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.

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.

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.

Ukraine was expected to occupy almost ** percent of the global production volume of sunflower seed in the marketing year 2024/25. Furthermore, the country's rapeseed production was forecast to account for over **** percent of the total.

This statistic shows the required increase from 2013 levels in agricultural production in order for projected demand in 2050 to be met. In order to meet the global food demand in 2050, agricultural production has to increase by **** percent worldwide.

Climate change has a profound impact on global agriculture, affecting crop yields, soil health, and farming sustainability. This synthetic dataset is designed to simulate real-world agricultural data, enabling researchers, data scientists, and policymakers to explore how climate variations influence food production across different regions.

🔍 Key Features: ✔️ Climate Variables – Simulated data on temperature changes, precipitation levels, and extreme weather events ✔️ Crop Productivity – Modeled impact of climate shifts on yields of key crops like wheat, rice, and corn ✔️ Regional Insights – Includes various geographic regions to analyze diverse climate-agriculture interactions ✔️ Ideal for Predictive Modeling – Supports climate risk assessment, food security studies, and sustainability research

📊 Dataset Overview: This dataset has been synthetically generated and does not contain real-world agricultural records. It is intended for academic learning, climate impact analysis, and machine learning applications in environmental studies.

📖 Columns Description: Region – Simulated geographic region Year – Modeled year of data collection Average_Temperature – Simulated temperature levels (°C) Precipitation – Modeled annual rainfall (mm) Crop_Yield – Synthetic yield data for selected crops (tons/hectare) Extreme_Weather_Events – Number of modeled extreme weather occurrences per year ⚠️ Disclaimer: This dataset is completely synthetic and should not be used for real-world climate policy decisions or agricultural forecasting. It is meant for educational purposes, research, and data science applications.

🔹 Use this dataset to analyze climate trends, build predictive models, and explore solutions for sustainable agriculture! 🌱📊

The Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 emission scenarios in 1997. Results of the initial study are presented at ESDIS' Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Finland FI: Cereal Yield: per Hectare data was reported at 3,574.100 kg/ha in 2016. This records a decrease from the previous number of 3,614.300 kg/ha for 2015. Finland FI: Cereal Yield: per Hectare data is updated yearly, averaging 2,946.600 kg/ha from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 3,760.400 kg/ha in 2009 and a record low of 1,363.600 kg/ha in 1962. Finland FI: Cereal Yield: per Hectare data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Finland – Table FI.World Bank.WDI: Agricultural Production and Consumption. Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. 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 and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

Sweden Agricultural Production: Crop: Spring Wheat data was reported at 298,600.000 Ton in 2017. This records a decrease from the previous number of 339,500.000 Ton for 2016. Sweden Agricultural Production: Crop: Spring Wheat data is updated yearly, averaging 231,080.000 Ton from Dec 1965 (Median) to 2017, with 53 observations. The data reached an all-time high of 549,600.000 Ton in 2013 and a record low of 119,220.000 Ton in 1970. Sweden Agricultural Production: Crop: Spring Wheat data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.B025: Agriculture Production.

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.

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...

The total beef production in the United States is estimated to be 26.96 billion pounds in 2023, down from 28.29 billion pounds in the previous year. Over the last two decades, the total U.S. beef production has fluctuated slightly but remained stable overall.

Beef retail in the United States Beef has the highest retail sales of any fresh meat in the United States, as of 2021. In that year, over 30 billion U.S. dollars worth of fresh beef were sold in the United States. The retail price for 100 percent ground beef in the United States was 4.8 U.S. dollars per pound in 2022, up from 3.95 U.S. dollars in 2020. Beef brisket, on the other hand, was priced on average around 8.84 U.S. dollars per pound in major grocery retailers.

U.S. beef consumption The United States consumes more beef than any other country in the world. Consumption of beef amounted to around 59 pounds per capita on an annual basis. This was projected to decrease slowly until 2032.

FAS's PSD Online data for those commodities published in the WASDE Report are reviewed and updated monthly by an interagency committee chaired by USDA's World Agricultural Outlook Board (WAOB), and consisting of: the Foreign Agricultural Service (FAS), the Economic Research Service (ERS), the Farm Service Agency (FSA), and the Agricultural Marketing Service (AMS). The international portion of the data is updated with input from agricultural attachés stationed at U.S. embassies around the world, FAS commodity analysts, and country and commodity analysts with ERS. The U.S. domestic component is updated with input from analysts in FAS, ERS, the National Agricultural Statistical Service, and FSA. Interagency work on the database is carried out under the aegis of the WAOB. The official USDA supply and use data is published monthly in: WAOB, World Agricultural Supply and Demand Estimates (WASDE); in the foreign agricultural commodity circular series issued by FAS; and in the regional situation and outlook reports and monthly commodity newsletters of ERS (see keywords Crops and Animal Products) data for horticultural products are usually published twice a year. Resources in this dataset:Resource Title: PSD Web API. File Name: Web Page, url: https://apps.fas.usda.gov/psdonline/app/index.html#/app/about Programmatically access Production, Supply, and Distribution data via Web API.

