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 million 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.

United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data was reported at 9,349.300 Head th in 2025. This records an increase from the previous number of 9,346.800 Head th for 2024. United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data is updated yearly, averaging 9,349.300 Head th from Dec 1926 (Median) to 2025, with 17 observations. The data reached an all-time high of 9,450.400 Head th in 2021 and a record low of 9,208.600 Head th in 2014. United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data remains active status in CEIC and is reported by Economic Research Service. The data is categorized under Global Database’s United States – Table US.RI018: Cattle Inventory.

This dataset contains hours per hours the activity level of cows on four farms. These data were used in Wagner et al. 2021. The dataset contains 4 files, one for each farm. Files are structured as follows: cow working id; date; hour (integer); the time (s) spent 'walking' during one hour (the cow is positioned 'in alleys'); the time (s) spent 'resting’ during one hour (the cow is in a resting area (typically cubicles)); the time (s) spent 'eating' during one hour (the cow is positioned at the feeding table); the activity level (unitless), which is the weighted sum of the time spent in each activity (with the following weights: -0.23 for resting, +0.16 for in alleys, and +0.42 for eating). Due to the weights, the hourly activity level can range from -828 (i.e. -0.23*3600) to 1512 (i.e. 0.42*3600); finally, for each of the 11 types of events, a boolean is provided, for the question "is there this type of event on this hour ?" (i.e. 1 means this type of event were reported for this hour; 0 means that this type of event were not reported for this hour). Note that in fact daily events are reported here at hourly scale. There are 11 types of events: oestrus; calving; lameness includes all types of lameness and issues on claw or leg; mastitis includes all types of mastitis; Event LPS is specific to experimentation as it is for administered lipopolysaccharide (LPS) in the mammary gland on one day to induce inflammation; Event acidosis stands for subacute ruminal acidosis; Event labelled 'other_disease’, which contains all other diseases such as colic, diarrhea, ketosis, milk fever or other infectious disease; Event accidents contains all types of accidents such as retained placenta or vaginal laceration; Event disturbance is mild intervention on animal (e.g. late feeding, alarm test, animal tied for injection, claw trimming, drying of the cow) and other issues on the day but that did not concerned management changes; Event mixing is for when cows were mixed or moved to another park. Event labelled ‘management_changes’ contains marginal management such as ration changes or bed filling. This event is reported in the dataset, but is not considered as influent on the animal behaviour; A final Boolean sum up the information on whether this hour is considered as normal (i.e. if all the booleans (without considering the management changes one) are equal to 0 then this hour can be considered as normal, then the boolean is set to 1). Dataset 2 was also used in Wagner et al., 2020 https://doi.org/10.1016/j.compag.2020.105233 and in Wagner et al., 2020, https://doi.org/10.1007/978-3-030-59491-6_32

Live Cattle rose to 227.35 USd/Lbs on July 23, 2025, up 1.06% from the previous day. Over the past month, Live Cattle's price has risen 2.62%, and is up 21.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Live Cattle - values, historical data, forecasts and news - updated on July of 2025.

China Livestock: Number: Cow data was reported at 105,085.102 Unit th in 2023. This records an increase from the previous number of 102,158.520 Unit th for 2022. China Livestock: Number: Cow data is updated yearly, averaging 103,974.569 Unit th from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 132,060.000 Unit th in 1995 and a record low of 88,344.899 Unit th in 2016. China Livestock: Number: Cow 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 .

To assess the magnitude of greenhouse gas (GHG) fluxes, nutrient runoff and leaching from dairy barnyards and to characterize factors controlling these fluxes, nine barnyards were built at the U.S. Dairy Forage Research Center Farm in Prairie du Sac, WI (latitude 43.33N, longitude 89.71W). The barnyards were designed to simulate outdoor cattle-holding areas on commercial dairy farms in Wisconsin. Each barnyard was approximately 7m x 7m; areas of barnyards 1-9 were 51.91, 47.29, 50.97, 46.32, 45.64, 46.30, 48.93, 48.78, 46.73 square meters, respectively. Factors investigated included three different surface materials (bark, sand, soil) and timing of cattle corralling. Each barnyard included a gravity drainage system that allowed leachate to be pumped out and analyzed. Each soil-covered barnyard also included a system to intercept runoff at the perimeter and drain to a pumping port, similar to the leachate systems. From October 2010 to October 2015, dairy heifers were placed onto experimental barnyards for approximately 7-day periods four times per year, generally in mid-spring, late-spring / early summer, mid-to-late summer and early-to-mid autumn. Heifers were fed once per day from total mixed rations consisting mostly of corn (maize) and alfalfa silages. Feed offered and feed refused were both weighed and analyzed for total nitrogen (N), carbon (C), phosphorus (P) and cell wall components (neutral detergent fiber, NDF). Leachate was pumped out of plots frequently enough to prevent saturation of surface materials and potential anaerobic conditions. Leachate was also pumped out the day before any gas flux measurements. Leachate total volume and nitrogen species were measured, and from “soil” barnyards the runoff was also measured. The starting bulk density, pH, total carbon (C) and total N of barnyard surface materials were analyzed. Decomposed bark in barnyards was replaced with new bark in 2013, before the spring flux measurements. Please note: the data presented here includes observations made in 2015; the original paper included observations through 2014 only. Gas fluxes (carbon dioxide, CO2; methane, CH4; ammonia, NH3; and nitrous oxide, N2O) were measured during the two days before heifers were corralled in barnyards, and during the two days after heifers were moved off the barnyards. During the first day of each two-day measurement period, gas fluxes were measured at two randomly selected locations within each barnyard. Each location was sampled once in the morning and once in the afternoon. During the second day, this procedure was repeated with two new randomly selected locations in each barnyard. This experiment was partially funded by a project called “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP). The Dairy CAP is funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP is to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP is improving life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities. Resources in this dataset:Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: BYD_Data_Dictionary.xlsxResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_Barnyards. File Name: BYD_Project_Data.xlsxResource Description: This is the complete data from the paper: Powell, J. M. & Vadas, P. A. (2016). Gas emissions from dairy barnyards. Animal Production Science, 56, 355-361. Data are separated into separate spreadsheet tabs.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: Data_Dictionary_BYD.csvResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Title: GHG Data. File Name: BYD_GHG.csvResource Description: Greenhouse gas flux dataResource Title: Intake Data. File Name: BYD_Intake.csvResource Title: Leachate Data. File Name: BYD_Leachate.csvResource Title: Runoff Data. File Name: BYD_Runoff.csvResource Title: Surface Data. File Name: BYD_Surface.csvResource Title: TMR Data. File Name: BYD_TMR.csvResource Description: Total mixed ration data

