Unraveling the Dynamics of Tar Spot Epidemics in Corn Fields: A Data-Driven Framework for Disease Development and Surveillance.

Abstract

Tar spot, caused by Phyllachora maydis Maubl, has significantly impacted U.S. corn production since its first detection in 2015. The elusive nature of this fungus has hindered advancement in disease characterization, especially pertaining to its spatiotemporal development. This study provides an in-depth analysis of field-scale epidemics by considering both temporal and vertical dynamics to ascertain the spatiotemporal intensification of this disease. The analysis involved generating high-resolution severity data across four growing seasons (2021 to 2024) in two production-style corn fields in northwest Indiana. We then applied population growth models and Markov chains to parameterize the temporal and vertical dynamics of tar spot progression within the corn canopy. From this, we found three distinct epidemiological phases: an establishment phase characterized by sporadic onset with <0.5% severity, a lag phase with widespread low severity (0.5 to 1%), and an exponential phase with rapid severity increases exceeding 40% in some cases. Beyond the conventional "bottom-up" paradigm, we observed diverse infection-like patterns influenced by canopy position, onset timing, and corn growth stage. Exponential growth models described severity intensification with an average apparent infection rate of 0.20/day, which was applicable across canopy positions, locations, and years. An aggregated Markov chain model, built from the 2021-2023 data, accurately estimated the 2024 epidemic's vertical-temporal progression and thus validated the framework developed from this study. Ultimately, this approach supports improved surveillance for targeted management by establishing a foundation for real-time detection and probabilistic modeling to aid in the enhancement of crop production.

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