Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)

Abstract

Multi-location experiments on maize were conducted from 2016 to 2019 at ten locations distributed across two agro-climatic zones (ACZ) i.e., ACZ-3 and ACZ-8 of Karnataka, India. Individual analysis of variance for each location-year combination showed significant differences among the hybrids; similarly, combined analysis showed a higher proportion of GE interaction variance than due to genotype. Mega-environments were identified using biplot approaches such as AMMI, GGE, and WAASB methodologies for the years 2016 to 2019. The BLUP method revealed a high correlation between grain yield and stability indices ranging from 0.67 to 1.0. Considering all three methods together, the three location pairs Arabhavi-Belavatagi, Bailhongal-Belavatagi, and Hagari-Sirguppa had three occurrences in the same mega-environment with a value of 0.67, and these location combinations consistently produced winning genotypes. Among the common winning genotypes identified, it was G7 during 2016 and 2017 and G10 during 2018 and 2019, based on WAASBY. The likelihood of Arabhavi-Nippani, Hagari-Mudhol, and Dharwad-Hagari occurring in the same mega-environment is minimal because they did not share the same winning genotype, with the exception of a small number of events. Despite being in the same agro-climatic zone, Arabhavi, Hagari, and Mudhol rarely had a winning genotype in common. An agro-climatic zone is grouped based on climatic and soil conditions which doesn’t consider GE interaction of cultivars thus, releasing the cultivars for commercial cultivation considering mega environments pattern would enhance the yield for the given target region.

Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot

“…Genotypes G3 and G11 (2022) and G5, G10, G8, and G11 (2023) did not fall into sectors, meaning that these varieties performed poorly in some/all environments [15,45,46]. To improve the accuracy of the test, it is recommended to increase the test period or test environment to better evaluate the varieties [27]. And in previous studies, the GGE biplot has been used to screen ideal varieties, such as in potato [19], sweet maize [47], baby corn maize [48], and sugar beet [45].…”

“…The GGE biplot can also be used for breeding-specific combining ability and general combining ability evaluation [17], disease evaluations [18], crop qualitative characteristics [19,20], and regional trial evaluations [21]. And the AMMI model GGE biplot has now been applied to cotton [21], oat [22], pigeonpea [23], cowpea [24], bambara groundnut [25], grain sorghum [26], as well as maize [27,28]. In previous studies, the yield of maize in one year was mostly analyzed by the AMMI model and GGE biplot or by a single method, and few were analyzed by combining the two methods for many years [11,[29][30][31][32].…”