Thesis (Ph. D.)--University of Wisconsin--Madison, 1960. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S. in plant pathology)--Washington State University, May 2009. / Title from PDF title page (viewed on May 26, 2009). "Department of Plant Pathology." Includes bibliographical references (p. 35-38).

No description available.

No description available.

Thesis (M.S.)--Missouri State University, 2009. / "May 2009." Includes bibliographical references (leaves 44-50). Also available online.

Ten winter wheat (Triticum aestivum L.) lines selected from the 1982 International Winter Wheat Mildew and Rust Nurseries were studied to characterize gene number and mode of inheritance of powdery mildew resistance. Two experiments were conducted: 1) each of the lines was crossed to the susceptible cultivar Chancellor, which lacks any known’ gene _ for resistance, and seedlings of the parental lines, F₁, F₂, BC₁ (Chancellor X F₁), and F₃ populations were inoculated with isolate 127 of Blumeria graminis (DC.) E. 0. Speer f. sp. tritici Em. Marchal in the greenhouse and evaluated for powdery mildew reaction; 2) the ten lines were crossed with each other and to each of 13 host differential lines with known genes for powdery mildew resistance, and 300 to 800 F₂ seedlings from each cross were evaluated. All parents were resistant (Infection Type = 1-3), except for ST1-25, which had an intermediate (IT = 4-5) reaction type. Genetic analyses of crosses revealed that the resistance in C39 and SI5 is conferred by three dominant genes (Pm2, 4b, and 6), and resistance in A55-2, R107, and Bulk PV63-6 is governed by one partially dominant gene (Pm4b). Results from F₂, F₃, and BC₁ populations derived from crosses between ‘Armada’ and Chancellor, were inconsistent, but indicated that Armada has at least one dominant gene for resistance, which likely is Pm4b as suggested by others. The resistance gene in OK75R3645 most likely is an allele at the Pm3 locus, and it is probable that the resistance gene in GO4779 is Pm1. Single recessive genes were identified in VPM1 (Pm4b) and ST1-25 (Pm8). / Ph. D.

MSCAGR / Department of Plant Production

The agriculture sectors in Canada are highly vulnerable to a wide range of inter-related weather risks linked to seasonal climate variability (e.g., El Ni ̃no Southern Oscillation(ENSO)), short-term extreme weather events (e.g., heatwaves), and emergent disease(e.g., grape powdery mildew). All of these weather-related risks can cause severe crop losses to agricultural crop yield and crop quality as Canada grows a wide range of farm products, and the changing weather conditions mainly drive farming practices. This dissertation presents three machine learning-based statistical models to assess the weather risks on the Canadian agriculture regions and to provide reliable risk forecasting to improve the decision-making of Canadian agricultural producers in farming practices. The first study presents a multi-scale, cluster-based Principal Component Analysis(PCA) approach to assess the potential seasonal impacts of ENSO to spring wheat and barley on agricultural census regions across the Canada prairies areas. Model prediction skills for annual wheat and barley yield have examined in multi-scale from spatial cluster approaches. The ’best’ spatial models were used to define spatial patterns of ENSO forcing on wheat and barley yields. The model comparison of our spatial model to non-spatial models shows spatial clustering and ENSO forcing have increase model performance of prediction skills in forecasting future cereal crop production. The second study presents a copula-Bayesian network approach to assess the impact of extreme high-temperature events (heatwave events) on the developments of regional crops across the Canada agricultural regions at the eco-district-scale. Relevantweather variables and heatwave variables during heatwave periods have identified and used as input variables for model learning. Both a copula-Bayesian network and Gaussian-based network modeling approach is evaluated and inter-compared. The copula approach based on ’vine copulas’ generated the most accurate predictions of heatwave occurrence as a driver of crop heat stress. The last study presents a stochastic, hybrid-Bayesian machine-learning approach to explore the complex causal relationships between weather, pathogen, and host for grape powdery mildew in an experimental farm in Quebec, Canada. This study explores a high-performance network model for daily disease risk forecast by using estimated development factors of pathogen and host from recorded daily weather variables. A fungicide strategy for disease control has presented by using the model outputs and forecasted future weather variability. The dissertation findings are beneficial to Canada’s agricultural sector. The inter-related weather risks explored by the three separate studies in multi-scales provide a better understanding of the interactions between changing weather conditions, extreme weather, and crop production. The research showcases new insights, methods, and tools for minimizing risk in agricultural decision-making / Graduate / 2021-08-19