Optimal Monitoring and Controlling of Invasive Species: The Case of Spotted Wing Drosophila in the United States Report as inadecuate

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Spotted wing drosophila (SWD) is an invasive pest having a devastating effect on soft-skinned fruits such as blueberries, raspberries, blackberries, strawberries, and cherries. Due to zero tolerance of SWD infested fruit in both fresh and frozen markets, current SWD management strategies consist mainly of preventive broad-spectrum insecticide sprays. Extension services across the United States are calling for management strategies that incorporate monitoring to reduce unnecessary insecticide sprays. Nonetheless, little is known about the economic benefits of these management strategies over the broad-spectrum insecticide sprays. In this paper, we develop a dynamic bioeconomic model to identify the cost-minimizing mix of SWD management strategies. We employ Bayesian methods in a dynamic simulation setting to evaluate the economic outcomes of alternative strategies involving insecticide sprays and monitoring combinations. We apply this model to a blueberry farmer making decisions to control SWD infestation. We find that the economic impacts of different SWD control strategies depend on the efficacy of the insecticide applied, the efficiency of monitoring traps, and also the action threshold selected. Overall, as the efficiency of monitoring traps improves, the management strategies which include monitoring are superior to the spray-only strategy. Also, growers can choose more liberal action thresholds when using monitoring traps with higher efficiency. In addition, monitor-to-initiate spray strategies perform better than the monitor-to-guide spray strategies in general.

Subject(s): Environmental Economics and Policy

Research Methods/ Statistical Methods

Resource /Energy Economics and Policy

Issue Date: 2016

Publication Type: Conference Paper/ Presentation

PURL Identifier: http://purl.umn.edu/236042

Series Statement: Paper


Record appears in: Agricultural and Applied Economics Association (AAEA) > 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts

Author: Fan, Xiaoli ; Gómez, Miguel ; Atallah, Shadi

Source: http://ageconsearch.umn.edu/record/236042?ln=en

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