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Increased productivity is the main contributor to growth in U.S. agriculture. This data set provides estimates of productivity growth in the U.S. farm sector for the 1948-2011 period, and estimates of the growth and relative levels of productivity across the States for the period 1960-2004.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.
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Note: Updates to this data product are discontinued. This data set provides farmgate and wholesale prices for select organic and conventional fruits and vegetables, wholesale prices for organic and conventional poultry (broilers) and eggs, as well as f.o.b. and spot prices for organic grain and feedstuffs. Prices are based on those reported by USDA Agricultural Marketing Service Market News, Organic Food Business News, and USDA National Agricultural Statistics Service.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.
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The food dollar series measures annual expenditures by U.S. consumers on domestically produced food. This data series is composed of three primary series—the marketing bill series, the industry group series, and the primary factor series—that shed light on different aspects of the food supply chain. The three series show three different ways to split up the same food dollar. Nominal DataThe FoodDollarDataNominal.xls file and the NominalData.csv file include statistics reported in current year dollars. In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2015UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)Real Data The FoodDollarDataReal.xls file and the FoodDollarDataReal.csv file include statistics reported in constant year 2009 dollars. Since the March 30, 2016 update, 2006 data in cents per domestic real food dollar units have been added to the real food dollar series.In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2014UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)
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Note: Updates to this data product are discontinued.
Data for public and private funding of food and agricultural research and development cover the years 1970-2009 (public) and 1970-2007 (private). Data are available as nominal figures and adjusted for inflation.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file For complete information, please visit https://data.gov.
This is a link to the different data products offered by the Economic Research Service from USDA.
Poverty Area MeasuresThis data product provides poverty area measures for counties across 50 States and Washington DC. The measures include indicators of high poverty areas, extreme poverty areas, persistent poverty areas, and enduring poverty areas for Decennial Census years 1960–2000 and for American Community Survey (ACS) 5-year periods spanning both 2007–11 and 2015–19.HighlightsThis data product uniquely provides poverty area measures at the census-tract level for decennial years 1970 through 2000 and 5-year periods spanning 2007–11 and 2015–19.The poverty area measure—enduring poverty—is introduced, which captures the entrenchment of high poverty in counties for Decennial Census years 1960–2000 and for ACS 5-year periods spanning 2007–11 and 2015–19. The same is available for census tracts beginning in 1970.High and extreme poverty area measures are provided for various data years, offering end-users the flexibility to adjust persistent poverty area measures to meet their unique needs.All measures are geographically standardized to allow for direct comparison over time and for census tracts within county analysis.Diverse geocoding is provided, which can be used for mapping/GIS applications, to link to supplemental data (e.g., USDA, Economic Research Service’s Atlas of Rural and Small-Town America), and to explore various spatial categories (e.g., regions and metro/nonmetro status). DefinitionsHigh poverty: areas with a poverty rate of 20.0 percent or more in a single time period.Extreme poverty: areas with a poverty rate of 40.0 percent or more in a single time period.Persistent poverty: areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods).Enduring poverty: areas with a poverty rate of 20.0 percent or more for at least 5 consecutive time periods, about 10 years apart, spanning approximately 40 years or more (baseline time period plus four or more evaluation time periods).Additional information about the measures can be found in the downloadable Excel file, which includes the documentation, data, and codebook for the poverty area measures (county and tract).The next update to this data product—planned for early 2023—is expected to include the addition of poverty area measures for the 5-year period 2017–21.Data SetLast UpdatedNext UpdatePoverty area measures (in CSV format)11/10/2022Poverty area measures11/10/2022Poverty Area MeasuresOverviewBackground and UsesERS's Legacy of Poverty Area MeasurementDocumentationDescriptions and MapsLast updated: Thursday, November 10, 2022For more information, contact: Tracey Farrigan and Austin SandersRecommended CitationU.S. Department of Agriculture, Economic Research Service. Poverty Area Measures, November 2022.
This data is from the 2017 release of the United States Department of Agriculture Economic Research Service (USDA ERS)'s Food Access Research Atlas.
The Research, Education, and Economics Information System (REEIS) is a source of information on the research, education and extension programs, projects and activities of the U. S. Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA), the USDA Forest Service, the USDA National Agricultural Statistics Service, the U. S. Patent and Trademark Office, U. S. Census Bureau, and the U. S. National Science Foundation. The system enables users to measure the impact and effectiveness of research, extension and education programs based on data related to agricultural research; forestry research; students, faculty and degrees related to agriculture; USDA partner institution snapshots; Food and nutrition research; 4-H programs; and agricultural snapshots of each state. Internet links to related agencies, institutions, and data bases are also included.
