This EnviroAtlas dataset summarizes by county the number of farm operations with cattle 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). Data regarding all three categories 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 cattle, 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, 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.

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.

The environmental impacts of beef cattle production and their effects on the overall sustainability of beef have become a national and international concern. Our objective was to quantify important environmental impacts of beef cattle production in the United States. Surveys and visits of farms, ranches and feedlots were conducted throughout seven regions (Northeast, Southeast, Midwest, Northern Plains, Southern Plains, Northwest and Southwest) to determine common practices and characteristics of cattle production. These data along with other information sources were used to create about 150 representative production systems throughout the country, which were simulated with the Integrated Farm System Model using local soil and climate data. The simulations quantified the performance and environmental impacts of beef cattle production systems for each region. A farm-gate life cycle assessment was used to quantify resource use and emissions for all production systems including traditional beef breeds and cull animals from the dairy industry. Regional and national totals were determined as the sum of the production system outputs multiplied by the number of cattle represented by each simulated system. The average annual greenhouse gas and reactive N emissions associated with beef cattle production over the past five years were determined to be 243 ± 26 Tg carbon dioxide equivalents (CO2e) and 1760 ± 136 Gg N, respectively. Total fossil energy use was found to be 569 ± 53 PJ and blue water consumption was 23.2 ± 3.5 TL. Environmental intensities expressed per kg of carcass weight produced were 21.3 ± 2.3 kg CO2e, 155 ± 12 g N, 50.0 ± 4.7 MJ, and 2034 ± 309 L, respectively. These farm-gate values are being combined with post farm-gate sources of packing, processing, distribution, retail, consumption and waste handling to produce a full life cycle assessment of U.S. beef. This study is the most detailed, yet comprehensive, study conducted to date to provide baseline measures for the sustainability of U.S. beef. Resources in this dataset:Resource Title: Appendix A. Supplementary Data - Tables S1 to S8 (docx). File Name: Web Page, url: https://ars.els-cdn.com/content/image/1-s2.0-S0308521X18305675-mmc1.docx Direct download, docx.

Table S1. Important characteristics of farms and ranches simulated throughout seven regions of the U.S.

Table S2. Important characteristics of representative finishing facilities simulated in seven regions of the U.S.

Table S3. Important characteristics of dairy farms simulated throughout seven regions of the U.S.

Table S4. Summary of 25 years of weather data (daily solar radiation, daily mean temperature, annual precipitation and daily wind speed)1 used to simulate beef cattle operations in each area of the eastern regions.

Table S5. Soil characteristics used for locations simulated across the U.S.

Table S6. Cattle numbers by state and region as obtained or estimated from NASS (2017).

Table S7. Cattle numbers by state and region divided between traditional beef and dairy breeds as obtained or estimated from NASS (2017).

Table S8. Important resource inputs and emissions from representative cow-calf, stocker / background and feedlot operations expressed per unit of final carcass weight (CW) produced.

This coverage contains estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Livestock holdings data are reported as either a number (for example, number of milk cows), number of farms, or in thousands of dollars. Livestock holdings estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year.

Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included.

The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970).

Livestock Census of Agriculture Counties United States

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' 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.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis 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.AttributesNote 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.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

Dataset Overview

This robust dataset delves into the world of biogas production from livestock farming across the United States, providing a pivotal tool for assessing renewable energy prospects. With a focus on biogas projects derived from various livestock such as cattle, dairy cows, poultry, and swine, this resource is invaluable for stakeholders in the farming industry, renewable energy sectors, and environmental policy-making. Each record encapsulates detailed information about a specific biogas project, making it a treasure trove for research, development, and strategic planning in the renewable energy domain.

