Fruits, other — 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
Fruits, other — Stock Variation is currently reported for 159 countries. The highest value is 564 1000 t in India; the lowest is -36 1000 t in Afghanistan.
The median across all reporting countries is 0 1000 t, and the mean is 5.55 1000 t.
The gap between the highest and lowest reporting country is a factor of about 16.
Over the past decade 44 countries rose and 53 fell. The largest increase was in China (up 2,050.0%), and the largest decrease in Afghanistan (down 800.0%).
Fruits, other — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | India | 564 1000 t | 2023 | up 198.4% | volatile |
| 2 | United States | 74 1000 t | 2023 | up 208.8% | volatile |
| 3 | China | 43 1000 t | 2023 | up 2,050.0% | volatile |
| 3 | Egypt | 43 1000 t | 2023 | up 1,333.3% | volatile |
| 5 | China, mainland | 37 1000 t | 2023 | up 145.7% | volatile |
| 6 | Thailand | 32 1000 t | 2023 | down 65.2% | volatile |
| 7 | Ecuador | 26 1000 t | 2023 | up 196.3% | volatile |
| 8 | Turkey | 25 1000 t | 2023 | up 457.1% | volatile |
| 8 | Türkiye | 25 1000 t | 2023 | up 457.1% | volatile |
| 10 | Saudi Arabia | 19 1000 t | 2023 | down 42.4% | falling |
| 11 | Germany | 17 1000 t | 2023 | — | volatile |
| 12 | Austria | 16 1000 t | 2023 | up 77.8% | volatile |
| 13 | Indonesia | 15 1000 t | 2023 | up 15.4% | volatile |
| 14 | Czechia | 13 1000 t | 2023 | up 1,400.0% | volatile |
| 15 | Armenia | 11 1000 t | 2023 | — | volatile |
| 16 | Sweden | 9 1000 t | 2023 | — | volatile |
| 17 | Algeria | 8 1000 t | 2023 | up 100.0% | volatile |
| 17 | Senegal | 8 1000 t | 2023 | up 700.0% | volatile |
| 17 | Slovenia | 8 1000 t | 2023 | — | volatile |
| 20 | Brazil | 7 1000 t | 2023 | up 600.0% | volatile |
| 21 | Kyrgyzstan | 4 1000 t | 2023 | — | volatile |
| 21 | Portugal | 4 1000 t | 2023 | up 100.0% | volatile |
| 21 | China, Taiwan Province of | 4 1000 t | 2023 | down 93.9% | volatile |
| 24 | Norway | 3 1000 t | 2023 | up 200.0% | volatile |
| 24 | Vietnam | 3 1000 t | 2023 | down 93.5% | volatile |
| 24 | Viet Nam | 3 1000 t | 2023 | down 93.5% | volatile |
| 27 | Colombia | 2 1000 t | 2023 | up 100.0% | volatile |
| 27 | Denmark | 2 1000 t | 2023 | up 100.0% | volatile |
| 27 | United Kingdom | 2 1000 t | 2023 | down 88.9% | volatile |
| 27 | Croatia | 2 1000 t | 2023 | — | volatile |
| 27 | Kazakhstan | 2 1000 t | 2023 | down 90.0% | volatile |
| 27 | Morocco | 2 1000 t | 2023 | down 33.3% | volatile |
| 27 | Oman | 2 1000 t | 2023 | up 100.0% | volatile |
| 27 | Pakistan | 2 1000 t | 2023 | down 85.7% | volatile |
| 27 | Syria | 2 1000 t | 2023 | — | volatile |
| 27 | Syrian Arab Republic | 2 1000 t | 2023 | — | volatile |
| 27 | Netherlands (Kingdom of the) | 2 1000 t | 2023 | down 95.3% | volatile |
| 27 | United Kingdom of Great Britain and Northern Ireland | 2 1000 t | 2023 | down 88.9% | volatile |
| 39 | Argentina | 1 1000 t | 2023 | up 111.1% | volatile |
| 39 | Belgium | 1 1000 t | 2023 | — | volatile |
| 39 | Burkina Faso | 1 1000 t | 2023 | — | volatile |
| 39 | Estonia | 1 1000 t | 2023 | — | volatile |
| 39 | Honduras | 1 1000 t | 2023 | up 200.0% | flat |
| 39 | Iraq | 1 1000 t | 2023 | up 133.3% | volatile |
| 39 | Israel | 1 1000 t | 2023 | up 125.0% | volatile |
| 39 | Jamaica | 1 1000 t | 2023 | up 200.0% | volatile |
| 39 | Kenya | 1 1000 t | 2023 | up 150.0% | volatile |
| 39 | Kuwait | 1 1000 t | 2023 | down 66.7% | volatile |
| 39 | Libya | 1 1000 t | 2023 | down 92.3% | volatile |
| 39 | Latvia | 1 1000 t | 2023 | — | volatile |
| 39 | Malawi | 1 1000 t | 2023 | — | volatile |
| 39 | Papua New Guinea | 1 1000 t | 2023 | — | volatile |
