Global Veal and Beef Consumption Per Capita by Country, 2024 Discover more data with ReportLinker!

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.

Detailed methods can be found in the publication, and highlights are provided below. The following original data sources were aggregated/disaggregated to a common hexagonal grid (cell size 290 km2, mean internode spacing 18.3 km): Gridded Livestock of the World (GLW 2), doi:10.1371/journal.pone.0096084, reporting year 2006, resolution 3 arcminutes (~5 km2 at equator); Gridded Population of the World (GPWv4), doi:10.7927/H4HX19NJ, reporting year 2010, resolution 30 arcseconds (~1 km at equator); GlobCover 2009, doi:10.1594/PANGAEA.787668, reporting year 2009, resolution 300m; FAOSTAT Fertilizers by Nutrient dataset (downloaded on 26 Feb 2018), http://www.fao.org/faostat/en/#data/RFN/metadata, reporting years 2002-2014, resolution national. ---Subnational methods and calculations Livestock densities, human population density, and cropland extent were summarized for each grid cell in a global hexagonal grid. This grid had consistent grid cell areas across latitudes, and was generated using the dggrid package (Barnes, 2016; Sahr, 2011) in the platform R (R Core Team, 2016). In the finer hexagonal grid, each grid cell had a mean area of 290 km2 and a mean internode spacing of 18.3 km. In the coarser grid, each grid cell had a mean side length of 95 km (mean hexagon area of 23,300 km2, mean internode spacing of 165 km), which was large enough to encompass megacities such as London and Paris along with peri-urban areas, but small enough to maintain subnational resolution in relatively small nations. For a minority of hexagonal grid cells, slight deviations in the dimensions were mathematically necessary to avoid overlapping cells and gaps over the world's surface (Barnes, 2016). Total manure P production in each grid cell was calculated by summing the contributions from each animal type, using animal-specific and nation-specific P excretion factors from Bouwman et al. (2017). For cattle we used 16.6 kg P per head yr-1 in Canada, USA, and Japan, 13.1 kg P per head yr-1 in the other OECD countries, and 8.75 kg P per head yr-1 in the remaining countries (Bouwman et al. 2017). For other animals we used 1.8 kg P per head yr-1 for pigs, 0.1 kg P per head yr-1 for chickens, 1.5 kg P per head yr-1 for sheep and goats for all countries (Bouwman et al. 2017). Cells with zero cropland extent were excluded from the analysis (and thus also gridcelldata.csv). --National methods and calculations We used nation-level P fertilizer data from FAOSTAT including import, export, agricultural use, and production for the most recent available years (2002-2014). FAOSTAT data were downloaded on 26 Feb 2018. Fertilizer data are reported annually, and we took the nation-specific means for each budgetary term over two different five year intervals (2010-2014, 2002-2006); these years deliberately exclude the global food crisis of 2007/2008 when the global phosphate rock price spiked by 400% (Chowdhury et al., 2017). A small number of countries had data gap years, requiring that the mean be calculated over fewer years. Import ratios, an indicator of fertilizer P import dependency, were calculated as net import : consumption, where net import = import - export. Recent fertilizer P consumption trends were summarized by calculating a consumption ratio of the 2010s to 2000s (2010-2014:2002-2006). Calculations involving P import ratios and consumption trends were conducted directly on FAO data, prior to disaggregation within the global grid. In cases where grid cells overlapped multiple countries, the nation representing the largest share of the grid cell was assigned to the whole cell using administrative data from Natural Earth. A minority of nations lacked P import or P consumption data and were excluded from P import ratio calculations. Nations that lacked P export data were assumed to have zero gross P export in these calculations. Attributes of the two compiled subnational datasets: "gridcelldata_fine.csv" and "gridcelldata_coarse.csv" (each row represents one hexagonal grid cell) nation: Name of the nation that possessed the largest share of the grid cell. lat: Decimal latitude of the grid cell centroid. lon: Decimal longitude of the grid cell centroid. crop_pct: Mean percent of land as cropland (i.e., cropland extent) within the grid cell. For coastal grid cells, only the land portion of the cell was used in this calculation. popd_indperkm2: Mean population density of the grid cell. manurep_kgperkm2: Calculated manure P production of the grid cell. This is the sum across multiple animal types using animal-specific, nation-specific P excretion factors from Bouwman et al. 2017. cattle_indperkm2: Mean cattle density of the grid cell. pigs_indperkm2: Mean pig density of the grid cell. chickens_indperkm2: Mean chicken density of the grid cell. sheep_indperkm2: Mean sheep density of the grid cell. goats_indperkm2: Mean goat density of the grid cell. pfertnatcons10s_metrictons: Mean nation-level P fertilizer consumption (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatimp_metrictons: Mean nation-level P fertilizer import (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT.pfertnatout_metrictons: Mean nation-level P fertilizer export (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatnetimpratio_unitless: Nation-level net fertilizer P import ratios ([import-export]/consumption) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatcons00s_metrictons: Mean nation-level P fertilizer consumption (P2O5 total nutrients) for years 2002-2006, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatconstr_unitless: Nation-level P fertilizer consumption trend, for the nation that possessed the largest share of the grid cell. This is the ratio of 2010s:2000s (that is, mean of 2010-2014 divided by mean of 2002-2006). Calculated from FAOSTAT.

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

Livestock Production: Milk: Per Capita Availability: Rajasthan data was reported at 1,171.000 g/Day in 2024. This records an increase from the previous number of 1,138.000 g/Day for 2023. Livestock Production: Milk: Per Capita Availability: Rajasthan data is updated yearly, averaging 538.500 g/Day from Mar 1999 (Median) to 2024, with 26 observations. The data reached an all-time high of 1,171.000 g/Day in 2024 and a record low of 353.000 g/Day in 2001. Livestock Production: Milk: Per Capita Availability: Rajasthan data remains active status in CEIC and is reported by Department of Animal Husbandry and Dairying. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIQ008: Livestock Production: Per Capita Availability.

Livestock Production: Egg: Per Capita Availability: Maharashtra data was reported at 56.000 Unit/Annum in 2022. This records an increase from the previous number of 52.000 Unit/Annum for 2021. Livestock Production: Egg: Per Capita Availability: Maharashtra data is updated yearly, averaging 35.000 Unit/Annum from Mar 1998 (Median) to 2022, with 25 observations. The data reached an all-time high of 56.000 Unit/Annum in 2022 and a record low of 31.000 Unit/Annum in 2001. Livestock Production: Egg: Per Capita Availability: Maharashtra data remains active status in CEIC and is reported by Department of Animal Husbandry and Dairying. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIQ008: Livestock Production: Per Capita Availability.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).