Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods

  • Kelly A. Patyk United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health, United States. https://orcid.org/0000-0003-1046-889X
  • Mary J. McCool-Eye United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health, United States. https://orcid.org/0000-0002-4199-4924
  • David D. South United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health, United States.
  • Christopher L. Burdett Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO, United States.
  • Susan A. Maroney Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO, United States. https://orcid.org/0000-0001-9417-9651
  • Andrew Fox United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health, United States.
  • Grace Kuiper Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO, United States. https://orcid.org/0000-0003-4651-6283
  • Sheryl Magzamen | sheryl.magzamen@colostate.edu Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO, United States. https://orcid.org/0000-0002-2874-3530

Abstract

Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

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Published
2020-12-10
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Section
Original Articles
Keywords:
Poultry, Farm, Population estimates, Distribution modelling, Remote sensing
Statistics
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How to Cite
Patyk, K. A., McCool-Eye, M. J., South, D. D., Burdett, C. L., Maroney, S. A., Fox, A., Kuiper, G., & Magzamen, S. (2020). Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.913