Fish, Seafood — Other uses (non-food) 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
Fish, Seafood — Other uses (non-food) is currently reported for 148 countries. The highest value is 1,000 1000 t in China (People’s Republic of); the lowest is 0 1000 t in China, Macao SAR.
The median across all reporting countries is 0 1000 t, and the mean is 25.58 1000 t.
Over the past decade 16 countries rose and 21 fell. The largest increase was in Panama (up 771.4%), and the largest decrease in Armenia (down 100.0%).
Fish, Seafood — Other uses: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | China (People’s Republic of) | 1,000 1000 t | 2023 | up 122.2% | rising |
| 1 | China, mainland | 1,000 1000 t | 2023 | up 122.2% | rising |
| 3 | India | 220 1000 t | 2023 | down 39.9% | falling |
| 4 | Chile | 212 1000 t | 2023 | up 583.9% | volatile |
| 5 | Belize | 205 1000 t | 2023 | up 606.9% | volatile |
| 6 | Republic of Korea | 180 1000 t | 2023 | up 500.0% | volatile |
| 7 | Guinea | 170 1000 t | 2023 | up 129.7% | volatile |
| 8 | Georgia | 142 1000 t | 2023 | — | volatile |
| 9 | Nauru | 120 1000 t | 2023 | — | rising |
| 10 | Kiribati | 64 1000 t | 2023 | up 700.0% | volatile |
| 11 | Panama | 61 1000 t | 2023 | up 771.4% | volatile |
| 12 | Indonesia | 59 1000 t | 2023 | down 57.9% | volatile |
| 13 | Peru | 55 1000 t | 2023 | — | volatile |
| 14 | Marshall Islands | 49 1000 t | 2023 | — | rising |
| 15 | Guinea-Bissau | 38 1000 t | 2023 | up 660.0% | volatile |
| 16 | Malaysia | 27 1000 t | 2023 | down 22.9% | falling |
| 17 | Oman | 26 1000 t | 2023 | up 100.0% | volatile |
| 18 | Maldives | 19 1000 t | 2023 | up 26.7% | rising |
| 18 | Tuvalu | 19 1000 t | 2023 | — | volatile |
| 20 | United Arab Emirates | 15 1000 t | 2023 | down 77.3% | volatile |
| 21 | Suriname | 14 1000 t | 2023 | up 7.7% | rising |
| 22 | Iceland | 12 1000 t | 2023 | unchanged | volatile |
| 23 | Liberia | 10 1000 t | 2023 | — | volatile |
| 23 | Solomon Islands | 10 1000 t | 2023 | — | volatile |
| 25 | Comoros | 9 1000 t | 2023 | down 73.5% | volatile |
| 26 | Denmark | 7 1000 t | 2023 | down 58.8% | volatile |
| 26 | Malta | 7 1000 t | 2023 | up 16.7% | rising |
| 28 | Samoa | 6 1000 t | 2023 | up 50.0% | rising |
| 29 | Nicaragua | 5 1000 t | 2023 | — | volatile |
| 30 | French Polynesia | 3 1000 t | 2023 | down 25.0% | falling |
| 30 | Romania | 3 1000 t | 2023 | up 50.0% | rising |
| 32 | Antigua and Barbuda | 2 1000 t | 2023 | down 33.3% | falling |
| 32 | Bahamas | 2 1000 t | 2023 | down 60.0% | falling |
| 32 | Barbados | 2 1000 t | 2023 | up 100.0% | rising |
| 32 | Greece | 2 1000 t | 2023 | — | volatile |
| 32 | New Caledonia | 2 1000 t | 2023 | — | volatile |
| 32 | Uruguay | 2 1000 t | 2023 | — | volatile |
| 32 | Vanuatu | 2 1000 t | 2023 | down 92.0% | volatile |
| 39 | Bulgaria | 1 1000 t | 2023 | — | volatile |
| 39 | Brazil | 1 1000 t | 2023 | down 66.7% | volatile |
