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Mapping the Global Distribution of Livestock

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Robinson, Timothy P., Wint, G. R. William, Conchedda, Giulia, Van Boeckel, Thomas P., Ercoli, Valentina, Palamara, Elisa, Cinardi, Giuseppina, D'Aietti, Laura, Hay, Simon I., Gilbert, Marius·2014·PLoS ONE

Abstract

Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.

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Research works printed but not linked: 53

  1. de Haan C, Gerber P, Opio C (2010) Structural Change in the Livestock Sector. In: Steinfeld H, Mooney HA, Schneider F and Neville LE, editors. Livestock in a Changing Landscape. Drivers, Consequences, and Responses. Volume 1. Washington D.C., USA: Island Press. pp. 35–51.
  2. Robinson TP, Thornton PK, Franceschini G, Kruska RL, Chiozza F, <etal>et al</etal>.. (2011) Global livestock production systems. Rome: Food and Agriculture Organization of the United Nations (FAO) and International Livestock Research Institute (ILRI). pp. 152.
  3. Robinson TP, Pozzi F (2011) Mapping supply and demand for animal-source foods to 2030. Animal Production and Health Working Paper No. 2. Rome: Food and Agriculture Organisation (FAO) of the United Nations. pp. 164.
  4. Narrod C, Tiongco M, Delgado C (2010) Socioeconomic Implications of the Livestock Industrialization Process. In: Steinfeld H, Mooney HA, Schneider F and Neville LE, editors. Livestock in a Changing Landscape. Drivers, Consequences, and Responses. Volume 1. Washington D.C., USA: Island Press. pp. 269–285.
  5. World Bank (2005) Managing the livestock revolution. Report no. 32725-GLB. Washington D.C. USA: World Bank. pp. 63.
  6. Solomon S, Qin D, Manning M, Chen Z, Marquis M, <etal>et al</etal>.. (2007) Climate Change 2007: The physical science basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M and Miller HL, editors. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press pp. 996.
  7. Steinfeld H, Mooney HA, Schneider F, Neville LE (2010) Livestock in a Changing Landscape. Drivers, Consequences, and Responses. Volume 1. Washington D.C., USA: Island Press. pp. 396.
  8. Bonfoh B, Schwabenbauer K, Wallinga D, Hartung J, Schelling E, <etal>et al</etal>.. (2010) Human Health Hazards Associated with Livestock Production. In: Steinfeld H, Mooney HA, Schneider F and Neville LE, editors. Livestock in a Changing Landscape. Drivers, Consequences, and Responses. Volume 1. Washington D.C., USA: Island Press. pp. 197–221.
  9. M Herrero, 2010, Smart Investments in Sustainable Food Production: Revisiting Mixed Crop-Livestock Systems, Science, 327, 822, 10.1126/science.1183725 · DOI · Paper page
  10. T Ito, 1998, Molecular basis for the generation in pigs of influenza A viruses with pandemic potential, J Virol, 7367, 10.1128/JVI.72.9.7367-7373.1998 · DOI · Paper page
  11. W Ma, 2009, The pig as a mixing vessel for influenza viruses: Human and veterinary implications, J Mol Genet Med, 158
  12. K Neumann, 2009, Modelling the spatial distribution of livestock in Europe, Landsc Ecol, 24, 1207, 10.1007/s10980-009-9357-5 · DOI
  13. DJ Prosser, 2011, Modelling the distribution of chickens, ducks, and geese in China, Agric Ecosyst Environ, 141, 381, 10.1016/j.agee.2011.04.002 · DOI
  14. TP Van Boeckel, 2011, Modelling the distribution of domestic ducks in Monsoon Asia, Agric Ecosyst Environ, 141, 373, 10.1016/j.agee.2011.04.013 · doi:10.1016/j.agee.2011.04.013
  15. A Letourneau, 2012, A land-use systems approach to represent land-use dynamics at continental and global scales, Environ Model Softw, 33, 61, 10.1016/j.envsoft.2012.01.007 · DOI
  16. R Naidoo, 2008, Global mapping of ecosystem services and conservation priorities, Proc Natl Acad Sci U S A, 105, 9495, 10.1073/pnas.0707823105 · DOI · Paper page
