Soyabeans — 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
Soyabeans — Stock Variation is currently reported for 176 countries. The highest value is 1,836 1000 t in China, mainland; the lowest is -8,725 1000 t in Brazil.
The median across all reporting countries is 0 1000 t, and the mean is -50.68 1000 t.
Over the past decade 39 countries rose and 28 fell. The largest increase was in Bangladesh (up 4,557.1%), and the largest decrease in Belarus (down 3,600.0%).
Soyabeans — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | China, mainland | 1,836 1000 t | 2023 | up 19.6% | volatile |
| 2 | China | 1,803 1000 t | 2023 | up 15.3% | volatile |
| 3 | Canada | 702 1000 t | 2023 | up 616.3% | volatile |
| 4 | India | 374 1000 t | 2023 | up 211.7% | volatile |
| 5 | Bangladesh | 312 1000 t | 2023 | up 4,557.1% | volatile |
| 6 | Serbia | 149 1000 t | 2023 | — | volatile |
| 7 | Ukraine | 130 1000 t | 2023 | down 21.7% | volatile |
| 8 | Zambia | 89 1000 t | 2023 | up 790.0% | volatile |
| 9 | Pakistan | 88 1000 t | 2023 | — | volatile |
| 10 | Egypt | 68 1000 t | 2023 | up 128.5% | volatile |
| 11 | France | 52 1000 t | 2023 | up 181.2% | volatile |
| 12 | United Kingdom of Great Britain and Northern Ireland | 47 1000 t | 2023 | up 194.0% | volatile |
| 13 | Mexico | 40 1000 t | 2023 | up 150.0% | volatile |
| 14 | Ghana | 34 1000 t | 2023 | — | volatile |
| 15 | Zimbabwe | 31 1000 t | 2023 | up 293.8% | volatile |
| 16 | Slovenia | 30 1000 t | 2023 | — | volatile |
| 17 | Italy | 19 1000 t | 2023 | down 36.7% | volatile |
| 18 | Burkina Faso | 15 1000 t | 2023 | up 650.0% | volatile |
| 19 | Cambodia | 12 1000 t | 2023 | — | volatile |
| 19 | Nepal | 12 1000 t | 2023 | — | volatile |
| 21 | Austria | 10 1000 t | 2023 | down 65.5% | volatile |
| 21 | Republic of Moldova | 10 1000 t | 2023 | down 61.5% | volatile |
| 23 | Angola | 9 1000 t | 2023 | up 350.0% | volatile |
| 24 | Iraq | 7 1000 t | 2023 | — | volatile |
| 25 | Ireland | 6 1000 t | 2023 | — | volatile |
| 25 | Romania | 6 1000 t | 2023 | down 91.8% | volatile |
| 25 | Uzbekistan | 6 1000 t | 2023 | up 700.0% | volatile |
| 28 | Tunisia | 5 1000 t | 2023 | up 150.0% | volatile |
| 29 | Denmark | 2 1000 t | 2023 | unchanged | volatile |
| 29 | Greece | 2 1000 t | 2023 | — | volatile |
| 31 | Finland | 1 1000 t | 2023 | up 116.7% | volatile |
| 31 | Honduras | 1 1000 t | 2023 | — | volatile |
| 31 | Côte d'Ivoire | 1 1000 t | 2023 | — | volatile |
| 31 | Lao People's Democratic Republic | 1 1000 t | 2023 | unchanged | flat |
| 35 | Albania | 0 1000 t | 2023 | — | flat |
| 35 | Armenia | 0 1000 t | 2023 | — | flat |
| 35 | Antigua and Barbuda | 0 1000 t | 2023 | — | flat |
| 35 | Australia | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Azerbaijan | 0 1000 t | 2023 | — | volatile |
| 35 | Belgium | 0 1000 t | 2023 | — | volatile |
| 35 | Bahrain | 0 1000 t | 2023 | — | volatile |
| 35 | Bahamas | 0 1000 t | 2023 | — | flat |
| 35 | Belize | 0 1000 t | 2023 | — | volatile |
| 35 | Barbados | 0 1000 t | 2023 | — | flat |
| 35 | Bhutan | 0 1000 t | 2023 | — | flat |
| 35 | Botswana | 0 1000 t | 2023 | — | volatile |
| 35 | Cameroon | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Congo | 0 1000 t | 2023 | — | flat |
| 35 | Colombia | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Cuba | 0 1000 t | 2019 | — | volatile |
| 35 | Cyprus | 0 1000 t | 2023 | — | flat |
| 35 | Czechia | 0 1000 t | 2023 | — | volatile |
| 35 | Algeria | 0 1000 t | 2023 | — | flat |
| 35 | Ecuador | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Estonia | 0 1000 t | 2023 | — | volatile |
| 35 | Fiji | 0 1000 t | 2023 | — | flat |
