Mega‐environment analysis and breeding for specific adaptation

Abstract

Mega-environment (ME) analysis is analysis of multi-year, multi-location crop variety trial data conducted in a target region of a crop to understand the magnitude and nature of genotype-by-environment interaction (GE) of the crop in the region.If repeatable GE patterns are identified, then the target region must be divided into subregions or MEs. Breeding and utilizing ME-specific cultivars will convert the repeatable GE into genotypic main effect (G) within ME, thereby improving heritability (selection reliability) and selection gain and maximize regional and overall productivity. If no repeatable GE is found, then the target region must be treated as a single ME and the GE must be accommodated by testing adequately, that is, at a sufficient number of locations in a sufficient number of years. This paper presents a theoretical framework of ME analysis, describes graphical tools to reveal the whichwon-where patterns in a genotype-by-environment dataset, and demonstrates LG (location-grouping) biplot analysis for revealing repeatable GE patterns and delineating MEs. The concept of G + GE or GGE, that is, GE relative to G, is emphasized. It is the relative GE that is the basis for ME analysis and breeding for specific adaptation; absolute magnitude of GE has little relevance for these purposes. Breeding ME-specific oat cultivars in Canada is demonstrated with a real-world example. INTRODUCTIONA mega-environment (ME) is a subregion within a crop's growing areas with similar genotypic responses and best performing cultivars. Mega-environment analysis is analysis of Abbreviations: AEZ, agro-ecological zone; BRDC, Brandon Research and

Exploring the trait‐yield association patterns in different oat mega‐environments of Canada

“…On the other hand, a large proportion of the breeding population is eliminated through independent culling for traits that are not strongly associated with yield, including the TYAs with short vectors as shown in the biplots (Figures 1,3,5, and 7). Indirect selection for yield based on strong TYAs (e.g., crown rust resistance for ME1) and independent culling based on weak TYAs (e.g., kernel weight and plant height for ME3) have been effective in developing superior crop cultivars in the past (e.g., Yan et al., 2023). However, selection based on visible characters alone is not effective enough to keep pace with the increasing population and increased need for food.…”

Genotype-by-environment interaction and selection of superior Physalis peruviana L. genotypes

“…The methodology proposed by Crossa et al (1991) and Gauch (1992;2013) using the AMMI1 model reduce the number of winning genotypes and identifying mega-environments. The mega-environment is a subregion with similar genotypic responses and better performing cultivars (Yan et al, 2023). The locations of this study were grouped in two mega-environments based on PC1 scores (Tab.…”

Two types of biplots to integrate multi‐trial and multi‐trait information for genotype selection

“…Key statistics related to genotype evaluation and selection, such as genetic correlation among test environments (Cooper & DeLacy, 1994) and heritability (or repeatability) across multi‐environment trials (Atlin et al., 2000; DeLacy et al., 1996; Nyquist, 1991; Yan, 2014), are all measures of the relative magnitude of G versus GE. It is the relative magnitude of G versus GE that matters to genotype evaluation and selection (Yan, Nilsen, et al., 2023). In the attempt to deal with GE, numerous stability indices have been proposed to quantify the genotypes’ contribution to GE, which were sometimes unduly emphasized in genotype selection while neglecting the much more important information of G. The GGE concept corrects such practices.…”

“…A clear path toward dealing with GE has emerged (Yan, 2016; Yan, Nilsen, et al., 2023). First, for a given crop and region, multi‐year and multi‐location trials must be conducted and analyzed to see if there is any repeatable GE, which is the basis to divide the target region into meaningful MEs.…”