Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

For grain yield stability analysis, genotype by environment interactions are crucial in properly identifying and discriminating between varieties and locations. Hence, this experiment was conducted with the objectives to evaluate the stability of soybean using additive main effects and multiplicative interaction (AMMI), multi-trait stability index (MTSI), weighted average absolute scores biplot (WAASB), Eberhart and Russell regression model, and genotype plus genotype by environment interaction (GGE) biplot analysis for grain yield of soybean genotypes and identified stable genotypes in the different soybean agroecologies of Ethiopia. Twenty-four soybean genotypes were planted at six soybean environments with RCBD in three replications in the 2015/2016 cropping season. Stability measures, namely, AMMI, AMMI stability value, and GGE biplot analysis were used to identify the high-yielding and stable genotypes across the testing environments. AMMI-1 biplot showed Pawe as the ideal environment; Bako as a favorable environment; Asosa an average environment; and the rest namely, Dimtu, Jimma, and Metu as unfavorable environments. On the other hand, AMMI-2 biplot analysis certain genotypes like Prichard, Spry, Delsoy 4710, and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by the GGE biplot analysis model. For other multivariate statistics used for this study, MTSI, WAASB, and regression models, stable and superior varieties for grain yield were revealed. Through the MTSI, the four genotypes, namely, Liu yue mang, SCS-1, Clarck-63k, and AFGAT, were found to be stable and superior over the rest tested genotypes. Overall, the genotypes SCS-1 and AGS-7-1 were stable across soybean growing environments and are recommended for mega environment production.