Aquatic Animals, Others — Export quantity 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
Aquatic Animals, Others — Export quantity is currently reported for 114 countries. The highest value is 90 1000 t in Malaysia; the lowest is 0 1000 t in China, Macao SAR.
The median across all reporting countries is 0 1000 t, and the mean is 3.34 1000 t.
Over the past decade 8 countries rose and 10 fell. The largest increase was in Republic of Korea (up 1,400.0%), and the largest decrease in France (down 100.0%).
Aquatic Animals, Others — Export quantity: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Malaysia | 90 1000 t | 2023 | up 1,025.0% | volatile |
| 2 | China | 64 1000 t | 2023 | down 59.2% | falling |
| 3 | China, mainland | 57 1000 t | 2023 | down 56.5% | falling |
| 4 | Thailand | 35 1000 t | 2023 | up 169.2% | rising |
| 5 | Mexico | 29 1000 t | 2023 | up 625.0% | volatile |
| 6 | Indonesia | 21 1000 t | 2023 | down 53.3% | falling |
| 7 | Republic of Korea | 15 1000 t | 2023 | up 1,400.0% | volatile |
| 8 | Russian Federation | 13 1000 t | 2023 | up 8.3% | rising |
| 9 | Canada | 9 1000 t | 2023 | unchanged | rising |
| 10 | Ecuador | 6 1000 t | 2023 | — | volatile |
| 10 | China, Hong Kong SAR | 6 1000 t | 2023 | down 76.0% | falling |
| 12 | India | 5 1000 t | 2023 | down 68.8% | volatile |
| 12 | Iceland | 5 1000 t | 2023 | up 400.0% | volatile |
| 14 | Chile | 4 1000 t | 2023 | down 42.9% | falling |
| 15 | Viet Nam | 3 1000 t | 2023 | up 200.0% | volatile |
| 16 | Honduras | 2 1000 t | 2023 | up 100.0% | volatile |
| 16 | Nicaragua | 2 1000 t | 2023 | unchanged | volatile |
| 16 | Melanesia | 2 1000 t | 2023 | — | volatile |
| 19 | Australia | 1 1000 t | 2023 | — | volatile |
| 19 | Spain | 1 1000 t | 2023 | down 75.0% | volatile |
| 19 | Greece | 1 1000 t | 2023 | — | volatile |
| 19 | Italy | 1 1000 t | 2023 | unchanged | volatile |
| 19 | Sri Lanka | 1 1000 t | 2023 | unchanged | rising |
| 19 | Pakistan | 1 1000 t | 2023 | — | volatile |
| 19 | Panama | 1 1000 t | 2023 | — | volatile |
| 19 | Philippines | 1 1000 t | 2023 | — | volatile |
| 19 | Papua New Guinea | 1 1000 t | 2023 | — | volatile |
| 19 | Saudi Arabia | 1 1000 t | 2023 | — | volatile |
| 19 | Türkiye | 1 1000 t | 2023 | — | volatile |
| 19 | Australia and New Zealand | 1 1000 t | 2023 | — | volatile |
| 19 | China, Taiwan Province of | 1 1000 t | 2023 | unchanged | volatile |
| 32 | Albania | 0 1000 t | 2023 | — | flat |
| 32 | United Arab Emirates | 0 1000 t | 2023 | — | flat |
| 32 | Austria | 0 1000 t | 2023 | — | flat |
| 32 | Belgium | 0 1000 t | 2023 | — | volatile |
| 32 | Bulgaria | 0 1000 t | 2023 | — | flat |
| 32 | Bahrain | 0 1000 t | 2023 | — | flat |
| 32 | Bahamas | 0 1000 t | 2023 | — | flat |
| 32 | Belarus | 0 1000 t | 2023 | — | flat |
| 32 | Belize | 0 1000 t | 2023 | — | flat |
| 32 | Brazil | 0 1000 t | 2023 | — | flat |
| 32 | Botswana | 0 1000 t | 2023 | — | flat |
| 32 | Switzerland | 0 1000 t | 2020 | — | flat |
| 32 | Costa Rica | 0 1000 t | 2023 | — | flat |
| 32 | Cuba | 0 1000 t | 2019 | — | flat |
| 32 | Czechia | 0 1000 t | 2023 | — | flat |
| 32 | Germany | 0 1000 t | 2023 | — | volatile |
| 32 | Denmark | 0 1000 t | 2023 | — | flat |
| 32 | Dominican Republic | 0 1000 t | 2023 | — | flat |
| 32 | Algeria | 0 1000 t | 2023 | — | flat |
| 32 | Estonia | 0 1000 t | 2023 | — | flat |
| 32 | Finland | 0 1000 t | 2023 | — | flat |
| 32 | Fiji | 0 1000 t | 2023 | — | volatile |
| 32 | France | 0 1000 t | 2023 | down 100.0% | volatile |
