Predicting the spatial distribution and demographics of commercial swine farms in the United States
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908623127445504 |
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| author | Sanchez, Felipe E. Lake, Thomas A. Galvis, Jason A. Jones, Chris Machado, Gustavo |
| author_facet | Sanchez, Felipe E. Lake, Thomas A. Galvis, Jason A. Jones, Chris Machado, Gustavo |
| contents | Data on livestock farm locations and demographics are essential for disease monitoring, risk assessment, and developing spatially explicit epidemiological models. Our semantic segmentation model achieved an F2 score of 92 % and a mean Intersection over Union of 76 %. An initial total of 194,474 swine barn candidates were identified in the Southeast (North Carolina = 111,135, South Carolina = 37,264 Virginia = 46,075) and 524,962 in the Midwest (Iowa = 168,866 Minnesota = 165,714 Ohio = 190,382). The post processing Random Forest classifier reduced false positives by 82 % in the Southeast and 88 % in the Midwest, resulting in 45,580 confirmed barn polygons. These were grouped into 16,976 predicted farms and classified into one of the four production types. Population sizes were then estimated using the Random Forest regression model, with prediction accuracy varying by production type. Across all farms, 87 % of predictions for operations with 1,000 2,000 pigs were within 500 pigs of the reference value, with nursery farms showing the highest agreement (R2= 0.82), followed by finisher farms (R2 = 0.77) and sow farms (R2 = 0.56). Our results revealed substantial gaps in the existing spatial and demographic data on U.S. swine production. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting the spatial distribution and demographics of commercial swine farms in the United States Sanchez, Felipe E. Lake, Thomas A. Galvis, Jason A. Jones, Chris Machado, Gustavo Applications Data on livestock farm locations and demographics are essential for disease monitoring, risk assessment, and developing spatially explicit epidemiological models. Our semantic segmentation model achieved an F2 score of 92 % and a mean Intersection over Union of 76 %. An initial total of 194,474 swine barn candidates were identified in the Southeast (North Carolina = 111,135, South Carolina = 37,264 Virginia = 46,075) and 524,962 in the Midwest (Iowa = 168,866 Minnesota = 165,714 Ohio = 190,382). The post processing Random Forest classifier reduced false positives by 82 % in the Southeast and 88 % in the Midwest, resulting in 45,580 confirmed barn polygons. These were grouped into 16,976 predicted farms and classified into one of the four production types. Population sizes were then estimated using the Random Forest regression model, with prediction accuracy varying by production type. Across all farms, 87 % of predictions for operations with 1,000 2,000 pigs were within 500 pigs of the reference value, with nursery farms showing the highest agreement (R2= 0.82), followed by finisher farms (R2 = 0.77) and sow farms (R2 = 0.56). Our results revealed substantial gaps in the existing spatial and demographic data on U.S. swine production. |
| title | Predicting the spatial distribution and demographics of commercial swine farms in the United States |
| topic | Applications |
| url | https://arxiv.org/abs/2511.00132 |