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.
