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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Grain Quality and Structure Research » Research » Publications at this Location » Publication #179972
Title: NURSERY LOCATION CLUSTERING BASED ON HARD WINTER WHEAT REGIONAL QUALITY EVALUATIONS

Author
item CLAYSHULTE, SALLEY - COLORADO STATE UNIV.
item HALEY, SCOTT - COLORADO STATE UNIV.
item CHAPMAN, PHILLIP - COLORADO STATE UNIV.
item Seabourn, Bradford
item Chung, Okkyung

Submitted to: Meeting Abstract
Publication Type: Other
Publication Acceptance Date: 4/22/2005
Publication Date: 5/22/2005
Citation: Clayshulte, S.R., Haley, S.D., Chapman, P.L., Seabourn, B.W., Chung, O.K. 2005. Nursery location clustering based on hard winter wheat regional quality evaluations. Third International Wheat Quality Conference Proceedings. Meeting Abstract. p.370

Interpretive Summary:

Technical Abstract: Environmental conditions significantly affect wheat quality characteristics from year to year. Understanding the effects of environment on quality characteristics and the similarities of genotype response to testing locations is important for breeders selecting test sites and evaluating experimental lines. Six years of quality data from the U.S. Hard Winter Wheat Southern Regional Performance Nursery (SRPN) and the Northern Regional Performance Nursery (NRPN) were analyzed to divide test sites into groups similar in their response to the environment. Cluster analysis divided the SRPN into four clusters based on milling-related variables and three clusters based on rheology-related variables. Locations were consistently placed in the same cluster for the rheology-related variables but not for the milling-related variables. For the NRPN, clustering based either on rheology-related or milling-related variables produced one cluster that contained 58% of the location-years. However, the remaining location-years were divided into two clusters based on milling-related variables and three clusters based on rheology-related variables. Principal component analysis of the SRPN data identified four principal components based on milling-related variables (explaining 78% of the total variability) and three principal components based on rheology-related variables (explaining 93% of the variability). Clustering based on these principal components revealed three clusters for the milling-related variables and four clusters in the rheology-related variables. Interpretation of the principal components allowed clusters to be characterized. Results from either clustering procedure indicate that only a few locations responded similarly to the environment from year to year.