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.

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.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: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

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

India Agricultural Production: Major Crops: Achievements: Pulses data was reported at 27.504 Ton mn in 2023. This records an increase from the previous number of 27.302 Ton mn for 2022. India Agricultural Production: Major Crops: Achievements: Pulses data is updated yearly, averaging 12.840 Ton mn from Mar 1956 (Median) to 2023, with 68 observations. The data reached an all-time high of 27.504 Ton mn in 2023 and a record low of 8.350 Ton mn in 1967. India Agricultural Production: Major Crops: Achievements: Pulses data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.

This table contains data at regional level on the number of persons employed on agricultural holdings, the corresponding annual work units (AWUs) and the number of holdings with workers.

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

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

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

Data on labour force refer to the period April to March of the year preceding the agricultural census.

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

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

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

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

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

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

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

Data available from: 2000

Status of the figures: The figures are final.

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

When will new figures be published? According to regular planning provisional figures for the current year are published in November and the definite figures will follow in March of the following year.

The NASS Census of Agriculture is a comprehensive dataset produced by the U.S. Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS). Conducted every five years, the census gathers detailed data on America’s farming and ranching operations. It covers a wide range of topics, including land use and ownership, farm and operator characteristics, production practices, income, expenditures, and the types and quantities of crops and livestock produced. The primary purpose of the Census of Agriculture is to provide accurate, objective, and meaningful statistical information that supports agricultural policy-making, business decisions, research, and rural development. It serves as a key resource for government agencies, policymakers, researchers, agribusinesses, and farmers themselves, helping to track trends and inform decisions at national, state, and county levels. Key features of the dataset include its breadth and depth—data are collected from all U.S. farms and ranches, regardless of size—and its granularity, offering insights down to the county level. The census uniquely gives voice to all agricultural producers, ensuring even small and specialized operations are represented, making it an essential tool for understanding the evolving landscape of American agriculture.

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.

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.

The dataset you've provided appears to capture agricultural data for Karnataka, specifically focusing on crop yields in Mangalore. Key features include the year of production, geographic details, and environmental conditions such as rainfall (measured in mm), temperature (in degrees Celsius), and humidity (as a percentage). Soil type, irrigation method, and crop type are also recorded, along with crop yields, market price, and season of growth (e.g., Kharif).

The dataset includes several columns related to crop production conditions and outcomes. For example, coconut crop data reveals a pattern of yields over different area sizes, showing how factors like rainfall, temperature, and irrigation influence production. Prices also vary, offering insights into the economic aspects of agriculture in the region. This information could be used to study the impact of environmental conditions and farming techniques on crop productivity, assisting in the development of optimized agricultural practices tailored for specific soil types, climates, and crop needs.

Success.ai’s Agricultural Data provides unparalleled access to verified profiles of agriculture and farming leaders worldwide. Sourced from over 700 million LinkedIn profiles, this dataset includes actionable insights and contact details for professionals shaping the global agricultural landscape. Whether your objective is to market agricultural products, establish partnerships, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Agricultural Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of farm owners, agricultural consultants, supply chain managers, agribusiness executives, and industry leaders. AI-validated data ensures 99% accuracy, minimizing wasted outreach and improving communication efficiency. Global Coverage Across Agricultural Sectors

Includes professionals from crop farming, livestock production, agricultural technology, and sustainable farming practices. Covers key regions such as North America, Europe, APAC, South America, and Africa. Continuously Updated Dataset

Real-time updates reflect role changes, organizational shifts, and emerging trends in agriculture and farming. Tailored for Agricultural Insights

Enriched profiles include professional histories, areas of specialization, and industry affiliations for deeper audience understanding. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of agricultural and farming professionals. 100M+ Work Emails: Communicate directly with decision-makers in agribusiness and farming. Enriched Professional Histories: Understand career trajectories, expertise, and organizational affiliations. Industry-Specific Segmentation: Target professionals in crop farming, agtech, and sustainable agriculture with precision filters. Key Features of the Dataset: Agriculture and Farming Professional Profiles

Identify and connect with farm operators, agricultural consultants, supply chain managers, and agribusiness leaders. Engage with professionals responsible for farm management, equipment procurement, and sustainable farming initiatives. Detailed Firmographic Data

Leverage insights into farm sizes, crop or livestock focus, geographic distribution, and operational scales. Customize outreach to align with specific farming practices or market needs. Advanced Filters for Precision Targeting

Refine searches by region, type of agriculture (crop farming, livestock, horticulture), or years of experience. Customize campaigns to address unique challenges such as climate adaptation or supply chain optimization. AI-Driven Enrichment

Enhanced datasets deliver actionable data for personalized campaigns, highlighting certifications, achievements, and key projects. Strategic Use Cases: Marketing Agricultural Products and Services

Promote farm equipment, crop protection solutions, or livestock management tools to decision-makers in agriculture. Engage with professionals seeking innovative solutions to enhance productivity and sustainability. Collaboration and Partnerships

Identify agricultural leaders for collaborations on sustainability programs, research projects, or community initiatives. Build partnerships with agribusinesses, cooperatives, or government bodies driving agricultural development. Market Research and Industry Analysis

Analyze trends in crop yields, livestock production, and agricultural technology adoption. Use insights to refine product development and marketing strategies tailored to evolving industry needs. Recruitment and Talent Acquisition

Target HR professionals and agricultural firms seeking skilled farm managers, agronomists, or agtech specialists. Support hiring for roles requiring agricultural expertise and leadership. Why Choose Success.ai? Best Price Guarantee

Access industry-leading Agricultural Data at the most competitive pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified agricultural data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted outreach and maximize engagement outcomes. Customizable Solutions

Tailor datasets to specific agricultural segments, regions, or areas of focus to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified agricultural profiles to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals in the agriculture sector, scaling your outreach efficiently. Success.ai’s Agricultural Data empowers you to connect with the leaders and innovators transforming global agriculture. With verified contact details, enriched professional profiles, and global reach, your marketing, partn...

This edition of the Abstract of Agricultural Statistics contains South African agricultural statistics of major importance that were available up to December 2017. The "Abstract" contains meaningful information on, inter alia, field crops, horticulture, livestock, important indicators and the contribution of agriculture.

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

Graph and download economic data for Employment Level - Agriculture and Related Industries (LNS12034560) from Jan 1948 to May 2025 about agriculture, 16 years +, household survey, employment, industry, and USA.

The Credit to Agriculture dataset provides national data for over 130 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fishing, including household producers, cooperatives, and agro-businesses. For some countries, the three subsectors of agriculture, forestry, and fishing are completely specified. In other cases, complete disaggregations are not available. The dataset also provides statistics on the total credit to all industries, indicators on the share of credit to agricultural producers, and an agriculture orientation index (AOI) for credit that normalizes the share of credit to agriculture over total credit by dividing it by the share of agriculture in gross domestic product (GDP). As such, it can provide a more accurate indication of the relative importance that banking sectors place on financing the sector. An AOI lower than 1 indicates that the agriculture sector receives a credit share lower than its contribution to the economy, while an AOI greater than 1 indicates a credit share to the agriculture sector greater than its economic contribution.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

Abstract

Agriculture is the predominant activity in the Kingdom of Tonga's economy, contributing more than 17% to the Gross Domestic Product (GDP) in 2012 - 2013. The first ever Agriculture Census of the Kingdom was conducted in 1985. The second Census was conducted in 2001, focusing on land tenure, land utilization, area and production of principal crops, livestock, agricultural implements and equipment, use of fertilizers, etc. including the various agricultural activities in which most of the households were engaged in. Although agriculture is the main factor in the economy of the Kingdom of Tonga, the database in this sector seems to be inadequate. There were quite several surveys conducted for this sector, however, an updated frame (list of holdings/parcels and its characteristics) is needed so these surveys will obtain more reliable estimates. There were important developments in agriculture within the fourteen-year period from the last census that should be captured like the use of forest trees within the farming system to enhance productivity and information on fisheries, which is becoming a very important sector of the Kingdom's economy. Considering the above issues, there is a great need to update the statistics on agriculture in order to determine its present situation and to use it for economic planning and policy-making.

