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Modeling the domestic poultry population in the United States: a novel approach leveraging remote sensing and synthetically generated data

Andrew Fox1*, Mary Jane Mccool-eye1, Kelly Patyk1, David South1, Chris Burdett2, Susan Maroney2, Sheryl Magzamen2 and Grace Kuiper2
  • 1 Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), United States
  • 2 Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, United States

Safeguarding the U.S. poultry industry from infectious disease is a top priority because of the potentially devastating effects of outbreaks to producers, the industry and the economy. Comprehensive and accurate poultry population demographic data are currently lacking in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. This study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modeling 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 farms. 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 in 594 U.S. counties with >100,000 birds in 34 states based on the 2012 USDA, NASS Census of Agriculture. 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 conterminous United States. Validation of the model illustrated that the hybrid model had higher locational accuracy and more realistic grouping patterns when compared to purely simulated data. The hybrid approach and the developed large-scale commercial poultry dataset have significant potential for application in animal disease spread modeling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

Acknowledgements

This work has been supported under USDA Cooperative Agreement 6000001724.

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Keywords: Poultry, farm, population estimates, distribution modelling, remote sensing

Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.

Presentation Type: Poster-no session

Topic: Spatial data sources, open data, accessibility and information integration

Citation: Fox A, Mccool-eye M, Patyk K, South D, Burdett C, Maroney S, Magzamen S and Kuiper G (2019). Modeling the domestic poultry population in the United States: a novel approach leveraging remote sensing and synthetically generated data. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00045

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Received: 30 May 2019; Published Online: 27 Sep 2019.

* Correspondence: Mx. Andrew Fox, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), Fort Collins, Colorado, United States, andrew.m.fox@aphis.usda.gov