Understanding and Managing Frogeye Leaf Spot through Network-Based Modeling in Soybean

TL;DR

This paper introduces a network-based model for Frogeye Leaf Spot in soybeans, incorporating real-field structure to improve disease management strategies and provide science-based guidance.

Contribution

It develops a novel network-based epidemiological model using Bayesian methods, addressing limitations of traditional models and informing targeted disease control.

Findings

Infection origin influences transmission routes.

Tillage practices do not significantly affect fungal spread.

Early targeted roguing is more effective than delayed or random removal.

Abstract

Frogeye Leaf Spot (FLS), caused by Cercospora sojina, poses a significant threat to soybean production, with yield losses of 30-60%. Traditional mass-action models assume homogeneous mixing, which rarely holds in real fields and limits their ability to inform FLS management. To address this, we developed a network-based model that incorporates real-field structure to improve FLS management in soybeans. Using approximate Bayesian computation, we estimated key epidemiological parameters and found that infection origin can shift the balance between transmission routes. Data analyses indicated that tillage and non-tillage plots did not differ significantly in fungal spread, decay, or disease severity. Finally, we show that early, targeted roguing is more effective than delayed or random removal. Together, these findings offer science-based guidance for FLS management and highlight the value of network-based models to inform agricultural disease control.