| 35 | Gabon | 0 1000 t | 2023 | — | flat |
| 35 | Georgia | 0 1000 t | 2023 | — | flat |
| 35 | Guinea | 0 1000 t | 2023 | — | flat |
| 35 | Gambia | 0 1000 t | 2023 | — | flat |
| 35 | Grenada | 0 1000 t | 2023 | — | flat |
| 35 | Guatemala | 0 1000 t | 2023 | down 100.0% | flat |
| 35 | Guyana | 0 1000 t | 2023 | down 100.0% | volatile |
| 35 | Haiti | 0 1000 t | 2023 | — | flat |
| 35 | Hungary | 0 1000 t | 2023 | down 100.0% | volatile |
| 35 | Iceland | 0 1000 t | 2023 | — | flat |
| 35 | Israel | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Jamaica | 0 1000 t | 2023 | — | flat |
| 35 | Jordan | 0 1000 t | 2023 | — | flat |
| 35 | Kyrgyzstan | 0 1000 t | 2023 | — | flat |
| 35 | Kiribati | 0 1000 t | 2023 | — | flat |
| 35 | Saint Kitts and Nevis | 0 1000 t | 2023 | — | flat |
| 35 | Kuwait | 0 1000 t | 2023 | — | flat |
| 35 | Lebanon | 0 1000 t | 2023 | — | flat |
| 35 | Liberia | 0 1000 t | 2023 | — | flat |
| 35 | Libya | 0 1000 t | 2023 | — | flat |
| 35 | Saint Lucia | 0 1000 t | 2023 | — | flat |
| 35 | Sri Lanka | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Lithuania | 0 1000 t | 2023 | — | volatile |
| 35 | Luxembourg | 0 1000 t | 2023 | — | flat |
| 35 | Madagascar | 0 1000 t | 2023 | — | flat |
| 35 | Maldives | 0 1000 t | 2023 | — | flat |
| 35 | Marshall Islands | 0 1000 t | 2023 | — | flat |
| 35 | North Macedonia | 0 1000 t | 2023 | — | flat |
| 35 | Malta | 0 1000 t | 2023 | — | flat |
| 35 | Montenegro | 0 1000 t | 2023 | — | flat |
| 35 | Mongolia | 0 1000 t | 2023 | — | flat |
| 35 | Mauritania | 0 1000 t | 2023 | — | flat |
| 35 | Mauritius | 0 1000 t | 2023 | — | flat |
| 35 | Namibia | 0 1000 t | 2023 | — | flat |
| 35 | New Caledonia | 0 1000 t | 2023 | — | volatile |
| 35 | Niger | 0 1000 t | 2023 | — | flat |
| 35 | Nicaragua | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Nauru | 0 1000 t | 2023 | — | flat |
| 35 | New Zealand | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Oman | 0 1000 t | 2023 | — | flat |
| 35 | Panama | 0 1000 t | 2023 | — | volatile |
| 35 | Philippines | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Papua New Guinea | 0 1000 t | 2023 | — | flat |
| 35 | Portugal | 0 1000 t | 2023 | down 100.0% | volatile |
| 35 | French Polynesia | 0 1000 t | 2023 | — | flat |
| 35 | Qatar | 0 1000 t | 2023 | — | flat |
| 35 | Rwanda | 0 1000 t | 2023 | down 100.0% | volatile |
| 35 | Senegal | 0 1000 t | 2023 | — | volatile |
| 35 | Solomon Islands | 0 1000 t | 2023 | — | flat |
| 35 | Sierra Leone | 0 1000 t | 2023 | — | flat |
| 35 | El Salvador | 0 1000 t | 2023 | — | volatile |
| 35 | Sao Tome and Principe | 0 1000 t | 2023 | — | flat |
| 35 | Suriname | 0 1000 t | 2023 | — | flat |
| 35 | Slovakia | 0 1000 t | 2023 | — | volatile |
| 35 | Sweden | 0 1000 t | 2023 | — | volatile |
| 35 | Eswatini | 0 1000 t | 2023 | — | flat |
| 35 | Seychelles | 0 1000 t | 2023 | — | flat |
| 35 | Tajikistan | 0 1000 t | 2023 | — | flat |
| 35 | Tonga | 0 1000 t | 2023 | — | flat |
| 35 | Trinidad and Tobago | 0 1000 t | 2023 | — | volatile |
| 35 | Tuvalu | 0 1000 t | 2023 | — | flat |
| 35 | Uganda | 0 1000 t | 2023 | — | flat |
| 35 | Uruguay | 0 1000 t | 2023 | — | volatile |
| 35 | Saint Vincent and the Grenadines | 0 1000 t | 2023 | — | volatile |
| 35 | Vanuatu | 0 1000 t | 2023 | — | flat |
| 35 | Samoa | 0 1000 t | 2023 | — | flat |
| 35 | Yemen | 0 1000 t | 2023 | — | flat |
| 35 | Micronesia | 0 1000 t | 2023 | — | flat |
| 35 | Timor-Leste | 0 1000 t | 2023 | — | volatile |
| 35 | Cabo Verde | 0 1000 t | 2023 | — | flat |
| 35 | Melanesia | 0 1000 t | 2023 | — | volatile |
| 35 | Polynesia | 0 1000 t | 2023 | — | flat |
