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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">JDS</journal-id>
<journal-title-group><journal-title>Journal of Data Science</journal-title></journal-title-group>
<issn pub-type="epub">1683-8602</issn><issn pub-type="ppub">1680-743X</issn><issn-l>1680-743X</issn-l>
<publisher>
<publisher-name>School of Statistics, Renmin University of China</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">JDS1146</article-id>
<article-id pub-id-type="doi">10.6339/24-JDS1146</article-id>
<article-categories><subj-group subj-group-type="heading">
<subject>Data Science in Action</subject></subj-group></article-categories>
<title-group>
<article-title>Evaluating a Method for Georeferencing Agricultural Fields<xref ref-type="fn" rid="j_jds1146_fn_001"><sup>✩</sup></xref></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Emmet</surname><given-names>Robert L.</given-names></name><email xlink:href="mailto:Robbie.Emmet@usda.gov">Robbie.Emmet@usda.gov</email><xref ref-type="aff" rid="j_jds1146_aff_001">1</xref><xref ref-type="corresp" rid="cor2">∗</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Hunt</surname><given-names>Kevin</given-names></name><xref ref-type="aff" rid="j_jds1146_aff_001">1</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Jennings</surname><given-names>Rachael</given-names></name><xref ref-type="aff" rid="j_jds1146_aff_001">1</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Daniel</surname><given-names>Kara</given-names></name><xref ref-type="aff" rid="j_jds1146_aff_001">1</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Abreu</surname><given-names>Denise A.</given-names></name><xref ref-type="aff" rid="j_jds1146_aff_001">1</xref>
</contrib>
<aff id="j_jds1146_aff_001"><label>1</label><institution>USDA National Agricultural Statistics Service</institution>, Washington, DC 20250, <country>United States of America</country></aff>
</contrib-group>
<author-notes>
<fn id="j_jds1146_fn_001"><label>✩</label>
<p>Disclaimer: <italic>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.</italic></p></fn><corresp id="cor2"><label>∗</label>Corresponding author. Email: <ext-link ext-link-type="uri" xlink:href="mailto:Robbie.Emmet@usda.gov">Robbie.Emmet@usda.gov</ext-link>.</corresp>
</author-notes>
<pub-date pub-type="ppub"><year>2024</year></pub-date><pub-date pub-type="epub"><day>9</day><month>8</month><year>2024</year></pub-date><volume>22</volume><issue>3</issue><fpage>423</fpage><lpage>435</lpage><supplementary-material id="S1" content-type="archive" xlink:href="jds1146_s001.zip" mimetype="application" mime-subtype="x-zip-compressed">
<caption>
<title>Supplementary Material</title>
<p>The primary data used in this paper are CIPSEA protected and are not allowed to be distributed by law.</p>
</caption>
</supplementary-material><history><date date-type="received"><day>13</day><month>12</month><year>2023</year></date><date date-type="accepted"><day>2</day><month>7</month><year>2024</year></date></history>
<permissions><copyright-statement>2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.</copyright-statement><copyright-year>2024</copyright-year>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Open access article under the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">CC BY</ext-link> license.</license-p></license></permissions>
<abstract>
<p>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 (&gt;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.</p>
</abstract>
<kwd-group>
<label>Keywords</label>
<kwd>area frame</kwd>
<kwd>georeferencing</kwd>
<kwd>list frame</kwd>
<kwd>record linkage</kwd>
<kwd>undercoverage</kwd>
</kwd-group>
</article-meta>
</front>
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