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Sampling Schemes for Policy Analyses Using Computer Simulation Experiments

Carriquiry1, Breidt, Lakshminarayan

  • 1Department of Statistics and CARD Iowa State University Ames, Iowa 50011, USA

Environmental Management
|May 16, 1998
PubMed
Summary
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This study proposes a cost-effective simulation experiment design for evaluating agricultural policies. By subsampling National Resources Inventory (NRI) data, it accurately assesses soil erosion impacts.

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Environmental Economics

Background:

  • Evaluating agricultural policies requires assessing policy-dependent and independent factors on environmental outcomes.
  • Simulation experiments are often used but can be costly and time-consuming due to numerous input combinations.
  • Existing methods face limitations in generating sufficient data for comprehensive policy impact assessments.

Purpose of the Study:

  • To develop an efficient method for designing computer simulation experiments in agricultural policy analysis.
  • To reduce the computational cost and time associated with generating data for a fraction of possible input combinations.
  • To provide a framework for evaluating environmental impacts, such as soil erosion and chemical leaching, under various policy scenarios.

Main Methods:

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  • The study proposes a simulation experiment design based on subsampling points from the 1992 National Resources Inventory (NRI).
  • This approach involves selecting a representative fraction of the available data points for simulation.
  • The methodology is illustrated by assessing soil erosion, comparing simulation results with observed data.

Main Results:

  • The proposed subsampling method significantly reduces the number of required data points for simulation experiments.
  • Estimates for soil erosion generated using the proposed procedure show good agreement with observed data from the Natural Resources Conservation Service (NRCS).
  • The approach demonstrates the feasibility of obtaining reliable impact assessments with a fraction of the data.

Conclusions:

  • The developed method offers a more efficient and cost-effective approach to designing simulation experiments for agricultural policy evaluation.
  • Subsampling the National Resources Inventory (NRI) provides a viable strategy for generating representative data.
  • This research contributes to better environmental and economic impact assessments of agricultural policies, particularly concerning nonpoint source pollution.