Monthly report on crop acreage, yield and production in major countries worldwide. Sources include reporting from FAS’s worldwide offices, official statistics of foreign governments, and analysis of economic data and satellite imagery.

The average for 2022 based on 188 countries was 108.5 index points. The highest value was in Senegal: 189.9 index points and the lowest value was in Malta: 53.8 index points. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

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 *** billion international U.S. dollars. Ukraine's and Russia's production amounted to ***** and ***** billion international U.S. dollars, respectively. This makes these countries the **** and *** ranked agricultural producers by production value.

This dataset contains detailed annual data on crop and livestock production compiled by the Food and Agriculture Organization (FAO) of the United Nations. It spans 1961 to 2023, covering more than 200 countries and territories, and includes:

🌾 Crops & Livestock: From staple grains like wheat and rice to niche items like anise or caraway.

🌍 Geographic Coverage: Global, with specific data for each country or region.

📆 Time Period: 1961–2023

📐 Metrics Provided: Area harvested (hectares) Production (tonnes) Yield (kg/ha)

This cleaned version of the dataset (NOFLAG) removes flags and notes, making it ideal for data analysis and machine learning projects.

Columns Column Name : Description Area Code : Numeric code for the country or region Area : Country or region name Item Code : Numeric code for the crop/livestock item Item : Name of the crop or livestock item Element Code : Numeric code indicating the metric type Element : Type of measurement (Area, Yield, Production) Unit : Unit of measurement (ha, t, kg/ha) Y1961 to Y2023 : Annual values for the metric in that year

Each row represents one (Country, Crop, Metric) combination across years.

Brazil was the leading agricultural producer in Latin America and the Caribbean in 2023. With *** billion international U.S. dollars. Mexico ranked second with an agricultural production value of **** billion U.S. dollars. Argentina ranked third with about ** billion U.S. dollars.

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

As of 2023, Niger registered the agricultural sector's highest contribution to the GDP in Africa, at over ** percent. Comoros and Ethiopia followed, with agriculture, forestry, and fishing accounting for approximately ** percent and ** percent of the GDP, respectively. On the other hand, Botswana, Djibouti, Libya, Zambia, and South Africa were the African countries with the lowest percentage of the GDP generated by the agricultural sector. Agriculture remains a pillar of Africa’s economy Despite the significant variations across countries, agriculture is a key sector in Africa. In 2022, it represented around ** percent of Sub-Saharan Africa’s GDP, growing by over *** percentage points compared to 2011. The agricultural industry also strongly contributes to the continent’s job market. The number of people employed in the primary sector in Africa grew from around *** million in 2011 to *** million in 2021. In proportion, agriculture employed approximately ** percent of Africa’s working population in 2021. Agricultural activities attracted a large share of the labor force in Central, East, and West Africa, which registered percentages over the regional average. On the other hand, North Africa recorded the lowest share of employment in agriculture, as the regional economy relies significantly on the industrial and service sectors. Cereals are among the most produced crops Sudan and South Africa are the African countries with the largest agricultural areas. Respectively, they devote around *** million and **** million hectares of land to growing crops. Agricultural production varies significantly across African countries in terms of products and volume. Cereals such as rice, corn, and wheat are among the main crops on the continent, also representing a staple in most countries. The leading cereal producers are Ethiopia, Nigeria, Egypt, and South Africa. Together, they recorded a cereal output of almost *** million metric tons in 2021. Additionally, rice production was concentrated in Nigeria, Egypt, Madagascar, and Tanzania.

The average for 2023 based on 176 countries was 3851 kg per hectar. The highest value was in Oman: 29147 kg per hectar and the lowest value was in Cape Verde: 23 kg per hectar. The indicator is available from 1961 to 2023. Below is a chart for all countries where data are available.

Abstract

The National Institute of Statistics and Census (INEC), through the Directorate of Agricultural and Economic Statistics (DEAGA) executed the Annual Agricultural Area and Production Survey (ESPAC), 2007.

This survey was carried out in Ecuador at the national level, in all provinces, except for the Galapagos and unassigned areas such as Las Golondrinas, Manga del Cura and El Piedrero. It covered all properties with total or partial agricultural activity, called Agricultural Production Units (UPAs). These were selected by the area sampling and list sampling, a methodology that is applied in this survey.

