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

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.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

References

Andronico, A, Courcoul, A, Bronner, A, Scoizec, A, Lebouquin-Leneveu, S, Guinat, C, Paul, MC, Durand, B, Cauchemez, S, 2019. Highly pathogenic avian influenza H5N8 in south-west France 2016-2017: A modeling study of control strategies. Epidemics 28:100340. DOI: https://doi.org/10.1016/j.epidem.2019.03.006

Aviagen Turkeys, 2018. Management Guidelines: Raising Commercial Turkeys. https://www.aviagenturkeys.us/uploads/2015/12/21/Aviagen%20Commercial%20Guide.pdf. Accessed: November 2, 2018.

Backer, JA, van Roermund, HJW, Fischer, EAJ, van Asseldonk, MAPM, Bergevoet, RHM, 2015. Controlling highly pathogenic avian influenza outbreaks: An epidemiological and economic model analysis. Prev Vet Med 121:142-50. DOI: https://doi.org/10.1016/j.prevetmed.2015.06.006

Bavinck, V, Bouma, A, van Boven, M, Bos, MEH, Stassen, E, Stegeman, JA, 2009. The role of backyard poultry flocks in the epidemic of highly pathogenic avian influenza virus (H7N7) in the Netherlands in 2003. Prev Vet Med 88:247-54. DOI: https://doi.org/10.1016/j.prevetmed.2008.10.007

Bell, DD, Weaver, WD (eds), Commercial Poultry Meat and Egg Production, 2002. Kluwer Academic Publishers, 819-827, 965-71. DOI: https://doi.org/10.1007/978-1-4615-0811-3_42

Bessell, PR, Shaw, DJ, Savill, NJ, Woolhouse, MEJ, 2010. Statistical modeling of holding level susceptibility to infection during the 2011 foot and mouth disease epidemic in Great Britain. Int J Infect Dis 14:e210-15. DOI: https://doi.org/10.1016/j.ijid.2009.05.003

Blundell, S, Opitz, D, Morris, M, Rao, R, 2008. Feature Analyst V5.0. ASPRS 2008 Annual Conference, 1-9.

Boender, GJ, Hagenaars, TJ, Bouma, A, Nodelijk, G, Elbers, ARW, de Jong, MCM, van Boven, M, 2007. Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Comput Biol 3:e71. DOI: https://doi.org/10.1371/journal.pcbi.0030071

Bonney, PJ, Malladi, S, Boender, GJ, Weaver, JT, Ssematimba, A, Halvorson, DA, Cardona, CJ, 2018. Spatial transmission of H5N2 highly pathogenic avian influenza between Minnesota poultry premises during the 2015 outbreak. PLoS ONE 13:e0204262. DOI: https://doi.org/10.1371/journal.pone.0204262

Bradhurst, RA, Roche, SE, East, IJ, Kwan, P, Garner, MG, 2015. A hybrid modeling approach to simulating foot-and-mouth disease outbreaks in Australian livestock. Front Environ Sci 3:17. DOI: https://doi.org/10.3389/fenvs.2015.00017

Bruhn, MC, Munoz, B, Cajka, J, Smith, G, Curry, RJ, Wagener, DK, Wheaton, WD, 2012. Synthesized population databases: A geospatial database of US poultry farms. Methods Rep RTI Press. DOI: https://doi.org/10.3768/rtipress.2012.mr.0023.1201

Burdett, CL, Kraus, BR, Garza, SJ, Miller, RS, Bjork, KE, 2015. Simulating the distribution of individual livestock farms and their populations in the United States: An example using domestic swine (Sus scrofa domesticus) farms. PLoS ONE 10:e0140338. DOI: https://doi.org/10.1371/journal.pone.0140338

Caggiano, MD, Tinkham, WT, Hoffman, C, Cheng, AS, Hawbaker, TJ, 2016. High resolution mapping of development in the wildlife-urban interface using object based image extraction. Heliyon 2:e00174. DOI: https://doi.org/10.1016/j.heliyon.2016.e00174

