China was the leading agricultural producer worldwide in 2023, with over a trillion international U.S. dollars. India ranked second, with an agricultural production value of 522 billion international U.S. dollars. Ukraine's and Russia's production amounted to 42.66 and 110.6 billion international U.S. dollars, respectively. This makes these countries the 20th and 5th ranked agricultural producers by production value.

The average for 2023 based on 166 countries was 9.91 percent. The highest value was in Niger: 47.81 percent and the lowest value was in Singapore: 0.03 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

European Agricultural Output Production Share by Country (Million Euros), 2023 Discover more data with ReportLinker!

Abstract

The World Bank’s research project on “Distortions to Agricultural Incentives” has produced a core database of Nominal Rates of Assistance to producers, or NRAs, together with a set of Consumer Tax Equivalents, or CTEs, for farm products and a set of Relative Rates of Assistance to farmers in 75 focus countries. This is a detailed core database.

The vast majority of the world’s poorest households depend on farming for their livelihood. In the past their earnings were often depressed by pro-urban and anti-agricultural biases of their own country’s policies. While progress has been made over the past two decades by numerous developing countries in reducing those policy biases, many trade-reducing price distortions remain intersectorally as well as within the agricultural sector of low-, middle- and high-income countries.

This project, in seeking to understand the extent, effects of and reasons behind that transformation, began by compiling new estimates of price distortions over the past half century. National country studies were undertaken in more than 50 countries in Africa, Asia, Latin America, and Europe’s transition economies. They were supplemented with similar estimates and analytical narratives of policy trends in 20 high-income countries. Together those countries account for more than 90 percent of the value of global agricultural output.

The core database provides nominal rates of assistance estimates for the main individual commodities that together account for about 70 percent of the value of farm production in those countries, as well as guesstimates of the NRA for the 30 percent of farm production not covered. Also estimated is the NRA for non-agricultural tradables so as to compute a relative rate of assistance. Consumer tax equivalents are also provided for the covered products in each focus country, along with value of production and consumption at undistorted prices and of trade for each covered product and for non-covered farm products. The working paper no. 4612, available as external resources, serves as the "methodology paper" for this first database.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

This database compiles secondary data originating from the National Statistical Offices of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.

The database includes information about major agricultural statistics such as structure of the agricultural sector, land use, livestock numbers, production of most important crops and livestock outputs as well as size of population at the level of provinces (oblasts). Most land use and production data are available for the three farm types, agricultural enterprises, individual farms, and households, and in aggregated form. Time coverage ranges from 1992 to 2017 (with few statistics additionally for 1991 and 2018), with some gaps in early years after independence.

The database is a product of the research project "ANICANET – Revitalising animal husbandry in Central Asia: A five-country analysis".

This statistic shows the total agricultural production in South East Asia in 2013, by country. In 2013, the total production of agricultural goods in Thailand was about 35 million U.S. dollars.

European Agricultural Output Subsidies by Country, 2023 Discover more data with ReportLinker!

Sudan had the largest agricultural land area in Africa in 2022, corresponding to around 112.7 million hectares. Following, South Africa and Nigeria had roughly 96.3 million and 69.8 million hectares of land under agricultural activities, respectively. In proportion to the total land area, Lesotho was the African country with the largest share of land devoted to agriculture.

European Agricultural Output Subsidies Share by Country (Million Euros), 2023 Discover more data with ReportLinker!

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.

Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by enabling comparisons across time and across countries and regions. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1991 and 2014(1991-2000,2001-2010 and 2010-2014). These TFP and PFP estimates were generated using the most recent data from Economic Research Service of the United States Department of Agriculture (ERS-USDA), the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), and national statistical sources.

Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by allowing for comparisons across time and across countries and regions. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1991 and 2014(1991-2000,2001-2010 and 2010-2014). These TFP and PFP estimates were generated using the most recent data on outputs and inputs from the Economic Research Service of the United States Department of Agriculture (ERS-USDA), an internationally consistent and comparable dataset on production and input quantities built using data from the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), supplemented with data from national statistical sources.

Graph and download economic data for Real farm output (A2000X1A020NBEA) from 2007 to 2023 about output, agriculture, real, GDP, and USA.

Exports of agricultural products from the United States of America came to 19173.8 billion U.S. dollars in 2022. The third-largest exporter of agricultural products was the Netherlands. This is likely due to the country's role as a major European shipping hub. Brazil exported agricultural products worth, 13640.55 U.S. dollars.

Description

This dataset contains information on crops and livestock products, sourced from FAOSTAT. It provides data for various countries and regions, covering annual statistics on harvested area, yield, and production. Data includes item codes, measurement units, and additional metadata such as flags indicating data reliability (e.g., estimated or official figures). The dataset supports agricultural and economic research for food production analysis.

Attributes

• Domain: Type of products (e.g., Crops and Livestock).
• Area: Geographical region or country.
• Element: Data type (e.g., harvested area, yield, production).
• Item: Specific product or commodity.
• Year: Year of data collection.
• Unit: Measurement unit for the data value.
• Value: The actual statistical measurement.
• Flag Description: Data reliability or source type.

*The Item Code (CPC) in the dataset refers to a standardized code used to identify specific agricultural products or items. It is derived from the Central Product Classification (CPC) system, which is an international standard maintained by the United Nations. This system is used to classify goods and services for economic analysis.*

Purpose of CPC Codes

Identification:

Each product, such as "Almonds, in shell" or "Wheat," is assigned a unique CPC code.

Standardization:

Facilitates international comparability and harmonization of data.

Economic Analysis:

Supports tracking of production, trade, and consumption statistics globally.

Example from Dataset

For instance:

• Item Code (CPC): 01371
• Item: "Almonds, in shell"

The CPC code helps ensure consistent identification and analysis of "Almonds, in shell" across datasets and countries.

Collection Methodology of the Dataset

Crop Production Data

1. Data Types and Collection:

• Data is primarily collected for harvested areas, though for permanent crops, it may reflect planted areas.

• Yields are computed using detailed area and production data, with higher reliability for temporary crops compared to permanent crops (e.g., coffee and cocoa).

2. Specific Crops:

• Cereals:

Data only covers crops harvested for dry grain, excluding those harvested for hay or silage. The area data corresponds to harvested areas unless only sown or cultivated areas are reported.

• Vegetables:

Statistics often refer to field crops grown for sale, excluding small-scale household gardens.

• Fruit:

Data covers fresh fruit production for food or processing but excludes production from wild plants or scattered trees.

3. Estimation and Reliability:

• Data for non-reporting countries are estimated.
• Data collection is inconsistent across countries, leading to variations in coverage for certain crops and regions.

4. Sources:

• Governments provide data through national publications and FAO questionnaires.
• Supplemental data from unofficial sources and international organizations ensure broader coverage.

Livestock Production Data

1. Livestock Numbers: - Covers all domestic animals, regardless of age or breeding purpose. Estimates are included for non-reporting countries or incomplete data.

2. Dairy and Egg Production: - Milk production includes whole fresh milk, excluding milk consumed by young animals. - Egg data may be derived from poultry numbers and estimated laying rates in countries lacking direct statistics.

3. Sources and Reliability: - Governments contribute through annual FAO questionnaires. Incomplete data is supplemented with estimates based on available indicators.

Time Reference

• Data is organized by calendar year, though some countries report based on agricultural or marketing years. When necessary, adjustments align these periods to a standard calendar year.

Totals and Aggregation

• Continental and world totals include only countries in the dataset. Rounding and coverage differences may cause discrepancies.

Data Sources and Enhancements

• Official government reports, supplemented by data from national and international organizations, aim to ensure completeness. Unofficial data may be incorporated when official data is lacking.

This comprehensive approach ensures that the dataset reflects a broad and detailed view of global agricultural production, though some data inconsistencies and gaps are acknowledged.

No Endorsement:

*The FAO does not endorse any specific interpretation, use, or analysis of this data beyond the context of its intended use for research, policy analysis, and decision-making. The FAO d...

Georgia Agricultural Output data was reported at 7,283.800 GEL mn in 2023. This records an increase from the previous number of 6,985.400 GEL mn for 2022. Georgia Agricultural Output data is updated yearly, averaging 2,262.100 GEL mn from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 7,283.800 GEL mn in 2023 and a record low of 1,570.000 GEL mn in 1995. Georgia Agricultural Output data remains active status in CEIC and is reported by National Statistics Office of Georgia. The data is categorized under Global Database’s Georgia – Table GE.B007: Agricultural Output and Output Volume Index.

Kyrgyzstan Gross Agricultural Output: Value: Farms data was reported at 118,009.900 KGS mn in 2016. This records a decrease from the previous number of 118,968.300 KGS mn for 2015. Kyrgyzstan Gross Agricultural Output: Value: Farms data is updated yearly, averaging 41,732.070 KGS mn from Dec 1996 (Median) to 2016, with 21 observations. The data reached an all-time high of 118,968.300 KGS mn in 2015 and a record low of 3,981.721 KGS mn in 1996. Kyrgyzstan Gross Agricultural Output: Value: Farms data remains active status in CEIC and is reported by National Statistical Committee of the Kyrgyz Republic. The data is categorized under Global Database’s Kyrgyzstan – Table KG.B004: Gross Agricultural Output: Volume and Value.

Ireland Agricultural Output: Vol: Barley data was reported at 1,043.000 Tonne th in 2017. This records a decrease from the previous number of 1,058.000 Tonne th for 2016. Ireland Agricultural Output: Vol: Barley data is updated yearly, averaging 919.500 Tonne th from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,263.000 Tonne th in 2014 and a record low of 646.000 Tonne th in 2005. Ireland Agricultural Output: Vol: Barley data remains active status in CEIC and is reported by Central Statistics Office of Ireland. The data is categorized under Global Database’s Ireland – Table IE.B017: Agricultural Output.

ML: Capture Fisheries Production data was reported at 102,486.000 Metric Ton in 2016. This records an increase from the previous number of 92,480.000 Metric Ton for 2015. ML: Capture Fisheries Production data is updated yearly, averaging 90,000.000 Metric Ton from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 132,900.000 Metric Ton in 1995 and a record low of 54,178.000 Metric Ton in 1985. ML: Capture Fisheries Production data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank.WDI: Agricultural Production and Consumption. Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes.; ; Food and Agriculture Organization.; Sum;

Graph and download economic data for Farm output: Farm products consumed on farms (B1008C1A027NBEA) from 1929 to 2023 about output, agriculture, consumption, GDP, and USA.