Identifying Mega‐Environments and Targeting Genotypes

Abstract

To maximize yield throughout a crop's heterogeneous growing region, despite differences in cultivar rankings from place to place due to genotype‐environment interactions, frequently it is necessary to subdivide a growing region into several relatively homogeneous mega‐environments and to breed and target adapted genotypes for each mega‐environment. The objectives of this study are to identify relevant criteria for evaluating mega‐environment analyses and to apply the Additive Main Effects and Multiplicative Interaction (AMMI) model to mega‐environment analysis. The proposed analysis is illustrated using a Louisiana corn (Zea mays L.) trial. Statistical strategies for identifying mega‐environments should meet four criteria: flexibility in handling yield trials with various designs, focus on that fraction of the total variation that is relevant for identifying mega‐environments, duality in giving integrated information on both genotypes and environments, and relevance for the primary objective of showing which genotypes win where. The AMMI model meets these criteria effectively when the usual biplots are supplemented with several new types of graphs designed to address questions about mega‐environments. Preliminary results indicate that a small and workable number of mega‐environments often suffices to exploit interactions and increase yields.

AMMI analysis to evaluate the adaptability and phenotypic stability of sugarcane genotypes

“…Classifying environments based on the winning genotypes (raw data), i.e., those with the highest means in each environment (Gauch and Zobel, 1997), six megaenvironments were formed (RB987935 in E1, E5 and E8; RB925211 in E2; RB935641 in E4 and E6; RB937570 in E3; RB987934 in E7; RB867515 in E9). On the other hand, based on the predicted means obtained from AMMI2 (F R Cornelius), three mega-environments were formed.…”

“…The additive main effect and multiplicative interaction (AMMI) method integrates analysis of variance (ANOVA) and principal component analysis (PCA) into a unified approach that can be used to analyse multilocation trials Crossa et al, 1990;Gauch and Zobel, 1996). AMMI uses analysis of variance to study the main effects of genotypes and environments and a principal component analysis for the residual multiplicative interaction among genotypes and environments.…”

“…Therefore, with this technique, one can readily identify productive cultivars with wide adaptability or mega-environments, as well as delimit the agronomic zoning of cultivars with specific adaptability and identify environments in which to conduct tests (Kempton, 1984;Gauch and Zobel, 1996;Ferreira et al, 2006). This study aimed to evaluate the adaptability and phenotypic stability of sugarcane genotypes in Minas Gerais state, Brazil, using the AMMI method.…”

“…The ideal genotype is one with high productivity and IPCA1 values close to zero. An undesirable genotype has low stability associated with low productivity (Kempton, 1984;Gauch and Zobel, 1996;Ferreira et al, 2006). Finally, the predictive averages were estimated according to the selected model.…”

A comparison of winter wheat cultivar rankings in groups of Polish locations

“…This result was similar to studies in which environmental effects were shown to be responsible for approximately 80% or more of the total experimental variation in wheat grain yield (Atlin and McRae 1994;Mohammadi et al 2010;Tapley et al 2013). Gauch and Zobel (1997) reported that locations accounted for approximately 80% of total variation, whereas genotype and G×E interactions each accounted for approximately 10%. In our study, relatively high values of variance components for the environmental effects (YR and YL(R)) confirmed that agro-ecological conditions in the trial locations varied, which caused most of the yield variation in the winter wheat PVTS.…”