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Evaluating a Method for Georeferencing Agricultural Fields✩
Volume 22, Issue 3 (2024): Special issue: The Government Advances in Statistical Programming (GASP) 2023 conference, pp. 423–435
Robert L. Emmet   Kevin Hunt   Rachael Jennings     All authors (5)

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https://doi.org/10.6339/24-JDS1146
Pub. online: 9 August 2024      Type: Data Science In Action      Open accessOpen Access

✩ Disclaimer: The findings and conclusions in this report are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.

Received
13 December 2023
Accepted
2 July 2024
Published
9 August 2024

Abstract

The US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has begun a modernization effort to supplement survey data with non-survey data to improve estimation of agricultural quantities. As part of this effort, NASS has begun georeferencing farms on its list frame by linking geospatial data on agricultural fields with farm records on the list frame. Although many farms can be linked to geospatial data acquired by the Farm Service Agency (FSA), this linkage is not possible for farmers who do not participate in FSA programs, which may include members of some underrepresented groups in US agriculture. Thus, NASS has developed a georeferencing process for non-FSA farms, combining automatic and manual field identification, county assessor parcel data, record linkage, and classification surveys. This process serves the dual purpose of linking farms already on the list frame to geospatial data sources and identifying new farms to add to NASS’s list frame. This report evaluates the output of the non-FSA georeferencing process for 11 states, with a focus on farms added to the list frame via georeferencing. Substantial percentages (>25% for each category) of the new farms added via georeferencing were urban or suburban farms, were small, had livestock, or were in counties with Amish settlements. The georeferencing process shows promise adding farms from these groups that have historically been less well covered in NASS surveys.

Supplementary material

 Supplementary Material
The primary data used in this paper are CIPSEA protected and are not allowed to be distributed by law.

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
area frame georeferencing list frame record linkage undercoverage

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