Search

Menu
Search
in Ecology

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

159 Views


Abstract

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.

Data availability

The datasets generated and analyzed during the current study are not publicly available to preserve farm-level anonymity, but county-level data are available from the corresponding author upon reasonable request.Environmental variables were obtained through the IBM Environmental Intelligence Suite (EIS) weather data API, a proprietary dataset accessible via paid license from IBM. Information regarding access to the IBM Environmental Intelligence Suite and its weather data products is available at: [https://www.ibm.com/products/environmental-intelligence](https:/www.ibm.com/products/environmental-intelligence) . Researchers with appropriate access to IBM Environmental Intelligence Suite can independently obtain equivalent environmental data using the methods described in this study.

References

  1. Barro, J. P., Neves, D. L., Ponte, D., Bradley, C. A. & E. M. & Frogeye leaf spot caused by Cercospora sojina: A review. Trop. Plant. Pathol. 48, 363–374 (2023a).

    Google Scholar 

  2. Grau, C. R., Dorrance, A. E., Bond, J. & Russin, J. S. Fungal diseases in soybeans: Improvement, production, and uses. Fungal Dis. 16, 679–763 (2004).

  3. Lehman, S. G. Frogeye leaf spot of soybean caused by Cercospora diazu Miura. J. Agric. Res. 35, 811–833 (1928).

    Google Scholar 

  4. Melchers, L. E. Diseases of cereal and forage crops in the United States in 1924. Plant. Dis. 40, 186 (1925).

    Google Scholar 

  5. Bradley, C. A. et al. An overview of frogeye leaf spot. Crop Prot. Netw. https://doi.org/10.31274/cpn-20190620-013 (2016).

    Google Scholar 

  6. Buchanan, M. & King, L. D. Carbon and phosphorus losses from decomposing crop residues in no-till and conventional till agroecosystems. Agron. J. 85, 631–638 (1993).

    Google Scholar 

  7. Mengistu, A. et al. Tillage, fungicide, and cultivar effects on frogeye leaf spot severity and yield in soybean. Plant. Dis. 98, 1476–1484 (2014).

    Google Scholar 

  8. Zhang, G. Cercospora sojina: Over-winter survival and fungicide resistance. Ph.D. Thesis, University of Illinois at Urbana-Champaign (2012).

  9. Cruz, C. D. & Dorrance, A. E. Characterization and survival of Cercospora sojina in Ohio. Plant. Health Prog. 10, 17 (2009).

    Google Scholar 

  10. Zhang, G. & Bradley, C. A. Survival of Cercospora sojina on soybean leaf debris in Illinois. Plant. Health Prog. 15, 92–96 (2014).

    Google Scholar 

  11. Singh, T. & Sinclair, J. B. Histopathology of Cercospora sojina in soybean seeds. Phytopathology 75, 185–189 (1985).

    Google Scholar 

  12. Wise, K. A. & Newman, M. E. Frogeye leaf spot. In Compendium of Soybean Diseases and Pests (eds. Hartman, G. L. et al.). 43–45 (APS, 2015).

  13. Sautua, F. J. et al. Detection and chemical control of Cercospora sojina infecting soybean seed in Argentina. Trop. Plant. Pathol. 43, 552–558 (2018).

    Google Scholar 

  14. Bohra, Y. Frog eye leaf spot in soybean: From symptoms to management. In Soybean Production Technology: Crop Pests and Diseases (ed. Singh, K. P., Singh, N. K. & Aravind, T.). 189–204 (Springer, 2025).

  15. Mian, M. A. R., Missaoui, A. M., Walker, D. R., Phillips, D. V. & Boerma, H. R. Frogeye leaf spot of soybean: A review and proposed race designations for isolates of Cercospora sojina Hara. Crop Sci. 48, 14–24 (2008).

    Google Scholar 

  16. Sepulcri, M. G., Moschini, R. C. & Carmona, M. A. Soybean frogeye leaf spot [Cercospora sojina]: First weather-based prediction models developed from weather station and satellite data. Adv. Appl. Agric. Sci. 3, 1–13 (2015).

