Unravelling the genetic variability and identification of vegetative heat tolerant lines in upland cotton (Gossypium hirsutum L.)

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Venkatesh Venkatesh, S. T. Kajjidoni, P Kariyannanavar, M. J. Pavithra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3942046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Euphytica → Version 1 posted 7 You are reading this latest preprint version Abstract A study was carried out to screen a set of 204 genotypes consisting of germplasm and advance breeding lines for heat tolerance. The study of genetic variability among these genotypes for various morpho-physiological traits revealed high PCV and GCV for number of dried squares, number of fruiting points and leaf area. The results of principal component analysis showed that the first six principal components with eigen values more than unity contributed 81.41% of the variability among genotypes and the traits like canopy temperature, relative water content, specific leaf weight, number of dried squares, plant height, number of fruiting points, leaf area and the phenological traits contributed significantly to total variability. The two genotypes viz. , CPD-424 and EC560323 were identified as highly vegetative heat tolerant lines which can be used as donor parents in the heat tolerance breeding programmes. heat stress genetic variability germplasm and multivariate analysis Figures Figure 1 Figure 2 Introduction The upland cotton ( Gossypium hirsutum L.) is a widely cultivated fibre crop species in the world. The recent unprecedented rise in temperature across the globe due to changing climate has shown the susceptibility of the upland cotton to heat stress. (Chattopadhyay et al., 2008 ; Thakare et al., 2014 ; Dendage et al., 2018 ). Hence, there is an urgent need for characterizing available germplasm to identify for heat tolerant genotypes to utilise them in improving heat tolerance in the face of changing climate scenario. The development of heat-tolerant cultivars is key to tackle heat stress caused by changing climate. Several heat stress screening methods based on morpho-physiological traits have been developed to identify heat tolerant lines. Both lab as well as field experiments were conducted to characterize understand and identify heat tolerant cotton genotypes (Karademir et al., 2012 ). The heat tolerance screening studies have also revealed the existence of natural variation for thermo tolerance in upland cotton by using various selection criteria, suggesting the scope for improving heat tolerance in Upland cotton (Majeed et al., 2019 ). Field screening provides ultimate proof for abiotic stress evaluation as it reveals the true expression of identified genotypes for a particular stress. Hence, an attempt was made to screen the genotypes during the summer, to ascertain the level of thermo tolerance and to identify relatively heat tolerant genotypes among the available germplasm and advance breeding lines. Material and methods The genetic material of 204 G. hirsutum lines comprising of selected 153 germplasm lines, indigenous and exotic collection, advanced breeding lines and released varieties. Four regional varietal checks viz. , Sahana, Surabhi, MCU 5 and ARBH 813 were also included for present study for the evaluation at Agricultural Research Station, Dharwad Farm during summer 2016-17. The augmented design was used for screening with 10 blocks and each treatment was hand dibbled in single row of 4.2 m length with inter-row spacing of 90 cm and 30 cm between plants within the row. The weekly weather data of summer 2016-17 during crop duration at experimental location presented in the Fig. 1 . The observations on morpho-physiological traits were recorded on five randomly selected plants. The recorded data was subjected for analysis of variance, estimation genetic variability parameters using Windostat software version 9.1 and principal component analysis (PCA) was carried out based on the mean data using Statistical Tool for Agricultural Research (STAR) version 2.0.1 software. Further based on the percentage of foliage retention all the genotypes were classified based on visual scoring using 0 to 10 scale, where 10 score equals to 100% retention of green foliage, which is referred as vegetative heat tolerance (Xu et al. 2000 ). Results The advanced breeding lines, cultivars and germplasm screened experienced heat stress (> 32°C) from the beginning of square initiation, days to flower initiation, till the crop senescence due to high temperature. The heat stress resulted in the expression of high variability among the genotypes for many traits and also induced early senescence among most of the genotypes. None of the genotypes could produce harvestable seed cotton, but, a few genotypes exhibited vegetative heat tolerance by retaining healthy green foliage. The present investigation was mainly carried out to study the genetic variability for morpho-physiological traits and also to identify important heat tolerance related traits through principal component analysis which is a dimensional reduction technique and to finally identify vegetative heat tolerant genotypes among the advanced breeding lines and germplasm. The analysis of variance for the genotypes revealed presence of significant differences among the genotypes for 15 plant morpho-physiological traits. A large range of variation was observed for morpho-physiological traits. Genotypes exhibited high PCV and GCV for number of dried squares, number of fruiting points and leaf area at 90 DAS, similarly, moderate PCV and GCV estimates were recorded for number of sympodia, number of nodes on main stem, plant height and specific leaf weight at 90 DAS indicating that these traits contributed markedly to the total variability (Table 1 ). Table 1 Estimates of genetic parameters for morpho-physiological among, germplasm, advance breeding lines and check varieties of G. hirsutum L. during summer 2016-17. Traits GCV (%) PCV (%) h² (broad sense) GA GAM (%) Days to square initiation 5.44 7.11 58.49 3.52 8.56 Days to 50 per cent squaring 4.33 6.17 49.35 3.02 6.27 Number of dried squares 33.55 35.45 89.55 9.41 65.40 Number of monopodia 9.97 14.38 48.03 0.19 14.23 Number of sympodia 