Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming
Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of f...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2024
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| Online Access: | https://hdl.handle.net/10568/163732 |
| _version_ | 1855535064863997952 |
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| author | Habib, Muhammad Ashraful Azam, Mohammad Golam Haque, Md. Ashraful Hassan, Lutful Khatun, Mst. Suhana Nayak, Swati Abdullah, Hasan Muhammad Ali, Essam A. Hossain, Nazmul Ullah, Riaz Sarker, Umakanta Ercisli, Sezai |
| author_browse | Abdullah, Hasan Muhammad Ali, Essam A. Azam, Mohammad Golam Ercisli, Sezai Habib, Muhammad Ashraful Haque, Md. Ashraful Hassan, Lutful Hossain, Nazmul Khatun, Mst. Suhana Nayak, Swati Sarker, Umakanta Ullah, Riaz |
| author_facet | Habib, Muhammad Ashraful Azam, Mohammad Golam Haque, Md. Ashraful Hassan, Lutful Khatun, Mst. Suhana Nayak, Swati Abdullah, Hasan Muhammad Ali, Essam A. Hossain, Nazmul Ullah, Riaz Sarker, Umakanta Ercisli, Sezai |
| author_sort | Habib, Muhammad Ashraful |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems. |
| format | Journal Article |
| id | CGSpace163732 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1637322025-12-02T10:59:51Z Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming Habib, Muhammad Ashraful Azam, Mohammad Golam Haque, Md. Ashraful Hassan, Lutful Khatun, Mst. Suhana Nayak, Swati Abdullah, Hasan Muhammad Ali, Essam A. Hossain, Nazmul Ullah, Riaz Sarker, Umakanta Ercisli, Sezai rice genotypes selection index irrigation yields breeding methods Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems. 2024-06-15 2024-12-18T16:45:38Z 2024-12-18T16:45:38Z Journal Article https://hdl.handle.net/10568/163732 en Open Access application/octet-stream Springer Habib, Muhammad Ashraful, Mohammad Golam Azam, Md Ashraful Haque, Lutful Hassan, Mst Suhana Khatun, Swati Nayak, Hasan Muhammad Abdullah et al. "Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming." Scientific Reports 14, no. 1 (2024): 13836 |
| spellingShingle | rice genotypes selection index irrigation yields breeding methods Habib, Muhammad Ashraful Azam, Mohammad Golam Haque, Md. Ashraful Hassan, Lutful Khatun, Mst. Suhana Nayak, Swati Abdullah, Hasan Muhammad Ali, Essam A. Hossain, Nazmul Ullah, Riaz Sarker, Umakanta Ercisli, Sezai Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title | Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title_full | Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title_fullStr | Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title_full_unstemmed | Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title_short | Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming |
| title_sort | climate smart rice oryza sativa l genotypes identification using stability analysis multi trait selection index and genotype environment interaction at different irrigation regimes with adaptation to universal warming |
| topic | rice genotypes selection index irrigation yields breeding methods |
| url | https://hdl.handle.net/10568/163732 |
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