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Model‐based county level crop estimates incorporating auxiliary sources of information

Author

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  • Andreea L. Erciulescu
  • Nathan B. Cruze
  • Balgobin Nandram

Abstract

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.

Suggested Citation

  • Andreea L. Erciulescu & Nathan B. Cruze & Balgobin Nandram, 2019. "Model‐based county level crop estimates incorporating auxiliary sources of information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 283-303, January.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:1:p:283-303
    DOI: 10.1111/rssa.12390
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    Citations

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    Cited by:

    1. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.
    2. Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers 15390, Institute of Labor Economics (IZA).
    3. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    4. Nandram, Balgobin & Cruze, Nathan B & Erciulescu, Andreea L & Chen, Lu, 2022. "Bayesian Small Area Models under Inequality Constraints with Benchmarking and Double Shrinkage," NASS Research Reports 327250, United States Department of Agriculture, National Agricultural Statistics Service.
    5. Lu Chen & Balgobin Nandram, 2023. "Bayesian Logistic Regression Model for Sub-Areas," Stats, MDPI, vol. 6(1), pages 1-23, January.
    6. Linda J. Young & Lu Chen, 2022. "Using Small Area Estimation to Produce Official Statistics," Stats, MDPI, vol. 5(3), pages 1-17, September.
    7. Lu Chen & Nathan B. Cruze & Linda J. Young, 2022. "Model-Based Estimates for Farm Labor Quantities," Stats, MDPI, vol. 5(3), pages 1-17, August.

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