Key Features:

📌 Project Name: The name of the biogas project. 📍**Project Type:** Type of the biogas project. 🌆 City: The city where the project is located. 🏞️ County: The county where the project is situated. 🗺️**State:** The state where the project is located. 🔬 Digester Type: Type of digester used in the project. 🔍 Status: Current status of the project. 📅 Year Operational: The year when the project became operational. 🐄 Animal/Farm Type(s): Types of animals or farms used in the project. 🐄 Cattle: Number of cattle involved. 🥛**Dairy:** Number of dairy cows involved. 🐔 Poultry: Number of poultry involved. 🐖 Swine: Number of swine involved. 🔄 Co-Digestion: Information on whether co-digestion is being used or not. 🌬️ Biogas Generation Estimate (cu-ft/day): Estimated daily biogas production. ⚡ Electricity Generated (kWh/yr): Estimated annual electricity generation. 💡 Biogas End Use(s): How the produced biogas is utilized. 🌿 LCFS Pathway?: Information on the Low Carbon Fuel Standard pathway. 🔌 Receiving Utility: The utility company receiving the biogas or electricity. 🌍 Total Emission Reductions (MTCO2e/yr): Estimated total emission reduction. 🏆 Awarded USDA Funding?: Information on whether the project received USDA funding or not. 📊 Operational Years:Number of years the project has been operational. 🦓 Total_Animals: Total number of animals involved in the project. 💨 Biogas_per_Animal (cu-ft/day): Estimated biogas production per animal. 🌱 Emission_Reduction_per_Year: Estimated annual emission reduction per animal. 🔋 Electricity_to_Biogas_Ratio: The ratio between electricity generation and biogas production. 🗑️ Total_Waste_kg/day: Estimated daily waste production. ⚙️ Waste_Efficiency: Efficiency of waste conversion to biogas. 🔧 Electricity_Efficiency: Efficiency of biogas conversion to electricity.

This dataset stands as a cornerstone for developing strategies that can enhance profitability for farmers, guide investment decisions for energy companies, and contribute significantly to environmental sustainability efforts.

The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management. This dataset encompasses a wide range of information, including farm location, land area, cow population, farm size, production dates, product details, brand information, quantities, pricing, shelf life, storage conditions, expiration dates, sales information, customer locations, sales channels, stock quantities, stock thresholds, and reorder quantities.

Features:

1. Location: The geographical location of the dairy farm.
2. Total Land Area (acres): The total land area occupied by the dairy farm.
3. Number of Cows: The number of cows present in the dairy farm.
4. Farm Size: The size of the dairy farm(in sq.km).
5. Date: The date of data recording.
6. Product ID: The unique identifier for each dairy product.
7. Product Name: The name of the dairy product.
8. Brand: The brand associated with the dairy product.
9. Quantity (liters/kg): The quantity of the dairy product available.
10. Price per Unit: The price per unit of the dairy product.
11. Total Value: The total value of the available quantity of the dairy product.
12. Shelf Life (days): The shelf life of the dairy product in days.
13. Storage Condition: The recommended storage condition for the dairy product.
14. Production Date: The date of production for the dairy product.
15. Expiration Date: The date of expiration for the dairy product.
16. Quantity Sold (liters/kg): The quantity of the dairy product sold.
17. Price per Unit (sold): The price per unit at which the dairy product was sold.
18. Approx. Total Revenue (INR): The approximate total revenue generated from the sale of the dairy product.
19. Customer Location: The location of the customer who purchased the dairy product.
20. Sales Channel: The channel through which the dairy product was sold (Retail, Wholesale, Online).
21. Quantity in Stock (liters/kg): The quantity of the dairy product remaining in stock.
22. Minimum Stock Threshold (liters/kg): The minimum stock threshold for the dairy product.
23. Reorder Quantity (liters/kg): The recommended quantity to reorder for the dairy product.

Potential Use-Case:

This dataset can be used by researchers, analysts, and businesses in the dairy industry for various purposes, such as:

1. Analyzing the performance of dairy farms based on location, land area, and cow population.
2. Understanding the sales and distribution patterns of different dairy products across various brands and regions.
3. Studying the impact of storage conditions and shelf life on the quality and availability of dairy products.
4. Analyzing customer preferences and buying behavior based on location and sales channels.
5. Optimizing inventory management by tracking stock quantities, minimum thresholds, and reorder quantities.
6. Conducting market research and trend analysis in the dairy industry.
7. Developing predictive models for demand forecasting and pricing strategies.

Note: This dataset includes data from the period between 2019 and 2022, and it specifically focuses on selected dairy brands operating in specific states and union territories of India. There is an intentional drift highlighted in the dataset's figures due to its opensource and creative license, currently !