    Google Scholar 

  17. Barro, J. P. et al. Meta-analytic modeling of the severity–yield relationships in soybean frogeye leaf spot epidemics. Plant. Dis. 107, 3422–3429 (2023b).

    Google Scholar 

  18. Balardin, R. S. Doenças na soja. Univ. Fed. Santa Maria 107 (2002).

  19. Bradley, C. A. et al. Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2015 to 2019. Plant. Health Prog. 22, 483–495 (2021).

    Google Scholar 

  20. Carmona, M. Damages caused by frogeye leaf spot and late season disease in soybean in Argentina and control criteria. Trop. Plant. Pathol. 3, 1356–1358 (2011).

    Google Scholar 

  21. Yorinori, J. T. A Mancha “olho-de-ra” (Cercospora sojina Hara) (EMBRAPA-CNPSo, 1988).

  22. Crop Protection Network. Disease Loss Calculator. https://loss.cropprotectionnetwork.org (2025).

  23. Crop Protection Network. Fungicide Efficacy for Control of Soybean Foliar Diseases. https://doi.org/10.31274/cpn-20190620-014 (2025).

  24. Dangal, N. K. et al. Fungicide Efficacy Guides for Foliar Diseases in Corn and Soybean: Development and Validation. Plant. Health Prog. https://doi.org/10.1094/PHP-02-25-0073-RS (2025).

    Google Scholar 

  25. Barro, J. P. et al. Efficacy and profitability of fungicides for managing frogeye leaf spot on soybean in the United States: A 10-year quantitative summary. Plant. Dis. 107, 3487–3496 (2023c).

    Google Scholar 

  26. Harrelson, B. C. et al. Assessment of quinone outside inhibitor sensitivity and frogeye leaf spot race of Cercospora sojina in Georgia soybean. Plant. Dis. 105, 2946–2954 (2021).

    Google Scholar 

  27. Mathew, F. M., Byamukama, E., Neves, D. L. & Bradley, C. A. Resistance to quinone outside inhibitor fungicides conferred by the G143A mutation in Cercospora sojina (causal agent of frogeye leaf spot) isolates from South Dakota soybean fields. Plant. Health Prog. 20, 104–105 (2019).

    Google Scholar 

  28. Mengistu, A., Kurtzweil, N. C. & Grau, C. R. First report of frogeye leaf spot (Cercospora sojina) in Wisconsin. Plant. Dis. 86, 1272–1272 (2002).

    Google Scholar 

  29. Neves, D. L., Chilvers, M. I., Jackson-Ziems, T. A., Malvick, D. K. & Bradley, C. A. Resistance to quinone outside inhibitor fungicides conferred by the G143A mutation in Cercospora sojina (causal agent of frogeye leaf spot) isolates from Michigan, Minnesota, and Nebraska soybean fields. Plant Health Prog. 21, 230–231 (2020).

    Google Scholar 

  30. Neves, D. L., Webster, R. W., Smith, D. L. & Bradley, C. A. The G143A mutation in the cytochrome b gene is associated with quinone outside inhibitor fungicide resistance in Cercospora sojina from soybean fields in Wisconsin. Plant. Health Prog. 23, 241–242 (2021).

    Google Scholar 

  31. Neves, D. L. et al. First detection of frogeye leaf spot in soybean fields in North Dakota and the G143A mutation in the cytochrome b gene of Cercospora sojina. Plant. Health Prog. 23, 269–271 (2022).

    Google Scholar 

  32. Piñeros-Guerrero, N., Neves, D. L., Bradley, C. A. & Telenko, D. E. Determining the distribution of QoI fungicide-resistant Cercospora sojina on soybean from Indiana. Plant. Dis. 107, 1012–1021. https://doi.org/10.1094/PDIS-08-22-1744-SR (2023).

