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Scientific data2018; 5; 180227; doi: 10.1038/sdata.2018.227

Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010.

Abstract: Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. We present a new version of the Gridded Livestock of the World (GLW 3) database, reflecting the most recently compiled and harmonized subnational livestock distribution data for 2010. GLW 3 provides global population densities of cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator). They are accompanied by detailed metadata on the year, spatial resolution and source of the input census data. Two versions of each species distribution are produced. In the first version, livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). In the second version, animal numbers are distributed homogeneously with equal densities within their census polygons (areal weighting) to provide spatial data layers free of any assumptions linking them to other spatial variables.
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Publication Date: 2018-10-30 PubMed ID: 30375994PubMed Central: PMC6207061DOI: 10.1038/sdata.2018.227Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article is about the creation of the Gridded Livestock of the World (GLW 3) database, which provides updated and more detailed information on the global distribution of different types of livestock in 2010.

Research Purpose

  • The study aims to develop the third version of the Gridded Livestock of the World (GLW 3), a global database showing the geographic distribution of different types of livestock. The database is useful for various applications, including socio-economic analysis in agriculture, food security assessment, evaluating environmental impact, and studying epidemiology.

Methodology

  • The GLW 3 database reflects the most recently compiled and harmonized subnational livestock distribution data from the year 2010.
  • The research provides global population densities of cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at approximately 0.083333 decimal degrees, or roughly 10 km at the equator.
  • Each data point in the database is accompanied by detailed metadata, which includes the year, the spatial resolution, and the source of the input census data.

Data Interpretation

  • The database features two versions of each species’ distribution. The first version, known as the dasymetric weighting method, disaggregates livestock numbers within census polygons according to weights set by statistical models using high-resolution spatial covariates. This process helps to achieve a more detailed and more accurate geographical distribution of the livestock populations.
  • The second version of the species distribution, called the areal weighting method, distributes animal numbers equally within their census polygons. This approach aims to deliver spatial data layers free of any assumptions relating them to other spatial variables, providing a more straightforward overview of the data.

Implications of the Study

  • With the GLW 3 database’s creation, researchers, governments, and organizations involved in various sectors will have access to accurate, detailed, and current data regarding the global distribution of livestock. This information can assist in planning agricultural activities, managing livestock resources, understanding food security, assessing environmental impacts, and studying disease epidemiology.

Cite This Article

APA
Gilbert M, Nicolas G, Cinardi G, Van Boeckel TP, Vanwambeke SO, Wint GRW, Robinson TP. (2018). Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci Data, 5, 180227. https://doi.org/10.1038/sdata.2018.227

Publication

Scientific data
ISSN: 2052-4463
NlmUniqueID: 101640192
Country: England
Language: English
Volume: 5
Pages: 180227
PII: 180227

Researcher Affiliations

Gilbert, Marius
  • Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.
  • Fonds National de la Recherche Scientifique (FNRS), Brussels, Belgium.
Nicolas, Gaëlle
  • Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.
Cinardi, Giusepina
  • Animal Production and Health Division (AGA), Food and Agriculture Organization of the United Nations, Rome, Italy.
Van Boeckel, Thomas P
  • Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
  • Center for Diseases Dynamics Economics and Policy, Washington DC, USA.
Vanwambeke, Sophie O
  • Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
Wint, G R William
  • Environment Research Group Oxford (ERGO), Department of Zoology, Oxford, United Kingdom.
Robinson, Timothy P
  • Animal Production and Health Division (AGA), Food and Agriculture Organization of the United Nations, Rome, Italy.

MeSH Terms

  • Agriculture / statistics & numerical data
  • Animals
  • Buffaloes
  • Cattle
  • Chickens
  • Ducks
  • Goats
  • Horses
  • Livestock
  • Population Density
  • Sheep
  • Swine

