Tea (including mate) — 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
Tea (including mate) — Stock Variation is currently reported for 181 countries. The highest value is 86 1000 t in China, mainland; the lowest is -391 1000 t in Türkiye.
The median across all reporting countries is 0 1000 t, and the mean is -1.2 1000 t.
Over the past decade 22 countries rose and 24 fell. The largest increase was in Lao People's Democratic Republic (up 300.0%), and the largest decrease in Türkiye (down 2,200.0%).
Tea (including mate) — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | China, mainland | 86 1000 t | 2023 | up 213.2% | volatile |
| 2 | China | 78 1000 t | 2023 | up 206.8% | volatile |
| 3 | Kenya | 29 1000 t | 2023 | up 131.5% | volatile |
| 4 | Thailand | 26 1000 t | 2023 | — | volatile |
| 5 | Lao People's Democratic Republic | 4 1000 t | 2023 | up 300.0% | volatile |
| 6 | Philippines | 2 1000 t | 2023 | up 300.0% | volatile |
| 6 | Uzbekistan | 2 1000 t | 2023 | up 100.0% | volatile |
| 6 | Zimbabwe | 2 1000 t | 2023 | up 133.3% | volatile |
| 9 | Italy | 1 1000 t | 2023 | — | volatile |
| 9 | Mauritania | 1 1000 t | 2023 | unchanged | volatile |
| 9 | Nepal | 1 1000 t | 2023 | — | flat |
| 9 | Syrian Arab Republic | 1 1000 t | 2023 | down 87.5% | volatile |
| 13 | Afghanistan | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Angola | 0 1000 t | 2023 | — | volatile |
| 13 | Albania | 0 1000 t | 2023 | — | flat |
| 13 | Argentina | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Armenia | 0 1000 t | 2023 | — | flat |
| 13 | Antigua and Barbuda | 0 1000 t | 2023 | — | flat |
| 13 | Australia | 0 1000 t | 2023 | — | volatile |
| 13 | Austria | 0 1000 t | 2023 | — | volatile |
| 13 | Azerbaijan | 0 1000 t | 2023 | — | volatile |
| 13 | Belgium | 0 1000 t | 2023 | — | volatile |
| 13 | Burkina Faso | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Bangladesh | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Bulgaria | 0 1000 t | 2023 | — | volatile |
| 13 | Bahrain | 0 1000 t | 2023 | — | volatile |
| 13 | Bahamas | 0 1000 t | 2023 | — | flat |
| 13 | Bosnia and Herzegovina | 0 1000 t | 2023 | — | flat |
| 13 | Belize | 0 1000 t | 2023 | — | flat |
| 13 | Brazil | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Barbados | 0 1000 t | 2023 | — | flat |
| 13 | Bhutan | 0 1000 t | 2023 | — | flat |
| 13 | Botswana | 0 1000 t | 2023 | — | flat |
| 13 | Canada | 0 1000 t | 2023 | — | volatile |
| 13 | Switzerland | 0 1000 t | 2023 | — | flat |
| 13 | Chile | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Cameroon | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Congo | 0 1000 t | 2023 | — | flat |
| 13 | Colombia | 0 1000 t | 2023 | — | flat |
| 13 | Comoros | 0 1000 t | 2023 | — | flat |
| 13 | Costa Rica | 0 1000 t | 2023 | — | flat |
| 13 | Cuba | 0 1000 t | 2019 | — | flat |
| 13 | Cyprus | 0 1000 t | 2023 | — | flat |
| 13 | Czechia | 0 1000 t | 2023 | — | volatile |
| 13 | Germany | 0 1000 t | 2023 | — | volatile |
| 13 | Djibouti | 0 1000 t | 2023 | — | flat |
| 13 | Denmark | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Dominican Republic | 0 1000 t | 2023 | — | flat |
| 13 | Algeria | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Ecuador | 0 1000 t | 2023 | — | flat |
| 13 | Egypt | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Spain | 0 1000 t | 2023 | — | volatile |
| 13 | Estonia | 0 1000 t | 2023 | — | flat |
| 13 | Finland | 0 1000 t | 2023 | — | flat |
| 13 | Fiji | 0 1000 t | 2023 | — | flat |
| 13 | France | 0 1000 t | 2023 | — | flat |
