Cottonseed — 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
Cottonseed — Stock Variation is currently reported for 146 countries. The highest value is 250 1000 t in Burkina Faso; the lowest is -161 1000 t in Uzbekistan.
The median across all reporting countries is 0 1000 t, and the mean is 1.68 1000 t.
The gap between the highest and lowest reporting country is a factor of about 2.
Over the past decade 20 countries rose and 14 fell. The largest increase was in Kenya (up 1,200.0%), and the largest decrease in Uzbekistan (down 3,120.0%).
Cottonseed — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Burkina Faso | 250 1000 t | 2023 | up 1,100.0% | volatile |
| 2 | Turkmenistan | 134 1000 t | 2023 | — | volatile |
| 3 | Pakistan | 132 1000 t | 2023 | up 232.0% | volatile |
| 4 | Myanmar | 94 1000 t | 2023 | — | volatile |
| 5 | Türkiye | 44 1000 t | 2023 | down 67.6% | volatile |
| 6 | Brazil | 25 1000 t | 2023 | up 149.0% | volatile |
| 7 | Kenya | 22 1000 t | 2023 | up 1,200.0% | volatile |
| 8 | Greece | 15 1000 t | 2023 | — | volatile |
| 9 | Kazakhstan | 14 1000 t | 2023 | up 240.0% | volatile |
| 10 | Zambia | 12 1000 t | 2023 | — | volatile |
| 10 | Republic of Korea | 12 1000 t | 2023 | — | volatile |
| 12 | Israel | 9 1000 t | 2023 | up 250.0% | volatile |
| 13 | Ethiopia | 8 1000 t | 2023 | down 20.0% | volatile |
| 14 | Ghana | 3 1000 t | 2023 | — | volatile |
| 15 | Azerbaijan | 2 1000 t | 2023 | up 300.0% | volatile |
| 15 | India | 2 1000 t | 2023 | down 98.0% | volatile |
| 15 | Kyrgyzstan | 2 1000 t | 2023 | up 166.7% | volatile |
| 15 | Malawi | 2 1000 t | 2023 | down 60.0% | volatile |
| 19 | Afghanistan | 1 1000 t | 2023 | — | volatile |
| 19 | Iran (Islamic Republic of) | 1 1000 t | 2023 | up 106.2% | volatile |
| 21 | Angola | 0 1000 t | 2023 | — | flat |
| 21 | Albania | 0 1000 t | 2023 | — | flat |
| 21 | United Arab Emirates | 0 1000 t | 2023 | down 100.0% | volatile |
| 21 | Antigua and Barbuda | 0 1000 t | 2023 | — | flat |
| 21 | Austria | 0 1000 t | 2023 | — | flat |
| 21 | Belgium | 0 1000 t | 2023 | — | flat |
| 21 | Bangladesh | 0 1000 t | 2023 | — | flat |
| 21 | Bulgaria | 0 1000 t | 2023 | — | volatile |
| 21 | Bahamas | 0 1000 t | 2023 | — | flat |
| 21 | Bosnia and Herzegovina | 0 1000 t | 2023 | — | flat |
| 21 | Barbados | 0 1000 t | 2023 | — | flat |
| 21 | Bhutan | 0 1000 t | 2023 | — | flat |
| 21 | Botswana | 0 1000 t | 2023 | — | flat |
| 21 | Canada | 0 1000 t | 2023 | — | volatile |
| 21 | Switzerland | 0 1000 t | 2023 | — | flat |
| 21 | Chile | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | Costa Rica | 0 1000 t | 2023 | — | flat |
| 21 | Cuba | 0 1000 t | 2019 | — | flat |
| 21 | Cyprus | 0 1000 t | 2023 | — | flat |
| 21 | Czechia | 0 1000 t | 2023 | — | flat |
| 21 | Germany | 0 1000 t | 2023 | — | flat |
| 21 | Denmark | 0 1000 t | 2023 | — | flat |
| 21 | Dominican Republic | 0 1000 t | 2023 | — | flat |
| 21 | Algeria | 0 1000 t | 2023 | — | flat |
