.V1, SD.V3, SD2.V1, and SD3.V3 combinations (Fig. 3 a). These findings imply that using the nine characteristics of cotton, it is feasible to distinguish between these genotypes and sowing dates. The correlation between each genotype and sowing date with a principal component is used to calculate the discriminatory power of the genotypes and sowing dates in each principal component. Important variables such as plant height (PH), boll weight (BW), crop growth rate (CGR), fiber length (FL), fiber strength (FS), lint yield (LY), seed cotton yield (SCY), and ginning out turn (GOT) were strongly associated with PC1, one of the traits that went into the PCA. This suggests that these traits were the primary drivers of the variation in the genotypes and sowing dates. Genotypes that loaded significantly on PC1, especially N-878, did better in these areas, especially when sown on April 22. According to PC2, which also found a substantial difference between genotypes sown on April 8 and June 6, genotypes such as SS-32 and BS-15 fared poorly, particularly on the late planting date of June 6 (Fig. 3 a) Scree plot The top two eigenvalues of the PCA for four genotypes under three sowing dates on different cotton variables were shown to represent the whole percentage of the variation in the dataset in the Scree plot (Fig. 3 b). Additionally, take note of the plot's split that divides the important from the unimportant parts. Components 1 and 2 are likely significant, according to the majority of academics. Discussion Sowing dates have a major effect on the vegetative and reproductive growth of the crop (Hallikeri et al., 2010 ). Cotton planted early experiences the hottest part of their reproductive cycle, resulting in a considerable drop in yield (Rahman et al., 2007). Furthermore, in late planting, crop faces heavy rains, cold temperature and shorter growth time which results in the reduction of crop yield and quality (Elayan et al., 2015 ). Sankaranarayanan et al. ( 2021 ) investigated the performance of long linted cotton genotypes (DLSA17, PA760, PA812, PA402, PA528 and K12) vs short stapled genotype (P. Dhanwanthrm) under two different sowing dates (4th August and 4th September). They found that short stapled cotton genotypes were on par than long linted cotton genotypes in terms of seed cotton yield. Among long linted genotypes, genotype PA 812 gave higher seed cotton yield at 4th August as compare to DLSA17, PA760, PA402, PA528 and K12, while PA 760 gave the higher fiber quality index then genotypes DLSA17, PA760, PA812, PA402, PA528. Sowing of genotypes on same dates not ensures ideal performance by all. Rather, performance of cotton genotypes is dependent on genotype to sowing date interaction. Ali et al. ( 2021 ) studied the response of different cotton genotypes (Bt. FH-142, Bt. MNH-886, FDH-170) to different sowing dates (21th April, 5th May and 20th May). At planting time 21th April Bt.MNH-886 showed good results for plant density, flowering, boll maturation and boll opening then FH-142, FDH-170. While, at 5th May FDH-170 showed more leaf area index (LAI) and net assimilation rate (NAR) and Bt. FH-142 had more crop growth rate, respectively. Planting of Bt. FH-142 at 21th April gave more total biomass production then Bt.FDH-17 and Bt. MNH 886. Deho et al. ( 2021 ) carried out an experiment on effect of planting dates to investigate the seed cotton yield and fiber quality of three cotton genotypes (NIA-Noori, Sadori and NIA-Ufaq) along with thirty-four elite lines of cotton crop under two different planting dates (20th March and 20th April). The elite lines of cotton performed well in comparison of cotton varieties on planting date 20th April. The elite lines showed higher staple length (mm), seed index (g), number of bolls per 5 plant, sympodial branches, and seed cotton yield were higher when cotton cultivar was planted at 20th April as compared to 20th March. Many other researchers also had studied the effect of sowing date on performance of cotton genotypes. Lashari et al. ( 2020 ) had reported that planting at 1 st May is better as compared to 1st April, 1st June and 15th June in Multan region. The genotypes differ for performance when planted on same date. More number of sympodial branches and plant height are reported in cotton variety ICI-2121 followed by IUB2013 then other genotypes maximum number of bolls per plant, boll weight, and seed cotton yield (kg/ha) was recorded in variety ICI-2121 followed by ICI-2424 and IUB-2013, regardless of planting dates. The researchers suggested that cotton variety ICI-2121 should be planted on 1st May in Multan zone for maximum yield (Lashari et al., 2020 ). Similarly, Iqbal et al. ( 2018 ) studied the effect of different planting times (1st April, 1st May, 1st June, 1st July) and genotypes (IUB-13, IUB-222, IUB-63) on seed cotton yield and reproductive development of cotton crop under agro-climatic conditions of Islamia University of Bahawalpur. Performance of cotton genotype IUB-13 was good when planted at 1st April in comparison with other genotypes. While genotypes (IUB-222, IUB-63) proved best for late sowing dates like 1st June, 1st July. Lowest yield was obtained by genotype IUB-63 because of small boll size. The adjustment of sowing time ensures an ideal temperature required for various growth stages of crop. The ideal sowing date for a genotype also varies with agro-ecologic conditions and location of a region. Sowing too early does not favor most of the developmental stages of crop. Seedling germination and emergence may be limited by low soil temperatures or a drop in air temperature at the end of March or the first 10-day period in April. As a result, planting should only be done when the environment is suitable for seed germination (Tuttolomondo et al., 2020 ). Germination is delayed in early sowing (March 15th), whereas quick and uniform germination is obtained by planting on or after April 15th. Maximum plant height and a greater no of bolls per plant can be obtained by planting between March 15th and April 15th (Kamran et al., 2017). The fluctuating temperature during development stage of crop has great impact on performance of genotypes. Sowing dates is associated with temperature 6 and had great impact on reproductive growth and yield parameters (Iqbal et al., 2018 ). Mehboob et al. ( 2020 ) evaluated the effect of temperature by adjusting sowing date on performance of cotton under agro-ecological conditions of Regional Agriculture Research Institute Bahawalpur (RARI) Pakistan. Planting on April 30th accounted for the highest leaf area index (LAI), total dry matter (TDM) and leaf area duration (LAD), maximum opened boll, average boll weight,100-seed weight and seed cotton yield of genotype MNH-886 as compared to 15 April, 15 and 30 May, 14 and29 June. Seed cotton weight per plant, number of bolls per plant and seed cotton yield declined with delayed sowing. June 1st to June 10this identified as optimum sowing dates (Copur et al., 2019). Improved number of bolls and seed cotton yield were observed in cultivar Stoneville 468. Promising ginning out turn (GOT) was observed in the cultivar PG 2018 while DP-499 was best in plant height, number of sympodial branches and seed cotton weight under studied edaphic and climatic conditions (Copur et al., 2019). Bilal et al. ( 2019 ) investigated the optimum sowing date for enhancing cotton production in Punjab, Pakistan. Three Bt. Cotton cultivars (BH-184, CIM-598, MNH-886) were planted at five different planting dates with the interval of 15 days (1st March, 15th March, 1st April, 15th April, 1st May, 15th May). They observed that maximum sympodial branches, number of bolls per plant, 100-seed weight, seed cotton yield and improved fiber length was produced, when cotton crop was planted on 15th April as compared to other sowing dates (1st March, 15th March, 1st April, 15th April, 1st May, 15th May). Higher seed cotton yield was obtained by cotton cultivar MNH-886 (Bilal et al., 2019 ). Similarly, Sharif et al. ( 2020 ) carried out an experiment at Cotton Research Station, Faisalabad to monitor influence of sowing dates on cotton growth, yield and fiber quality of cotton genotypes. The genotypes planted on 10th April gave increased seed cotton yield as compared to planted on 10th May. under some circumstances, various sowing dates not always show variability in fiber quality. However, the genotypes differ for fiber quality due to difference in their genetic makeup. For example, Sharif and his coworkers ( 2020 ) had reported that genotype FH-6071 produced more yield as compared to genotype FH-152, but FH-6071 had improved fiber quality than other genotypes planted on various dates. Abbas and Ahmad, (2018) examined the effect of different sowing times and cultivars on cotton fiber quality. They found 7 that the late planted cotton crop on June 15th produced improved staple-length, staple-strength and uniformity-index-ratio while highest micronaire values were recorded at early sown crop on May 1st. Cotton Cultivar MNH-886 accounts for improved quality parameters in late sowing dates. In Southern Punjab, Pakistan, early planted crop (February 14 and March 1) required more days to begin emergence, squaring, blooming, boll formation, boll opening and 50% plant germination. While, less days to begin emergence with complete and uniform germination, squaring, flowering, boll formation, boll opening is reported in late sown (30th April and 15th May) crop. However, yield parameters like number of sympodial branches, bolls per plant, and boll weight were enhanced in planting date May 15th. Similarly, planting on March 16th produced maximum ginning out turn, fiber length, strength, fineness, and uniformity. The researcher recommended that for higher seed cotton yield and lint yield having better quality fiber, BT cotton be planted on March 16th (Shah et al., 2017 ). The performance of cotton genotypes and lint quality is also influenced by prevailing agro-ecological conditions of a region and agronomic practices. Under irrigated and rainfed environment the genotypes perform differently. Particularly, rainfed conditions had a significant impact on