The main objective of the survey is to provide information on the agricultural sector, referring to planted, sown, and/or harvested areas, production and sales of permanent/transient crops, animal/livestock breeding, as well as, the employment of labor. This was done in order to have information for formulating crop plans and diversification of agricultural production, formulating price rules and, incentives to improve agricultural production. It was also done to establish a system of equitable distribution of production in the different areas of the country, contribute to the sectoral economic analysis, as well as, the preparation and execution of import and export policies for agricultural products so that the country, through the national government, can promote policies that strengthen the agricultural sector.

Geographic coverage

National Coverage.

Analysis unit

Agricultural holdings

Universe

All properties with total or partial agricultural activity called Agricultural Production Units (UPAs), selected in the sample.

The survey covers the rural area of continental Ecuador. However, populated centers, the province of Galapagos and areas not assigned to a province such as Las Golondrinas, Manga del Cura and El Piedrero are excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Annual Agricultural Area and Production Survey (ESPAC) 2007 uses the multi-frame sampling methodology (MMM), which consists of a combination of Sample Area Frame (MMA) and a Sample List Frame (MML). The sample frames of each of the provinces of the territory used in the III National Agricultural Census of the year 2000 are applied.

The MMA sampling consists of dividing the total area of the country into small areas without omission, called Primary Sampling Units (UPMs). A UPM is an area of 10 km2 on average and is delimited by natural and/ or cultural barrier that is easily identifiable on the ground. For the operation of the survey, these areas were outlined on an aerial photograph and a map. A second division is made as each UPM is divided into a specific number of Sampling Segment (SM). An SM is an extension of land with an area of approximately 2 km2 or 200 hectares, delimited by natural and/ or cultural barriers easily identifiable on the ground. The set of all SMs constitutes the area frame and covers the continental territory excluding the province of Galapagos and the areas not assigned to any province. The SMs are clearly marked and delineated on an aerial photograph and generally contain one or more Agricultural Production Units (UPAs) or one or several non-UPAs.

The MML is a statistical procedure that consists of extracting information from all or a sample of the elements found in the list frame. This list frame is a directory prepared by the National Institute of Statistics and Census (INEC) where the UPAs that meet certain pre-established criteria are recorded. In the MML are the main UPAs, identified by INEC, the Ministry of Agriculture and Livestock (MAG) and the private sector, based on their importance in terms of contribution to the countries agricultural production.

The sample size consists of randomly selecting a subsample of 2,000 SMs from the area frame, and a list of 4,000 UPAs.

Sampling deviation

There were no deviations from the original sample design. All sampled segments and sampling units were visited.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

In the Annual Agricultural Area and Production Survey 2008, the Expert System is used. This is a computer system that automatically produces coding once the data is entered. This system also allows for individual review and validation, after which the database is generated. Usually, after data collection, the questionnaire is delivered to the digitizer/ operator who enters the information into the computer in the Expert System. Once the information is entered, it is encrypted, validated and verified. This validation process is implemented in the system to check for inconsistencies and errors. In certain cases, the questionnaire is delivered to the field staff so that the information is verified again, and the data is re-entered. After this, a database is created, followed by processing and analysis to generate results to be published.

Response rate

In the Annual Agricultural Area and Production Survey of the year 2007, the non-response rate, as well as, other rejection indicators go hand in hand with the sample design implemented, so this rate is not unique or general. This is because some SMs are not investigated due to different reasons, such as: rejections, transportation problems, etc.

Please note that the following information along with the dataset is taken from http://www.fao.org/faostat/en/#data/QC From the Food and Agriculture Organization of the United Nations (FAO)

Context

Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Tree nuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity, and yield. The objective is to comprehensively cover the production of all primary crops for all countries and regions in the world. Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only

If FAO is to carry out its work successfully it will need to know where and why hunger and malnutrition exist, what forms they take, and how widespread they are. Such data will serve as a basis for making plans, determining the efficacy of measures used, and measuring progress from time to time.