| 13 | Gabon | 0 1000 t | 2023 | — | flat |
| 13 | Georgia | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Ghana | 0 1000 t | 2023 | — | volatile |
| 13 | Guinea | 0 1000 t | 2023 | — | flat |
| 13 | Gambia | 0 1000 t | 2023 | — | volatile |
| 13 | Guinea-Bissau | 0 1000 t | 2023 | — | flat |
| 13 | Greece | 0 1000 t | 2023 | — | volatile |
| 13 | Grenada | 0 1000 t | 2023 | — | flat |
| 13 | Guatemala | 0 1000 t | 2023 | — | flat |
| 13 | Guyana | 0 1000 t | 2023 | — | flat |
| 13 | Honduras | 0 1000 t | 2023 | — | flat |
| 13 | Croatia | 0 1000 t | 2023 | — | flat |
| 13 | Haiti | 0 1000 t | 2023 | — | flat |
| 13 | Hungary | 0 1000 t | 2023 | — | volatile |
| 13 | Indonesia | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | India | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Ireland | 0 1000 t | 2023 | — | volatile |
| 13 | Iraq | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Iceland | 0 1000 t | 2023 | — | flat |
| 13 | Israel | 0 1000 t | 2023 | — | flat |
| 13 | Jamaica | 0 1000 t | 2023 | — | flat |
| 13 | Jordan | 0 1000 t | 2023 | — | flat |
| 13 | Kazakhstan | 0 1000 t | 2023 | down 100.0% | flat |
| 13 | Cambodia | 0 1000 t | 2023 | — | flat |
| 13 | Kiribati | 0 1000 t | 2023 | — | flat |
| 13 | Saint Kitts and Nevis | 0 1000 t | 2023 | — | flat |
| 13 | Kuwait | 0 1000 t | 2023 | — | volatile |
| 13 | Lebanon | 0 1000 t | 2023 | down 100.0% | flat |
| 13 | Liberia | 0 1000 t | 2023 | — | flat |
| 13 | Libya | 0 1000 t | 2023 | — | volatile |
| 13 | Saint Lucia | 0 1000 t | 2023 | — | flat |
| 13 | Sri Lanka | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Lesotho | 0 1000 t | 2023 | — | flat |
| 13 | Lithuania | 0 1000 t | 2023 | — | flat |
| 13 | Luxembourg | 0 1000 t | 2023 | — | flat |
| 13 | Latvia | 0 1000 t | 2023 | — | flat |
| 13 | Morocco | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Madagascar | 0 1000 t | 2023 | — | flat |
| 13 | Maldives | 0 1000 t | 2023 | — | flat |
| 13 | Mexico | 0 1000 t | 2023 | — | volatile |
| 13 | Marshall Islands | 0 1000 t | 2023 | — | flat |
| 13 | North Macedonia | 0 1000 t | 2023 | — | flat |
| 13 | Malta | 0 1000 t | 2023 | — | flat |
| 13 | Myanmar | 0 1000 t | 2023 | — | volatile |
| 13 | Montenegro | 0 1000 t | 2023 | — | flat |
| 13 | Mongolia | 0 1000 t | 2023 | down 100.0% | flat |
| 13 | Mozambique | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Mauritius | 0 1000 t | 2023 | — | flat |
| 13 | Malawi | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Malaysia | 0 1000 t | 2023 | — | flat |
| 13 | Namibia | 0 1000 t | 2023 | — | volatile |
| 13 | New Caledonia | 0 1000 t | 2023 | — | flat |
| 13 | Niger | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Nigeria | 0 1000 t | 2023 | — | volatile |
| 13 | Nicaragua | 0 1000 t | 2023 | — | flat |
| 13 | Norway | 0 1000 t | 2023 | — | flat |
| 13 | Nauru | 0 1000 t | 2023 | — | flat |
| 13 | New Zealand | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Oman | 0 1000 t | 2023 | — | flat |
| 13 | Panama | 0 1000 t | 2023 | — | flat |
| 13 | Peru | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Papua New Guinea | 0 1000 t | 2023 | — | volatile |
| 13 | Poland | 0 1000 t | 2023 | up 100.0% | flat |
| 13 | Portugal | 0 1000 t | 2023 | — | volatile |
| 13 | Paraguay | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | French Polynesia | 0 1000 t | 2023 | — | flat |
| 13 | Qatar | 0 1000 t | 2023 | — | flat |
| 13 | Romania | 0 1000 t | 2023 | — | flat |
| 13 | Rwanda | 0 1000 t | 2023 | — | volatile |
| 13 | Saudi Arabia | 0 1000 t | 2023 | — | flat |
