Analysis of Multi-Environment Trials of Rainfed Barley in Warm Regions of Iran

Abstract

Multi-environment trials (MET) are commonly conducted in plant breeding programs to identify superior genotypes and to determine mega-environments in a targeted region. The main objectives of the study were to graphically analyze MET data from rainfed barley (Hordeum vulgare L.) and interpret genotype-by-environment (GE) interaction using GGE biplot methodology and Kang's two parameters, i.e., yield-stability statistic (YSi) and rank-sum (RS). The analyses were performed on grain yield data of 18 rainfed barley genotypes, derived from an Iran/ICARDA joint project, grown at three representative rainfed barley locations in warm areas of Iran during three cropping seasons (2007–2009). Combined ANOVA indicated that the environment accounted for 87.8% of total variation, followed by GE interaction. The large magnitude of the GE interaction relative to genotypic effect suggested the possible existence of sub-environmental groups for the genotypes. Collective analyses of yearly and combined GGE biplots were used to identify high-yielding genotypes and their areas of adaptation, and suggested the existence of two rainfed barley mega-environments. The “discriminating power vs. the representative view” of the GGE biplot identified Moghan as the location with high discriminating ability and greater representativeness, suggesting the possibility of testing genotypes adapted to warm rainfed areas at this location. The YSi and RS statistics as well as GGE biplot gave similar results for simultaneously selecting for high yield and stability. Based on the simultaneous selection index, the genotypes G9 (ChiCm/An57//Albert/3/Alger/Ceres362-1-1) and G13 (Onslow/Arda2) could be recommended for commercial release in warm rainfed areas of Iran. The study also indicated that YSi and RS were as efficient as the GGE biplot method in selecting high-yielding and stable genotypes under variable environments.