The U.S. Environmental Protection Agency (EPA) needs a comprehensive method to evaluate the human health and environmental effects of alternative agricultural pest management strategies. This project explored the utility of Adaptive Environmental Assessment (AEA) techniques for meeting this need. The project objectives were to produce models for environmental impact analysis, improve communications, identify research needs and data requirements, and demonstrate a process for resolving conflicts. The project was structured around the construction (in an initial 2 1/2-day workshop) and examination (in a second 2 1/2-day workshop) of a simulation model of a corn agroecosystem.
The model conceptualized at the first workshop simulates the effect of corn agrecosystem decisions on crop production, economic returns, and environmental indicators. The model is composed of five interacting submodels: 1) a Production Strategies submodel which makes decisions concerning tillage, planting, fertilizer and pesticide applications, and harvest; 2) a Hydrology/Chemical Transport submodel which represents soil hydrology, erosion, and concentrations of fertilizers and pesticides in the soil, runoff, surface waters, and percolation; 3) a Vegetation submodel which simulates growth of agricultural crops (corns and soybeans) and weeds; 4) a Pests submodel which calculates pest population levels and resulting crop damage; and 5) an Environmental Effects submodel which calculates indicators of potential fish kills, human health effects, and wildlife habitat. The most persistent data gaps encountered in quantifying the model were coefficients to relate environmental consequences to alternative pest management strategies.
While the model developed in the project is not yet accurate enough to be used for real-world decisions about the use of pesticides on corn, it does contain the basic structure upon which such a model could be built. More importantly at this stage of development, the project has shown that very complex systems can be modeled in short periods of time and that the process of building such models increases understanding among disciplinary specialists and between diverse institutional interests. This process can be useful to EPA as the agency cooperates with other institutions to meet its responsibilities in less costly ways.
Activities at the second 2 1/2-day workshop included a review of the model, incorporation of necessary corrections, simulation of policy scenarios, and examination of techniques to address remaining institutional conflicts. Participants were divided into three groups representing environmental, production or industry, and regulatory interests. Each group developed scenarios that would be most appealing to their particular interest and the scenarios were simulated by the agroecosystem computer model. Negotiators from each of the interest groups decided whether a hypothetical herbicide should be relabeled and if certain restrictions should be imposed on its use. Other participants functioned as experts and consultants on caucus teams. A solution to the hypothetical problem was successfully negotiated.
Workshop participants and project staff agreed that the model and processes developed during the project should be used in training students, extension specialists, farmers, researchers, and chemical producers in collaborative problem solving methods. More productive research can be planned, and more realistic models of complex systems can be built in this way. More importantly, greater trust of decisionmakers in computer models, better understanding by technical experts about disciplines other than their own, and improved cooperation between institutional interests can be achieved. This trust, understanding, and cooperation are critical ingredients in solving problems that are too complex to be resolved by independent disciplinary activity and unilateral decision authority.
|Title||Evaluating environmental and economic consequences of alternative pest management strategies: results of modeling workshops|
|Authors||Richard L. Johnson, Austin K. Andrews, Gregor T.L. Auble, Richard A. Ellison, David B. Hamilton, James E. Roelle, Peter J. McNamee|
|Publication Subtype||Other Report|
|Record Source||USGS Publications Warehouse|