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Andreea L. Erciulescu

National Agricultural Statistics Service/National Institute of Statistical Sciences



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Nathan B. Cruze

USDA National Agricultural Statistics Service



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Balgobin Nandram

Worcester Polytechnic Institute and USDA National Agricultural Statistics Service



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255 – Advances in Small Area/Domain Estimation

Model-Based County-Level Crop Estimates Incorporating Auxiliary Sources of Information

Sponsor: Survey Research Methods Section
Keywords: Auxiliary Data, Benchmarking, Crop Acreage Estimates, Hierarchical Bayes, Small Area Estimation

Andreea L. Erciulescu

National Agricultural Statistics Service/National Institute of Statistical Sciences

Nathan B. Cruze

USDA National Agricultural Statistics Service

Balgobin Nandram

Worcester Polytechnic Institute and USDA National Agricultural Statistics Service

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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