A Data-Driven Framework for Tar Spot Monitoring, Modeling, and Management in Corn

Tar spot, caused by Phyllachora maydis, is a destructive foliar disease that has threatened U.S. corn production since 2015. Despite research advances, early detection and mechanistic understanding of tar spot epidemics remain limited, posing challenges for effective management. This presentation synthesizes insights from seven years of research addressing key gaps in tar spot epidemiology and severity prediction. Central is a multi-year study (2021–2024) conducted across seven Indiana site-years, employing high-resolution severity assessments, deterministic growth models, and Markov chains to evaluate epidemic dynamics across canopy positions. Three distinct phases were identified: establishment (< 0.5% severity), lag (0.5–1%), and exponential ( >50%), with growth rates averaging 0.21/day. Contrary to conventional bottom-up infection and spread assumptions, onset timing and progression varied by canopy position and corn growth stage, underscoring the limitations of generalized management recommendations. Together, these findings support more targeted, sustainable strategies and lay the groundwork for real-time detection, improved surveillance, and optimized fungicide timing.