ArticleHeliyon2024
Analysis of genotype-by-environment interaction effect in barely genotypes using AMMI and GGE biplot methods.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Investigating the spatial adaptation for iron and zinc content using AMMI and GGE biplot model in lentil (Lens culinaris L.).BMC plant biology · 2026Article
- Integrating AMMI, Bayesian-AMMI, BLUP, and GGE biplot models to improve the identification of high-yielding and stable barley genotypes.BMC plant biology · 2026Article
- Multi-trait stability index in the selection of high-yielding and stable barley genotypes.Journal of applied genetics · 2026Article
- Evaluating barley genotypes for yield stability using integrated model- and rank-based stability statistics.BMC plant biology · 2025Article
- Multi-environment evaluation and identification of Tartary buckwheat (Fagopyrum tataricum Gaertn.) genotypes for superior agronomic and nutritional potential in the North-Western Himalayas.Scientific reports · 2025Article
- Enhancing tomato drought resilience with organic amendments and local landraces.Scientific reports · 2025Article
- Cross-talk between stability parameters and selection models: a new procedure for improving the identification of the superior genotypes in multi-environment trials.BMC research notes · 2025Article
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5 authors.
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Abstract
Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021-2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.
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