Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The Organic INTEGRITY Database is a certified organic operations database that contains up-to-date and accurate information about operations that may and may not sell as organic, deterring fraud, increases supply chain transparency for buyers and sellers, and promotes market visibility for organic operations. Only certified operations can sell, label, or represent products as organic, unless exempt or excluded from certification. The INTEGRITY database improves access to certified organic operation information by giving industry and public users an easier way to search for data with greater precision than the formerly posted Annual Lists of Certified Operations. You can find a certified organic farm or business, or search for an operation with specific characteristics such as:

The status of an operation: Certified, Surrendered, Revoked, or Suspended The scopes for which an operation is certified: Crops, Livestock, Wild Crops, or Handling

The organic commodities and services that operations offer. A new, more structured classification system (sample provided) will help you find more of what you’re looking for and details about the flexible taxonomy can be found in the INTEGRITY Categories and Items list. Resources in this dataset:Resource Title: Organic INTEGRITY Database. File Name: Web Page, url: https://organic.ams.usda.gov/integrity/Default.aspx Find a specific certified organic farm or business, or search for an operation with specific characteristics. Listings come from USDA-Accredited Certifying Agents. Historical Annual Lists of Certified Organic Operations and monthly snapshots of the full data set are available for download on the Data History page. Only certified operations can sell, label or represent products as organic, unless exempt or excluded from certification.

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

Farm Data Management System Market Outlook

The global farm data management system market size was valued at USD 3.2 billion in 2023 and is projected to reach USD 9.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The market is driven by the increasing adoption of advanced technologies in agriculture to enhance productivity and efficiency, coupled with growing concerns over sustainable farming practices and food security.

The integration of sophisticated technologies such as IoT, AI, and satellite imagery in farm data management systems is significantly propelling market growth. These advanced technologies enable farmers to collect, analyze, and interpret vast amounts of data, leading to informed decision-making. For instance, IoT devices can monitor soil conditions, weather patterns, and crop health in real-time, providing valuable insights that help optimize resource utilization and crop yields. This technological shift not only enhances productivity but also contributes to sustainable farming practices by reducing waste and minimizing environmental impact.

Another major growth factor is the increasing need for efficient farm management due to the rising global population. With the world population expected to reach 9.7 billion by 2050, there is an escalating demand for food, which in turn requires farmers to maximize their output. Farm data management systems play a pivotal role in this scenario by enabling precision farming. Precision farming allows for the targeted application of inputs such as water, fertilizers, and pesticides, which ensures optimal plant growth and reduces the likelihood of overuse and wastage. Consequently, this contributes to higher crop productivity and better resource management.

Government initiatives and funding are also critical drivers of the farm data management system market. Governments worldwide are increasingly recognizing the importance of modernizing agricultural practices to ensure food security and environmental sustainability. Subsidies, grants, and policy support for the adoption of smart farming technologies are encouraging farmers to invest in farm data management systems. These government interventions not only provide financial support but also raise awareness about the benefits of advanced farming technologies, accelerating market growth.

Regionally, North America held the largest market share in 2023, attributed to the high adoption rate of advanced agricultural technologies and substantial investment in research and development. Europe follows closely, driven by stringent regulations on sustainable farming and strong government support. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing population, and a growing need for efficient agricultural practices. Countries like India and China are investing heavily in smart farming technologies to enhance agricultural productivity and meet the rising food demand.

Component Analysis

The farm data management system market is segmented by component into software, hardware, and services. The software segment is anticipated to hold the largest share owing to its crucial role in data collection, analysis, and interpretation. Advanced software solutions facilitate real-time monitoring and decision-making, which are integral to modern farming practices. These software solutions often integrate with IoT devices and other sensors to gather data on various parameters such as soil moisture, weather conditions, and crop health. This data is then processed using algorithms and analytics to provide actionable insights, helping farmers optimize their operations.

Hardware is another critical component, encompassing devices such as sensors, GPS units, drones, and other IoT devices. These hardware components are essential for the effective collection of data from the farm. Sensors, for instance, can measure soil moisture levels, temperature, and nutrient content, while drones offer aerial imaging and monitoring capabilities. The data collected by these devices is indispensable for precision farming, as it allows for accurate assessment and management of farming activities. The hardware segment is expected to grow steadily, driven by the increasing adoption of IoT and automation technologies in agriculture.

The services segment includes consulting, installation, maintenance, and support services. As farm data management systems become more sophisticated, the demand for professional services to support these sys

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 is a historical record of every sheep that has been to the Animal Services Unit Ripley site.

Longitude and latitude, state, address, name, and zip code of Farmers Markets in the United States

Graph and download economic data for Net farm income, USDA (B1448C1A027NBEA) from 1967 to 2023 about USDA, agriculture, Net, income, GDP, and USA.

The size of the farm management software and data analytics market worldwide from 2021 to 2026 is expected grow with a CAGR of 17.47 percent. In 2020, the global farm management software and data analytics market was valued at 1.1 billion U.S. dollars worldwide. North America has a significant presence in the market due to quicker development and implementation of data based technology applied towards agriculture, with a market size of almost half a billion U.S. dollars.

United States Farm Debt Delinquent: RELF: Nonperforming data was reported at 1.640 USD bn in Jun 2018. This records an increase from the previous number of 1.570 USD bn for Mar 2018. United States Farm Debt Delinquent: RELF: Nonperforming data is updated quarterly, averaging 0.530 USD bn from Mar 1991 (Median) to Jun 2018, with 110 observations. The data reached an all-time high of 1.950 USD bn in Mar 2011 and a record low of 0.300 USD bn in Sep 1994. United States Farm Debt Delinquent: RELF: Nonperforming data remains active status in CEIC and is reported by Federal Reserve Bank of Kansas City. The data is categorized under Global Database’s United States – Table US.KB012: Agriculture Financing: Farm Debt Outstanding.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and housed animals, at regional level, by general farm type.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Reference date for livestock is 1 April and for crops 15 May.

In 2022, equidae are not part of the Agricultural Census. This affects the farm type and the total number of farms in the Agricultural Census. Farms with horses, ponies and donkeys that were previously classified as ‘specialist grazing livestock' could be classified, according to their dominant activity, as another farm type in 2022.

From 2020 onwards, the SO2017, based on the years 2015 to 2019, will apply (see also the explanation for SO: Standard Output).

From 2018 onwards the number of calves for fattening, pigs for fattening, chicken and turkey are adjusted in the case of temporary breaks in the production cycle (e.g. sanitary cleaning). The agricultural census is a structural survey, in which adjustment for temporary breaks in the production cycle is a.o. relevant for the calculation of the economic size of the holding, and its farm type. In the livestock surveys the number of animals on the reference day is relevant, therefore no adjustment for temporary breaks in the production cycle are made. This means that the number of animals in the tables of the agricultural census may differ from those in the livestock tables (see ‘links to relevant tables and relevant articles).

From 2017 onwards, animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of the combined data collection. The I&R registers are the responsibility of RVO (Netherlands Enterprise Agency). Since 2017, cattle numbers are derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly at RVO. Poultry data is collected via the designated database Poultry Information System Poultry (KIP) from Avined. Avined is a branch organization for the egg and poultry meat sectors. Avined passes the data on to the central database of RVO. Due to the transition to the use of I&R registers, a change in classification will occur for sheep and goats from 2018 onwards.

Since 2016, information of the Dutch Business Register is used to define the agricultural census. Registration in the Business Register with an agricultural standard industrial classification code (SIC), related to NACE/ISIC, (in Dutch SBI: ‘Standaard BedrijfsIndeling’) is leading to determine whether there is an agricultural holding. This aligns the agricultural census as closely as possible to the statistical regulations of Eurostat and the (Dutch) implementation of the definition of 'active farmer' as described in the common agricultural policy.

