Comparison of shifted multiplicative model,rank correlation,and biplot analysis for clustering winter wheat production environments |
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Authors: | Subas Malla Amir M H Ibrahim Rich Little Steve Kalsbeck Karl D Glover Cuirong Ren |
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Institution: | (1) Plant Science Department, South Dakota State University, Brookings, SD 57007, USA;(2) Department of Soil and Crop Sciences, Texas A&M University, 2474 TAMU, College Station, TX 77843, USA; |
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Abstract: | 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. |
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