In support of the strategic plans and programmes of the Kingdom of Tonga on agriculture, the Government has decided to conduct the Agriculture Census (AC). This census is envisioned to:

a) Provide benchmark or basic data on structure of agricultural holdings and their main characteristics; b) Use this information to develop a regular system of agricultural statistics; c) Build up some important village level statistics; d) Establish a technical and organizational foundation on which to build up a comprehensive and integrated system of food and agricultural statistics; and e) Provide a frame from which samples can be drawn to study certain aspects of agricultural activities in greater depth. f) Provide information on community (village) statistics.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The Census covered all individuals and households.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The questionnaire was designed in collaboration between FAO, Ministry of Agriculture and the TNSO. It was designed in such a way that data items were efficiently encoded and processed using the software package CSPro.

The questionnaire was developed in English, but enumerators were specifically trained to be able to clearly translate these questions to other languages and dialect used in the country.

The questionnaires were designed in 13 sub-sections which are:

A. Identification particulars B. Household demographic and economic information C. Household engagement in agricultural subsectors D. Usage of land E. Food crops F. Agricultural practices G. Livestock H. Fisheries I. Forestry J. Handicrafts K. Labour L. Machinery and equipment M. Agricultural income and loan for all subsectors

Information collected for each sub-sections:

Section A: This section basically include the IDs for the households and include: - Village number - Census block - Household number - Sample - Type of holding

Section B: - name - relationship - sex - age - economic activity - educational attainment

Section D: - Bush allotment - Town allotment

Section E: - Exisiting crops - Crops harvested

Section F: - Fertilizer - Pesticides - Irrigation - Community farming

Section G: - Beef cattle - Dairy cattle - Pig - Horse - Sheep - Goat - Chicken - Duck - Dog / Cat - Veterinary services

Section H: - Fishing type - Fish income / sales - Purpose of fishing

Section I: - Number of trees - uses of trees

Section J: - Handicraft materials - Handicrafts sold - Value of handicrafts

Section K / L - Number of laborers - Days worked - Hours worked - Machinery used

Section M: - Income from agriculture - Loans - Drawbacks

Cleaning operations

VERIFICATION AND CODING

The data editing process begins when the completed questionnaires were returned to the national statistics office (NSO) for checking by the coders. This include checking that all fields are correctly filled, skipped pattern are properly followed, missing fields and so forth. Once the questionnaires are verified to be correct, then coding begins where certain variables are coded to their respective codes for capturing in the data entry screen - codes include Village code, Crops and Trees codes.

IN-BUILT EDITING

The data entry application for the 2015 Agriculture Census was designed using the software package CSPro where all necessary checks were incorporated to allow the Data Entry Operators (DEO) to verify data while doing data entry. With all the in-built checks, this ensures capturing good quality data efficiently and effectively. The in-built checks include range checks, skip and filtering questions and consistent and logic checks. With these in-built checks ensures good quality data is captured while entering and this greatly helps in the final batch editing.

SECONDARY EDITING

After the completion of the data entry, the final editing process was done. This include verification of questionnaires that all are captured and the actual running of the batch editing program on the whole data. Since most of the checks were done during the data entry phase, the batch editing process mostly involves verifying those errors that were missed or could not be solved during data entry, checking on those responses which have been coded 'missing' and trying to impute or verify by referring to the respective questionnaire and fixing 'outliers' responses. Frequencies on each variable were also checked to verify any inconsistencies between variables. The batch editing logic program was ren twice when it was decided to finalize the data. Some missing values were not fixed as they were not able to be verified, examples of these are mostly on money values and number of crops/trees.

Response rate

The final national response rate was 89% with 16,122 households enumerated out of the 18,043 total households.

Data appraisal

There were 12 data entry operators, 7 computers and a server used for data entry. The 12 operators took turns in doing data entry as well as coding and verification of questionnaires before they were entered. The data entry was done manually was done in the head office to allow for easy access to the books.

