This data set was produced in complement to GLW published by Gilbert et al. (2018) to contain the latest subnational pig distribution data to available to date (November 2018) in support of the risk assessment of the ongoing African Swine Fever epidemics. GLW v3 dataset are organised around a pivot year, 2010 for GLW v3, which correspond to the median year of the subnational data set. In this release, the latest sub-national data sets have been integrated with a particular focus on Asia, with, for example, new data from China (2015) and Indonesia (2017), and much higher or more recent data for other countries such as Thailand or Vietnam. All country totals have been standardized to match the 2015 FAOSTAT numbers, in order to be as close a possible to the present pig stock. Please go through the 1_Pg_2015_Metadata.html file for more information about this dataset.

This dataset contains the most up to date version of GLW 4 for the reference year 2020 for the following species: buffalo, cattle, sheep, goats, pigs and chicken. The individual species datasets are available at global extent and 5 minutes of arc resolution (approx. 10 km at the equator).

The fourth version of GLW, compared to the previous ones, reflects the most recently compiled and harmonized subnational livestock distribution data and much more detailed metadata.

The layers contain the density of animals per km², with weight estimated by the Random Forest model. The livestock species modelled include: buffaloes, cattle, chickens, goats, pigs and sheep.

All datasets are licensed through a Creative Commons Attribution 4.0 International License.

References

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

• Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs

• Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Data publication: 2024-07-15

Supplemental Information:

Unit: head/pixel or birds/pixel

Data type: Float64

No data value: No data

Spatial resolution: Approximately 10km (0.08333 degrees)

Spatial extent: World

Spatial Reference System (SRS): EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Contact points:

Resource Contact: Dominik Wisser (FAO-NSAL)

Metadata Contact: Giuseppina Cinardi (FAO-NSAL)

Data lineage:

Recommentations on data representation

The standard lat/long visualisation of the global raster datasets tends to visually over-represent animal densities in pixels located in northern latitudes as they cover a much lower surface on earth than those close to the equator. Thus, altough the data files are distributed in lat/long, we recommend the use of an equal-area projection for a proper representation of densities of our livestock data.

Resource constraints:

Public-use data under the CC BY-NC-SA 3.0 IGO license.

Online resources:

Buffalo: metadata

Chicken: metadata

Cattle: metadata

Goats: metadata

Pigs: metadata

Sheep: metadata

Data for download: All species density

Data for download: Buffalo density

Data for download: Chicken density

Data for download: Cattle density

Data for download: Goats density

Data for download: Pigs density

Data for download: Sheep density

A spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 world regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs) for the year 2000. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system.

Lineage: A livestock systems classification updated by Robinson et al (2011) was used as the starting point. It is based on agro-ecological differentiation (arid, humid and temperate/tropical highland areas), which helps in establishing the composition of diets for animals in different regions and agro-agroecologies and in the future to elicit the impacts that climate change might have on feed resources and land use. We differentiated 8 different types of livestock systems in 28 geographical regions of the world for this study. Numbers of animals for each of these systems and regions were estimated using the data of Wint and Robinson (2007) for the year 2000.

For ruminants (cattle, sheep and goats), we disaggregated the dairy and beef cattle herds using livestock demographic data for total cattle, sheep and goats and the dairy females for each species, respectively, from FAOSTAT. We used herd dynamics models parameterised for each region and production system using reproduction and mortality rates obtained from extensive literature reviews to estimate herd composition. For monogastrics (pigs and poultry), we only differentiated two systems: smallholder and industrial production systems. The allocation of poultry, eggs and pork production was done on the basis of knowledge of the total product output from these two systems from national information from selected countries in the different regions, applied to the respective region.

Biomass consumption and productivity estimations from different species in each region and system followed a three stage process. First, feed availability of four main types of feeds (grass, crop residues, grains, occasional feeds) was estimated using hybrid maps of grassland productivity and EPIC model output (Havlik et al 2013) for humid and temperate regions of the world. Crop residue availability was estimated using the SPAM cropland layers (You et al 2014) and coefficients of stover use for animal feeding and harvest indexes for different parts of the world. Grain availability for animal production was taken from the FAO Commodity balance sheets and the availability of occasional feeds like cut and carry grasses and legumes was obtained from literature reviews.

