In 2023, around 140 million tons of poultry meat were consumed worldwide, making it the most consumed type of meat globally. Pork was the second most consumed meat worldwide, followed by beef and veal. Leading consumers The per capita consumption of meat is forecast to grow in every part of the world by 2031. OECD countries had the highest per capita consumption of meat from 2019 to 2021, at 69.5 kilograms of retail weight per person. The world average per capita consumption is only about 34.1 kilograms. Shift towards meat substitutes Meat production is a significant greenhouse gas emitter and beef specifically emits more greenhouse gases than any other food product. Because of this and other climate change threats caused by meat production, such as deforestation, meat alternatives have been on the rise. It is projected that by 2040, 25 percent of all “meat” consumed will be vegan meat alternatives and only 40 percent of consumption will be from traditionally produced meat.

Context

Meat consumption is related to living standards, diet, livestock production and consumer prices, as well as macroeconomic uncertainty and shocks to GDP. Compared to other commodities, meat is characterised by high production costs and high output prices. Meat demand is associated with higher incomes and a shift - due to urbanisation - to food consumption changes that favour increased proteins from animal sources in diets. While the global meat industry provides food and a livelihood for billions of people, it also has significant environmental and health consequences for the planet.

This dataset was refreshed in 2018, with world meat projections up to 2026 are presented for beef and veal, pig, poultry, and sheep. Meat consumption is measured in thousand tonnes of carcass weight (except for poultry expressed as ready to cook weight) and in kilograms of retail weight per capita. Carcass weight to retail weight conversion factors are: 0.7 for beef and veal, 0.78 for pig meat, and 0.88 for both sheep meat and poultry meat. Excludes Iceland but includes all EU 28 member countries.

Content

The csv file has 5 columns:

• LOCATION = the country code name
• SUBJECT = The type of meat(pig, beef, etc)
• TIME = the year the data was recorded
• MEASURE = the measure used to show the value
• VALUE = The value, according to the measure

Acknowledgements

https://data.oecd.org/agroutput/meat-consumption.htm OECD/FAO (2017), “OECD-FAO Agricultural Outlook”, OECD Agriculture statistics (database). doi: dx.doi.org/10.1787/agr-outl-data-en (Accessed on 24 January 2018)

Inspiration

Ask questions such as:

1. What country had the most meat consumption?
2. What years had the most worldwide meat consumption?
3. Is there any correlation between the type of meat and the amount being consumed?

Forecast: Chicken Meat Consumption Per Capita in India 2023 - 2027 Discover more data with ReportLinker!

Beef consumption in the United States reached a new high in 2021, when 30 billion pounds were consumed. This is an increase in consumption of about 8.7 percent compared to the previous year and the highest consumption recorded during the period under consideration.

United States beef production

The United States is the world’s top producer of beef and veal. In 2022, production exceeding 12.6 million metric tons. To keep up with the production demand, the U.S. was home to about 30 million beef cows in 2022, more than three times the number of dairy cows recorded that year.

The shift towards plant-based foods

There is a large trend among Generation Z consumers to adopt a more vegetarian or vegan diet. Over half of Gen Z consumers are mostly vegetarian, at a minimum, as of 2022. 21 percent, however, are completely vegan, meaning they eat no animal products at all. With this shift away from animal proteins, it is no surprise that the consumption of meat substitutes is expected to exponentially grow within the next several years. By 2027, U.S. meat substitute consumption is forecast to reach 292.5 million kilograms.

This product provides information on the per capita consumption of meats (beef, veal, mutton/lamb, pork and poultry) in Canada and United States for a thirty-year period. Trend of Beef and Poultry consumption comparison is included.

United States Long Term Projections: Per Capita Meat Consumption: Beef data was reported at 55.258 lb in 2034. This records a decrease from the previous number of 55.380 lb for 2033. United States Long Term Projections: Per Capita Meat Consumption: Beef data is updated yearly, averaging 55.360 lb from Dec 2022 (Median) to 2034, with 13 observations. The data reached an all-time high of 59.250 lb in 2024 and a record low of 52.840 lb in 2027. United States Long Term Projections: Per Capita Meat Consumption: Beef data remains active status in CEIC and is reported by U.S. Department of Agriculture. The data is categorized under Global Database’s United States – Table US.RI016: Agricultural Projections: Meat Consumption.