    Google Scholar 

  33. Onofre, R. B., Neves, D. L. & Bradley, C. A. Cercospora sojina isolates from Kansas soybean fields with the G143A mutation conferring resistance to quinone outside inhibitor fungicides. Plant. Health Prog. 25, 144–145 (2024).

    Google Scholar 

  34. Standish, J. R., Tomaso-Peterson, M., Allen, T. W., Sabanadzovic, S. & Aboughanem-Sabanadzovic, N. Occurrence of QoI fungicide resistance in Cercospora sojina from Mississippi soybean. Plant. Dis. 99, 1347–1352. https://doi.org/10.1094/PDIS-02-15-0157-RE (2015).

    Google Scholar 

  35. Zhang, G. et al. Widespread occurrence of quinone outside inhibitor fungicide-resistant isolates of Cercospora sojina, causal agent of frogeye leaf spot of soybean, in the United States. Plant. Health Prog. 19, 295–302 (2018).

    Google Scholar 

  36. Zhou, T. & Mehl, H. L. Rapid quantification of the G143A mutation conferring fungicide resistance in Virginia populations of Cercospora sojina using pyrosequencing. Crop Prot. 127, 104942 (2020).

    Google Scholar 

  37. Bandara, A. Y. et al. Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States I: Foliar fungicides vs foliar diseases. PLoS ONE. 15, e0234390. https://doi.org/10.1371/journal.pone.0234390 (2020).

    Google Scholar 

  38. Russell, P. E. A century of fungicide evolution. J. Agric. Sci. 143, 11–25 (2005).

    Google Scholar 

  39. Hollomon, D. W. Fungicide resistance: facing the challenge – A review. Plant. Prot. Sci. 51, 170–176. https://doi.org/10.17221/42/2015-PPS (2015).

    Google Scholar 

  40. Neves, D. L. & Bradley, C. A. Baseline sensitivity of Cercospora sojina and Corynespora cassiicola to pydiflumetofen. Crop Prot. 147, 105461 (2021).

    Google Scholar 

  41. Zhang, G., Neves, D. L., Krausz, K. & Bradley, C. A. Sensitivity of Cercospora sojina to demethylation inhibitor and methyl benzimidazole carbamate fungicides. Crop Prot. 149, 105765 (2021).

    Google Scholar 

  42. Mueller, D. S., Wise, K. A., Dufault, N. S., Bradley, C. A. & Chilvers, M. A. (eds.) Fungicides for Field Crops (APS, 2013).

  43. Madden, L. V., Hughes, G. & Bosch, F. V. D. The Study of Plant Disease Epidemics (APS, 2007).

  44. Alves, K. S., Shah, D. A., Dillard, H. R., Ponte, D. & Pethybridge, S. J. E. M. Safer and smarter: Leveraging interpretation-guided modeling and data merging of disease and environmental data for plant disease risk prediction. Phytopathology 10, 1329–1343 https://doi.org/10.1094/PHYTO-01-25-0008-FI (2025).

  45. Jensen, A., Brown, P., Groves, K. & Morshed, A. Next generation crop protection: A systematic review of trends in modelling approaches for disease prediction. Comput. Electron. Agric. 234, 110245 (2025).

    Google Scholar 

  46. Shah, D. A. et al. Into the trees: Random forests for predicting Fusarium head blight epidemics of wheat in the United States. Phytopathology 113, 1483–1493 (2023).

    Google Scholar 

  47. Magarey, R. D., Travis, J. W., Russo, J. M., Seem, R. C. & Magarey, P. A. Decision support systems: Quenching the thirst. Plant. Dis. 86, 4–14 (2002).

    Google Scholar 

  48. Bishop, B. Applying foliar fungicides on soybeans using predictive models. MSc Thesis. Iowa State University. https://doi.org/10.31274/cc-20240624-233 (2022).

  49. Huang, C. Y. et al. Study on forecasting the epidemiology of frogeye leaf spot and yield loss in soyabean. Soybean Sci. 17, 48–52 (1998).