The second step consisted of developing feasible diets for each species in each region and production system. The proportions of each feed in the diet of each species was obtained from extensive information available in the literature and from databases and feeding practice surveys at key research centres in the world (i.e. FAO, ILRI). Data on feed quality was obtained from the databases containing regional feed composition data for each feed (Herrero et al 2008). The third step consisted of estimating productivity. For ruminants, the information on the quantity and quality of the different feeds was then used to parameterise an IPCC tier 3 digestion and metabolism model (RUMINANT, Herrero et al 2002), as described in Herrero et al (2008) and Thornton and Herrero (2010). The model estimated productivity (milk, meat), methane emissions and manure and nitrogen excretion. For monogastrics, information on feed quality was used to estimate feed intake, productivity and feed use efficiency using standard nutrient requirements guidelines (NRC 2008). The estimation of methane and nitrous oxide emissions from manure, and of nitrous oxide from pastures followed an IPCC tier 2 approach, for each species, system and region. Further details are available in the Supplementary Information of Herrero et al. 2013.

All information on animal production (bovine milk, bovine meat, sheep and goat milk, sheep and goat meat, pork, poultry and eggs) and for grains as feed was harmonised with FAOSTAT’s commodity balance sheets at national level following an iterative procedure restricted to deviate +/- 20% from the statistical data in FAOSTAT.

The size of the collection is 1.32 GB, 192 zip files.

Gridded Livestock of the World v3 This dataset contains the most up to date version of GLW 3 for the reference year 2010 and the following species: cattle, sheep, goats, buffaloes, horses, pigs, chickens and ducks. The individual species datasets are available at global extent and 5 minutes of arc resolution (approx. 10 km at the equator), and national extent 30 seconds of arc resolution (approx. 1 km at the equator) will be added as they become available. GLW 3 mainly differs from previous GLW versions in that the input data has been improved, the downscaling algorithm has been updated (Random Forest) and much more detailed metadata has been provided. All datasets are licensed through a Creative Commons Attribution 4.0 International License. Animal Density using the dasymetric method (DA). This method assigns different weights to different pixels based on high resolution environmental predictor variables and Random Forest models, and the animal census counts are distributed according to these weights. This layer contains the DA density of animals per pixel, with weight estimated by the Random Forest model. The DA GLW models provide an estimate of how livestock species may be distributed within census areas. However, spatial predictors (e.g. human population density, vegetation indices, topography, etc.) that are used to derived the downscaling weights may introduce some uncontrolled counfonding effects for users willing to quantify the effect of livestock alongside these spatial predictors on an outcome. Similarly, the DA models may introduce circularity for users willing to use livestock data to study their impact on some these spatial factors, such as land-use, for example. Unit : heads/km² Data type: Float64 No data value -9999 Spatial resolution: Approximately 10km (0.08333 degrees) Spatial extent: World Spatial Reference System (SRS): EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Introduction

Animal welfare is important because there are so many animals around the world suffering from being used for entertainment, food, medicine, fashion, scientific advancement, and as exotic pets. Every animal deserves to have a good life where they enjoy the benefits of the Five Domains.

About Dataset

We aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.

This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.

Estimates for fish are more uncertain, but when we include them, these numbers more than double.

These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.

On this page, you can find all of our data, and writing on animal welfare.

File 1: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct deaths only. For example, the pork numbers include only the deaths of pigs slaughtered for food.

File 2: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct and indirect deaths. For example, the pork numbers include the deaths of pigs slaughtered for food (direct) but also those who die pre-slaughter and feed fish given to those pigs (indirect).

File 3: The estimated quantity of edible meat produced per animal, measured in kilograms.

File 4: Different location on time span = 2013 - 2020

File 5: Share of hens in cages Share of hens housed in a barn or aviary Share of non-organic, free-range hens Share of organic, free-range hens Share of laying hens in unknown housing

File 6: Number of eggs from hens in organic, free-range farms Number of eggs from hens in non-organic, free-range farms Number of eggs from hens in barns Number of eggs from hens in (enriched) cages

File 7: Estimated number of farmed decapod crustaceans Estimated number of farmed decapod crustaceans (upper bound) Estimated number of decapod crustaceans (lower bound)

File 8: Estimated number of farmed fish Estimated number of farmed fish (upper bound) Estimated number of farmed fish (lower bound)

File 9: Share of cage-free eggs Share of all eggs that are produced in cage-free housing systems. This includes barns, pasture and free-range (non-organic and organic) eggs.