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

This data set includes measurements of greenhouse gas (GHG) and ammonia fluxes from dairy manure, with accompanying measurements of manure physical and chemical characteristics. The manure was collected from two farms in the Great Lakes region and subjected to varying treatments of anaerobic digestion and liquid-solid separation. Farm 1 was a private farm with a 2,560-cow diary herd. Manure was collected three times daily using skid steers. Both digestion and separation of manure were performed at Farm 1. Farm 2 was the USDA Dairy Forage Research Center in Prairie du Sac, WI with a 350-cow herd and manure collected by scrape daily. Farm 2 had a separator but no digester. Gas fluxes from manure of each treatment type were monitored both from manure storage barrels ("Storage_GHG" tab of dataset), and from field-applied manure ("Field_GHG" tab). The "Manure" tab gives information about the manure chemical and physical characteristics after treatment (i.e. after digestion and/or separation) and during barrel storage. The "Soil" tab gives information about soil chemical contents during the time period of flux measurements from field-applied manure. Manure storage was during November 2013 - May 2014. In May 2014 the stored manure was surface-applied and immediately incorporated on 3.3 m^2 plots at Farm 2 in a randomized block design, at a rate of 320 kg N/ha. Field corn (maize) was planted in the plots. Note that gas fluxes are given as cumulative mass flux over the monitoring period, with sampling approximately once a week during storage (November 2013 - May 2014) and field monitoring (May 2014 - September 2014). The instrument used to measure both storage barrel and field fluxes was a "Gasmet" brand Fourier Transform Infrared (FTIR) Spectroscopy gas analyzer. Each flux sample was taken over 7 minutes with gas concentrations measured every 20 seconds. Flux data from different manure fraction "treatments" are reported as the measured fluxes, and also as the fluxes normalized to a raw manure (i.e. whole, wet manure) weight basis. This experiment is part of the 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 for: Carbon Dioxide, Methane, Nitrous Oxide, and Ammonia Emissions from Digested and Separated Dairy Manure during Storage and Land Application; updated data set. File Name: DairyCAP_WI_manure_dictionary_01302017.csvResource Software Recommended: Microsoft Excel 2013,url: https://products.office.com/en-us/excel Resource Title: Carbon Dioxide, Methane, Nitrous Oxide, and Ammonia Emissions from Digested and Separated Dairy Manure during Storage and Land Application; updated data set . File Name: DairyCAP_WI_manure_data_01302017.xlsxResource Description: This data set includes measurements of greenhouse gas (GHG) and ammonia fluxes from dairy manure, with accompanying measurements of manure physical and chemical characteristics. The manure was collected from two farms in the Great Lakes region and subjected to varying treatments of anaerobic digestion and liquid-solid separation. Farm 1 was a private farm with a 2,560-cow diary herd. Manure was collected three times daily using skid steers. Both digestion and separation of manure were performed at Farm 1. Farm 2 was the USDA Dairy Forage Research Center in Prairie du Sac, WI with a 350-cow herd and manure collected by scrape daily. Farm 2 had a separator but no digester. Gas fluxes from manure of each treatment type were monitored both from manure storage barrels ("Storage_GHG" tab of dataset), and from field-applied manure ("Field_GHG" tab). The "Manure" tab gives information about the manure chemical and physical characteristics after treatment (i.e. after digestion and/or separation) and during barrel storage. The "Soil" tab gives information about soil chemical contents during the time period of flux measurements from field-applied manure. Manure storage was during November 2013 - May 2014. In May 2014 the stored manure was surface-applied and immediately incorporated on 3.3 m^2 plots at Farm 2 in a randomized block design, at a rate of 320 kg N/ha. Field corn (maize) was planted in the plots. Note that gas fluxes are given as cumulative mass flux over the monitoring period, with sampling approximately once a week during storage (November 2013 - May 2014) and field monitoring (May 2014 - September 2014). The instrument used to measure both storage barrel and field fluxes was a "Gasmet" brand Fourier Transform Infrared (FTIR) Spectroscopy gas analyzer. Each flux sample was taken over 7 minutes with gas concentrations measured every 20 seconds. Flux data from different manure fraction "treatments" are reported as the measured fluxes, and also as the fluxes normalized to a raw manure (i.e. whole, wet manure) weight basis. This experiment is part of the 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. This data set was updated on January 30, 2017, in order to harmonize some units and nomenclature with other Dairy CAP data sets.Resource Software Recommended: Microsoft Excel 2013,url: https://products.office.com/en-us/microsoft-excel-2013

