Evidence map›Paper›PMID 41957394›Full record

ArticleScientific reports2026

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

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 and 17 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Jose F González-AcuñaDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, 50011, USA.
Tom W AllenDelta Research and Extension Center, Mississippi State University, Stoneville, MS, 38776, USA.
Mandy D BishDivision of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA.
Carl A BradleyDepartment of Plant Pathology, University of Kentucky, Princeton, KY, 42445, USA.
Boris X CamilettiDepartment of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Martin I ChilversDepartment of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA.
Nabin K DangalDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, 50011, USA.
Mercedes M Diaz-AriasBayer Crop Science, St. Louis, MO, 63141, USA.
Ahmad M FakhourySchool of Agricultural Sciences, Southern Illinois University, Carbondale, IL, 62901, USA.
Travis R FaskeLonoke Extension Center, University of Arkansas Division of Agriculture, Lonoke, AR, 72086, USA.
Mark L GleasonDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, 50011, USA.
Bryan C HansenDepartment of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA.
Heather M KellyDepartment of Entomology and Plant Pathology, West Tennessee Research and Education Center, University of Tennessee, Jackson, TN, 38301, USA.
Horacio D Lopez-NicoraDepartment of Plant Pathology, The Ohio State University, Columbus, OH, 43210, USA.
LeAnn LuxDepartment of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA.
Dean K MalvickDepartment of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA.
Dylan MangelDepartment of Plant Pathology, University of Nebraska-Lincoln, Lincoln, 68583, NE, USA.
Samuel G MarkellDepartment of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA.
Daren S MuellerDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, 50011, USA.
Paul P PriceMacon Ridge Research Station, LSU AgCenter, Winnsboro, LA, 71295, USA.
Hope Renfroe-BectonDepartment of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA.
Jessica M SchererDepartment of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA.
Edward J SikoraDepartment of Entomology and Plant Pathology, Auburn University, Auburn, AL, 36849, USA.
Damon L SmithDepartment of Plant Pathology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Adam StriegelBayer Crop Science, Storm Lake, IA, 50588, USA.
Darcy E P TelenkoDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN, 47907, USA.
Richard W WebsterDepartment of Plant Pathology, Microbiology, and Biotechnology, North Dakota State University, Fargo, ND, 58108, USA. richard.webster@ndsu.edu.

Funding

USDA IOW04108
6 · The paper itself

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.

Indexed as

CercosporaGlycine maxPlant DiseasesEnvironmentFungicides, IndustrialHumidityPlant LeavesRandom ForestTemperatureUnited StatesFungicides, IndustrialDecision support systemsPredictive modeling

Identifiers

PMID41957394
PMCPMC13201582

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.