Dent, JE, Kiss, IZ, Kao, R, Arnold, M, 2011. The potential spread of highly pathogenic avian influenza virus via dynamic contacts between poultry premises in Great Britain. BMC Vet Res 7:59. DOI: https://doi.org/10.1186/1746-6148-7-59

Emelyanova, IA, Donald, GE, Miron, DJ, Henry, DA, Garner, MG, 2009. Probabilistic modelling of cattle farm distribution in Australia. Environ Model Assess 14:449-65. DOI: https://doi.org/10.1007/s10666-008-9140-z

Economic Research Service (ERS), 2019. Poultry and Eggs. United States Department of Agriculture. https://www.ers.usda.gov/topics/animal-products/poultry-eggs/. Accessed: May 8, 2019.

European Space Agency (ESA), 2009. Spectral signatures. http://www.esa.int/SPECIALS/Eduspace_EN/SEMPNQ3Z2OF_2.html. Accessed: June 26, 2019.

Fairchild, BD, Basic Introduction to Broiler House Environmental Control, 2005 August. Retrieved from http://www.thepoultrysite.com/articles/386/basic-introduction-to-broiler-housing-environmental-control/. Accessed: February 16, 2018.

Freire, S, Santos, T, Navarro, A, Soares, F, Silva, JD, Afonso, N, Fonseca, A, Tenedório, J, 2014. Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery. ISPRS J Photogramm 90:1-9. DOI: https://doi.org/10.1016/j.isprsjprs.2013.12.009

Johnson, KK, Seeger, RM, Marsh, TL, 2016. Local economies and highly pathogenic avian influenza. Choices, Quarter 2. http://www.choicesmagazine.org/choices-magazine/theme-articles/economic-consequences-of-highly-pathogenic-avian-influenza/local-economies-and-highly-pathogenic-avian-influenza.

Khan, SU, O’Sullivan, TL, Poljak, Z, Alsop, J, Greer, AL, 2018. Modeling livestock population structure: a geospatial database for Ontario swine farms. BMC Vet Res 14:31. DOI: https://doi.org/10.1186/s12917-018-1362-y

Maroney, S, McCool-Eye, MJ, Fox, A, Burdett, CL, In press. Identifying commercial poultry operations from high resolution imagery to support animal health emergencies. Geospat Health.

Martin, MK, Helm, J, Patyk, KA, 2015. An approach for de-identification of point locations of livestock premises for further use in disease spread modeling. Prev Vet Med 120:131-40. DOI: https://doi.org/10.1016/j.prevetmed.2015.04.010

Meadows, AJ, Mundt, CC, Keeling, MJ, Tildesley, MJ, 2018. Disentangling the influence of livestock vs. farm density on livestock disease epidemics. Ecosphere 9:e02294. DOI: https://doi.org/10.1002/ecs2.2294

Melius, C, 2007. Developing poultry facility type information from USDA agricultural census data for use in epidemiologic and economic models. Department of Homeland Security. Lawrence Livermore National Laboratory. Livermore, CA. DOI: https://doi.org/10.2172/926044

Melius, C, Robertson, A, Hullinger, P, 2006. Developing livestock facility type information from USDA agricultural census data for use in epidemiologic and economic models. Department of Homeland Security Lawrence Livermore National Laboratory. Livermore, CA. DOI: https://doi.org/10.2172/1036849

National Agricultural Statistics Service (NASS), 2012. 2012 Census of Agriculture. United States. Appendix A. Census of Agriculture Methodology. United States Department of Agriculture.

National Agricultural Statistics Service (NASS), 2015. USDA Poultry Production Data. Fact Sheet. United States Department of Agriculture. https://www.usda.gov/sites/default/files/documents/nass-poultry-stats-factsheet.pdf.

