Thesis
Disease screening and statistical strategies for predicting varietal performance in wheat
Washington State University
Master of Science (MS), Washington State University
2017
Handle:
https://hdl.handle.net/2376/105769
Abstract
Spatial variation is a common problem for agricultural researchers performing field experiments largely due to environmental factors. Such variation can play a major role when screening for soil-borne diseases like Fusarium crown rot (FCR), particularly in the Pacific Northwest (PNW) United States. FCR is caused by Fusarium pseudograminearum and F. culmorum and it is a common disease that infects wheat in the PNW. The disease incurs average yield losses between 10% and 35%. Recently, FCR has become more prevalent due to the increased adoption of conservation tillage and no-till practices. There is currently little known about available sources of FCR resistance in soft-white winter (SWW) wheat adapted to the PNW. We screened 106 SWW wheat genotypes, adapted to the PNW, for resistance to FCR in both the greenhouse and the field. We predicted that we would see a wide range in FCR resistance in the evaluated genotypes. In the greenhouse, we found 22 genotypes that were significantly more resistant to FCR comparted to the susceptible cultivar, 'Madsen'. The field results were highly variable due to environmental variation and interference from other soil-borne pathogens. The partially-resistant genotypes identified in this study can be used to incorporate new sources of FCR resistance into regional breeding programs. We also wanted to determine if the use of linear mixed models with spatial covariance structures (SLMM) could better control for spatial variation in the Washington State University Extension Cereal Variety Testing Program's (WSU-CVT) SWW wheat variety trials. We evaluated yield data from 143 environments over 22 locations, seven years, and five precipitation zones using five different SLMM compared to the randomized complete block (RCB) and alpha-lattice designs (PBLR). Using Akaike Information Criterion and likelihood ration tests, we found that SLMM performed better in 86% of environments compared to RCB and PBLR designs. We also found that SLMM eliminated spatial trends in plot residual errors and changed genotype rankings at individual environments compared to the RCB and PBLR designs. This information can be used to assist WSU-CVT and regional breeding programs in performing more efficient and effective analyses of their field data.
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Details
- Title
- Disease screening and statistical strategies for predicting varietal performance in wheat
- Creators
- Dylan Lee Larkin
- Contributors
- Kimberly A. Garland-Campbell (Chair)Arron Hyrum Carter (Committee Member) - Washington State University, Crop and Soil Sciences, Department ofTimothy C. Paulitz (Committee Member) - Washington State University, Plant Pathology, Department of
- Awarding Institution
- Washington State University
- Academic Unit
- Crop and Soil Sciences, Department of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525006001842
- Language
- English
- Resource Type
- Thesis