Analysis of Genotype by Environment Interactions using AMMI and GGE Biplot Methods for Soybean Germplasm Lines

Background: Soybean breeding program face challenges due to genotype-by-environment interaction (GEI), where genotypes exhibit varying performance across different environments, complicating the identification of stable, high-yield cultivars. This study aimed to elucidate GEI’s impact on soybean yield and identify genotypes with superior adaptation and yield stability across diverse environments. Methods: Fifty genotypes were evaluated over three years in a randomized block design with three replications per environment, assessing yield components and other agronomic traits. Stability performance was analyzed using general linear methods, GGE biplot, AMMI analysis and ASV rank analysis. Result: The GGE biplot explained 75.10% of the total variation (PC1: 30.93%, PC2: 44.8%), while the AMMI model’s first two interaction principal component axes (IPCA1: 59.3%, IPCA2: 40.7%) explained the GEI variation. AMMI analysis identified G26 (JS 22-88) and G39 (JS 22-101) as the most stable, high-performing genotypes, whereas GGE biplot analysis identified G9 (JS 22-71).

Authors

Publication Details

Journal
Legume Research - An International Journal
Published
2025-07-18
DOI
https://doi.org/10.18805/lr-5478
Citations
1
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
1.75
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article

Analysis of Genotype by Environment Interactions using AMMI and GGE Biplot Methods for Soybean Germplasm Lines

Yogendra Singh, Vikrant Khare, Manoj Kumar Shrivastava, Pawan K. Amrate et al.
1 citations
Legume Research - An International Journal
Soybean genetics and cultivation
1.75
article

Analysis of Genotype by Environment Interactions using AMMI and GGE Biplot Methods for Soybean Germplasm Lines

Yogendra Singh, Vikrant Khare, Manoj Kumar Shrivastava, Pawan K. Amrate, Monika Soni
article en
1 citations

Abstract

Background: Soybean breeding program face challenges due to genotype-by-environment interaction (GEI), where genotypes exhibit varying performance across different environments, complicating the identification of stable, high-yield cultivars. This study aimed to elucidate GEI’s impact on soybean yield and identify genotypes with superior adaptation and yield stability across diverse environments. Methods: Fifty genotypes were evaluated over three years in a randomized block design with three replications per environment, assessing yield components and other agronomic traits. Stability performance was analyzed using general linear methods, GGE biplot, AMMI analysis and ASV rank analysis. Result: The GGE biplot explained 75.10% of the total variation (PC1: 30.93%, PC2: 44.8%), while the AMMI model’s first two interaction principal component axes (IPCA1: 59.3%, IPCA2: 40.7%) explained the GEI variation. AMMI analysis identified G26 (JS 22-88) and G39 (JS 22-101) as the most stable, high-performing genotypes, whereas GGE biplot analysis identified G9 (JS 22-71).

Legume Research - An International Journal(Of)
Life in Land
Openalex Percentile: Top 13%
Soybean genetics and cultivation
1.75
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