José F. González-Acuña, Tom W. Allen, Mandy Bish, Carl A. Bradley, Boris X. Camiletti, Martin I. Chilvers, Nabin K. Dangal, Mercedes M. Diaz-Arias, Ahmad M. Fakhoury, Travis Faske, Mark L. Gleason, Bryan Hansen, Heather Kelly, H. D. Lopez-Nicora, LeAnn Lux, Dean K. Malvick, Dylan Mangel, Samuel G. Markell, Daren S. Mueller, Paul P. Price, Hope Renfroe-Becton, Jessica M. Scherer, Edward J. Sikora, Damon L. Smith, Adam Striegel, Darcy E. P. Telenko, Richard W. Webster
Frogeye leaf spot (FLS), caused by Cercospora sojina, is a common soybean disease across the U.S. Fungicides are a key management tool, particularly when susceptible cultivars are planted; however, widespread QoI resistance has raised concern about overreliance on the remaining effective fungicide classes. Protecting these chemical classes is essential for long-term sustainability, particularly under narrow profit margins. To develop an FLS prediction model that supports more efficient fungicide use, environmental and epidemiological data from multiple site-years were analyzed in 2024 using correlation analysis, logistic regression (LR), and machine-learning approaches. The most effective model combined a 30-day moving average (ma) of daily hours of relative humidity (RH) ≥ 80% and maximum temperature (°C) in a LR model. FLS risk peaked when the 30-d ma of daily hours of RH ≥ 80% was 15–20 h and maximum temperature was 24–36 °C. When daily hours of RH ≥ 80% averaged < 5 h, risk remained low regardless of temperature. Random forest and support vector machine models achieved greater accuracy and sensitivity than LR but showed poorer specificity. This research provides a strong epidemiological foundation for improving decision-making and advancing integrated disease management. The resulting prediction model is deployed in a public decision support system ( https://cropprotectionnetwork.org/crop-disease-forecasting ), enabling real-time FLS risk assessments and promoting stewardship-minded fungicide use.