Content

Statistical concepts and definitions - Areas refer to the area under cultivation. Area under cultivation means the area that corresponds to the total sown area, but after the harvest it excludes ruined areas (e.g. due to natural disasters). If the same land parcel is used twice in the same year, the area of this parcel can be counted twice. For tree crops, some countries provide data in terms of number of trees instead of in area. This number is then converted to an area estimate using typical planting density conversions. Production means the harvested production. Harvested production means production including on-holding losses and wastage, quantities consumed directly on the farm and marketed quantities, indicated in units of basic product weight. Harvest year means the calendar year in which the harvest begins. Yield means the harvested production per ha for the area under cultivation. Seed quantity comprises all amounts of the commodity in question used during the reference period for reproductive purposes, such as seed or seedlings. Whenever official data are not available, seed figures can be estimated either as a percentage of production or by multiplying a seed rate (the average amount of seed needed per hectare planted) with the planted area of the particular crop of the subsequent year. Usually, the average seed rate in any given country does not vary greatly from year to year.

Statistical unit: Agriculture holdings cultivated for the production of crops.

Statistical population: All areas cultivated with crops in a country.

Reference area: All countries of the world and geographical aggregates according to the United Nations M-49 list.

Time coverage: 1961-2018 (up to 2017 for all elements computed from FBS framework, e.g. seed, derived/processed commodities)

Periodicity: Annual

Pelase note that the information on flags and units used can be found along with the dataset.

Acknowledgements

Food and Agriculture Organization of the United Nations (FAO), Statistics Division (ESS), Environment Statistics team, Mr. Salar Tayyib. Source - http://www.fao.org/faostat/en/#data/QC/metadata

Inspiration

Initially found this dataset when I was working on a school project regarding the crops most popular in spain. Found this extremely useful in determining the most popular crops and visualizing the same.

The main aims of statistic on the overall distribution of land and agricultural production are: offering precise information on the area, production and destination of agricultural output in the Basque Country, determining the sown area and expected output of the main agricultural crops (according to a pre-established calendar) and determining the area under protected crops and nurseries. The information obtained also provides the basis to compile the annual accounts for the agri-food sector. More information in the https://www.euskadi.eus/web01-a1estadi/es/ departmental statistical portal.

Global Agriculture Gross Production by Country, 2023 Discover more data with ReportLinker!

The Relationship between Crop Production and Climate Change

Explore the Relationship between Crop Production and Climate Change over time

About this dataset

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. Crop yields are the harvested production per unit of harvested area for crop products. In most cases yield data are not recorded but are obtained by dividing the production data by the data on the area harvested. The actual yield that is captured on a farm depends on several factors such as the crop's genetic potential, the amount of sunlight, water, and nutrients absorbed by the crop, the presence of weeds and pests. This indicator is presented for wheat, maize, rice, and soybean. Crop production is measured in tonnes per hectare.

This dataset includes information on crop production from 2010-2016

How to use the dataset

https://www.kaggle.com/usda/crop-production

Crop production is an important economic activity that affects commodity prices and macroeconomic uncertainty. This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare, in thousand hectares, and in thousand tonnes.

This dataset can be used to examine the effect of different crops on climate change and to compare yields between different climates

Research Ideas

• Determining how various factors affect crop production and yields
• Comparing crop yields between different types of crops
• Examining the impact of climate change on crop production

Acknowledgements

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: crop_production.csv | Column name | Description | |:---------------|:------------------------------------------------------------| | LOCATION | The country or region where the crop is grown. (String) | | INDICATOR | The indicator used to measure the crop production. (String) | | SUBJECT | The subject of the indicator. (String) | | MEASURE | The measure of the indicator. (String) | | FREQUENCY | The frequency of the data. (String) | | TIME | The time period of the data. (String) | | Value | The value of the indicator. (Float) | | Flag Codes | The flag codes of the data. (String) |

The average for 2022 based on 20 countries was 106.4 index points. The highest value was in Dominican Republic: 136.2 index points and the lowest value was in Haiti: 72.1 index points. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

🌾 European Wheat Production (1961–Present)

This dataset offers a detailed historical view of wheat production across European countries, spanning from 1961 onwards. It's ideal for agricultural trend analysis, economic forecasting, and food security research. The dataset includes annual production data, country-wise figures, and year-over-year changes to provide context on growth and fluctuations in wheat output over time.