Beef traded flat at 296.10 BRL/15KG on July 23, 2025. Over the past month, Beef's price has fallen 6.80%, but it is still 28.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Beef - values, historical data, forecasts and news - updated on July of 2025.

BackgroundBovine viral diarrhea is one of the diseases that cause huge economic losses in animal husbandry. Many countries or regions have successively introduced eradication plans, but BVDV still has a high prevalence in the world. This meta-analysis aims to investigate the prevalence and risk factors of BVDV in the world in recent 10 years, and is expected to provide some reference and theoretical basis for BVDV control plans in different regions.MethodRelevant articles published from 2010 to 2021 were mainly retrieved from NCBI, ScienceDirect, Chongqing VIP, Chinese web of knowledge (CNKI), web of science and Wanfang databases.Results128 data were used to analyze the prevalence of BVDV from 2010 to 2021. BVDV antigen prevalence rate is 15.74% (95% CI: 11.35–20.68), antibody prevalence rate is 42.77% (95% CI: 37.01–48.63). In the two databases of antigen and antibody, regions, sampling time, samples, detection methods, species, health status, age, sex, breeding mode, and seasonal subgroups were discussed and analyzed, respectively. In the antigen database, the prevalence of dairy cows in the breed subgroup, ELISA in the detection method subgroup, ear tissue in the sample subgroup, and extensive breeding in the breeding mode were the lowest, with significant differences. In the antibody database, the prevalence rate of dairy cows in the breed subgroup and intensive farming was the highest, with a significant difference. The subgroups in the remaining two databases were not significantly different.ConclusionThis meta-analysis determined the prevalence of BVDV in global cattle herds from 2010 to 2021. The prevalence of BVDV varies from region to region, and the situation is still not optimistic. In daily feeding, we should pay attention to the rigorous and comprehensive management to minimize the spread of virus. The government should enforce BVDV prevention and control, implement control or eradication policies according to local conditions, and adjust the policies in time.

This dataset includes the scripts to reproduce the models presented in the paper. The cleaned data used for the analyses is also available. Abstract of the article: Precision farming technology, including GPS collars with biologging, has revolutionized remote livestock monitoring in extensive grazing systems. High resolution accelerometry can be used to infer the behavior of an animal. Previous behavioral classification studies using accelerometer data have focused on a few key behaviors and were mostly conducted in controlled situations. Here, we conducted behavioral observations of 38 beef cows (Hereford, Limousine, Charolais, Simmental/NRF/Hereford mix) free-ranging in rugged, forested areas, and fitted with a commercially available virtual fence collar (Nofence) containing a 10Hz tri-axial accelerometer. We used random forest models to calibrate data from the accelerometers on both commonly documented (e.g., feeding, resting, walking) and rarer (e.g., suckling calf, head butting, allogrooming) behaviors. Our goal was to assess pre-processing decisions including different running mean intervals (smoothing window of 1, 5, or 20 seconds), collar orientation and feature selection (orientation-dependent versus orientation-independent features). We identified the 10 most common behaviors exhibited by the cows. Models based only on orientation-independent features did not perform better than models based on orientation-dependent features, despite variation in how collars were attached (direction and tightness). Using a 20 seconds running mean and orientation-dependent features resulted in the highest model performance (model accuracy: 0.998, precision: 0.991, and recall: 0.989). We also used this model to add 11 rarer behaviors (each < 0.1% of the data; e.g. head butting, throwing head, self-grooming). These rarer behaviors were predicted with less accuracy because they were not observed at all for some individuals, but overall model performance remained high (accuracy, precision, recall >98%). Our study suggests that the accelerometers in the Nofence collars are suitable to identify the most common behaviors of free-ranging cattle. The results of this study could be used in future research for understanding cattle habitat selection in rugged forest ranges, herd dynamics, or responses to stressors such as carnivores, as well as to improve cattle management and welfare.