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Clustering of Environments of Southern Soft Red Winter Wheat Region for Milling and Baking Quality Attributes
- Collaku, A., Harrison, S. A., Finney, P. L., Van Sanford, D. A.
- Crop science 2002 v.42 no.1 pp. 58
- Triticum aestivum, milling quality, baking quality, geographical variation, cultivars, genotype-environment interaction, protein content, water holding capacity, temporal variation, phenotypic variation, genotype, Texas, Louisiana, Arkansas, Mississippi, Georgia, Florida, South Carolina, North Carolina, Virginia, Maryland, Kentucky, Tennessee
- Division of regional nursery test sites into homogenous subregions contributes to more efficient evaluation and better differentiation of cultivars. Data from the Uniform Southern Soft Red Winter Wheat Nursery (USSRWWN) were analyzed to group testing sites into relatively homogenous subregions for milling and baking quality (MBQ) attributes. Environmental effects due to years accounted for over 50% of the total variation for protein content (P) and 42% for alkaline water retention capacity (AWRC). Genotype effect accounted for 63% of the total variation for softness equivalence (SE), and 37% for flour yield (FLY). A significant genotype × location (G×L) interaction occurred for FLY and P. However, the G×L variance component accounted for a small proportion of the total phenotypic variance, suggesting that clustering would be more beneficial for resource efficiency than for increasing differentiation of genotypes. A hierarchical cluster analysis was used to group locations on the basis of G×L interaction effects for FLY, P, AWRC, and SE. Cluster analysis divided the USSRWWN into two main subregions within which the G×L interaction was reduced by over 90% for FLY and by 60% for P. Although this classification is not entirely consistent with the geographic distribution of locations, clusters do follow general geographic-climatic-disease regions. Our results suggest that the USSR-WWN can be divided into subregions to reduce the resources expended on evaluation of MBQ attributes. This classification of locations could be useful in breeding for specific adaptability within subregions.