National Agricultural Statistics Service (NASS), 2019. 2017 Census of Agriculture. United States. Summary and State Data. Volume 1. Geographic Area Series. Part 51. AC-17-A-51. United States Department of Agriculture.

Neumann, K, Elbersen, BS, Verburg, PH, Staritsky, I, Perez-Soba, M, de Vries, W, Rienks, WA, 2009. Modelling the spatial distribution of livestock in Europe. Landscape Ecol 24:1207-22. DOI: https://doi.org/10.1007/s10980-009-9357-5

Patyk, KA, Helm, J, Martin, MK, Forde-Folle, KN, Olea-Popelka, FJ, Hokanson, JE, Fingerlin, T, Reeves, A, 2013. An epidemiologic simulation model of the spread and control of highly pathogenic avian influenza (H5N1) among commercial and backyard poultry flocks in South Carolina, United States. Prev Vet Med 110:510-24. DOI: https://doi.org/10.1016/j.prevetmed.2013.01.003

Penn State Extension, 2018. Small-flock turkey production. https://extension.psu.edu/small-flock-turkey-production. Accessed: November 2, 2018.

Porphyre, T, Auty, HK, Tildesley, MJ, Gunn, GJ, Woolhouse, MEJ, 2013. Vaccination against foot-and-mouth disease: Do initial conditions affect its benefit? PloS ONE 8:e77616. DOI: https://doi.org/10.1371/journal.pone.0077616

Prosser, DJ, Wu, J, Ellis, EC, Gale, F, Van Boeckel, TP, Wint, W, Robinson, T, Xiao, X, Gilbert, M, 2011. Modelling the distribution of chickens, ducks, and geese in China. Agric Ecosyst Environ 141:381-9. DOI: https://doi.org/10.1016/j.agee.2011.04.002

R Core Team, 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Reeves, A, 2012. Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry. PhD thesis. Colorado State University, Fort Collins, CO.

Sanson, R, 1993. The development of a decision support system for an animal disease emergency. PhD thesis. Massey University, Palmerston North, NZ.

Seitzinger, AH, Paarlberg, PL, 2016. Regionalization of the 2014 and 2015 Highly Pathogenic Avian Influenza Outbreaks. Choices, Quarter 2. http://www.choicesmagazine.org/choices-magazine/theme-articles/economic-consequences-of-highly-pathogenic-avian-influenza/regionalization-of-the-2014-and-2015-highly-pathogenic-avian-influenza-outbreaks.

Smith, G, Dunipace, S, 2011. How backyard poultry flocks influence the effort required to curtail avian influenza epidemics in commercial poultry flocks. Epidemics 3:71-5. DOI: https://doi.org/10.1016/j.epidem.2011.01.003

Souvestre, M, Guinat, C, Niqueux, E, Robertet, L, Croville, G, Paul, M, Schmitz, A, Bronner, A, Eterradossi, N, Guerin, J-L, 2019. Role of backyard flocks in transmission dynamics of highly pathogenic avian influenza A (H5N8) clade 2.3.4.4, France, 2016-2017. Emerg Infect Dis 25:551-4. DOI: https://doi.org/10.3201/eid2503.181040

Spröhnle, K, Tiede, D, Schoepfer, E, Füreder, P, Svanberg, A, Rost, T. 2014. Earth observation-based dwelling detection approaches in a highly complex refugee camp environment – a comparative study. Remote Sens 6:9277-97. DOI: https://doi.org/10.3390/rs6109277

Stenkamp-Strahm, C, Patyk, K, McCool-Eye, MJ, Fox, A, Humphreys, J, James, A, South, D, Magzamen, S, 2020. Using geospatial methods to measure the risk of environmental persistence of avian influenza virus in South Carolina. Spat Spatiotemporal Epidemiol. DOI: https://doi.org/10.1016/j.sste.2020.100342

