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Interpreting genotype × environment interaction in bread wheat (Triticum aestivum L.) genotypes using nonparametric measures

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The objectives of this study were to compare nonparametric stability measures, and to identify promising high-yield and stable bread wheat (Triticum aestivum L.) genotypes in 7 environments during 2003-2005 in the central Black Sea region of Turkey. | Turk J Agric For 33 (2009) 127-137 © TÜBİTAK doi:10.3906/tar-0803-28 Interpreting Genotype × Environment Interaction in Bread Wheat (Triticum aestivum L.) Genotypes Using Nonparametric Measures Zeki MUT1, Nevzat AYDIN2, Hasan Orhan BAYRAMOĞLU2, Hasan ÖZCAN2 1 Ondokuz Mayıs University, Bafra Vocational School, Department of Technical Programs, 55400 Bafra, Samsun - TURKEY 2 Black Sea Agricultural Research Institute, PK. 39, Samsun - TURKEY Received: 21.03.2008 Abstract: The objectives of this study were to compare nonparametric stability measures, and to identify promising high-yield and stable bread wheat (Triticum aestivum L.) genotypes in 7 environments during 2003-2005 in the central Black Sea region of Turkey. The bread wheat genotypes (20 advanced lines and 5 cultivars) were grown in a randomized complete block design with 4 replications in 7 different environments. Three nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in 7 environments. Combined ANOVA and nonparametric tests (Kubinger, Hildebrand, and De Kroon/Van der Laan) of genotype × environment interaction indicated the presence of significant crossover and non-crossover interactions, as well as significant differences between genotypes. In this study high TOP values (proportion of environments in which a genotype ranked in the top third) and low rank-sum values (sum of ranks of mean yield and Shukla’s stability variance) were associated with high mean yield. Nonetheless, results of the other nonparametric tests were negatively correlated with mean yield. In the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis (PCA) results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum) G7 (VONA//KS75210/TAM101),