Toward near-real-time forecasts of airborne crop pests: Aphid invasions of cereal grains in North America

Authors: KORALEWSKI, TOMASZ - Texas A&M University; WANG, HSIAO-HSUAN - University Of Kentucky; GRANT, WILLIAM - Texas A&M University; LAFOREST, JOSEPH - University Of Georgia; BREWER, MICHAEL - Texas Agrilife Research; Elliott, Norman - Norm; WESTBROOK, JOHN - Retired ARS Employee

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/27/2020
Publication Date: 12/1/2020
Citation: Koralewski, T.E., Wang, H., Grant, W.E., LaForest, J.H., Brewer, M.J., Elliott, N.C., Westbrook, J.K. 2020. Toward near-real-time forecasts of airborne crop pests: Aphid invasions of cereal grains in North America. Computers and Electronics in Agriculture. 179:105861. https://doi.org/10.1016/j.compag.2020.105861.
DOI: https://doi.org/10.1016/j.compag.2020.105861

Interpretive Summary: Airborne invasions of crop pests pose a significant challenge in agriculture. Novel infestations, such as the recently observed invasion of sugarcane aphid on sorghum in the Great Plains of the U.S., occur rapidly and require fast management actions to mitigate economic losses to the sorghum crop. Area-wide integrated pest management has been a recognized strategic response to such problems. Management tactics may benefit from analysis of pest infestation data and from predictive simulation modeling. Within such a framework, a predictive simulation model could provide short- and long-term information on pest status and geographic occurrence. We describe a prototype of a mathematical modeling framework that could be used to forecast sugarcane aphid invasions of sorghum in near-real-time. Within the framework, a modeling platform that simulates spread of aphid infestations of sorghum geographically and is linked to a web-based mapping system for documenting the invasive sugarcane aphid distribution and spread over time, which can provide near real time support to pest managers. The interconnectivity of the modeling process and sorghum field sampling through the mapping system (EDDMapS) allows for regular model updates, which can support short-term infestation forecasts to pest managers. Practical application of the system could support informed decisions on field sampling and the need for pesticide application throughout the geographic region impacted by the sugarcane aphid and could facilitate informed decisions on deployment of sugarcane aphid resistant sorghum varieties. This framework could be expanded to include other invasive pests.

Technical Abstract: Airborne invasions of crop pests pose a significant challenge in agriculture. Novel infestations, such as the recently observed invasion of sugarcane aphid [Melanaphis sacchari (Zehntner) (Hemiptera:Aphididae)] on sorghum [Sorghum bicolor (L.) Moench] in the Great Plains of the U.S., emerge rapidly and require fast management actions to mitigate economic losses. Areawide integrated pest management has been a recognized strategic response to such problems. Management tactics may benefit from analysis of pest infestation data and from predictive simulation modeling. Within such a framework, a predictive simulation model could provide short- and long-term decision support. We describe a prototype of a computational framework that could be used to forecast sugarcane aphid invasions of sorghum in near-real-time, as supported by timely field reports on current aphid infestation status. Within the framework, a modeling platform that simulates spread of aphid infestations of sorghum is linked to EDDMapS, a web-based mapping system for documenting invasive species and agronomic pest distributions. The interconnectivity of the modeling process and sorghum field sampling through EDDMapS allows for regular updates of the internal states of the model, which support short-term infestation forecasts based on field data feeds. Practical application of the system could support informed short-term decisions on pesticide application and field sampling, as well as longer-term decisions regarding crop variety deployment.