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

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.

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.

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.

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.

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.

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.

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

This data product provides agricultural output, input and total factor productivity (TFP) growth rates, but not levels, across the countries and regions of the world in a consistent, comparable way, for 1961-2010.

Spain Agricultural Output Production was up 2.4% in 2020, compared to the previous year.

In 2019, Food Net Production Per Capita Index in Laos jumped by 3.1% compared to a year earlier.

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.

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.

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.

Jordan Agricultural Production Area data was reported at 505.793 Donum th in 2016. This records an increase from the previous number of 487.727 Donum th for 2015. Jordan Agricultural Production Area data is updated yearly, averaging 401.656 Donum th from Dec 1994 (Median) to 2016, with 23 observations. The data reached an all-time high of 508.687 Donum th in 2014 and a record low of 271.483 Donum th in 1996. Jordan Agricultural Production Area data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Jordan – Table JO.B011: Agricultural Production Area.

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.

The particularities of agriculture, as a sector which ensures food supply, result from many factors, including the multilateral interaction between the environment and human activity. The extent of human intervention in the food production process is usually measured with the amount of capital expenditure. Therefore, the food production potential and the resulting food security depend on both natural and economic factors. This paper identifies the current status of food security in different countries around the world, considering both aspects (physical and economic availability) combined together. The variables published by FAO were used together with a variable estimated based on the author’s own methodology to identify 8 groups of countries characterized by economic development level, net trade in agricultural products, and selected variables related to agriculture and food situation. As shown by this study, the degree to which food security is ensured with domestic supply varies strongly across the globe. Domestic production provides a foundation for food security in wealthy countries, usually located in areas with favorable conditions for agriculture (including North America, Australia, New Zealand, Kazakhstan) and in countries which, though characterized by a relatively small area of arable land per capita, demonstrate high production intensity (mainly European countries). International trade largely contributes to food security in Middle East and North African countries as well as in selected South American countries which are net importers of food products. The most problematic food situation continues to affect Sub-Saharan Africa and Central Asia.

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

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.

This dataset encompasses extensive information on crop production in India, spanning multiple years and offering insights into agricultural trends and patterns. The dataset consists of over 246,000 records, capturing a wide array of variables related to crop production, and is intended to facilitate advanced analyses such as predictive modeling and the extraction of key insights for stakeholders in the agri-food sector.

Temporal Coverage: - The dataset covers multiple years, providing a longitudinal view of crop production trends in India. This temporal dimension is crucial for analyzing changes over time and understanding long-term patterns.

Geographical Scope: - Data is collected across various states and regions of India, reflecting the diverse agricultural landscape of the country. Regional variations in crop production can be analyzed to identify local factors affecting yields.

Crop Types: - The dataset includes information on various crop types grown across different regions. This classification allows for detailed analysis of specific crops, their production levels, and their sensitivity to various factors.

Production Metrics: - Metrics related to crop production such as yield (e.g., tons per hectare), total production volume, and harvested area are included. These metrics are essential for evaluating productivity and efficiency.

Data Quality and Completeness: - The dataset is likely to include a mix of structured and unstructured data. Data quality may vary, and preprocessing steps such as cleaning and normalization may be necessary to ensure accurate analyses.

Applications and Objectives:

Predictive Modeling: - The primary goal of analyzing this dataset is to develop predictive models for crop production. By leveraging historical data, machine learning algorithms can forecast future production levels and identify potential risks.

Insight Extraction: - The dataset provides an opportunity to uncover key indicators and metrics that significantly influence crop production. Insights can help stakeholders make informed decisions regarding crop management, resource allocation, and policy formulation.

Trend Analysis: - Longitudinal analysis of the data can reveal trends and patterns in crop production, helping to understand how factors such as technological advancements, policy changes, and environmental conditions affect agriculture.

Stakeholder Collaboration: - The dataset supports the development of collaboration platforms that connect various stakeholders in the agri-food sector. By integrating data from multiple sources, stakeholders can collaborate more effectively to address challenges and optimize production.

Key Features: 1. State_Name: Represents the name of the state in India where the crop data was recorded. 2. District_Name: Specifies the district within the state where the crop data was collected. 3. Crop_Year: Indicates the year in which the crop was harvested. 4. Season: Denotes the agricultural season (e.g., Kharif, Rabi) during which the crop was grown. 5. Crop: Identifies the type of crop that was cultivated. 6. Area: Represents the total land area used for cultivating the crop. 7. Production: Indicates the total quantity of the crop produced from the specified area.