Abstract:
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In 2011, USDA's National Agricultural Statistics Service started the complete implementation of the County Agricultural Production Survey (CAPS). CAPS is an annual survey to provide accurate county-level acreage and production estimates of approved federal and state crop commodities. The current top-down method of producing official county-level estimates that satisfy the county-district-state benchmarking constraint is an expert assessment incorporating multiple sources of information. We propose a model-based method that combines the CAPS survey acreage data with auxiliary data and improves county-level survey estimation, while providing measures of uncertainty for the county-level acreage estimates. Auxiliary sources of information include remote sensing, weather data, and planted acreage administrative data from other USDA agencies. A novel hierarchical Bayesian subarea-level model is proposed and implemented, with an additional hierarchical level for the sampling variances. County-level model-based acreage estimates have lower coefficients of variation than the corresponding county-level survey acreage estimates. Top-down benchmarking methods are investigated and the final acreage estimates satisfy the county-district-state benchmarking constraint.
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