lint yield and fiber quality of cotton (Ayele et al., 2020 ). The 10 to 14 weeks and 13 to 15 weeks after sowing are crucial plant growth phases for square and boll formation and therefore moisture supply during these conditions may significantly affect yield and quality of fiber under various environments. The average number of squares per plant are dramatically decreased between weeks 10 to 14 after planting under rainfed conditions, while the average number of bolls per plant are reduced during weeks 13 to 15 after planting (Ayele et al., 2020 ). The interactive effect of planting time and other agronomic practices also affect cotton productivity in a region. For example, it is reported that cotton productivity was increased by 14.2% through transplanting of seedlings than direct sowing. Early planting at 1st March resulted in a significant increase in productivity than 1st May. Normally, high temperatures coincide with the May planting and peak blooming times in different cotton growing areas, the practice of planting cotton by transplanting seedlings and early sowing could be successfully strategy. Planting 8 time and weed management practices had sown minimum weed density, weed dry weight of cotton cultivar when planted on August 1st with integrated weed management practices (Hariharasudhan et al., 2017). Studies on interaction of sowing window and nitrogen regimes had showed that application of 120 kgha-1 N offer maximum plant growth, lint yield, and seed cotton yield if the crop is planted on July 1st to mid of July under rainfed conditions of Sudan. Mohamed et al. 2016 ). Similarly, under prevailing conditions of Tandojam (Sindh), planting on 1st May is considered optimum and offers maximum ginning out turn (GOT) and seed cotton yield than late planting. Jamro et al. ( 2017 ) suggested that that cotton crop should not be planted after 10th May in Tandojam and proposed varieties Haridost and Sindh-1 for general cultivation in Tandojam for maximum seed cotton yield. Usman et al. (2016) is of the opinion that planting cotton varieties of central institute Mulan Pakistan (CIM-599 followed by CIM-599) earlier than April 19 results in higher vegetative growth rather than lint yield, but late planted cotton resulted in flowering and boll development in Dera Ismail Khan region. Due to unfavorable environmental conditions and a shorter growing period, earlier and later planting than April 19th results in lower cotton yield with poor quality (Usman et al., 2016). Similarly, as research was conducted in BARI Chakwal to investigate the influence of different sowing dates on different genotypes. It was observed that the cotton genotypes that were sown on 20th April. Higher seed cotton yield, no of bolls and improved quality parameters were obtained in 20th April sowing followed by 10th May and 30th May. Climate change has a great impact on vegetative as well as reproductive growth of cotton crop. In order to get appropriate climate, it is necessary to evaluate best sowing date for cotton crop. Salih. (2019) observed that cotton varieties sown at the end of march showed highest plant height as well as seed cotton yield as compared to late sown varieties. Conclusion To sum up, this study has effectively illustrated the possibility of growing cotton in the rainfed environment. Four distinct cotton genotypes (N-878, IUB-2013, SS-32, and BS-15) were evaluated for performance over three sowing dates. It was discovered that genotype N-878 performed better in terms of growth, yield, and fiber quality, especially when sown on April 22. The significance of maximizing planting windows for increasing both yield and quality under the region's unique climatic conditions is highlighted by the fact that this sowing date regularly beat earlier and later planting dates. PCA analysis successfully brought attention to the significance of the interactions between genotype and sowing date. The combination that performed the best was N-878, which was sowed on April 22 and provided the highest yield and fiber quality. Cotton output in the rainfed areas of Pothwar and other comparable places may increase as a result of farmers using this information to make better decisions. The PCA's findings have important ramifications. The April 22 planting date is crucial, according to the research, especially for the N-878 genotype, which continuously surpassed other combinations in terms of yield and quality. Accordingly, April 22 is the best day to plant in order to maximize cotton yield in the rainfed area. The PCA also demonstrated the versatility and excellent performance of N-878, which makes it a solid contender for additional breeding initiatives targeted at improving cotton yield in rainfed conditions. In order to increase cotton yields in unpredictable rainfed circumstances, farmers and policymakers in the Pothwar region can use these data to guide their decision on the best cotton genotype and sowing date. In order to further maximize cotton output in comparable agro-ecological zones, future study might use the PCA as a basis to examine the performance of different genotypes or improve planting techniques. Declarations Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Authors’ contributions Khan HY and Khan NM performed the experiments, analyzed the data, prepared figures and tables, authored and reviewed drafts of the article. Mujtaba G and Khan MA assisted in performing the experiments and performing plant analysis. Khan N, Kashif M and Ashraf HMA improved the written language. Khan H, Khan NM and Mujtaba G conceived and designed the experiments and approved the final draft. Khan N and Kashif M also helped in using IBM SPSS Statistical Package. All authors read and approved the final manuscript. References Abbas, Q. and Shakeel Ahmad. Effect of Different Sowing Times and Cultivars on Cotton Fiber Quality under Stable Cotton-Wheat Cropping System in Southern Punjab, Pakistan. Pakistan J. Life Social Sci. 16 , 2 (2018). Ahmad, S., Iqbal, M., Muhammad, T. & Mehmood, A. Shakeel Ahmad, and Mirza Hasanuzzaman. Cotton productivity enhanced through transplanting and early sowing. Acta Scientiarum Biol. Sci. 40 , 1–7 (2018). Ali, A., Qamar, R., Safdar, M. E., Saleem, S. & Ullah, S. Muhammad Arshad Javed, and Syed Wasim Hasan. Development and growth: influence of sowing dates on performance of cotton cultivars. 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The multivariate approach and influence of characters in selecting superior soybean genotypes. Afr. J. Agric. Res. 8 (30), 4162–4169 (2013). Wold, S., Esbensen, K. & Geladi, P. Principal component analysis. Chemometr. Intell. Lab. Syst. 2 , 1–3 (1987). Zeng, Linghe, W. R. et al. Bourland. Genotype-by-environment interaction effects on lint yield of cotton cultivars across major regions in the US cotton belt. J. Cotton Sci. 18 (1), 75–84 (2014). Additional Declarations No competing interests reported. Supplementary Files supplementaryData.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5336733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381454128,"identity":"9343e796-198d-4b15-bc16-b83e88d915a7","order_by":0,"name":"Haider Hayat Khan","email":"","orcid":"","institution":"PMAS Arid Agriculture University, Rawalpindi, Punjab, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Haider","middleName":"Hayat","lastName":"Khan","suffix":""},{"id":381454129,"identity":"94e649f3-ab74-4c1e-9f0b-c8237c324632","order_by":1,"name":"Ghulam Mujtaba","email":"","orcid":"","institution":"PMAS Arid Agriculture University, Rawalpindi, Punjab, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Ghulam","middleName":"","lastName":"Mujtaba","suffix":""},{"id":381454130,"identity":"a3de34f2-9b6a-4d75-a02e-48546e4e5b0f","order_by":2,"name":"Nasir Mehmood Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJCCAwwMEgwMzOyHH3yoAHKZmRuI1cKTZjjjDEgLI2EtMGAgzdkGogloMWc//vBw5Q4Lefl2hgRjxnm10fztQC0/Krbh1GLZk2Nw8OwZCcPGZsYDjwu3Hc+dcZixgbHnzG3cjjmQw3CwsU2CsZkZaMvMbcdyG4BamBnb8Gg5//wBSIt9GzPQL7xzjuXOJ6jlRoIBSEtiD1hLQ03uBsJa3oC1JM8AB/KxA7kbgVoO4vXL+fTHHxvb6mzn9x8HRmVNXe6884cPPvhRgVsLOjgMJg8QrR4I6khRPApGwSgYBSMEAADZxV7rJtnSKQAAAABJRU5ErkJggg==","orcid":"","institution":"PMAS Arid Agriculture University, Rawalpindi, Punjab, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Nasir","middleName":"Mehmood","lastName":"Khan","suffix":""},{"id":381454131,"identity":"9e006aa3-6a20-49fa-bc1f-947fd3a64ad3","order_by":3,"name":"Nanak Khan","email":"","orcid":"","institution":"Balochistan Agriculture College, Quetta, Balochistan, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Nanak","middleName":"","lastName":"Khan","suffix":""},{"id":381454132,"identity":"9fff60d4-1a10-4e19-b57e-78f441f9a742","order_by":4,"name":"Muhammad Kashif","email":"","orcid":"","institution":"Crop Science Institute, National Agricultural Research Centre, Islamabad, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Kashif","suffix":""},{"id":381454133,"identity":"94ecaff7-123a-42be-bc69-76625d08a6ec","order_by":5,"name":"Hafiz Muhammad Aftab Ashraf","email":"","orcid":"","institution":"Services \u0026 General Administration Department (S\u0026GAD), Lahore, Punjab, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Hafiz","middleName":"Muhammad Aftab","lastName":"Ashraf","suffix":""},{"id":381454134,"identity":"778ec619-6987-4c1e-9c88-5eac7676e598","order_by":6,"name":"Muhammad Amaid Khan","email":"","orcid":"","institution":"University of Haripur, Haripur, Khyber Pakhtunkhwa, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Amaid","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-10-26 09:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5336733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5336733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70088124,"identity":"81d1eae7-4300-45e5-8df6-7690b1661e4a","added_by":"auto","created_at":"2024-11-28 08:30:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14550,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s Correlation matrix (r) for nine crop traits of four different cotton genotypes under four different sowing dates, r value: 0.0 to 0.2—very weak fit, 0.2 to 0.4—weak fit, 0.4 to 