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 SEDAC's 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).

Comprehensive agricultural production and food security data worldwide

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.

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.

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

Sustainably feeding the next generation is often described as one of the most pressing “grand challenges” facing the 21st century. Generally, scholars propose addressing this problem by increasing agricultural production, investing in technology to boost yields, changing diets, or reducing food waste. In this paper, we explore whether global food production is nutritionally balanced by comparing the diet that nutritionists recommend versus global agricultural production statistics. Results show that the global agricultural system currently overproduces grains, fats, and sugars while production of fruits and vegetables and protein is not sufficient to meet the nutritional needs of the current population. Correcting this imbalance could reduce the amount of arable land used by agriculture by 51 million ha globally but would increase total land used for agriculture by 407 million ha and increase greenhouse gas emissions. For a growing population, our calculations suggest that the only way to eat a nutritionally balanced diet, save land and reduce greenhouse gas emissions is to consume and produce more fruits and vegetables as well as transition to diets higher in plant-based protein. Such a move will help protect habitats and help meet the Sustainable Development Goals.

Laos LA: Production Index: 2014-2016: Food data was reported at 114.840 2014-2016=100 in 2022. This records an increase from the previous number of 107.390 2014-2016=100 for 2021. Laos LA: Production Index: 2014-2016: Food data is updated yearly, averaging 19.895 2014-2016=100 from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 114.840 2014-2016=100 in 2022 and a record low of 7.240 2014-2016=100 in 1962. Laos LA: Production Index: 2014-2016: Food data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank.WDI: Agricultural Production Index. Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.;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.

The Global Food Security Index was designed and constructed by Economist Impact and is supported by Corteva Agriscience. The Economist Impact team exercises full and final editorial control over all content, including data gathering, analysis and forecasting. The 2022 GFSI is the 11th edition of the index. Economist Impact updates the model annually to capture year-on-year changes in structural factors impacting food security.

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The GFSI considers the issues of: 1. Affordability : Measures the ability of consumers to purchase food, their vulnerability to price shocks and the presence of Programmes and policies to support consumers when shocks occur. 2. Availability : Measures agricultural production and on-farm capabilities, the risk of supply disruption, national capacity to disseminate food and research efforts to expand agricultural output. 3. Quality and Safety : Measures the variety and nutritional quality of average diets, as well as the safety of food. 4. Sustainability and Adaptation : Assesses a country's exposure to the impacts of climate change; its susceptibility to natural resource risks; and how the country is adapting to these risks.

Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.

Incomplete or outdated versions of the dataset from the same source: - https://www.kaggle.com/datasets/dorbicycle/world-foodfeed-production - https://www.kaggle.com/datasets/sofiacosousa/meat-supply-per-person

Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (RSQ) and normalized root mean square error (nRMSE). Overall, the most robustly performing ensemble learning technique was majority voting yielding RSQ and nRMSE values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached RSQ and nRMSE ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at 1×1km spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.

Japan JP: Production Index: 2004-2006: Food data was reported at 92.180 2004-2006=100 in 2016. This records a decrease from the previous number of 95.790 2004-2006=100 for 2015. Japan JP: Production Index: 2004-2006: Food data is updated yearly, averaging 100.820 2004-2006=100 from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 116.360 2004-2006=100 in 1986 and a record low of 67.790 2004-2006=100 in 1961. Japan JP: Production Index: 2004-2006: Food data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Agricultural Production Index. Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

The Food and Agriculture Organization of the United Nations (FAO) in collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a global agro-ecological assessment database for rational land-use planning options. The database is available on a CD-ROM entitled, Global Agro-ecological Assessment for Agriculture in the Twenty-first Century. Digital maps produced from this database can be downloaded from the FAO UN GeoNetwork [http://www.fao.org/geonetwork/srv/en/main.search].

The Global Agro-ecological Assessment for Agriculture in the Twenty-first Century provides a comprehensive global assessment of the world’s agricultural ecology based on the Agro-Ecological Zones (AEZ) methodology developed by FAO and IIASA. The AEZ approach is a GIS-based modeling framework that combines land evaluation methods with socioeconomic and multiple-criteria analysis to evaluate spatial and dynamic aspects of agriculture. The results of the Global AEZ assessment are estimated by grid cell and aggregated to national, regional, and global levels. They include identification of areas with specific climate, soil, and terrain constraints to crop production; estimation of the extent and productivity of rain-fed and irrigated cultivable land and potential for expansion; quantification of cultivation potential of land currently in forest ecosystems; and impacts of climate change on food production potential, geographical shifts of cultivable land, and implications for food security. Such national-level information with global coverage is critical for knowledge-based decision making for sustainable agricultural development.

The Global AEZ CD-ROM is described at [http://www.fao.org/icatalog/search/dett.asp?aries_id=103307]. The CD-ROM contains information on soil, terrain and climatic conditions worldwide, which forms the basis for a global assessment of potential crop productivity. Numerous downloadable maps, tables, a report on the methodology and results, and an executive summary report illustrating the main findings may be also found on the CD-ROM. The CD-ROM can be ordered from: FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org). The price of the CD-ROM is US$40.

IIASA provides a Global AEZ web site at [http://www.iiasa.ac.at/Research/LUC/SAEZ/in47.htm] which complements the CD-ROM. Background information and all of the data available on the Global Agro-ecological Assessment CD-ROM can be accessed directly from this web site. The data include about 100 geographical data sets in raster format, spreadsheets with detailed country and regional results, and appendices with technical details. The report, Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results (PDF 2059K), can be also downloaded from the web site.

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.

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.

Brazil Agricultural Production: Area data was reported at 77,332,547.000 ha in Mar 2019. This records an increase from the previous number of 76,979,570.000 ha for Feb 2019. Brazil Agricultural Production: Area data is updated monthly, averaging 63,520,595.000 ha from Mar 1998 (Median) to Mar 2019, with 253 observations. The data reached an all-time high of 78,211,347.000 ha in Sep 2017 and a record low of 33,993,254.000 ha in Jan 1999. Brazil Agricultural Production: Area data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIA001: Agricultural Area.

This dataset is the basis for the International Food Security Assessment, 2016-2026 released in June 2016. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Excel file listing For complete information, please visit https://data.gov.

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, required safety net financing, and corresponding estimates expressed on the Integrated Phase Classification (IPC) scale. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage. This model has been extended to express outputs on the IPC scale by converting estimates using a nonlinear beta regression estimated on a normalized range, and distributionally adjusted using a smooth threshold transformation.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

Sugar cane was the most produced crop worldwide in 2023. The largest proportion of sugar cane was produced in the Americas, specifically Brazil. Sugar cane production is a major cause of tropical deforestation. In 2023, more than *** million metric tons of oil palm fruit were produced in Asia. Oil palm fruit is also a significant contributor to deforestation, especially in Indonesia.