The definition of the agricultural census based on information from the Dutch Business Register mainly affects the number of holdings, a clear deviation of the trend occurs. The impact on areas (except for other land and rough grazing) and the number of animals (except for sheep, horses and ponies) is limited. This is mainly due to the holdings that are excluded as a result of the new delimitation of agricultural holdings (such as equestrian centres, city farms and organisations in nature management).

In 2011 there were changes in geographic assignment of holdings with a foreign main seat. This may influence regional figures, mainly in border regions.

Until 2010 the economic size of agricultural holdings was expressed in Dutch size units (in Dutch NGE: 'Nederlandse Grootte Eenheid'). From 2010 onwards this has become Standard Output (SO). This means that the threshold for holdings in the agricultural census has changed from 3 NGE to 3000 euro SO. For comparable time series the figures for 2000 up to and including 2009 have been recalculated, based on SO coefficients and SO typology. The latest update took place in 2016.

Data available from: 2000

Status of the figures: The figures are final.

Changes as of March 28, 2025: the final figures for 2024 have been added.

Payments made by the Department of Agriculture, Farm Service Agency to US agricultural producers participating in Farm Bill programs including commodity, price support, disaster assistance and conservation. Payments may be searched by payee, program, year, commodity, state, county, farm, payment date and amount paid.

COMET-FARM is a whole farm and ranch carbon and greenhouse gas accounting system.

These files from Statistics Canada present Census of Agriculture data allocated by standard census geographic polygons: Provinces and Territories (PR), Census Agricultural Regions (CAR), Census Divisions (CD) and Census Consolidated Subdivisions (CCS). Five datasets are provided: 1. Agricultural operation characteristics: includes information on farm type, operating arrangements, paid agricultural work and financial characteristics of the agricultural operation. 2. Land tenure and management practices: includes information on land use, land tenure, agricultural practices, land inputs, technologies used on the operation and the renewable energy production on the operation. 3. Crops: includes information on hay and field crops, vegetables (excluding greenhouse vegetables), fruits, berries, nuts, greenhouse productions and other crops. 4. Livestock, poultry and bees: includes information on livestock, poultry and bees. 5. Characteristics of farm operators: includes information on age, sex and the hours of works of farm operators. Note: For all the datasets, confidential values have been assigned a value of -1. Correction notice: On January 18, 2023, selected estimates have been corrected for selected variables in the following 2021 Census of Agriculture domains: Direct sales of agricultural products to consumers (Agricultural operations category), Succession plan for the agricultural operation (Agricultural operators category), and Renewable energy production (Use, tenure and practices category).

Census of Agriculture, 2001 to date. Farms classified by farm type.

Comprehensive dataset of 61 Farm bureaus in France as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Big Data Analytics in Agriculture Market Outlook

The global big data analytics in agriculture market is anticipated to witness substantial growth from 2024 to 2032. In 2023, the market size was valued at approximately USD 2.5 billion, and it is projected to reach around USD 8.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 14.1%. Several factors are driving this impressive growth, including the increasing adoption of precision farming techniques and the heightened need for sustainable agricultural practices to meet the rising global food demand. As the agriculture industry shifts towards more data-driven methodologies, big data analytics emerges as a critical tool for enhancing productivity and efficiency.

One of the significant growth factors propelling the big data analytics in agriculture market is the rise in global population, which has resulted in an increased demand for food. To cope with this demand, farmers and agribusinesses are turning to technology-driven solutions such as big data analytics to optimize production processes and maximize yield. Big data analytics provides insights into various agricultural practices, helping to improve crop management and resource utilization. Additionally, the pressure to adopt environmentally friendly practices is encouraging the use of analytics to minimize waste and optimize resource usage, thereby supporting sustainable agriculture.