| 39 | Romania | 1 1000 t | 2023 | up 103.7% | volatile |
| 39 | Ukraine | 1 1000 t | 2023 | unchanged | volatile |
| 39 | South Africa | 1 1000 t | 2023 | down 91.7% | volatile |
| 39 | Melanesia | 1 1000 t | 2023 | down 50.0% | volatile |
| 39 | Caribbean | 1 1000 t | 2023 | down 66.7% | volatile |
| 39 | China, Hong Kong SAR | 1 1000 t | 2023 | down 93.8% | volatile |
| 59 | Angola | 0 1000 t | 2023 | — | flat |
| 59 | Antigua and Barbuda | 0 1000 t | 2023 | — | volatile |
| 59 | Australia | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Azerbaijan | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Bulgaria | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Bahamas | 0 1000 t | 2023 | down 100.0% | flat |
| 59 | Bosnia and Herzegovina | 0 1000 t | 2023 | — | volatile |
| 59 | Belarus | 0 1000 t | 2023 | — | volatile |
| 59 | Barbados | 0 1000 t | 2023 | — | volatile |
| 59 | Bhutan | 0 1000 t | 2023 | — | flat |
| 59 | Botswana | 0 1000 t | 2023 | down 100.0% | flat |
| 59 | Canada | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Switzerland | 0 1000 t | 2023 | — | volatile |
| 59 | Chile | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Cote d'Ivoire | 0 1000 t | 2023 | — | volatile |
| 59 | Costa Rica | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Cyprus | 0 1000 t | 2023 | — | volatile |
| 59 | Dominican Republic | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Finland | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Fiji | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | France | 0 1000 t | 2023 | — | volatile |
| 59 | Grenada | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Guyana | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Haiti | 0 1000 t | 2023 | — | volatile |
| 59 | Hungary | 0 1000 t | 2023 | — | volatile |
| 59 | Ireland | 0 1000 t | 2023 | — | volatile |
| 59 | Iceland | 0 1000 t | 2023 | — | volatile |
| 59 | Cambodia | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Saint Kitts and Nevis | 0 1000 t | 2023 | — | volatile |
| 59 | Laos | 0 1000 t | 2023 | — | flat |
| 59 | Lebanon | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Saint Lucia | 0 1000 t | 2023 | — | volatile |
| 59 | Sri Lanka | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Lithuania | 0 1000 t | 2023 | — | volatile |
| 59 | Luxembourg | 0 1000 t | 2023 | — | volatile |
| 59 | Moldova | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Maldives | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | North Macedonia | 0 1000 t | 2023 | — | volatile |
| 59 | Myanmar | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Montenegro | 0 1000 t | 2023 | — | volatile |
| 59 | Mongolia | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Mauritania | 0 1000 t | 2023 | — | volatile |
| 59 | Mauritius | 0 1000 t | 2023 | — | volatile |
| 59 | Malaysia | 0 1000 t | 2023 | — | volatile |
| 59 | Namibia | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | New Caledonia | 0 1000 t | 2023 | — | volatile |
| 59 | Niger | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Nigeria | 0 1000 t | 2023 | — | flat |
| 59 | Nicaragua | 0 1000 t | 2023 | — | volatile |
| 59 | Nepal | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | New Zealand | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Panama | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Peru | 0 1000 t | 2023 | down 100.0% | flat |
| 59 | Rwanda | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Solomon Islands | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | El Salvador | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Serbia | 0 1000 t | 2023 | — | flat |