Meat Consumption per Capita: CF: Tambov Region data was reported at 84.000 kg in 2022. This stayed constant from the previous number of 84.000 kg for 2021. Meat Consumption per Capita: CF: Tambov Region data is updated yearly, averaging 65.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 84.000 kg in 2022 and a record low of 49.000 kg in 2001. Meat Consumption per Capita: CF: Tambov Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

A spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 world regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs) for the year 2000. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system.

Lineage: A livestock systems classification updated by Robinson et al (2011) was used as the starting point. It is based on agro-ecological differentiation (arid, humid and temperate/tropical highland areas), which helps in establishing the composition of diets for animals in different regions and agro-agroecologies and in the future to elicit the impacts that climate change might have on feed resources and land use. We differentiated 8 different types of livestock systems in 28 geographical regions of the world for this study. Numbers of animals for each of these systems and regions were estimated using the data of Wint and Robinson (2007) for the year 2000.

For ruminants (cattle, sheep and goats), we disaggregated the dairy and beef cattle herds using livestock demographic data for total cattle, sheep and goats and the dairy females for each species, respectively, from FAOSTAT. We used herd dynamics models parameterised for each region and production system using reproduction and mortality rates obtained from extensive literature reviews to estimate herd composition. For monogastrics (pigs and poultry), we only differentiated two systems: smallholder and industrial production systems. The allocation of poultry, eggs and pork production was done on the basis of knowledge of the total product output from these two systems from national information from selected countries in the different regions, applied to the respective region.

Biomass consumption and productivity estimations from different species in each region and system followed a three stage process. First, feed availability of four main types of feeds (grass, crop residues, grains, occasional feeds) was estimated using hybrid maps of grassland productivity and EPIC model output (Havlik et al 2013) for humid and temperate regions of the world. Crop residue availability was estimated using the SPAM cropland layers (You et al 2014) and coefficients of stover use for animal feeding and harvest indexes for different parts of the world. Grain availability for animal production was taken from the FAO Commodity balance sheets and the availability of occasional feeds like cut and carry grasses and legumes was obtained from literature reviews.

The second step consisted of developing feasible diets for each species in each region and production system. The proportions of each feed in the diet of each species was obtained from extensive information available in the literature and from databases and feeding practice surveys at key research centres in the world (i.e. FAO, ILRI). Data on feed quality was obtained from the databases containing regional feed composition data for each feed (Herrero et al 2008). The third step consisted of estimating productivity. For ruminants, the information on the quantity and quality of the different feeds was then used to parameterise an IPCC tier 3 digestion and metabolism model (RUMINANT, Herrero et al 2002), as described in Herrero et al (2008) and Thornton and Herrero (2010). The model estimated productivity (milk, meat), methane emissions and manure and nitrogen excretion. For monogastrics, information on feed quality was used to estimate feed intake, productivity and feed use efficiency using standard nutrient requirements guidelines (NRC 2008). The estimation of methane and nitrous oxide emissions from manure, and of nitrous oxide from pastures followed an IPCC tier 2 approach, for each species, system and region. Further details are available in the Supplementary Information of Herrero et al. 2013.

All information on animal production (bovine milk, bovine meat, sheep and goat milk, sheep and goat meat, pork, poultry and eggs) and for grains as feed was harmonised with FAOSTAT’s commodity balance sheets at national level following an iterative procedure restricted to deviate +/- 20% from the statistical data in FAOSTAT.

The size of the collection is 1.32 GB, 192 zip files.

Meat Consumption per Capita: CF: Ryazan Region data was reported at 67.000 kg in 2022. This records an increase from the previous number of 63.000 kg for 2021. Meat Consumption per Capita: CF: Ryazan Region data is updated yearly, averaging 58.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 75.000 kg in 1990 and a record low of 50.000 kg in 2000. Meat Consumption per Capita: CF: Ryazan Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

Meat Consumption per Capita: CF: Tula Region data was reported at 70.000 kg in 2022. This records a decrease from the previous number of 72.000 kg for 2021. Meat Consumption per Capita: CF: Tula Region data is updated yearly, averaging 61.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 81.000 kg in 1990 and a record low of 45.000 kg in 2000. Meat Consumption per Capita: CF: Tula Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

Meat Consumption per Capita: NC: Stavropol Territory data was reported at 80.000 kg in 2022. This records a decrease from the previous number of 82.000 kg for 2021. Meat Consumption per Capita: NC: Stavropol Territory data is updated yearly, averaging 65.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 82.000 kg in 2021 and a record low of 39.000 kg in 2000. Meat Consumption per Capita: NC: Stavropol Territory data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

Meat Consumption per Capita: CF: Ivanovo Region data was reported at 65.000 kg in 2022. This records an increase from the previous number of 64.000 kg for 2021. Meat Consumption per Capita: CF: Ivanovo Region data is updated yearly, averaging 54.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 70.000 kg in 1990 and a record low of 36.000 kg in 2000. Meat Consumption per Capita: CF: Ivanovo Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

This dataset contains data from 30 participants who completed the same questionnaire on meat consumption 12 times. The participant’s opinion was perturbed on each of the 11 items and measured to what extent this changed the participant’s scores on the questionnaire. It is a unique dataset that can be used for several purposes (Hoekstra et al., 2018).

