SAMPLING SCHEMES FOR POLICY ANALYSES USING COMPUTER-SIMULATION EXPERIMENTS

Evaluating the environmental and economic impacts oi agricultural poli cies is not a simple task. A systematic approach to evaluation would i nclude the effect of policy-dependent factors (such as tillage practic es, crop rotations, and chemical use) as well as the effect of policy- independent covariates (such as weather, topography, and soil attribut es) on response variables (such as amount of soil eroded or chemical l eached into the groundwater). For comparison purposes, the effects of these input combinations on the response variable would have to be ass essed under competing policy scenarios. Because the number of input co mbinations is high in most problems, and because policies to be evalua ted are often not in use at the time of the study, practitioners have resorted to simulation experiments to generate data. However, generati ng data from simulation models is often costly and time consuming; thu s, the number of input combinations in a study may be limiting even in simulation experiments. In this paper, we discuss the problem of desi gning computer simulation experiments that require generating data for just a fraction of the possible input combinations. We propose an app roach that is based on subsampling the 1992 National Resources Invento ry (NRI) points. We illustrate the procedure by assessing soil erosion in a situation where there are ''observed'' data [reported by the Nat ural Resources Conservation Service (NRCS)] for comparison. Estimates for soil erosion obtained using the procedure we propose are in good a greement with NRCS reported values.