0.7 –intermediate fit, 0.7 to 0.9—strong fit, 0.9 to 1.0—very strong fit. PH plant height (cm), BW boll weight (g), CGR crop growth (gm\u003csup\u003e-2\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e), FF fiber fineness (µg/inch), FL fiber length (mm), FS fiber strength (g tex\u003csup\u003e-1\u003c/sup\u003e), LY lint yield (kg/ha), SCY seed cotton yield (kg/ha), GOT ginning out turn (%),\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/9027e8e6f054f09872a5e0ea.png"},{"id":70088128,"identity":"cc4b7216-519c-480e-9e8f-63a5d3cd2501","added_by":"auto","created_at":"2024-11-28 08:30:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15606,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot of PC1 and PC2 showing the contribution and relationship of 4 genotypes and 3 sowing dates for various traits in cotton. Cotton parameters fiber fineness (FF), boll weight (BW), seed cotton yield (SCY), fiber strength (FS), crop growth rate (CGR), lint yield (LY), ginning out turn (GOT), plant height (PH), fiber length (FL) are the scores of principal component analysis. The symbols used in biplots represent treatments (sowing dates with genotypes), SD1.V1 (sowing date 1 with genotype 1), SD1.V2 (sowing date 1 with genotype 2), SD1.V3 (sowing date 1 with genotype 3), SD1.V4 (sowing date 1 with genotype 4), SD2.V1 (sowing date 2 with genotype 1), SD2.V2 (sowing date 2 with genotype 2), SD2.V3 (sowing date 2 with genotype 3), SD2.V4 (sowing date 2 with genotype 4), SD3.V1 (sowing date 3 with genotype 1), SD3.V2 (sowing date 3 with genotype 2), SD3.V3 (sowing date 3 with genotype 3), SD3.V4 (sowing date 3 with genotype 4), SD4.V1 (sowing date 4 with genotype 1), SD4.V2 (sowing date 4 with genotype 2), SD4.V3 (sowing date 4 with genotype 3), SD4.V4 (sowing date 4 with genotype 4).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/3e817b8b3029726642133961.png"},{"id":70088723,"identity":"5c827655-02e2-4a15-93d3-7dc03fd9aaf8","added_by":"auto","created_at":"2024-11-28 08:38:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26238,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Scores Plot of PC1 and PC2 showing the contribution and relationship of 4 genotypes and 3 sowing dates, and (b) scree plot of PCA between eigen values and principal component number\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/af83f7bd4b447d876d549903.png"},{"id":70088125,"identity":"245b932a-5c82-4e94-8e2c-b548aaa3e282","added_by":"auto","created_at":"2024-11-28 08:30:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44812,"visible":true,"origin":"","legend":"\u003cp\u003eDaily maximum and minimum temperatures (◦C), rainfall (mm) and solar radiation (MJ m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e) during 2021 cotton growing season at study site URFK Chakwal.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/dd718afee5ad011c31e1b76b.png"},{"id":83264812,"identity":"f8157cfc-3a26-40c3-8ab1-8ea0a8506d4a","added_by":"auto","created_at":"2025-05-22 05:32:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1183547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/2a7e708c-f52c-472e-b78d-509e244900f2.pdf"},{"id":70088126,"identity":"4e5aed96-d02b-4331-81cd-13d235ac18d2","added_by":"auto","created_at":"2024-11-28 08:30:21","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147658,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336733/v1/f3fcd341faae59c926dd0a2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCotton is the world\u0026rsquo;s major industrial crop (Tuttolomondo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that supports the world's largest textile industries, worth \u003cspan\u003e$\u003c/span\u003e600\u0026nbsp;billion annually (Shuli et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is back bone of Pakistan economy with 55% contribution to export earnings. In Pakistan, cotton is cultivated on an area of 2.079 mha with production of 7.064\u0026nbsp;million bales. The crop accounts for 3.1% of total value addition in agriculture and 0.6% of gross domestic product (GDP) of Pakistan (Pakistan Economic Survey, 2020\u0026ndash;2021).\u003c/p\u003e \u003cp\u003eIn spite of being largest traded commodity, there is a steady decline in the area and production of cotton in the country over the last eight years due to various biotic, abiotic constraints and mushroom growth of sugar mills in the core area of cotton cultivation. Cotton production had dropped 22.8% to 7.064\u0026nbsp;million bales, down from 9.148\u0026nbsp;million bales the previous year (Pakistan Economic Survey 2020\u0026ndash;2021). Similarly, area under cotton cultivation is declined by 17.4% to 2.79 mha against area of 2.517 mha. High temperatures, rainfall, water availability, sowing and harvesting dates, land adaptability, and rainfall pattern are among the other major constraints that affected cotton production (Balathandayutham \u0026amp; Mayilswami. 2015; Sharif et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sankaranarayanan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change has influenced cotton production system in a number of ways, and the consequences are likelsy to have an impact on economy of the country. The seed cotton production is determined by the climatic conditions that prevail during the squaring, flowering, and boll development stages. In Pakistan high temperature during flowering is a major cause for reduced cotton yield. Climate conditions and agronomic techniques like plant density, sowing timing, irrigation, and fertilization can all affect fiber and seed yield (Guzman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For better growth and development, cotton requires 28.5\u0026ndash;35\u0026deg;C temperature (Shuli et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Pakistan, cotton is mainly cultivated in Sindh and Punjab. Climate of these provinces is very dry and hot where mercury level in summer rises to 41\u0026ndash;47\u0026deg;C and sometimes 50\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe fluctuating climatic conditions require re-standardizing and adjusting the cotton sowing window (Sankaranarayanan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for various genotypes. 2 Adjusting sowing times has proven to be an effective management strategy for increasing seed cotton yield (Sankaranarayanan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sowing time determines not only the growth and production components, but also the fiber quality of cotton (Shah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, a crucial management strategy for limiting the effects of abiotic variables such as drought and heat stress is planting of cotton at optimum sowing time.\u003c/p\u003e \u003cp\u003eSelection of best cultivar and optimum sowing date accounts for a considerable improvement in yield and quality parameters of cotton (Deho et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Growth and yield response of cotton genotypes is dependent on interaction of genotypes and sowing dates. Cultivar selection and management of optimum sowing dates are essential elements that have a great impact on yield and quality parameters of cotton (Zeng et al., 2014). Optimum sowing dates for different cultivars may differ according to environmental conditions of the region. (Usman et al., 2016). Best adopted cultivars ensure higher yield, fiber quality, tolerance against adverse conditions and early maturity. By sowing cotton cultivars at different times like early sowing, normal sowing and late sowing, we can appraise best cultivar for higher yield and quality (Usman et al., 2016) for diverse climatic conditions.\u003c/p\u003e \u003cp\u003eThe cotton production can be increased by expanding area under its cultivation and by increasing per acre yield. The increase in area seems impracticable due to sugarcane adoption by the farmers in core area of cotton cultivation due to more profitability and risk of failure. However, cotton production can be increased by exploring new pockets for its cultivation. The determination of genotype-by-environment interaction at various planting times could be a technique for achieving optimum cotton seed and lint yields under new agro-ecological conditions (Tuttolomondo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKeeping in view the aforementioned challenges, the current study had been designed to explore the influence of different sowing dates on the growth and yield of various cotton genotypes under rainfed conditions in Pothwar region. The given piece of work was carried out with objective of: (a) Exploring possibilities of cotton cultivation under rainfed conditions of Pothwar, (b) Evaluating best genotypes of cotton for cultivation under rainfed conditions of Pothwar and (c) Determining best sowing time for cotton cultivation in Pothwar region under rainfed conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study was conducted at University Research Farm Koont, Rawalpindi (PMAS-AAUR) in the growing year 2021 to determine the best growing cultivar and optimum sowing date of cotton crop in agro-climatic conditions of Pothwar. After the harvesting of wheat crop, rotavator was used to plow the soil. The land was prepared by ploughing with planking after it rains to make the soil fine and leveled. The study was comprised of two factors viz., genotypes and sowing dates. Four genotypes of cotton (IUB-2013, N-878, SS-32, BS-15) was planted on three different sowing dates depending on rainfall. RCBD factorial design was used with four replicates. Sowing date was kept in main plots and cotton cultivars was sown in subplots. The crop was flat sown manually by dibbling method, planting four seeds per hole. The plant-plant distance of 25 cm and row-row distance of 75cm was maintained Thinning was done to one plant per hole when the seedlings established. Each treatment will consist of five lines. The recommended dose of N, P, K at120, 90, 80 kg ha\u003csup\u003e− 1\u003c/sup\u003e, respectively, was applied. Nitrogen fertilizer was applied in three splits viz. 1/3 at basal, 1/3 at flowering stage and 1/3 at boll formation stage. Pest scouting was done on weekly basis to monitor the pest infestation and for suitable measures.