The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Cattle productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Many cattle production commodity fields are broken out into 6 or 7 ranges based on the number of head of cattle. For space reasons, a general sample of the fields is listed here.Commodities included in this layer: Cattle, (Excl Cows) - Inventory - Inventory of Cattle, (Excl Cows): (By number of head)Cattle, (Excl Cows) - InventoryCattle, (Excl Cows) - Operations with Inventory - Inventory of Cattle, (Excl Cows): (By number of head)Cattle, (Excl Cows) - Operations with InventoryCattle, Calves - Operations with Sales - Sales of Calves: (By number of head)Cattle, Calves - Operations with SalesCattle, Calves - Sales, Measured in Head - Sales of Calves: (By number of head)Cattle, Calves - Sales, Measured in HeadCattle, Calves, Veal, Raised or Sold - Number of OperationsCattle, Cows - Inventory; Cattle, Cows - Operations with InventoryCattle, Cows, Beef - Inventory - Inventory of Beef Cows: (By number of head)Cattle, Cows, Beef - InventoryCattle, Cows, Beef - Operations with Inventory - Inventory of Beef Cows: (By number of head)Cattle, Cows, Beef - Operations with InventoryCattle, Cows, Milk - Inventory - Inventory of Milk Cows: (By number of head)Cattle, Cows, Milk - InventoryCattle, Cows, Milk - Operations with Inventory - Inventory of Milk Cows: (By number of head)Cattle, Cows, Milk - Operations with InventoryCattle, >= 500 lbs - Operations with Sales - Sales of Cattle >= 500 lbs: (By number of head)Cattle, >= 500 lbs - Operations with SalesCattle, >= 500 lbs - Sales, Measured in Head - Sales of Cattle >= 500 lbs: (By number of head)Cattle, >= 500 lbs - Sales, Measured in HeadCattle, Heifers, >= 500 lbs, Milk Replacement, Production Contract - Operations with ProductionCattle, Heifers, >= 500 lbs, Milk Replacement, Production Contract - Production, Measured in HeadCattle, Incl Calves - Inventory - Inventory of Cattle, Incl Calves: (By number of head)Cattle, Incl Calves - InventoryCattle, Incl Calves - Operations with Inventory - Inventory of Cattle, Incl Calves: (By number of head)Cattle, Incl Calves - Operations with InventoryCattle, Incl Calves - Operations with Sales - Sales of Cattle, Incl Calves: (By number of head)Cattle, Incl Calves - Operations with SalesCattle, Incl Calves - Sales, Measured in US Dollars ($)Cattle, Incl Calves - Sales, Measured in Head - Sales of Cattle, Incl Calves: (By number of head)Cattle, Incl Calves - Sales, Measured in HeadCattle, On Feed - Inventory - Inventory of Cattle On Feed: (By number of head)Cattle, On Feed - InventoryCattle, On Feed - Operations with Inventory - Inventory of Cattle On Feed: (By number of head)Cattle, On Feed - Operations with InventoryCattle, On Feed - Operations with Sales For Slaughter - Sales of Cattle On Feed: (By number of head)Cattle, On Feed - Operations with Sales For SlaughterCattle, On Feed - Sales For Slaughter, Measured in Head - Sales of Cattle On Feed: (By number of head)Cattle, On Feed - Sales For Slaughter, Measured in HeadCattle, Production Contract, On Feed - Operations with ProductionCattle, Production Contract, On Feed - Production, Measured in HeadGeography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

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.

In the U.S., there have been approximately three times more beef cows than dairy cows each year since 2001. As of 2024, 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 2022, over 28 billion pounds of beef were produced in the United States. U.S. milk production and consumption The United States was among the top consumers of milk worldwide in 2022, surpassed only by India and the European Union. The annual consumption of milk in the U.S. that year was just under 21 million metric tons. 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 228 billion pounds by 2023. California and Wisconsin were the top producing states as of 2022, producing about 41.8 and 31.9 billion pounds of milk, respectively.

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.

This EnviroAtlas dataset summarizes by county the number of farm operations with dairy cows and the number of heads they manage. The data come from the Census of Agriculture, which is administered every five years by the US Department of Agriculture (USDA), and include the years 2002, 2007, 2012, and 2017. The Census classifies cattle managed on operations as beef cows, dairy cows, or other cattle (which encompasses heifers, steers, bulls, and calves). Only data regarding dairy cows are displayed in this layer. Operations are categorized into small, medium, or large, based on how many heads they manage. For each county and Census year, the dataset reports the number of farm operations that manage dairy cows, the number of heads on their property at the end of the Census year, and a breakdown of the operations into small, medium, and large. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

This dataset provides information on the number of milk cows, production of milk per cow and total milk production by state and region in the United States from the year 1970 to 2021.

Live Cattle rose to 232.95 USd/Lbs on July 30, 2025, up 1.40% from the previous day. Over the past month, Live Cattle's price has risen 10.53%, and is up 24.42% 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.

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

Feeder Cattle rose to 339.82 USd/Lbs on July 31, 2025, up 0.12% from the previous day. Over the past month, Feeder Cattle's price has risen 11.12%, and is up 34.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Feeder Cattle - values, historical data, forecasts and news - updated on July of 2025.

Cow Health Monitoring System Market Outlook

The global cow health monitoring system market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.8% during the forecast period. The growth of this market is significantly driven by the increasing adoption of advanced technologies in the agriculture sector, growing awareness regarding animal health and productivity, and rising demand for dairy and meat products.

One of the primary growth factors for the cow health monitoring system market is the increasing demand for efficient livestock management practices. Farmers and livestock managers are continually seeking ways to enhance the productivity and health of their herds. Advanced cow health monitoring systems provide real-time data on various health parameters, enabling timely and informed decisions to improve animal welfare and farm productivity. The integration of IoT and AI-based solutions is further augmenting the accuracy and reliability of these systems, making them indispensable tools for modern farming.

Another significant factor propelling the market's growth is the rising incidence of livestock diseases and the subsequent need for early disease detection and prevention. As diseases like mastitis, lameness, and metabolic disorders can have devastating effects on herd health and farm profitability, the implementation of health monitoring systems becomes crucial. These systems help in early detection of health issues, allowing for prompt intervention and reducing the risk of widespread outbreaks. This not only improves the overall health of the herd but also enhances the economic viability of farming operations.

The growing emphasis on sustainable farming practices is also contributing to the market's expansion. There is an increasing awareness about the environmental impact of livestock farming, and farmers are under pressure to adopt practices that minimize their carbon footprint. Cow health monitoring systems aid in optimizing resource use, reducing waste, and improving feed efficiency, all of which are crucial for sustainable farming. By ensuring that cows are healthy and productive, these systems help in achieving higher yields with lower environmental impact.

Regionally, the market exhibits diverse growth patterns. North America and Europe are leading the market due to the early adoption of advanced farming technologies and strong emphasis on animal welfare. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by increasing livestock farming activities, government initiatives to boost agricultural productivity, and rising disposable incomes that fuel demand for dairy and meat products. In contrast, regions like Latin America and the Middle East & Africa are also showing promising growth prospects due to the gradual adoption of modern farming practices and technology.

Component Analysis

The cow health monitoring system market is segmented by component into hardware, software, and services. Hardware components include sensors, RFID tags, and wearable devices that are crucial for collecting health data from the cows. These devices are designed to withstand harsh farm environments and provide accurate data over long periods. The demand for robust and reliable hardware is increasing as farmers seek to implement comprehensive monitoring systems that offer real-time insights into animal health and behavior.

Software solutions play a pivotal role in analyzing the data collected by hardware devices. Advanced software platforms utilize algorithms and machine learning models to interpret health indicators and predict potential health issues. These platforms often come with user-friendly interfaces that allow farmers to monitor their herds from any location. The integration of cloud-based solutions is gaining popularity as it facilitates seamless data access and management. Such software solutions are not only enhancing the efficiency of monitoring systems but also providing valuable analytics that can drive better decision-making processes.

Services constitute another critical component of the market, encompassing installation, maintenance, and training services. Effective installation and regular maintenance are essential for the optimal functioning of cow health monitoring systems. Training services ensure that farmers and farm workers are well-versed in using these advanced systems to their full potential. The demand for comprehensive service packages

The National Agricultural Statistics Service 2012 Census of Agriculture - AnimalsPrepared by Larry Heard, NMCDC, larryheard@gmail.comSource: United States Department of Agriculture 2012 Census of Agriculture, http://www.agcensus.usda.gov/The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them.Maps and statistics from the 2012 Census of Agriculture are organized into five broad categories:Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries.Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment.Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs.Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers.Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.ArcGIS Map Service: http://arcgis-ersarcgism3xl-1157953884.us-east-1.elb.amazonaws.com/arcgis/rest/services/NASS/livestockanimals/MapServer

This statistic shows the number of bovine tuberculosis infected cattle detected at slaughter in the U.S. from 2003 to 2017. According to the data, there were 38 infected cattle detected at slaughter in 2003 and just 13 cattle detected in 2017. Bovine tuberculosis is an infectious disease that is transmittable to both humans and cattle. TB is spread between cattle through the inhalation of infectious particles in the air or through infected feed.

Value per head of livestock at July 1, Canada and provinces (in dollars). Data are available on an annual basis.

