Estimating probability of visual detection of exotic pests and diseases in the grains industry—An expert elicitation approach

Abstract

Participants in the grains industry undertake general surveillance monitoring of grain crops for early detection of pests and diseases. Evaluating the adequacy of monitoring to ensure successful early detection relies on understanding the probability of detection of the relevant exotic crop pests and diseases. Empirical data on probability of detection is often not available. Our aim was to both gain a better understanding of how agronomists undertake visual crop surveillance, and use this insight to help inform structured expert judgments about the probability of early detection of various exotic grain pests and diseases. In our study we surveyed agronomists under a state funded program to identify survey methods used to undertake visual inspection of grain crops, and their confidence in detecting pests and diseases using the associated methods. We then elicited expert judgments on the probabilities of visual detection by agronomists of key exotic pests and diseases, and compared these estimates with the self-assessments of confidence made by agronomists. Results showed that agronomists used a systematic approach to visual crop inspection but that they were not confident in detecting exotic pests and diseases, with the exception of pest and diseases that affect leaves. They were most confident in visually detecting Barley stripe rust and Russian wheat aphid; however, confidence in detecting the latter was influenced by recent training. Expert judgments on the ability of agronomists to visually detect exotic pests and diseases early was in accordance with agronomists’ self-rated confidence of detection but highlighted uncertainty around the ability of agronomists in detecting non-leaf pests and diseases. The outcomes of the study demonstrated the utility of structured expert elicitation as a cost-effective tool for reducing knowledge gaps around the sensitivity of general surveillance for early detection, which in turn improves area freedom estimates.