Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

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

This table provides the number of employees in the agriculture sector, and agricultural operations with at least one employee, by province. A breakdown by full-time, part-time and seasonal employees is also available.

Artificial intelligence is deployed in agriculture mainly in field, livestock, and indoor farming in 2019. Field farming is the main farming type where AI is used in agriculture, with a market share of more than ***. The overall AI in agriculture market had a size of almost *** billion U.S. dollars in 2019 and is forecast to grow to more than *** billion U.S. dollars by 2024.

This table provides the number of employees in the agriculture sector, and agricultural operations with at least one employee, by industry in Canada. A breakdown by full-time, part-time and seasonal employees is also available.

The value of artificial intelligence in the global agriculture market was estimated to be around *** billion U.S. dollars in 2023 and is forecast to grow to about *** billion U.S. dollars by 2028.

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.

Balance sheet of the agricultural sector and ratios as at December 31 in dollars unless otherwise noted (Canada and the provinces).

According to Cognitive Market Research, the global agriculture analytics market size is USD 1.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 13.1% from 2024 to 2031. Market Dynamics of Agriculture Analytics Market

Key Drivers for Agriculture Analytics Market

Expanding Government Schemes and Policies-Rising government initiatives around the world are bolstering agricultural modernization initiatives with subsidies, grants, and regulations. These are driving the adoption of analytics solutions for environmental monitoring and precision agriculture in response to rising consumer and regulatory demands for sustainable farming practices. The widespread adoption of big data in farming, which allows farmers to gain useful knowledge that will lead to improved crop yields, is driving the demand for agriculture analytics.

Key Restraints for Agriculture Analytics Market

Inadequate access to reliable and fast internet can impede the ability to gather and analyze data in real-time, which is essential for accurate agricultural analytics. This could slow market growth. Industry expansion is being impeded by factors such as the high cost of data collecting and processing. Introduction of the Agriculture Analytics Market

Agriculture analytics is the practice of analyzing data, using technology, and applying statistical methods to understand and manage resources, weather, crop yields, and market trends better. Improved sustainability, less risk, and maximum productivity can be achieved through well-informed decision-making by farmers and other stakeholders. Increasing government initiatives to implement new agricultural techniques is the main development factor for the agriculture analytics industry. More and more data is being generated in the agricultural industry at an exponential rate. Another factor fueling the expansion of agriculture analytics is the rising use of Internet of Things devices that gather data from linked agricultural machinery like smart tractors and drones. Moreover, there has been a strong push to improve farm offerings, and the result is a dramatic increase in investment and technical innovation in the agricultural sector around the world. Weather data analytics, crop growth monitoring, land preparation, and other analytics tools are becoming more popular among farmland owners as a means to optimize agricultural operations. Due to these, the demand for the agriculture analytics market will grow in the coming years.

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.

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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

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The Economic Accounts for Agriculture (EAA) provide detailed information on the preformance and the income of the agricultural sector. The purpose is to analyse the production process of the agricultural industry and the income derived from it. The accounts are therefore based on the industry concept. The EAA are detailed…

This table provides the number of temporary foreign workers, the number of jobs filled by temporary foreign workers and the number of operations with at least one temporary foreign worker in the agriculture sector, by category of farm revenue in Canada. A breakdown by the following farm revenue categories is available: Less than $100,000, $100,000 to $249,999, $250,000 to $499,999, $500,000 to $999,999, $1,000,000 to $1,999,999 and $2,000,000 and over.

This statistic illustrates the number of people employed in agriculture in the United Kingdom (UK) from 2003 to 2023. In 2023, the workforce in the agriculture sector was made up of approximately 419 thousand people. Additionally, the share of food sector employment in the total workforce in the United Kingdom can be found at the following. UK agriculture Agriculture is a large part of the economy in the United Kingdom. In 2021, the projected gross value added of agriculture was about 12.1 billion British pounds, an increase from previous years. The total agricultural land area in the United Kingdom was about 18.1 million hectares in 2022 and has remained fairly constant since 2003. Crop production in the UK The value of fresh vegetable production in the UK has been increasing since 2014 and was valued at about 1.8 billion Great British pounds in 2022. The value of potato production has fluctuated in recent years, but had a value of 705 million Great British pounds in 2022 with about 4.8 million tons of potatoes harvested.

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across the United Republic of 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 aggregated 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 access conditions are provided in the data processing and data access 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

The total global market size for agriculture technology-as-a-service stood at about 1.8 billion U.S. dollars in 2023. Software-as-a-service is responsible for the majority of the market with about 1.1 billion U.S. dollars.