All Title Author
Keywords Abstract

PLOS ONE  2014 

Mapping the Global Distribution of Livestock

DOI: 10.1371/journal.pone.0096084

Full-Text   Cite this paper   Add to My Lib


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 (, as will be the maps of other livestock types as they are produced.


[1]  FAO (2009) The State of Food and Agriculture. Livestock in the Balance. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO).
[2]  FAO (2011) World Livestock 2011 - Livestock in food security. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO).
[3]  Alexandratos N, Bruinsma J (2012) World Agriculture Towards 2030/2050. The 2012 Revision. Global Perspective Studies Team. ESA Working Paper No. 12-03. Rome, Italy FAO, Food and Agriculture Organization of the United Nations.
[4]  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.
[5]  Robinson TP, Thornton PK, Franceschini G, Kruska RL, Chiozza F, et al.. (2011) Global livestock production systems. Rome: Food and Agriculture Organization of the United Nations (FAO) and International Livestock Research Institute (ILRI). pp. 152.
[6]  Otte J, Costales A, Dijkman J, Pica-Ciamarra U, Robinson T, et al.. (2012) Livestock sector development for poverty reduction: an economic and policy perspective. Livestock's many virtues. Rome: Food and Agriculture Organization of the United Nations (FAO). 161 p.
[7]  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.
[8]  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.
[9]  World Bank (2005) Managing the livestock revolution. Report no. 32725-GLB. Washington D.C. USA: World Bank. pp. 63.
[10]  Solomon S, Qin D, Manning M, Chen Z, Marquis M, et al.. (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.
[11]  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.
[12]  Bonfoh B, Schwabenbauer K, Wallinga D, Hartung J, Schelling E, et al.. (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.
[13]  Herrero M, Thornton PK, Notenbaert AM, Wood S, Msangi S, et al. (2010) Smart Investments in Sustainable Food Production: Revisiting Mixed Crop-Livestock Systems. Science 327: 822–825.
[14]  Ito T, Couceiro JN, Kelm S, Baum LG, Krauss S, et al. (1998) Molecular basis for the generation in pigs of influenza A viruses with pandemic potential. J Virol 7367–7373.
[15]  Ma W, Kahn RE, Richt JA (2009) The pig as a mixing vessel for influenza viruses: Human and veterinary implications. J Mol Genet Med 158–166.
[16]  FAO (2007) Gridded Livestock of the World, 2007, by G.R.W. Wint and T.P. Robinson. Food and Agriculture Organization of the United Nations, Rome. 131 p.
[17]  Neumann K, Elbersen BS, Verburg PH, Staritsky I, Pérez-Soba M, et al. (2009) Modelling the spatial distribution of livestock in Europe. Landsc Ecol 24: 1207–1222.
[18]  Prosser DJ, Wu J, Ellis EC, Gale F, Van Boeckel TP, et al. (2011) Modelling the distribution of chickens, ducks, and geese in China. Agric Ecosyst Environ 141: 381–389.
[19]  Van Boeckel TP, Prosser D, Franceschini G, Biradar C, Wint W, et al. (2011) Modelling the distribution of domestic ducks in Monsoon Asia. Agric Ecosyst Environ 141: 373–380.
[20]  Letourneau A, Verburg PH, Stehfest E (2012) A land-use systems approach to represent land-use dynamics at continental and global scales. Environ Model Softw 33: 61–79.
[21]  Naidoo R, Balmford A, Costanza R, Fisher B, Green RE, et al. (2008) Global mapping of ecosystem services and conservation priorities. Proc Natl Acad Sci U S A 105: 9495–9500.
[22]  Herrero M, Thornton PK, Kruska R, Reid RS (2008) Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030. Agric Ecosyst Environ 126: 122–137.
[23]  Steinfeld H, Gerber P, Wassenar T, Castel V, Rosales M, et al.. (2006) Livestock's long shadow: Environmental issues and options. Rome, Italy: Food and Agriculture Organization (FAO).
[24]  Thornton PK, van de Steeg J, Notenbaert A, Herrero M (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: 113–127.
[25]  Cecchi G, Wint W, Shaw A, Marletta A, Mattioli R, et al. (2010) Geographic distribution and environmental characterization of livestock production systems in Eastern Africa. Agric Ecosyst Environ 135: 98–110.
[26]  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.
[27]  Cecchi G, Mattioli RC, Slingenbergh J, de la Rocque S (2008) Land cover and tsetse fly distributions in sub-Saharan Africa. Med Vet Entomol 22: 364–373.
[28]  Gilbert M, Xiao X, Pfeiffer DU, Epprecht M, Boles S, et al. (2008) Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia. Proc Natl Acad Sci U S A 105: 4769–4774.
[29]  Muzaffar SB, Takekawa JY, Prosser DJ, Newman SH, Xiao X (2010) Rice Production Systems and Avian Influenza: Interactions between Mixed-Farming Systems, Poultry and Wild Birds. Waterbirds 33: 219–230.
[30]  Sumption K, Rweyemamu M, Wint W (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: 5–13.
[31]  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.