\u003c/p\u003e\u003ch2\u003eClimate description\u003c/h2\u003e\u003cp\u003ePakistan Meteorological Department (PMD) data on climatic variables, such as daily minimum and maximum temperatures (◦C) and rainfall (mm), were gathered. However, the FAO approach was used to compute solar radiation (Mj m\u003csup\u003e− 2\u003c/sup\u003e day\u003csup\u003e− 1\u003c/sup\u003e). The average annual temperature of URFK Chakwal is 22.4°C, placing it in the medium rainfall zone (van Ogtrop et al., 2014). Weather conditions prevailed during the study season at URF Chakwal have been depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003ePlant height (cm) was measured from the base of the stem to terminal bud of 20 tagged plants in cm. Plant height was recorded at maturity stage by using measuring rod. Boll Weight of seed cotton per boll was determined by dividing total weight of bolls by total numbers of collected bolls from 20 representative tagged plants of each treatment. Crop Growth Rate (gm\u003csup\u003e− 2\u003c/sup\u003e day\u003csup\u003e− 1\u003c/sup\u003e) of each genotype in interaction with each sowing date was calculated. Dry weight of selected plants was recorded at two different stages. CGR was calculated by using a formula:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CGR=\\:\\frac{W2-W1}{P\\:(t1-t2)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere;\u003c/p\u003e\u003cp\u003eP = ground area\u003c/p\u003e\u003cp\u003eW1 = Dry weight of plants recorded at time t1\u003c/p\u003e\u003cp\u003eW2 = Dry weight of plants recorded at time t2\u003c/p\u003e\u003cp\u003eGinning Out Turn (%) (GOT) is the ratio of ginned lint to seed cotton yield. GOT was calculated by a formula:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:GOT=\\:\\frac{Lint\\:Weight\\:\\left(g\\right)}{SeedCotton\\:Yield\\:\\left(g\\right)}\\:x\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eLint Yield (kg/ha), after ginning seed cotton of selected plants, lint yield of each treatment was calculated by using electric balance. The assessment of quality parameter (fiber length and fiber strength) was carried out from CCRI Multan. One of the key factors that directly affects yarn quality and can lead to expensive disruptions or breakage in the yarn manufacturing process is the length of the cotton fiber, also known as the staple length. The irregularity of the yarn is directly influenced by the length of the fibers; yarn with longer fibers has a stronger tenacity.\u003c/p\u003e\u003cp\u003eThe majority of spinning mills currently use the High-Volume Instrument (HVI) and the Advanced Fiber Information System (AFIS) to measure the quality of cotton fiber. Based on a fiber bundle testing method, the HVI system aids spinning specialists in managing bale lay down in warehouses and is utilized for fiber classification. We have chosen to use the HVI equipment, which measures light attenuation through a combed beard of fibers to determine fiber length. Samples are placed in the HVI baskets, where cotton fibers (lint) are captured by a comb. Fiber beards are created by combed fibers that have been clamped such that each fiber is parallel to the others. After that, light is passed through these beards by the device, and sensors analyze the difference in light attenuation across the fiber beard to determine how long the cotton fibers are.\u003c/p\u003e\u003cp\u003eThe sowing dates can have an impact on the fiber fineness of different cotton genotypes. Fiber fineness refers to the thickness or diameter of the individual cotton fiber which is an important characteristic in determining the quality of cotton. Micronaire values are typically used to express the fineness of cotton fiber. After being weighed, the fibers are compressed to a certain volume in a chamber. Micronaire values (Table\u0026nbsp;1) are calculated by passing a regulated airflow through the prepared sample based on the permeability through the fibers. Lord (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1956\u003c/span\u003e) demonstrated that the fineness and maturity of cotton fiber were correlated with resistance to airflow.\u003c/p\u003e\u003cp\u003eM × H = 3.86 × Mic\u003csup\u003e2\u003c/sup\u003e + 18.16 × Mic + 13\u003c/p\u003e\u003cp\u003ewhere M is maturity ratio, H is linear density, and mic is micronaire. The classification standard of cotton fiber micronaire values is summarized in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eTable.1. Classification standard of cotton fiber micronaire value (Saville., 1999),\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicornaire Value (µg/inch)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatings\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow 3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery fine\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.1–3.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFine\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.0–4.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.0–5.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoarse\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver 6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery coarse\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe seed cotton yield (kg/ha) from each treatment was taken by adding seed cotton yield of first picking and second picking and expressed in kg/ha.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis was performed by the analysis of variance using SPSS. The treatment means was compared by Least Significant Difference test (α = 0.05). Principle component analysis (PCA) was performed with Origin Pro v 2024b. The correlations between the quantitative variables were determined using Pearson correlation coefficient formula.\u003c/p\u003e\u003cp\u003eThe PCA was carried-out based on the biplot method in Origin Pro v 2024b in order to investigate how sowing dates (SDs) and Genotypes (Vs) altered cotton parameters. The PCA is a multiple-variate tool that examines the data using a variety of related, measured dependent variables in order to extract the essential information from the data, designate it as a collection of novel orthogonal variables called principal components (PC), and demonstrate the relationships between the observations and variables (Mishra et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The variables thought to best describe the system qualities were those with high factor loading and main components with high eigenvalues. As a result, only PCs with eigenvalues of ≥ 1 (Kaiser, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1960\u003c/span\u003e) is taken into consideration. Component scores, often referred to as factor scores and loadings, provide an explanation of the obtained findings of a PCA (Wold et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant height (cm)\u003c/h2\u003e \u003cp\u003ePlant height is a main parameter to investigate the crop growth of the all crops. Plant height varied on different sowing dates. An experiment was conducted to investigate the variation of plant height of the four different cultivars of cotton under rainfed condition on three different sowing dates in Pothwar region. Results of our study showed that an average plant height of cultivar N-878 (92.233 cm) was significantly higher results than others cultivars on all sowing dates especially on 22nd of the April as followed by other two dates of 8th April and 6th of June respectively. Plant height of cultivar IUB-2013 (89.667 cm) also performed best at second sowing date after cultivar N-878 under same conditions, (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After IUB-2013, SS-32 showed decent results on an average followed by BS-15. Anova showed that plant height and different sowing dates had no significant relationship (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Results of our study are in line with Jamro et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) who also found the 2nd half of the April month showed great results of cotton crop yield in the rainfed area as compared to the other sowing dates.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Plant Height (cm) of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSowing date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.733B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.025A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.092C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.911AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.956BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.722A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.744C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLSD values: varieties (4.0223); sowing date (3.4834); S \u0026times;V (6.9668)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBoll weight (g)\u003c/h3\u003e\n\u003cp\u003eAverage boll weight is the major factor that affect cotton yield. The difference in the average boll weight of four different cotton cultivars is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The interaction of sowing data and genotype was non-significant for average boll weight. The results demonstrated that the sowing date of 22nd April showed significantly better (0.31g) than the other two sowing dates on an average boll weight. There was no significant difference in boll weight for sowing on 1st (0.28g) and 3rd sowing (0.71g). The average boll weight of the cotton genotypes sown on 8th April (0.28g) was higher than the average boll weight of the cotton genotypes sown on 6th June (0.71g) but the difference was not significant. The N-878 had shown significantly better results than other three varieties in all the three sowing dates followed by the IUB-2013 which shows better results than SS-32 and BS-15 although, these differences were significant when the varieties were sown on 8th April as compared to other two sowing dates where the difference was not significant. This could be due to the better water and nutrient storage capacity of the N-878 and IUB-2013 as compared to the other two varieties which made them more favorable to increase their boll weight. Our results are in line with (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) who also found that the cotton genotypes performed better when sowed in the mid of April.