Genotype by Environment Interaction on Tropical Maize Hybrids Under Normal Irrigation and Waterlogging Conditions

Abstract

Unpredictable rainfall in the tropics often increases the risk of waterlogging or even flooding in agricultural lands, hindering the efforts to fulfill maize demands. Breeding maize for waterlogging tolerance is necessary yet challenging since performing varietal testing on a set of hybrids might be biased toward the presence of genotype and environment interaction (GEI). This study aimed to elucidate the GEI effects on yield and related agronomic traits of tropical maize hybrids under normal irrigation and waterlogging conditions and to assess the adaptability of these hybrids in such conditions using several stability models. Ten hybrids including two commercial checks were evaluated across 14 environments under normal and waterlogging conditions in Indonesia from 2018 to 2020. Waterlogging imposed at the V6 stage for ten consecutive days significantly hampered the plant height and ear height, slightly delayed flowering dates, and reduced yield and yield components. The genotype, location, and genotype by location effects were significant on yield, but the genotype by waterlogging effect was not. Stress tolerance index is highly significantly correlated (p < 0.01) with yield in both normal (r = 0.90) and waterlogging (r = 0.96) conditions. The GGE biplot analysis on yield revealed five sectors, two mega-environments, and five vertex genotypes. This study indicated the possibility of breeding maize hybrids tolerant to waterlogging (G05), as well as high-yielding hybrids under both conditions (G07).

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

“…The significance of the genotype-by-environment interaction shows that the genotypes' agronomic traits vary from one location to another, while the non-significance of genotype-by-environment interaction for some traits showed that the traits did not diverge from one location to another. In the corn hybrid study for grain yield, Haruna et al (2017); Azrai et al (2022), andHudson et al (2022) reported findings in multi-environment trials where the main proportion of total phenotypic variation was ascribed to the environment and comparatively minor sources of variation to genotype, while in this present study, a larger proportion of variation was attributed to the genotypes. This study revealed very highly significant differences in genotypes and significant differences in environments, which could be credited to environmental conditions, the genetic diversity of the genotypes, and the effects of the interaction between the genotypes and environments.…”

“…The two concepts focus on environments in terms of their ability to detect the best genotypes (discriminativeness) and to adequately represent the test environments (representativeness) (Yan and Tinker, 2006;Oladosu et al, 2017;Khan et al, 2021). The use of multi-environmental trials is very beneficial because it helps to avoid overestimation of heritability and genetic variance, which are always observed with one location experiment (Azrai et al, 2022). Our study revealed the environments closely related, demonstrating the discriminativeness ability and the representativeness of the test environments.…”

“…Similarly, the GEI study is a means through which plant breeders select the ideal and best genotypes for commercial purposes, which is not without challenges. The partitioning of the environment indicates that the sources of variation could be due to experimental sites, climatic conditions of the sites, or growing season of the crops (Oladosu et al, 2017;Khan et al, 2021;Azrai et al, 2022).…”

“…The size of the projection determines the stability of the genotypes; thus, the longer the projection, the more unstable the genotype, and the shorter the projection, the more stable the genotype (Yan et al, 2007). The stability analysis of yield complements the genotype ranking analysis in determining the adaptability and stability of genotypes based on their agronomic performances across the test locations (Azrai et al, 2022). In this study for YPP, FPW, TNPP, HSP, LOS, and WOS the shortest projection was identified as G8, G8, G13, G7, G8, and G4, respectively, and therefore identified as the most stable.…”

Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset

“…Interpretation of the genotype x environment interaction is essential to increase productivity with the positioning of the cultivar in the microregion and its predictability of performance according to the sowing date [1]. Traditionally, analysis methods of the genotype x environment interaction have been used for the positioning of cultivars, however, the interpretations obtained after obtaining data and generalizations about the environment do not allow the application or extrapolation of results for all regions [2][3]. However, when the environments are well characterized, conclusions on cultivar performance are extrapolatable, as used nitrogen levels in rice [4] or used water deficit environments in maize [5].…”