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 41 references
  1. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/GIVQ75
    doi: 10.7910/DVN/GIVQ75google scholar: lookup
  2. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/5U8MWI
    doi: 10.7910/DVN/5U8MWIgoogle scholar: lookup
  3. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/BLWPZN
    doi: 10.7910/DVN/BLWPZNgoogle scholar: lookup
  4. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/OCPH42
    doi: 10.7910/DVN/OCPH42google scholar: lookup
  5. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/7Q52MV
    doi: 10.7910/DVN/7Q52MVgoogle scholar: lookup
  6. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/33N0JG
    doi: 10.7910/DVN/33N0JGgoogle scholar: lookup
  7. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/SUFASB
    doi: 10.7910/DVN/SUFASBgoogle scholar: lookup
  8. Gilbert M., net al.n. 2018. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/ICHCBH
    doi: 10.7910/DVN/ICHCBHgoogle scholar: lookup
  9. Robinson T. Global livestock production systems. 152 pp (2011).
  10. Steinfeld H, Gerber P, Wassenaar T D, Castel V, de Haan C. Livestock’s long shadow: environmental issues and options. (FAO, 2006).
  11. Slingenbergh J. World Livestock 2013: changing disease landscapes. (Food and Agriculture Organization of the United Nations FAO, 2013).
  12. Gilbert M, Xiao X, Robinson TP. Intensifying poultry production systems and the emergence of avian influenza in China: a 'One Health/Ecohealth' epitome.. Arch Public Health 2017;75:48.
    pmc: PMC5702979pubmed: 29209498doi: 10.1186/s13690-017-0218-4google scholar: lookup
  13. Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, Teillant A, Laxminarayan R. Global trends in antimicrobial use in food animals.. Proc Natl Acad Sci U S A 2015 May 5;112(18):5649-54.
    pmc: PMC4426470pubmed: 25792457doi: 10.1073/pnas.1503141112google scholar: lookup
  14. Van Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, Levin SA, Bonhoeffer S, Laxminarayan R. Reducing antimicrobial use in food animals.. Science 2017 Sep 29;357(6358):1350-1352.
    pmc: PMC6510296pubmed: 28963240doi: 10.1126/science.aao1495google scholar: lookup
  15. Wint W, Robinson T. Gridded Livestock of the World. (Food and Agriculture Organization, 2007).
  16. Robinson TP, Wint GR, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, Cinardi G, D'Aietti L, Hay SI, Gilbert M. Mapping the global distribution of livestock.. PLoS One 2014;9(5):e96084.
    pmc: PMC4038494pubmed: 24875496doi: 10.1371/journal.pone.0096084google scholar: lookup
  17. Nicolas G, Robinson TP, Wint GR, Conchedda G, Cinardi G, Gilbert M. Using Random Forest to Improve the Downscaling of Global Livestock Census Data.. PLoS One 2016;11(3):e0150424.
    pmc: PMC4792414pubmed: 26977807doi: 10.1371/journal.pone.0150424google scholar: lookup
  18. Channan S, Collins K, Emanuel W R. Global mosaics of the standard MODIS land cover type data. (University of Maryland and the Pacific Northwest National Laboratory, 2014).
  19. Pesaresi M. GHS Built-up Grid, Derived from Landsat, Multitemporal (1975, 1990, 2000, 2014) European Commission, Joint Research Centre (JRC). PID. (2015).
  20. Dudley N. Guidelines for applying protected area management categories. (Iucn, 2008).
  21. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2012.
  22. Hijmans R J, van Etten J. raster: Geographic data analysis and modeling. R Package Version 2, 15 (2014).
  23. Bivand R. Package ‘rgdal’. (2017).
  24. Bivand R. Package ‘maptools’. (2017).
  25. Liaw A, Wiener M. Classification and regression by randomForest. R News 2, 18–22 (2002).
  26. Hollings T. How do you find the Green Sheep? A critical review of the use of remotely sensed imagery to detect and count animals. Methods Ecol. Evol 9, 881–892 (2018).
  27. Cecchi G. Geographic distribution and environmental characterization of livestock production systems in Eastern Africa. Agric. Ecosyst. Environ 135, 98–110 (2010).
  28. Gilbert M, Conchedda G, Van Boeckel TP, Cinardi G, Linard C, Nicolas G, Thanapongtharm W, D'Aietti L, Wint W, Newman SH, Robinson TP. Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs.. PLoS One 2015;10(7):e0133381.
    pmc: PMC4521704pubmed: 26230336doi: 10.1371/journal.pone.0133381google scholar: lookup
  29. Center for International Earth Science Information Network - CIESIN - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Land and Water Area. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), (2016).
  30. UNEP-WCMC, IUCN. The World Database on Protected Areas (WDPA). (UNEP-WCMC, 2010).
  31. Tatem AJ. WorldPop, open data for spatial demography.. Sci Data 2017 Jan 31;4:170004.
    pmc: PMC5283060pubmed: 28140397doi: 10.1038/sdata.2017.4google scholar: lookup
  32. Dobson J E, Bright E A, Coleman P R, Durfee R C, Worley B A. LandScan: a global population database for estimating populations at risk. Photogramm. Eng. Remote Sens. 66, 849–857 (2000).
  33. Center for International Earth Science Information Network - CIESIN - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), (2016).
  34. Nelson A. Travel time to major cities: A global map of Accessibility. Global Environment Monitoring Unit—Joint Research Centre of the European Commission: Ispra, Italy. (2008).
  35. LDAAC. Global 30 Arc-Second Elevation Data Set GTOPO30. (Land Process Distributed Active Archive Center, 2004).
  36. Scharlemann JP, Benz D, Hay SI, Purse BV, Tatem AJ, Wint GR, Rogers DJ. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.. PLoS One 2008 Jan 9;3(1):e1408.
    pmc: PMC2171368pubmed: 18183289doi: 10.1371/journal.pone.0001408google scholar: lookup
  37. Jones P G, Thornton P K. Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environ. Sci. Policy 12, 427–437 (2009).
  38. Zhang X. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).
  39. Fritz S, See L, McCallum I, You L, Bun A, Moltchanova E, Duerauer M, Albrecht F, Schill C, Perger C, Havlik P, Mosnier A, Thornton P, Wood-Sichra U, Herrero M, Becker-Reshef I, Justice C, Hansen M, Gong P, Abdel Aziz S, Cipriani A, Cumani R, Cecchi G, Conchedda G, Ferreira S, Gomez A, Haffani M, Kayitakire F, Malanding J, Mueller R, Newby T, Nonguierma A, Olusegun A, Ortner S, Rajak DR, Rocha J, Schepaschenko D, Schepaschenko M, Terekhov A, Tiangwa A, Vancutsem C, Vintrou E, Wenbin W, van der Velde M, Dunwoody A, Kraxner F, Obersteiner M. Mapping global cropland and field size.. Glob Chang Biol 2015 May;21(5):1980-92.
    pubmed: 25640302doi: 10.1111/gcb.12838google scholar: lookup
  40. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JR. High-resolution global maps of 21st-century forest cover change.. Science 2013 Nov 15;342(6160):850-3.
    pubmed: 24233722doi: 10.1126/science.1244693google scholar: lookup
  41. Fick S E, Hijmans R J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