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on boll weight (g) of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0. 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2922B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2722C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3333A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2567C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLSD values: varieties (0.44); sowing date (0.0087)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCrop growth rate (gm day)\u003c/h3\u003e\n\u003cp\u003eDynamics of crop growth rate of the four different cotton cultivars as affected by the three different sowing dates is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Results of our study showed that there was no significant difference between sowing dates and cotton genotypes in terms of crop growth rate. CGR was higher when the cotton cultivars were sown on 8th April as compared to the other two sowing dates on an average. Although, this difference was significant when the cultivars were sown on 6th June but the difference was not significant when the crops were sown on 22nd April. The N-878 showed the best results on all the three sowing dates followed by IUB-2013 during the sowing dates of 8th and 22nd April but during the sowing of 6th June SS32 showed best results than IUB.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on CGR (gm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3225B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3517A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2925C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLSD values: varieties (0.0248); sowing date (0.0215); S \u0026times;V (0.0429)\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFiber fineness (µg/inch)\u003c/h3\u003e\n\u003cp\u003eFiber fineness is the crucial parameter for evaluating quality of cotton. Fiber fineness is the thickness or the diameter of individual cotton fibers. Effect of different sowing dates on the fiber fineness of four different genotypes of cotton has been shown in the Table\u0026nbsp;5. The results showed that Fiber finesses of all the cultivars when they were sown on 22nd April were significantly better than the sowing dates of 8th April and 6th June. The Study revealed that cotton genotype N-878 showed significantly better results than all the other three varieties during all the three sowing dates. The statistical analysis showed that among the interaction between cotton genotypes and sowing date the variety(N-878) planted on 22 April showed best result in case of fiber fineness followed by the cotton genotypes (SS-32 and BS-15) that were both planted on 6 June respectively, genotype N-878 having The mean LSD for fiber fineness is 3.98 having the highest mean as compared to other genotypes (SS-32, BS-15 and IUB-2013) having the means of 3.784, 3.747 and 3.733 respectively. While the LSD mean for sowing date having the highest fiber fineness value of 3.967 for two sowing dates 8th April and 22nd April thus the fiber fineness has no effect on the genotypes that are sown on 8th April and 22nd April 2022, Similarly the sowing date 6 June 2022 having the lowest LSD mean of 3.622 which affected the fiber fineness of cotton. Ullah et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that cotton genotype grown in the mid of April showed better results of fiber fineness as compared to other sowing dates.\u003c/p\u003e \u003cp\u003eTable.5. Effect of sowing dates on Fiber Fineness (\u0026micro;g/inch) of various cotton genotypes under rainfed conditions of Pothwar.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6225B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9675A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8450A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7333B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7844B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9811A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7478B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLSD values: varieties (0.1877); sowing date (0.1626); S \u0026times;V (0.3251)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFiber length (mm)\u003c/h3\u003e\n\u003cp\u003eFiber length is an important factor affecting cotton quality. It refers to the physical length of individual fibers. Variation in fiber length of the four different genotypes of cotton which are sown on three different sowing dates in rainfed areas of the Pothwar is shown in the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Results of the study illustrate non-significant difference between cotton genotypes and sowing dates. The LSD mean of sowing date 22nd April 2022 have the highest value of 30.14 in fiber length followed by sowing date 8th April 2022 and 6th June 2022 having the mean LSD value of 28.08 and 25.93 respectively. While in case of comparing the mean LSD for variety effect to fiber length named N-878 showed an exceptional performance on an average having the mean LSD value of 30.58 while there is no significance difference between genotypes IUB-2013, BS-15 and SS-32 in case of fiber length. The overall results showed that the best average of the cotton genotypes regarding fiber length was found during the sowing date of 22nd April.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Fiber Length (mm) of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.082B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.142A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.934C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.541B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.408C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.581A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.681BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLSD values: varieties (1.9237); sowing date (1.6660); S \u0026times;V (3.3319)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFiber strength (g tex\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/h2\u003e \u003cp\u003eEffect of different sowing dates on the fiber strength of four different cultivars of cotton has been shown in the Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Statistical analysis shows that there is no significant difference between the interaction of sowing date and cotton genotypes. Fiber finesses of all the cultivars when they were sown on 22nd April were significantly having the LSD mean of 28.75 for sowing date better than the sowing dates of 8th April having the LSD mean of 26.43 followed by 6th June having the mean LSD value of 25.07 least fiber strength was observed in the 6th June sowing date. The Study revealed that N-878 showed significantly better results having the Mean LSD value of 29.95 followed by IUB-2013 having the LSD mean value of 27.02 while there is no significant difference between the cotton genotypes SS-32 and BS-15 having the mean LSD value of 25.55 and 24.64 respectively. The results of our study are in line with (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) who also found that the cotton genotypes which were sown during the month of April give good results as compared to the late sowing dates. He observed improved fiber strength in the genotypes that were sown in the mid of April.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Fiber Strength (g tex\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) various cotton genotypes rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.43B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.88A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.08C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.02B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.55C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.96A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.64C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLSD values: varieties (1.4685); sowing date (1.2718); S \u0026times;V (2.5436)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLint yield (kg/ha)\u003c/h3\u003e\n\u003cp\u003eDynamics of lint yield of the four different genotypes of cotton crop as affected by the three different sowing dates have been shown in the Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e8\u003c/span\u003e. There is no significant difference between the sowing dates and genotypes of cotton affecting the lint yield. Results of the study illustrate that the cotton genotype named N-878 showed a very good performance on an average having the LSD mean of 261.45 while there was no significant difference between IUB-2013 and SS-32 while there is a significant difference between the N-878 and BS-15 during all the three sowing dates but its peak results were obtained during the sowing date of 22nd April (238.08). The Results showed that genotypes sown on 22nd April performed significantly better having the Mean LSD value of 4.64 as compare to all other sowing dates 8th April and 6th June in case of lint yield. The Reason behind the higher lint yield by N-878 could be its higher plant height and its good adaptation to the environment than all the other varieties. Malik et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) has also observed maximum lint yield was recorded in the cotton genotypes that were planted in the mid of April.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Lint Yield kg/ha of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e245.08AB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e261.