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<p>Frogeye leaf spot (FLS), caused by <i>Cercospora sojina</i>, 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&#xa0;h and maximum temperature was 24–36&#xa0;°C. When daily hours of RH ≥ 80% averaged &lt; 5&#xa0;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 (<a href="https://cropprotectionnetwork.org/crop-disease-forecasting">https://cropprotectionnetwork.org/crop-disease-forecasting</a><i>)</i>, enabling real-time FLS risk assessments and promoting stewardship-minded fungicide use.</p>

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Modeling the environment-related risk of frogeye leaf spot (Cercospora sojina) in soybean across the United States

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Jose F. González-Acuña,
  • Tom W. Allen,
  • Mandy D. Bish,
  • Carl A. Bradley,
  • Boris X. Camiletti,
  • Martin I. Chilvers,
  • Nabin K. Dangal,
  • Mercedes M. Diaz-Arias,
  • Ahmad M. Fakhoury,
  • Travis R. Faske,
  • Mark L. Gleason,
  • Bryan C. Hansen,
  • Heather M. Kelly,
  • Horacio D. Lopez-Nicora,
  • LeAnn Lux,
  • Dean K. Malvick,
  • Dylan Mangel,
  • Samuel G. Markell,
  • Daren S. Mueller,
  • Paul P. Price III,
  • Hope Renfroe-Becton,
  • Jessica M. Scherer,
  • Edward J. Sikora,
  • Damon L. Smith,
  • Adam Striegel,
  • Darcy E. P. Telenko,
  • Richard W. Webster

摘要

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.