Announcement of Final Thesis Defense Plant Pathology Ph.D. Degree: Jill Check
April 28, 2025 1:30PM - 2:30PM
Date: April 28th, 2025
Time: 1:30 pm in PSSB A271
Zoom: https://msu.zoom.us/j/94159442429
Meeting ID: 941 5944 2429
Passcode: PLP
THE EPIDEMIOLOGY AND MANAGEMENT OF FIELD CROP DISEASES IN MICHIGAN
Members of the Examining Committee and their Department:
1. Dr. Martin Chilvers – Plant, Soil and Microbial Sciences
2. Dr. Jaime Willbur – Plant, Soil and Microbial Sciences
1. Dr. Younsuk Dong – Biosystems and Agricultural Engineering
2. Dr. Richard Wade Webster – Plant Pathology, North Dakota State University,
ABSTRACT
The continental climate of Michigan provides favorable conditions for a range of fungal pathogens that infect field crops. This dissertation focuses on two key fungal pathogens: Phyllachora maydis, which causes tar spot of corn and is emerging in the region, and Sclerotinia sclerotiorum, a long-established pathogen causing Sclerotinia stem rot on soybean, dry bean and potato. Both are highly influenced by environmental conditions, resulting in variable yield impacts and management difficulties. A field trial was conducted to investigate cultural disease management strategies for tar spot, finding that nitrogen application rate had no effect, while low planting density increased disease severity. However, reducing planting density was not an economically viable management strategy. Instead, hybrid susceptibility consistently and significantly reduced disease severity, highlighting the importance of hybrid selection in tar spot management. Spore traps and a qPCR assay were used to quantify P. maydis spore release and dispersal across six environments. Logistic regression modeling revealed spore release and dispersal was greatly influenced by temperature and humidity, achieving 85% accuracy. Although spore detection before disease symptom onset wasn’t achieved, this approach shows promise for future warning systems. Supervised machine learning and on-site weather monitoring systems were applied to predict S. sclerotiorum apothecia presence using data from 20 site-years representing irrigated soybean, dry bean and potato fields. Decision tree models achieved up to 87% accuracy, demonstrating promising potential for a future multi-crop prediction tool for irrigated environments. The findings from this dissertation contribute advancements in plant pathogen epidemiology and disease management for Michigan field crop production.
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