Wine — 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
Wine — Stock Variation is currently reported for 180 countries. The highest value is 203 1000 t in Netherlands (Kingdom of the); the lowest is -650 1000 t in Spain.
The median across all reporting countries is 0 1000 t, and the mean is 0.1833 1000 t.
Over the past decade 33 countries rose and 32 fell. The largest increase was in Portugal (up 5,900.0%), and the largest decrease in North Macedonia (down 560.0%).
Wine — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Netherlands (Kingdom of the) | 203 1000 t | 2023 | up 2,355.6% | volatile |
| 2 | France | 145 1000 t | 2023 | up 3,000.0% | volatile |
| 3 | Portugal | 116 1000 t | 2023 | up 5,900.0% | volatile |
| 4 | Australia and New Zealand | 46 1000 t | 2023 | up 4,500.0% | volatile |
| 5 | Czechia | 44 1000 t | 2023 | — | volatile |
| 6 | Italy | 33 1000 t | 2023 | down 89.8% | volatile |
| 7 | Australia | 24 1000 t | 2023 | up 71.4% | volatile |
| 8 | New Zealand | 22 1000 t | 2023 | up 269.2% | volatile |
| 9 | Croatia | 18 1000 t | 2023 | — | volatile |
| 10 | Morocco | 10 1000 t | 2023 | — | volatile |
| 11 | Argentina | 8 1000 t | 2023 | down 87.3% | volatile |
| 12 | Hungary | 7 1000 t | 2023 | down 84.8% | volatile |
| 13 | Bulgaria | 5 1000 t | 2023 | up 400.0% | volatile |
| 13 | Belarus | 5 1000 t | 2023 | up 66.7% | volatile |
| 15 | China | 3 1000 t | 2023 | up 101.8% | volatile |
| 15 | Georgia | 3 1000 t | 2023 | down 66.7% | volatile |
| 15 | Viet Nam | 3 1000 t | 2023 | up 200.0% | volatile |
| 15 | China, Taiwan Province of | 3 1000 t | 2023 | up 400.0% | volatile |
| 19 | Armenia | 1 1000 t | 2023 | unchanged | volatile |
| 19 | Brazil | 1 1000 t | 2023 | up 106.2% | volatile |
| 19 | Colombia | 1 1000 t | 2023 | down 66.7% | volatile |
| 19 | Egypt | 1 1000 t | 2023 | — | volatile |
| 19 | Guatemala | 1 1000 t | 2023 | unchanged | volatile |
| 19 | Iceland | 1 1000 t | 2023 | — | volatile |
| 19 | Israel | 1 1000 t | 2023 | down 50.0% | volatile |
| 19 | Myanmar | 1 1000 t | 2023 | unchanged | volatile |
| 19 | Peru | 1 1000 t | 2023 | up 150.0% | flat |
| 19 | Sweden | 1 1000 t | 2023 | — | volatile |
| 19 | Democratic Republic of the Congo | 1 1000 t | 2023 | unchanged | volatile |
| 19 | Republic of Korea | 1 1000 t | 2023 | down 66.7% | volatile |
| 19 | Russian Federation | 1 1000 t | 2023 | up 100.5% | volatile |
| 19 | China, Macao SAR | 1 1000 t | 2023 | — | volatile |
| 33 | Afghanistan | 0 1000 t | 2023 | — | flat |
| 33 | Angola | 0 1000 t | 2023 | — | volatile |
| 33 | Albania | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | United Arab Emirates | 0 1000 t | 2023 | — | flat |
| 33 | Antigua and Barbuda | 0 1000 t | 2023 | — | volatile |
| 33 | Austria | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Azerbaijan | 0 1000 t | 2023 | — | volatile |
| 33 | Belgium | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Burkina Faso | 0 1000 t | 2023 | — | volatile |
| 33 | Bangladesh | 0 1000 t | 2023 | — | flat |
| 33 | Bahrain | 0 1000 t | 2023 | — | volatile |
| 33 | Bahamas | 0 1000 t | 2023 | — | volatile |
| 33 | Bosnia and Herzegovina | 0 1000 t | 2023 | — | flat |
| 33 | Belize | 0 1000 t | 2023 | — | flat |
| 33 | Barbados | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Bhutan | 0 1000 t | 2023 | — | flat |
| 33 | Botswana | 0 1000 t | 2023 | — | flat |
| 33 | Canada | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Switzerland | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Chile | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Cameroon | 0 1000 t | 2023 | — | volatile |
| 33 | Congo | 0 1000 t | 2023 | down 100.0% | flat |