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e248.08A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e237.49B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246.20AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241.94AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252.01A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234.52B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLSD values: varieties (12.01); sowing date (10.402); S \u0026times;V (20.804)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSeed cotton yield (kg/ha)\u003c/h3\u003e\n\u003cp\u003eSeed Cotton yield refers to the amount of raw cotton produced per unit of land area. Seed cotton yield is important parameter that indicates the production of cotton crop. Influence of different sowing dates on the seed cotton yield of four different genotypes of cotton has been shown in the Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Statistical analysis shows that there is no significant difference between the interaction of sowing date and cotton genotypes. Our study indicates that higher seed cotton yield was obtained by cotton genotype N-878 when it was sown during 22nd April (1809) followed by cotton genotypes IUB-2013 (1185) in 22nd April Sowing. Cotton genotype BS-15 showed minimum seed cotton yield in all three sowing but among all three sowing dates least seed cotton yield was obtained in 8th April (599.3). Results indicates that variety N8- 78 was best genotype under rainfed conditions of Pothwar. Similarly, sowing date 22nd April showed good results for all cotton genotypes. Our results are in line with the study of (Malik et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). He showed that higher seed cotton yield was obtained in 20th April sowing followed by 10th May and 20th May.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Seed Cotton Yield (kg/ha) of various cotton genotypes under rainfed conditions of Pothwar.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e927.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1401.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e721.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e599.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e912.6B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1185.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e977.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1809.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e839.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1203A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e777.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e714.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e979.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e614.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e771.2C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e963.5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1031.1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1170A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e684.4C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLSD values: varieties (101.51); sowing date (87.913); S \u0026times;V (175.83)\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGinning out turn (%)\u003c/h2\u003e \u003cp\u003eVariation in GOT (%) of the four different cultivars of cotton which were sown on three different sowing dates in rainfed areas of the Pakistan has been shown in the Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The statistical analysis shows that between interaction between cotton genotypes and sowing dates in case of GOT is not significant. Results of our study showed that GOT of the varieties was significantly higher when they were sown on 22nd April having the LSD mean of 38.30 as followed by other two dates of 8th April having the LSD mean of 36.46 and 6th June having the LSD mean of 35.33 there is no significant difference between these two sowing dates in case of GOT respectively. The N-878 variety showed the best results regarding GOT in all the three sowing dates as compared to all the other three varieties which might be due to the well adaptation to the environment of Pothwar region and its drought resistance capacity. Whereas, in general on comparative basis the IUB-2013 showed best results in all three sowing seasons after the N-878. After IUB-2013, SS-32 showed decent results on an average followed by BS-15. The overall results showed that the best average of the cotton varieties regarding GOT was found during the sowing date of 22nd April. Results of our research are in line with (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in which he noticed the higher GOT in the genotypes that were sown in the mid of April. Malik et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) has also observed improved ginning out turn (%) was 24 recorded in the cotton genotypes that were planted in the mid of April.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of sowing dates on Ginning out turn (%) of various cotton genotypes under rainfed conditions of Pothwar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSowing date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.465B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.307A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.338B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.718C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLSD values: varieties (1.5426); sowing date (1.3359); S \u0026times;V (2.6719)\u003c/p\u003e \u003cp\u003eValues sharing common letters did not differ significantly (α\u0026thinsp;=\u0026thinsp;.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePearson correlation matrix\u003c/h2\u003e \u003cp\u003eThe Pearson correlation among the 9 numerical traits of four genotypes of American cotton under the impact of three different sowing dates is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, below. Plant height (cm) showed highly strong positive to weak positive significant association with CGR (r\u0026thinsp;=\u0026thinsp;0.93), FL (r\u0026thinsp;=\u0026thinsp;0.91), GOT (r\u0026thinsp;=\u0026thinsp;0.91), FS (r\u0026thinsp;=\u0026thinsp;0.9), LY (0.83), BW (0.82), SCY (r\u0026thinsp;=\u0026thinsp;0.67) and FF (r\u0026thinsp;=\u0026thinsp;0.35). A highly strong positive and intermediate positive significant association was found for the traits like FS (r\u0026thinsp;=\u0026thinsp;0.98), CGR (r\u0026thinsp;=\u0026thinsp;0.93), GOT (r\u0026thinsp;=\u0026thinsp;0.93), LY (r\u0026thinsp;=\u0026thinsp;0.9), FL (r\u0026thinsp;=\u0026thinsp;0.86), SCY (r\u0026thinsp;=\u0026thinsp;0.62) and FF (r\u0026thinsp;=\u0026thinsp;0.58) with the boll weight. The lint yield (LY) revealed highly strong positive (r\u0026thinsp;=\u0026thinsp;0.93) to weak positive (r\u0026thinsp;=\u0026thinsp;0.39) significant association was found with FS (r\u0026thinsp;=\u0026thinsp;0.93), GOT (r\u0026thinsp;=\u0026thinsp;0.92), CGR (r\u0026thinsp;=\u0026thinsp;0.91), BW (r\u0026thinsp;=\u0026thinsp;0.9), PH (r\u0026thinsp;=\u0026thinsp;0.83), SCY (r\u0026thinsp;=\u0026thinsp;0.7) and FF (r\u0026thinsp;=\u0026thinsp;0.39) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The features are ranked by their correlations, and the blue bars represent lowest\u0026thinsp;+\u0026thinsp;ve correlations while red bar indicates highly strong positive correlations. The deeper the red color, the stronger the correlations while deeper the blue color, the weaker the correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis (PCA)\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eBiplot\u003c/h2\u003e \u003cp\u003eOne useful method for describing the relationships between genotypes and sowing dates for different cotton properties is principal component analysis. To see how the four cotton genotypes; IUB-2013, N-878, SS-32, BS-15 (V1, V2, V3 and V4) and three sowing dates (SD1, SD2 and SD3) relate to one another, the first two PCs were plotted. At each axis of differentiation, PCA shows the significance of the biggest contributor to the overall variation ((Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eBased on PC1 and PC2, the PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) displayed four genotypes that differed genetically based on the scattering pattern. A good level of genetic variety was shown by the biplot's genotype dispersion. In terms of seed production, the genotypes that were closest to one another differed little or not at all. The PC1 and PC2 biplots almost validated the cluster analysis grouping. Through hybridization, distant genotypes might be used as varied parents to expand the cotton genetic base since they showed more diversity in seed output. According to Vianna et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the majority of the genotypes found by the PCA were clustered in the same cluster, suggesting that they were comparable. Thus, it was feasible to create a second selection that focused on seed cotton output based on the homogeneity that existed in the groups. The X-axis accounts for 79.95% of the variability, the Y-axis for 9.51% of the extra original variability, and PC1 and PC2 for 89.46% of the overall variation. By redrawing the contour with estimated coefficients for the associated principal component, the two initial PCs were plotted to examine correlations between the four genotypes and three distinct sowing dates with the principal component of cotton seed yield. The plot now displays the association between the genotypes that had reasonably substantial loading on both the PCA1 and PCA2 axes, according to the correlation coefficients between the genotypes and sowing dates.\u003c/p\u003e \u003cp\u003eThe associations between the four cotton genotypes (IUB-2013, N-878, SS-32, BS-15) and the three sowing dates (April 8, April 22, and June 6) were graphically shown using the PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A considerable degree of genetic diversity was demonstrated by the genotypes' excellent distribution. Plotting near to one another revealed comparable traits, but plotting farther apart revealed more genetic differences. Interestingly, when sowed on April 22 (SD2), the genotype N-878 (V2) was located farthest along the positive axis of PC1, demonstrating its better performance across important characteristics such as lint yield, fiber strength, and seed cotton yield. This implies that N-878 (V2) is exceptionally adapted to rainfed environment, especially when planted on April 22 (SD2).