    Google Scholar 

  50. Yang, C. & Wang, J. A mathematical model for frogeye leaf spot epidemics in soybean. Math. Biosci. Eng. 21, 1144–1166 (2023).

    Google Scholar 

  51. Sievert, C. plotly: Create interactive web graphics via plotly.js. R package version 4.10.4. https://CRAN.R-project.org/package=plotly (2025).

  52. Posit Team. RStudio: Integrated development environment for R. https://posit.co (Posit Software, 2025).

  53. Camera, J. N., Ghissi, V. C., Reis, E. M. & Deuner, C. C. The combined effects of temperature and leaf wetness periods on soybean frogeye leaf spot intensity. Semin. Ciênc Agrár. 37, 77–84 (2016).

    Google Scholar 

  54. El Jarroudi, M. et al. Weather-based predictive modeling of Cercospora beticola infection events in sugar beet in Belgium. J. Fungi. 7, 777 (2021).

    Google Scholar 

  55. Lavilla, M., Martinez, M., Ivancovich, A. & Díaz-Paleo, A. Modelo predictivo de la severidad del tizón foliar por Cercospora kikuchii mediante variables meteorológicas. Agron. Mesoam. 34, 53248 (2023).

    Google Scholar 

  56. Sentelhas, P. C. et al. Suitability of relative humidity as an estimator of leaf wetness duration. Agr Meteorol. 148, 392–400 (2008).

    Google Scholar 

  57. Adaskaveg, J. E., Michailides, T. & Eskalen, A. Fungicides, bactericides, biocontrols, and natural products for deciduous tree fruit and nut, citrus, strawberry, and vine crops in California. Univ Calif. Agric. Nat. Resour. (2025).

  58. Wieben, C. M. Estimated annual agricultural pesticide use by major crop or crop group for states of the conterminous United States, 1992–2017. https://www.sciencebase.gov/catalog/item/5d88c231e4b0c4f70d0ab2c6 (U.S. Geological Survey Data Release, 2019).

  59. Couëdel, A. et al. Assessing environment types for maize, soybean and wheat in the United States as determined by spatio-temporal variation in drought and heat stress. Agric. Meteorol. 307, 108513 (2021).

    Google Scholar 

  60. Mikel, M. A., Diers, B. W., Nelson, R. L. & Smith, H. H. Genetic diversity and agronomic improvement of North American soybean germplasm. Crop Sci. 50, 1219–1229 (2010).

    Google Scholar 

  61. Ray, R. V. Effects of pathogens and disease on plant physiology. In Agrios’ Plant Pathology (ed. Oliver, R. P.). 63–92 (Academic, (2024).

  62. Kandel, Y. R. et al. Analysis of yield and economic response from foliar fungicide and insecticide applications to soybean in the North Central United States. Plant. Health Prog. 17, 232–238 (2016).

    Google Scholar 

  63. Cunniffe, N. J. et al. Thirteen challenges in modelling plant diseases. Epidemics 10, 6–10. https://doi.org/10.1016/j.epidem.2014.06.002 (2015).

    Google Scholar 

  64. Reich, J. & Chatterton, S. Predicting field diseases caused by Sclerotinia sclerotiorum: A review. Plant. Pathol. 72, 3–18 (2023).

    Google Scholar 

  65. Gent, D. H. & Del Ponte, E. M. Plant disease warning systems. In Agrios’ Plant Pathology. 247–257 (Academic, 2024).

  66. Shah, D. A. et al. A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States. Sci. Rep. 11, 18769 (2021).

    Google Scholar 

  67. Fehr, W. R., Caviness, C. E., Burmood, D. T. & Pennington, J. S. Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Sci. 11, 929–931 (1971).

    Google Scholar 

  68. Kyveryga, P. M., Blackmer, T. M. & Mueller, D. S. When do foliar pyraclostrobin fungicide applications produce profitable soybean yield responses? Plant. Health Prog. 14, 6 (2013).

    Google Scholar 

  69. Price, T., Purvis, M. & Pruitt, H. A quantifiable disease severity rating scale for frogeye leaf spot of soybean. Plant. Health Prog. 17, 27–29 (2016).