| 33 | Comoros | 0 1000 t | 2023 | — | flat |
| 33 | Costa Rica | 0 1000 t | 2023 | — | volatile |
| 33 | Cuba | 0 1000 t | 2019 | — | flat |
| 33 | Germany | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Djibouti | 0 1000 t | 2023 | — | flat |
| 33 | Denmark | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Dominican Republic | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Algeria | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Ecuador | 0 1000 t | 2023 | — | volatile |
| 33 | Estonia | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Ethiopia | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Finland | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Fiji | 0 1000 t | 2023 | — | volatile |
| 33 | Gabon | 0 1000 t | 2023 | — | volatile |
| 33 | Ghana | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Guinea | 0 1000 t | 2023 | — | volatile |
| 33 | Gambia | 0 1000 t | 2023 | — | flat |
| 33 | Guinea-Bissau | 0 1000 t | 2023 | — | flat |
| 33 | Greece | 0 1000 t | 2023 | — | volatile |
| 33 | Grenada | 0 1000 t | 2023 | — | volatile |
| 33 | Guyana | 0 1000 t | 2023 | — | flat |
| 33 | Honduras | 0 1000 t | 2023 | — | flat |
| 33 | Haiti | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Indonesia | 0 1000 t | 2023 | — | volatile |
| 33 | India | 0 1000 t | 2023 | — | flat |
| 33 | Ireland | 0 1000 t | 2023 | — | volatile |
| 33 | Iraq | 0 1000 t | 2023 | — | flat |
| 33 | Jamaica | 0 1000 t | 2023 | — | volatile |
| 33 | Jordan | 0 1000 t | 2023 | — | flat |
| 33 | Kazakhstan | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Kenya | 0 1000 t | 2023 | — | volatile |
| 33 | Kyrgyzstan | 0 1000 t | 2023 | — | volatile |
| 33 | Cambodia | 0 1000 t | 2023 | — | flat |
| 33 | Kiribati | 0 1000 t | 2023 | — | flat |
| 33 | Saint Kitts and Nevis | 0 1000 t | 2023 | — | flat |
| 33 | Lebanon | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Liberia | 0 1000 t | 2023 | — | volatile |
| 33 | Libya | 0 1000 t | 2023 | — | flat |
| 33 | Saint Lucia | 0 1000 t | 2023 | — | volatile |
| 33 | Sri Lanka | 0 1000 t | 2023 | — | flat |
| 33 | Lesotho | 0 1000 t | 2023 | — | flat |
| 33 | Lithuania | 0 1000 t | 2023 | — | volatile |
| 33 | Luxembourg | 0 1000 t | 2023 | — | volatile |
| 33 | Latvia | 0 1000 t | 2023 | — | volatile |
| 33 | Madagascar | 0 1000 t | 2023 | — | flat |
| 33 | Maldives | 0 1000 t | 2023 | — | volatile |
| 33 | Mexico | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Marshall Islands | 0 1000 t | 2023 | — | flat |
| 33 | Malta | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Montenegro | 0 1000 t | 2023 | — | volatile |
| 33 | Mongolia | 0 1000 t | 2023 | — | volatile |
| 33 | Mozambique | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Mauritania | 0 1000 t | 2023 | — | flat |
| 33 | Mauritius | 0 1000 t | 2023 | — | volatile |
| 33 | Malawi | 0 1000 t | 2023 | — | volatile |
| 33 | Malaysia | 0 1000 t | 2023 | — | flat |
| 33 | Namibia | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | New Caledonia | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Niger | 0 1000 t | 2023 | — | flat |
| 33 | Nigeria | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Nicaragua | 0 1000 t | 2023 | — | volatile |
| 33 | Norway | 0 1000 t | 2023 | — | volatile |
| 33 | Nepal | 0 1000 t | 2023 | — | flat |
| 33 | Nauru | 0 1000 t | 2023 | — | flat |
| 33 | Oman | 0 1000 t | 2023 | — | flat |
| 33 | Pakistan | 0 1000 t | 2023 | — | flat |
| 33 | Panama | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Philippines | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Papua New Guinea | 0 1000 t | 2023 | — | flat |