| 13 | Solomon Islands | 0 1000 t | 2023 | — | flat |
| 13 | Sierra Leone | 0 1000 t | 2023 | — | flat |
| 13 | El Salvador | 0 1000 t | 2023 | — | flat |
| 13 | Serbia | 0 1000 t | 2023 | — | flat |
| 13 | Sao Tome and Principe | 0 1000 t | 2023 | — | flat |
| 13 | Suriname | 0 1000 t | 2023 | — | flat |
| 13 | Slovakia | 0 1000 t | 2023 | — | flat |
| 13 | Slovenia | 0 1000 t | 2023 | — | flat |
| 13 | Sweden | 0 1000 t | 2023 | — | flat |
| 13 | Eswatini | 0 1000 t | 2023 | — | volatile |
| 13 | Seychelles | 0 1000 t | 2023 | — | flat |
| 13 | Tajikistan | 0 1000 t | 2023 | — | volatile |
| 13 | Turkmenistan | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Tonga | 0 1000 t | 2023 | — | flat |
| 13 | Trinidad and Tobago | 0 1000 t | 2023 | — | flat |
| 13 | Tunisia | 0 1000 t | 2023 | — | flat |
| 13 | Tuvalu | 0 1000 t | 2023 | — | flat |
| 13 | Uganda | 0 1000 t | 2023 | — | flat |
| 13 | Ukraine | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Uruguay | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Saint Vincent and the Grenadines | 0 1000 t | 2023 | — | flat |
| 13 | Vanuatu | 0 1000 t | 2023 | — | flat |
| 13 | Samoa | 0 1000 t | 2023 | — | flat |
| 13 | Yemen | 0 1000 t | 2023 | — | volatile |
| 13 | Zambia | 0 1000 t | 2023 | — | flat |
| 13 | Micronesia | 0 1000 t | 2023 | — | flat |
| 13 | Timor-Leste | 0 1000 t | 2023 | — | flat |
| 13 | Cabo Verde | 0 1000 t | 2023 | — | flat |
| 13 | Melanesia | 0 1000 t | 2023 | — | volatile |
| 13 | Polynesia | 0 1000 t | 2023 | — | flat |
| 13 | Democratic Republic of the Congo | 0 1000 t | 2023 | — | volatile |
| 13 | Caribbean | 0 1000 t | 2023 | — | flat |
| 13 | Bolivia (Plurinational State of) | 0 1000 t | 2023 | — | flat |
| 13 | Iran (Islamic Republic of) | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Republic of Moldova | 0 1000 t | 2023 | — | flat |
| 13 | Russian Federation | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2023 | — | flat |
| 13 | Viet Nam | 0 1000 t | 2023 | up 100.0% | volatile |
| 13 | Australia and New Zealand | 0 1000 t | 2023 | — | volatile |
| 13 | Côte d'Ivoire | 0 1000 t | 2023 | — | volatile |
| 13 | China, Taiwan Province of | 0 1000 t | 2023 | down 100.0% | volatile |
| 13 | Netherlands (Kingdom of the) | 0 1000 t | 2023 | — | volatile |
| 13 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | volatile |
| 13 | Micronesia (Federated States of) | 0 1000 t | 2023 | — | flat |
| 13 | China, Macao SAR | 0 1000 t | 2023 | — | flat |
| 172 | Ethiopia | -1 1000 t | 2023 | down 200.0% | volatile |
| 172 | Republic of Korea | -1 1000 t | 2023 | unchanged | volatile |
| 174 | Kyrgyzstan | -2 1000 t | 2023 | — | flat |
| 174 | Pakistan | -2 1000 t | 2023 | up 60.0% | volatile |
| 174 | Senegal | -2 1000 t | 2023 | — | flat |
| 177 | Belarus | -4 1000 t | 2023 | — | volatile |
| 178 | China, Hong Kong SAR | -8 1000 t | 2023 | — | volatile |
| 179 | United Republic of Tanzania | -9 1000 t | 2023 | down 1,000.0% | volatile |
| 180 | United Arab Emirates | -31 1000 t | 2023 | down 542.9% | volatile |
| 181 | Türkiye | -391 1000 t | 2023 | down 2,200.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Eastern Asia 78 1000 t
- South-eastern Asia 32 1000 t
- Low Income Food Deficit Countries (LIFDCs) 23 1000 t
- Net Food Importing Developing Countries (NFIDCs) 23 1000 t
- Eastern Africa 22 1000 t
- Africa 21 1000 t
- Land Locked Developing Countries (LLDCs) 6 1000 t
- Middle Africa 2 1000 t
- European Union (27) 1 1000 t
- Northern Europe 1 1000 t
- Southern Europe 1 1000 t
- Oceania 0 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.