16.29 19.14 72.45 2.23 28.56 Number of nodes on main stem 14.90 18.60 64.19 2.54 24.60 Number of fruiting points 41.30 44.31 86.89 15.65 79.30 Days to flower initiation 4.23 5.43 60.79 4.42 6.80 Days to 50 per cent flowering 4.39 5.69 59.58 5.03 6.99 Plant height (cm) 17.92 18.70 91.88 13.43 35.38 SPAD value at 90 DAS 6.11 10.30 35.12 3.08 7.45 Canopy temperature at 90 DAS (°C) 7.12 8.04 78.35 4.64 12.98 Relative water content at 90 DAS (%) 6.21 9.38 43.77 6.47 8.46 Specific leaf weight at 90 DAS (mg cm − 2 13.91 17.28 64.77 1.46 23.05 Leaf area at 90 DAS (cm 2 ) 28.46 28.87 97.20 21.42 57.80 Principal component analysis (PCA) is a standard dimensional reduction technique and it is a best tool to extract and interpret relevant information on the traits from complex data sets. In the present study, PCA was utilized to know the relative contribution of the individual as well as set of morpho-physiological traits to the total variation observed among the genotypes. The PCA analysis revealed that six components out of 16 had eigen values above one with a cumulative proportion of 81.41% (Table 2 ). PC1 explained 25.14%, PC2 accounted for 21.55% of the total variation. Canopy temperature, relative water content and specific leaf weight were the main contributors of PC1, while, number of dried squares, plant height, number of fruiting points, leaf area, relative water content, specific leaf weight were important contributors of PC2. All the phenological traits also majorly contributed to both PC1 and PC2. Using the factor scores of PC1 and PC2, a PC biplot was drawn to know the pattern of genotype grouping. In the biplot each trait was represented as a vector and the length of vector was proportionate to the ability to distinguish genotypes (Fig. 2 ). Table 2 Principal component analysis on morpho-physiological and phenological traits of upland cotton Statistics PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 Proportion of variance 0.25 0.22 0.12 0.08 0.08 0.07 0.06 0.05 0.03 0.02 0.02 0.01 0.00 0.00 0.00 0.00 Cumulative proportion 0.25 0.47 0.59 0.67 0.74 0.81 0.87 0.92 0.95 0.97 0.98 0.99 0.99 1.00 1.00 1.00 Eigen values 4.02 3.45 1.94 1.28 1.22 1.12 0.89 0.76 0.53 0.27 0.25 0.13 0.07 0.04 0.03 0.02 The experimental season encountered with extreme summer high temperature at reproductive phase which continued till the end of the crop senescence. The experimental crop suffered from high temperature stress (> 32°C) from 4th week onwards till the crop senescence. Heat sensitivity was observed in most of the genotypes which resulted in the failure of reproductive phase on account of high square drying. Interestingly few genotypes retained healthy green foliage indicating their ability to with stand high temperatures or their ability to tolerate the heat stress (Table 3 ). Table 3 Classification of 204 germplasm for green foliage retention (Xu et al. 2000 ) Genotypes Number of Genotypes Scale (0–10) EC560323, CPD-424 2 10 RHC-0811, NH-152, EC560409, RAHC1019, TSH − 322, TAH-235, 6 6 128333 Acala 44, LRA-5166, Khandwa-3, HAGH-148, PS-20-2-1, CSH-3088, CNH07-16, RAHC1017 8 5 IC357196 (EL-508), IC356874 (Blight Master), IC35700 (Coker 100 Stable), IC357200 (EL592), IC357226 (EWLS x Tide water sp), IC356780 (B58-1290), IC358790 (SRT 1), EC138566 (Coker 310), EC128334 (Steninile-20), IC359047 (Soubagya), EC141679 (AC-241-1), SIMA-1,EC143506 (NC-177-166-30), JB WR-13 (MESR-17), JB WR-23 (NSP-18), EC137592 (Tanaroon-2-78),IC359963(DCB-348),EC479,543370A02N62, 543374A02N68,LRK-516 (Anjali), CNH-120 (MB),EC548182, EC559012, EC559021,EC560399, FQT-1, FQT − 8, FQT − 9, FQT − 10,FQT − 21, FQT − 28, FQT − 35,CPD-819, CPD-812 HAG-1055, ARBH-818, ARB-760, HBS-13-1, CPD-824, TSH-9975, CPD-423, CPD-1050,Raj-2,ARB-08-4/15, HAG-08-1002,Abadhita,JK-119, Laxmi,AK-32,CAK-023A, NH-615,PH-93, Khandwa-2, MCU-13,SVPR-2, F-2164, LH-2076,Sumangala,CPD-1009, CPD-745, 706M6M, BN, RAH-221,HBS-123, HBS-110, SRT-1, ADB-39, 127,RAH-110, RAH-162, HS-289, L-761, NH-630, NH-111-11, RS-2013/SGNR, RS-810/SGNR, CSHH-198 M/Sirsa,HLS-321729,RDT-2,RDT-3, RDT-13, RDT-17, RDT-34, Tiny-boll, CPD-425, CPD-426, DRC-305, PRS-74, CPD-468, CPD-428, CPD-437, CPD-433, CPD-432,AH-107, ACP-71,CPD-476,CPD-420, RAS-303,Sharada, Pusa-2-93,CPD-445, CPD-447, CPD-431, CPD-464, CPD-463,CPD-419, TCH-1740,CPD-824 1, 543404A03N107,543405A03N108, 3412A03N122,543421A03N137, 129014 368,CNH-36 380, EC560342, EC560358, EC560359, EC560393, EC560400,EC560408, EC560423,RDT-11, RDT-14, RDT-21, C-6030-P1,C-6030-P7,C-6030-P4,RAH-1003,GJHV-407,CCH-11-1,CNH-1107, CPD-867, RS-2622, FQT-3,Suray,BS-23,CPD-1401, ARBH-1402,RAH-1066,ARBH-1501,GSHV-173,RAH 1069,ARBH-1502,RB-602,CPD-1501,SHM-55,BGDS 1055,F 2532,GJHV-517,RAH-1271,CPD-1502,GSHV-172, PBH 21,BGDS 1033,CCH 15 − 2,RAH 1271, AKH-09-5,BGDS1033,CPD-1551,ARBH-1551,NDLH-2028-2,BGDS1055,RAH1069,CPD-1552, NDLH-2005-4,GBHV-198,ARBH-1552,IH11—10,GBHV-195, ARBC1501,RS2814,ANGC1501,DSC-1501,GSHV171, LHDP1,GSHV-180,DSC1552,ARBC1551,GTHV-13/32,GISV-272,543396A03N99,DSC-1551,AKH-780,MCU5_C1,SAHANA_C2,SURABHI_C3,ARBH813_C4 188 0 Discussion Out of various traits under study, number of dried squares, number of fruiting points and leaf area at 90 DAS exhibited high degree of variability among genotypes under heat stress which do not exhibit such magnitude of variability under normal growing seasonal conditions. The early susceptibility/tolerance of a genotypes to heat stress depends on extent of drying of squares and leaf area, which can be exploited for breeding of better yielding cultivars under high temperature regimes. Abro et al. ( 2015 ) also reported high genetic variation for morpho-physiological traits responsible for heat tolerance among the upland genotypes. Considerable changes in the estimates of heritability were observed for most of the morpho-physiological traits studied. Number of dried squares, plant height and leaf area studied during summer heat screening registered very high estimates of heritability values equal to or more than 90.00 per cent. The number of sympodia, number of nodes, number of fruiting points, days to flower initiation, canopy temperature at 90 DAS and specific leaf weight at 90 DAS registered high estimates of heritability. However, the number of dried squares, number of