Pesticide regulatory actions and the development of pest resistance: a dynamic bioeconomic model

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1991-07-03
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Virginia Tech
Abstract

Pest resistance to pesticides can have severe impacts on both commercial agriculture and the environment. But many resistance problems are exacerbated because pest susceptibility is a dynamic, common-property resource subject to inefficient allocation by the market. Theoretically, the impact of resistance can be mitigated through regulatory management of the control technology set. However, the current pesticide regulatory process does not include resistance considerations in its quantitative analyses due to the computational difficulties encountered when trying to optimize complex bioeconomic models. As a result, regulatory efforts may actually promote increased susceptibility depletion and the rapid emergence of resistance. This study overcame these problems by forming a dynamic bioeconomic model that combined: 1) a widely accepted genetic simulator used by entomologists; 2} an aggregate economic surplus model with nationwide regulatory relevance; and 3) an improved simulation optimization algorithm that conserved computational resources. For the purpose of illustration, the bioeconomic model was parameterized to represent the U.S. apple production system.

Information generated through optimization of the dynamic bioeconomic model suggested that resistance becomes quantitatively important when planning horizons exceed 10 years, confirming that the economic performance of the production system becomes severely sub-optimal when susceptibility depletion is not incorporated into decision-making. Furthermore, insecticide withdrawals from an initial control technology set led to large additional losses in economic surplus, although the exact magnitude of these impacts varied depending on the characteristics of the insecticide withdrawn. Substantial withdrawal-induced losses in of the planning horizon, and they were accompanied by temporal shifts in insecticide applications. The need to incorporate a dynamic, bioeconomic simulation analysis in the regulatory process was demonstrated by comparing statically optimal and extant insecticide use recommendations with the dynamically̅optimal solutions. Optimal solutions drastically reduced economic surplus losses, although they did lead to increased levels of insecticide use. Ultimately, management of the resistance/regulation nexus requires that both current economic data and the time–dynamics of system biology play a prominent role in the benefits assessment process. This can only be accomplished if an investment is made in the necessary basic research and model development.

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