This study aims to acquaint breeders of the need to use statistical tools that will help resolve the identification of consistently better performing genotypes across various environmental conditions. It also aim to reveal the relationship among the various statistical methods used to describe genotype × environment interaction (GEI) and cultivar stability. A mixed model with fixed genotypes and random environments were used for the analysis of variance (ANOVA). In the present study, twenty released bread wheat cultivars were evaluated during the 2009 main cropping season using a randomized complete block design (RCBD) with three replications at seven different environments. The combined ANOVA revealed the presence of a highly significant GEI (p < 0.01) for grain yield indicating its influence on cultivar selection and recommendation. Spearman’s rank correlation coefficient revealed a perfect correspondence between Wricke’s ecovalence (Wi) and Shukla’s statbility variance (s2). These stability measures also showed a highly significant positive rank correlation with deviation from regression (S2di), coefficient of determination( ri2), AMMI stability value (ASV), variance of ranks (Si(2)), rank sum (R-sum), and mean absolute rank difference (Si(1)) indicating their similarity in cultivar ranking. The principal component analysis (PCA) clearly showed three groupings of the statistical methods as the static concept of stability, the dynamic concept of stability and yield performance measures. Therefore, it is imperative to consider one stability measure from the dynamic concept and one from the yield performance measures for efficient cultivar recommendation.
Key words: Bread wheat, genotype × environment interaction (GEI), principal component analysis (PCA), rank correlation, stability measures.
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