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Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations
Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The “drought hot spot” in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404259/ https://www.ncbi.nlm.nih.gov/pubmed/37543669 http://dx.doi.org/10.1038/s41598-023-38581-0 |
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author | Ajay, B. C. Kumar, Narendra Kona, Praveen Gangadhar, K. Rani, Kirti Rajanna, G. A. Bera, S. K. |
author_facet | Ajay, B. C. Kumar, Narendra Kona, Praveen Gangadhar, K. Rani, Kirti Rajanna, G. A. Bera, S. K. |
author_sort | Ajay, B. C. |
collection | PubMed |
description | Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The “drought hot spot” in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limited rainfall, with over 50% rainfall variability. According to reports, 30 out of 200 groundnut cultivars in India are supposed to possess drought-tolerant characteristics. However, these cultivars are yet to be evaluated in areas that are prone to drought. This study tested these drought-tolerant genotypes in naturally drought-prone areas of Anantapur under rainfed conditions from Kharif 2017 to 2019. Pod yield and rainfall-use-efficiency (RUE) were measured for these genotypes. Genotype and genotype*environment interactions affected pod yield and RUE (GEI). The AMMI model exhibits significant season-to-season variability within the same area with environmental vectors > 90° angles. GGE biplot suggested the 2018 wet season for drought-resistant cultivar identification. Kadiri5 and GPBD5 were the most drought-tolerant cultivars for cultivation in Anantapur and adjacent regions. These types could also be used to generate drought-tolerant groundnut variants for drought-prone regions. |
format | Online Article Text |
id | pubmed-10404259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104042592023-08-07 Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations Ajay, B. C. Kumar, Narendra Kona, Praveen Gangadhar, K. Rani, Kirti Rajanna, G. A. Bera, S. K. Sci Rep Article Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The “drought hot spot” in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limited rainfall, with over 50% rainfall variability. According to reports, 30 out of 200 groundnut cultivars in India are supposed to possess drought-tolerant characteristics. However, these cultivars are yet to be evaluated in areas that are prone to drought. This study tested these drought-tolerant genotypes in naturally drought-prone areas of Anantapur under rainfed conditions from Kharif 2017 to 2019. Pod yield and rainfall-use-efficiency (RUE) were measured for these genotypes. Genotype and genotype*environment interactions affected pod yield and RUE (GEI). The AMMI model exhibits significant season-to-season variability within the same area with environmental vectors > 90° angles. GGE biplot suggested the 2018 wet season for drought-resistant cultivar identification. Kadiri5 and GPBD5 were the most drought-tolerant cultivars for cultivation in Anantapur and adjacent regions. These types could also be used to generate drought-tolerant groundnut variants for drought-prone regions. Nature Publishing Group UK 2023-08-05 /pmc/articles/PMC10404259/ /pubmed/37543669 http://dx.doi.org/10.1038/s41598-023-38581-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ajay, B. C. Kumar, Narendra Kona, Praveen Gangadhar, K. Rani, Kirti Rajanna, G. A. Bera, S. K. Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title | Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title_full | Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title_fullStr | Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title_full_unstemmed | Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title_short | Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
title_sort | integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404259/ https://www.ncbi.nlm.nih.gov/pubmed/37543669 http://dx.doi.org/10.1038/s41598-023-38581-0 |
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