| 39 | Spain | 1 1000 t | 2023 | unchanged | volatile |
| 39 | Mauritius | 1 1000 t | 2023 | — | volatile |
| 39 | United Kingdom of Great Britain and Northern Ireland | 1 1000 t | 2023 | down 50.0% | falling |
| 44 | Afghanistan | 0 1000 t | 2023 | — | flat |
| 44 | Angola | 0 1000 t | 2023 | — | flat |
| 44 | Albania | 0 1000 t | 2023 | — | flat |
| 44 | Argentina | 0 1000 t | 2023 | — | flat |
| 44 | Armenia | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Australia | 0 1000 t | 2023 | — | flat |
| 44 | Austria | 0 1000 t | 2023 | — | flat |
| 44 | Azerbaijan | 0 1000 t | 2023 | — | flat |
| 44 | Belgium | 0 1000 t | 2023 | — | volatile |
| 44 | Bangladesh | 0 1000 t | 2023 | — | volatile |
| 44 | Bahrain | 0 1000 t | 2023 | — | flat |
| 44 | Bosnia and Herzegovina | 0 1000 t | 2018 | — | flat |
| 44 | Belarus | 0 1000 t | 2023 | — | flat |
| 44 | Botswana | 0 1000 t | 2023 | — | flat |
| 44 | Canada | 0 1000 t | 2023 | — | flat |
| 44 | Switzerland | 0 1000 t | 2023 | — | flat |
| 44 | Cameroon | 0 1000 t | 2023 | — | flat |
| 44 | Colombia | 0 1000 t | 2017 | — | flat |
| 44 | Costa Rica | 0 1000 t | 2023 | — | flat |
| 44 | Cyprus | 0 1000 t | 2023 | — | volatile |
| 44 | Czechia | 0 1000 t | 2023 | — | flat |
| 44 | Germany | 0 1000 t | 2023 | — | volatile |
| 44 | Dominican Republic | 0 1000 t | 2023 | — | flat |
| 44 | Algeria | 0 1000 t | 2023 | — | flat |
| 44 | Ecuador | 0 1000 t | 2023 | — | volatile |
| 44 | Egypt | 0 1000 t | 2020 | — | flat |
| 44 | Estonia | 0 1000 t | 2023 | — | flat |
| 44 | Finland | 0 1000 t | 2023 | — | flat |
| 44 | Fiji | 0 1000 t | 2019 | — | volatile |
| 44 | France | 0 1000 t | 2023 | — | volatile |
| 44 | Gabon | 0 1000 t | 2023 | — | flat |
| 44 | Ghana | 0 1000 t | 2023 | — | flat |
| 44 | Grenada | 0 1000 t | 2023 | — | flat |
| 44 | Guatemala | 0 1000 t | 2023 | — | volatile |
| 44 | Guyana | 0 1000 t | 2023 | — | flat |
| 44 | Honduras | 0 1000 t | 2023 | — | flat |
| 44 | Croatia | 0 1000 t | 2023 | — | flat |
| 44 | Hungary | 0 1000 t | 2023 | — | flat |
| 44 | Ireland | 0 1000 t | 2023 | — | flat |
| 44 | Iraq | 0 1000 t | 2023 | — | flat |
| 44 | Israel | 0 1000 t | 2023 | — | volatile |
| 44 | Italy | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Jamaica | 0 1000 t | 2023 | — | flat |
| 44 | Jordan | 0 1000 t | 2023 | — | flat |
| 44 | Kazakhstan | 0 1000 t | 2023 | — | flat |
| 44 | Kenya | 0 1000 t | 2023 | — | flat |
| 44 | Kyrgyzstan | 0 1000 t | 2023 | — | flat |
| 44 | Cambodia | 0 1000 t | 2023 | — | flat |
| 44 | Saint Kitts and Nevis | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Kuwait | 0 1000 t | 2023 | — | flat |
| 44 | Lebanon | 0 1000 t | 2023 | — | flat |
| 44 | Libya | 0 1000 t | 2020 | — | flat |
| 44 | Saint Lucia | 0 1000 t | 2023 | — | flat |
| 44 | Sri Lanka | 0 1000 t | 2023 | — | flat |
| 44 | Lesotho | 0 1000 t | 2023 | — | flat |
| 44 | Lithuania | 0 1000 t | 2023 | — | flat |
| 44 | Luxembourg | 0 1000 t | 2023 | — | flat |