The data entry screen was designed using the CSPro software.

The data entry application was well designed and efficient during the actual data entry as a lot of testing was done during the pilot census. Questionnaires from the pilot census was used to test the application and relevant modifications were done, hence, by the time the actual data processing commences, the application was fully developed and ready. This ensures the capturing of good quality and reliable data from the questionnaires.

TECHNICAL NOTES AND DEFINITIONS 1. Agriculture active household is a household that is active in any of the agriculture activities: cropping; livestock; fisheries; forestry and handicraft. A household is active in any of these agriculture activities if it can be classified into either: subsistence, semi subsistence or commercial. 2. A household is non active in agriculture if it cannot be classified into any of the agriculture activities: cropping; livestock; fishery; forestry or handicraft. 3. Subsistence is a type of agriculture activities (cropping; livestock; fishing; forestry or handicraft making) in which most of the produce is consumed by the farmer and his family, leaving nothing to be marketed. 4. Semi-subsistence is a type of agriculture activities in which some of the produces are to be consumed by the farmer and his family and some of them are to be marketed. 5. Commercial is a type of agriculture activities in which most of the produces are to be marketed. 6. For the 2015 Agricultural Census, the following definition was used to classify the levels of agriculture activities whether it was subsistence; semi-subsistence or commercial.

Crop was based on total cultivated land area a) Subsistence: 0 < and <= 1 acres Semi Subsistence: 1 acres < and <= 8 acres Commercial: > 8 acres

Livestock was based on type and number of livestock kept a) Subsistence: If Milk Cattle or Beef Cattle =1 or Sow = 1 b) Semi Subsistence: If Milk Cattle or Beef Cattle = 2 - 100 or Sow = 2 - 25 c) Commercial: If Milk Cattle or Beef Cattle > 100 or Sow > 25 or Egg layer > 0 or Broiler > 0

Fishery was based households or organizations' response to the question on purpose of their fishing activities; whether it was for subsistence; semi-subsistence or commercial.

Forestry was based on number of high value trees and timber trees that households have grown at the time of the census a) Subsistence: number of trees 1 - 4 b) Semi Subsistence: number of trees 5 - 100 c) Commercial: number of trees 101 - 999

Handicraft was based on proportion of handicraft being sold a) Subsistence: no handicraft sold b) Semi Subsistence: 1% - 75 % sold c) Commercial: 76% - 100% sold

LIMITATIONS A Pilot Census was conducted which the questionnaires received were used to test the data entry application. This allowed to redefine the questionnaires as well as the data entry application to ensure that it everything was efficiently designed to capture reliable data. Like any other census, the 2015 Agriculture Census (AGC) has its own limitations. These are summarized as follows:

-

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

Introduction

Agricultural Robots Statistics: The transition that agriculture, the backbone of human civilization, is currently experiencing is largely driven by advanced technology. Among the technological innovations in agriculture, agricultural robots—often referred to as "bots"—are at the forefront of this change, disrupting traditional farming practices. Agricultural robots are autonomous machines that perform various tasks, including planting, harvesting, monitoring crop health, and managing livestock.

Their increasing acceptance is due to their ability to enhance efficiency, address the shortage of agricultural labor, and meet the rising demand for food with higher productivity. This article will present statistics on agricultural robots for 2025, highlighting key trends in the market and the factors contributing to their growth.

Agriculture Production Yield: Horticultural: Sweet Corn data was reported at 381.000 kg/Decare in 2017. This records a decrease from the previous number of 449.000 kg/Decare for 2016. Agriculture Production Yield: Horticultural: Sweet Corn data is updated yearly, averaging 390.000 kg/Decare from Dec 1996 (Median) to 2017, with 21 observations. The data reached an all-time high of 671.000 kg/Decare in 2008 and a record low of 0.000 kg/Decare in 1999. Agriculture Production Yield: Horticultural: Sweet Corn data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.B011: Agricultural Production Yield.