Technological advancements in data processing and analysis are also playing a crucial role in the market's expansion. The integration of the Internet of Things (IoT) with big data analytics allows for real-time data gathering from various agricultural equipment and sensors. This capability enables the precise monitoring of farm conditions, leading to data-driven decision-making processes that optimize crop growth, pest control, and harvesting schedules. Furthermore, advancements in machine learning and artificial intelligence are enhancing the predictive capabilities of big data analytics, allowing for better anticipation of weather patterns, disease outbreaks, and market trends, which are vital for strategic planning and risk management in agriculture.

Another significant growth factor is the increased investment in agricultural technology by both government and private sectors. Governments around the world are recognizing the importance of agricultural technology in ensuring food security and are therefore investing in research and development initiatives. Additionally, venture capitalists and private firms are funding startups that specialize in agricultural analytics, further propelling market growth. The collaboration between technology companies and agricultural stakeholders is resulting in the development of innovative solutions that are tailored to the specific needs of the agricultural sector, thereby enhancing the market uptake of big data analytics.

From a regional perspective, North America holds a significant share of the big data analytics in agriculture market due to the presence of advanced agricultural practices and the early adoption of technology. Meanwhile, the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period. This growth can be attributed to the increasing population in countries like China and India, which is driving the demand for food and pushing the agricultural sector to adopt advanced technologies. Additionally, government initiatives in these regions to support technological integration in agriculture are further aiding market growth. Europe is also witnessing steady growth, with an increasing focus on sustainable farming practices and the utilization of analytics to enhance productivity.

Component Analysis

The component segment of the big data analytics in agriculture market comprises software, hardware, and services, each playing a vital role in the effective deployment and utilization of data analytics in agriculture. Software solutions in this market are particularly critical, as they provide the platforms and applications necessary for data collection, analysis, and visualization. These software applications range from farm management systems to predictive analytics tools that help farmers make informed decisions about crop planting, pest control, and resource management. With advancements in cloud computing and AI, software solutions are becoming more sophisticated, offering enhanced functionalities and user-friendly interfaces that cater to the specific needs of the agricultural sector.

Hardware components, such as sensors, drones, and IoT devices, are essential for the col

Vietnam Number of Livestock Farms data was reported at 20,869.000 Unit in 2016. This records an increase from the previous number of 15,068.000 Unit for 2015. Vietnam Number of Livestock Farms data is updated yearly, averaging 10,924.000 Unit from Dec 2011 (Median) to 2016, with 6 observations. The data reached an all-time high of 20,869.000 Unit in 2016 and a record low of 6,048.000 Unit in 2011. Vietnam Number of Livestock Farms data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.RI014: Farm Statistics.