Data is archived here: https://doi.org/10.5281/zenodo.4818011Data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.This data and code archive contains the following files and folders:* READMEDescription: text file with this description* flowchart.pdfDescription: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.* runAll.shDescription: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)* Folder "DataRaw"Description: folder for raw data filesThis folder contains the following files:- DataRaw/COWS.xlsxDescription: MS-Excel file with the number of cows per countySource: USDA NASS QuickstatsObservations: All available counties and years from 2002 to 2012- DataRaw/milk_state.xlsxDescription: MS-Excel file with average monthly milk yields per cowSource: USDA NASS QuickstatsObservations: All available states from 1981 to 2018- DataRaw/TMAX.csvDescription: CSV file with daily maximum temperaturesSource: PRISM Climate Group (spatially averaged)Observations: All counties from 1981 to 2018- DataRaw/VPD.csvDescription: CSV file with daily maximum vapor pressure deficitsSource: PRISM Climate Group (spatially averaged)Observations: All counties from 1981 to 2018- DataRaw/countynamesandID.csvDescription: CSV file with county names, state FIPS codes, and county FIPS codesSource: US Census BureauObservations: All counties- DataRaw/statecentroids.csvDescriptions: CSV file with latitudes and longitudes of state centroidsSource: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" functionObservations: All states* Folder "DataGenerated"Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).* Folder "Results"Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).* Folder "Figures"Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.* Folder "Tables"Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.* Folder "logFiles"Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.* PrepareCowsData.RDescription: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses* PrepareWeatherData.RDescription: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses* PrepareMilkData.RDescription: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses* CalcFrequenciesTHI_Temp.RDescription: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state* CalcAvgTHI.RDescription: R script that calculates the average THI in each state* PreparePanelTHI.RDescription: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins* PreparePanelTemp.RDescription: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins* PreparePanelFinal.RDescription: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses* EstimateTrendsTHI.RDescription: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set* EstimateModels.RDescription: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications* CalcCoefStateYear.RDescription: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification* SearchWeightMonths.RDescription: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term* TestModelSpec.RDescription: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10* CreateFigure1a.RDescription: R script that creates subfigure a of Figure 1* CreateFigure1b.RDescription: R script that creates subfigure b of Figure 1* CreateFigure2a.RDescription: R script that creates subfigure a of Figure 2* CreateFigure2b.RDescription: R script that creates subfigure b of Figure 2* CreateFigure2c.RDescription: R script that creates subfigure c of Figure 2* CreateFigure3.RDescription: R script that creates the subfigures of Figure 3* CreateFigure4.RDescription: R script that creates the subfigures of Figure 4* CreateFigure5_TableS6.RDescription: R script that creates the subfigures of Figure 5 and Table S6* CreateFigureS1.RDescription: R script that creates Figure S1* CreateFigureS2.RDescription: R script that creates Figure S2* CreateTableS2_S3_S7.RDescription: R script that creates Tables S2, S3, and S7* CreateTableS4_S5.RDescription: R script that creates Tables S4 and S5* CreateTableS8.RDescription: R script that creates Table S8* CreateTableS9.RDescription: R script that creates Table S9

United States - Producer Price Index by Commodity: Farm Products: Slaughter Cows and Bulls was 332.97300 Index 1982=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Farm Products: Slaughter Cows and Bulls reached a record high of 332.97300 in June of 2025 and a record low of 40.70000 in March of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Farm Products: Slaughter Cows and Bulls - last updated from the United States Federal Reserve on July of 2025.

Beef rose to 294.35 BRL/15KG on July 31, 2025, up 0.05% from the previous day. Over the past month, Beef's price has fallen 5.25%, but it is still 26.90% 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 August of 2025.

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.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Slaughter Cattle (WPS0131) from Jan 1967 to Jun 2025 about slaughter, cattle, livestock, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Nitrogen Source Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Mandan, North Dakota Use of dietary amendments to reduce nitrogen (N) in excreta represents a possible strategy to decrease greenhouse gas (GHG) emissions from livestock. In this regard, ingestion of small amounts of condensed quebracho tannin has been found to reduce N concentration in livestock urine. In this study, we sought to quantify the effects of tannin-affected cattle urine, normal cattle urine, and NH4NO3 in solution on greenhouse gas flux. Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) flux was measured using static chamber methodology from the three N treatments and a no application control over a six-week period in a mixed grass prairie in west-central North Dakota, USA. Over the course of the study, average CO2 emission was greatest from normal urine (335 ± 8 mg C m-2 hr-1) and least from the control (229 ± 19 mg C m-2 hr-1), with intermediate fluxes for the tannin urine and NH4NO3 treatments (290 ± 27 and 286 ± 54 mg C m-2 hr-1, respectively). Methane uptake was prevalent throughout the study, as soil conditions were predominantly warm and dry. Uptake of CH4 was greatest within the control (-30 ± 2 µg C m-2 hr-1) and least in the tannin urine treatment (-12 ± 4 µg C m-2 hr-1). Uptake of CH4 was over 40% less within the tannin urine treatment as compared to normal urine, and may have been repressed by the capacity of tannin to bind monooxygenases responsible for CH4 oxidation. Average N2O emission from NH4NO3 solution was more than twice that of all other treatments. Though the tannin urine treatment possessed 34% less N than normal cattle urine, cumulative N2O emission between the treatments did not differ. Results from this study suggest the use of condensed quebracho tannin as a dietary amendment for livestock does not yield GHG mitigation benefits in the short-term. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/40cfe233-a757-4049-b1e8-eb37b1c017e0