Adaptability and stability of maize genotypes in growing regions of central Brazil

Abstract

This study aimed to estimate and compare parameters of adaptability and stability for maize grain yield in a variety of environments by different projection methods. Data from experiments on 36 maize genotypes, in simple lattice 6x6, in 2012/13 season performed at nine growing locations in central Brazil were used. Adaptability and stability analyses were performed using the methods of Lin & Binns (1988) with decomposition, MHPRVG through REML/BLUP, AMMI-Biplot, and GGE-Biplot analysis. These methods have similarities in terms of genotype ordering but differ in precision and amount of information provided on genotype-environment (GxE) interactions. When compared to GGE-Biplot, AMMI method retained a good percentage of the total square sum, based on pattern of GxE interaction. The method of Lin & Binns with decomposition is similar to MHPRVG, but these one is more accurate, practice and informative. MHPRVG and GGE-Biplot methods should be used together to select the most promising genotypes. The genotypes G5 and G8 can be recommended for cultivation in central Brazil due to their adaptability, stability, and yield.

Assessment of yield performance and stability of hybrids and populations of tropical maize across multiple environments in Southeastern Brazil

“…Many studies have reported the use of complementary methods to assess the yield performance and stability of maize genotypes across multiple environments to improve reliability in the selection of superior maize cultivars adapted and stable to a target region (Abakemal et al., 2016; Faria et al., 2017; Mebratu et al., 2019; Olayiwola et al., 2021; Rezende et al., 2020; Singamsetti et al., 2021; Yamamoto et al., 2021). In our study, we found that among ANOVA methods, Eberhart and Russell and GGE biplot presented results similar in terms of stability of hybrids and populations.…”

“…In addition, the use of genotype main effect plus genotype × environment interaction (GGE) biplot (Yan et al., 2000) and harmonic mean of the relative performance of the predicted genetic values (HMRPGV) (Resende, 2007) methods, which are based on fixed models (sum of squares based) and mixed models (variance based), respectively, for studying GEI in maize breeding programs has increased considerably in the last two decades (Akinwale et al., 2014; Alwala et al., 2010; Elias et al., 2016; Faria et al., 2017; Oliveira et al., 2017; Rezende et al., 2021; van Eeuwijk et al., 2016). The employment of more than one method to handle the GEI and to identify high‐yielding and stable genotypes may increase the reliability of cultivar recommendations and has been endorsed by many maize breeders since the results provided may complement each other and help in the interpretation of data from breeding and agronomic field evaluation trials (Badu‐Apraku et al., 2012; Faria et al., 2017; Rezende et al., 2020, 2021; Yamamoto et al., 2021). Therefore, the use of complementary and alternative statistical methods has led to the study of GEI and the recommendation of maize cultivars being more accurate and precise than the use of only one approach.…”

Genotype by Environment Interaction on Early-Maturing and High-Yield Maize Hybrids

“…The genotype-by-environment interactions (GEI) and the stability of genotypes tested in different locations can be assessed using the AMMI analysis method [56][57][58]. The AMMI analysis is done when there is a significant genotypeby-environment interaction (GEI) [59].…”

Adaptability and Phenotypic Stability of Upland Rice Genotypes in Mozambique

“…The environments were divided into two groups according to the red lines that came out from the origin of the biplot, thus forming two mega-environments. The winning genotypes for the sectors are the genotypes of the vertices at the intersection of the two sides of the polygon whose perpendicular lines form the boundary of that sector (Yan et al, 2007) and are classified as the most reactive to the en- vironmental stimulus, however, those within the polygon are less reactive (Yamamoto et al, 2021).…”

“…Therefore, the vertices of the polygon are formed by the genotypes G1, G2, G3, and G4 being the most reactive to the environmental stimulus. When genotypes are in the vertices where it is not possible to define which sector they belong to or have no related environments these genotypes were not sensitive to any of the grouped environments (Yamamoto et al, 2021). Genotype G4 is the vertex of the sector that encompasses the mega-envi-ronment1 composed of environments E1, E2, and E3.…”