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Model‐based county level crop estimates incorporating auxiliary sources of information
Author(s) -
Erciulescu Andreea L.,
Cruze Nathan B.,
Nandram Balgobin
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12390
Subject(s) - benchmarking , small area estimation , estimation , survey data collection , agriculture , production (economics) , statistics , constraint (computer aided design) , sampling (signal processing) , computer science , geography , mathematics , business , engineering , economics , geometry , systems engineering , archaeology , filter (signal processing) , marketing , estimator , computer vision , macroeconomics
Summary In 2011, the US Department of Agriculture's National Agricultural Statistics Service started the complete implementation of the County Agricultural Production Survey (CAPS). The 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 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 data, weather data and planted acreage administrative data from other US agencies. A 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.