GENOTYPE BY ENVIRONMENT INTERACTION AND GRAIN YIELD STABILITY OF MAIZE (Zea mays L.) THREE WAY HYBRIDS IN ETHIOPIA

Abstract

The yield performance of maize genotypes is highly influenced by environmental factors and genotype by environment interaction (GEI). The presence of GEI makes it difficult to select the best performing as well as the most stable genotypes. Therefore, conducting multi-environment trials, appropriate analysis of the data and interpretation of the result is important to develop improved maize cultivars effectively in Ethiopia. This study was carried out to assess the effect of GEI on maize grain yield and yield related traits; and to determine stability of three way hybrid maize for grain yield performance in Ethiopia. Twenty–three maize three way hybrids were evaluated at six environments namely: Asossa, Bako, Jimma, Pawe, Wendogenet and Ambo during the 2022 main growing season. The stability of the hybrids was assessed with multivariate techniques including, additive main effects and multiplicative interaction (AMMI) and genotype and GEI (GGE) biplot models. The environment, genotype and the GEI effects were significant at p< 0.001, p< 0.001, and p< 0.05, respectively. This revealed the predominant effects of both environmental and genetic factors on maize grain yield in this study. The analysis of variance based on AMMI indicated significant genotype, environment and GEI effects; accounting for 13.99%, 63.31% and 7.21%, respectively, to the total variation. The first interaction principal component (IPCA1) captured most of the interactions, 39%, and the second interaction principal component (IPCA2) explained additional 29%. In general, the first two interaction principal components captured 68% of the GEI variation. Graphical AMMI biplot analysis and AMMI based stability index were used to identify maize genotypes with the highest yield and stable performances across environments. Accordingly, AMMI stability parameters identified genotype 5 as high yielding and stable genotype. The graphical view of the GGE-biplot further confirmed the same genotypes as high yielding and stable across the tested environments. The polygon view of the GGE biplot showed that environments used in this study were clustered into two mega-environments, with different winning genotypes. Both AMMI and GGE approaches allowed the identification of stable and high yielding genotype, genotype 5, with yield advantage of 10.33 % over the best check variety (BH661) in this study, which can be promoted to variety verification trial.