A comprehensive image dataset of various types of reknown cows in Bangladesh. This dataset contains a total of 263 HD image data in JPG,JPEG,PNG format. The cow breed is collected from different area in Bangladesh .There are 4 types of cows in this dataset which contains un-uniform numbers of image data: 1.Holstain Friesian (69 images) 2.Jersey (73 images) 3.Local Deshi (56 images) 4.Sahiwal (65 images) Each of the variety images are unique and captured from various angle so that the cows size, shape and color can be recognize sharply during image processing. Also, the Dataset is in ZIP format where it contains root folder "Cow Breed.zip" which contains 4 sub folders for each breed of cow. Moreover, this dataset is a state-of-the-art cow breed dataset.

Genetic modification of farm animals has not been well accepted by the public. Some modifications have the potential to improve animal welfare. One such example is the use of gene editing (i.e. CRISPR (clustered regularly interspaced short palindromic repeats)) to spread the naturally occurring POLLED gene, as these genetically hornless animals would not need to experience the painful procedures used to remove the horns or horn buds. The aim of the current study was to assess public attitudes regarding the use of GM to produce polled cattle. United States (US) citizens (n = 598), recruited via Amazon Mechanical Turk, were asked “Do you think genetically modifying cows to be hornless would be…”, and responded using a 7-point Likert scale (1 = a very bad thing, 4 = neither good nor bad, 7 = a very good thing). Participants were then asked to indicate if they would be willing to consume products from these modified animals. We excluded 164 of the original 598 participants for not completing the survey, failing any of three attention check questions, or providing no or unintelligible qualitative responses. Respondents were then asked to provide a written statement explaining their answers; these reasons were subjected to qualitative analysis. Comparison of Likert scale ratings between two groups was done using the Wilcoxon rank-sum test, and comparisons between more than two groups were done using the Kruskal-Wallis rank test. More people responded that the modification would be good (Likert ≥ 5; 65.7%) than bad (Likert ≤ 3; 23.1%), and that they would be willing to consume products from these animals (Likert ≥ 5; 66.0%) versus not consume these products (Likert ≤ 3; 22.6%). Qualitative analysis of the text responses showed that participant reasoning was based on several themes including animal welfare, uncertainty about the technology, and worker well-being. In conclusion, many participants reported positive attitudes towards GM polled cattle; we suggest that people may be more likely to support GM technologies when these are perceived to benefit the animal.