sympodia, number of nodes on main stem, number of fruiting points, plant height, specific leaf weight at 90 DAS and leaf area at 90 DAS recorded high level of broad sense heritability coupled with high value of GAM. This indicates role of additive gene action for expression of these traits, which can be improved by phenotypic selection. The canopy temperature at 90 DAS exhibited high level of broad sense heritability coupled with moderate value of GAM. The high temperature regime exhibited higher genetic variability, broad sense heritability and genetic advance for most of the morphological traits studied. High heritability coupled with high GAM was also reported for chlorophyll content and relative cell injury percentage in previous cotton heat tolerance studies in cotton (Khan et al. 2008 ). The arrangement of trait vectors in the biplot reveals the association patterns of traits studied. All the morphological traits were represented as an isolated cluster of vectors indicating that these traits have positive inter trait association. Similarly, phenological traits were also represented in a separate cluster of vectors revealing that these traits were highly correlated among themselves. Interestingly, the physiological traits were mainly split in to two opposite clusters indicating these two cluster of traits are highly correlated in negative direction. Leaf area, specific leaf weight, SPAD chlorophyll meter reading were grouped as a single cluster indicating that they were highly correlated in positive direction, while the canopy temperature and specific leaf weight were grouped together and were placed as separate clusters in the opposite direction to that of other physiological traits indicating the negative correlation among the two clusters of physiological traits. The genetic material in this study was largely structured based on phenological and morphological traits, leaf area and canopy temperature among physiological traits contributed considerably to the total variation. A threshold ambient temperature of 32°C is considered to assert the presence of heat stress based on the fact that the temperatures above 32°C sharply decreases the seed cotton yield (Dabbert et al., 2017 ). The temperatures of 32°C and above have been shown to have negative impact on boll retention (Reddy et al., 1999 ). The number of days with above 32°C across the crop growth period was recorded and 94 such warmer days were observed in the present study. The maximum day temperature also reached up to 39°C during crop period which caused severe square drying. Among the genotypes few retained green foliage indicating their ability to with stand high temperatures or their ability to tolerate the heat stress. Based on the percentage of foliage retention (Xu et al., 2000 ), the genotypes were classified by visual score using 0 to 10 scale, where 10 score equals to 100% retention of foliage. Two genotypes, CPD-424 and EC560323 exhibited very high vegetative heat tolerance by retaining 80 and 70 percent of foliage during peak flowering stage. These two accessions can be used as donors for transferring vegetative heat tolerance in the background of high yielding cultivars. Similarly, other genotypes viz. , TSH-322, RAHC1019, RHC-0811, NH-152, EC560409 and TAH-235 retained 60% of the foliage. Likewise, ARBC-1501, CNH07-16, RAHC1017, 128333-Acala-44, LRA-5166, Khandwa-3, HAGH-148, CSH-3088 and PS-20-2-1 retained 50% of the vegetation and these can be further used as a source to improve heat tolerance in upland cotton (Table 3 ). Earlier, Wu et al. ( 2014 ) identified vegetative heat tolerant upland germplasm accessions based on chlorophyll fluorescence measurement during heat stress under field condition. Demirel et al. ( 2016 ) also identified 16 heat tolerant genotypes based on the traits such as hypocotyl dry weight, leaf pigment contents and cellular respiration which were significantly correlated with previously known yield of ten cultivars grown in the hot field conditions. Conclusion The study confirmed that, heat stress resulted in the expression of high variability among the genotypes for many traits and also induced early senescence among most of the genotypes. The identified vegetative heat tolerant germplasm and advanced breeding lines needs to be subjected for physiological and cellular levels analyses for deciphering the heat tolerance mechanisms. Further these genotypes can be utilized as a source of donors to improve heat tolerance in upland cotton. Declarations Author contributions Venkatesh and S. T. Kajjidoni designed the experiment.Venkatesh grew plants and performed the phenotypic evaluation. P. Kariyannanavar, M. J Pavithra and Venkatesh analyzed the data. P. Kariyannanavar and Venkatesh wrote the draft manuscript. S. T. Kajjidoni revised the draft manuscript. All authors made contributions and approved the final manuscript. Acknowledgment Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad for providing material to conduct research effectively in time. Conflict of Interest The authors have no conflict of interest References Abro, S., Rajput, M. T., Khan, M. A., Sial, M. A., & Tahir, S. S. (2015). Screening of cotton ( Gossypium hirsutum L.) genotypes for heat tolerance. Pak. J. Bot , 47 (6): 2085-2091. Chattopadhyay, N., Samual, R. P., & Banerjee, S. K. (2008). Effect of weather on growth and yield of cotton grown in the dry farming tract of peninsular India. Mausam , 59 (3): 339-346. Dabbert, T. A., Pauli, D., Sheetz, R., & Gore, M. A. (2017). Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton. Euphytica , 213 (1): 1-17. Demirel, U., Çopur, O., & Gür, A. (2016). Early-stage screening for heat tolerance in cotton. Plant Breeding , 135 (1): 80-89. Dendage, V. R., Khobragade A. M., Bagade A. B., & Chavan K. K., (2018). Agroclimatic characterization of cotton crop under parbhani district. International Journal of Current Microbiology and Applied Sciences , 6: 1021-1034. Karademir, E., Karademir, Ç.,Ekinci, R., Başbağ, S., & Başal, H. (2012). Screening cotton varieties ( Gossypium hirsutum L.) for heat tolerance under field conditions. African Journal of Agriculture Research , 7 (47): 6335-6342. Khan, A. I., Khan, I. A., & Sadaqat, H. A. (2008). Heat tolerance is variable in cotton ( Gossypium hirsutum L.) and can be exploited for breeding of better yielding cultivars under high temperature regimes. Pakistan Journal of Botany, 40 (5): 2053-2058. Majeed, S., Malik, T. A., Rana, I. A., & Azhar, M. T. (2019). Antioxidant and Physiological Responses of Upland Cotton Accessions Grown Under High-Temperature Regimes. Iran Journal of Science and Technology Transforming Agriculture Sciences , 43 (6): 2759-2768. Reddy, K. R., Davidonis, G. H., Johnson, A. S., & Vinyard, B. T., (1999). Temperature regime and carbon dioxide enrichment alter cotton boll development and fiber properties. Agronomy Journal , 91 (5), 851-858. STAR, version 2.0.1 2014. Biometrics and Breeding Informatics, PBGB Division, International Rice Research Institute, Los Banos, Laguna. Thakare, H. S., Shrivastava, P. K., & Bardhan, K., (2014). Impact of weather parameters on cotton productivity at Surat (Gujarat), India. Journal of Applied and Natural Sciences , 6 (2): 599-604. Windostat Version 9.1 from indostat services, Hyderabad. Wu, T., Weaver, D.B., Locy, R.D., McElroy, S., & van Santen, E. (2014). Identification of vegetative heat tolerant upland cotton ( Gossypium hirsutum L.) germplasm utilizing chlorophyll fluorescence measurement during heat stress. Plant Breeding , 133 (2):250-255. Xu, W., Rosenow, D. T., & Nguyen, H. T., (2000). Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. Plant Breeding , 119 (4): 365-367. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Euphytica → Version 1 posted Editorial decision: Revision requested 30 Mar, 2024 Reviews received at journal 09 Mar, 2024 Reviewers agreed at journal 18 Feb, 2024 Reviewers invited by journal 18 Feb, 2024 Submission checks completed at journal 12 Feb, 2024 Editor assigned by journal 12 Feb, 2024 First submitted to journal 09 Feb, 2024 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. We do this by developing innovative software and high quality services for the global research community. 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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-3942046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272548775,"identity":"48d96922-ab42-4807-a18d-13cf0da83a0d","order_by":0,"name":"Venkatesh Venkatesh","email":"","orcid":"","institution":"University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Venkatesh","middleName":"","lastName":"Venkatesh","suffix":""},{"id":272548776,"identity":"5cbb2041-fefd-4e4c-9c52-bcbc87cdbe3c","order_by":1,"name":"S. T. Kajjidoni","email":"","orcid":"","institution":"University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"T.","lastName":"Kajjidoni","suffix":""},{"id":272548777,"identity":"43d20236-8d4f-43eb-9287-919c8776ed5a","order_by":2,"name":"P Kariyannanavar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYHACNoaEin8MDDzMB4AcCRkitZwBquZhSwBp4SFOC2MbSAuPAYhHWIs5++FnDx6w3cmT7znz+dWNGgseBvbDRzfg02LZk2ZukMDzrNjgbO8265xjQIfxpKXdwKfF4EAOm0SCBHPiBn7ebcZANg/QO2b4tZx/A9RiwJw4v5/nmXHOP2K03ADZknA4seFsD/Pj3DYitFjOeGYmkXAgrdjgzDEz5tw+CR42Qn4x509+Jvnznw0wxJIff875VifHz374GH6HQekEIGaTALHY8ClH18L8gZDqUTAKRsEoGJkAAKRtSQuaVaofAAAAAElFTkSuQmCC","orcid":"","institution":"University of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"P","middleName":"","lastName":"Kariyannanavar","suffix":""},{"id":272548778,"identity":"e0881f5f-ef1f-4394-bc33-5fbd0bcc236d","order_by":3,"name":"M. 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Pavithra","email":"","orcid":"","institution":"University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"J.","lastName":"Pavithra","suffix":""}],"badges":[],"createdAt":"2024-02-09 05:29:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3942046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3942046/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10681-024-03361-y","type":"published","date":"2024-06-14T15:28:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51136989,"identity":"a5f68a84-b355-4ce0-acae-097eaedd01c7","added_by":"auto","created_at":"2024-02-14 18:38:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341286,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly weather data of summer 2016-17 during crop duration at ARS, Dharwad farm\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3942046/v1/b030a9f2925da71b17a1d459.png"},{"id":51136990,"identity":"24f5e595-eb14-407c-85db-9a0ef993f872","added_by":"auto","created_at":"2024-02-14 18:38:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24871,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot of PC1 Vs PC2 for morpho-physiological traits under study based on 204 upland cotton germplasm and advanced breeding lines.\u003c/p\u003e\n\u003cp\u003eDSI: Days to square initiation DFS: Days to 50 per cent squaring DS: Number of dried squares NM: Number of monopodia\u003c/p\u003e\n\u003cp\u003eNS: Number of sympodia NN: Number of nodes on main stem NFP: Number of fruiting points DFI: Days to flower initiation\u003c/p\u003e\n\u003cp\u003eDFF: Days to 50 per cent flowering PH: Plant height (cm) SPAD90: SPAD value at 90 DAS CT90: Canopy temperature at 90 DAS (°C) RWC90: Relative water content at 90 DAS (%) SLW90: Specific leaf weight at 90 DAS (mg cm\u003csup\u003e-2\u003c/sup\u003e) LA90: Leaf area at 90 DAS (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3942046/v1/d588fb587ca1afe5090d7d85.png"},{"id":58823275,"identity":"db2d502d-e3e8-4ed2-8735-005ff02c23df","added_by":"auto","created_at":"2024-06-21 16:57:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":704875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3942046/v1/b601e0c9-12fa-4802-b6c0-ab0d1032f214.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unravelling the genetic variability and identification of vegetative heat tolerant lines in upland cotton (Gossypium hirsutum L.)