Volume 13, Issue 40 (12-2021)
jcb 2021, 13(40): 50-59 |
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Alishah O, Vanda M, Rahemi M, Hekmat M, Fathi M, VafaeiTabar M, et al . (2021). Genotype by Environment Interaction and yield Stability Assessment in new promising Cotton Genotypes by Parametric, AMMI and GGE Biplot Analysis Methods. jcb. 13(40), 50-59. doi:10.52547/jcb.13.40.50
URL: http://jcb.sanru.ac.ir/article-1-1256-en.html
URL: http://jcb.sanru.ac.ir/article-1-1256-en.html
Omran Alishah 
, Mitra Vanda , M.Reza Rahemi , M.Hassan Hekmat , Mohsen Fathi , M.Reza VafaeiTabar , Ali Naderi Arefi , Majid JafarAghaei , S.Yaghoum Sidmasoumi , M.Magher Khorshidi
, Mitra Vanda , M.Reza Rahemi , M.Hassan Hekmat , Mohsen Fathi , M.Reza VafaeiTabar , Ali Naderi Arefi , Majid JafarAghaei , S.Yaghoum Sidmasoumi , M.Magher Khorshidi
Cotton Research Institute of Iran, ARREO, Gorgan, Iran
Abstract: (1829 Views)
Extended Abstract
Introduction and Objective: Study of genotype (G) × environment (E) interaction and identifying of stable and high yielding cultivars are important and essential component of cotton breeding programs dedicated for cultivar development.
Materials and Methods: Nine new promising cotton genotypes (Gossypium hirsutum L.) along with two commercial cultivars (control) were evaluated in a randomized complete block design (RCBD) with four replications at eight locations including Hashemabad, Karkandeh, Darab, Khodafarin, Garmsar, Varamin, Isfahan and Moghan during two crop seasons (2018-2019).
Results: Combined analysis of variance (ANOVA) across environments indicated significant variation among genotypes and G × E interaction for seed cotton yield. Significant G×E variation allowed for subsequent analysis of genotype stability statistics and AMMI analysis. Regarding AMMI analysis, two principle components contributed for 81.0 % of the variation among the G×E interaction. The Varamin and 92-34 genotypes have shown general adaptability with average yield across environments. The Golestan, ANBK and ANB414 were identified as high yielding and the most stable genotypes based on parametric models (Cvi, bi and Xi), AMMI and GGE Biplot models. It was concluded that AMMI biplot clearly facilitate identification of mega-environments and cultivars for specific recommendations.
Conclusion: The results of study indicated that ANB414, ANBK and Golestan could be suited for cultivation across the test environments.
Introduction and Objective: Study of genotype (G) × environment (E) interaction and identifying of stable and high yielding cultivars are important and essential component of cotton breeding programs dedicated for cultivar development.
Materials and Methods: Nine new promising cotton genotypes (Gossypium hirsutum L.) along with two commercial cultivars (control) were evaluated in a randomized complete block design (RCBD) with four replications at eight locations including Hashemabad, Karkandeh, Darab, Khodafarin, Garmsar, Varamin, Isfahan and Moghan during two crop seasons (2018-2019).
Results: Combined analysis of variance (ANOVA) across environments indicated significant variation among genotypes and G × E interaction for seed cotton yield. Significant G×E variation allowed for subsequent analysis of genotype stability statistics and AMMI analysis. Regarding AMMI analysis, two principle components contributed for 81.0 % of the variation among the G×E interaction. The Varamin and 92-34 genotypes have shown general adaptability with average yield across environments. The Golestan, ANBK and ANB414 were identified as high yielding and the most stable genotypes based on parametric models (Cvi, bi and Xi), AMMI and GGE Biplot models. It was concluded that AMMI biplot clearly facilitate identification of mega-environments and cultivars for specific recommendations.
Conclusion: The results of study indicated that ANB414, ANBK and Golestan could be suited for cultivation across the test environments.
Type of Study: Research |
Subject:
General
Received: 2021/05/5 | Revised: 2022/11/30 | Accepted: 2021/09/18 | Published: 2022/01/16
Received: 2021/05/5 | Revised: 2022/11/30 | Accepted: 2021/09/18 | Published: 2022/01/16
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