Addressing Biological Invasions in Agriculture with Big Data in an Informatics Age

Rebecca A. Clement (), Hyoseok Lee, Nicholas C. Manoukis, Yelena M. Pacheco, Fallon Ross, Mark S. Sisterson and Christopher L. Owen
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Rebecca A. Clement: Pest Identification Technology Laboratory, United States Department of Agriculture, Animal and Plant Health Inspection Service, Plant Protection and Quarantine, Science & Technology, Fort Collins, CO 80526, USA
Hyoseok Lee: Oak Ridge Institute for Science and Education, Oak Ridge Associated Universities, Oak Ridge, TN 37830, USA
Nicholas C. Manoukis: Daniel K. Inouye US Pacific Basin Agricultural Research Center, United States Department of Agriculture, Agricultural Research Service, Hilo, HI 96720, USA
Yelena M. Pacheco: Systematic Entomology Laboratory, United States Department of Agriculture, Agricultural Research Service, Washington, DC 20013, USA
Fallon Ross: United States Department of Agriculture, Natural Resources Conservation Service, Red Cloud, NE 68970, USA
Mark S. Sisterson: San Joaquin Valley Agricultural Sciences Center, United States Department of Agriculture, Agricultural Research Service, Parlier, CA 93648, USA
Christopher L. Owen: Systematic Entomology Laboratory, United States Department of Agriculture, Agricultural Research Service, Washington, DC 20013, USA

Agriculture, 2025, vol. 15, issue 11, 1-34

Abstract: Big data approaches are rapidly expanding across many fields of science and are seeing increasing application, yet the use of big data in research related to invasive species lags. Big data can play a key role in predicting, detecting, preventing, controlling, and eradicating biological invasions. Here, we assess terms in the literature related to big data, biological invasions, and agriculture and review sources of big data, including museum records, crowdsourcing observations, natural history collections, and DNA-based information. These sources can be combined with environmental data to build models, predict the origins of invasive species, and develop control methods. To harness the power of data for agricultural biological invasions, several action areas are recommended to streamline processes and improve data sources.

Keywords: invasive species; agricultural pests; machine learning; occurrence data (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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