  17. M Herrero, 2008, Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030, Agric Ecosyst Environ, 126, 122, 10.1016/j.agee.2008.01.017 · DOI
  18. PK Thornton, 2009, The impacts of climate change on livestock and livestock systems in developing countries: A review of what we know and what we need to know, Agric Syst, 101, 113, 10.1016/j.agsy.2009.05.002 · DOI · Paper page
  19. G Cecchi, 2010, Geographic distribution and environmental characterization of livestock production systems in Eastern Africa, Agric Ecosyst Environ, 135, 98, 10.1016/j.agee.2009.08.011 · DOI
  20. Cecchi G, Mattioli R (2009) Geospatial datasets and analyses for an environmental approach to African trypanosomiasis. PAAT (Programme Against African Trypanosomiasis). Technical and Scientific Series. No. 9. Rome: Food and Agriculture Organization of the United Nations. pp. 80.
  21. G Cecchi, 2008, Land cover and tsetse fly distributions in sub-Saharan Africa, Med Vet Entomol, 22, 364, 10.1111/j.1365-2915.2008.00747.x · doi:10.1111/j.1365-2915.2008.00747.x
  22. M Gilbert, 2008, Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia, Proc Natl Acad Sci U S A, 105, 4769, 10.1073/pnas.0710581105 · doi:10.1073/pnas.0710581105
  23. SB Muzaffar, 2010, Rice Production Systems and Avian Influenza: Interactions between Mixed-Farming Systems, Poultry and Wild Birds, Waterbirds, 33, 219, 10.1675/063.033.s116 · doi:10.1675/063.033.s116
  24. K Sumption, 2008, Incidence and Distribution of Foot-and-Mouth Disease in Asia, Africa and South America; Combining Expert Opinion, Official Disease Information and Livestock Populations to Assist Risk Assessment, Transbound Emerg Dis, 55, 5, 10.1111/j.1865-1682.2007.01017.x · doi:10.1111/j.1865-1682.2007.01017.x
  25. Mangen MJ, Otte J, Pfeiffer D, Chilonda P (2002) Bovine brucellosis in sub-Saharan Africa: estimation of sero-prevalence and impact on meat and milk offtake potential. Livestock Policy Discussion Paper No. 8 Rome: Food and Agriculture of the United Nations - Livestock Information and Policy Branch, AGAL. pp. 58.
  26. G Hendrickx, 2001, Spatial trypanosomosis management: from data-layers to decision making, Trends Parasitol, 17, 35, 10.1016/S1471-4922(00)01852-3 · doi:10.1016/s1471-4922(00)01852-3
  27. A Shaw, 2014, Mapping the economic benefits to livestock keepers from intervening against bovine trypanosomosis in Eastern Africa, Prev Vet Med, 113, 197, 10.1016/j.prevetmed.2013.10.024 · DOI · Paper page
  28. G Franceschini, 2009, The Global Livestock Impact Mapping System (GLIMS) as a tool for animal health applications, Vet Ital, 491
  29. AE Gaughan, 2013, High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8, e55882, 10.1371/journal.pone.0055882 · DOI · Paper page
  30. C Linard, 2012, Population Distribution, Settlement Patterns and Accessibility across Africa in 2010, PLoS ONE, 7, e31743, 10.1371/journal.pone.0031743 · DOI · Paper page
  31. Dudley N (2008) Guidelines for Applying Protected Area Management Categories. International Union for the Conservation of Nature (IUCN). Gland, Switzerland. pp. 106. · DOI · Paper page
  32. Rogers DJ (1997) Satellite Imagery and the Prediction of Tsetse Distributions in East Africa. In Diagnosis and Control of Livestock Diseases using Nuclear and related techniques Vienna, Austria: International Atomic Energy Agency. pp. 397–420.
  33. DJ Rogers, 2000, Satellites, space, time and the African trypanosomiases, Adv Parasitol, 47, 129, 10.1016/S0065-308X(00)47008-9 · DOI
  34. DJ Rogers, 1996, Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data, Ann Trop Med Parasitol, 90, 225, 10.1080/00034983.1996.11813049 · DOI · Paper page
