How many cattle are in the world? The global live cattle population amounted to about 1.57 billion heads in 2023, up from approximately 1.51 billion in 2021. Cows as livestock The domestication of cattle began as early as 10,000 to 5,000 years ago. From ancient times up to the present, cattle are bred to provide meat and dairy. Cattle are also employed as draft animals to plow the fields or transport heavy objects. Cattle hide is used for the production of leather, and dung for fuel and agricultural fertilizer. In 2022, India was home to the highest number of milk cows in the world. Cattle farming in the United States Cattle meat such as beef and veal is one of the most widely consumed types of meat across the globe, and is particularly popular in the United States. The United States is the top producer of beef and veal of any country worldwide. In 2021, beef production in the United States reached 12.6 million metric tons. Beef production appears to be following a positive trend in the United States. More than 33.07 million cattle were slaughtered both commercially and in farms annually in the United States in 2019, up from 33 million in the previous year.

The dataset contains year- and continent-wise compiled data of global livestock population. The livestock covered in the dataset include Buffaloes, Sheep, Goats, Pigs, Horses, Asses, Mules, Camels, Camelids, Chickens, Ducks, Geese and guinea fowls, Turkeys, Rabbits, Hares, Beehives, Goats, etc. and the continents covered include Africa, America, Europe, Oceania, etc.

Ethiopia had the highest number of cattle in Africa as of 2023, nearly ** million heads. United Republic of Tanzania possessed the second-highest bovine animal stock on the continent, with about ** million heads. In 2022, Africa had over *** million heads of cattle, one of the major species raised for livestock farming on the continent.

India's cattle inventory amounted to about *** million in 2023. In comparison, the global cattle population stood at over ***********, India had the highest cattle population followed by Brazil, China and the United States that year. Where are cattle bred in India? As one of the leading dairy producers and consumers worldwide, cattle in the south Asian country were bred mainly in the rural areas. However, its population was spread unevenly across the vast land. Uttar Pradesh ranked first in terms of milk production, followed by Rajasthan, and Madhya Pradesh in 2023. Contextualizing the holiness of the Indian cow Considered a sacred animal by Hindus in India, the cow is associated with several gods and goddesses. This deep religious and cultural significance has led to communal tensions. In 2014, the government established the Rashtriya Gokul Mission (RGM) to conserve and develop indigenous breeds of cows and buffaloes. While the general goal was well-received, it aligns with the underlying Hindu nationalist narrative of the current government.

The dataset contains All India compiled data on the quinquennial livestock and poultry population from the year 1956 to 2019. The livestock covered in the dataset include Cattle, Buffaloes, Sheep, Goats, Horses and ponies, Camels, Pigs, Mules, Donkeys, Yaks, etc.

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 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

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Households who are rearing livestock or kept poultry

Universe

Livestock or poultry household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Livestock/poultry farming housing units were systematically selected and canvassed.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC livestock and poultry questionnaire was divided into the following sections: - Identification/description of holdings - Funds, employment and earnings/wages - Livestock - Poultry - Fixed assets - Sales - Stock - Subsidy

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 99.3 percent was recorded at housing units level.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

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/

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)

The Livestock dataset contains four variables which provide census data for Cattle, Hogs, Horses (horses, donkeys and camels), and Sheep (sheep and goats). These census data are based on 1990 statistics.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

Livestock: Number: Beef Cattle: Tibet data was reported at 5,555.000 Unit th in 2022. This records an increase from the previous number of 5,447.000 Unit th for 2021. Livestock: Number: Beef Cattle: Tibet data is updated yearly, averaging 4,713.000 Unit th from Dec 2008 (Median) to 2022, with 15 observations. The data reached an all-time high of 5,555.000 Unit th in 2022 and a record low of 4,513.000 Unit th in 2012. Livestock: Number: Beef Cattle: Tibet 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: Large Animals: Cow .

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/

How many cows are in the U.S.? The United States is home to approximately **** million cattle and calves as of 2025, dropping slightly from the 2024 value. Cattle farming in the United States There are over ***** times more beef cows than milk cows living in the United States. Raising cattle is notoriously expensive, not only in terms of land, feed, and equipment, but also in terms of the environmental impact of consuming beef. Beef and milk have the highest carbon footprints of any type of food in the United States. U.S. milk market The volume of milk produced in the United States has been steadily increasing over the last several years. In 2024, total milk production in the U.S. was about ***** billion pounds, up from ***** billion pounds in 2010. ********** is the leading producer of milk of any U.S. state, generating approximately ** billion pounds of milk in 2023. Wisconsin came in second, producing about **** billion pounds of milk in that year.

Livestock Insurance Market size was valued at USD 3.25 Billion in 2024 and is projected to reach USD 6.02 Billion by 2031, growing at a CAGR of 8% from 2024 to 2031.

Global Livestock Insurance Market Drivers

Increasing Frequency of Extreme Weather Events and Natural Disasters: The increasing frequency of extreme weather events and natural calamities has a substantial impact on livestock farmers, driving up demand for livestock insurance. According to the Food and Agriculture Organization (FAO) of the United Nations, between 2008 and 2018, agriculture accounted for around 20% of disaster-related economic losses in developing countries, with livestock accounting for 36%. This disturbing trend is mostly driven by livestock's vulnerability to climate-related threats such as droughts, floods, and diseases, which are compounded by shifting weather patterns.

Growing Livestock Population and Intensification of Farming Practices: The expanding livestock population and intensification of farming practices are driving demand for livestock insurance, as global demand for animal products rises. According to the World Bank, globally meat production is expected to rise by 13% between 2020 and 2029, reaching 366 million tons in 2029. This increase in livestock numbers not only expands operations, but also increases financial risks connected with disease outbreaks, market swings, and production issues.

