Oilcrops, Other — Residuals 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
Oilcrops, Other — Residuals is currently reported for 90 countries. The highest value is 3 1000 t in Argentina; the lowest is -257 1000 t in India.
The median across all reporting countries is -0.5 1000 t, and the mean is -8.08 1000 t.
Over the past decade 13 countries rose and 9 fell. The largest increase was in Argentina (up 200.0%), and the largest decrease in India (down 12,950.0%).
Oilcrops, Other — Residuals: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Argentina | 3 1000 t | 2023 | up 200.0% | volatile |
| 2 | Australia | 0 1000 t | 2023 | — | volatile |
| 2 | Belgium | 0 1000 t | 2023 | down 100.0% | volatile |
| 2 | Bulgaria | 0 1000 t | 2023 | — | volatile |
| 2 | Colombia | 0 1000 t | 2023 | — | volatile |
| 2 | Djibouti | 0 1000 t | 2023 | — | volatile |
| 2 | Ecuador | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Georgia | 0 1000 t | 2023 | — | volatile |
| 2 | Greece | 0 1000 t | 2023 | — | volatile |
| 2 | Israel | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Kazakhstan | 0 1000 t | 2023 | — | volatile |
| 2 | Kenya | 0 1000 t | 2023 | — | volatile |
| 2 | Laos | 0 1000 t | 2023 | — | volatile |
| 2 | Lithuania | 0 1000 t | 2023 | — | volatile |
| 2 | Latvia | 0 1000 t | 2023 | — | volatile |
| 2 | Mexico | 0 1000 t | 2023 | — | volatile |
| 2 | Mongolia | 0 1000 t | 2023 | — | volatile |
| 2 | Malaysia | 0 1000 t | 2023 | — | volatile |
| 2 | Nicaragua | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Papua New Guinea | 0 1000 t | 2023 | — | volatile |
| 2 | Poland | 0 1000 t | 2023 | — | volatile |
| 2 | Romania | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Russia | 0 1000 t | 2023 | — | volatile |
| 2 | Rwanda | 0 1000 t | 2023 | — | volatile |
| 2 | Solomon Islands | 0 1000 t | 2023 | — | volatile |
| 2 | El Salvador | 0 1000 t | 2023 | — | volatile |
| 2 | Slovakia | 0 1000 t | 2023 | — | volatile |
| 2 | Slovenia | 0 1000 t | 2023 | — | volatile |
| 2 | Sweden | 0 1000 t | 2023 | — | volatile |
| 2 | East Timor | 0 1000 t | 2023 | — | volatile |
| 2 | Trinidad and Tobago | 0 1000 t | 2023 | — | volatile |
| 2 | Turkey | 0 1000 t | 2023 | — | volatile |
| 2 | Uganda | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Vietnam | 0 1000 t | 2023 | — | volatile |
| 2 | Vanuatu | 0 1000 t | 2023 | — | volatile |
| 2 | South Africa | 0 1000 t | 2023 | up 100.0% | volatile |
| 2 | Timor-Leste | 0 1000 t | 2023 | — | volatile |
| 2 | Melanesia | 0 1000 t | 2023 | — | volatile |
| 2 | Caribbean | 0 1000 t | 2023 | — | volatile |
| 2 | Iran (Islamic Republic of) | 0 1000 t | 2023 | down 100.0% | volatile |
| 2 | Russian Federation | 0 1000 t | 2023 | — | volatile |
| 2 | Viet Nam | 0 1000 t | 2023 | — | volatile |
| 2 | Türkiye | 0 1000 t | 2023 | — | volatile |
| 2 | Australia and New Zealand | 0 1000 t | 2023 | — | volatile |
| 2 | Lao People's Democratic Republic | 0 1000 t | 2023 | — | volatile |
| 146 | Belarus | -1 1000 t | 2023 | — | volatile |
| 146 | Brazil | -1 1000 t | 2023 | — | volatile |
| 146 | Denmark | -1 1000 t | 2023 | — | volatile |
| 146 | Estonia | -1 1000 t | 2023 | — | volatile |
| 146 | United Kingdom | -1 1000 t | 2023 | — | volatile |
| 146 | Indonesia | -1 1000 t | 2023 | up 99.4% | volatile |
| 146 | Pakistan | -1 1000 t | 2023 | — | volatile |
| 146 | Thailand | -1 1000 t | 2023 | — | volatile |
| 146 | Ukraine | -1 1000 t | 2023 | — | volatile |
| 146 | Netherlands (Kingdom of the) | -1 1000 t | 2023 | — | volatile |
| 146 | United Kingdom of Great Britain and Northern Ireland | -1 1000 t | 2023 | — | volatile |
| 157 | United Arab Emirates | -2 1000 t | 2023 | — | volatile |
| 157 | China | -2 1000 t | 2023 | up 50.0% | volatile |
| 157 | Nigeria | -2 1000 t | 2023 | — | volatile |
| 157 | Panama | -2 1000 t | 2023 | — | volatile |
| 157 | