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 dataset is part of the European H2020 project ICAERUS regarding the livestock monitoring use case. More information here : https://icaerus.eu/

This dataset was built to develop cow detection and counting models using drone images.

The directory contains the dataset encompasses around 1100 raw .jpeg images from drone (DJI mavic 3 Enterprise or Thermal) of grazing areas where cattle graze collected between June and October 2023.

The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position.

Images were taken from altitudes of 30m, 60m and mostly 100m. The Aerial images contain many .exif methadata (drone, camera, altitude etc.).

There is a strong imbalance between images with "cattle" and image with "no cattle" representative of areas to monitor and weather conditions.

Data organization :

The data are organized as first in a directory by farm (Mauron, Derval or Jalogny) where the images were collected and then by a second directory called JPGImages and finally another directory by

flight planned. Images can be found there.

Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XXX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XXX representing a mission number.

An annotation directory is also present in flight directory and contains label files. The name of the annotation directory depends on the type of the annotation format used

(for now : PASCAL_VOC1.1 and YOLO_V1). One annotation file referred to an unique image and have the same name except the extension.

Annotations files contain bounding boxes coordinates produced using CVAT.io. Depending on the using model it can be preferable to use PASCAL_VOC1.1 or YOLO_V1 annotations.

Other versions of this dataset will be available in 2024. The authors of the dataset are opened to any collaboration regarding animal counting models.

For more information, please contact: adrien.lebreton@idele.fr

Cattle Breed

## Overview

Cattle Breed is a dataset for object detection tasks - it contains Breed annotations for 826 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset includes 1747 images. Cows-buffalo are annotated in YOLOv8 format.

class_id center_x center_y width height

The following pre-processing was applied to each image:

Auto-orientation of pixel data (with EXIF-orientation stripping) Resize to 640x640 (Stretch) No image augmentation techniques were applied.

Examples:-

0 0.3234375 0.421875 0.1015625 0.346875

0 0.5859375 0.53203125 0.2828125 0.575

0 0.1359375 0.584375 0.271875 0.525

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

The USDA-Agricultural Research Service Central Plains Experimental Range (CPER) is a Long-Term Agroecosystem Research (LTAR) network site located ~20 km northeast of Nunn, in north-central Colorado, USA. In 1939, scientists established the Long-term Grazing Intensity study (LTGI) with four replications of light, moderate, and heavy grazing. Each replication had three 129.5 ha pastures with the grazing intensity treatment randomly assigned. Today, one replication remains. Light grazing occurs in pasture 23W (9.3 Animal Unit Days (AUD)/ha, targeted for 20% utilization of peak growing-season biomass), moderate grazing in pasture 15E (12.5 AUD/ha, 40% utilization), and heavy grazing in pasture 23E (18.6 AUD/ha, 60% utilization). British- and continental-breed yearling cattle graze the pastures season-long from mid-May to October except when forage limitations shorten the grazing season. Individual raw data on cattle entry and exit weights, as well as weights every 28-days during the grazing season are available from 2000 to 2019. Cattle entry and exit weights are included in this dataset. Weight outliers (± 2 SD) are flagged for calculating summary statistics or performing statistical analysis. Resources in this dataset:Resource Title: Data Dictionary for LTGI Cattle weights on CPER (2000-2019). File Name: LTGI_2000-2019_data_dictionary.csvResource Description: Data dictionary for data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities Resource Title: LTGI Cattle weights on CPER (2000-2019). File Name: LTGI_2000-2019_all_weights_published.csvResource Description: Data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities

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.

Water buffalo (Bubalus bubalis L.) is an important livestock species worldwide. Like many other livestock species, water buffalo lacks high quality and continuous reference genome assembly, required for fine-scale comparative genomics studies. In this work, we present a dataset, which characterizes genomic differences between water buffalo genome and the extensively studied cattle (Bos taurus Taurus) reference genome. This data set is obtained after alignment of 14 river buffalo whole genome sequencing datasets to the cattle reference. This data set consisted of 13, 444 deletion CNV regions, and 11,050 merged mobile element insertion (MEI) events within the upstream regions of annotated cattle genes. Gene expression data from cattle and buffalo were also presented for genes impacted by these regions. This study sought to characterize differences in gene content, regulation and structure between taurine cattle and river buffalo (2n=50) (one extant type of water buffalo) using the extensively annotated UMD3.1 cattle reference genome as a basis for comparisons. Using 14 WGS datasets from river buffalo, we identified 13,444 deletion CNV regions (Supplemental Table 1) in river buffalo, but not identified in cattle. We also presented 11,050 merged mobile element insertion (MEI) events (Supplemental Table 2) in river buffalo, out of which, 568 of them are within the upstream regions of annotated cattle genes. Furthermore, our tissue transcriptomics analysis provided expression profiles of genes impacted by MEI (Supplemental Tables 3–6) and CNV (Supplemental Table 7) events identified in this study. This data provides the genomic coordinates of identified CNV-deletions and MEI events. Additionally, normalized read count of impacted genes, along with their adjusted p-values of statistical analysis were presented (Supplemental Tables 3–6). Genomic coordinates of identified CNV-deletion and MEI events, and Ensemble gene names of impacted genes (Supplemental Tables 1 and 2) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by MEI in liver (Supplemental Tables 3 and 4) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by MEI in muscle (Supplemental Tables 5 and 6) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by CNV deletions in river buffalo (Supplemental Table 7) Public assessment of this dataset will allow for further analyses and functional annotation of genes that are potentially associated with phenotypic difference between cattle and water buffalo. Raw read data of whole genome and transcriptome sequencing were deposited to NCBI Bioprojects. Resources in this dataset:Resource Title: Genomic structural differences between cattle and River Buffalo identified through comparative genomic and transcriptomic analysis. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340918305183 Data in Brief presenting a dataset which characterizes genomic differences between water buffalo genome and the extensively studied cattle (Bos taurus Taurus) reference genome. This data set is obtained after alignment of 14 river buffalo whole genome sequencing datasets to the cattle reference. This data set consisted of 13, 444 deletion CNV regions, and 11,050 merged mobile element insertion (MEI) events within the upstream regions of annotated cattle genes. Gene expression data from cattle and buffalo were also presented for genes impacted by these regions. Tables are with this article. Raw read data of whole genome and transcriptome sequencing were deposited to NCBI Bioprojects as the following: PRJNA350833 (https://www.ncbi.nlm.nih.gov/bioproject/?term=350833) PRJNA277147 (https://www.ncbi.nlm.nih.gov/bioproject/?term=277147) PRJEB4351 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4351)

The Department of Agriculture Food and the Marine (DAFM) maintains cattle traceability on a central database called the Animal Identification and Movement (AIM) database. The AIM system records all births, movements and disposals in accordance with EU requirements and therefore traces all bovines from birth to slaughter. DAFM produces an annual AIM Bovine Statistics Report containing comprehensive data on the national bovine herd. This table contains data taken from the AIM stats report from 2011-2021 on: calf births categorised as beef and dairy. They are classified as such depending on whether the sire breed type is a Beef or Dairy Breed. Calf births broken down by the breed of the sire. It contains data on the top 6 breeds only as these account for approx 90% of all births. Slaughter in DAFM slaughter plants of male beef animals. Age at slaughter in DAFM slaughter plants of female beef animals. Whether bovine animals were slaughtered at DAFM slaughter plants or local authority abattoirs, how many were on farm deaths, how many still born deaths and how many bovines were exported. Categorising bovine herds into those with less than 25 animals, 25-49, 50-74, 75-99, 100-149 and 150+

Cow And Buffalo

## Overview

Cow And Buffalo is a dataset for object detection tasks - it contains Animal annotations for 240 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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

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

This table contains 8 series, with data for years 1930 - 1990 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (4 items: Montreal;Toronto;Winnipeg;Calgary); Type of livestock (4 items: Slaughter steers, good;Slaughter cows, good;Feeder steers, good;Calves veal, good and choice).

This data package includes survey questions from beef cattle producers collectively operating in at least 31 counties in at least 7 states (California, Illinois, Missouri, Nebraska, New Mexico, Oklahoma, Texas) - "at least" because there were some respondents who chose not to provide the _location of their operation. Responses were collected between January 22, 2020 and May 31, 2021. Most of the surveys were administered in person at the 2020 Southwest Beef Symposium in Amarillo, TX. The survey was also placed online and an additional few responses were collected through the online survey. These data represent a sample of convenience as no formal sampling scheme was employed in soliciting responses. Survey responses are summarized in the publication, Snapshot of Rancher Perspectives on Creative Cattle Management Options (Elias et. al, 2020).The purpose of gathering these data was to learn more about the characteristics of beef cattle producers in the region and to gauge producer interest in precision livestock ranching technologies and heritage cattle – both strategies being researched by the Sustainable Southwest Beef Project to support sustainability of ranching operations in the Southwest and Southern Plains regions of the US.

Live Cattle fell to 240.50 USd/Lbs on October 17, 2025, down 1.41% from the previous day. Over the past month, Live Cattle's price has risen 3.50%, and is up 28.31% 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 October of 2025.

China Livestock: Number: Cow: Beef Cattle data was reported at 84,541.000 Unit th in 2022. This records an increase from the previous number of 80,044.000 Unit th for 2021. China Livestock: Number: Cow: Beef Cattle data is updated yearly, averaging 68,386.000 Unit th from Dec 2008 (Median) to 2022, with 15 observations. The data reached an all-time high of 84,541.000 Unit th in 2022 and a record low of 52,533.000 Unit th in 2008. China Livestock: Number: Cow: Beef Cattle data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Number of Livestock: Large Animals: Cow .

This dataset comprises spatial and temporal data on ruminant livestock distributions on detailed spatial level. We collected statistics on cattle, sheep and goats distribution for 43 countries and territories in Europe: for 31 countries and territories we collected data on the level of local administrative units, for 8 countries on regional level (NUTS 3), and on national level (NUTS 0) for 4 countries. We provide data for over 73 thousand administrative units, making them more detailed than publicly provided by the European Statistical Office (which reports data for 325 adminstrative units). The data are available for the periods corresponding to 2000, 2010 and 2020.

Harmonizing to livestock units:

We harmonized the data to livestock units (LSU), making them easy to use and compare between different countries and regions. We used livestock coefficients provided by EUROSTAT (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Livestock_unit_(LSU) ) to harmonize numbers of different livestock types, with more detail on the processing provided in the accompanying data_sources.xlsx file.File description:

The data-set consists of the following files:

a spreadsheet describing in detail the sources of data and how the data was processed (file data_sources.xlsx)

three geopackage files (archived as a zip file) for each year (2000, 2010, 2020) for harmonized ruminant livestock numbers for all 43 countries and territories (livestock2000.zip, livestock2010.zip, livestock2020.zip )

three geopackage files (archived as a zip file) for each year (2000, 2010, 2020) for sheep and goats for Poland (as the numbers are provided on a different level for these two livestock type for Poland) (poland_sheep_goats2000.zip , poland_sheep_goats2010.zip , poland_sheep_goats2020.zip )

a geopackage file (archived as a zip file) with (estimated) shares of cattle grazing for each country, in many cases for subnational units (grazing_share.zip )

Source information:

The raw data on livestock numbers are available from each country individually, and we provide the sources in the data_sources.xlsx file , to enable future updates.

This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.

Cattle markets, where livestock producers may buy and sell cattle and calves, act as major hubs in the shipment network that connect cattle populations across the United States (U.S.). Cattle markets can then provide insight into the integration of the U.S. cattle industry, thus informing how regional price fluctuations can influence cattle prices nationally. Despite biosecurity measures and regulatory compliance from livestock markets, commingling and re-distribution of animals from multiple sources may elevate the risk of disease spread and make tracing animal movements more complex, which could pose significant challenges if a transboundary animal disease (TAD) were introduced into the U.S. Therefore, knowing the size and location of cattle markets in the U.S. is critical to understanding cattle industry market dynamics and enhancing pandemic scenario modeling efforts. In this article, we present a list of cattle markets, their locations, and estimated quarterly cattle sales. We compiled a list of 1,619 known cattle markets with and without market sales data from 1,131 counties across the U.S. from 2012-2016. To estimate unknown market sales data, we fit a spatial autoregressive lag model to annual county-level market sales data and used the fit to predict annual sales in counties that lacked sales information. County-level sales data provide important insight into the structure of the U.S. cattle industry. The dataset can be used to improve national-scale cattle movement models, livestock disease models, and inform TAD surveillance efforts.

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280. Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.