{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2218"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2218","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean","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 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Weerarathna, Chinthaka"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wang, Jin","Le, Thien; Ma, Ziwei; Wang, Xiunan","College of Arts and Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2027,"date_issued":"2027-01-01T08:00:00Z","date_published":"2027-01-01T08:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["Cercospora","Plant diseases--Epidemiology--Mathematical models","Soybean--Diseases and pests--Control"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1037","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Jin","Le, Thien; Ma, Ziwei; Wang, Xiunan","College of Arts and Sciences"]},{"key":"dc:creator","label":"Author","values":["Weerarathna, Chinthaka"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12-01T08:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2027-01-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cercospora","Plant diseases--Epidemiology--Mathematical models","Soybean--Diseases and pests--Control"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/1037"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Mathematics","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean"]}]}],"canonical_facts":{"dc:contributor":["Wang, Jin","Le, Thien; Ma, Ziwei; Wang, Xiunan","College of Arts and Sciences"],"dc:creator":["Weerarathna, Chinthaka"],"dc:date":["2025-12-01T08:00:00Z"],"dc:date.available":["2027-01-01T08:00:00Z"],"dc:description":["Dept. of Mathematics","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.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 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."],"dc:identifier":["https://scholar.utc.edu/theses/1037"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Cercospora","Plant diseases--Epidemiology--Mathematical models","Soybean--Diseases and pests--Control"],"dc:title":["Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:28Z"}