Purpose: The need to assess the sustainability attributes of the United States beef industry is underscored by its importance to food security locally and globally. A life cycle assessment (LCA) of the US beef value chain was conducted to develop baseline information on the environmental impacts of the industry including metrics of the cradle-to-farm gate (feed production, cow-calf, and feedlot operations) and post-farm gate (packing, case-ready, retail, restaurant, and consumer) segments. Methods: Cattle production (cradle-to-farm gate) data were obtained using the integrated farm system model (IFSM) supported with production data from the Roman L. Hruska US Meat Animal Research Center (USMARC). Primary data for the packing and case-ready phases were obtained from packers that jointly processed nearly 60% of US beef while retail and restaurant primary data represented 8 and 6%, respectively, of each sector. Consumer data were obtained from public databases and literature. The functional unit or consumer benefit (CB) was 1 kg of consumed, boneless, edible beef. The relative environmental impacts of processes along the full beef value chain were assessed using a third party validated BASF Corporation Eco-Efficiency Analysis methodology. Results and discussion: Value chain LCA results indicated that the feed and cattle production phases were the largest contributors to most environmental impact categories. Impact metrics included water emissions (7005 L diluted water eq/CB), cumulative energy demand (1110 MJ/CB), and land use (47.4 m2a eq/CB). Air emissions were acidification potential (726 g SO2 eq/CB), photochemical ozone creation potential (146.5 g C2H4 eq/CB), global warming potential (48.4 kg CO2 eq/CB), and ozone depletion potential (1686 μg CFC11 eq/CB). The remaining metrics calculated were abiotic depletion potential (10.3 mg Ag eq/CB), consumptive water use (2558 L eq/CB), and solid waste (369 g municipal waste eq/CB). Of the relative points adding up to 1 for each impact category, the feed phase contributed 0.93 to the human toxicity potential. Conclusions: This LCA is the first of its kind for beef and has been third party verified in accordance with ISO 14040:2006a and 14044:2006b and 14045:2012 standards. An expanded nationwide study of beef cattle production is now being performed with region-specific cattle production data aimed at identifying region-level benchmarks and opportunities for further improvement in US beef sustainability. Resources in this dataset:Resource Title: Electronic Supplementary Material ESM 1 - Tables S1 to S11 (docx). File Name: Web Page, url: https://static-content.springer.com/esm/art:10.1007/s11367-018-1464-6/MediaObjects/11367_2018_1464_MOESM1_ESM.docx Direct download, docx. Table S1: Feed phase input data (resource use and emissions) from USMARC and IFSM simulations used in the U.S. beef life cycle impact assessment and sources of their life-cycle inventories (LCI). Table S2: Cattle phase input data (resource use and emissions) from USMARC and IFSM simulations in the U.S. beef life cycle impact assessment and the sources of their respective life-cycle inventories (LCI). Table S3: Packing and case-ready phases input data (resource use and emissions) used in the U.S. beef life cycle impact assessment and the sources of their respective life-cycle inventories (LCI). Allocation factor of case-ready (i.e. % packaged at case ready) = 0.63. Table S4: Retail and consumer phases input data (resource use and emissions) used in U.S. beef life cycle impact assessment and their respective life-cycle inventory (LCI) sources. Allocation factor for retail and consumer (i.e. at-home consumption portion of total consumption sold through retail) = 0.47. Table S5: Restaurant phase input data (resource use and emissions) used in U.S. beef life cycle impact assessment and their respective life-cycle inventory (LCI) sources. Allocation factor (i.e. restaurant fraction of total beef consumption) = 0.53. Table S6: Essential raw materials considered in the U.S. beef life cycle impact assessment and respective weighting factors used for the determination of their Abiotic Depletion Potential (ADP). Table S7: Scoring system for toxic properties described by H-phrases for U.S. beef life cycle impact assessment (Landsiedel and Saling (2002) before our modification). Table S8: Land occupation and transformation weighting factors for U.S. beef life cycle impact assessment based on Ecosystem Damage Potentials (EDPs) from the Ecoinvent 2.2 life cycle inventory database (Frischknecht et al. 2005). Table S9: Air emissions and their respective weighting (equivalence) factors used in U.S. beef life cycle impact assessment. Table S10: Solid waste relative disposal costs used in U.S. beef life cycle impact assessment (Klein 2011). Table S11: Water emissions categories and their respective weighting factors based on regional regulatory limits used in the U.S. beef life cycle assessment.

This extensive dataset offers a comprehensive look into various biogas projects across the United States, shining a light on the intersection of waste management and renewable energy. Representing a crucial resource for environmental research and sustainable development, each entry in the dataset encapsulates a distinct biogas project, providing valuable insights into its operational, environmental, and technological facets.

Key Features:

📌 Project Name: The name of the biogas project. 📍 Project Type: Type of the biogas project. 🌆 City: The city where the project is located. 🏞️ County: The county where the project is situated. 🗺️ State: The state where the project is located. 🔬 Digester Type: Type of digester used in the project. 🔍 Status: Current status of the project. 📅 Year Operational: The year when the project became operational. 🐄 Animal/Farm Type(s): Types of animals or farms used in the project. 🐄 Cattle: Number of cattle involved. 🥛 Dairy: Number of dairy cows involved. 🐔** Poultry:** Number of poultry involved. 🐖 **Swine: **Number of swine involved. 🔄 Co-Digestion: Information on whether co-digestion is being used or not. 🌬️ Biogas Generation Estimate (cu-ft/day): Estimated daily biogas production. ⚡ Electricity Generated (kWh/yr): Estimated annual electricity generation. 💡 Biogas End Use(s): How the produced biogas is utilized. 🌿 LCFS Pathway?: Information on the Low Carbon Fuel Standard pathway. 🔌 Receiving Utility: The utility company receiving the biogas or electricity. 🌍 Total Emission Reductions (MTCO2e/yr): Estimated total emission reduction. 🏆 Awarded USDA Funding?: Information on whether the project received USDA funding or not. 📊 Operational Years: Number of years the project has been operational. 🦓 Total_Animals: Total number of animals involved in the project. 💨 Biogas_per_Animal (cu-ft/day): Estimated biogas production per animal. 🌱 Emission_Reduction_per_Year: Estimated annual emission reduction per animal. 🔋 Electricity_to_Biogas_Ratio: The ratio between electricity generation and biogas production. 🗑️ Total_Waste_kg/day: Estimated daily waste production. ⚙️ Waste_Efficiency: Efficiency of waste conversion to biogas. 🔧 Electricity_Efficiency: Efficiency of biogas conversion to electricity.

This dataset serves as an instrumental tool in advancing our understanding of biogas projects and their role in the broader context of renewable energy and sustainable waste management. It highlights the technological, operational, and environmental aspects of biogas projects, offering insights into areas where improvements can drive enhanced efficiency and sustainability. The data underscores the potential of biogas as a pivotal element in transitioning towards more sustainable energy and waste management practices, contributing significantly to environmental conservation efforts.

Washington State Department of Agriculture regulates dairy farm compliance with state water quality and food safety law. This includes regular inspections of dairy production fields and facilities. The milking facilities, which generally represent the heart of the operation, are mapped for internal and public use.This dataset includes all active cow dairy milking facilities. The data are updated quarterly. The dataset includes information about the spatial distribution of dairies in Washington State and information about each business itself. Pursuant to WAC 16-06-210, some information is expressed in ranges to meet non-disclosure requirements.The following is a description of the attributes included with the WA Dairies dataset:

Field
Description


AG ID
The agency given identification number assigned at the initial licensing of the dairy.


Facility Size
This is a general summary of the farm size. For DNMP purposes, size is determined by mature (milking + dry) animal numbers; with a dairy herd of up to 199 animals being a Small, 200-699 being medium, and 700 or greater being Large.


Business Name
The name which appears on the milking license.


Site Address
The street address of the farm milking facility (not the business mailing address).


Site City
The city wherein lies the milking facility.


County
The county wherein lies the milking facility.


DNMP Region
The Dairy Nutrient Management Program Region wherein lies the milking facility.


CAFO Status
This field denotes whether or not the dairy milking license has an associated Confined Animal Feeding Operation (CAFO) permit.


CAFO ID
The permit identification number for the associated dairy.


Range Current Acres
The current and approximate acreage of land application or farming production land associated with the dairy.


Range Current Milking
The current and approximate number of milking animals currently in rotation.


Range Current Dry
The current and approximate number of mature dry animals currently in rotation.


Range Current Heifers
The current and approximate number of heifers (ages 6 months old to fresh) currently in rotation.


Range Current Calves
The current and approximate number of calves (ages 0 to 6 months) currently in rotation.


Latitude (WGS84)
Latitude Datum World Geodetic System 1984


Longitude (WGS84)
Longitude Datum World Geodetic System 1984


WRIA
The Water Resources Inventory Area (WRIA) wherein lies the milking facility.


Conservation District
The Conservation District serving the dairy business.


DNMA Status
Indicates whether the dairy is currently licensed and is regulated under food safety laws and dairy nutrient management act requirements.

Beef traded flat at 321.60 BRL/15KG on December 1, 2025. Over the past month, Beef's price has risen 0.44%, but it is still 8.58% lower 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 December of 2025.