  35. Rogers DJ, Williams BG (1994) Tsetse distribution in Africa: seeing the wood and the trees. In: Edwards PJ, May RM, Webb NR, editors. Large-scale ecology and conservation biology. 35th symposium of the Britush Ecological Society with the Society for Conservation Biology. Chapter 11. pp. 249–273. Oxford, UK: Blackwell Scientific Publications.
  36. JPW Scharlemann, 2008, Global data for ecology and epidemiology: A novel algorithm for temporal fourier processing MODIS data, PLoS ONE, 3
  37. X Zhang, 2003, Monitoring vegetation phenology using MODIS, Remote Sens Environ, 84, 471, 10.1016/S0034-4257(02)00135-9 · DOI · Paper page
  38. Jones P (1987) Current availability and deficiencies in data relevant to agro-ecological studies in the geographic area covered by the IARCS. In: A.HBunting (Ed), Agricultural Environments. CAB International, Wallingford, UK, pp. 69–83.
  39. PG Jones, 2009, Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change, Environ Sci Policy, 12, 427, 10.1016/j.envsci.2008.08.006 · DOI · Paper page
  40. RJ Hijmans, 2005, Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology, 25, 1965, 10.1002/joc.1276 · DOI · Paper page
  41. Griguolo S, Mazzanti M (1996) ADDAPIX: pixel-by-pixel classification for zoning and monitoring. Rome: FAO Technical Report SD:GCP/INt/578/NET. Revised May 2000. Rome, Italy. pp. 76.
  42. Zuur A, Ieno EN, Smith GM (2007) Analysing Ecological Data. XXVI, 672 p. · DOI · Paper page
  43. M Gilbert, 2007, Avian influenza, domestic ducks and rice agriculture in Thailand, Agric Ecosyst Environ, 119, 409, 10.1016/j.agee.2006.09.001 · DOI
  44. Weimin M (2010) Distribution and characteristics of duck-fish farming systems in Eastern China. Smallholder Poultry Production. Rome: Food and Agriculture of the United Nations (FAO). pp. 28.
  45. MacLeod M, Gerber P, Opio C, Henderson B, Dietze K, <etal>et al</etal>.. (2013) Greenhouse gas emissions from pig and chicken supply chains – A global life cycle assessment Rome: Food and Agriculture Organization (FAO) of the United Nations. pp. 196.
  46. V Martin, 2011, Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China, PLoS Pathogens, 7, 10.1371/journal.ppat.1001308 · DOI
  47. D Butler, 2013, Mapping the H7N9 avian flu outbreaks, Nature
  48. P Horby, 2013, H7N9 is a virus worth worrying about, Nature
  49. Openshaw S (1983) The modifiable areal unit problem. Concepts and techniques in modern geography. Norwich, UK: Geo Books Vol. 38
  50. TP Van Boeckel, 2012, Predicting the distribution of intensive poultry farming in Thailand, Agric Ecosyst Environ, 149, 144, 10.1016/j.agee.2011.12.019 · DOI
  51. S Fritz, 2009, Geo-wiki.org: The use of crowdsourcing to improve global land cover, Remote Sens, 1, 345, 10.3390/rs1030345 · DOI · Paper page
  52. S Fritz, 2012, Geo-Wiki: An online platform for improving global land cover, Environ Model Softw, 31, 110, 10.1016/j.envsoft.2011.11.015 · DOI · Paper page
  53. JH Elith, 2006, Novel methods improve prediction of species' distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x · DOI · Paper page

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Metadata

Authors
Robinson, T. P., Wint, G. R. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., Cinardi, G., D'Aietti, L., Hay, S. I., & Gilbert, M.
Venue
PLoS ONE
Published
2014-05-29
Volume
9
Issue
5
Pages
e96084
ISSN
1932-6203 (ISSN-L)
OpenAlex
W1981012806
Cited by
890
References
1
FWCI
102.17
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Mapping the Global Distribution of Livestock. https://app.cochat.ai/papers/10.1371/journal.pone.0096084
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Robinson, T. P., Wint, G. R. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., Cinardi, G., D'Aietti, L., Hay, S. I., & Gilbert, M. (2014). Mapping the Global Distribution of Livestock. PLoS ONE. https://doi.org/10.1371/journal.pone.0096084

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