Citations

This article has been cited 91 times.
  1. Thézé J, Ambroset C, Barry S, Masseglia S, Colin A, Tricot A, Tardy F, Bailly X. Genome-wide phylodynamic approach reveals the epidemic dynamics of the main Mycoplasma bovis subtype circulating in France.. Microb Genom 2023 Jul;9(7).
    doi: 10.1099/mgen.0.001067pubmed: 37486749google scholar: lookup
  2. Leijten F, Lantz C Baldos U, Johnson JA, Sim S, Verburg PH. Projecting global oil palm expansion under zero-deforestation commitments: Direct and indirect land use change impacts.. iScience 2023 Jun 16;26(6):106971.
    doi: 10.1016/j.isci.2023.106971pubmed: 37332602google scholar: lookup
  3. Luong T, Be TB, Hoang MD, Hoang TTH, Pham QT, Tran TMH, Ho HD, Long PT, Blackburn JK. Incidence and Spatial distribution of Human and Livestock Anthrax in Cao Bang Province, Vietnam (2004-2020).. Vector Borne Zoonotic Dis 2023 May;23(5):306-309.
    doi: 10.1089/vbz.2022.0072pubmed: 37140464google scholar: lookup
  4. Wuepper D, Le Clech S, Zilberman D, Mueller N, Finger R. Countries influence the trade-off between crop yields and nitrogen pollution.. Nat Food 2020 Nov;1(11):713-719.
    doi: 10.1038/s43016-020-00185-6pubmed: 37128040google scholar: lookup
  5. Rulli MC, D'Odorico P, Galli N, Hayman DTS. Land-use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats.. Nat Food 2021 Jun;2(6):409-416.
    doi: 10.1038/s43016-021-00285-xpubmed: 37118224google scholar: lookup
  6. Hawkins JW, Komarek AM, Kihoro EM, Nicholson CF, Omore AO, Yesuf GU, Ericksen PJ, Schoneveld GC, Rufino MC. High-yield dairy cattle breeds improve farmer incomes, curtail greenhouse gas emissions and reduce dairy import dependency in Tanzania.. Nat Food 2022 Nov;3(11):957-967.
    doi: 10.1038/s43016-022-00633-5pubmed: 37118219google scholar: lookup
  7. Zhao C, Wang Y, Tiseo K, Pires J, Criscuolo NG, Van Boeckel TP. Geographically targeted surveillance of livestock could help prioritize intervention against antimicrobial resistance in China.. Nat Food 2021 Aug;2(8):596-602.
    doi: 10.1038/s43016-021-00320-xpubmed: 37118162google scholar: lookup
  8. Rahimi J, Mutua JY, Notenbaert AMO, Marshall K, Butterbach-Bahl K. Heat stress will detrimentally impact future livestock production in East Africa.. Nat Food 2021 Feb;2(2):88-96.
    doi: 10.1038/s43016-021-00226-8pubmed: 37117410google scholar: lookup
  9. Colston JM, Chernyavskiy P, Gardner L, Fang B, Houpt E, Swarup S, Badr H, Zaitchik B, Lakshmi V, Kosek M. The Planetary Child Health and Enterics Observatory (Plan-EO): a Protocol for an Interdisciplinary Research Initiative and Web-Based Dashboard for Climate-Informed Mapping of Enteric Infectious Diseases and their Risk Factors and Interventions in Low- and Middle-Income Countries.. Res Sq 2023 Apr 18;.
    doi: 10.21203/rs.3.rs-2640564/v1pubmed: 36993232google scholar: lookup
  10. Mulchandani R, Wang Y, Gilbert M, Van Boeckel TP. Global trends in antimicrobial use in food-producing animals: 2020 to 2030.. PLOS Glob Public Health 2023;3(2):e0001305.
    doi: 10.1371/journal.pgph.0001305pubmed: 36963007google scholar: lookup
  11. Zhan N, Liu W, Ye T, Li H, Chen S, Ma H. High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020.. Sci Data 2023 Mar 18;10(1):142.
    doi: 10.1038/s41597-023-02050-0pubmed: 36932126google scholar: lookup
  12. Olson G, Jeske W, Iqbal O, Krupa E, Farooqui A, Siddiqui F, Hoppensteadt D, Kouta A, Fareed J. Potency Adjusted Blended Heparin of Bovine, Ovine, and Porcine Heparin Exhibit Comparable Biologic Effects to Referenced Single-Sourced Porcine Heparin.. Clin Appl Thromb Hemost 2023 Jan-Dec;29:10760296231163251.
    doi: 10.1177/10760296231163251pubmed: 36908199google scholar: lookup
  13. Telford C, Nyakarahuka L, Waller L, Kitron U, Shoemaker T. Geostatistical Modeling and Prediction of Rift Valley Fever Seroprevalence among Livestock in Uganda.. Am J Trop Med Hyg 2023 Apr 5;108(4):712-721.
    doi: 10.4269/ajtmh.22-0555pubmed: 36878208google scholar: lookup
  14. Braczkowski AR, O'Bryan CJ, Lessmann C, Rondinini C, Crysell AP, Gilbert S, Stringer M, Gibson L, Biggs D. The unequal burden of human-wildlife conflict.. Commun Biol 2023 Feb 23;6(1):182.
    doi: 10.1038/s42003-023-04493-ypubmed: 36823291google scholar: lookup
  15. Wiethase JH, Critchlow R, Foley C, Foley L, Kinsey EJ, Bergman BG, Osujaki B, Mbwambo Z, Kirway PB, Redeker KR, Hartley SE, Beale CM. Pathways of degradation in rangelands in Northern Tanzania show their loss of resistance, but potential for recovery.. Sci Rep 2023 Feb 22;13(1):2417.