Rising Awareness and Government Support for Agricultural Insurance: Rising awareness and government support for agricultural insurance, including livestock insurance, are helping farmers protect their livelihoods. For instance, in India, the government's Livestock Insurance Scheme experienced significant growth, with the number of insured animals jumping from 5.6 million in 2019-20 to 7.9 million in 2020-21—a 41% increase in just one year, according to the Department of Animal Husbandry and Dairying.

Livestock population (bovines, dairy cows, pigs, sheep, goats) by NUTS 2 region; expressed in 1000 heads. Data are derived from livestock surveys that is carried out in November/December.

Livestock Monitoring Market Outlook

According to our latest research, the global livestock monitoring market size reached USD 5.6 billion in 2024, reflecting a robust trajectory driven by the adoption of advanced technologies in animal husbandry. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 16.1 billion by 2033. This impressive growth is primarily fueled by the increasing demand for real-time monitoring solutions, the rising need for operational efficiency on farms, and the growing emphasis on animal health and productivity across the globe.

One of the primary growth factors for the livestock monitoring market is the escalating need for precision livestock farming. Farmers and agribusinesses are increasingly adopting smart technologies to monitor animal health, optimize feeding, and streamline breeding processes. The integration of IoT devices, sensors, and advanced analytics has enabled real-time tracking of livestock, which not only minimizes losses due to disease outbreaks but also enhances overall productivity. The ability to collect and analyze data continuously allows for early detection of health issues, timely interventions, and improved resource management, all of which contribute to higher yields and profitability. Additionally, the growing awareness of animal welfare and the need for traceability in food production chains further drive the adoption of livestock monitoring solutions worldwide.

Another significant driver is the increasing pressure on the agricultural sector to meet the food demands of a rapidly growing global population. With urbanization and changing dietary preferences leading to higher consumption of animal-based products, livestock farmers are compelled to maximize output while maintaining sustainable practices. Livestock monitoring systems facilitate optimized feeding management, efficient milk harvesting, and precise breeding management, thereby ensuring that productivity targets are met without compromising animal health. Furthermore, regulatory mandates for food safety and animal traceability in several countries have prompted farmers to invest in advanced monitoring technologies, thereby accelerating market growth.

The proliferation of wireless connectivity and advancements in sensor technologies have also played a pivotal role in market expansion. Modern livestock monitoring systems leverage cloud computing, artificial intelligence, and big data analytics to provide actionable insights, automate routine tasks, and reduce labor costs. As a result, both small and large-scale farms are witnessing improved operational efficiency and reduced instances of livestock mortality. The cost-effectiveness of these solutions, coupled with government initiatives to promote smart farming, has further encouraged adoption. The market is also benefiting from the entry of new players offering innovative hardware, software, and service models tailored to diverse farming needs.

In recent years, Dairy Cow Health Monitoring has emerged as a critical component of livestock management, particularly within the dairy sector. This advancement is driven by the need to ensure the well-being of dairy cows, which directly impacts milk production and quality. By employing sophisticated monitoring systems, farmers can track vital health indicators such as temperature, heart rate, and activity levels, enabling early detection of illnesses and reducing the risk of disease outbreaks. These systems not only improve animal welfare but also enhance the efficiency of dairy operations by minimizing downtime and optimizing herd management. As the dairy industry continues to evolve, the integration of health monitoring technologies is becoming increasingly essential for maintaining competitive advantage and ensuring sustainable production practices.

Regionally, North America continues to hold a dominant share in the livestock monitoring market, owing to the early adoption of digital technologies, established infrastructure, and strong presence of leading market players. However, Asia Pacific is emerging as the fastest-growing region, driven by the rapid modernization of the agricultural sector, increasing livestock population, and supportive government policies. Europe also presents significant growth opportunities due to stringent animal welfare regulations and the widesp

A global high resolution data base of animal population densities and associated methane (CH4) emissions has been developed at the NASA Goddard Institute for Space Studies (NASA/GISS). The animal population statistics were based primarily on the Food and Agriculture Organization (FAO) compilations and other sources. The animals were distributed using a 1-degree by 1-degree resolution data base of countries and land-use (Matthews, 1983). The animals included are cattle and dairy cows, water buffalo, sheep, goats, camels, pigs, horses, and caribou. Estimates of methane production from each animal type were applied to the animal populations to yield a global distribution of methane emissions by animals. About 55% of the global annual emissions was concentrated between 25 N and 55 N. Estimates of methane emissions from animals were based on the research by Crutzen et al. (1986).

In the U.S., there have been approximately three times more beef cows than dairy cows each year since 2001. As of 2025, it was estimated that there were about 28 million beef cows and only about 9.3 million dairy cows. Beef vs. dairy cows Both beef and dairy cows are bred for their respective purposes and farmers often look for different qualities in each. Dairy cows are often bigger, as they can produce a larger volume of milk. Beef cows, on the other hand, are generally shorter, and there is more emphasis on their muscle growth, among other qualities. In 2024, over 26 billion pounds of beef were produced in the United States. U.S. milk production and consumption The United States is among the top consumers of milk worldwide, surpassed only by India and the European Union. The annual consumption of milk in the U.S. that year was about 20 million metric tons in 2024. To keep up with this level of consumption, milk production in the U.S. has increased by over 60 billion pounds since 1999 and is expected to exceed 227 billion pounds by 2025. California and Wisconsin were the top producing states as of 2024, producing about 42 and 32 billion pounds of milk, respectively.

The share of major livestock types (equidae, cattle, sheep, goats, pigs and poultry) in total livestock population expressed in livestock units (LSU); based on Farm Structure Survey data.