| 33 | Poland | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Paraguay | 0 1000 t | 2023 | — | volatile |
| 33 | French Polynesia | 0 1000 t | 2023 | — | volatile |
| 33 | Qatar | 0 1000 t | 2023 | — | volatile |
| 33 | Romania | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Rwanda | 0 1000 t | 2023 | — | flat |
| 33 | Saudi Arabia | 0 1000 t | 2023 | — | flat |
| 33 | Senegal | 0 1000 t | 2023 | — | flat |
| 33 | Solomon Islands | 0 1000 t | 2023 | — | flat |
| 33 | Sierra Leone | 0 1000 t | 2023 | — | flat |
| 33 | El Salvador | 0 1000 t | 2023 | — | flat |
| 33 | Serbia | 0 1000 t | 2023 | — | flat |
| 33 | Sao Tome and Principe | 0 1000 t | 2023 | — | flat |
| 33 | Suriname | 0 1000 t | 2023 | — | flat |
| 33 | Slovenia | 0 1000 t | 2023 | — | volatile |
| 33 | Eswatini | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Seychelles | 0 1000 t | 2023 | — | volatile |
| 33 | Thailand | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Tajikistan | 0 1000 t | 2023 | — | flat |
| 33 | Turkmenistan | 0 1000 t | 2023 | — | volatile |
| 33 | Tonga | 0 1000 t | 2023 | — | flat |
| 33 | Trinidad and Tobago | 0 1000 t | 2023 | — | flat |
| 33 | Tunisia | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Tuvalu | 0 1000 t | 2023 | — | flat |
| 33 | Uganda | 0 1000 t | 2023 | — | flat |
| 33 | Ukraine | 0 1000 t | 2023 | — | volatile |
| 33 | Uruguay | 0 1000 t | 2023 | — | volatile |
| 33 | Uzbekistan | 0 1000 t | 2023 | — | volatile |
| 33 | Saint Vincent and the Grenadines | 0 1000 t | 2023 | — | flat |
| 33 | Vanuatu | 0 1000 t | 2023 | — | flat |
| 33 | Samoa | 0 1000 t | 2023 | — | flat |
| 33 | Yemen | 0 1000 t | 2023 | — | flat |
| 33 | Zambia | 0 1000 t | 2023 | — | volatile |
| 33 | Zimbabwe | 0 1000 t | 2023 | — | flat |
| 33 | Micronesia | 0 1000 t | 2023 | — | flat |
| 33 | Timor-Leste | 0 1000 t | 2023 | — | flat |
| 33 | Cabo Verde | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Melanesia | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Polynesia | 0 1000 t | 2023 | — | volatile |
| 33 | Caribbean | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | Bolivia (Plurinational State of) | 0 1000 t | 2023 | — | volatile |
| 33 | Iran (Islamic Republic of) | 0 1000 t | 2023 | — | flat |
| 33 | Syrian Arab Republic | 0 1000 t | 2023 | — | flat |
| 33 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2023 | — | flat |
| 33 | Türkiye | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | China, mainland | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Côte d'Ivoire | 0 1000 t | 2023 | up 100.0% | volatile |
| 33 | Lao People's Democratic Republic | 0 1000 t | 2023 | — | flat |
| 33 | United Republic of Tanzania | 0 1000 t | 2023 | down 100.0% | volatile |
| 33 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | volatile |
| 33 | Micronesia (Federated States of) | 0 1000 t | 2023 | — | flat |
| 175 | Cyprus | -1 1000 t | 2023 | — | volatile |
| 175 | Slovakia | -1 1000 t | 2023 | up 83.3% | volatile |
| 175 | China, Hong Kong SAR | -1 1000 t | 2023 | down 200.0% | volatile |
| 178 | Republic of Moldova | -3 1000 t | 2023 | up 50.0% | volatile |
| 179 | North Macedonia | -23 1000 t | 2023 | down 560.0% | volatile |
| 180 | Spain | -650 1000 t | 2023 | down 158.3% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Western Europe 348 1000 t
- Americas 318 1000 t
- Northern America 306 1000 t
- United States of America 306 1000 t
- World 295 1000 t
- Eastern Europe 60 1000 t
- Oceania 46 1000 t
- Net Food Importing Developing Countries (NFIDCs) 15 1000 t
- Africa 14 1000 t
- Asia 13 1000 t
- Northern Africa 11 1000 t
- South America 10 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.