    Google Scholar 

  70. Webster, R. W. et al. Uncovering the environmental conditions required for Phyllachora maydis infection and tar spot development on corn in the United States for use as predictive models for future epidemics. Sci. Rep. 13, 17064. https://doi.org/10.1038/s41598-023-44338-6 (2023).

    Google Scholar 

  71. Payne, A. F. & Smith, D. L. Development and evaluation of two pecan scab prediction models. Plant. Dis. 96, 1358–1364 (2012).

    Google Scholar 

  72. Sentelhas, P. C., Monteiro, J. E. B. A. & Gillespie, T. J. Electronic leaf wetness duration sensor: Why it should be painted. Int. J. Biometeorol. 48, 202–205 (2004).

    Google Scholar 

  73. Sentelhas, P. C. et al. Spatial variability of leaf wetness duration in different crop canopies. Int. J. Biometeorol. 49, 363–370 (2005).

    Google Scholar 

  74. Gleason, M. L. et al. Obtaining weather data for input to crop disease-warning systems: Leaf wetness duration as a case study. Sci. Agric. 65, 76–87 (2008).

    Google Scholar 

  75. Willbur, J. F. et al. Weather-based models for assessing the risk of Sclerotinia sclerotiorum apothecial presence in soybean (Glycine max) fields. Plant. Dis. 102, 73–84 (2018).

    Google Scholar 

  76. R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2025).

  77. Kuhn, M. Classification and Regression Training. R package version 6.0-94. https://CRAN.R-project.org/package=caret (2025).

  78. Liaw, A., Wiener, M. & randomForest Breiman and Cutler’s random forests for classification and regression. R package version 4.7–1.1. https://CRAN.R-project.org/package=randomForest (2025).

  79. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. e1071: Misc functions of the Department of Statistics, TU Wien. R package version 1.7–16. https://CRAN.R-project.org/package=e1071 (2025).

Download references

Acknowledgements

We acknowledge the numerous research staff and students from several research groups whose efforts made this work possible. Their contributions in establishing, maintaining and monitoring experiments, as well as data collection, were vital to the development of this study. We sincerely thank them for their time, expertise, and care in generating the dataset that underpinned this research.

Funding

This research was partially supported by Iowa State University USDA Hatch Project IOW04108 and the soybean checkoff through the United Soybean Board, North Central Soybean Research Program, Atlantic Soybean Council, Mid-South Soybean Board, Southern Soybean Research Program, Indiana Soybean Alliance, and Louisiana Soybean and Feed Grains Research and Promotion Board.

Author information

Authors and Affiliations

  1. Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, 50011, USA

    Jose F. González-Acuña, Nabin K. Dangal, Mark L. Gleason & Daren S. Mueller

  2. Delta Research and Extension Center, Mississippi State University, Stoneville, MS, 38776, USA

    Tom W. Allen

  3. Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA

    Mandy D. Bish

  4. Department of Plant Pathology, University of Kentucky, Princeton, KY, 42445, USA

    Carl A. Bradley

  5. Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Boris X. Camiletti

  6. Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA

    Martin I. Chilvers

  7. Bayer Crop Science, St. Louis, MO, 63141, USA

    Mercedes M. Diaz-Arias

  8. School of Agricultural Sciences, Southern Illinois University, Carbondale, IL, 62901, USA

    Ahmad M. Fakhoury

  9. Lonoke Extension Center, University of Arkansas Division of Agriculture, Lonoke, AR, 72086, USA

    Travis R. Faske

  10. Department of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA

    Bryan C. Hansen, Samuel G. Markell, Hope Renfroe-Becton, Jessica M. Scherer & Richard W. Webster

  11. Department of Entomology and Plant Pathology, West Tennessee Research and Education Center, University of Tennessee, Jackson, TN, 38301, USA

