240-6 Hard Winter Wheat in Washington State: Mega-Environment and Multivariate Statistical Analysis.

Poster Number 239

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: II
Tuesday, November 4, 2014
Long Beach Convention Center, Exhibit Hall ABC
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Melba Ruth Salazar-Gutierrez1, Bernardo Chaves1, Jakarat Anothai1, Jerry Johnson2 and Gerrit Hoogenboom1, (1)Washington State University, Prosser, WA
(2)The University of Georgia, Griffin, GA
Multi-environment trials of crop breeding lines are conducted annually to identify superior varieties and to develop an understanding of the crop for a target region. The objectives of this study were to determine whether subdividing the hard winter wheat (Triticum aestivum L.) production areas in the eastern section of the State of Washington into mega-environment and to classify wheat genotypes and existing relations among agronomic traits.  Grain yield, protein percentage, plant height and heading date were obtained for 17 winter wheat genotypes from the WSU Variety Testing Program conducted at locations in eastern Washington State from 2000 to2012. The genotype and genotype x environment (GGE) biplot method was used to subdivide the wheat production areas into subregions. In addition, Principal Component Analysis (PCA) and Cluster analysis was conducted to determine the main source of variability of winter wheat varieties. All statistical analysis was performed using the Statistical Analysis System (SAS) V.9.3. The preliminary results showed that the GGE biplot analysis based on the averaged seven varieties that were common to the trials conducted from 2000 to 2006 and the 10 varieties that were common to trials conducted from 2007 to 2012 separated Walla Walla from other locations, indicating that the subdivision of wheat production areas into mega-environment is justified for eastern Washington. PCA analysis identified that two components explained 81.6% of the variation. The first component protein concentration and the second component was plant height. Both yield and protein concentration responded to the variability in environmental conditions. The cluster analysis placed the genotypes into three groups; the first group had the highest yield followed by the medium and low yield groups. The use of environmental characterization and the PCA analysis can potentially be a good statistical to aid wheat improvement in Washington and the Pacific Northwest.
See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: II