NUOnet Vision: Efficient use of nutrients to optimize production and product quality of food for animals and humans, fuel and fiber in a sustainable manner that contributes to ecosystem services. This record contains the DET and Data Dictionary for NUOnet - the data files may be found at https://usdaars.maps.arcgis.com/apps/MapSeries/index.html?appid=e90392a99d5c427487c6c37cf6d47844 Best nutrient management practices are critical for maintaining profitable economic returns, sustaining higher yields, lowering environmental impacts, optimizing nutritional quality, and providing ecosystem services. Best management practices that improve nutrient use efficiencies can reduce nutrient losses from agricultural systems. However, we need to improve our understanding of biological, physical and chemical influences on nutrient processes. For instance, crop use efficiency of nitrogen (N), the primary macronutrient regulating yield and protein content, can be reduced by processes such as denitrification (N2O and N2 emission), leaching (NH4-N, NO3-N, and organic-N), ammonia (NH3-N,) volatilization, surface runoff and erosion, disease, and non-crop competition. Similarly, we need to obtain more information about biological and physical cycles of nutrients, especially phosphorus (P), including factors that influence nutrient availability from fertilizers, crop residues, cover crops, manures, and other byproducts. We need a better understanding of relationships between soil biological communities and ecosystems, including plant roots and root exudates, and availability and uptake of macro- and micro-nutrients. In addition, we need information regarding how these practices impact yields, organoleptic qualities, and the macro- and micro-nutritional composition of plants. This information will improve our ability to develop best nutrient management practices. Optimal soil nutrient levels are critical for maximizing economic returns, increasing sustainable yields, lowering environmental impacts, sustaining ecosystem services and optimizing nutritional and organoleptic qualities of human and animal foods. Efficient management practices are crucial for increasing economic returns for land managers in a sustainable manner while producing high quality of food for animals and humans with reduced off-site transfer of nutrients from agricultural areas in watersheds. Optimizing N and P inputs requires more information about nutrient inputs from fertilizers, manures, composts, agricultural byproducts, cover crops, and other nutrient sources in addition to nutrient cycling within soils. This requires data from long-term nutrient management studies across a wide range of soils, crops, and environmental conditions. Land management needs are to connect nutrient management practices for crops with nutrient use efficiency; crop quality; crop chemical composition and nutritional value, quality and acceptability for animal and human health. Development of databases that enable the scientific exploration of connections among data generated from diverse research efforts such as nutrient management, fate and ecosystem service outcomes, nutritional composition of crops, and animal and human health, is needed. Nitrogen is a key nutrient that enhances agricultural yield and protein content, but multiple N loss pathways, as previously mentioned, reduce crop N use efficiency (NUE). Implementing proper management practices is needed to reduce N losses from agricultural systems. ARS has multidisciplinary scientific teams with expertise in soils, ecological engineering, hydrology, livestock management and nutrition, horticulture, crop breeding, human and animal nutrition, post-harvest management and processing, and other areas, and intentional collaboration among these teams offers opportunities to rapidly improve NUE and crop quality and reduce off-site N losses. Similarly, increased P use efficiencies are needed to enhance and ensure sustainable agricultural production and to reduce environmental degradation of water sources. Manure is a valuable source of P and it can be used as a soil amendment to reduce crop production costs. However, there is a need to improve our understanding of the biological and physical cycles of soil P, as well as to obtain more information about P supplies from fertilizer, crop residues, cover crops, manure, and byproducts, and livestock nutrition impacts on manure properties. There is also a need for a better understanding of soil biological communities and ecosystems, including plant roots and root exudates and how their interactions with crops and community ecology affect yield and the uptake of macro- and micro-nutrients and the ultimate nutritional composition and organoleptic qualities of the crop. Studies documenting the responses of crop-associated biological communities to management practices and genetic technologies implemented across multiple environments (e.g., soil types and chemistries, hydrologic regimes, climates) will improve our understanding of gaps in macro- and micro-nutrient management strategies. A goal of the USDA-ARS is to increase agricultural production and quality while reducing environmental impacts. The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows. Increasing our understanding of stocks and flows could help in the identification of knowledge gaps as well as areas where increased efficiencies can be achieved at a national level. NUOnet could also be used to develop tools to derive cost-benefit curves associated with nutrient management improvement scenarios and assess local, regional and national impacts of off-site nutrient loss. Understanding how agricultural production impacts human health is a challenge, and the database could be used to link crop management strategies to crop chemical composition to human consumption patterns and ultimately to human health outcomes. A national database will also be very important for development and evaluation of new technologies such as real-time sensing or other proximal and remote sensing technologies that enable assessment of nutrient use efficiencies, particularly at the grower level. The database could also be used to develop analyses that will contribute to the recommendation of policies for resource allocations that will most effectively fulfill the goals of the Grand Challenge. Such a national database with contributions from peers across different national programs could also enhance collaborations between ARS, universities, and extension specialists, as well as with producers, industry, and other partners. See the NUOnet Home Page for more information about this database and strategic goals. Resources in this dataset:Resource Title: GRACEnet-NUOnet Data Dictionary. File Name: GRACEnet-NUOnet_DD.csvResource Title: NUOnet Data Entry Template. File Name: DET_NATRES_NUO.zipResource Description: A multi-tab worksheet for data entry. Users can customize fields to be mandatory, set minimum and maximum values, and run a validation on fields as specified by the user.

https://gpsr.ars.usda.gov/html/NUOnet_DET/DET_NATRES_NUO.xlsm