Bovine Meat — Stock Variation by country
A 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...
What the numbers show
Bovine Meat — Stock Variation is currently reported for 137 countries. The highest value is 38 1000 t in Canada; the lowest is -73 1000 t in Argentina.
The median across all reporting countries is 0 1000 t, and the mean is 0.0146 1000 t.
Over the past decade 28 countries rose and 36 fell. The largest increase was in Azerbaijan (up 1,800.0%), and the largest decrease in Argentina (down 1,725.0%).
Bovine Meat — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Canada | 38 1000 t | 2023 | up 1,366.7% | volatile |
| 2 | China | 37 1000 t | 2023 | down 7.5% | volatile |
| 3 | China, mainland | 32 1000 t | 2023 | down 25.6% | volatile |
| 4 | Belgium | 22 1000 t | 2023 | — | volatile |
| 5 | Austria | 18 1000 t | 2023 | — | volatile |
| 5 | United Republic of Tanzania | 18 1000 t | 2023 | up 260.0% | volatile |
| 7 | Azerbaijan | 17 1000 t | 2023 | up 1,800.0% | volatile |
| 8 | Kyrgyzstan | 14 1000 t | 2023 | up 800.0% | volatile |
| 9 | Portugal | 12 1000 t | 2023 | up 400.0% | volatile |
| 10 | Denmark | 10 1000 t | 2023 | — | volatile |
| 11 | Czechia | 9 1000 t | 2023 | — | volatile |
| 11 | Greece | 9 1000 t | 2023 | — | volatile |
| 13 | Croatia | 6 1000 t | 2023 | — | volatile |
| 13 | Italy | 6 1000 t | 2023 | — | volatile |
| 13 | Namibia | 6 1000 t | 2023 | — | volatile |
| 13 | Vietnam | 6 1000 t | 2023 | — | volatile |
| 13 | Viet Nam | 6 1000 t | 2023 | — | volatile |
| 18 | Guatemala | 5 1000 t | 2023 | up 118.5% | volatile |
| 18 | Netherlands (Kingdom of the) | 5 1000 t | 2023 | — | volatile |
| 20 | Brazil | 4 1000 t | 2023 | up 300.0% | volatile |
| 20 | Cambodia | 4 1000 t | 2023 | down 63.6% | volatile |
| 20 | Ukraine | 4 1000 t | 2023 | — | volatile |
| 20 | China, Hong Kong SAR | 4 1000 t | 2023 | up 233.3% | volatile |
| 24 | Lithuania | 3 1000 t | 2023 | — | volatile |
| 25 | Colombia | 2 1000 t | 2023 | up 100.0% | volatile |
| 25 | Rwanda | 2 1000 t | 2023 | up 100.0% | volatile |
| 25 | Slovenia | 2 1000 t | 2023 | unchanged | falling |
| 28 | Hungary | 1 1000 t | 2023 | up 114.3% | volatile |
| 28 | Iraq | 1 1000 t | 2023 | down 66.7% | volatile |
| 28 | Niger | 1 1000 t | 2023 | up 105.3% | volatile |
| 28 | Pakistan | 1 1000 t | 2023 | down 50.0% | volatile |
| 28 | Philippines | 1 1000 t | 2023 | down 83.3% | volatile |
| 28 | Thailand | 1 1000 t | 2023 | up 133.3% | volatile |
| 28 | China, Taiwan Province of | 1 1000 t | 2023 | — | volatile |
| 28 | China, Macao SAR | 1 1000 t | 2023 | — | volatile |
| 36 | Afghanistan | 0 1000 t | 2023 | down 100.0% | flat |
| 36 | Angola | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | United Arab Emirates | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Armenia | 0 1000 t | 2023 | — | volatile |
| 36 | Australia | 0 1000 t | 2023 | — | flat |
| 36 | Burkina Faso | 0 1000 t | 2023 | down 100.0% | flat |
| 36 | Bangladesh | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Bulgaria | 0 1000 t | 2023 | — | volatile |
| 36 | Belarus | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Bhutan | 0 1000 t | 2023 | — | volatile |
| 36 | Botswana | 0 1000 t | 2023 | — | volatile |
| 36 | Switzerland | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Chile | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Cote d'Ivoire | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Cameroon | 0 1000 t | 2023 | — | volatile |
| 36 | Congo | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Comoros | 0 1000 t | 2023 | — | volatile |
| 36 | Costa Rica | 0 1000 t | 2023 | — | volatile |
| 36 | Cyprus | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Dominican Republic | 0 1000 t | 2023 | — | volatile |
| 36 | Algeria | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Spain | 0 1000 t | 2023 | — | volatile |
| 36 | Estonia | 0 1000 t | 2023 | — | volatile |
| 36 | Ethiopia | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | United Kingdom | 0 1000 t | 2023 | — | volatile |
| 36 | Georgia | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Ghana | 0 1000 t | 2023 | — | volatile |
| 36 | Guinea | 0 1000 t | 2023 | — | volatile |
| 36 | Gambia | 0 1000 t | 2023 | up 100.0% | flat |
| 36 | Honduras | 0 1000 t | 2023 | — | volatile |
| 36 | India | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Israel | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Jamaica | 0 1000 t | 2023 | — | volatile |
| 36 | Jordan | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Kazakhstan | 0 1000 t | 2023 | — | volatile |
| 36 | Kenya | 0 1000 t | 2023 | — | volatile |
| 36 | Kuwait | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Lebanon | 0 1000 t | 2023 | — | volatile |
