Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing 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.

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.

The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (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 as an output file compatible for updating databases and spreadsheets, or save a link for future use. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. County level data are also available via Quick Stats. The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. The download data files contain planted and harvested area, yield per acre and production. NASS develops these estimates from data collected through:

hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture

the Census of Agriculture conducted every five years providing state- and county-level aggregates Resources in this dataset:Resource Title: Quick Stats database. File Name: Web Page, url: https://quickstats.nass.usda.gov/ Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search.

Link Function: information

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 2002 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for the U.S. Virgin Islands is the second census in the U.S. Virgin Islands conducted by NASS. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years. The 2007 census is the 14th census of agriculture of the U.S. Virgin Islands. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in the U.S. Virgin Islands. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with the 1982 Economic Censuses covering manufacturing, mining, construction, retail trade, wholesale trade, service industries, and selected transportation activities. After 1982, the agriculture census reverted to a 5-year cycle. Data in this publication are for the calendar year 2007, and inventory data reflect what was on hand on December 31, 2007. This is the same reference period used in the 2002 census. Prior to the 2002 census, data was collected in the summer for the previous 12 months, with inventory items counted as what was on hand as of July 1 of the year the data collection was done.

Objectives: The census of agriculture is the leading source of statistics about the U.S. Virgin Islands’s agricultural production and the only source of consistent, comparable data at the island level. Census statistics are used to measure agricultural production and to identify trends in an ever changing agricultural sector. Many local programs use census data as a benchmark for designing and evaluating surveys. Private industry uses census statistics to provide a more effective production and distribution system for the agricultural community.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was a farm, defined as "any place from which USD 500 or more of agricultural products were produced and sold, or normally would had been sold, during the calendar year 2007". According to the census definition, a farm is essentially an operating unit, not an ownership tract. All land operated or managed by one person or partnership represents one farm. In the case of tenants, the land assigned to each tenant is considered a separate farm, even though the landlord may consider the entire landholding to be one unit rather than several separate units.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Method of Enumeration As in the previous censuses of the U.S. Virgin Islands, a direct enumeration procedure was used in the 2007 Census of Agriculture. Enumeration was based on a list of farm operators compiled by the U.S. Virgin Islands Department of Agriculture. This list was compiled with the help of the USDA Farm Services Agency located in St. Croix. The statistics in this report were collected from farm operators beginning in January of 2003. Each enumerator was assigned a list of individuals or farm operations from a master enumeration list. The enumerators contacted persons or operations on their list and completed a census report form for all farm operations. If the person on the list was not operating a farm, the enumerator recorded whether the land had been sold or rented to someone else and was still being used for agriculture. If land was sold or rented out, the enumerator got the name of the new operator and contacted that person to ensure that he or she was included in the census.

(b) Frame The census frame consisted of a list of farm operators compiled by the U.S. Virgin Islands DA. This list was compiled with the help of the USDA Farm Services Agency, located in St. Croix.

(c) Complete and/or sample enumeration methods The census was a complete enumeration of all farm operators registered in the list compiled by the United States of America in the CA 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire (report form) for the CA 2007 was prepared by NASS, in cooperation with the DA of the U.S. Virgin Islands. Only one questionnaire was used for data collection covering topics on:

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The questionnaire of the 2007 CA covered 12 of the 16 core items' recommended for the WCA 2010 round.

Cleaning operations

DATA PROCESSING The processing of the 2007 Census of Agriculture for the U.S. Virgin Islands was done in St. Croix. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for clarity and completeness. Reporting errors in units of measures, illegible entries, and misplaced entries were corrected. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies identified, but not corrected by the computer, were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and any inconsistencies or potential problems were then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by making corrections to individual data records.

Sampling error estimates

The accuracy of these tabulated data is determined by the joint effects of the various nonsampling errors. No direct measures of these effects have been obtained; however, precautionary steps were taken in all phases of data collection, processing, and tabulation of the data in an effort to minimize the effects of nonsampling errors.