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe upland cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) is a widely cultivated fibre crop species in the world. The recent unprecedented rise in temperature across the globe due to changing climate has shown the susceptibility of the upland cotton to heat stress. (Chattopadhyay et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Thakare et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dendage et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hence, there is an urgent need for characterizing available germplasm to identify for heat tolerant genotypes to utilise them in improving heat tolerance in the face of changing climate scenario. The development of heat-tolerant cultivars is key to tackle heat stress caused by changing climate. Several heat stress screening methods based on morpho-physiological traits have been developed to identify heat tolerant lines. Both lab as well as field experiments were conducted to characterize understand and identify heat tolerant cotton genotypes (Karademir et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The heat tolerance screening studies have also revealed the existence of natural variation for thermo tolerance in upland cotton by using various selection criteria, suggesting the scope for improving heat tolerance in Upland cotton (Majeed et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Field screening provides ultimate proof for abiotic stress evaluation as it reveals the true expression of identified genotypes for a particular stress. Hence, an attempt was made to screen the genotypes during the summer, to ascertain the level of thermo tolerance and to identify relatively heat tolerant genotypes among the available germplasm and advance breeding lines.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThe genetic material of 204 \u003cem\u003eG. hirsutum\u003c/em\u003e lines comprising of selected 153 germplasm lines, indigenous and exotic collection, advanced breeding lines and released varieties. Four regional varietal checks \u003cem\u003eviz.\u003c/em\u003e, Sahana, Surabhi, MCU 5 and ARBH 813 were also included for present study for the evaluation at Agricultural Research Station, Dharwad Farm during summer 2016-17. The augmented design was used for screening with 10 blocks and each treatment was hand dibbled in single row of 4.2 m length with inter-row spacing of 90 cm and 30 cm between plants within the row. The weekly weather data of summer 2016-17 during crop duration at experimental location presented in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The observations on morpho-physiological traits were recorded on five randomly selected plants. The recorded data was subjected for analysis of variance, estimation genetic variability parameters using Windostat software version 9.1 and principal component analysis (PCA) was carried out based on the mean data using Statistical Tool for Agricultural Research (STAR) version 2.0.1 software.\u003c/p\u003e \u003cp\u003eFurther based on the percentage of foliage retention all the genotypes were classified based on visual scoring using 0 to 10 scale, where 10 score equals to 100% retention of green foliage, which is referred as vegetative heat tolerance (Xu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe advanced breeding lines, cultivars and germplasm screened experienced heat stress (\u0026gt;\u0026thinsp;32\u0026deg;C) from the beginning of square initiation, days to flower initiation, till the crop senescence due to high temperature. The heat stress resulted in the expression of high variability among the genotypes for many traits and also induced early senescence among most of the genotypes. None of the genotypes could produce harvestable seed cotton, but, a few genotypes exhibited vegetative heat tolerance by retaining healthy green foliage.\u003c/p\u003e \u003cp\u003eThe present investigation was mainly carried out to study the genetic variability for morpho-physiological traits and also to identify important heat tolerance related traits through principal component analysis which is a dimensional reduction technique and to finally identify vegetative heat tolerant genotypes among the advanced breeding lines and germplasm. The analysis of variance for the genotypes revealed presence of significant differences among the genotypes for 15 plant morpho-physiological traits. A large range of variation was observed for morpho-physiological traits. Genotypes exhibited high PCV and GCV for number of dried squares, number of fruiting points and leaf area at 90 DAS, similarly, moderate PCV and GCV estimates were recorded for number of sympodia, number of nodes on main stem, plant height and specific leaf weight at 90 DAS indicating that these traits contributed markedly to the total variability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of genetic parameters for morpho-physiological among, germplasm, advance breeding lines and check varieties of \u003cem\u003eG. hirsutum\u003c/em\u003e L. during summer 2016-17.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eh\u0026sup2;\u003c/p\u003e \u003cp\u003e(broad sense)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGAM\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to square initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to 50 per cent squaring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of dried squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of monopodia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sympodia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of nodes on main stem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fruiting points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to flower initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to 50 per cent flowering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPAD value at 90 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanopy temperature at 90 DAS (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative water content at 90 DAS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific leaf weight at 90 DAS (mg cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf area at 90 DAS (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) is a standard dimensional reduction technique and it is a best tool to extract and interpret relevant information on the traits from complex data sets. In the present study, PCA was utilized to know the relative contribution of the individual as well as set of morpho-physiological traits to the total variation observed among the genotypes. The PCA analysis revealed that six components out of 16 had eigen values above one with a