Task: The dataset can be used to study causal discovery algorithms.

Summary:

• Size of dataset: 360 x 11
• Task: Causal Discovery
• Data Type: Discrete
• Dataset Scope: Standalone
• Ground Truth: Estimated
• Temporal Structure: Static
• License: CC BY-4.0
• Missing Values: No

Missingness Statement: There are no missing values.

Features: Each measurement is a a six-level factor with levels 1 (completely disagree) to 6 (completely agree)

• moral: Eating meat is morally wrong
• nutr: Meat contains important nutrients for your body
• envir: The production of meat if harmful for the environment
• infer: Animals are inferior to people
• suff: By consuming meat you contribute to animal suffering
• tax: There should be a tax on meat
• taste: I like the taste of meat
• death: Meat reminds me of death and suffering of animals
• sad: If I had to stop eating meat I would feel sad
• guilty: If I eat meat I feel guilty
• disg: If I eat meat I feel disgust

Files:

• Data.csv: dateset
• GroundTruth.csv: Estimated causal directions by Waldorp et al. (2021) via a conditional invariant prediction method.

BackgroundThe burden of ischemic stroke (IS) linked to high consumption of red meat is on the rise. This study aimed to analyze the mortality and disability-adjusted life years (DALYs) trends for IS attributed to high red meat intake in China between 1990 and 2019 and to compare these trends with global trends.MethodsThis study extracted data on IS attributed to diets high in red meat in China from 1990 to 2019 from the Global Burden of Disease Study (GBD) database. Key measures, including mortality, DALYs, age-standardized mortality rates (ASMR), and age-standardized DALYs rates (ASDR), were used to estimate the disease burden. The estimated annual percentage change and joinpoint regression models were employed to assess the trends over time. An age-period-cohort analysis was used to assess the contribution of a diet high in red meat to the age, period, and cohort effects of IS ASMR and ASDR.ResultsBetween 1990 and 2019, deaths and DALYs from IS attributed to a diet high in red meat in China, along with corresponding age-standardized rates, significantly increased. The overall estimated annual percentage change for the total population and across sex categories ranged from 1.01 to 2.08. The average annual percentage changes for overall ASDR and ASMR were 1.4 and 1.33, respectively, with male ASDR and ASMR average annual percentage changes at 1.69 and 1.69, respectively. Contrastingly, female ASDR and ASMR average annual percentage changes were 1.07 and 0.87, respectively. Except for a few periods of significant decrease in females, all other periods indicated a significant increase or nonsignificant changes. Incidence of IS linked to a diet high in red meat rose sharply with age, displaying increasing period and cohort effects in ASDR. Female ASMR period and cohort effect ratios initially increased and then decreased, whereas the male ratio showed an upward trend.ConclusionThis study comprehensively analyzed epidemiological characteristics that indicated a marked increase in mortality and DALYs from IS attributable to high red meat consumption, contrasting with a global downtrend. This increase was more pronounced in males than females. This research provides valuable insights for enhancing IS prevention in China.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

Meat Consumption per Capita: SF: Krasnodar Territory data was reported at 91.000 kg in 2022. This records an increase from the previous number of 90.000 kg for 2021. Meat Consumption per Capita: SF: Krasnodar Territory data is updated yearly, averaging 68.000 kg from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 91.000 kg in 2022 and a record low of 36.000 kg in 1999. Meat Consumption per Capita: SF: Krasnodar Territory data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HB006: Household Food Consumption per Capita: Meat.

Regional meat consumption per capita (kg/person/year).

This table contains 168 series, with data for years 1941 - 1976 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (10 items: Canada;Prince Edward Island;Nova Scotia;New Brunswick; ...); Type of poultry meat (2 items: Goose;Duck); Components (3 items: Total birds;Bird meat consumed by producers;Bird meat sold); Estimates (3 items: Production;Weight;Value).