Using oral swabs to collect the remnants of stomach content regurgitation during rumination in dairy cows can replicate up to 70% of the ruminal bacterial community, offering potential for broad-scale population-based studies on the rumen microbiome. The swabs collected from dairy cows often vary widely with respect to sample quality, likely due to several factors such as time of sample collection and cow rumination behavior, which may limit the ability of a given swab to accurately represent the ruminal microbiome. One such factor is the color of the swab, which can vary significantly across different cows. Here, we hypothesize that darker-colored swabs contain more rumen contents, thereby better representing the ruminal bacterial community than lighter-colored swabs. To address this, we collected oral swabs from 402 dairy cows and rumen samples from 13 cannulated cows on a research farm in Wisconsin, United States and subjected them to 16S rRNA sequencing. In addition, given that little is known about the ability of oral swabs to recapitulate the ruminal fungal community, we also conducted ITS sequencing of these samples. To correlate swab color to the microbiota we developed and utilized a novel imaging approach to colorimetrically quantify each swab from a range of light to dark. We found that swabs with increasing darkness scores were significantly associated with increased bacterial alpha diversity (p 

The MAMA experiment (Manure Application Methods for Alfalfa-Grass) was designed to evaluate nutrient and pathogen losses with conventional and improved liquid dairy manure management practices for alfalfa-grass production. Observations from MAMA have also been used for parameterization and validation of computer simulation models of greenhouse gas (GHG) emissions from dairy farms (Gaillard et al., in preparation). The experiment included five treatments: shallow injection of manure, aerator/banded manure (subsurface deposition), banded manure (trailing foot application), broadcast manure, and no manure (i.e. control). The five treatments were replicated three times in a randomized complete block design. This experiment was performed as part of the Dairy CAP, described below. The experiment was conducted at the Marshfield Research Station of the University of Wisconsin and the USDA Agricultural Research Service (ARS) in Marshfield, WI (Wood County, Latitude 44.641445, Longitude -90.133526). Soils at the research station are from the Withee soil series, fine-loamy, mixed, superactive, frigid Aquic Glossudalf, with 2% slope. Each of the fifteen experimental plots was approximately 7.3 x 12.8 meters, oriented across slope. A weather station was at the south edge of the research field and centered east-west. A weather station for snow data was located 420 meters south of the field. The experiment was initiated on May 16, 2013 by planting alfalfa (Medicago sativa) on plots that were in a corn (Zea mays) and soybean (Glycine max) rotation during the previous five years. All plots were planted with cultivar "Nexgrow-6422Q 19," using a 10-foot Brillion forage seeder. Planting rate was 19 kg seed per hectare. Alfalfa forage was harvested by cutting at 3 inches (~8 cm) height. Alfalfa was harvested once in 2013, three times in 2014 and 2015, and four times in 2016. Forage characteristics were measured at the University of Wisconsin Soil and Forage Lab in Marshfield (total P and total K) and at the Marshfield ARS (dry matter, total N and total C) The manure applied in this experiment was from the dairy herd at the Marshfield Research Station. Cows were fed a diet of 48% dry matter, 17.45% protein, and 72.8% total digestible nutrients. Liquid slurry manure, including feces, urine, and bedding, was collected and stored in a lagoon on the site. Manure was withdrawn from the lagoon, spread on the plots and sampled for analysis all on the same day, once per year shortly after an alfalfa harvest. Manure samples were analyzed at the University of Wisconsin Soil and Forage Lab in Marshfield (NH4-N, total P and total K) and at the Marshfield ARS (pH, dry matter, volatile solids, total N and total C). GHG fluxes from soil (CO2, CH4, N2O) were measured using static chambers as described in Parkin and Venterea (2010). In addition, ammonia fluxes (NH3) from soil were measured using a dynamic chamber method (Svensson, 1994; Misselbrook and Hansen, 2001). Additional soil chemical and physical characteristics were measured as noted in the data dictionary and other metadata of the MAMA data set, included here. This experiment was part of “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP), 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 was 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 has improved 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_MAMA. File Name: Data_dictionary_DairyCAP_MAMA.xlsxResource Description: This is the data dictionary for the DairyCAP_MAMA experiment, which was conducted at the USDA-ARS research station in Marshfield, WI.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data dictionary DairyCAP MAMA. File Name: Data_Dictionary_DairyCAP_MAMA.csvResource Description: This is the data dictionary for the DairyCAP_MAMA dataset.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_MAMA. File Name: DairyCAP_MAMA.xlsxResource Description: Data from Manure Application Methods for Alfalfa-grass (MAMA) experiment at the USDA-ARS research station in Marshfield, WI.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel

The quality of the animal-human relationship and, consequently, the welfare of animals can be improved by gentle interactions such as stroking and talking. The perception of different stimuli during these interactions likely plays a key role in their emotional experience, but studies are scarce. During experiments, the standardization of verbal stimuli could be increased by using a recording. However, the use of a playback might influence the perception differently than “live” talking, which is closer to on-farm practice. Thus, we compared heifers' (n = 28) reactions to stroking while an experimenter was talking soothingly (“live”) or while a recording of the experimenter talking soothingly was played (“playback”). Each animal was tested three times per condition and each trial comprised three phases: pre-stimulus, stimulus (stroking and talking) and post-stimulus. In both conditions, similar phrases with positive content were spoken calmly, using long low-pitched vowels. All tests were video recorded and analyzed for behaviors associated with different affective states. Effects on the heifers' cardiac parameters were assessed using analysis of heart rate variability. Independently of the auditory stimuli, longer durations of neck stretching occurred during stroking, supporting our hypothesis of a positive perception of stroking. Observation of ear positions revealed longer durations of the “back up” position and less ear flicking and changes of ear positions during stroking. The predicted decrease in HR during stroking was not confirmed; instead we found a slightly increased mean HR during stroking with a subsequent decrease in HR, which was stronger after stroking with live talking. In combination with differences in HRV parameters, our findings suggest that live talking might have been more pleasurable to the animals and had a stronger relaxing effect than “playback.” The results regarding the effects of the degree of standardization of the stimulus on the variability of the data were inconclusive. We thus conclude that the use of recorded auditory stimuli to promote positive affective states during human-animal interactions in experimental settings is possible, but not necessarily preferable.

IntroductionThere is a growing interest in utilizing seaweed in ruminant diets for mitigating enteric methane (CH4) emissions while improving animal health. Chondrus crispus is a red seaweed that grows in the Gulf of Maine (United States) and has shown to suppress CH4 production in vitro. Organic dairy producers in Maine are currently feeding seaweed due to herd health promoting benefits. However, large-scale adoption depends on technical and financial factors, as well as validation from pilot studies.MethodsA survey was developed to identify barriers and drivers towards the adoption of CH4-reducing algal-based feeds. Concurrently, a randomized complete block design study was conducted to investigate the effect of C. crispus on enteric CH4 emissions and milk production in a typical Maine organic dairy farm. Twenty-two organically certified Holstein and Jersey cows averaging 29 ± 6.8 kg of milk/d and 150 ± 69 days in milk, were blocked and randomly assigned to a control diet without C. crispus (0CC), or with 6% [dry matter (DM) basis] C. crispus (6CC). Samples were collected on the last week of the 2-wk covariate period, and wk 3, 5, 8, and 10 after initiation of treatments for a total of 12 weeks. Gaseous emissions were measured using a GreenFeed unit. Data were analyzed using the MIXED procedure of SAS with repeated measures over time.ResultsAll survey respondents (n = 35; 54% response rate) were familiar with seaweeds as feed, and 34% were already users. Producers who were willing to pay 0.64 USD/cow/d on average for a CH4-reducing algal-based feed, also stated the need for co-benefits in terms of cattle health and performance as a requirement for adoption. Feeding 6CC decreased enteric CH4 production by 13.9% compared with 0CC (401 vs. 466 g/d). Further, milk yield (mean = 27.1 kg/d), CH4 intensity (mean = 15.2 g of CH4/kg of energy corrected milk), and concentrations and yields of milk fat and true protein were not affected by treatments.DiscussionProducer receptiveness to CH4-reducing algal-based feeds will not only be dependent on purchase price, but also on co-benefits and simplicity of integration into existing feed practices. Feeding C. crispus at 6% of the diet DM decreased CH4 production in dairy cows by 13.9% without negative effects on milk yield and composition. Identifying the bioactive compounds in C. crispus is critical to understand the effect of this red seaweed on mitigating enteric CH4 emissions in dairy cows.