Due to the humid continental climate of Michigan, field crops suffer from a plethora of fungal pathogens causing infections of roots, stems, foliage and reproductive structures. In 2022, corn, soybean, potatoes and dry beans accounted for 6.2 million acres in Michigan, approximately 65% of the state’s agricultural production land. Phyllachora maydis is a fungal pathogen of corn that is emerging in the region, causing tar spot of corn. Sclerotinia sclerotiorum is a long-established fungal pathogen across many economically important hosts produced in Michigan, including leguminous and vegetable crops. Both P. maydis and S. sclerotiorum are highly influenced by their environment, resulting in erratic and unpredictable year-to-year economic impacts due to the prevailing weather conditions regionally. Therefore, the focus of this dissertation was to improve our understanding of these pathogens’ interactions with their environment to create new disease management recommendations and tools. In chapter 1, I provide a review of the current literature on P. maydis and S. sclerotiorum biology, infection strategy and the management of their respective diseases. I also briefly discuss the use of spore traps in plant pathogen epidemiology research and disease management, as well as the history and current state of predictive modeling for white mold management. In chapter 2, I conducted a field trial to address key questions about cultural management of tar spot of corn. This study established that neither high nor low nitrogen application rate influences tar spot development and that low planting density increases tar spot severity. However, an economic analysis of the optimal planting density revealed that increasing planting density to reduce tar spot severity was not a viable management strategy. Therefore, other disease management strategies, such as planting partially resistant hybrids, should be employed rather than altering established agronomic practices. In chapter 3, I used a previously developed P. maydis qPCR assay, Burkard and rotating-arm spore traps to quantify spore capture in tar spot-infested fields. Using data collected from 6 different environments, logistic regression modeling showed that spore release was negatively related to maximum precipitation rate, minimum and mean temperature, and maximum relative humidity, and positively related to minimum wind speed. The best performing logistic regression model used mean temperature and maximum relative humidity to predict the capture of P. maydis spores, achieving a balanced accuracy of 85%. As the spore traps were used in this study, they were not able to detect spores prior to the onset of visible disease symptoms but were able to detect spores prior to tar spot incidence reaching 100%. With future improvement of spore trap deployment and qPCR sensitivity, Michigan farmers could benefit from an early tar spot warning system to aid in expediting disease management decision making. In chapter 4, I used on-site weather monitoring systems and supervised machine learning to predict S. sclerotiorum apothecia presence in irrigated environments. Apothecia monitoring was conducted in soybean, dry bean and potato fields representing 20 site-years to develop a multi-crop apothecia prediction model. Decision tree (DT) models performed best, with an accuracy of 77% when developed using gridded weather data and 89% after incorporating soil weather data collected on-site. Interpretability of models did not have to be sacrificed to achieve a high prediction accuracy, as the DT developed using untransformed features performed comparably to the DT developed using principal components. Through this study, a robust framework for developing multi-crop S. sclerotiorum apothecia prediction models was demonstrated, but future model validation will be required to assess the value of the prediction models for SSR disease control. Lastly, in chapter 5, I discuss the conclusions and impacts from the work described herein. Overall, these studies contribute to the current understanding of P. maydis and S. sclerotiorum epidemiology and the management of their associated diseases on Michigan field crops.
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