Title page for ETD etd-07132007-143149
|Type of Document
||Kazmierczak, Richard Francis
||Pesticide regulatory actions and the development of pest resistance : a dynamic bioeconomic model
|Norton, George W.
|Johnson, Thomas G.
|Pease, James W.
|Rajotte, Edwin G.
|Taylor, Daniel B.
- Pesticide resistance Research
- Pesticides Research
|Date of Defense
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on the characteristics of the insecticide withdrawn.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 dynamically·optimal
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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