| 44 | Latvia | 0 1000 t | 2023 | — | flat |
| 44 | Morocco | 0 1000 t | 2023 | — | flat |
| 44 | Madagascar | 0 1000 t | 2023 | — | flat |
| 44 | Mexico | 0 1000 t | 2023 | — | volatile |
| 44 | North Macedonia | 0 1000 t | 2023 | — | flat |
| 44 | Myanmar | 0 1000 t | 2023 | — | flat |
| 44 | Mongolia | 0 1000 t | 2023 | — | flat |
| 44 | Mauritania | 0 1000 t | 2023 | — | flat |
| 44 | Namibia | 0 1000 t | 2023 | — | volatile |
| 44 | Nigeria | 0 1000 t | 2023 | — | flat |
| 44 | Norway | 0 1000 t | 2023 | — | flat |
| 44 | Nepal | 0 1000 t | 2023 | — | volatile |
| 44 | New Zealand | 0 1000 t | 2023 | — | flat |
| 44 | Pakistan | 0 1000 t | 2023 | — | flat |
| 44 | Philippines | 0 1000 t | 2023 | — | flat |
| 44 | Papua New Guinea | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Poland | 0 1000 t | 2023 | — | flat |
| 44 | Portugal | 0 1000 t | 2023 | — | flat |
| 44 | Qatar | 0 1000 t | 2023 | — | flat |
| 44 | Rwanda | 0 1000 t | 2023 | — | flat |
| 44 | Saudi Arabia | 0 1000 t | 2023 | — | flat |
| 44 | Senegal | 0 1000 t | 2023 | — | flat |
| 44 | El Salvador | 0 1000 t | 2023 | — | flat |
| 44 | Serbia | 0 1000 t | 2023 | — | flat |
| 44 | Sao Tome and Principe | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Slovak Republic | 0 1000 t | 2023 | — | volatile |
| 44 | Slovenia | 0 1000 t | 2023 | — | flat |
| 44 | Sweden | 0 1000 t | 2023 | — | flat |
| 44 | Eswatini | 0 1000 t | 2023 | — | flat |
| 44 | Seychelles | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Thailand | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Tonga | 0 1000 t | 2023 | — | flat |
| 44 | Trinidad and Tobago | 0 1000 t | 2023 | — | flat |
| 44 | Tunisia | 0 1000 t | 2023 | — | flat |
| 44 | Uganda | 0 1000 t | 2023 | — | flat |
| 44 | Ukraine | 0 1000 t | 2023 | — | flat |
| 44 | Uzbekistan | 0 1000 t | 2023 | — | flat |
| 44 | Saint Vincent and the Grenadines | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | Yemen | 0 1000 t | 2019 | — | volatile |
| 44 | South Africa | 0 1000 t | 2023 | — | flat |
| 44 | Zambia | 0 1000 t | 2023 | — | flat |
| 44 | Zimbabwe | 0 1000 t | 2019 | — | flat |
| 44 | China, Hong Kong SAR | 0 1000 t | 2023 | — | flat |
| 44 | Russian Federation | 0 1000 t | 2023 | — | flat |
| 44 | Australia and New Zealand | 0 1000 t | 2023 | — | flat |
| 44 | China, Taiwan Province of | 0 1000 t | 2023 | — | flat |
| 44 | Netherlands (Kingdom of the) | 0 1000 t | 2023 | down 100.0% | volatile |
| 44 | 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 2,891 1000 t
- Americas 569 1000 t
- Net Food Importing Developing Countries (NFIDCs) 406 1000 t
- Low Income Food Deficit Countries (LIFDCs) 277 1000 t
- European Union (27) 22 1000 t
- Northern Europe 20 1000 t
- Northern America 7 1000 t
- United States of America 7 1000 t
- Western Europe 1 1000 t
- Land Locked Developing Countries (LLDCs) 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.