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

description:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer with a ground resolution of 56 meters. The CDL is produced using satellite imagery from the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. Ancillary classification inputs include: the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2001 is used as non-agricultural training and validation data. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

Constraints:
Users of our Cropland Data Layer (CDL) and associated raster and vector data files are solely responsible for interpretations made from these products. The CDL is provided "as is". USDA-NASS does not warrant results you may obtain by using the Cropland Data Layer. Feel free to contact our staff at (HQ_RD_OD@nass.usda.gov) if technical questions arise in the use of our Cropland Data Layer. NASS does provide considerable metadata and substantial statistical performance measures in the Frequently Asked Questions (FAQ's) section on the CDL website and on the CD-ROM and/or DVD. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer with a ground resolution of 56 meters. The CDL is produced using satellite imagery from the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. Ancillary classification inputs include: the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2001 is used as non-agricultural training and validation data. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

Constraints:
Users of our Cropland Data Layer (CDL) and associated raster and vector data files are solely responsible for interpretations made from these products. The CDL is provided "as is". USDA-NASS does not warrant results you may obtain by using the Cropland Data Layer. Feel free to contact our staff at (HQ_RD_OD@nass.usda.gov) if technical questions arise in the use of our Cropland Data Layer. NASS does provide considerable metadata and substantial statistical performance measures in the Frequently Asked Questions (FAQ's) section on the CDL website and on the CD-ROM and/or DVD. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

description: The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2014 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.; abstract: The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2014 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

The USDA's National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers are only a few examples.

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Households who are rearing livestock or kept poultry

Universe

Livestock or poultry household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Livestock/poultry farming housing units were systematically selected and canvassed.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC livestock and poultry questionnaire was divided into the following sections: - Identification/description of holdings - Funds, employment and earnings/wages - Livestock - Poultry - Fixed assets - Sales - Stock - Subsidy

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 99.3 percent was recorded at housing units level.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

This dataset presents final honey production estimates for 1986-1992. Data represents producers with five colonies or more, and covers number of honey producing colonies, yield per colony, honey production, stocks held by producers, average price received by producers at point of first sale, and value of production. At the national level, revisions of estimates of honey were one to four percent.

  Collection Organization: National Agricultural Statistics Service

  Collection Methodology: Surveys of the farm universe are made
  several times each year and estimates are adopted based on
  survey data and any other available administrative data that
  would support estimate levels.

  Collection Frequency: Annually.

  Update Characteristics: Updated in its entirety.

  STATISTICAL INFORMATION: The data reside in two ASCII text files.
  LANGUAGE: English
  ACCESS/AVAILABILITY:
  Data Center: National Agricultural Statistics Service
  Dissemination Media: Diskette, Internet home page
  File Format: ASCII delimited
  Access Instructions: Call NASS at 1-800-999-6779 for historical
  series data available on diskette. For historical series data
  available online, connect to the Internet home page at Cornell
  University.

  Or connect at the NASS Internet home page.
  URL: 'http://www.nass.usda.gov/index.asp'

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)

The USDA-NASS 2002 New Jersey Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of New Jersey This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University. The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000. There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm

The Local Food Marketing Practices Survey (LFMPS) is a dataset created by the U.S. Department of Agriculture's National Agricultural Statistics Service (NASS) to track marketing practices of farms selling locally or regionally produced agricultural food products. First conducted in 2015 and updated in 2020 as part of the Census of Agriculture, it provides benchmark data on direct-to-consumer and direct-to-intermediate-market sales, including revenue, channels (e.g., farmers' markets, CSAs, on-farm sales), and operational characteristics. Key features include exclusion of farms not engaged in local sales, detailed breakdowns of sales by state (e.g., California accounted for the largest share of direct sales in 2020), and insights into trends like the dominance of direct-to-consumer marketing (77% of operations in 2020). The dataset supports policy development, academic research, and industry analysis by quantifying the economic impact of local food systems. For example, in 2020, 147,307 operations generated $9.0 billion through direct marketing. Unique aspects include its focus on branded regional products and granular data on sales distribution (e.g., direct-to-consumer sales constituted 33% of total direct sales despite being the most common channel). Data is accessible via the NASS Quick Stats database.

"Agricultural Chemical Usage, Field Crops Summary" contains state and U.S. fertilizer and pesticide use data for corn, cotton, peanuts, rice, sorghum, soybeans, wheat, fall potatoes. Includes pesticide use by active ingredient, application rates, and acres treated.

  Collection Organization: ERS and NASS

  Collection Methodology: The information presented is the result
  of a sample survey conducted for the crop year (end of harvest
  for previous crop through harvest of current crop). A random
  sample of fields was selected with probability proportional to
  size, using information, obtained earlier in the year, from two
  surveys of farm operators. Personal interviews were used to
  obtain the information. Chemical data were collected at the
  product level and converted to active ingredient for
  summarization.

  Collection Frequency: Annual survey.

  Update Characteristics: Dataset not updated.

