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Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method

Drought is one of the main abiotic stresses that seriously influences cotton production. Many indicators can be used to evaluate cotton drought tolerance, but the key indicators remain to be determined. The objective of this study was to identify effective cotton drought tolerance indicators from 19...

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Autores principales: Sun, FengLei, Chen, Qin, Chen, QuanJia, Jiang, Menghui, Gao, Wenwei, Qu, YanYing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299416/
https://www.ncbi.nlm.nih.gov/pubmed/34305956
http://dx.doi.org/10.3389/fpls.2021.619926
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author Sun, FengLei
Chen, Qin
Chen, QuanJia
Jiang, Menghui
Gao, Wenwei
Qu, YanYing
author_facet Sun, FengLei
Chen, Qin
Chen, QuanJia
Jiang, Menghui
Gao, Wenwei
Qu, YanYing
author_sort Sun, FengLei
collection PubMed
description Drought is one of the main abiotic stresses that seriously influences cotton production. Many indicators can be used to evaluate cotton drought tolerance, but the key indicators remain to be determined. The objective of this study was to identify effective cotton drought tolerance indicators from 19 indices, including morphology, photosynthesis, physiology, and yield-related indices, and to evaluate the yield potential of 104 cotton varieties under both normal and drought-stress field conditions. Combined with principal component analysis (PCA) and a regression analysis method, the results showed that the top five PCs among the 19, with eigenvalues > 1, contributed 65.52, 63.59, and 65.90% of the total variability during 2016 to 2018, respectively, which included plant height (PH), effective fruit branch number (EFBN), single boll weight (SBW), transpiration rate (Tr) and chlorophyll (Chl). Therefore, the indicator dimension decreased from 19 to 5. A comparison of the 19 indicators with the 5 identified indicators through PCA and a combined regression analysis found that the results of the final cluster of drought tolerance on 104 cotton varieties were basically consistent. The results indicated that these five traits could be used in combination to screen cotton varieties or lines for drought tolerance in cotton breeding programs, and Zhong R2016 and Xin lu zao 45 exhibited high drought tolerance and can be selected as superior parents for good yield performance under drought stress.
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spelling pubmed-82994162021-07-24 Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method Sun, FengLei Chen, Qin Chen, QuanJia Jiang, Menghui Gao, Wenwei Qu, YanYing Front Plant Sci Plant Science Drought is one of the main abiotic stresses that seriously influences cotton production. Many indicators can be used to evaluate cotton drought tolerance, but the key indicators remain to be determined. The objective of this study was to identify effective cotton drought tolerance indicators from 19 indices, including morphology, photosynthesis, physiology, and yield-related indices, and to evaluate the yield potential of 104 cotton varieties under both normal and drought-stress field conditions. Combined with principal component analysis (PCA) and a regression analysis method, the results showed that the top five PCs among the 19, with eigenvalues > 1, contributed 65.52, 63.59, and 65.90% of the total variability during 2016 to 2018, respectively, which included plant height (PH), effective fruit branch number (EFBN), single boll weight (SBW), transpiration rate (Tr) and chlorophyll (Chl). Therefore, the indicator dimension decreased from 19 to 5. A comparison of the 19 indicators with the 5 identified indicators through PCA and a combined regression analysis found that the results of the final cluster of drought tolerance on 104 cotton varieties were basically consistent. The results indicated that these five traits could be used in combination to screen cotton varieties or lines for drought tolerance in cotton breeding programs, and Zhong R2016 and Xin lu zao 45 exhibited high drought tolerance and can be selected as superior parents for good yield performance under drought stress. Frontiers Media S.A. 2021-07-09 /pmc/articles/PMC8299416/ /pubmed/34305956 http://dx.doi.org/10.3389/fpls.2021.619926 Text en Copyright © 2021 Sun, Chen, Chen, Jiang, Gao and Qu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Sun, FengLei
Chen, Qin
Chen, QuanJia
Jiang, Menghui
Gao, Wenwei
Qu, YanYing
Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title_full Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title_fullStr Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title_full_unstemmed Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title_short Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method
title_sort screening of key drought tolerance indices for cotton at the flowering and boll setting stage using the dimension reduction method
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299416/
https://www.ncbi.nlm.nih.gov/pubmed/34305956
http://dx.doi.org/10.3389/fpls.2021.619926
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