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

Livestock play a key role in global food systems as the main source of animal protein (milk, meat and eggs), contribute to crop productivity through the provision of draught power and manure, and to the livelihoods and nutrition of poor households in low- and middle-income countries1 (LMICs). Livestock farming has a major impact on the environment, through greenhouse gas (GHG) emissions from enteric fermentation and manure, disruption of nitrogen and phosphorous cycles and indirect impacts on biodiversity and other ecosystem services through overgrazing and land-use change2. Livestock farming also bears public health implications through its role in food-borne disease transmission, the emergence and spread of infectious zoonotic diseases3 such as avian influenza4, Q-fever and MERS and its contribution to the global burden of antimicrobial resistance, linked to the routine abuse of those drugs in livestock production.5,6 Detailed, contemporary data sets on the global distribution of the most important species of farmed animals have a wide range of applications in understanding the social, economic, environmental, epidemiological and public health impacts of the livestock sector. The gridded livestock of the world database (GLW 1) produced in 2007 had three objectives7: i) to collect, harmonize and disseminate subnational global livestock data, ii) to predict livestock numbers in areas with missing census counts (gap-filling), and iii) to provide a statistically-informed estimate of how livestock may be distributed within census units (downscaling). GLW 1 was produced at a spatial resolution of 0.0416666 decimal degrees (approximately 5 km at the equator). In 2014, an updated GLW 2 was published, benefiting from the availability of finer-scale and more contemporary input census data, from the improvement of the processing and from higher spatial resolution predictor variables that were used for downscaling8. In this paper, we describe a new global, subnational livestock dataset (GLW 3) generated using Random Forests (RF), a machine-learning technique recently shown to provided more accurate gapfilling and disaggregation of livestock data than did the previously-used multivariate regression methods9. In addition to that important change in methodology, GLW 3 differs from the previous ones in three ways. For each species, we now provide a detailed report that includes comprehensive metadata on the input census data for each country (e.g. year, resolution and source) and goodness-of-fit metrics of the models by continent and by the size of the administrative unit from which the census data came. This enables users to assess the quality of the estimates for each combination of species, country and size of census unit. All species distributions are now available in two representations, termed dasymetric (DA) and arealweighted (AW). The DA models correspond to previous GLW versions, whereby different animal densities are assigned to different pixels within a given census polygon according to the RF models. In contrast, the AW models simply spread individuals of a census polygon evenly, and the density of animals in each pixel corresponds to the average number of animals per km2 of suitable land in the census unit. The AW models were introduced because the spatial predictor variables used in the downscaling algorithms (e.g. human population density, vegetation indices and topography) may introduce uncontrolled confounding effects or circularity for users wishing to study livestock distribution numbers independently of any other spatial variables. The AW models are free of the influence of other spatial predictor variables, at the cost of displaying cruder distribution patterns, especially in large census areas containing a wide range of different environmental, land-use and farming conditions. In polygons where input census data were missing, the AW model simply includes the aggregated predictions of the DA models, and a separate layer is provided for the user that distinguishes between predictions and census observations. GLW 3 provides global data (DA, AW and prediction status) at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator), as the higher spatial resolution of previous GLW versions could be misleading in areas where the census data were of poor quality. Future versions of GLW will differentiate stocks according to production systems for ruminant (meat vs. dairy) and monogastric species (intensive vs. extensive, meat vs. egg production). Higher resolution models for individual countries where the census data can support such predictions will also be produced