    Heather M. Kelly

  12. Department of Plant Pathology, The Ohio State University, Columbus, OH, 43210, USA

    Horacio D. Lopez-Nicora

  13. Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA

    LeAnn Lux

  14. Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA

    Dean K. Malvick

  15. Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, 68583, NE, USA

    Dylan Mangel

  16. Macon Ridge Research Station, LSU AgCenter, Winnsboro, LA, 71295, USA

    Paul P. Price III

  17. Department of Entomology and Plant Pathology, Auburn University, Auburn, AL, 36849, USA

    Edward J. Sikora

  18. Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Damon L. Smith

  19. Bayer Crop Science, Storm Lake, IA, 50588, USA

    Adam Striegel

  20. Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, 47907, USA

    Darcy E. P. Telenko

Authors

  1. Jose F. González-Acuña
    View author publications

    Search author on:PubMed Google Scholar

  2. Tom W. Allen
    View author publications

    Search author on:PubMed Google Scholar

  3. Mandy D. Bish
    View author publications

    Search author on:PubMed Google Scholar

  4. Carl A. Bradley
    View author publications

    Search author on:PubMed Google Scholar

  5. Boris X. Camiletti
    View author publications

    Search author on:PubMed Google Scholar

  6. Martin I. Chilvers
    View author publications

    Search author on:PubMed Google Scholar

  7. Nabin K. Dangal
    View author publications

    Search author on:PubMed Google Scholar

  8. Mercedes M. Diaz-Arias
    View author publications

    Search author on:PubMed Google Scholar

  9. Ahmad M. Fakhoury
    View author publications

    Search author on:PubMed Google Scholar

  10. Travis R. Faske
    View author publications

    Search author on:PubMed Google Scholar

  11. Mark L. Gleason
    View author publications

    Search author on:PubMed Google Scholar

  12. Bryan C. Hansen
    View author publications

    Search author on:PubMed Google Scholar

  13. Heather M. Kelly
    View author publications

    Search author on:PubMed Google Scholar

  14. Horacio D. Lopez-Nicora
    View author publications

    Search author on:PubMed Google Scholar

  15. LeAnn Lux
    View author publications

    Search author on:PubMed Google Scholar

  16. Dean K. Malvick
    View author publications

    Search author on:PubMed Google Scholar

  17. Dylan Mangel
    View author publications

    Search author on:PubMed Google Scholar

  18. Samuel G. Markell
    View author publications

    Search author on:PubMed Google Scholar

  19. Daren S. Mueller
    View author publications

    Search author on:PubMed Google Scholar

  20. Paul P. Price III
    View author publications

    Search author on:PubMed Google Scholar

  21. Hope Renfroe-Becton
    View author publications

    Search author on:PubMed Google Scholar

  22. Jessica M. Scherer
    View author publications

    Search author on:PubMed Google Scholar

  23. Edward J. Sikora
    View author publications

    Search author on:PubMed Google Scholar

  24. Damon L. Smith
    View author publications

    Search author on:PubMed Google Scholar

  25. Adam Striegel
    View author publications

    Search author on:PubMed Google Scholar

  26. Darcy E. P. Telenko
    View author publications

    Search author on:PubMed Google Scholar

  27. Richard W. Webster
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.F.G. and R.W.W. ran the statistical analysis and co-wrote the manuscript draft. R.W.W. is the corresponding author. All authors contributed to the collection of data and reviewed and edited the iterative manuscript drafts.

Corresponding author

Correspondence to
Richard W. Webster.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (download XLSX )

Supplementary Material 2 (download XLSX )

Supplementary Material 3 (download XLSX )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

González-Acuña, J.F., Allen, T.W., Bish, M.D. et al. Modeling the environment-related risk of frogeye leaf spot (Cercospora sojina) in soybean across the United States.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-46975-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-46975-z

Keywords

  • Predictive modeling
  • Decision support systems

Source: Ecology - nature.com

Author Correction: Trade-offs in the externalities of pig production are not inevitable

Short-term effects of biochar amendment on root–microbe interactions in natural and peri-urban soils

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close