| 21 | Ecuador | 0 1000 t | 2023 | — | flat |
| 21 | Egypt | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | Estonia | 0 1000 t | 2023 | — | flat |
| 21 | Finland | 0 1000 t | 2023 | — | flat |
| 21 | Fiji | 0 1000 t | 2023 | — | flat |
| 21 | France | 0 1000 t | 2023 | — | flat |
| 21 | Georgia | 0 1000 t | 2023 | — | flat |
| 21 | Guinea | 0 1000 t | 2023 | — | flat |
| 21 | Gambia | 0 1000 t | 2023 | — | flat |
| 21 | Guinea-Bissau | 0 1000 t | 2023 | — | flat |
| 21 | Grenada | 0 1000 t | 2023 | — | flat |
| 21 | Guatemala | 0 1000 t | 2023 | — | flat |
| 21 | Honduras | 0 1000 t | 2023 | — | flat |
| 21 | Croatia | 0 1000 t | 2023 | — | flat |
| 21 | Haiti | 0 1000 t | 2023 | — | flat |
| 21 | Hungary | 0 1000 t | 2023 | — | flat |
| 21 | Indonesia | 0 1000 t | 2023 | — | volatile |
| 21 | Ireland | 0 1000 t | 2023 | — | flat |
| 21 | Iraq | 0 1000 t | 2023 | — | flat |
| 21 | Iceland | 0 1000 t | 2023 | — | flat |
| 21 | Italy | 0 1000 t | 2023 | down 100.0% | volatile |
| 21 | Jamaica | 0 1000 t | 2020 | — | flat |
| 21 | Jordan | 0 1000 t | 2023 | — | flat |
| 21 | Cambodia | 0 1000 t | 2023 | — | flat |
| 21 | Saint Kitts and Nevis | 0 1000 t | 2023 | — | flat |
| 21 | Kuwait | 0 1000 t | 2023 | — | flat |
| 21 | Lebanon | 0 1000 t | 2023 | — | flat |
| 21 | Sri Lanka | 0 1000 t | 2023 | — | flat |
| 21 | Lithuania | 0 1000 t | 2023 | — | flat |
| 21 | Luxembourg | 0 1000 t | 2023 | — | flat |
| 21 | Latvia | 0 1000 t | 2023 | — | flat |
| 21 | Morocco | 0 1000 t | 2023 | — | volatile |
| 21 | Madagascar | 0 1000 t | 2023 | — | flat |
| 21 | Maldives | 0 1000 t | 2023 | — | flat |
| 21 | North Macedonia | 0 1000 t | 2023 | — | flat |
| 21 | Malta | 0 1000 t | 2023 | — | flat |
| 21 | Montenegro | 0 1000 t | 2023 | — | flat |
| 21 | Mauritania | 0 1000 t | 2023 | — | flat |
| 21 | Mauritius | 0 1000 t | 2023 | — | flat |
| 21 | Namibia | 0 1000 t | 2023 | — | volatile |
| 21 | Niger | 0 1000 t | 2023 | — | flat |
| 21 | Nicaragua | 0 1000 t | 2023 | — | volatile |
| 21 | Norway | 0 1000 t | 2023 | — | flat |
| 21 | Nepal | 0 1000 t | 2023 | — | flat |
| 21 | New Zealand | 0 1000 t | 2023 | — | flat |
| 21 | Oman | 0 1000 t | 2023 | — | flat |
| 21 | Panama | 0 1000 t | 2020 | — | flat |
| 21 | Peru | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | Philippines | 0 1000 t | 2023 | — | flat |
| 21 | Papua New Guinea | 0 1000 t | 2023 | — | flat |
| 21 | Poland | 0 1000 t | 2023 | — | flat |
| 21 | Portugal | 0 1000 t | 2023 | — | flat |
| 21 | Paraguay | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | Qatar | 0 1000 t | 2023 | — | volatile |
| 21 | Romania | 0 1000 t | 2023 | — | flat |
| 21 | Rwanda | 0 1000 t | 2023 | — | flat |
| 21 | Saudi Arabia | 0 1000 t | 2023 | — | flat |
| 21 | Senegal | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | El Salvador | 0 1000 t | 2023 | — | flat |
| 21 | Slovakia | 0 1000 t | 2023 | — | flat |