    doi: 10.1038/s41598-023-29358-6pubmed: 36813819google scholar: lookup
  16. Brouillet A, Sultan B. Livestock exposure to future cumulated climate-related stressors in West Africa.. Sci Rep 2023 Feb 15;13(1):2698.
    doi: 10.1038/s41598-022-22544-ypubmed: 36792631google scholar: lookup
  17. Lobato-Bailón L, García-Ulloa M, Santos A, Guixé D, Camprodon J, Florensa-Rius X, Molleda R, Manzano R, P Ribas M, Espunyes J, Dias-Alves A, Marco I, Migura-Garcia L, Martínez-Urtaza J, Cabezón O. The fecal bacterial microbiome of the Kuhl's pipistrelle bat (Pipistrellus kuhlii) reflects landscape anthropogenic pressure.. Anim Microbiome 2023 Feb 4;5(1):7.
    doi: 10.1186/s42523-023-00229-9pubmed: 36739423google scholar: lookup
  18. Meng N, Wang L, Qi W, Dai X, Li Z, Yang Y, Li R, Ma J, Zheng H. A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015.. Sci Data 2023 Feb 2;10(1):68.
    doi: 10.1038/s41597-023-01970-1pubmed: 36732526google scholar: lookup
  19. Tan LM, Hung DN, My DT, Walker MA, Ha HTT, Thai PQ, Hung TTM, Blackburn JK. Spatial analysis of human and livestock anthrax in Dien Bien province, Vietnam (2010-2019) and the significance of anthrax vaccination in livestock.. PLoS Negl Trop Dis 2022 Dec;16(12):e0010942.
    doi: 10.1371/journal.pntd.0010942pubmed: 36538536google scholar: lookup
  20. Schooler SL, Finnegan SP, Fowler NL, Kellner KF, Lutto AL, Parchizadeh J, van den Bosch M, Zubiria Perez A, Masinde LM, Mwampeta SB, Boone HM, Gantchoff MG, Hill JE, Kautz TM, Wehr NH, Fyumagwa R, Belant JL. Factors influencing lion movements and habitat use in the western Serengeti ecosystem, Tanzania.. Sci Rep 2022 Nov 7;12(1):18890.
    doi: 10.1038/s41598-022-22053-ypubmed: 36344560google scholar: lookup
  21. Cai Q, Zeng N, Zhao F, Han P, Liu D, Lin X, Chen J. The impact of human and livestock respiration on CO(2) emissions from 14 global cities.. Carbon Balance Manag 2022 Nov 3;17(1):17.
    doi: 10.1186/s13021-022-00217-7pubmed: 36326963google scholar: lookup
  22. Kittiwan N, Calland JK, Mourkas E, Hitchings MD, Murray S, Tadee P, Tadee P, Duangsonk K, Meric G, Sheppard SK, Patchanee P, Pascoe B. Genetic diversity and variation in antimicrobial-resistance determinants of non-serotype 2 Streptococcus suis isolates from healthy pigs.. Microb Genom 2022 Nov;8(11).
    doi: 10.1099/mgen.0.000882pubmed: 36326658google scholar: lookup
  23. Berdugo M, Gaitán JJ, Delgado-Baquerizo M, Crowther TW, Dakos V. Prevalence and drivers of abrupt vegetation shifts in global drylands.. Proc Natl Acad Sci U S A 2022 Oct 25;119(43):e2123393119.
    doi: 10.1073/pnas.2123393119pubmed: 36252001google scholar: lookup
  24. Huang RM, van Aarde RJ, Pimm SL, Chase MJ, Leggett K. Mapping potential connections between Southern Africa's elephant populations.. PLoS One 2022;17(10):e0275791.
    doi: 10.1371/journal.pone.0275791pubmed: 36219597google scholar: lookup
  25. Agoltsov VA, Podshibyakin DV, Padilo LP, Chernykh OY, Popova OM, Stupina LV, Solotova NV. Analysis of peste des petits ruminants virus spread and the risk of its introduction into the territory of the Russian Federation.. Vet World 2022 Jul;15(7):1610-1616.
    doi: 10.14202/vetworld.2022.1610-1616pubmed: 36185524google scholar: lookup
  26. Fernández-Bastit L, Roca N, Romero-Durana M, Rodon J, Cantero G, García Ó, López C, Pérez M, López R, Carrillo J, Izquierdo-Useros N, Blanco J, Clotet B, Pujols J, Vergara-Alert J, Segalés J, Lorca-Oró C. Susceptibility of Domestic Goat (Capra aegagrus hircus) to Experimental Infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) B.1.351/Beta Variant.. Viruses 2022 Sep 9;14(9).
    doi: 10.3390/v14092002pubmed: 36146808google scholar: lookup
  27. Meza DK, Mollentze N, Broos A, Tello C, Valderrama W, Recuenco S, Carrera JE, Shiva C, Falcon N, Viana M, Streicker DG. Ecological determinants of rabies virus dynamics in vampire bats and spillover to livestock.. Proc Biol Sci 2022 Sep 14;289(1982):20220860.
    doi: 10.1098/rspb.2022.0860pubmed: 36069012google scholar: lookup
  28. Meisner J, Kato A, Lemerani MM, Mwamba Miaka E, Ismail Taban A, Wakefield J, Rowhani-Rahbar A, Pigott DM, Mayer JD, Rabinowitz PM. The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense: A causal inference-based approach.. PLoS Negl Trop Dis 2022 Aug;16(8):e0010155.
    doi: 10.1371/journal.pntd.0010155pubmed: 36037205google scholar: lookup
  29. Bottinelli M, Gastaldelli M, Picchi M, Dall'Ora A, Cristovao Borges L, Ramírez AS, Matucci A, Catania S. The Monitoring of Mycoplasma gallisepticum Minimum Inhibitory Concentrations during the Last Decade (2010-2020) Seems to Reveal a Comeback of Susceptibility to Macrolides, Tiamulin, and Lincomycin.. Antibiotics (Basel) 2022 Jul 29;11(8).
