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

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  • Christopher L Burdett
  • Brian R Kraus
  • Sarah J Garza
  • Ryan S Miller
  • Kathe E Bjork

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iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality.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 Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed.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

Suggested Citation

  • Christopher L Burdett & Brian R Kraus & Sarah J Garza & Ryan S Miller & Kathe E Bjork, 2015. "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, Public Library of Science, vol. 10(11), pages 1-21, November.
  • Handle: RePEc:plo:pone00:0140338
    DOI: 10.1371/journal.pone.0140338
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    References listed on IDEAS

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    1. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    2. Brian Roe & Elena G. Irwin & Jeff S. Sharp, 2002. "Pigs in Space: Modeling the Spatial Structure of Hog Production in Traditional and Nontraditional Production Regions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 259-278.
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    1. Chaiban, Celia & Biscio, Christophe & Thanapongtharm, Weerapong & Tildesley, Michael & Xiao, Xiangming & Robinson, Timothy P. & Vanwambeke, Sophie O. & Gilbert, Marius, 2019. "Point pattern simulation modelling of extensive and intensive chicken farming in Thailand: Accounting for clustering and landscape characteristics," Agricultural Systems, Elsevier, vol. 173(C), pages 335-344.
    2. Stefan Sellman & Kimberly Tsao & Michael J Tildesley & Peter Brommesson & Colleen T Webb & Uno Wennergren & Matt J Keeling & Tom Lindström, 2018. "Need for speed: An optimized gridding approach for spatially explicit disease simulations," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-27, April.
    3. Jing Yi & Samantha Cohen & Sarah Rehkamp & Patrick Canning & Miguel I. Gómez & Houtian Ge, 2023. "Overcoming data barriers in spatial agri‐food systems analysis: A flexible imputation framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 686-701, September.

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