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Simulation Models Evaluation of Pest Resistance Development to Refuge in the Bag
Concepts Related to Pioneer Submission version 1.0
(30 pp, 211 KB) (EPA/600/R-10/055) January 2010

The USEPA, under its administration of the Federal Insecticide Fungicide and Rodenticide Act
(FIFRA), requires the registration of all pesticides and pesticidal materials. The GM crops containing pesticidal traits are subject to FIFRA registration requirements. Concerns relating to future environmental effects of these crops can be investigated only using simulation models. Model application in this instance offers an analysis of the useful lifetime for the pesticidal traits. USEPA has clearly supported the use of these traits as replacements of broad spectrum pesticides that have significant environmental footprints.

Resistance management (RM) simulation models are designed as deterministic, stochastic, and
spatially explicit analytical methodologies. Simulation models can provide a realistic assessment of the risk of resistance evolution given the allele frequencies used as initial conditions. New concepts of PIP crop deployment such as refuge-in-the-bag require a detailed evaluation to ensure the claims associated with each concept offer the sustainable protection of the crop. Simulation models offer a means to evaluate the threat of crop loss through the development of resistance in the near future.

This collaborative research effort was designed to evaluate the relative merit of refuge-in-bag
concepts as submitted by Pioneer Hi-Bred International, Inc. This research specifically analyzed the relative risks related to the control of western corn rootworm in block refuge deployment and 5% seedmixture scenarios.

Research Design
To understand the relative risks related to the evolution of resistance in western corn rootworm between the currently mandated block refuge deployment and a 5% seed-mixture deployment as submitted Pioneer Hi-Bred International, Inc., two different models were developed and evaluated. The two models consisted of a modification of a spatially-explicit stochastic model (Caprio et al. 2006) and a simpler, frequency-based deterministic model. The latter could be run in a single simulation mode with a graphical interface to enter parameters or in a risk-assessment mode capable of running thousands of simulations to estimate the effects of parameter uncertainty. Realistic estimates for several parameters using in the deterministic model were developed through use of the stochastic model.

Impacts and Outcomes
This research was designed to provide substantive information about the operation and
capabilities of different resistance management models to assist the regulatory expert in its proper use and interpretation of results.


John Glaser

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