Ammi, gge-biplot, and joint regression technique as a tool in measuring G × E interaction in 3-way cross maize (Zea mays L.) hybrids

Abstract

Genotype by environment (GE) interaction has a large impact on selecting adapted and predictable genotypes. Therefore, it is necessary to evaluate maize genotypes across different environments, seasons or locations for a successful selection. Twelve 3-way cross maize hybrids obtained from the International Institute of Tropical Agriculture (IITA) were evaluated on the field of the Federal University of Agriculture, Abeokuta, Nigeria (latitude 7? 15? N and longitude 3? 25? E) across three growing seasons of 2021 and 2022. The experiment was laid out with three replicates. Additive main effect and multiplicative interaction (AMMI), genotype (G) plus GE (GGE) biplot and joint regression techniques were used to identify stable and high-yielding genotypes. The AMMI analysis showed that the total variances in the yield of the three-way maize hybrids accounted for by G, environment (E) and GE interaction were 30.6%, 44.19% and 25.31%, respectively. Based on the AMMI biplot, the genotypes LW1701-10 and OBA SUPER-9, which combined high yield with stability, were the most desirable. The GGE biplot showed that hybrids LW1701-10, OBA SUPER-9 and LW1701-6 were the most stable and desirable genotypes. The joint regression technique showed that the performance of the genotypes could not be revealed in a linear manner as the deviation component variance accounted for 81.05% and identified LW1701-6, LW1701-16, LW1701-12, LW1701-21, LW1701-4 as stable and desirable genotypes. The study revealed that the GGE and AMMI models were more effective than the joint regression technique in examining yield stability of maize hybrids. The study deals with the comparison of AMMI, GGE biplot and joint regression techniques.