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Title: CLUSTERING OF ENVIRONMENTS OF SOUTHERN SOFT RED WHEAT REGION FOR MILLING AND BAKING QUALITY ATTRIBUTES

Author
item COLLAKU, A - LOUISIANA STATE UNIV
item HARRISON, S - LOUISIANA STATE UNIV
item Finney, Patrick
item VAN SANFORD, D - UNIV OF KENTUCKY

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/11/2000
Publication Date: N/A
Citation: N/A

Interpretive Summary: Statistical analyses showed that the South Eastern Wheat growing region of the United States could be divided into two subregions in order to more efficiently evaluate wheat lines and cultivars and to better indicate which lines and cultivars possess superior milling and baking characteristics. This research benefits the University- and private wheat breeding programs in the South Eastern wheat growing region of the United States by reducing their resource needs required to evaluate milling and baking potential of wheat lines and cultivars.

Technical Abstract: 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 Flour Yield (FLY) and Protein(P). Genotype effect accounted for 63% of the total variation for softness equivalence (SE), suggesting a strong genotypic base for this trait. A significant genotype x location (GxL) interaction occurred for FLY and P, indicating that clustering of locations into subregions could be beneficial. However, the GxL 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 based on GxL interaction effects for FLY, P, AWRC and SE. Cluster analysis divided the USSRWWN into two main subregions within which GxL interaction was reduced by 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 USSRWWN 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.