Cultivar evaluation and mega-environment investigation based on the GGE biplot

Cultivar evaluation and mega-environment identification are among the most important objectives of multi-environment trials (MET). Although the measur ed yield is a combined result of effects of genotype (G), environment (E), and genotype X environment interaction (GE), only G and GE are relevant to cultivar evaluation and mega-environment identification. This paper present s a GGE (i.e., G + GE) biplot, which is constructed by the first two symmet rically scaled principal components (PC1 and PC2) derived from singular val ue decomposition of environment-centered MET data. The GGE biplot graphical ly displays G plus GE of a MET in a way that facilitates visual cultivar ev aluation and mega-environment identification. When applied to yield data of the 1989 through 1998 Ontario winter wheat (Triticum aestivum L.) performa nce trials, the GGE biplots clearly identified yearly winning genotypes and their winning niches. Collective analysis of the yearly biplots suggests t wo winter wheat mega-environments in Ontario: a minor mega-environment (eas tern Ontario) and a major one (southern and western Ontario), the latter be ing traditionally divided into three subareas. There were frequent crossove r GE interactions within the major mega-environment but the location groupi ngs were variable across years. It therefore could not be further divided i nto meaningful subareas. It was revealed that in most years PC1 represents a proportional cultivar response across locations, which leads to noncrosso ver GE interactions, while PC2 represents a disproportional cultivar respon se across locations, which is responsible for any crossover GE interactions . Consequently, genotypes with large PC1 scores tend to give higher average yield, and locations with large PC1 scores and near-zero PC2 scores facili tates identification of such genotypes.