Lets diving in dataset and create some excellent notebook for visualization and types of prediction. So, Good luck.

By Hannah Ritchie, Pablo Rosado and Max Roser (Our world in data)

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator.In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). For the detailed background and Metadata visit: https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/

This dataset provides livestock data for US Counties within the contiguous US. Census data of cattle, poultry (fowl), hogs, horses and sheep are provided. These data are estimated counts for 1990 based on an average of 1987 and 1992 census data from US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

This dataset contains zooarchaeological data relevant to cattle, sheep/goat, and pigs from lowland northern Italy, and an associated R analysis and visualisation script. This dataset supports the journal article:A. Trentacoste, A. Nieto-Espinet, S. Guimarães Chiarelli, and Valenzuela-Lamas. (2023). Systems change: Investigating climatic and environmental impacts on livestock production in lowland Italy between the Bronze Age and Late Antiquity (c. 1700 BC - AD 700). Quaternary International 662–663:26-36. https://doi.org/10.1016/j.quaint.2022.11.005

The majority of the data were collected under the auspices of the ERC-Starting Grant ZooMWest – Zooarchaeology and Mobility in the Western Mediterranean: Husbandry production from the Late Bronze Age to the Late Antiquity (award number 716298), funded by the European Research Council Agency (ERCEA) under the direction of Sílvia Valenzuela-Lamas (2017–2022). This work built on previous data collection undertaken for Trentacoste's (2014) PhD thesis. The dataset was also expanded with support from a Gerda Henkel Stifling Scholarship (AZ 44/F/20) awarded to A. Trentacoste.

The chronological timespan of the dataset is between the Middle Bronze Age and Late Antiquity (c. 1700 BC - AD 700). For details on the methodology underlying the creation of the dataset see Trentacoste et al. (2018) and Trentacoste et al. (2021).

Zooarchaeological data were collected from published sources (see references file), with the exception of some data for the sites of Spina, Vidulis and Aquileia. Metadata for these sites were available in the published literature, but individual data were collected from the archive papers of Italian zooarchaeologist Alfredo Reidel (1925–2014). We are grateful to Francesco Boschin (Università degli Studi di Siena) for access to the archive.

The dataset includes:

Raw biometric data for post-cranial bones for cattle, sheep/goat, pigs, and wild boar on a specimen level. Measurement abbreviations follow Von den Driesch (1976) and Davis (1996; only humerus HT and HTC). File: NItaly_Livestock_Metric_Data.csv

NISP (Number of Identified Specimens) data for site phases with over 100 identified cattle/sheep/goat/pig specimens. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv

Location coordinates and information on environmental context: mean, min, and/or max values for a 5km radius for sites with NISP data. Elevation information was taken from Shuttle Radar Topography Mission (SRTM) terrain data from the U.S. Geological Survey (90m resolution; Jarvis et al., 2008). Precipitation data were from World Clim 2.1 (average monthly climate data for 1970–2000, 30 arc-sec; Fick and Hijmans, 2017), and solar irradiance data were from Global Solar Atlas 2.0 (9 arc-sec; developed and operated by Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP); https://globalsolaratlas.info). Soil characteristics were derived from LUCAS topsoil data (500m; Ballabio et al., 2016): clay, silt, sand, and coarse fragments content (%), bulk density, and Available Water Capacity (AWC) for the topsoil fine earth fraction. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv

Bibliographic information for each assemblage with indication of whether NISP and/or biometric data was used. File: NItaly_Livestock_References.csv

R script file for the analyses and visualisations in the above journal article. File: NItaly_Livestock_SysChange_Script.R

If you re-use this data, please cite this dataset and the associated journal articles as relevant.

China Farm Price: Live Pig data was reported at 15.090 RMB/kg in 23 Apr 2025. This records an increase from the previous number of 15.050 RMB/kg for 16 Apr 2025. China Farm Price: Live Pig data is updated daily, averaging 15.230 RMB/kg from Jan 2009 (Median) to 23 Apr 2025, with 811 observations. The data reached an all-time high of 39.800 RMB/kg in 30 Oct 2019 and a record low of 9.680 RMB/kg in 07 Apr 2010. China Farm Price: Live Pig data remains active status in CEIC and is reported by National Development and Reform Commission. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Livestock Breeding Condition.