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Title: Predicting Stored Grain Insect Population Densities Using an Electronic Probe Trap

Author
item Flinn, Paul
item Opit, George
item Throne, James

Submitted to: Journal of Economic Entomology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2009
Publication Date: 7/15/2009
Citation: Flinn, P.W., Opit, G.P., Throne, J.E. 2009. Predicting Stored Grain Insect Population Densities Using an Electronic Probe Trap. Journal of Economic Entomology 102: 1696-1704.

Interpretive Summary: Manual sampling of insects in stored grain is a laborious and time consuming process. Automation of grain sampling should help to increase the adoption of stored-grain integrated pest management. We field-tested a new commercial electronic grain probe trap (Insector™) in two bins, each containing 32.6 tonnes of wheat, over a two-year period. We developed statistical models to convert trap catch into insects per kilogram of wheat. An expert system, Stored Grain Advisor Pro, was modified to automatically obtain data from Insector™ and to estimate the numbers of insects in the grain for three different species. Management decisions using Insector™ trap-catch estimates for rusty grain beetle, lesser grain borer and red flour beetle numbers were similar to those made using grain sample estimates for most sampling dates. However, because of the similarity in size of the lesser grain borer and red flour beetle, the software was unable to tell the difference between these two species. In the central and southern portions of the US, where both species frequently occur, it may be necessary to determine the proportion of each species present in the grain by manual inspection of trap catch. The combination of SGA Pro with the OPI Insector™ system should prove to be a useful tool for automatic monitoring of insect pests in stored grain.

Technical Abstract: Manual sampling of insects in stored grain is a laborious and time consuming process. Automation of grain sampling should help to increase the adoption of stored-grain integrated pest management. A new commercial electronic grain probe trap (OPI Insector™) has recently been marketed. We field tested OPI Insector™ electronic grain probes in two bins, each containing 32.6 tonnes of wheat, over a two-year period. We developed new statistical models to convert Insector™ catch into insects per kg. We compared grain sample estimates of insect density (insects/kg wheat) taken near each Insector™ to the model-predicted insect density using Insector™ counts. An existing expert system, Stored Grain Advisor Pro, was modified to automatically read the Insector database and use the appropriate model to estimate Cryptolestes ferrugineus (Stephens), Rhyzopertha dominica (Fabricius), and Tribolium castaneum (Herbst) density from trap catch counts. Management decisions using Insector™ trap-catch estimates for insect density were similar to those made using grain sample estimates of insect density for most sampling dates. However, because of the similarity in size of R. dominica and T. castaneum, the software was unable to differentiate counts between these two species. In the central and southern portions of the US, where both species frequently occur, it may be necessary to determine the proportion of each species present in the grain by manual inspection of trap catch. The combination of SGA Pro with the OPI Insector™ system should prove to be a useful tool for automatic monitoring of insect pests in stored grain.