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.

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

This dataset contains data on number of pigs in Latvia in 1919-1939. Dataset "Number of Pigs in Latvia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

This dataset contains data on number of pigs in Estonia in 1919-1939. Dataset "Number of Pigs in Estonia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

This dataset contains the global distribution of pigs in 2010 expressed in total number of pigs per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3). Please go through the 1_Pg_2010_Metadata.html file for more information about this dataset and the set of included files.

Context

Meat consumption is related to living standards, diet, livestock production and consumer prices, as well as macroeconomic uncertainty and shocks to GDP. Compared to other commodities, meat is characterised by high production costs and high output prices. Meat demand is associated with higher incomes and a shift - due to urbanisation - to food consumption changes that favour increased proteins from animal sources in diets. While the global meat industry provides food and a livelihood for billions of people, it also has significant environmental and health consequences for the planet.

This dataset was refreshed in 2018, with world meat projections up to 2026 are presented for beef and veal, pig, poultry, and sheep. Meat consumption is measured in thousand tonnes of carcass weight (except for poultry expressed as ready to cook weight) and in kilograms of retail weight per capita. Carcass weight to retail weight conversion factors are: 0.7 for beef and veal, 0.78 for pig meat, and 0.88 for both sheep meat and poultry meat. Excludes Iceland but includes all EU 28 member countries.

Content

The csv file has 5 columns:

• LOCATION = the country code name
• SUBJECT = The type of meat(pig, beef, etc)
• TIME = the year the data was recorded
• MEASURE = the measure used to show the value
• VALUE = The value, according to the measure

Acknowledgements

https://data.oecd.org/agroutput/meat-consumption.htm OECD/FAO (2017), “OECD-FAO Agricultural Outlook”, OECD Agriculture statistics (database). doi: dx.doi.org/10.1787/agr-outl-data-en (Accessed on 24 January 2018)

Inspiration

Ask questions such as:

1. What country had the most meat consumption?
2. What years had the most worldwide meat consumption?
3. Is there any correlation between the type of meat and the amount being consumed?

This dataset contains data on number of pigs in Vitebsk Province (within the current borders Belarus, Latvia and Russia) in 1897-1914. Dataset "Number of Pigs in Vitebsk Province (Belarus, Latvia and Russia), 1897-1914" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

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.

The eruption of invasive wild pigs (IWPs) Sus scrofa throughout the world exemplifies the need to understand the influences of exotic and non-native species expansions. In particular, the continental USA is precariously threatened by a rapid expansion of IWPs, and a better understanding of the rate and process of spread can inform strategies that will limit the expansion. We developed a spatially and temporally dynamic model to examine three decades (1982–2012) of IWP expansion, and predict the spread of IWPs throughout the continental USA, relative to where IWPs previously inhabited. We used the model to predict where IWPs are likely to invade next. The average rate of northward expansion increased from 6.5 to 12.6 km per year, suggesting most counties in the continental USA could be inhabited within the next 3–5 decades. The spread of IWPs was primarily associated with expansion into areas with similar environmental characteristics as their previous range, with the exception of spreading into colder regions. We identified that climate change may assist spread into northern regions by generating milder winters with less snow. Otherwise, the spread of IWPs was not dependent on agriculture, precipitation, or biodiversity at the county level. The model correctly predicted 86% of counties that were invaded during 2012, and those predictions indicate that large portions of the USA are in immediate danger of invasion. Synthesis and applications. Anti-invasion efforts should focus along the boundaries of current occupied range to stop natural expansion, and anti-invasion policies should focus on stopping anthropogenic transport and release of invasive wild pigs (IWPs). Our results demonstrate the utility of a spatio-temporal examination to inform strategies for limiting the spread of IWPs.

Livestock: Number: Pig Stock: Guangdong data was reported at 20,492.028 Unit th in 2023. This records a decrease from the previous number of 21,958.560 Unit th for 2022. Livestock: Number: Pig Stock: Guangdong data is updated yearly, averaging 21,301.037 Unit th from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 23,922.791 Unit th in 2009 and a record low of 13,337.864 Unit th in 2019. Livestock: Number: Pig Stock: Guangdong data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Number of Livestock: Pig Stock.

2530 Global export shipment records of Pig with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.