This data product provides agricultural output, input and total factor productivity (TFP) growth rates, but not levels, across the countries and regions of the world in a consistent, comparable way, for 1961-2010.
This dataset contains Supplemental Data at the county level from the U.S. Department of Agriculture (USDA) Food Environment Atlas website. ERS (Economic Research Service, USDA) researchers and others who analyze conditions in "rural" America most often study conditions in nonmetropolitan (nonmetro) areas, defined on the basis of counties. Counties are the standard building block for collecting economic data and for conducting research to track and explain regional population and economic trends.
Data was last updated on the USDA website in September 2020.
Any data elements with numerical values reflect figures at the locality-level. See column descriptions for details. For more information on all metrics in this dataset, see the Food Environment Atlas Data Access and Documentation Downloads website.
How much do fruits and vegetables cost? ERS estimated average prices for 153 commonly consumed fresh and processed fruits and vegetables.
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Limited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some Americans to eat a healthy diet. Expanding the availability of nutritious and affordable food by developing and equipping grocery stores, small retailers, corner markets and farmers’ markets in communities with limited access is an important part of the Healthy Food Financing Initiative. There are many ways to define which areas are considered "food deserts" and many ways to measure food store access for individuals and for neighborhoods. Most measures and definitions take into account at least some of the following indicators of access:
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Note: Updates to this data product are discontinued. Aquaculture is the production of aquatic animals and plants under controlled conditions for all or part of their lifecycle. This data product provides statistics on domestically grown catfish and trout and U.S. imports and exports of fish and shellfish that may be products of aquaculture, such as salmon, shrimp, and oysters.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ZIP file of CSV formatted data Web page links to Excel files For complete information, please visit https://data.gov.
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
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The ERS content and data APIs (including our pre-made widgets for embedding charts) are currently out of service while we redesign our site. Check back here for updates--we'll keep you informed as to the progress. Contact us at webadmin@ers.usda.gov with questions.
The Data APIs provide programmatic access to select data sets.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: https://www.ers.usda.gov/developer/ For complete information, please visit https://data.gov.
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All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: API access page For complete information, please visit https://data.gov.
U.S. Government Workshttps://www.usa.gov/government-works
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The data are from the Census Bureau's Consolidated Federal Funds Reports on Federal expenditures and obligations for grants, salaries and wages, procurements, direct payments, direct loans, guaranteed loans, and insurance obtained from Federal Government agencies. Each file contains Federal outlays by program for each county in the State. County population is also included so that per capita spending can be calculated. Summary of the data for the United States and a ZIP file for all States are also available.
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The U.S. Bioenergy Statistics are a source of information on biofuels intended to present a picture of the renewable energy industry and its relationship to agriculture. Where appropriate, data are presented in both a calendar year and the relevant marketing year timeframe to increase utility to feedstock-oriented users. The statistics highlight the factors that influence the demand for agricultural feedstocks for biofuels production; for instance, numerous tables emphasize the relationship between energy and commodity markets.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.
The Agricultural Resource Management Survey (ARMS) is a dataset created by the U.S. Department of Agriculture (USDA), jointly administered by the Economic Research Service (ERS) and the National Agricultural Statistics Service (NASS). It serves as the primary source of information on farm production practices, resource use, financial conditions, and the economic well-being of U.S. farm households. The dataset collects detailed, farm-level data through annual surveys, covering topics such as crop production, input costs, income, and sustainability practices. Its purpose is to inform USDA, Congress, and industry stakeholders about agricultural trends, enabling evidence-based policy and program decisions. For example, ARMS data supports evaluations of farm subsidies, environmental programs, and market dynamics. Key features include its comprehensive scope, combining financial and operational metrics, and its representative sampling of U.S. farms and ranches. Unique aspects include the use of advanced statistical methods like the delete-a-group jackknife for variance estimation and the availability of data via an API and bulk files for researchers. ARMS is also critical for developing models like unit process data for crop production, enhancing agricultural research and sustainability studies. (Word count: 198)
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Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Query tool For complete information, please visit https://data.gov.
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Increased productivity is the main contributor to growth in U.S. agriculture. This data set provides estimates of productivity growth in the U.S. farm sector for the 1948-2011 period, and estimates of the growth and relative levels of productivity across the States for the period 1960-2004.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.