[32]  Hendrickx G, de La Rocque S, Reid R, Wint W (2001) Spatial trypanosomosis management: from data-layers to decision making. Trends Parasitol 17: 35–41.
[33]  Shaw A, Cecchi G, Wint G, Mattioli R, Robinson T (2014) Mapping the economic benefits to livestock keepers from intervening against bovine trypanosomosis in Eastern Africa. Prev Vet Med 113: 197–210.
[34]  Franceschini G, Robinson TP, Morteo K, Dentale D, Wint GRW, et al. (2009) The Global Livestock Impact Mapping System (GLIMS) as a tool for animal health applications. Vet Ital 491–499.
[35]  Gaughan AE, Stevens FR, Linard C, Jia P, Tatem AJ (2013) High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015. PLoS ONE 8: e55882.
[36]  Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ (2012) Population Distribution, Settlement Patterns and Accessibility across Africa in 2010. PLoS ONE 7: e31743.
[37]  R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
[38]  Eastman JR (2009) IDRISI Taiga (Worcester, MA: Clark University).
[39]  European Commission Joint Research Centre (2003) Global Land Cover 2000 database. Available: Accessed 2014 April 16.
[40]  Dudley N (2008) Guidelines for Applying Protected Area Management Categories. International Union for the Conservation of Nature (IUCN). Gland, Switzerland. pp. 106.
[41]  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.
[42]  Rogers DJ (2000) Satellites, space, time and the African trypanosomiases. Adv Parasitol 47: 129–171.
[43]  Rogers DJ, Hay SI, Packer MJ (1996) Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Ann Trop Med Parasitol 90: 225–241.
[44]  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.
[45]  Scharlemann JPW, Benz D, Hay SI, Purse BV, Tatem AJ, et al. (2008) Global data for ecology and epidemiology: A novel algorithm for temporal fourier processing MODIS data. PLoS ONE 3.
[46]  Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, et al. (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84: 471–475.
[47]  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.
[48]  Jones PG, Thornton PK (2009) Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environ Sci Policy 12: 427–437.
[49]  Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978.
[50]  CIESIN, IFPRI, World Bank and CIAT(2004) Global Rural Urban Mapping Project (GRUMP), Beta Version. New York, Center for Internation Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); The World Bank & Centro Internacional de Agricultura Tropical (CIAT). Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available: Accessed 2014 April 16.
[51]  Nelson A (2008) Travel time to major cities: A global map of Accessibility. Global Environment Monitoring Unit - Joint Research Centre of the European Commission, Ispra Italy. Available: Accessed 2014 April 16.
[52]  USGS-EROS (1996) Global 30 Arc-Second Elevation Data Set. 1996. US Geological Survey (USGS) - Earth Resources Observation Systems (EROS). EROS Data Center (EDC) Distributed Active Archive Center (DAAC), Sioux Falls, South Dakota, USA. Available: Accessed 2014 April 16.
[53]  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.
[54]  Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, et al. (2001) Terrestrial ecoregions of the world: A new map of life on Earth. BioScience 51: 933–938.
[55]  Zuur A, Ieno EN, Smith GM (2007) Analysing Ecological Data. XXVI, 672 p.
[56]  Gilbert M, Xiao X, Chaitaweesub P, Kalpravidh W, Premashthira S, et al. (2007) Avian influenza, domestic ducks and rice agriculture in Thailand. Agric Ecosyst Environ 119: 409–415.
[57]  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.
[58]  MacLeod M, Gerber P, Opio C, Henderson B, Dietze K, et al.. (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.
[59]  Martin V, Pfeiffer DU, Zhou X, Xiao X, Prosser DJ, et al. (2011) Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China. PLoS Pathogens 7.
[60]  Butler D (2013) Mapping the H7N9 avian flu outbreaks. Nature doi:101038/nature201312863.
[61]  Horby P, Tatem AJ, Huang Z, Gilbert M, Robinson TP, et al. (2013) H7N9 is a virus worth worrying about. Nature doi:101038/496399a 496(7446): 399.
[62]  Openshaw S (1983) The modifiable areal unit problem. Concepts and techniques in modern geography. Norwich, UK: Geo Books Vol. 38
[63]  Van Boeckel TP, Thanapongtharm W, Robinson T, D'Aietti L, Gilbert M (2012) Predicting the distribution of intensive poultry farming in Thailand. Agric Ecosyst Environ 149: 144–153.
[64]  Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, et al. (2009) The use of crowdsourcing to improve global land cover. Remote Sens 1: 345–354.
[65]  Fritz S, McCallum I, Schill C, Perger C, See L, et al. (2012) Geo-Wiki: An online platform for improving global land cover. Environ Model Softw 31: 110–123.
[66]  Elith JH, Graham CP, Anderson R, Dudík M, Ferrier S, et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.


comments powered by Disqus

Contact Us


微信:OALib Journal