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 Quick Stats database is the most comprehensive tool for accessing agricultural data published by NASS. It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results, or save a link for future use.Data is available By StateBy SubjectCrops and PlantsDemographicsEconomic and PricesEnvironmentalLivestock and AnimalsResearch, Science, and TechnologyThere are 12 data files, each in gzip'd TSV format.

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

National Agricultural Statistics Service account, Iteration 1, Fiscal year 2024

Apportionment file 11245781 retrieved from OMB public records

The graph shows the citations of ^'s papers published in each year.

National Agricultural Statistics Service account, Iteration 2, Fiscal year 2023

A long term series of all the main agriculture census items collected in the June census. Source agency: Scottish Government Designation: National Statistics Language: English Alternative title: Abstract of Scottish Agricultural Statistics

Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (RSQ) and normalized root mean square error (nRMSE). Overall, the most robustly performing ensemble learning technique was majority voting yielding RSQ and nRMSE values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached RSQ and nRMSE ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at 1×1km spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.

This site provides interactive access to data from NASS, as part of a cooperative effort among USDA, the USDA Regional Pest Management Centers and the NSF Center for Integrated Pest Management (CIPM). All data available have been previously published by NASS and have been consolidated at the state level. Commodity acreages and active ingredient agricultural chemical use (% acres treated, ai/acre/treatment, average number of treatments, ai/acre, total ai used) data are available. All data can be searched by commodity, year, state and active ingredient. For more details on methodology, please see NASS website. Search results can be obtained in web format and as downloadable Excel files. For each individual active ingredient, commodity, year and statistic, dynamic U.S. maps of each use statistic can be generated. Agricultural chemical usage statistic data can also be seen in a graphical format. Currently, this site contains the data from 1990. We will continue to update the database annually. As this site is enhanced, we will also provide means and totals of the statistics over years, states, and commodities. This project is funded by USDA, The Cooperative State Research, Education, and Extension Service (CSREES), project award No. 2001-34366-10324. Resources in this dataset:Resource Title: Agricultural Chemical Use Program Data. File Name: Web Page, url: https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Chemical_Use/#data Since 2009, the release of chemical use surveys is available through Quick Stats. The following materials are available for each survey: highlights fact sheet, a methodology paper, and a set of data tables featuring commonly requested information.

This dataset encompasses extensive information on crop production in India, spanning multiple years and offering insights into agricultural trends and patterns. The dataset consists of over 246,000 records, capturing a wide array of variables related to crop production, and is intended to facilitate advanced analyses such as predictive modeling and the extraction of key insights for stakeholders in the agri-food sector.

Temporal Coverage: - The dataset covers multiple years, providing a longitudinal view of crop production trends in India. This temporal dimension is crucial for analyzing changes over time and understanding long-term patterns.

Geographical Scope: - Data is collected across various states and regions of India, reflecting the diverse agricultural landscape of the country. Regional variations in crop production can be analyzed to identify local factors affecting yields.

Crop Types: - The dataset includes information on various crop types grown across different regions. This classification allows for detailed analysis of specific crops, their production levels, and their sensitivity to various factors.

Production Metrics: - Metrics related to crop production such as yield (e.g., tons per hectare), total production volume, and harvested area are included. These metrics are essential for evaluating productivity and efficiency.

Data Quality and Completeness: - The dataset is likely to include a mix of structured and unstructured data. Data quality may vary, and preprocessing steps such as cleaning and normalization may be necessary to ensure accurate analyses.

Applications and Objectives:

Predictive Modeling: - The primary goal of analyzing this dataset is to develop predictive models for crop production. By leveraging historical data, machine learning algorithms can forecast future production levels and identify potential risks.

Insight Extraction: - The dataset provides an opportunity to uncover key indicators and metrics that significantly influence crop production. Insights can help stakeholders make informed decisions regarding crop management, resource allocation, and policy formulation.

Trend Analysis: - Longitudinal analysis of the data can reveal trends and patterns in crop production, helping to understand how factors such as technological advancements, policy changes, and environmental conditions affect agriculture.

