Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean

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 gain insights into 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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Year dc:date.available

2027

Author dc:creator

• Weerarathna, Chinthaka

Contributors dc:contributor

• Wang, Jin
• Le, Thien; Ma, Ziwei; Wang, Xiunan
• College of Arts and Sciences

• Cercospora
• Plant diseases--Epidemiology--Mathematical models
• Soybean--Diseases and pests--Control

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/1037

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-2218