Comparison of shifted multiplicative model, rank correlation, and biplot analysis for clustering winter wheat production environments
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Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) environment (E) interactions according to the Azzalini-Cox test. G E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman's rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs. 2010 Springer Science+Business Media B.V.