Announcing: Final Thesis Defense Seminar Plant Pathology PhD Ryan Hamilton (Chilvers lab) presents:
July 6, 2026 10:00AM - 11:00AM
From Detection to Prediction: Molecular Epidemiology of
Soybean Soilborne Disease Systems for Risk-Informed Management
Members of the Examining Committee and their Department:
- Dr. Martin Chilvers – Plant, Soil and Microbial Sciences
- Dr. Jaime Willbur – Plant, Soil and Microbial Sciences
- Dr. Timothy Miles – Plant, Soil and Microbial Sciences
- Dr. Linda Hanson – USDA-ARS/Plant, Soil and Microbial Sciences
ABSTRACT
Soybean production is continually challenged by soilborne pathogens that reduce yield, complicate management decisions, and contribute to substantial economic losses. Among the most important of these pathogens are Fusarium virguliforme, a causal agent of sudden death syndrome (SDS), and soybean cyst nematode (SCN – Heterodera glycines). Although advances in diagnostics have improved pathogen detection, disease outcomes often depend on complex interactions among pathogen pressure, environmental conditions, host genetics, and management practices. Consequently, pathogen abundance alone frequently provides useful, but limited information regarding disease risk or crop performance. This research investigated soybean soilborne disease systems through the integration of molecular diagnostics, epidemiology, and quantitative analysis across multiple spatial and biological scales. Species-specific TaqMan qPCR assays targeting SCN and F. virguliforme were deployed within an operational surveillance framework to evaluate relationships among molecular abundance, pathogen distribution and co-occurrence, and disease risk. Multivariate longitudinal datasets were also generated to examine associations among pathogen DNA abundance, SDS symptom expression, yield loss, and environmental factors across diverse soybean production environments. Additional studies evaluated the performance of fungicidal and biological seed treatments under varying levels of disease pressure. Collectively, the results demonstrate that disease outcomes are influenced not only by pathogen presence but also by pathogen pressure, environmental context, and management conditions. The findings support a transition from reactive pathogen detection toward predictive, risk-informed approaches that integrate molecular and epidemiological information. Broadly, this work provides a framework for advancing molecular surveillance, disease-risk classification, and precision disease management.
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