Managing White Mold in Soybeans: Understanding the Field-Scale Spatial Distribution and Genotypic Diversity of Sclerotinia sclerotiorum

White mold in soybeans, caused by Sclerotinia sclerotium (S.s.), is the second most devastating disease of soybeans in the world. The disease causes an average of 2.5% yield loss in soybean production annually in Pennsylvania (PA), which equates to approximately $6,000,000. However, many growers still do not scout for white mold or have not been able to manage it well. My thesis aims to increase our understanding of the biology of S. s. at the field-scale, which will improve sampling strategies, crop loss estimations, and field experimental designs. Studying the genotypic diversity of S.s. populations at a field scale will help improve management strategies like fungicide applications. Eight fields were selected for soil sampling of thirty-five quadrats. S.s. sclerotia were manually removed and isolated in the lab. DNA was extracted for 286 isolates and genotyped by fragment analysis. Microsatellite regions of the DNA were amplified at 10 loci and PCR products were analyzed by capillary electrophoresis. The same fields were also scouted to quantify the disease incidence at each quadrat. Lastly, soybean fields in PA and New York were monitored and scouted to conduct a validation of the Sporecaster mobile application for forecasting white mold risk. Across both years, the maximum number of sclerotia in a field was 3.3 sclerotia/kg soil and the maximum disease incidence for a field was 14%. The spatial distribution of the pathogen at a field-scale was randomly distributed and only one field showed aggregation. Genotypic results indicated 83 multilocus genotypes across PA. Despite a high genotypic diversity, populations at a field scale were clonal and showed little evidence of outcrossing. The Sporecaster mobile application had a two-year average accuracy of 57-74% at predicting white mold disease incidence. Our increased knowledge of the pathogen and use of the Sporecaster app will help improve management recommendations and guide future research of white mold management tactics.