Project Details:

Development of Best Management Guidelines for White Mold in PA

Parent Project: Development of Best Management Guidelines for White Mold in PA
Checkoff Organization:Pennsylvania Soybean Promotion Board
Categories:Soybean diseases
Organization Project Code:PSB-R2022-01
Project Year:2022
Lead Principal Investigator:Paul Esker (Pennsylvania State University)
Co-Principal Investigators:

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

The persistent annual risk of white mold requires the development of a proactive approach to understanding the importance of different risk factors and farm-level economics to incorporate new changes on the farm. Research and extension in this project are focused on investigating best management practices for the control of white mold. We are taking a multi-tiered 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 adopting different tactics and the farm scale.

Project Objectives

(1) Conduct in-person surveys to quantify the extent and perceived risk of white mold for soybean production, and
(2) 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.
(3) Conduct molecular analysis to elucidate multilocus genotypes (unique groups) of S. sclerotiorum and their distribution and genetic diversity at the field and regional scale, and
(4) Perform fungicide sensitivity assays to determine the status of S. sclerotiorum resistance or reduced sensitivity to fungicides.
(5) Further validate the Sporecaster mobile application in the Northeast U.S. climate for forecasting white mold risk.

Project Deliverables

(1) Develop an isolate collection that will provide pathogen material for nursery development that enables a more precise evaluation of genetic material, foliar fungicides, and other production practices like in-furrow treatment of sclerotia.
(2) New knowledge relating the importance of different factors that influence white mold risk since data are based on grower practices and production fields.
(3) Economic quantification at the farm level of best management practices that may be applied to combat white mold.
(4) Method development that is transferable to other states and can be applied in other legume crops.
(5) Long-term, we hypothesize that this approach will enable the development of a risk assessment tool that takes farm-inputted production information and quantifies the risk to classify the farm into one of three different areas: always at low risk, having a moderate risk, and always at high risk since each group will require a different best management recommendation.
(6) Training key stakeholder groups on identifying best management tools to combat white mold based on actual field histories.
(7) Training the next generation of scientists and farmers using real-world, on-farm data information.

Progress of Work

Updated August 23, 2022:
See uploaded file.

View uploaded report PDF file

Final Project Results

Benefit to Soybean Farmers

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.

Performance Metrics

Project Years