Stakeholder Collaboration: - The dataset supports the development of collaboration platforms that connect various stakeholders in the agri-food sector. By integrating data from multiple sources, stakeholders can collaborate more effectively to address challenges and optimize production.

Key Features: 1. State_Name: Represents the name of the state in India where the crop data was recorded. 2. District_Name: Specifies the district within the state where the crop data was collected. 3. Crop_Year: Indicates the year in which the crop was harvested. 4. Season: Denotes the agricultural season (e.g., Kharif, Rabi) during which the crop was grown. 5. Crop: Identifies the type of crop that was cultivated. 6. Area: Represents the total land area used for cultivating the crop. 7. Production: Indicates the total quantity of the crop produced from the specified area.

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 publication has been discontinued, as it has been superseded by and comes under Agriculture in the UK now. This publication provides an easy-to-reference statistics on UK Agriculture, complementing its more comprehensive sister publication Agriculture in the UK.

Designation: Official Statistics Alternative title: Agricultural Statistics in your Pocket

How many cows are in the U.S.? The United States is home to approximately 87.1 million cattle and calves as of 2024, dropping slightly from the 2023 value. Cattle farming in the United States There are over three times more beef cows than milk cows living in the United States. Raising cattle is notoriously expensive, not only in terms of land, feed, and equipment, but also in terms of the environmental impact of consuming beef. Beef and milk have the highest carbon footprints of any type of food in the United States. U.S. milk market The volume of milk produced in the United States has been steadily increasing over the last several years. In 2023, total milk production in the U.S. was about 228.3 billion pounds, up from 192.9 billion pounds in 2010. California is the leading producer of milk of any U.S. state, generating approximately 42 billion pounds of milk in 2022. Wisconsin came in second, producing about 31.9 billion pounds of milk in that year.

A direct internet link to Solomon Island's agriculture statistics at a glance and other related information.

Rajasthan is a state of India that has been incorporating trending agricultural methods due to the scarcity of the water as a resource and thus becomes a unique hotspot for data scientists and machine learning engineers to use their predictive analysis methods in the domain of agriculture for future.

This dataset provides comprehensive agricultural data for Rajasthan, India from 2018 to 2019. It contains four of his CSV files: Crop_Production_Data.csv, Water_Usage_Data.csv, Soil_Analysis_Data.csv, and Crop_Price_Data.csv. This dataset will be a valuable resource for researchers and data scientists working in the agricultural sector, enabling them to analyze and model various aspects of agricultural practices and market dynamics in Rajasthan.

1. Crop_Production_Data.csv: This file contains crop production information such as district, crop type, season, area (hectares), yield (quintals) and production (tonnes). Researchers and analysts can use this data to study crop productivity, analyze seasonal variations, and identify patterns of crop production in different districts of Rajasthan.
2. water_usage_data.csv: The Water_usage_data.csv file provides insight into water usage in agriculture. This includes details such as district, crop type, irrigation method used, water consumption (liters/ha), and water availability (liters/ha). This information can be used to analyze water management practices, assess the efficiency of different irrigation methods, and study the impact of water availability on crop production.
3. Soil analysis_data.csv: This file contains the soil quality, including area, soil type, pH, percentage of organic matter, nitrogen content (kg/ha), phosphorus content (kg/ha), and potassium content (kg/ha). Contains analytical data. Researchers and scientists can use this data to study soil properties, assess soil fertility, and understand the nutrient content of different types of soils in Rajasthan.
4. Crop_price_data.csv: The Crop_price_data.csv file contains data about crop prices for different markets (mandi) in different counties. This includes information such as district, crop type, market, date and price (INR/100 weight approx). This dataset is useful for studying crop price trends, analyzing market trends and developing crop price forecasting models in Rajasthan.

Researchers and data scientists can use this dataset to perform a variety of analyzes and studies, including:

• Crop productivity analysis and forecasting.
• Research on water management and irrigation optimization.
• Soil fertility assessment and soil health analysis.
• Market analysis and crop price forecasting.
• Evaluation of agricultural policies and interventions.
• Assessment of the impact of climate change on agriculture in Rajasthan.