Stevenson, MA, Sanson, RL, Miranda, AO, Lawrence, KA, Morris, RS, 2007. Decision support systems for monitoring and maintaining health in food animal populations. N Z Vet J 55:264-72. DOI: https://doi.org/10.1080/00480169.2007.36780

Stevenson, MA, Sanson, RL, Stern, MW, O’Leary, BD, Sujau, M, Moles-Benfell, N, Morris, RS, 2013. InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations. Prev Vet Med 109:10-24. DOI: https://doi.org/10.1016/j.prevetmed.2012.08.015

Terregino, C, De Nardi, R, Guberti, V, Scremin, M, Raffini, E, Martin, AM, Cattoli, G, Bonfanti, L, Capua, I, 2007. Active surveillance for avian influenza viruses in wild birds and backyard flocks in Northern Italy during 2004 to 2006. Avian Pathol 36:337-44. DOI: https://doi.org/10.1080/03079450701488345

Tildesley, MJ, House, TA, Bruhn, MC, Curry, RJ, O’Neil, M, Allpress, JLE, Smith, G, Keeling, MJ, 2010. Impact of spatial clustering on disease transmission and optimal control. PNAS 107:1041-6. DOI: https://doi.org/10.1073/pnas.0909047107

Tildesley, MJ, Ryan, SJ, 2012. Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models. PLoS Comput Biol 8:e1002723. DOI: https://doi.org/10.1371/journal.pcbi.1002723

Tomassen, FHM, de Koeijer, A, Mourits, MCM, Dekker, A, Bouma, A, Huirne, RBM, 2002. A decision-tree to optimize control measures during the early state of a foot-and-mouth disease epidemic. Prev Vet Med 54:301-24. DOI: https://doi.org/10.1016/S0167-5877(02)00053-3

TomTom Inc., 2012. Street Centerline GIS dataset [data file]. [producer]. Redlands, CA: Environmental System Research Institute (ESRI) [distributor].

United States Defense Mapping Agency, 2014. Urban Areas GIS Dataset, Digital Chart of the World [data file]. Washington, D.C. [producer] Redlands, CA: Environmental System Research Institute (ESRI) [distributor].

United States Department of Agriculture (USDA), 2016. Geospatial Data Gateway: Direct NAIP Download. https://datagateway.nrcs.usda.gov/. Accessed: July 25, 2018.

United States Geological Survey (USGS), 2014. National Hydrography GIS dataset [data file]. Washington, D.C. [producer] Redlands, CA:

Environmental System Research Institute (ESRI) [distributor].

United States Geological Survey (USGS), 2019. What is remote sensing and what is it used for? https://www.usgs.gov/faqs/what-remote-sensing-and-what-it-used?qt-news_science_products=7#qt-news_science_products. Accessed: June 20, 2019.

van Andel, M, Jewell, C, McKenzie, J, Hollings, T, Robinson, A, Burgman, M, Bingham, P, Carpenter, T, 2017. Predicting farm-level animal populations using environmental and socioeconomic variables. Prev Vet Med 145:121-32. DOI: https://doi.org/10.1016/j.prevetmed.2017.07.005

van Andel, M, Hollins, T, Bradhurst, R, Robinson, A, Burgman, M, Gates, MC, Bingham, P, Carpenter, T, 2018. Does size matter to models? Exploring the effect of herd size on outputs of a herd-level disease spread simulator. Front Vet Sci 5:78. DOI: https://doi.org/10.3389/fvets.2018.00078

Werkman, M, Tildesley, MJ, Brooks-Pollock, E, Keeling, MJ, 2016. Preserving privacy whilst maintaining robust epidemiological predictions. Epidemics 17:35-41. DOI: https://doi.org/10.1016/j.epidem.2016.10.004

Published
2020-12-10
Info
Issue
Section
Original Articles
Keywords:
Poultry, Farm, Population estimates, Distribution modelling, Remote sensing
Statistics
  • Abstract views: 1089

  • PDF: 232
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