\u003c/p\u003e \u003cp\u003eThe strongest positive correlations between genotypes and sowing dates were seen for SD3.V4 with SD3.V2, SD3.V1 with SD1.V2 and SD1.V4, and SD2.V2 with SD1.V1, SD.V3, SD2.V1, and SD3.V3 combinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These findings imply that using the nine characteristics of cotton, it is feasible to distinguish between these genotypes and sowing dates. The correlation between each genotype and sowing date with a principal component is used to calculate the discriminatory power of the genotypes and sowing dates in each principal component.\u003c/p\u003e \u003cp\u003eImportant variables such as plant height (PH), boll weight (BW), crop growth rate (CGR), fiber length (FL), fiber strength (FS), lint yield (LY), seed cotton yield (SCY), and ginning out turn (GOT) were strongly associated with PC1, one of the traits that went into the PCA. This suggests that these traits were the primary drivers of the variation in the genotypes and sowing dates. Genotypes that loaded significantly on PC1, especially N-878, did better in these areas, especially when sown on April 22. According to PC2, which also found a substantial difference between genotypes sown on April 8 and June 6, genotypes such as SS-32 and BS-15 fared poorly, particularly on the late planting date of June 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eScree plot\u003c/h2\u003e \u003cp\u003eThe top two eigenvalues of the PCA for four genotypes under three sowing dates on different cotton variables were shown to represent the whole percentage of the variation in the dataset in the Scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Additionally, take note of the plot's split that divides the important from the unimportant parts. Components 1 and 2 are likely significant, according to the majority of academics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSowing dates have a major effect on the vegetative and reproductive growth of the crop (Hallikeri et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Cotton planted early experiences the hottest part of their reproductive cycle, resulting in a considerable drop in yield (Rahman et al., 2007). Furthermore, in late planting, crop faces heavy rains, cold temperature and shorter growth time which results in the reduction of crop yield and quality (Elayan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sankaranarayanan et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated the performance of long linted cotton genotypes (DLSA17, PA760, PA812, PA402, PA528 and K12) vs short stapled genotype (P. Dhanwanthrm) under two different sowing dates (4th August and 4th September). They found that short stapled cotton genotypes were on par than long linted cotton genotypes in terms of seed cotton yield. Among long linted genotypes, genotype PA 812 gave higher seed cotton yield at 4th August as compare to DLSA17, PA760, PA402, PA528 and K12, while PA 760 gave the higher fiber quality index then genotypes DLSA17, PA760, PA812, PA402, PA528. Sowing of genotypes on same dates not ensures ideal performance by all. Rather, performance of cotton genotypes is dependent on genotype to sowing date interaction. Ali et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the response of different cotton genotypes (Bt. FH-142, Bt. MNH-886, FDH-170) to different sowing dates (21th April, 5th May and 20th May). At planting time 21th April Bt.MNH-886 showed good results for plant density, flowering, boll maturation and boll opening then FH-142, FDH-170. While, at 5th May FDH-170 showed more leaf area index (LAI) and net assimilation rate (NAR) and Bt. FH-142 had more crop growth rate, respectively. Planting of Bt. FH-142 at 21th April gave more total biomass production then Bt.FDH-17 and Bt. MNH 886. Deho et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) carried out an experiment on effect of planting dates to investigate the seed cotton yield and fiber quality of three cotton genotypes (NIA-Noori, Sadori and NIA-Ufaq) along with thirty-four elite lines of cotton crop under two different planting dates (20th March and 20th April). The elite lines of cotton performed well in comparison of cotton varieties on planting date 20th April. The elite lines showed higher staple length (mm), seed index (g), number of bolls per 5 plant, sympodial branches, and seed cotton yield were higher when cotton cultivar was planted at 20th April as compared to 20th March.\u003c/p\u003e \u003cp\u003eMany other researchers also had studied the effect of sowing date on performance of cotton genotypes. Lashari et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) had reported that planting at 1 st May is better as compared to 1st April, 1st June and 15th June in Multan region. The genotypes differ for performance when planted on same date. More number of sympodial branches and plant height are reported in cotton variety ICI-2121 followed by IUB2013 then other genotypes maximum number of bolls per plant, boll weight, and seed cotton yield (kg/ha) was recorded in variety ICI-2121 followed by ICI-2424 and IUB-2013, regardless of planting dates. The researchers suggested that cotton variety ICI-2121 should be planted on 1st May in Multan zone for maximum yield (Lashari et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, Iqbal et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) studied the effect of different planting times (1st April, 1st May, 1st June, 1st July) and genotypes (IUB-13, IUB-222, IUB-63) on seed cotton yield and reproductive development of cotton crop under agro-climatic conditions of Islamia University of Bahawalpur. Performance of cotton genotype IUB-13 was good when planted at 1st April in comparison with other genotypes. While genotypes (IUB-222, IUB-63) proved best for late sowing dates like 1st June, 1st July. Lowest yield was obtained by genotype IUB-63 because of small boll size.\u003c/p\u003e \u003cp\u003eThe adjustment of sowing time ensures an ideal temperature required for various growth stages of crop. The ideal sowing date for a genotype also varies with agro-ecologic conditions and location of a region. Sowing too early does not favor most of the developmental stages of crop. Seedling germination and emergence may be limited by low soil temperatures or a drop in air temperature at the end of March or the first 10-day period in April. As a result, planting should only be done when the environment is suitable for seed germination (Tuttolomondo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Germination is delayed in early sowing (March 15th), whereas quick and uniform germination is obtained by planting on or after April 15th. Maximum plant height and a greater no of bolls per plant can be obtained by planting between March 15th and April 15th (Kamran et al., 2017).\u003c/p\u003e \u003cp\u003eThe fluctuating temperature during development stage of crop has great impact on performance of genotypes. Sowing dates is associated with temperature 6 and had great impact on reproductive growth and yield parameters (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mehboob et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) evaluated the effect of temperature by adjusting sowing date on performance of cotton under agro-ecological conditions of Regional Agriculture Research Institute Bahawalpur (RARI) Pakistan. Planting on April 30th accounted for the highest leaf area index (LAI), total dry matter (TDM) and leaf area duration (LAD), maximum opened boll, average boll weight,100-seed weight and seed cotton yield of genotype MNH-886 as compared to 15 April, 15 and 30 May, 14 and29 June. Seed cotton weight per plant, number of bolls per plant and seed cotton yield declined with delayed sowing. June 1st to June 10this identified as optimum sowing dates (Copur et al., 2019). Improved number of bolls and seed cotton yield were observed in cultivar Stoneville 468. Promising ginning out turn (GOT) was observed in the cultivar PG 2018 while DP-499 was best in plant height, number of sympodial branches and seed cotton weight under studied edaphic and climatic conditions (Copur et al., 2019). Bilal et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated the optimum sowing date for enhancing cotton production in Punjab, Pakistan. Three Bt. Cotton cultivars (BH-184, CIM-598, MNH-886) were planted at five different planting dates with the interval of 15 days (1st March, 15th March, 1st April, 15th April, 1st May, 15th May). They observed that maximum sympodial branches, number of bolls per plant, 100-seed weight, seed cotton yield and improved fiber length was produced, when cotton crop was planted on 15th April as compared to other sowing dates (1st March, 15th March, 1st April, 15th April, 1st May, 15th May). Higher seed cotton yield was obtained by cotton cultivar MNH-886 (Bilal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, Sharif et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) carried out an experiment at Cotton Research Station, Faisalabad to monitor influence of sowing dates on cotton growth, yield and fiber quality of cotton genotypes. The genotypes planted on 10th April gave increased seed cotton yield as compared to planted on 10th May. under some circumstances, various sowing dates not always show variability in fiber quality. However, the genotypes differ for fiber quality due to difference in their genetic makeup. For example, Sharif and his coworkers (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) had reported that genotype FH-6071 produced more yield as compared to genotype FH-152, but FH-6071 had improved fiber quality than other genotypes planted on various dates. Abbas and Ahmad, (2018) examined the effect of different sowing times and cultivars on cotton fiber quality. They found 7 that the late planted cotton crop on June 15th produced improved staple-length, staple-strength and uniformity-index-ratio while highest micronaire values were recorded at early sown crop on May 1st. Cotton Cultivar MNH-886 accounts for improved quality parameters in late sowing dates.