Charcoal rot, caused by the soilborne fungus Macrophomina phaseolina (Mp), poses a serious threat to soybean health and harvests on a global scale. The disease thrives in dry, hot conditions and can quietly devastate crops before symptoms even appear. Now, researchers have developed high-resolution risk maps that predict where charcoal rot is most likely to occur—based on the soil beneath the surface.
Published in Phytopathology, the study was led by Horacio Lopez-Nicora, Assistant Professor of Soybean Pathology and Nematology at The Ohio State University, and Sandip Mondal, postdoctoral researcher in the Lopez-Nicora Lab.
The research team collected soil samples from 297 soybean fields across seven departments in Paraguay, a key soybean-producing region in South America. Their analysis revealed that Mp abundance is highest in the southeastern region of the country, where soils tend to be more acidic and richer in clay.
"By using geostatistical mapping and predictive modeling, we're giving farmers the ability to forecast disease risk before it becomes a problem," said Lopez-Nicora.
What makes this approach so powerful is its proactive nature. Rather than wait to observe symptoms after damage has occurred, the maps use measurable soil characteristics, such as pH, clay content, and cation exchange capacity, to assess risk across the landscape. These indicators were found to be strong predictors of Mp colony-forming units, with lower pH showing a significant negative correlation with fungal abundance.
The team used geostatistical and spatial regression models to uncover not only which fields were most vulnerable but also how Mp clusters in specific areas instead of being evenly distributed. Moran's I statistic, which measures spatial correlation, confirmed that the disease shows clear geographic patterns—something traditional scouting methods often miss.
"Charcoal rot doesn't spread evenly across a field. It pops up in the right conditions, and those conditions are often hiding in the soil," said Mondal. "This tool helps uncover those high-risk zones."
The ability to identify these hotspots has direct implications for precision agriculture. The insights from this study get growers closer to targeted approaches to managing charcoal rot—adjusting planting dates, rotating crops, and applying soil amendments only where needed. Doing so reduces unnecessary input costs and helps protect yields under increasingly variable climate conditions.
Emile Gluck-Thaler, Assistant Professor at the University of Wisconsin–Madison and co-author of the study, said, "The implications go beyond individual growers. For policymakers, the study offers a scalable strategy to support food security by integrating soil data and disease forecasting into national crop protection programs."
"What excites us the most about this research is its potential to revolutionize how we approach disease management in agriculture," said Lopez-Nicora. "This isn't just about soybeans or one disease—it's a demonstration of how spatial data and soil science can transform farming into a more predictive, efficient, and sustainable system."
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