The persistent annual risk of white mold requires development of a proactive approach to understanding the importance of different risk factors, as well as farm-level economics to incorporate new changes on the farm. Research and extension in this project is focused on investigating best management practices for the control of white mold. We are taking a multitiered approach in this project, incorporating an increased understanding of pathogen diversity, spatial sampling for Sclerotinia sclerotiorum, testing and validating existing prediction models developed in the Midwest to see if they perform similarly in the Northeastern US. We are also working directly with farmers to determine what management tactics would be feasible in their farm operation, recognizing that we may need to make individual recommendations based on the likelihood of adoption of different tactics and the farm scale.

1. Conduct a soil survey to obtain isolates from different fields with different histories of white mold (established fields, new finds in fields with more recent history of the disease, and among other fields where concerns and questions have been raised),
2. Develop a paper-based survey to quantify the extent and perceived risk of white mold for soybean production, and
3. Conduct a case study on-farm assessment of best management practices that incorporates field history (independent study project), crop rotation, and cost of new equipment if rotation practices are changed.

In the second year of the project, we achieved the following:
1. We were able to continue our sampling program, both at the site level, as well as field sampling. In 2020, we obtained samples from eight locations.
2. To date, we have collected approximately 196 isolates of S. sclerotiorum that will enable us to commence with looking at the genetic diversity in the state, which will be important for making best recommendations. We further have another 120 isolates from the spatial studies on white mold that will be used to understand the genetic diversity at the local scale.
3. At the end of 2020, we launched our survey to improve our knowledge and understanding of white mold in Pennsylvania, as well as learn what management practices are most likely to be considered as part of the overall farm production practices across the state.
4. We successfully tested the Sporecaster model, developed in the Midwest, across 23 locations in Pennsylvania. Results showed that the model overestimated the risk of white mold, which led to the developers to examine the primary weather data source given the spatial scale that is needed for accurate estimates across microclimates. We expect to validate the new forecasts again in 2021.
5. Both students (Karen Luong and Tyler McFeaters), working on thesis research for white mold, presented their research at the Plant Health 2020 Online conference in August 2020.

Updated August 20, 2021:

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Results from this research will provide a novel approach to tackling the white mold issue, not only for Pennsylvania, but also for the Northeastern USA, where the microclimate variability can greatly modify the risk from field-to-field, and valley-to-valley. The ultimate end-goal for this project is to get to a phase where using an information technology platform and risk assessment tool (i.e., application) and field-collected data, we will be able to provide a white mold risk tool that focuses on classification of the field and farm both within- and across-growing season.