In the marketing year 2024/2025, China was the leading wheat producing country with production volume of about ***** million metric tons. This was followed by the European Union with production volume of over *** million metric tons. Wheat productionWheat is the second most important grain that is cultivated in the United States, following only corn. Wheat is a cereal crop that can be classified into five major classes. These 5 wheat categories are comprised of: hard red winter, hard red spring, soft red winter, white and durum wheat. Each class has a different end-use and the cultivation tends to be region-specific. Hard red winter wheat is mainly cultivated in the Great Plains area ranging from Montana to Texas. This type is primarily used for the manufacturing of bread flour. Hard red spring wheat is mainly grown in the Northern Plains area. Their wheat ears are mostly taken for protein blending uses. Durum wheat, which is primarily grown in North Dakota and Montana, is well-known for their excellent qualities for producing pasta. The wheat class everyone is familiar with from their breakfast cereal is known as white wheat.Almost every U.S. state is involved in agricultural production of wheat. The latest figures show that North Dakota, Kansas and Montana were the leading wheat producing states among the United States.

The "Global Rice Production Statistics Dataset" presents detailed information on rice production in different countries, including annual production amounts, acreage used for cultivation, yield per hectare, and rice consumption per person. This dataset highlights the importance of rice farming worldwide, illustrating its role in supporting local economies and ensuring food security on a global scale.

Column Descriptions:

Country: The name of the country contributing to global rice production.

Rice Production (Tons): The total annual rice production in tons by each country, reflecting their contribution to the global rice supply.

Rank of Rice Production: The ranking of the country based on its annual rice production volume compared to other rice-producing nations.

Rice Production Per Person (Kg): The amount of rice produced per person in kilograms annually, indicating the consumption and availability of rice on a per capita basis.

Rank of Rice Production Per Person: The ranking of the country based on rice production per person, providing insights into consumption patterns and availability.

Rice Acreage (Hectare): The total land area in hectares dedicated to rice cultivation in each country, showcasing the extent of rice farming practices.

Rank of Rice Acreage: The ranking of the country based on the acreage dedicated to rice cultivation, highlighting the scale of rice farming operations.

Rice Yield (Kg / Hectare): The average rice yield in kilograms per hectare of land, indicating the productivity and efficiency of rice production practices.

Rank of Rice Yield: The ranking of the country based on rice yield per hectare, showcasing the effectiveness of agricultural practices in maximizing rice output.

This dataset aims to provide a comprehensive overview of global rice production statistics, emphasizing the diversity in production levels, agricultural practices, and consumption patterns across different countries.

Sweden Agricultural Production: Crop: Winter Wheat data was reported at 3,000,000.000 Ton in 2017. This records an increase from the previous number of 2,502,100.000 Ton for 2016. Sweden Agricultural Production: Crop: Winter Wheat data is updated yearly, averaging 1,347,200.000 Ton from Dec 1965 (Median) to 2017, with 53 observations. The data reached an all-time high of 3,000,000.000 Ton in 2017 and a record low of 407,910.000 Ton in 1966. Sweden Agricultural Production: Crop: Winter Wheat data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.B025: Agriculture Production.

The timeline shows the crop value of the dry pulse and vegetable industry in the United States from 2014 to 2023, by type. In 2023, the crop value of dry beans, peas and lentils in the U.S. amounted to approximately 1.67 billion U.S. dollars.

Germany DE: Agriculture Value Added per Worker: 2010 Price data was reported at 41,992.961 USD in 2016. This records an increase from the previous number of 40,221.633 USD for 2015. Germany DE: Agriculture Value Added per Worker: 2010 Price data is updated yearly, averaging 25,326.241 USD from Dec 1991 (Median) to 2016, with 26 observations. The data reached an all-time high of 42,328.069 USD in 2009 and a record low of 15,927.567 USD in 1994. Germany DE: Agriculture Value Added per Worker: 2010 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Agricultural Production and Consumption. Agriculture value added per worker is a measure of agricultural productivity. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1-5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Data are in constant 2010 U.S. dollars.; ; Derived from World Bank national accounts files and Food and Agriculture Organization, Production Yearbook and data files.; Weighted average;