Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia

Abstract

Genotype by environment interaction (GEI) is a phenomenon that occurs in heterogeneous environments that slows breeding progress by preventing the selection of superior cultivars for breeding and commercialization. Therefore, the objectives of this study were to find out how GEI impacts soybean output and to identify the most adapted and stable genotypes. Moreover, to look at the possibility of other mega environments for testing in the future. The experiments were grown for two years in a four-replicated randomized block design at each environment. Over the course of several harvests, yield components, days to flowering, days to maturity, plant height, the number of pods per plants, the number of seeds per plant, hundred seed weight and grain yield per hectare were evaluated in the main for 2018 and 2019.To analyze the stability performance of the genotypes, general linear method, GGE and Additive main effect and multiplicative interaction effects analysis (AMMI) and ASV rank analysis were applied. The GGE biplot revealed that the GGE biplots explained 74.29% of the total variation distributed as,56.69% and 17.62% of sum of squares between principal component PC1 and PC2, respectively whereas, AMMI model, the first two interaction principal component axes (IPCA1 and IPCA2) explained 47.74% and 26.62% of the variation due to GEI, respectively, exposed genotypes identified the five as best performer. The results from the four distinct stability statistics AMMI biplot (G8, G2, G1, G11), ASV (G1, G11; (GSI; G9, G1, G11) and (GGE: G2, G8, G9) are taken into account together with the genotypes` grand mean. The genotypes JM-CLK/CRFD-15-SD (G8) and 5002T (G1), which rank among the best and have the highest seed output, are suitable for hybridization as a parent and commercial production. Therefore, genotypes JM-CLK/CRFD-15-SD (G8) and 5002T(G1) have the highest seed output were among the best and thus could be recommended for release as a new soybean varieties cultivation across.

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

“…This finding aligns with reports by Liu et al. ( 2017 ), Hailemariam Habtegebriel ( 2022 ), and Susanto et al. ( 2023 ), underscoring the significant influence of G × E interaction on soybean yield, which reflects the varied responses of genotypes to different environments.…”

Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods

“…GGE biplot analysis enables the understanding of the value of various test environments in terms of relative discrimination between genotypes and the associations between them for various traits. An ideal environment should have a high PC1 score and zero scores for PC 2 [Habtegebriel 2022]. The present study also obtained the same result.…”

“…The genotypes found in the inner circle are the most desirable among the genotypes present in the outer circle [Ali et al, 2017, Firew et al, 2019and Habtegebriel 2022]. If no genotype was located inside the inner circle, genotypes that were located next to or closer to the inner circle were regarded as ideal [Khan et al, 2021].…”

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

“…Relations between environments were presented in Figure 4 . Smaller angle between two environments means higher correlation between the two environments demonstrating co-occurrence for the genotype ranking or high level of repeatability, whereas the larger angle between two environments indicates less correlation or contradicting genotypic performance ( Habtegebriel, 2022 ). Therefore, E1, E3, E4, E5, E6, and E12 had smaller angles between one another forming a group of environments (Group I) where the sorghum genotypes had comparable performance and genotypic ranks.…”

“…To assess the yield stability of the sorghum genotypes, IPCA scores and AMMI stability estimates were used in the present study. According to Habtegebriel (2022) , IPCA1 score of each sorghum variety could be used as a parameter to judge the yield stability of the sorghum varieties. Mushoriwa et al.…”