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May 2019 Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges
Nathan B. Cruze, Andreea L. Erciulescu, Balgobin Nandram, Wendy J. Barboza, Linda J. Young
Statist. Sci. 34(2): 301-316 (May 2019). DOI: 10.1214/18-STS687


In the United States, county-level estimates of crop yield, production, and acreage published by the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS) play an important role in determining the value of payments allotted to farmers and ranchers enrolled in several federal programs. Given the importance of these official county-level crop estimates, NASS continually strives to improve its crops county estimates program in terms of accuracy, reliability and coverage. In 2015, NASS engaged a panel of experts convened under the auspices of the National Academies of Sciences, Engineering, and Medicine Committee on National Statistics (CNSTAT) for guidance on implementing models that may synthesize multiple sources of information into a single estimate, provide defensible measures of uncertainty, and potentially increase the number of publishable county estimates. The final report titled Improving Crop Estimates by Integrating Multiple Data Sources was released in 2017. This paper discusses several needs and requirements for NASS county-level crop estimates that were illuminated during the activities of the CNSTAT panel. A motivating example of planted acreage estimation in Illinois illustrates several challenges that NASS faces as it considers adopting any explicit model for official crops county estimates.


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Nathan B. Cruze. Andreea L. Erciulescu. Balgobin Nandram. Wendy J. Barboza. Linda J. Young. "Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges." Statist. Sci. 34 (2) 301 - 316, May 2019.


Published: May 2019
First available in Project Euclid: 19 July 2019

MathSciNet: MR3983330
Digital Object Identifier: 10.1214/18-STS687

Rights: Copyright © 2019 Institute of Mathematical Statistics


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Vol.34 • No. 2 • May 2019
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