Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms
Identifieur interne : 000017 ( Pmc/Curation ); précédent : 000016; suivant : 000018Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms
Auteurs : Christopher L. Burdett [États-Unis] ; Brian R. Kraus [États-Unis] ; Sarah J. Garza [États-Unis] ; Ryan S. Miller [États-Unis] ; Kathe E. Bjork [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (
Url:
DOI: 10.1371/journal.pone.0140338
PubMed: 26571497
PubMed Central: 4646625
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<author><name sortKey="Burdett, Christopher L" sort="Burdett, Christopher L" uniqKey="Burdett C" first="Christopher L." last="Burdett">Christopher L. Burdett</name>
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<front><div type="abstract" xml:lang="en"><p>Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (<italic>Sus scrofa domesticus</italic>
) as an example species, we customized iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality. We used a weighted sampling design to collect data on the presence and absence of farms and used them to develop a national-scale distribution model that predicted the distribution of individual farms at a 100 m resolution. We implemented microsimulation algorithms that simulated the populations and locations of individual farms using output from our imputed Census of Agriculture dataset and distribution model. Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed. Using aerial photography, we confirmed the presence or absence of livestock farms at 10,238 locations and found livestock farms were correlated with open areas, cropland, and roads, and also areas with cooler temperatures and gentler topography. The distribution of swine farms was highly variable, but cross-validation of our distribution model produced an area under the receiver-operating characteristics curve value of 0.78, which indicated good predictive performance. Verification analyses showed FLAPS accurately imputed and simulated Census of Agriculture data based on absolute percent difference values of < 0.01% at the state-to-national scale, 3.26% for the county-to-state scale, and 0.03% for the individual farm-to-county scale. Our output data have many applications for risk management of agricultural systems including epidemiological studies, food safety, biosecurity issues, emergency-response planning, and conflicts between livestock and other natural resources.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26571497</article-id>
<article-id pub-id-type="pmc">4646625</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0140338</article-id>
<article-id pub-id-type="publisher-id">PONE-D-15-21463</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (<italic>Sus scrofa domesticus</italic>
) Farms</article-title>
<alt-title alt-title-type="running-head">Simulating Livestock Farms in the United States</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Burdett</surname>
<given-names>Christopher L.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref rid="cor001" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Kraus</surname>
<given-names>Brian R.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Garza</surname>
<given-names>Sarah J.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="econtrib001"><sup>‡</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Miller</surname>
<given-names>Ryan S.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="econtrib001"><sup>‡</sup>
</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Bjork</surname>
<given-names>Kathe E.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>Colorado State University, Department of Biology, Fort Collins, Colorado, United States of America</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Centers for Animal Health and Epidemiology, Fort Collins, Colorado, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>van Boven</surname>
<given-names>Michiel</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>National Institute for Public Health and the Environment, NETHERLANDS</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: CB BK KB RM. Performed the experiments: CB BK KB RM SG. Analyzed the data: CB BK SG KB. Contributed reagents/materials/analysis tools: KB RM. Wrote the paper: CB BK KB RM SG.</p>
</fn>
<fn fn-type="other" id="econtrib001"><p>‡ These authors also contributed equally to this work.</p>
</fn>
<corresp id="cor001">* E-mail: <email>Chris.Burdett@colostate.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>16</day>
<month>11</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<volume>10</volume>
<issue>11</issue>
<elocation-id>e0140338</elocation-id>
<history><date date-type="received"><day>13</day>
<month>8</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>28</day>
<month>8</month>
<year>2015</year>
</date>
</history>
<permissions><license xlink:href="https://creativecommons.org/publicdomain/zero/1.0/"><license-p>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/publicdomain/zero/1.0/">Creative Commons CC0</ext-link>