| 59 | Suriname | 0 1000 t | 2023 | — | volatile |
| 59 | Slovakia | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Tajikistan | 0 1000 t | 2023 | — | volatile |
| 59 | Turkmenistan | 0 1000 t | 2023 | — | flat |
| 59 | East Timor | 0 1000 t | 2023 | — | volatile |
| 59 | Tunisia | 0 1000 t | 2023 | — | volatile |
| 59 | Uruguay | 0 1000 t | 2023 | down 100.0% | flat |
| 59 | Samoa | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Yemen | 0 1000 t | 2023 | — | flat |
| 59 | Zambia | 0 1000 t | 2023 | — | flat |
| 59 | Zimbabwe | 0 1000 t | 2023 | — | volatile |
| 59 | Micronesia | 0 1000 t | 2023 | — | volatile |
| 59 | Timor-Leste | 0 1000 t | 2023 | — | volatile |
| 59 | Polynesia | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Democratic Republic of the Congo | 0 1000 t | 2023 | — | volatile |
| 59 | Bolivia (Plurinational State of) | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Republic of Korea | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Republic of Moldova | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2023 | down 100.0% | volatile |
| 59 | Australia and New Zealand | 0 1000 t | 2023 | up 100.0% | volatile |
| 59 | Côte d'Ivoire | 0 1000 t | 2023 | — | volatile |
| 59 | Lao People's Democratic Republic | 0 1000 t | 2023 | — | flat |
| 59 | United Republic of Tanzania | 0 1000 t | 2023 | down 100.0% | flat |
| 59 | Micronesia (Federated States of) | 0 1000 t | 2023 | — | volatile |
| 59 | China, Macao SAR | 0 1000 t | 2023 | — | volatile |
| 175 | Albania | -1 1000 t | 2023 | up 50.0% | volatile |
| 175 | Ghana | -1 1000 t | 2023 | down 120.0% | flat |
| 175 | Madagascar | -1 1000 t | 2023 | down 133.3% | volatile |
| 175 | Philippines | -1 1000 t | 2023 | down 102.3% | volatile |
| 179 | Bangladesh | -2 1000 t | 2023 | down 140.0% | volatile |
| 179 | Italy | -2 1000 t | 2023 | — | volatile |
| 181 | Ethiopia | -3 1000 t | 2023 | — | volatile |
| 181 | Eswatini | -3 1000 t | 2023 | — | volatile |
| 183 | Guatemala | -5 1000 t | 2023 | down 600.0% | volatile |
| 184 | Poland | -6 1000 t | 2023 | down 175.0% | volatile |
| 184 | Russia | -6 1000 t | 2023 | down 128.6% | volatile |
| 184 | Russian Federation | -6 1000 t | 2023 | down 128.6% | volatile |
| 187 | Mexico | -9 1000 t | 2023 | down 228.6% | volatile |
| 188 | Greece | -12 1000 t | 2023 | — | volatile |
| 189 | United Arab Emirates | -17 1000 t | 2023 | down 13.3% | volatile |
| 190 | Spain | -21 1000 t | 2023 | up 82.8% | volatile |
| 190 | Iran (Islamic Republic of) | -21 1000 t | 2023 | down 250.0% | volatile |
| 192 | Uzbekistan | -29 1000 t | 2023 | down 341.7% | volatile |
| 193 | Afghanistan | -36 1000 t | 2023 | down 800.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- World 822 1000 t
- Asia 627 1000 t
- Southern Asia 507 1000 t
- Americas 98 1000 t
- Northern America 74 1000 t
- United States of America 74 1000 t
- Africa 60 1000 t
- Northern Africa 54 1000 t
- South-Eastern Asia 50 1000 t
- Eastern Asia 48 1000 t
- Western Asia 45 1000 t
- European Union (27) 36 1000 t
- South America 36 1000 t
- Western Europe 36 1000 t
- Europe 35 1000 t
- Northern Europe 19 1000 t
- Net Food Importing Developing Countries (NFIDCs) 17 1000 t
- Western Africa 8 1000 t
- Small island developing States (SIDS) 3 1000 t
- Eastern Europe 3 1000 t
- Oceania 2 1000 t
- Middle Africa 0 1000 t
- Eastern Africa -1 1000 t
- Southern Africa -2 1000 t
- Central America -13 1000 t
- Central Asia -23 1000 t
- Southern Europe -23 1000 t
- Least Developed Countries (LDCs) -30 1000 t
- Low Income Food Deficit Countries (LIFDCs) -50 1000 t
- Land Locked Developing Countries (LLDCs) -51 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.