| 32 | Gabon | 0 1000 t | 2023 | — | flat |
| 32 | Georgia | 0 1000 t | 2017 | — | flat |
| 32 | Guyana | 0 1000 t | 2023 | — | flat |
| 32 | Croatia | 0 1000 t | 2023 | — | flat |
| 32 | Haiti | 0 1000 t | 2023 | — | volatile |
| 32 | Hungary | 0 1000 t | 2023 | — | flat |
| 32 | Ireland | 0 1000 t | 2023 | — | volatile |
| 32 | Jamaica | 0 1000 t | 2023 | — | flat |
| 32 | Kenya | 0 1000 t | 2023 | — | flat |
| 32 | Kiribati | 0 1000 t | 2023 | — | flat |
| 32 | Kuwait | 0 1000 t | 2023 | — | flat |
| 32 | Lithuania | 0 1000 t | 2023 | — | flat |
| 32 | Luxembourg | 0 1000 t | 2023 | — | flat |
| 32 | Latvia | 0 1000 t | 2023 | — | flat |
| 32 | Madagascar | 0 1000 t | 2023 | — | volatile |
| 32 | Maldives | 0 1000 t | 2023 | down 100.0% | volatile |
| 32 | Malta | 0 1000 t | 2017 | — | flat |
| 32 | Myanmar | 0 1000 t | 2023 | — | volatile |
| 32 | Mongolia | 0 1000 t | 2023 | — | flat |
| 32 | Mozambique | 0 1000 t | 2023 | — | volatile |
| 32 | Mauritania | 0 1000 t | 2018 | — | flat |
| 32 | Mauritius | 0 1000 t | 2023 | — | flat |
| 32 | Namibia | 0 1000 t | 2023 | — | flat |
| 32 | New Caledonia | 0 1000 t | 2023 | — | flat |
| 32 | Norway | 0 1000 t | 2023 | — | flat |
| 32 | New Zealand | 0 1000 t | 2023 | — | flat |
| 32 | Oman | 0 1000 t | 2023 | — | flat |
| 32 | Peru | 0 1000 t | 2023 | down 100.0% | volatile |
| 32 | Poland | 0 1000 t | 2023 | — | flat |
| 32 | Portugal | 0 1000 t | 2023 | — | volatile |
| 32 | French Polynesia | 0 1000 t | 2023 | — | flat |
| 32 | Qatar | 0 1000 t | 2023 | — | flat |
| 32 | Romania | 0 1000 t | 2023 | — | flat |
| 32 | Senegal | 0 1000 t | 2023 | — | flat |
| 32 | Solomon Islands | 0 1000 t | 2023 | — | flat |
| 32 | El Salvador | 0 1000 t | 2023 | — | flat |
| 32 | Serbia | 0 1000 t | 2018 | — | flat |
| 32 | Slovakia | 0 1000 t | 2023 | — | flat |
| 32 | Slovenia | 0 1000 t | 2023 | — | flat |
| 32 | Sweden | 0 1000 t | 2023 | — | flat |
| 32 | Seychelles | 0 1000 t | 2023 | — | flat |
| 32 | Tonga | 0 1000 t | 2023 | — | flat |
| 32 | Trinidad and Tobago | 0 1000 t | 2023 | — | flat |
| 32 | Tunisia | 0 1000 t | 2023 | — | flat |
| 32 | Ukraine | 0 1000 t | 2023 | — | flat |
| 32 | Uruguay | 0 1000 t | 2017 | — | flat |
| 32 | Vanuatu | 0 1000 t | 2023 | — | flat |
| 32 | Yemen | 0 1000 t | 2023 | — | flat |
| 32 | Micronesia | 0 1000 t | 2023 | — | flat |
| 32 | Cabo Verde | 0 1000 t | 2023 | — | flat |
| 32 | Polynesia | 0 1000 t | 2023 | — | flat |
| 32 | Caribbean | 0 1000 t | 2023 | — | volatile |
| 32 | Iran (Islamic Republic of) | 0 1000 t | 2023 | — | volatile |
| 32 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2020 | — | flat |
| 32 | United Republic of Tanzania | 0 1000 t | 2023 | — | flat |
| 32 | Netherlands (Kingdom of the) | 0 1000 t | 2023 | — | volatile |
| 32 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | volatile |
| 32 | Democratic People's Republic of Korea | 0 1000 t | 2018 | — | flat |
| 32 | Micronesia (Federated States of) | 0 1000 t | 2023 | — | flat |
| 32 | China, Macao SAR | 0 1000 t | 2023 | — | flat |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- World 338 1000 t
- Asia 257 1000 t
- South-eastern Asia 149 1000 t
- Eastern Asia 100 1000 t
- Americas 54 1000 t
- Central America 34 1000 t
- Europe 22 1000 t
- Eastern Europe 14 1000 t
- Northern America 11 1000 t
- South America 9 1000 t
- Southern Asia 8 1000 t
- Net Food Importing Developing Countries (NFIDCs) 6 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.