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James F Campbell (Jim)

Director

Dr. James Campbell
Center Director

USDA-ARS-CGAHR-SPIERU
ATTN: James Campbell
1515 College Avenue
Manhattan, KS  66502

james.campbell@usda.gov
james.f.campbell@usda.gov
Telephone: 785.776.2717
www.ars.usda.gov/pa/cgahr/spieru/campbell

    /ARSUserFiles/11034/Campbell photo.jpg

EDUCATION
B.S., Environmental Science, Rutgers University
M.S., Entomology, Rutgers University
Ph.D., Entomology, University of California Davis

RESEARCH INTERESTS
Jim Campbell is a Research Entomologist with the USDA ARS Center for Grain and Animal Health Research in Manhattan, KS. His research interests include the behavior of stored product insects and their natural enemies and how the use of behavioral information can improve the management of insect pests.

More Information     ORCID     Research Summaries

ALL PUBLICATIONS
via ARIS System     via Google Scholar

RECENT PUBLICATIONS<
Density-mediated foraging behavioral responses of Rhyzopertha dominica (Coleoptera: Bostrichidae) and Tribolium castaneum (Coleoptera: Tenebrionidae) - (Peer Reviewed Journal)
Ponce Jr, M.A., Ranabhat, S., Bruce, A., Van Winkle, T., Campbell, J.F., Morrison III, W.R. 2024. Density-mediated foraging behavioral responses of Rhyzopertha dominica (Coleoptera: Bostrichidae) and Tribolium castaneum (Coleoptera: Tenebrionidae). Scientific Reports. 14:12259. https://doi.org/10.1038/s41598-024-62277-8.
Numerical simulation of pyrethrin aerosol deposition - (Peer Reviewed Journal)
Aerosolize insecticide spray distributions and relationships to storage insect efficacies - (Peer Reviewed Journal)
Brabec, D.L., Lanka, S., Campbell, J.F., Arthur, F., Scheff, D.S., Zhu, K. 2023. Aerosolize insecticide spray distributions and relationships to storage insect efficacies. Insects. 14(12. Article 914. https://doi.org/10.3390/insects14120914.
Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms - (Peer Reviewed Journal)
Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms. Journal of Stored Products Research. 104. Article 102196. https://doi.org/10.1016/j.jspr.2023.102196.
The behavioral response to the putative necromones from dead Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae) in traps by conspecifics as a function of density and time since capture - (Peer Reviewed Journal)
Harman, R.H., Morrison III, W.R., Bruce, A., Ranabhat, S., Quellhorst, H.E., Wilkins, R.V., Campbell, J.F., Gerken, A.R. 2023. The behavioral response to the putative necromones from dead Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae) in traps by conspecifics as a function of density and time since capture. Environmental Entomology. 52(6):1020-1032. https://doi.org/10.1093/ee/nvad098.
Identifying common stored product insects using automated deep learning methods - (Peer Reviewed Journal)
Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Identifying common stored product insects using automated deep learning methods. Journal of Stored Products Research. 103. Article 102166. https://doi.org/10.1016/j.jspr.2023.102166.
Behavioral response of unmated female Plodia interpunctella Hübner (Lepidoptera: Pyralidae) to synthetic sex pheromone lure - (Peer Reviewed Journal)
Gerken, A.R., Dryer, D.L., Abts, S.R., Campbell, J.F. 2022. Behavioral response of unmated female Plodia interpunctella Hübner (Lepidoptera: Pyralidae) to synthetic sex pheromone lure. Environmental Entomology. 51(6):1200-1209. https://doi.org/10.1093/ee/nvac087.
Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning - (Peer Reviewed Journal)
Bisgin, H., Bera, T., Wu, L., Ding, H., Bisgin, N., Liu, Z., Pava-Ripoll, M., Barnes, A., Campbell, J.F., Vyas, H., Furlanello, C., Tong, W., Xu, J. 2022. Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2022.952424.
Fumigation monitoring and modeling of hopper-bottom railcars loaded with corn grits - (Peer Reviewed Journal)
Brabec, D.L., Kaloudis, E., Athanassiou, C., Campbell, J.F., Agrafioti, P., Scheff, D.S., Bantas, S., Sotiroudas, V. 2022. Fumigation monitoring and modeling of hopper-bottom railcars loaded with corn grits. Journal of Biosystems Engineering. 47:358-369. https://doi.org/10.1007/s42853-022-00148-8.
The genome of Rhyzopertha dominica (Fab.) (Coleoptera: Bostrichidae): Adaptation for success - (Peer Reviewed Journal)
Oppert, B.S., Muszewska, A., Steczkiewicz, K., Šatovic-Vukšic, E., Plohl, M., Fabrick, J.A., Vinokurov, K.S., Koloniuk, I., Johnston, J., Smith, T.P., Guedes, R.C., Terra, W.R., Ferreira, C., Dias, R.O., Chaply, K.A., Elpidina, E.N., Tereshchenkova, V., Mitchell, M.F., Jenson, A.J., Mckay, R., Shan, T., Cao, X., Xiong, C., Jiang, H., Morrison III, W.R., Koren, S., Schlipalius, D., Lorenzen, M.D., Bansal, R., Wang, Y., Perkin, L.C., Poelcheau, M., Friesen, K.S., Olmstead, M.L., Scully, E.D., Campbell, J.F., et al. 2022. The genome of Rhyzopertha dominica (Fab.) (Coleoptera: Bostrichidae): Adaptation for success. Genes. 13(3). Article 446. https://doi.org/10.3390/genes13030446.