ABSTRACT:
Estimating variables, such as erosion rates, across a diverse soil and water resource base is a problem of interest in natural resource management. Here, we propose an alternative to the predominant soils (PS) approach of Stoneman, Brown, and Spivey. Our method called Gaussian quadrature (GQ), is adapted from the numerical integration literature. Two experiments compare GQ with PS. The first plugs sample input into a simulation model to approximate erosion rates, surface runoff, and crop yield for a region. The second estimates erosion in three regions using the Universal Soil Loss Equation (USLE). Results for the GQ samples are compared to results for the full population and results from random samples. GQ sampling tends to be more effective, particularly with respect to measures related to the heterogeneity of the population, such as the variance and skewness, than PS or random sampling. Judiciously used GQ sample selection permits reductions in the number of soils sampled with only a moderate loss of accuracy.
Footnotes
Channing Arndt is assistant professor in the Department of Agricultural Economics at Purdue University, West Lufayette, Indiana. Barbara Fecso is a program analyst in the Conservation Operations Division of the Natural Resources Conservation Service (NRCS) at the U.S. Department of Agriculture (USDA), Washington, D.C. Paul V. Preckel is a professor in the Department of Agricultural Economics at Pardue University in West Lafayette, Indiana. Bruce Stoneman is assistant Virginia State soil scientist for the Natural Resources Conservation Service (NRCS) at the U.S. Department of Agriculture (USDA) in Richmond: Virginia.
- Copyright 2001 by the Soil and Water Conservation Society
This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.