Abstract

The main objective of this study was to compare the several statistical models, i.e., joint regression analysis (JRA), additive main effect and multiplicative interaction (AMMI) and genotype and genotype × environment (GE) interaction (GGE) biplot, for analyzing of GE interaction for grain yield of rainfed safflower multi-environment trials. The effectiveness of each model was compared for identifying the best performing genotypes across environments, identifying the best genotypes for mega-environment differentiation and evaluating the yield and stability performance. Grain yield data of 13 cold-tolerant safflower breeding lines along with a check cultivar grown in three rainfed research stations for two cropping seasons were used. Environment (E) main effect accounted for 57.1% of total variation, compared to 8.8 and 34.1% for G and GE interaction effects, respectively. Spearman’s rank correlation analysis indicated that the three methods (GGE biplot, AMMI analysis and JRA) were significantly correlated (P < 0.01) in ranking of genotypes for static (biological) stability, suggesting that they can be used interchangeably. All three methods identified genotypes G4 and G9 as the most stable genotypes with low-yielding performance, and the breeding line G3 as high-yielding stable genotype across environments. Based on the results, the Maragheh was an ideal test location with a demonstrated high efficiency in selecting new cultivars with a wide adaptability. The main conclusions were the similarity between the dominant genotypes in the three models. The GGE biplot was more versatile and flexible and provided a better understanding of GE interaction than the other methods. Positive increase in yield and yield stability is attributable predominately to genetic improvement in safflower breeding lines. The breeding line 415/338 could serve as a good genetic source for both high yielding and stability in safflower breeding programs for highland cold rainfed areas of Iran.

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