| 35 | Democratic Republic of the Congo | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | China, Hong Kong SAR | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Australia and New Zealand | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Netherlands (Kingdom of the) | 0 1000 t | 2023 | up 100.0% | volatile |
| 35 | Micronesia (Federated States of) | 0 1000 t | 2023 | — | flat |
| 35 | China, Macao SAR | 0 1000 t | 2023 | — | volatile |
| 135 | Chile | -1 1000 t | 2023 | — | volatile |
| 135 | Kenya | -1 1000 t | 2023 | unchanged | volatile |
| 135 | Peru | -1 1000 t | 2023 | unchanged | volatile |
| 135 | Venezuela (Bolivarian Republic of) | -1 1000 t | 2023 | unchanged | flat |
| 139 | Switzerland | -2 1000 t | 2023 | — | volatile |
| 139 | Myanmar | -2 1000 t | 2023 | up 96.5% | volatile |
| 141 | Latvia | -3 1000 t | 2023 | — | volatile |
| 141 | Morocco | -3 1000 t | 2023 | — | volatile |
| 141 | Caribbean | -3 1000 t | 2023 | up 40.0% | volatile |
| 144 | Bulgaria | -4 1000 t | 2023 | down 500.0% | volatile |
| 144 | Dominican Republic | -4 1000 t | 2023 | — | volatile |
| 146 | Mozambique | -6 1000 t | 2023 | — | volatile |
| 146 | Syrian Arab Republic | -6 1000 t | 2023 | up 73.9% | volatile |
| 148 | Bosnia and Herzegovina | -7 1000 t | 2023 | down 177.8% | volatile |
| 148 | Saudi Arabia | -7 1000 t | 2023 | up 68.2% | volatile |
| 148 | United Republic of Tanzania | -7 1000 t | 2023 | down 133.3% | volatile |
| 151 | Costa Rica | -12 1000 t | 2023 | up 29.4% | volatile |
| 152 | Malaysia | -15 1000 t | 2023 | up 11.8% | volatile |
| 153 | Nigeria | -20 1000 t | 2023 | up 84.7% | volatile |
| 153 | Norway | -20 1000 t | 2023 | down 322.2% | volatile |
| 155 | Republic of Korea | -26 1000 t | 2023 | — | volatile |
| 156 | United Arab Emirates | -27 1000 t | 2023 | up 25.0% | volatile |
| 157 | Viet Nam | -30 1000 t | 2023 | down 400.0% | volatile |
| 158 | China, Taiwan Province of | -33 1000 t | 2023 | down 206.5% | volatile |
| 159 | Croatia | -39 1000 t | 2023 | down 18.2% | volatile |
| 160 | Spain | -47 1000 t | 2023 | — | volatile |
| 160 | Kazakhstan | -47 1000 t | 2023 | down 687.5% | volatile |
| 162 | Malawi | -49 1000 t | 2023 | down 1,533.3% | volatile |
| 163 | Thailand | -51 1000 t | 2023 | up 40.7% | volatile |
| 164 | Germany | -60 1000 t | 2023 | down 137.0% | volatile |
| 165 | Ethiopia | -81 1000 t | 2023 | — | volatile |
| 166 | Poland | -92 1000 t | 2023 | — | volatile |
| 167 | Türkiye | -111 1000 t | 2023 | up 15.9% | volatile |
| 168 | Indonesia | -113 1000 t | 2023 | down 370.8% | volatile |
| 169 | Democratic People's Republic of Korea | -120 1000 t | 2018 | down 110.5% | volatile |
| 170 | Iran (Islamic Republic of) | -122 1000 t | 2023 | down 2,540.0% | volatile |
| 171 | Belarus | -148 1000 t | 2023 | down 3,600.0% | volatile |
| 172 | Bolivia (Plurinational State of) | -316 1000 t | 2023 | down 464.3% | volatile |
| 173 | Paraguay | -347 1000 t | 2023 | down 321.0% | volatile |
| 174 | Russian Federation | -621 1000 t | 2023 | — | volatile |
| 175 | Argentina | -3,500 1000 t | 2023 | down 218.5% | volatile |
| 176 | Brazil | -8,725 1000 t | 2023 | down 1,334.1% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Asia 1,893 1000 t
- Eastern Asia 1,587 1000 t
- Southern Asia 688 1000 t
- Net Food Importing Developing Countries (NFIDCs) 663 1000 t
- Least Developed Countries (LDCs) 508 1000 t
- Africa 220 1000 t
- Western Africa 210 1000 t
- Low Income Food Deficit Countries (LIFDCs) 110 1000 t
- Southern Europe 106 1000 t
- Northern Africa 70 1000 t
- Northern Europe 33 1000 t
- Central America 29 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.