  STATISTICAL INFORMATION:

  The data reside in one ASCII text file.
  LANGUAGE:

  English
  ACCESS/AVAILABILITY:

  Data Center: National Agricultural Statistics Service
  Dissemination Media: Diskette, Internet home page
  File Format: ASCII delimited
  Access Instructions: Call NASS at 1-800-999-6779 for historical
  series data available on diskette. For historical series data
  available online, connect to the Internet home page at Cornell
  University.

  Or connect at the NASS Internet home page.

        URL: 'http://www.nass.usda.gov/index.asp'

description: The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF: "The USDA-NASS 2003 Indiana Cropland Data Layer is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between the dates of April 11, 2003 and August 26, 2003. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The Indiana data layer is aggregated to 13 standardized categories for display purposes with the emphasis being agricultural land cover. This is part of an annual series in which several states are categorized annually based on the extensive field observations collected during the annual NASS June Agricultural Survey. However, no farmer reported data is included or derivable on the Cropland Data Layer CD-ROM."; abstract: The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF: "The USDA-NASS 2003 Indiana Cropland Data Layer is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between the dates of April 11, 2003 and August 26, 2003. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The Indiana data layer is aggregated to 13 standardized categories for display purposes with the emphasis being agricultural land cover. This is part of an annual series in which several states are categorized annually based on the extensive field observations collected during the annual NASS June Agricultural Survey. However, no farmer reported data is included or derivable on the Cropland Data Layer CD-ROM."

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. 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).

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across Tanzania by collecting comprehensive data on various aspects of the agricultural sector. This survey is crucial for policy formulation, development planning, and service delivery, providing reliable data to monitor and evaluate national and international development frameworks.

The 2022/23 survey is particularly significant as it informs the monitoring and evaluation of key agricultural development strategies and frameworks. The collected data will contribute to the Tanzania Development Vision 2025, Zanzibar Development Vision 2020, the Five-Year Development Plan 2021/22–2025/26, the National Strategy for Growth and Reduction of Poverty (NSGRP) known as MKUKUTA, and the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) known as MKUZA. The survey data also supports the evaluation of Sustainable Development Goals (SDGs) and Comprehensive Africa Agriculture Development Programme (CAADP). Key indicators for agricultural performance and poverty monitoring are directly measured from the survey data.

The 2022/23 AASS provides a detailed descriptive analysis and related tables on the main thematic areas. These areas include household members and holder identification, field roster, seasonal plot and crop rosters (Vuli, Masika, and Dry Season), permanent crop production, crop harvest use, seed and seedling acquisition, input use and acquisition (fertilizers and pesticides), livestock inventory and changes, livestock production costs, milk and eggs production, other livestock products, aquaculture production, and labor dynamics. The 2022/23 AASS offers an extensive dataset essential for understanding the current state of agriculture in Tanzania. The insights gained will support the development of policies and interventions aimed at enhancing agricultural productivity, sustainability, and the livelihoods of farming communities. This data is indispensable for stakeholders addressing challenges in the agricultural sector and promoting sustainable agricultural development.

STATISTICAL DISCLOSURE CONTROL (SDC) METHODS HAVE BEEN APPLIED TO THE MICRODATA, TO PROTECT THE CONFIDENTIALITY OF THE INDIVIDUAL DATA COLLECTED. USERS MUST BE AWARE THAT THESE ANONYMIZATION OR SDC METHODS MODIFY THE DATA, INCLUDING SUPPRESSION OF SOME DATA POINTS. THIS AFFECTS THE AGREGATED VALUES DERIVED FROM THE ANONYMIZED MICRODATA, AND MAY HAVE OTHER UNWANTED CONSEQUENCES, SUCH AS SAMPLING ERROR AND BIAS. ADDITIONAL DETAILS ABOUT THE SDC METHODS AND DATA ACESS CONDITIONS ARE PROVIDED IN THE DATA PROCESSING AND DATA ACESS CONDITIONS BELOW.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS-2022/23) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2022/23 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, the EAs (primary sampling units) were stratified into 2-3 strata within each region and then selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the codes of District and Council which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection was conducted . In hamlets with more than 200 households, twelve (12) agricultural households were drawn from the PHC 2022 list with a simple random sampling without replacement procedure in each sampled hamlet. In hamlets with 200 households or less, a listing exercise was carried out in each sampled hamlet, and twelve (12) agricultural households were selected with a simple random sampling without replacement procedure. A total of 1,352 PSUs were selected from the 2022 Population and Housing Census frame, of which 1,234 PSUs were from Mainland Tanzania and 118 from Zanzibar. A total number of 16,224 agricultural households were sampled (14,808 households from Mainland Tanzania and 1,416 from Zanzibar).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022/23 Annual Agricultural Survey used two main questionnaires consolidated into a single questionnaire within the CAPIthe CAPI System, Smallholder Farmers and Large-Scale Farms Questionnaire. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The 2022/23 AASS questionnaire covered 23 sections which are:

1. COVER; The cover page included the title of the survey, survey year (2022/23), general instructions for both the interviewers and respondents. It sets the context for the survey and also it shows the survey covers the United Republic of Tanzania.

2. SCREENING: Included preliminary questions designed to determine if the respondent or household is eligible to participate in the survey. It checks for core criteria such as involvement in agricultural activities.

3. START INTERVIEW: The introductory section where basic details about the interview are recorded, such as the date, location, and interviewer’s information. This helped in the identification and tracking of the interview process.

4. HOUSEHOLD MEMBERS AND HOLDER IDENTIFICATION: Collected information about all household members, including age, gender, relationship to the household head, and the identification of the main agricultural holder. This section helped in understanding the demographic composition of the agriculture household.

5. FIELD ROSTER: Provided the details of the various agricultural fields operated by the agriculture household. Information includes the size, location, and identification of each field. This section provided a comprehensive overview of the land resources available to the household.

6. VULI PLOT ROSTER: Focused on plots used during the Vuli season (short rainy season). It includes details on the crops planted, plot sizes, and any specific characteristics of these plots. This helps in assessing seasonal agricultural activities.

7. VULI CROP ROSTER: Provided detailed information on the types of crops grown during the Vuli season, including quantities produced and intended use (e.g., consumption, sale, storage). This section captures the output of short rainy season farming.

8. MASIKA PLOT ROSTER: Similar to Section 4 but focuses on the Masika season (long rainy season). It collects data on plot usage, crop types, and sizes. This helps in understanding the agricultural practices during the primary growing season.

9. MASIKA CROP ROSTER: Provided detailed information on crops grown during the Masika season, including production quantities and uses. This section captures the output from the main agricultural season.

10. PERMANENT CROP PRODUCTION: Focuses on perennial or permanent crops (e.g., fruit trees, tea, coffee). It includes data on the types of permanent crops, area under cultivation, production volumes, and uses. This section tracks long-term agricultural investments.

11. CROP HARVEST USE: In this, provided the details how harvested crops are utilized within the household. Categories included consumption, sale, storage, and other uses. This section helps in understanding food security and market engagement.

12. SEED AND SEEDLINGS ACQUISITION: Collected information on how the agriculture household acquires seeds and seedlings, including sources (e.g., purchased, saved, gifted) and types (local, improved, etc). This section provided insights into input supply chains and planting decisions based on the households, or head.

13. INPUT USE AND ACQUISITION (FERTILIZERS AND PESTICIDES): It provided the details of the use and acquisition of agricultural inputs such as fertilizers and pesticides. It included information on quantities used, sources, and types of inputs. This section assessed the input dependency and agricultural practices.

14. LIVESTOCK IN STOCK AND CHANGE IN STOCK: The questionnaire recorded the

The Tenure, Ownership, and Transition of Agricultural Land (TOTAL) dataset is a comprehensive survey conducted by the U.S. Department of Agriculture’s National Agricultural Statistics Service (NASS) and Economic Research Service (ERS). It examines agricultural landowners, including non-operator landlords, to analyze land tenure, ownership structures, and transition plans. The dataset includes detailed information on land income, expenses, debt, assets, landlord demographics, and intentions for land succession or sale. Created as a follow-up to the 2012 Census of Agriculture, the TOTAL survey aims to track shifts in farmland ownership and inform policies affecting agricultural sustainability. Its key features include granular data on rental arrangements, intergenerational transitions, and the financial dynamics of landholding. Notably, it distinguishes between operator-owned and rented lands, offering insights into challenges like land fragmentation and access for beginning farmers. The 2014 iteration highlighted trends such as increasing non-family rental arrangements and the role of trusts in land succession. Unique aspects of TOTAL include its focus on non-operator stakeholders and integration with surveys like the Agricultural Resource Management Survey (ARMS) to enhance data depth. It serves as a critical resource for researchers, policymakers, and agricultural groups addressing land-use changes, rural economic development, and food system resilience. By quantifying transitions from agricultural to developed land uses, TOTAL supports efforts to preserve farmland and sustain agricultural economies.

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.