cumulative proportion of 81.41% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PC1 explained 25.14%, PC2 accounted for 21.55% of the total variation. Canopy temperature, relative water content and specific leaf weight were the main contributors of PC1, while, number of dried squares, plant height, number of fruiting points, leaf area, relative water content, specific leaf weight were important contributors of PC2. All the phenological traits also majorly contributed to both PC1 and PC2. Using the factor scores of PC1 and PC2, a PC biplot was drawn to know the pattern of genotype grouping. In the biplot each trait was represented as a vector and the length of vector was proportionate to the ability to distinguish genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal component analysis on morpho-physiological and phenological traits of upland cotton\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePC7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePC8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePC9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePC10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePC11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePC12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003ePC13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePC14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003ePC15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003ePC16\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative proportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigen values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe experimental season encountered with extreme summer high temperature at reproductive phase which continued till the end of the crop senescence. The experimental crop suffered from high temperature stress (\u0026gt;\u0026thinsp;32\u0026deg;C) from 4th week onwards till the crop senescence. Heat sensitivity was observed in most of the genotypes which resulted in the failure of reproductive phase on account of high square drying. Interestingly few genotypes retained healthy green foliage indicating their ability to with stand high temperatures or their ability to tolerate the heat stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of 204 germplasm for green foliage retention (Xu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale (0\u0026ndash;10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC560323, CPD-424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHC-0811, NH-152, EC560409, RAHC1019, TSH \u0026minus;\u0026thinsp;322, TAH-235,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e128333 Acala 44, LRA-5166, Khandwa-3, HAGH-148, PS-20-2-1, CSH-3088, CNH07-16, RAHC1017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC357196 (EL-508), IC356874 (Blight Master), IC35700 (Coker 100 Stable), IC357200 (EL592), IC357226 (EWLS x Tide water sp), IC356780 (B58-1290), IC358790 (SRT 1), EC138566 (Coker 310), EC128334 (Steninile-20), IC359047 (Soubagya), EC141679 (AC-241-1), SIMA-1,EC143506 (NC-177-166-30), JB WR-13 (MESR-17), JB WR-23 (NSP-18), EC137592 (Tanaroon-2-78),IC359963(DCB-348),EC479,543370A02N62, 543374A02N68,LRK-516 (Anjali), CNH-120 (MB),EC548182, EC559012, EC559021,EC560399, FQT-1, FQT \u0026minus;\u0026thinsp;8, FQT \u0026minus;\u0026thinsp;9, FQT \u0026minus;\u0026thinsp;10,FQT \u0026minus;\u0026thinsp;21, FQT \u0026minus;\u0026thinsp;28, FQT \u0026minus;\u0026thinsp;35,CPD-819, CPD-812 HAG-1055, ARBH-818, ARB-760, HBS-13-1, CPD-824, TSH-9975, CPD-423, CPD-1050,Raj-2,ARB-08-4/15, HAG-08-1002,Abadhita,JK-119, Laxmi,AK-32,CAK-023A, NH-615,PH-93, Khandwa-2, MCU-13,SVPR-2, F-2164, LH-2076,Sumangala,CPD-1009, CPD-745, 706M6M, BN, RAH-221,HBS-123, HBS-110, SRT-1, ADB-39, 127,RAH-110, RAH-162, HS-289, L-761, NH-630, NH-111-11, RS-2013/SGNR, RS-810/SGNR, CSHH-198 M/Sirsa,HLS-321729,RDT-2,RDT-3, RDT-13, RDT-17, RDT-34, Tiny-boll, CPD-425, CPD-426, DRC-305, PRS-74, CPD-468, CPD-428, CPD-437, CPD-433, CPD-432,AH-107, ACP-71,CPD-476,CPD-420, RAS-303,Sharada, Pusa-2-93,CPD-445, CPD-447, CPD-431, CPD-464, CPD-463,CPD-419, TCH-1740,CPD-824 1, 543404A03N107,543405A03N108, 3412A03N122,543421A03N137,\u0026nbsp;129014 368,CNH-36 380, EC560342, EC560358, EC560359,\u0026nbsp; EC560393,\u0026nbsp; EC560400,EC560408, EC560423,RDT-11,\u0026nbsp; RDT-14,\u0026nbsp;RDT-21,\u0026nbsp;C-6030-P1,C-6030-P7,C-6030-P4,RAH-1003,GJHV-407,CCH-11-1,CNH-1107, CPD-867, RS-2622, FQT-3,Suray,BS-23,CPD-1401, ARBH-1402,RAH-1066,ARBH-1501,GSHV-173,RAH 1069,ARBH-1502,RB-602,CPD-1501,SHM-55,BGDS 1055,F 2532,GJHV-517,RAH-1271,CPD-1502,GSHV-172, PBH 21,BGDS 1033,CCH 15\u0026thinsp;\u0026minus;\u0026thinsp;2,RAH 1271, AKH-09-5,BGDS1033,CPD-1551,ARBH-1551,NDLH-2028-2,BGDS1055,RAH1069,CPD-1552, NDLH-2005-4,GBHV-198,ARBH-1552,IH11\u0026mdash;10,GBHV-195, ARBC1501,RS2814,ANGC1501,DSC-1501,GSHV171, LHDP1,GSHV-180,DSC1552,ARBC1551,GTHV-13/32,GISV-272,543396A03N99,DSC-1551,AKH-780,MCU5_C1,SAHANA_C2,SURABHI_C3,ARBH813_C4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOut of various traits under study, number of dried squares, number of fruiting points and leaf area at 90 DAS exhibited high degree of variability among genotypes under heat stress which do not exhibit such magnitude of variability under normal growing seasonal conditions. The early susceptibility/tolerance of a genotypes to heat stress depends on extent of drying of squares and leaf area, which can be exploited for breeding of better yielding cultivars under high temperature regimes. Abro et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also reported high genetic variation for morpho-physiological traits responsible for heat tolerance among the upland genotypes.