\u003c/p\u003e \u003cp\u003eIn Southern Punjab, Pakistan, early planted crop (February 14 and March 1) required more days to begin emergence, squaring, blooming, boll formation, boll opening and 50% plant germination. While, less days to begin emergence with complete and uniform germination, squaring, flowering, boll formation, boll opening is reported in late sown (30th April and 15th May) crop. However, yield parameters like number of sympodial branches, bolls per plant, and boll weight were enhanced in planting date May 15th. Similarly, planting on March 16th produced maximum ginning out turn, fiber length, strength, fineness, and uniformity. The researcher recommended that for higher seed cotton yield and lint yield having better quality fiber, BT cotton be planted on March 16th (Shah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe performance of cotton genotypes and lint quality is also influenced by prevailing agro-ecological conditions of a region and agronomic practices. Under irrigated and rainfed environment the genotypes perform differently. Particularly, rainfed conditions had a significant impact on lint yield and fiber quality of cotton (Ayele et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The 10 to 14 weeks and 13 to 15 weeks after sowing are crucial plant growth phases for square and boll formation and therefore moisture supply during these conditions may significantly affect yield and quality of fiber under various environments. The average number of squares per plant are dramatically decreased between weeks 10 to 14 after planting under rainfed conditions, while the average number of bolls per plant are reduced during weeks 13 to 15 after planting (Ayele et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interactive effect of planting time and other agronomic practices also affect cotton productivity in a region. For example, it is reported that cotton productivity was increased by 14.2% through transplanting of seedlings than direct sowing. Early planting at 1st March resulted in a significant increase in productivity than 1st May. Normally, high temperatures coincide with the May planting and peak blooming times in different cotton growing areas, the practice of planting cotton by transplanting seedlings and early sowing could be successfully strategy. Planting 8 time and weed management practices had sown minimum weed density, weed dry weight of cotton cultivar when planted on August 1st with integrated weed management practices (Hariharasudhan et al., 2017). Studies on interaction of sowing window and nitrogen regimes had showed that application of 120 kgha-1 N offer maximum plant growth, lint yield, and seed cotton yield if the crop is planted on July 1st to mid of July under rainfed conditions of Sudan. Mohamed et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, under prevailing conditions of Tandojam (Sindh), planting on 1st May is considered optimum and offers maximum ginning out turn (GOT) and seed cotton yield than late planting. Jamro et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggested that that cotton crop should not be planted after 10th May in Tandojam and proposed varieties Haridost and Sindh-1 for general cultivation in Tandojam for maximum seed cotton yield.\u003c/p\u003e \u003cp\u003eUsman et al. (2016) is of the opinion that planting cotton varieties of central institute Mulan Pakistan (CIM-599 followed by CIM-599) earlier than April 19 results in higher vegetative growth rather than lint yield, but late planted cotton resulted in flowering and boll development in Dera Ismail Khan region. Due to unfavorable environmental conditions and a shorter growing period, earlier and later planting than April 19th results in lower cotton yield with poor quality (Usman et al., 2016). Similarly, as research was conducted in BARI Chakwal to investigate the influence of different sowing dates on different genotypes. It was observed that the cotton genotypes that were sown on 20th April. Higher seed cotton yield, no of bolls and improved quality parameters were obtained in 20th April sowing followed by 10th May and 30th May.\u003c/p\u003e \u003cp\u003eClimate change has a great impact on vegetative as well as reproductive growth of cotton crop. In order to get appropriate climate, it is necessary to evaluate best sowing date for cotton crop. Salih. (2019) observed that cotton varieties sown at the end of march showed highest plant height as well as seed cotton yield as compared to late sown varieties.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, this study has effectively illustrated the possibility of growing cotton in the rainfed environment. Four distinct cotton genotypes (N-878, IUB-2013, SS-32, and BS-15) were evaluated for performance over three sowing dates. It was discovered that genotype N-878 performed better in terms of growth, yield, and fiber quality, especially when sown on April 22. The significance of maximizing planting windows for increasing both yield and quality under the region's unique climatic conditions is highlighted by the fact that this sowing date regularly beat earlier and later planting dates.\u003c/p\u003e \u003cp\u003ePCA analysis successfully brought attention to the significance of the interactions between genotype and sowing date. The combination that performed the best was N-878, which was sowed on April 22 and provided the highest yield and fiber quality. Cotton output in the rainfed areas of Pothwar and other comparable places may increase as a result of farmers using this information to make better decisions.\u003c/p\u003e \u003cp\u003eThe PCA's findings have important ramifications. The April 22 planting date is crucial, according to the research, especially for the N-878 genotype, which continuously surpassed other combinations in terms of yield and quality. Accordingly, April 22 is the best day to plant in order to maximize cotton yield in the rainfed area. The PCA also demonstrated the versatility and excellent performance of N-878, which makes it a solid contender for additional breeding initiatives targeted at improving cotton yield in rainfed conditions.\u003c/p\u003e \u003cp\u003eIn order to increase cotton yields in unpredictable rainfed circumstances, farmers and policymakers in the Pothwar region can use these data to guide their decision on the best cotton genotype and sowing date. In order to further maximize cotton output in comparable agro-ecological zones, future study might use the PCA as a basis to examine the performance of different genotypes or improve planting techniques.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKhan HY and Khan NM performed the experiments, analyzed the data, prepared figures and tables, authored and reviewed drafts of the article. Mujtaba G and Khan MA assisted in performing the experiments and performing plant analysis. Khan N, Kashif M and Ashraf HMA improved the written language. Khan H, Khan NM and Mujtaba G conceived and designed the experiments and approved the final draft. Khan N and Kashif M also helped in using IBM SPSS Statistical Package. \u0026nbsp; All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas, Q. and Shakeel Ahmad. Effect of Different Sowing Times and Cultivars on Cotton Fiber Quality under Stable Cotton-Wheat Cropping System in Southern Punjab, Pakistan. \u003cem\u003ePakistan J. Life Social Sci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 2 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad, S., Iqbal, M., Muhammad, T. \u0026amp; Mehmood, A. Shakeel Ahmad, and Mirza Hasanuzzaman. 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Bourland. Genotype-by-environment interaction effects on lint yield of cotton cultivars across major regions in the US cotton belt. \u003cem\u003eJ. Cotton Sci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 75\u0026ndash;84 (2014).\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":"Gossypium hirsutum, sowing dates, water stress, rainfed cotton, Pothwar region. PCA analysis","lastPublishedDoi":"10.21203/rs.3.rs-5336733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5336733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCotton production has been drastically affected by the climate change through its effect on agronomic practices such as sowing time and adoption of genotypes to various agro-ecological conditions. Interaction of genotypes and sowing date is an important strategy to analyze cotton yield and quality in a rainfed environment. Various morphological, and yield parameters including plant height, boll weight, crop growth rate, lint yield, seed cotton yield, ginning out turn (GOT), fiber fineness, fiber length and fiber strength was recorded. Results of the study revealed that N-878 performed best on an average during all the growing seasons followed by IUB-2013, SS-32 and BS-15 respectively. The best yield and quality were achieved when the cotton cultivars were sown on 22nd April followed by 8th April and 6th June respectively. This study will be helpful to look into performance of genotypes and possibilities of cotton cultivation under rainfed conditions of Pothwar. Also, sowing window of studied cotton genotypes will be explored for cotton cultivation under rainfed conditions of Pothwar. In addition, the present piece of work will provide future direction for research on cotton in the Pothwar region.\u003c/p\u003e","manuscriptTitle":"Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-28 08:30:16","doi":"10.21203/rs.3.rs-5336733/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":"017b9e4e-516c-4559-8948-2086bd8a1f86","owner":[],"postedDate":"November 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40631962,"name":"Biological sciences/Plant sciences"},{"id":40631963,"name":"Biological sciences/Plant sciences/Plant development"},{"id":40631964,"name":"Biological sciences/Plant sciences/Plant physiology"},{"id":40631965,"name":"Biological sciences/Plant sciences/Plant stress responses"}],"tags":[],"updatedAt":"2025-05-22T05:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-28 08:30:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5336733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5336733","identity":"rs-5336733","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}