public domain dedication</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0140338.pdf"></self-uri>
<abstract><p>Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (<italic>Sus scrofa domesticus</italic>
) as an example species, we customized iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality. We used a weighted sampling design to collect data on the presence and absence of farms and used them to develop a national-scale distribution model that predicted the distribution of individual farms at a 100 m resolution. We implemented microsimulation algorithms that simulated the populations and locations of individual farms using output from our imputed Census of Agriculture dataset and distribution model. Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed. Using aerial photography, we confirmed the presence or absence of livestock farms at 10,238 locations and found livestock farms were correlated with open areas, cropland, and roads, and also areas with cooler temperatures and gentler topography. The distribution of swine farms was highly variable, but cross-validation of our distribution model produced an area under the receiver-operating characteristics curve value of 0.78, which indicated good predictive performance. Verification analyses showed FLAPS accurately imputed and simulated Census of Agriculture data based on absolute percent difference values of < 0.01% at the state-to-national scale, 3.26% for the county-to-state scale, and 0.03% for the individual farm-to-county scale. Our output data have many applications for risk management of agricultural systems including epidemiological studies, food safety, biosecurity issues, emergency-response planning, and conflicts between livestock and other natural resources.</p>
</abstract>
<funding-group><funding-statement>This work was funded by the U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Center for Epidemiology and Animal Health (<ext-link ext-link-type="uri" xlink:href="http://www.aphis.usda.gov/wps/portal/?1dmy&urile=wcm:path:/APHIS_Content_Library/SA_Our_Focus/SA_Animal_Health/SA_Program_Overview/SA_CEAH">http://www.aphis.usda.gov/wps/portal/?1dmy&urile=wcm:path:/APHIS_Content_Library/SA_Our_Focus/SA_Animal_Health/SA_Program_Overview/SA_CEAH</ext-link>
). Additional funding was provided by the U.S. Department of Health and Human Services, Food and Drug Administration, Center for Food Safety and Applied Nutrition (<ext-link ext-link-type="uri" xlink:href="http://www.fda.gov/AboutFDA/CentersOffices/OfficeofFoods/CFSAN/">http://www.fda.gov/AboutFDA/CentersOffices/OfficeofFoods/CFSAN/</ext-link>
) through an interagency grant to APHIS-VS-CEAH. Grant numbers supporting this work include USDA grant numbers 11-9208-0301 (interagency grant from FDA to USDA represented here) and 13-9208-0336. These grants were received by CB, who served as principle investigator at Colorado State University. The co-authors at APHIS-VS-CEAH had subordinate roles in study design, data analysis, publication and manuscript preparation.</funding-statement>
</funding-group>
<counts><fig-count count="5"></fig-count>
<table-count count="4"></table-count>
<page-count count="21"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>We used two sources of data to build the Farm Location and Animal Population Simulator (FLAPS) model that is introduced in this paper. First, our source for demographic and coarse-grained geographic data was the 2012 Census of Agriculture. All these data are freely available online from (<ext-link ext-link-type="uri" xlink:href="http://www.agcensus.usda.gov/Publications/2012/">www.agcensus.usda.gov/Publications/2012/</ext-link>
). Second, the fine-grained geographic data we collected to simulate the locations of individual farms are publicly available at (<ext-link ext-link-type="uri" xlink:href="http://datadryad.org/">datadryad.org/</ext-link>
) (DOI: doi:<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5061/dryad.46pb3">10.5061/dryad.46pb3</ext-link>
).</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>We used two sources of data to build the Farm Location and Animal Population Simulator (FLAPS) model that is introduced in this paper. First, our source for demographic and coarse-grained geographic data was the 2012 Census of Agriculture. All these data are freely available online from (<ext-link ext-link-type="uri" xlink:href="http://www.agcensus.usda.gov/Publications/2012/">www.agcensus.usda.gov/Publications/2012/</ext-link>
). Second, the fine-grained geographic data we collected to simulate the locations of individual farms are publicly available at (<ext-link ext-link-type="uri" xlink:href="http://datadryad.org/">datadryad.org/</ext-link>
) (DOI: doi:<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5061/dryad.46pb3">10.5061/dryad.46pb3</ext-link>
).</p>
</notes>
</front>
</pmc>
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