| 36 | Sri Lanka | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Lesotho | 0 1000 t | 2023 | — | volatile |
| 36 | Luxembourg | 0 1000 t | 2023 | — | volatile |
| 36 | Morocco | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Madagascar | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Mexico | 0 1000 t | 2023 | — | volatile |
| 36 | Myanmar | 0 1000 t | 2023 | — | volatile |
| 36 | Montenegro | 0 1000 t | 2023 | — | volatile |
| 36 | Mauritania | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Mauritius | 0 1000 t | 2023 | — | volatile |
| 36 | Malawi | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Malaysia | 0 1000 t | 2023 | — | flat |
| 36 | New Caledonia | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Nigeria | 0 1000 t | 2023 | — | volatile |
| 36 | Nicaragua | 0 1000 t | 2023 | — | volatile |
| 36 | Norway | 0 1000 t | 2023 | — | volatile |
| 36 | Nepal | 0 1000 t | 2023 | — | volatile |
| 36 | New Zealand | 0 1000 t | 2023 | — | volatile |
| 36 | Oman | 0 1000 t | 2023 | — | volatile |
| 36 | Panama | 0 1000 t | 2023 | — | volatile |
| 36 | Papua New Guinea | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Paraguay | 0 1000 t | 2023 | — | flat |
| 36 | Romania | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Russia | 0 1000 t | 2023 | — | volatile |
| 36 | Saudi Arabia | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Senegal | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Solomon Islands | 0 1000 t | 2023 | down 100.0% | flat |
| 36 | Sierra Leone | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Slovakia | 0 1000 t | 2023 | — | volatile |
| 36 | Sweden | 0 1000 t | 2023 | — | volatile |
| 36 | Eswatini | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Syria | 0 1000 t | 2023 | — | volatile |
| 36 | Tajikistan | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Turkmenistan | 0 1000 t | 2023 | — | volatile |
| 36 | Tunisia | 0 1000 t | 2023 | — | volatile |
| 36 | Turkey | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Uruguay | 0 1000 t | 2023 | — | flat |
| 36 | Uzbekistan | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | South Africa | 0 1000 t | 2023 | — | flat |
| 36 | Zambia | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Zimbabwe | 0 1000 t | 2023 | up 100.0% | volatile |
| 36 | Melanesia | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Democratic Republic of the Congo | 0 1000 t | 2023 | — | volatile |
| 36 | Caribbean | 0 1000 t | 2023 | — | volatile |
| 36 | Bolivia (Plurinational State of) | 0 1000 t | 2023 | — | volatile |
| 36 | Iran (Islamic Republic of) | 0 1000 t | 2023 | — | volatile |
| 36 | Republic of Korea | 0 1000 t | 2023 | — | volatile |
| 36 | Russian Federation | 0 1000 t | 2023 | — | volatile |
| 36 | Syrian Arab Republic | 0 1000 t | 2023 | — | volatile |
| 36 | Türkiye | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | Australia and New Zealand | 0 1000 t | 2023 | — | volatile |
| 36 | Côte d'Ivoire | 0 1000 t | 2023 | down 100.0% | volatile |
| 36 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | volatile |
| 36 | Democratic People's Republic of Korea | 0 1000 t | 2018 | — | volatile |
| 185 | Indonesia | -1 1000 t | 2023 | up 85.7% | volatile |
| 186 | Finland | -3 1000 t | 2023 | — | volatile |
| 187 | Latvia | -5 1000 t | 2023 | — | volatile |
| 188 | Mongolia | -7 1000 t | 2023 | down 600.0% | volatile |
| 189 | Egypt | -11 1000 t | 2023 | down 650.0% | volatile |
| 190 | United States | -39 1000 t | 2023 | — | volatile |
| 191 | Poland | -41 1000 t | 2023 | up 8.9% | volatile |
| 192 | Germany | -61 1000 t | 2023 | — | volatile |
| 193 | Ireland | -66 1000 t | 2023 | — | volatile |
| 194 | Argentina | -73 1000 t | 2023 | down 1,725.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Asia 75 1000 t
- Low Income Food Deficit Countries (LIFDCs) 37 1000 t
- Southern Europe 35 1000 t
- Eastern Asia 30 1000 t
- Land Locked Developing Countries (LLDCs) 29 1000 t
- Least Developed Countries (LDCs) 27 1000 t
- Eastern Africa 20 1000 t
- Western Asia 19 1000 t
- Africa 18 1000 t
- Central Asia 15 1000 t
- Net Food Importing Developing Countries (NFIDCs) 15 1000 t
- South-Eastern Asia 11 1000 t
- Southern Africa 6 1000 t
- Central America 4 1000 t
- Western Africa 2 1000 t
- Southern Asia 1 1000 t
- Oceania 0 1000 t
- Small island developing States (SIDS) 0 1000 t
- Middle Africa 0 1000 t
- Northern America -1 1000 t
- Northern Africa -11 1000 t
- Western Europe -16 1000 t
- Eastern Europe -27 1000 t
- United States of America -39 1000 t
- World -40 1000 t
- Northern Europe -61 1000 t
- Americas -64 1000 t
- South America -67 1000 t
- Europe -69 1000 t
- European Union (27) -73 1000 t
About this data
A 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 capita 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.