Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh
During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE a...
| Autores principales: | , , , , |
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| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
Research Square Platform LLC
2021
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| Acceso en línea: | https://hdl.handle.net/10568/164168 |
| _version_ | 1855542872846106624 |
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| author | Khatun, Marium Islam, A. K. M. Aminul Islam, M. Rafiqul Khan, M. A. Rahman Hossain, M. Kamal |
| author_browse | Hossain, M. Kamal Islam, A. K. M. Aminul Islam, M. Rafiqul Khan, M. A. Rahman Khatun, Marium |
| author_facet | Khatun, Marium Islam, A. K. M. Aminul Islam, M. Rafiqul Khan, M. A. Rahman Hossain, M. Kamal |
| author_sort | Khatun, Marium |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone. |
| format | Preprint |
| id | CGSpace164168 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Research Square Platform LLC |
| publisherStr | Research Square Platform LLC |
| record_format | dspace |
| spelling | CGSpace1641682025-02-19T14:27:09Z Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh Khatun, Marium Islam, A. K. M. Aminul Islam, M. Rafiqul Khan, M. A. Rahman Hossain, M. Kamal During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone. 2021-11-02 2024-12-19T12:53:32Z 2024-12-19T12:53:32Z Preprint https://hdl.handle.net/10568/164168 en Open Access Research Square Platform LLC Khatun, Marium; Islam, A. K. M. Aminul; Islam, M. Rafiqul; Khan, M. A. Rahman and Hossain, M. Kamal. 2021. Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh. Research Square, [pre-print]; 17 p. |
| spellingShingle | Khatun, Marium Islam, A. K. M. Aminul Islam, M. Rafiqul Khan, M. A. Rahman Hossain, M. Kamal Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title | Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title_full | Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title_fullStr | Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title_full_unstemmed | Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title_short | Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh |
| title_sort | grain yield stability analysis using ammi and gge biplot models in different breeding zones of bangladesh |
| url | https://hdl.handle.net/10568/164168 |
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