\u003c/p\u003e \u003cp\u003eConsiderable changes in the estimates of heritability were observed for most of the morpho-physiological traits studied. Number of dried squares, plant height and leaf area studied during summer heat screening registered very high estimates of heritability values equal to or more than 90.00 per cent. The number of sympodia, number of nodes, number of fruiting points, days to flower initiation, canopy temperature at 90 DAS and specific leaf weight at 90 DAS registered high estimates of heritability. However, the number of dried squares, number of sympodia, number of nodes on main stem, number of fruiting points, plant height, specific leaf weight at 90 DAS and leaf area at 90 DAS recorded high level of broad sense heritability coupled with high value of GAM. This indicates role of additive gene action for expression of these traits, which can be improved by phenotypic selection. The canopy temperature at 90 DAS exhibited high level of broad sense heritability coupled with moderate value of GAM. The high temperature regime exhibited higher genetic variability, broad sense heritability and genetic advance for most of the morphological traits studied. High heritability coupled with high GAM was also reported for chlorophyll content and relative cell injury percentage in previous cotton heat tolerance studies in cotton (Khan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe arrangement of trait vectors in the biplot reveals the association patterns of traits studied. All the morphological traits were represented as an isolated cluster of vectors indicating that these traits have positive inter trait association. Similarly, phenological traits were also represented in a separate cluster of vectors revealing that these traits were highly correlated among themselves. Interestingly, the physiological traits were mainly split in to two opposite clusters indicating these two cluster of traits are highly correlated in negative direction. Leaf area, specific leaf weight, SPAD chlorophyll meter reading were grouped as a single cluster indicating that they were highly correlated in positive direction, while the canopy temperature and specific leaf weight were grouped together and were placed as separate clusters in the opposite direction to that of other physiological traits indicating the negative correlation among the two clusters of physiological traits. The genetic material in this study was largely structured based on phenological and morphological traits, leaf area and canopy temperature among physiological traits contributed considerably to the total variation.\u003c/p\u003e \u003cp\u003eA threshold ambient temperature of 32\u0026deg;C is considered to assert the presence of heat stress based on the fact that the temperatures above 32\u0026deg;C sharply decreases the seed cotton yield (Dabbert et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The temperatures of 32\u0026deg;C and above have been shown to have negative impact on boll retention (Reddy et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The number of days with above 32\u0026deg;C across the crop growth period was recorded and 94 such warmer days were observed in the present study. The maximum day temperature also reached up to 39\u0026deg;C during crop period which caused severe square drying. Among the genotypes few retained green foliage indicating their ability to with stand high temperatures or their ability to tolerate the heat stress. Based on the percentage of foliage retention (Xu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the genotypes were classified by visual score using 0 to 10 scale, where 10 score equals to 100% retention of foliage. Two genotypes, CPD-424 and EC560323 exhibited very high vegetative heat tolerance by retaining 80 and 70 percent of foliage during peak flowering stage. These two accessions can be used as donors for transferring vegetative heat tolerance in the background of high yielding cultivars. Similarly, other genotypes \u003cem\u003eviz.\u003c/em\u003e, TSH-322, RAHC1019, RHC-0811, NH-152, EC560409 and TAH-235 retained 60% of the foliage. Likewise, ARBC-1501, CNH07-16, RAHC1017, 128333-Acala-44, LRA-5166, Khandwa-3, HAGH-148, CSH-3088 and PS-20-2-1 retained 50% of the vegetation and these can be further used as a source to improve heat tolerance in upland cotton (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Earlier, Wu et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identified vegetative heat tolerant upland germplasm accessions based on chlorophyll fluorescence measurement during heat stress under field condition. Demirel et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also identified 16 heat tolerant genotypes based on the traits such as hypocotyl dry weight, leaf pigment contents and cellular respiration which were significantly correlated with previously known yield of ten cultivars grown in the hot field conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study confirmed that, heat stress resulted in the expression of high variability among the genotypes for many traits and also induced early senescence among most of the genotypes. The identified vegetative heat tolerant germplasm and advanced breeding lines needs to be subjected for physiological and cellular levels analyses for deciphering the heat tolerance mechanisms. Further these genotypes can be utilized as a source of donors to improve heat tolerance in upland cotton.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenkatesh and S. T. Kajjidoni\u003csup\u003e\u0026nbsp;\u003c/sup\u003edesigned the experiment.Venkatesh\u0026nbsp;grew plants and performed the phenotypic evaluation. P. Kariyannanavar, M. J Pavithra and Venkatesh analyzed the data. P. Kariyannanavar\u0026nbsp;and\u0026nbsp;Venkatesh\u0026nbsp;wrote the draft manuscript.\u0026nbsp;S. T. Kajjidoni\u003csup\u003e\u0026nbsp;\u003c/sup\u003erevised the draft manuscript. All authors made contributions and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003eDepartment of Genetics and Plant Breeding,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eUniversity of Agricultural Sciences, Dharwad for providing material to conduct research effectively in time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe authors have no conflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbro, S., Rajput, M. T., Khan, M. A., Sial, M. A., \u0026amp; Tahir, S. S. (2015). Screening of cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) genotypes for heat tolerance. \u003cem\u003ePak. J. Bot\u003c/em\u003e, \u003cstrong\u003e47\u003c/strong\u003e(6): 2085-2091.\u003c/li\u003e\n\u003cli\u003eChattopadhyay, N., Samual, R. P., \u0026amp; Banerjee, S. K. (2008). Effect of weather on growth and yield of cotton grown in the dry farming tract of peninsular India. \u003cem\u003eMausam\u003c/em\u003e, \u003cstrong\u003e59\u003c/strong\u003e(3): 339-346.