    doi: 10.3390/antibiotics11081021pubmed: 36009890google scholar: lookup
  30. Legnardi M, Raizman E, Beltran-Alcrudo D, Cinardi G, Robinson T, Falzon LC, Djomgang HK, Okori E, Parida S, Njeumi F, Benfield CTO. Peste des Petits Ruminants in Central and Eastern Asia/West Eurasia: Epidemiological Situation and Status of Control and Eradication Activities after the First Phase of the PPR Global Eradication Programme (2017-2021).. Animals (Basel) 2022 Aug 10;12(16).
    doi: 10.3390/ani12162030pubmed: 36009619google scholar: lookup
  31. Zhou Y, Yang L, Han X, Han J, Hu Y, Li F, Xia H, Peng L, Boschiero C, Rosen BD, Bickhart DM, Zhang S, Guo A, Van Tassell CP, Smith TPL, Yang L, Liu GE. Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history.. Genome Res 2022 Aug 17;32(8):1585-601.
    doi: 10.1101/gr.276550.122pubmed: 35977842google scholar: lookup
  32. Chen J, Shi H, Wang X, Zhang Y, Zhang Z. Effectiveness of China's Protected Areas in Mitigating Human Activity Pressure.. Int J Environ Res Public Health 2022 Jul 30;19(15).
    doi: 10.3390/ijerph19159335pubmed: 35954691google scholar: lookup
  33. Meisner J, Kato A, Lemerani M, Miaka EM, Ismail AT, Wakefield J, Rowhani-Rahbar A, Pigott D, Mayer J, Rabinowitz P. A time-series approach to mapping livestock density using household survey data.. Sci Rep 2022 Aug 3;12(1):13310.
    doi: 10.1038/s41598-022-16118-1pubmed: 35922452google scholar: lookup
  34. Jayasinghe P, Ramilan T, Donaghy DJ, Pembleton KG, Barber DG. Comparison of Nutritive Values of Tropical Pasture Species Grown in Different Environments, and Implications for Livestock Methane Production: A Meta-Analysis.. Animals (Basel) 2022 Jul 14;12(14).
    doi: 10.3390/ani12141806pubmed: 35883354google scholar: lookup
  35. Van Damme M, Clarisse L, Stavrakou T, Wichink Kruit R, Sellekaerts L, Viatte C, Clerbaux C, Coheur PF. On the weekly cycle of atmospheric ammonia over European agricultural hotspots.. Sci Rep 2022 Jul 19;12(1):12327.
    doi: 10.1038/s41598-022-15836-wpubmed: 35853953google scholar: lookup
  36. Desvars-Larrive A, Käsbohrer A. Surveillance and Control of African Swine Fever in the Early Phase of the COVID-19 Pandemic, March-May 2020: A Multi-Country E-Survey.. Front Vet Sci 2022;9:867631.
    doi: 10.3389/fvets.2022.867631pubmed: 35774983google scholar: lookup
  37. Hope A, Mugenyi A, Esterhuizen J, Tirados I, Cunningham L, Garrod G, Lehane MJ, Longbottom J, Mangwiro TC, Opiyo M, Stanton M, Torr SJ, Vale GA, Waiswa C, Selby R. Scaling up of tsetse control to eliminate Gambian sleeping sickness in northern Uganda.. PLoS Negl Trop Dis 2022 Jun;16(6):e0010222.
    doi: 10.1371/journal.pntd.0010222pubmed: 35767572google scholar: lookup
  38. Tagwireyi P, Ndebele M, Chikurunhe W. Climate change diminishes the potential habitat of the bont tick (Amblyomma hebraeum): evidence from Mashonaland Central Province, Zimbabwe.. Parasit Vectors 2022 Jun 28;15(1):237.
    doi: 10.1186/s13071-022-05346-zpubmed: 35765035google scholar: lookup
  39. Evans MV, Drake JM. A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface.. Ecohealth 2022 Jun;19(2):246-258.
    doi: 10.1007/s10393-022-01599-3pubmed: 35666334google scholar: lookup
  40. Barber-Cross T, Filazzola A, Brown C, Dettlaff MA, Batbaatar A, Grenke JSJ, Peetoom Heida I, Cahill JF Jr. A global inventory of animal diversity measured in different grazing treatments.. Sci Data 2022 May 16;9(1):209.
    doi: 10.1038/s41597-022-01326-1pubmed: 35577900google scholar: lookup
  41. Zhang T, Wang T, Niu Q, Xu L, Chen Y, Gao X, Gao H, Zhang L, Liu GE, Li J, Xu L. Transcriptional atlas analysis from multiple tissues reveals the expression specificity patterns in beef cattle.. BMC Biol 2022 Mar 29;20(1):79.
    doi: 10.1186/s12915-022-01269-4pubmed: 35351103google scholar: lookup
  42. Liu L, Xu W, Lu X, Zhong B, Guo Y, Lu X, Zhao Y, He W, Wang S, Zhang X, Liu X, Vitousek P. Exploring global changes in agricultural ammonia emissions and their contribution to nitrogen deposition since 1980.. Proc Natl Acad Sci U S A 2022 Apr 5;119(14):e2121998119.
    doi: 10.1073/pnas.2121998119pubmed: 35344440google scholar: lookup
  43. Piipponen J, Jalava M, de Leeuw J, Rizayeva A, Godde C, Cramer G, Herrero M, Kummu M. Global trends in grassland carrying capacity and relative stocking density of livestock.. Glob Chang Biol 2022 Jun;28(12):3902-3919.
