Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms

PLoS One. 2015 Nov 16;10(11):e0140338. doi: 10.1371/journal.pone.0140338. eCollection 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 (Sus scrofa domesticus) 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iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality.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 Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed.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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animal Husbandry / methods*
  • Animals
  • Computer Simulation
  • Ecology
  • Geography
  • Livestock*
  • Models, Statistical
  • Population Dynamics
  • Probability
  • ROC Curve
  • Risk Management
  • Sus scrofa*
  • United States

Grants and funding

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 (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). 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 (http://www.fda.gov/AboutFDA/CentersOffices/OfficeofFoods/CFSAN/) 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.