\u003c/li\u003e\n\u003cli\u003eDabbert, T. A., Pauli, D., Sheetz, R., \u0026amp; Gore, M. A. (2017). Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton. \u003cem\u003eEuphytica\u003c/em\u003e, \u003cstrong\u003e213\u003c/strong\u003e(1): 1-17.\u003c/li\u003e\n\u003cli\u003eDemirel, U., \u0026Ccedil;opur, O., \u0026amp; G\u0026uuml;r, A. (2016). Early-stage screening for heat tolerance in cotton. \u003cem\u003ePlant Breeding\u003c/em\u003e, \u003cstrong\u003e135\u003c/strong\u003e(1): 80-89.\u003c/li\u003e\n\u003cli\u003eDendage, V. R., Khobragade A. M., Bagade A. B., \u0026amp; Chavan K. K., (2018). Agroclimatic characterization of cotton crop under parbhani district. \u003cem\u003eInternational Journal of Current Microbiology and Applied Sciences\u003c/em\u003e, 6: 1021-1034.\u003c/li\u003e\n\u003cli\u003eKarademir, E., Karademir, \u0026Ccedil;.,Ekinci, R., Başbağ, S., \u0026amp; Başal, H. (2012). Screening cotton varieties (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) for heat tolerance under field conditions. \u003cem\u003eAfrican Journal of Agriculture Research\u003c/em\u003e, \u003cstrong\u003e7\u003c/strong\u003e(47): 6335-6342.\u003c/li\u003e\n\u003cli\u003eKhan, A. I., Khan, I. A., \u0026amp; Sadaqat, H. A. (2008). Heat tolerance is variable in cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) and can be exploited for breeding of better yielding cultivars under high temperature regimes. \u003cem\u003ePakistan Journal of Botany,\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e(5): 2053-2058.\u003c/li\u003e\n\u003cli\u003eMajeed, S., Malik, T. A., Rana, I. A., \u0026amp; Azhar, M. T. (2019). Antioxidant and Physiological Responses of Upland Cotton Accessions Grown Under High-Temperature Regimes. \u003cem\u003eIran Journal of Science and Technology Transforming Agriculture Sciences\u003c/em\u003e, \u003cstrong\u003e43\u003c/strong\u003e(6): 2759-2768.\u003c/li\u003e\n\u003cli\u003eReddy, K. R., Davidonis, G. H., Johnson, A. S., \u0026amp; Vinyard, B. T., (1999). Temperature regime and carbon dioxide enrichment alter cotton boll development and fiber properties. \u003cem\u003eAgronomy Journal\u003c/em\u003e, \u003cstrong\u003e91\u003c/strong\u003e(5), 851-858.\u003c/li\u003e\n\u003cli\u003eSTAR, version 2.0.1 2014. Biometrics and Breeding Informatics, PBGB Division, International Rice Research Institute, Los Banos, Laguna.\u003c/li\u003e\n\u003cli\u003eThakare, H. S., Shrivastava, P. K., \u0026amp; Bardhan, K., (2014). Impact of weather parameters on cotton productivity at Surat (Gujarat), India. \u003cem\u003eJournal of Applied and Natural Sciences\u003c/em\u003e, \u003cstrong\u003e6\u003c/strong\u003e(2): 599-604.\u003c/li\u003e\n\u003cli\u003eWindostat Version 9.1 from indostat services, Hyderabad.\u003c/li\u003e\n\u003cli\u003eWu, T., Weaver, D.B., Locy, R.D., McElroy, S., \u0026amp; van Santen, E. (2014). Identification of vegetative heat tolerant upland cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) germplasm utilizing chlorophyll fluorescence measurement during heat stress. \u003cem\u003ePlant Breeding\u003c/em\u003e, \u003cstrong\u003e133\u003c/strong\u003e(2):250-255.\u003c/li\u003e\n\u003cli\u003eXu, W., Rosenow, D. T., \u0026amp; Nguyen, H. T., (2000). Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. \u003cem\u003ePlant Breeding\u003c/em\u003e, \u003cstrong\u003e119\u003c/strong\u003e(4): 365-367.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"heat stress, genetic variability, germplasm and multivariate analysis","lastPublishedDoi":"10.21203/rs.3.rs-3942046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3942046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA study was carried out to screen a set of 204 genotypes consisting of germplasm and advance breeding lines for heat tolerance. The study of genetic variability among these genotypes for various morpho-physiological traits revealed high PCV and GCV for number of dried squares, number of fruiting points and leaf area. The results of principal component analysis showed that the first six principal components with eigen values more than unity contributed 81.41% of the variability among genotypes and the traits like canopy temperature, relative water content, specific leaf weight, number of dried squares, plant height, number of fruiting points, leaf area and the phenological traits contributed significantly to total variability. The two genotypes \u003cem\u003eviz.\u003c/em\u003e, CPD-424 and EC560323 were identified as highly vegetative heat tolerant lines which can be used as donor parents in the heat tolerance breeding programmes.\u003c/p\u003e","manuscriptTitle":"Unravelling the genetic variability and identification of vegetative heat tolerant lines in upland cotton (Gossypium hirsutum L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-14 18:38:21","doi":"10.21203/rs.3.rs-3942046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-30T14:31:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-09T19:04:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4babfc9c-7c96-4a97-b8b7-4e7df863fc23","date":"2024-02-18T21:09:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-18T16:50:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-13T02:06:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-13T02:06:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2024-02-09T05:22:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f2d2cc7-75b8-4fe5-946f-79f4fe44fce6","owner":[],"postedDate":"February 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:28:48+00:00","versionOfRecord":{"articleIdentity":"rs-3942046","link":"https://doi.org/10.1007/s10681-024-03361-y","journal":{"identity":"euphytica","isVorOnly":false,"title":"Euphytica"},"publishedOn":"2024-06-14 15:28:48","publishedOnDateReadable":"June 14th, 2024"},"versionCreatedAt":"2024-02-14 18:38:21","video":"","vorDoi":"10.1007/s10681-024-03361-y","vorDoiUrl":"https://doi.org/10.1007/s10681-024-03361-y","workflowStages":[]},"version":"v1","identity":"rs-3942046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3942046","identity":"rs-3942046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}