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

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)

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.

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 …Show full descriptionA 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. The metadata and files (if any) are available to the public.

This dataset describes habitat suitability for feral pig breeding and persistence in northern Australia during the dry season. It is the result of a spatially-explicit, resource-based and regional-scale habitat model that integrated expert knowledge on feral pig breeding requirements and home range movements as well as seasonal variability in environmental conditions.

The modelled habitat suitability index (HSI) can theoretically range between 0 and 100, with higher values indicating better habitat quality for feral pig breeding. Due to modelling methods and assumptions, HSI values in this dataset effectively range between 11 and 81. They can be broadly classified as follows: HSI ≥ 60 = highly suitable habitat; HSI ≥ 40 = moderately suitable habitat; HSI < 40 = unsuitable habitat.

Predicted habitat suitability should not be confused with actual feral pig occurrence. Individuals may be sighted at any time in unsuitable breeding habitat. Conversely, suitable breeding habitat may remain unoccupied. While there is a link between habitat suitability and population density, this may not always be straightforward (i.e. comparable habitat may carry vastly different actual or potential densities depending on the nature and quality of available resources).

Feral pig habitat suitability in northern Australia was modelled for two seasonal scenarios. The dry season scenario captured unfavourable conditions during the late dry season, when resources required by feral pigs are generally scarce and scattered across the region. It was developed using spatial proxies averaged across two months (October/November) over five years (2010 to 2014). Seasonal model results were validated against four independent distributional data sets.

Underlying model parameters were elicited from experts. This dataset represents results from an expert-averaged model run. The model contained a variable "Disturbance stress" for which no spatial proxies were available. In this dataset, we assumed a uniformly “high” intensity and frequency of control activities, which likely overestimated disturbance and may undervalue habitat suitability in situations where there is actually little management.

A detailed description of modelling methods and assumptions is provided in Froese et al. 2017 (https://doi.org/10.1371/journal.pone.0177018).

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)

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.