An Empirical Best Linear Unbiased Prediction Approach to Small-Area Estimation of Crop Parameters

• Activity Number: 174
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Michael E. Bellow*+ and Partha Lahiri
• Companies: U.S. Department of Agriculture and University of Maryland
• Keywords: small-area estimation ; components of variance ; predictor variables

Abstract

Accurate county (small-area) level estimation of crop and livestock items is an important priority for the USDA's National Agricultural Statistics Service (NASS). We consider an empirical best linear unbiased prediction (EBLUP) method for combining multiple data sources to estimate crop harvested area (and potentially other crop parameters) at the county level. This method assumes a linear mixed model that relates survey reported harvested area to both unit (farm) and area (county) level covariates, with variance components estimated using a technique which ensures strictly positive consistent estimation of the model variance. A parametric bootstrap method that incorporates all sources of uncertainty can be used to estimate variability parameters. Results of a study comparing the proposed EBLUP method with standard ratio and regression type estimators and a synthetic estimator for corn and soybeans in seven states in the Midwestern grain belt region of the US are discussed.