📁 Columns

• Year: The calendar year of the data point
• Value: Wheat production volume
• Unit: Unit of measurement (e.g., tonnes)
• Flag: Data quality indicator (e.g., Estimated, Official)
• Country: The country of production
• Item: The crop (Wheat)
• Domain: Category of data (Production)
• Metric: Measurement type (Production)
• YoY Change: Year-over-year percentage change in production

📌 Use Cases

• Time-series analysis of wheat production trends
• Forecasting agricultural output
• Comparative analysis of European countries
• Studying the impact of policy or climate events on crop yield

📚 Source

This dataset is compiled from publicly available agricultural production records. Data cleaning and structuring have been applied for ease of analysis.

Feel free to explore, analyze, and build models based on this dataset. 🌾📈

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

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.

Per capita production of selected agricultural products in CC and EPC in 2021.

Recent studies argue that cross-country labor productivity differences are much larger in agriculture than in the aggregate. We reexamine the agricultural productivity data underlying this conclusion using new evidence from disaggregate sources. We find that for the world's staple grains—maize, rice, and wheat—cross-country differences in the quantity of grain produced per worker are enormous according to both micro- and macrosources. Our findings validate the idea that understanding agricultural productivity is at the heart of understanding world income inequality.

Crop production statistics provide annual data on agricultural areas, harvested production, and yields of various crops in EU Member States, EFTA countries, and candidate and potential candidate countries. These figures are crucial for understanding agricultural trends, food security, and the economic performance of the agricultural sector.

The crop production statistics collected by Eurostat refer to the following types of annual data:

• Sown area, harvested production, yield of annual arable land crops, including main area for most important categories of arable land crops
• Main area and harvested production of permanent grasslands (production data is available only from 2025 reference period onwards)
• Harvested area and harvested production of fresh vegetables and strawberries, main area of total fresh vegetables, and flowers and ornamental plants, and production of cultivated mushrooms
• Production area and harvested production of permanent crops for human consumption, and main area for main categories of permanent crops, including nurseries and other permanent crops.

From the 2025 reference year onwards, crop production statistics are collected in accordance with https://eur-lex.europa.eu/eli/reg/2022/2379/oj/eng" target="_parent">Regulation (EU) 2022/2379 on Statistics on Agricultural Input and Output (SAIO) adopted in 2022 and its https://eur-lex.europa.eu/eli/reg_impl/2023/1538/oj" target="_parent">Commission Implementing Regulation (EU) 2023/1538. This new framework repeals the previously applicable https://eur-lex.europa.eu/eli/reg/2009/543/oj/eng" target="_parent">Regulation (EC) No 543/2009, https://eur-lex.europa.eu/eli/reg_del/2015/1557/oj" target="_parent">Commission Delegated Regulation (EU) No 2015/1557 and the https://ec.europa.eu/eurostat/documents/749240/7023703/CROP-ESS-2015-25-15-amended-by-ESS-2020-42-6-Annual-crop-statistics.pdf/1c6a527d-1d27-c20f-4e0f-284e262da75c" target="_parent">European Statistical System (ESS) agreement.

The areas are expressed in 1 000 hectares, the harvested quantities in 1 000 tonnes and the yields in tonne/ha. The production and yield data are available in EU standard humidity (https://ec.europa.eu/eurostat/databrowser/view/apro_cpsh1/default/table?lang=en&category=agr.apro.apro_crop.apro_cp.apro_cpsh" target="_parent">apro_cpsh1) and in national humidity (https://ec.europa.eu/eurostat/databrowser/view/apro_cpnh1/default/table?lang=en&category=agr.apro.apro_crop.apro_cp.apro_cpnh" target="_parent">apro_cpnh1). The information concerns more than 100 crops.

Regional metadata

The crop area and production data are available at national (NUTS 0) level, but for some crops regional figures are available as well at NUTS 1 and 2 levels, except for Germany, that is only available at NUTS 1 level. Please note that for chapters where no metadata for regional data has been specified the regional metadata is identical to the metadata for the national data.

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".

keywords: Agricultural production, Agrarian reform, Climate change, Animal husbandry, farm, agriculture job, agricultural population, agrarian society, rural sociology, agronomy, agrarian structure, Central Asia, climate change