    doi: 10.1111/gcb.16174pubmed: 35320616google scholar: lookup
  44. Seck I, Lo MM, Fall AG, Diop M, Ciss M, Cêtre-Sossah CB, Faye C, Lo M, Gaye AM, Coste C, Squarzoni-Diaw C, Alambedji RB, Sall B, Apolloni A, Lancelot R. Identification of drivers of Rift Valley fever after the 2013-14 outbreak in Senegal using serological data in small ruminants.. PLoS Negl Trop Dis 2022 Feb;16(2):e0010024.
    doi: 10.1371/journal.pntd.0010024pubmed: 35108284google scholar: lookup
  45. Semenchuk P, Plutzar C, Kastner T, Matej S, Bidoglio G, Erb KH, Essl F, Haberl H, Wessely J, Krausmann F, Dullinger S. Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity.. Nat Commun 2022 Feb 1;13(1):615.
    doi: 10.1038/s41467-022-28245-4pubmed: 35105884google scholar: lookup
  46. Afshari Safavi E. Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features.. Trop Anim Health Prod 2022 Jan 14;54(1):55.
    doi: 10.1007/s11250-022-03073-2pubmed: 35029707google scholar: lookup
  47. Battini M, Renna M, Giammarino M, Battaglini L, Mattiello S. Feasibility and Reliability of the AWIN Welfare Assessment Protocol for Dairy Goats in Semi-extensive Farming Conditions.. Front Vet Sci 2021;8:731927.
    doi: 10.3389/fvets.2021.731927pubmed: 34746279google scholar: lookup
  48. Pautienius A, Dudas G, Simkute E, Grigas J, Zakiene I, Paulauskas A, Armonaite A, Zienius D, Slyzius E, Stankevicius A. Bulk Milk Tank Samples Are Suitable to Assess Circulation of Tick-Borne Encephalitis Virus in High Endemic Areas.. Viruses 2021 Sep 5;13(9).
    doi: 10.3390/v13091772pubmed: 34578353google scholar: lookup
  49. Nisbet EG, Dlugokencky EJ, Fisher RE, France JL, Lowry D, Manning MR, Michel SE, Warwick NJ. Atmospheric methane and nitrous oxide: challenges alongthe path to Net Zero.. Philos Trans A Math Phys Eng Sci 2021 Nov 15;379(2210):20200457.
    doi: 10.1098/rsta.2020.0457pubmed: 34565227google scholar: lookup
  50. Neila-Ibáñez C, Casal J, Hennig-Pauka I, Stockhofe-Zurwieden N, Gottschalk M, Migura-García L, Pailler-García L, Napp S. Stochastic Assessment of the Economic Impact of Streptococcus suis-Associated Disease in German, Dutch and Spanish Swine Farms.. Front Vet Sci 2021;8:676002.
    doi: 10.3389/fvets.2021.676002pubmed: 34490389google scholar: lookup
  51. Thornton P, Nelson G, Mayberry D, Herrero M. Increases in extreme heat stress in domesticated livestock species during the twenty-first century.. Glob Chang Biol 2021 Nov;27(22):5762-5772.
    doi: 10.1111/gcb.15825pubmed: 34410027google scholar: lookup
  52. Michelitsch A, Wernike K, Ulrich L, Mettenleiter TC, Beer M. SARS-CoV-2 in animals: From potential hosts to animal models.. Adv Virus Res 2021;110:59-102.
    doi: 10.1016/bs.aivir.2021.03.004pubmed: 34353482google scholar: lookup
  53. El Moustaid F, Thornton Z, Slamani H, Ryan SJ, Johnson LR. Predicting temperature-dependent transmission suitability of bluetongue virus in livestock.. Parasit Vectors 2021 Jul 30;14(1):382.
    doi: 10.1186/s13071-021-04826-ypubmed: 34330315google scholar: lookup
  54. Fuks D, Marom N. Sheep and wheat domestication in southwest Asia: a meta-trajectory of intensification and loss.. Anim Front 2021 May;11(3):20-29.
    doi: 10.1093/af/vfab010pubmed: 34158986google scholar: lookup
  55. Taylor C, Brodbelt DC, Dobson B, Catchpole B, O'Neill DG, Stevens KB. Spatio-temporal distribution and agroecological factors associated with canine leptospirosis in Great Britain.. Prev Vet Med 2021 Aug;193:105407.
    doi: 10.1016/j.prevetmed.2021.105407pubmed: 34153840google scholar: lookup
  56. Di Nardo A, Ferretti L, Wadsworth J, Mioulet V, Gelman B, Karniely S, Scherbakov A, Ziay G, Özyörük F, Parlak Ü, Tuncer-Göktuna P, Hassanzadeh R, Khalaj M, Dastoor SM, Abdollahi D, Khan EU, Afzal M, Hussain M, Knowles NJ, King DP. Evolutionary and Ecological Drivers Shape the Emergence and Extinction of Foot-and-Mouth Disease Virus Lineages.. Mol Biol Evol 2021 Sep 27;38(10):4346-4361.
    doi: 10.1093/molbev/msab172pubmed: 34115138google scholar: lookup
  57. Kummu M, Heino M, Taka M, Varis O, Viviroli D. Climate change risks pushing one-third of global food production outside the safe climatic space.. One Earth 2021 May 21;4(5):720-729.
    doi: 10.1016/j.oneear.2021.04.017pubmed: 34056573google scholar: lookup
  58. Grewar JD, Kotze JL, Parker BJ, van Helden LS, Weyer CT. An entry risk assessment of African horse sickness virus into the controlled area of South Africa through the legal movement of equids.. PLoS One 2021;16(5):e0252117.
    doi: 10.1371/journal.pone.0252117pubmed: 34038466google scholar: lookup
