Data from: Simulating the distribution of individual livestock farms and their populations in the united states: an example using domestic swine (Sus scrofa domesticus) farms

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Census of Agriculture and imputed unpublished state- or county-level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Approximately 19% of county-level pig population totals were unpublished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.