| 21 | Slovenia | 0 1000 t | 2023 | — | flat |
| 21 | Sweden | 0 1000 t | 2023 | — | flat |
| 21 | Eswatini | 0 1000 t | 2023 | — | flat |
| 21 | Thailand | 0 1000 t | 2023 | — | flat |
| 21 | Tunisia | 0 1000 t | 2023 | — | volatile |
| 21 | Uganda | 0 1000 t | 2023 | — | volatile |
| 21 | Uruguay | 0 1000 t | 2023 | — | flat |
| 21 | Vanuatu | 0 1000 t | 2020 | — | flat |
| 21 | Samoa | 0 1000 t | 2021 | — | flat |
| 21 | Yemen | 0 1000 t | 2023 | — | flat |
| 21 | Melanesia | 0 1000 t | 2023 | — | flat |
| 21 | Polynesia | 0 1000 t | 2021 | — | flat |
| 21 | Caribbean | 0 1000 t | 2023 | — | flat |
| 21 | Bolivia (Plurinational State of) | 0 1000 t | 2023 | — | flat |
| 21 | China, Hong Kong SAR | 0 1000 t | 2019 | — | flat |
| 21 | Russian Federation | 0 1000 t | 2023 | — | flat |
| 21 | Syrian Arab Republic | 0 1000 t | 2023 | — | volatile |
| 21 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2023 | up 100.0% | volatile |
| 21 | Viet Nam | 0 1000 t | 2023 | — | flat |
| 21 | Côte d'Ivoire | 0 1000 t | 2023 | — | volatile |
| 21 | Lao People's Democratic Republic | 0 1000 t | 2023 | — | flat |
| 21 | China, Taiwan Province of | 0 1000 t | 2023 | — | flat |
| 21 | Netherlands (Kingdom of the) | 0 1000 t | 2023 | — | flat |
| 21 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | flat |
| 21 | Democratic People's Republic of Korea | 0 1000 t | 2018 | — | flat |
| 130 | Colombia | -1 1000 t | 2023 | — | volatile |
| 130 | Spain | -1 1000 t | 2023 | up 50.0% | volatile |
| 130 | Malaysia | -1 1000 t | 2023 | down 150.0% | volatile |
| 130 | Nigeria | -1 1000 t | 2023 | up 88.9% | volatile |
| 134 | Democratic Republic of the Congo | -2 1000 t | 2023 | — | volatile |
| 135 | Cameroon | -4 1000 t | 2023 | up 69.2% | volatile |
| 136 | Mozambique | -7 1000 t | 2023 | up 46.2% | volatile |
| 137 | Tajikistan | -8 1000 t | 2023 | down 200.0% | volatile |
| 138 | Mexico | -20 1000 t | 2023 | down 1,100.0% | volatile |
| 139 | China | -29 1000 t | 2023 | — | volatile |
| 139 | China, mainland | -29 1000 t | 2023 | — | volatile |
| 141 | United Republic of Tanzania | -41 1000 t | 2023 | down 241.4% | volatile |
| 142 | Argentina | -45 1000 t | 2023 | up 15.1% | volatile |
| 143 | Zimbabwe | -53 1000 t | 2023 | down 562.5% | volatile |
| 144 | Australia | -68 1000 t | 2023 | down 236.0% | volatile |
| 144 | Australia and New Zealand | -68 1000 t | 2023 | down 236.0% | volatile |
| 146 | Uzbekistan | -161 1000 t | 2023 | down 3,120.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- World 750 1000 t
- Net Food Importing Developing Countries (NFIDCs) 442 1000 t
- Northern America 436 1000 t
- United States of America 436 1000 t
- Americas 395 1000 t
- Least Developed Countries (LDCs) 289 1000 t
- Asia 249 1000 t
- Western Africa 233 1000 t
- Land Locked Developing Countries (LLDCs) 197 1000 t
- Africa 160 1000 t
- Southern Asia 137 1000 t
- South-eastern Asia 93 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.