  59. Di Minin E, Slotow R, Fink C, Bauer H, Packer C. A pan-African spatial assessment of human conflicts with lions and elephants.. Nat Commun 2021 May 20;12(1):2978.
    doi: 10.1038/s41467-021-23283-wpubmed: 34017002google scholar: lookup
  60. Lord JS. Changes in Rice and Livestock Production and the Potential Emergence of Japanese Encephalitis in Africa.. Pathogens 2021 Mar 4;10(3).
    doi: 10.3390/pathogens10030294pubmed: 33806470google scholar: lookup
  61. de Miguel R, Arrieta M, Rodríguez-Largo A, Echeverría I, Resendiz R, Pérez E, Ruiz H, Pérez M, de Andrés D, Reina R, de Blas I, Luján L. Worldwide Prevalence of Small Ruminant Lentiviruses in Sheep: A Systematic Review and Meta-Analysis.. Animals (Basel) 2021 Mar 11;11(3).
    doi: 10.3390/ani11030784pubmed: 33799908google scholar: lookup
  62. Otieno FT, Gachohi J, Gikuma-Njuru P, Kariuki P, Oyas H, Canfield SA, Blackburn JK, Njenga MK, Bett B. Modeling the spatial distribution of anthrax in southern Kenya.. PLoS Negl Trop Dis 2021 Mar;15(3):e0009301.
    doi: 10.1371/journal.pntd.0009301pubmed: 33780459google scholar: lookup
  63. Utaaker KS, Chaudhary S, Kifleyohannes T, Robertson LJ. Global Goat! Is the Expanding Goat Population an Important Reservoir of Cryptosporidium?. Front Vet Sci 2021;8:648500.
    doi: 10.3389/fvets.2021.648500pubmed: 33748221google scholar: lookup
  64. Hawkins J, Yesuf G, Zijlstra M, Schoneveld GC, Rufino MC. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector.. Sci Rep 2021 Feb 18;11(1):4190.
    doi: 10.1038/s41598-021-83475-8pubmed: 33602970google scholar: lookup
  65. Tiseo K, Huber L, Gilbert M, Robinson TP, Van Boeckel TP. Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030.. Antibiotics (Basel) 2020 Dec 17;9(12).
    doi: 10.3390/antibiotics9120918pubmed: 33348801google scholar: lookup
  66. Rothman-Ostrow P, Gilbert W, Rushton J. Tropical Livestock Units: Re-evaluating a Methodology.. Front Vet Sci 2020;7:556788.
    doi: 10.3389/fvets.2020.556788pubmed: 33330685google scholar: lookup
  67. Câmara RJF, Bueno BL, Resende CF, Balasuriya UBR, Sakamoto SM, Reis JKPD. Viral Diseases that Affect Donkeys and Mules.. Animals (Basel) 2020 Nov 25;10(12).
    doi: 10.3390/ani10122203pubmed: 33255568google scholar: lookup
  68. Ramos B, Pereira AC, Reis AC, Cunha MV. Estimates of the global and continental burden of animal tuberculosis in key livestock species worldwide: A meta-analysis study.. One Health 2020 Dec;10:100169.
    doi: 10.1016/j.onehlt.2020.100169pubmed: 33134472google scholar: lookup
  69. Walsh MG, Sawleshwarkar S, Hossain S, Mor SM. Whence the next pandemic? The intersecting global geography of the animal-human interface, poor health systems and air transit centrality reveals conduits for high-impact spillover.. One Health 2020 Dec 20;11:100177.
    doi: 10.1016/j.onehlt.2020.100177pubmed: 33052311google scholar: lookup
  70. Brunner N, Kühleitner M. The growth of domestic goats and sheep: A meta study with Bertalanffy-Pütter models.. Vet Anim Sci 2020 Dec;10:100135.
    doi: 10.1016/j.vas.2020.100135pubmed: 32885092google scholar: lookup
  71. Morgoglione ME, Bosco A, Maurelli MP, Alves LC, Saralli G, Bruni G, Cringoli G, Rinaldi L. A 10-Year Surveillance of Eimeria spp. in Cattle and Buffaloes in a Mediterranean Area.. Front Vet Sci 2020;7:410.
    doi: 10.3389/fvets.2020.00410pubmed: 32851006google scholar: lookup
  72. Lycett SJ, Pohlmann A, Staubach C, Caliendo V, Woolhouse M, Beer M, Kuiken T. Genesis and spread of multiple reassortants during the 2016/2017 H5 avian influenza epidemic in Eurasia.. Proc Natl Acad Sci U S A 2020 Aug 25;117(34):20814-20825.
    doi: 10.1073/pnas.2001813117pubmed: 32769208google scholar: lookup
  73. Jung M, Dahal PR, Butchart SHM, Donald PF, De Lamo X, Lesiv M, Kapos V, Rondinini C, Visconti P. A global map of terrestrial habitat types.. Sci Data 2020 Aug 5;7(1):256.
    doi: 10.1038/s41597-020-00599-8pubmed: 32759943google scholar: lookup
  74. Hardcastle AN, Osborne JCP, Ramshaw RE, Hulland EN, Morgan JD, Miller-Petrie MK, Hon J, Earl L, Rabinowitz P, Wasserheit JN, Gilbert M, Robinson TP, Wint GRW, Shirude S, Hay SI, Pigott DM. Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability.. Int J Infect Dis 2020 Oct;99:362-372.
    doi: 10.1016/j.ijid.2020.07.043pubmed: 32738486google scholar: lookup
  75. Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA).. JAMIA Open 2020 Jul;3(2):306-317.
    doi: 10.1093/jamiaopen/ooaa005pubmed: 32734172google scholar: lookup
