Variable importance ranks for individual covariates used for predicting the distribution of swine farms in the conterminous U.S.

<p>^a Covariates with prefix <i>d</i> are measured as distance to the environmental or anthropogenic feature.</p><p>Each run is an iteration of a 5-fold cross-validation where 80% of the dataset was used for model building and 20% used for model testing. Quadratic forms of these covariates were used when their AIC values were less than the linear forms (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.s003" target="_blank">S1 Table</a>).</p

Landscape characteristics associated with livestock farms in the United States

This data describes the environmental or anthropogenic features occurring at locations where livestock farms are either present or absent in the conterminous United States. The spatial coordinates of locations have been removed so they do not contain any personally identifiable information. Please see accompanying ReadMe file for definitions of field names

Mean absolute percent differences for states in our county-to-state IPF verification analysis.

<p>This map depicts the reaggregation of our county-level estimates of swine populations to the state-level totals from which they were derived. The most missing data in the Census of Agriculture occurs at the county-level, and this missing data precludes the high accuracy (mean absolute percent differences of ≤ 0.03%) our IPF algorithms achieved at the other two hierarchical scales (individual farms to counties, and states to the national total). For the nine states with absolute percent differences of > 5%, we overlaid the percent of total U.S. swine population occurring in each state. Collectively, these nine states comprised only 2.24% of the total U.S. swine industry.</p

The structure of the FLAPS model.

<p>The FLAPS population simulation model consists of three interactive sub-models: (1) a missing-data model, (2) a distribution model, and (3) a simulation model. The output of the missing-data and distribution models provides input data for the simulation model. The definitions of the acronyms are: (1) IPF = iterative proportional fitting, and (2) LR = logistic regression.</p

Example of Census of Agriculture data from 2012 for the entire U.S. (including Alaska and Hawaii) showing the paired nature of the frequency distributions for the number of swine farms and individual pigs.

<p>^a The total number of farms or population occurring within each of seven farm/population-size bins. Data from Table 19, 2012 U.S. Census of Agriculture [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref016" target="_blank">16</a>].</p><p>^b Grand totals for the farm and population data types representing the total number of swine farms and total swine population for the entire U.S.</p><p>The number of swine farms is not confidential information and is published for all hierarchical levels of the Census of Agriculture. In contrast, the number of individual pigs can reveal socioeconomic information about individual farms and can be redacted, most commonly for county totals and subtotals due to fewer farms in these finer resolution categories.</p

The probability surface used to simulate the locations of individual farms throughout the conterminous United States.

<p>The blue to red color scheme represents a gradient of low to high predicted probability values at a 100 m resolution.</p

The density of domestic swine (A) farms, and (B) populations in the conterminous United States.

<p>Data are from 2012 Census of Agriculture [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref016" target="_blank">16</a>]. Counties colored black in (B) are those counties where swine population data were withheld to ensure respondent confidentiality.</p

A flow chart of iterative steps in the simulation model based on algorithms used to place individual farms with both geographic (i.e., location) and demographic (i.e., population) attributes.

<p>A flow chart of iterative steps in the simulation model based on algorithms used to place individual farms with both geographic (i.e., location) and demographic (i.e., population) attributes.</p

Covariates used to model the distribution of swine farms in the United States.

<p>^a Covariates with prefix <i>d</i> are measured as linear distances (m) to the environmental or anthropogenic feature.</p><p>^b Sources and references: Land-cover categories: 2006 National Land Cover Dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref021" target="_blank">21</a>]; Topography: National Elevation Dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref022" target="_blank">22</a>]; Climate: WORLDCLIM database [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref023" target="_blank">23</a>]; Transportation: Environmental Systems Research Institute (ESRI) World Transportation [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140338#pone.0140338.ref024" target="_blank">24</a>].</p><p>^c Open areas = Cropland + Pasture + Grassland + low and medium intensity Developed areas</p><p>Covariates used to model the distribution of swine farms in the United States.</p

Model-selection analysis for logistic regression modeling of swine farm distribution in the conterminous U.S.

<p>^a Covariates with prefix <i>d</i> are measured as distance to the environmental or anthropogenic feature.</p><p>Results are shown for models with <i>AIC</i>Δ_<i>i</i> ≤ 2.0 and all these models were used to develop model-averaged coefficients for our final distribution model.</p