Soybean sudden death syndrome (SDS) is an important disease of soybean in Kansas, resulting in millions of dollars of economic loss annually. Together, the objectives proposed in this study will help producers better manage this disease on their farms through better detection, prediction of risk, and through recommendations for cropping system adjustments (plant population and row spacing). Our proposed field trials will be used as “living classrooms” through in-field extension events. This work is synergistic with several ongoing projects in the lab of Dr. Rodrigo Onofre. This project will provide broad, practical training for one M.S. student.

Objective 1: Evaluate the influence of agronomic practices on soybean sudden death syndrome (SDS) through on-farm trials evaluating row spacing, plant population, and variety selection.
Objective 2: Develop a sudden death syndrome prediction tool for predicting disease prior planting.
Objective 3: Evaluate genetic diversity of Fusarium spp. from soybeans causing SDS in Kansas.
Objective 4: Validate a rapid diagnostic tool for SDS based on loop-mediated isothermal amplification (LAMP).
Objective 5: Generate and promote data-driven best management practices based on results of objectives 1, 2 and 3.

Through the work described in this proposal we will generate a better understanding of how plant population and row spacing is contributing to SDS risk. We will develop a fast, reliable and accurate diagnostic tool to better serve our growers. We will identify important pre-planting weather predictors to inform risk of SDS for the coming season, so decisions. Through this project we will establish an isolate collection from Kansas that will be used for the current objectives and future work such as efficacy of seed treatments. This work will allow us to begin to understand the diversity of the SDS pathogen across soybean regions of Kansas. Finally, we will have tangible outputs that will allow us to provide data-driven SDS management recommendations for Kansas soybean growers, crop agents, and the ag industry through dynamic extension programming.