  76. Khan AU, Sayour AE, Melzer F, El-Soally SAGE, Elschner MC, Shell WS, Moawad AA, Mohamed SA, Hendam A, Roesler U, Neubauer H, El-Adawy H. Seroprevalence and Molecular Identification of Brucella spp. in Camels in Egypt.. Microorganisms 2020 Jul 13;8(7).
    doi: 10.3390/microorganisms8071035pubmed: 32668648google scholar: lookup
  77. Quan TM, Thao TTN, Duy NM, Nhat TM, Clapham H. Estimates of the global burden of Japanese encephalitis and the impact of vaccination from 2000-2015.. Elife 2020 May 26;9.
    doi: 10.7554/eLife.51027pubmed: 32450946google scholar: lookup
  78. Zubkova M, Boschetti L, Abatzoglou JT, Giglio L. Changes in Fire Activity in Africa from 2002 to 2016 and Their Potential Drivers.. Geophys Res Lett 2019 Jul 16;46(13):7643-7653.
    doi: 10.1029/2019gl083469pubmed: 32440032google scholar: lookup
  79. Yanase T, Murota K, Hayama Y. Endemic and Emerging Arboviruses in Domestic Ruminants in East Asia.. Front Vet Sci 2020;7:168.
    doi: 10.3389/fvets.2020.00168pubmed: 32318588google scholar: lookup
  80. Miao D, Dai K, Zhao GP, Li XL, Shi WQ, Zhang JS, Yang Y, Liu W, Fang LQ. Mapping the global potential transmission hotspots for severe fever with thrombocytopenia syndrome by machine learning methods.. Emerg Microbes Infect 2020 Dec;9(1):817-826.
    doi: 10.1080/22221751.2020.1748521pubmed: 32212956google scholar: lookup
  81. Wang J, Li J, Liu F, Cheng Y, Su J. Characterization of Salmonella enterica Isolates from Diseased Poultry in Northern China between 2014 and 2018.. Pathogens 2020 Feb 4;9(2).
    doi: 10.3390/pathogens9020095pubmed: 32033063google scholar: lookup
  82. Da Re D, Gilbert M, Chaiban C, Bourguignon P, Thanapongtharm W, Robinson TP, Vanwambeke SO. Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.. PLoS One 2020;15(1):e0221070.
    doi: 10.1371/journal.pone.0221070pubmed: 31986146google scholar: lookup
  83. Sandom CJ, Middleton O, Lundgren E, Rowan J, Schowanek SD, Svenning JC, Faurby S. Trophic rewilding presents regionally specific opportunities for mitigating climate change.. Philos Trans R Soc Lond B Biol Sci 2020 Mar 16;375(1794):20190125.
    doi: 10.1098/rstb.2019.0125pubmed: 31983340google scholar: lookup
  84. Karimi A, Jones K. Assessing national human footprint and implications for biodiversity conservation in Iran.. Ambio 2020 Sep;49(9):1506-1518.
    doi: 10.1007/s13280-019-01305-8pubmed: 31955400google scholar: lookup
  85. Semango G, Hamilton CM, Kreppel K, Katzer F, Kibona T, Lankester F, Allan KJ, Thomas KM, Claxton JR, Innes EA, Swai ES, Buza J, Cleaveland S, de Glanville WA. The Sero-epidemiology of Neospora caninum in Cattle in Northern Tanzania.. Front Vet Sci 2019;6:327.
    doi: 10.3389/fvets.2019.00327pubmed: 31681800google scholar: lookup
  86. Umar S, Teillaud A, Aslam HB, Guerin JL, Ducatez MF. Molecular epidemiology of respiratory viruses in commercial chicken flocks in Pakistan from 2014 through to 2016.. BMC Vet Res 2019 Oct 21;15(1):351.
    doi: 10.1186/s12917-019-2103-6pubmed: 31638995google scholar: lookup
  87. Leta S, Fetene E, Mulatu T, Amenu K, Jaleta MB, Beyene TJ, Negussie H, Revie CW. Modeling the global distribution of Culicoides imicola: an Ensemble approach.. Sci Rep 2019 Oct 2;9(1):14187.
    doi: 10.1038/s41598-019-50765-1pubmed: 31578399google scholar: lookup
  88. Hicks JT, Dimitrov KM, Afonso CL, Ramey AM, Bahl J. Global phylodynamic analysis of avian paramyxovirus-1 provides evidence of inter-host transmission and intercontinental spatial diffusion.. BMC Evol Biol 2019 May 24;19(1):108.
    doi: 10.1186/s12862-019-1431-2pubmed: 31126244google scholar: lookup
  89. Vigiak O, Grizzetti B, Udias-Moinelo A, Zanni M, Dorati C, Bouraoui F, Pistocchi A. Predicting biochemical oxygen demand in European freshwater bodies.. Sci Total Environ 2019 May 20;666:1089-1105.
    doi: 10.1016/j.scitotenv.2019.02.252pubmed: 30970475google scholar: lookup
  90. Becker DJ, Nachtmann C, Argibay HD, Botto G, Escalera-Zamudio M, Carrera JE, Tello C, Winiarski E, Greenwood AD, Méndez-Ojeda ML, Loza-Rubio E, Lavergne A, de Thoisy B, Czirják GÁ, Plowright RK, Altizer S, Streicker DG. Leukocyte Profiles Reflect Geographic Range Limits in a Widespread Neotropical Bat.. Integr Comp Biol 2019 Nov 1;59(5):1176-1189.
    doi: 10.1093/icb/icz007pubmed: 30873523google scholar: lookup
  91. Hankerson BR, Schierhorn F, Prishchepov AV, Dong C, Eisfelder C, Müller D. Modeling the spatial distribution of grazing intensity in Kazakhstan.. PLoS One 2019;14(1):e0210051.
    doi: 10.1371/journal.pone.0210051pubmed: 30633752google scholar: lookup
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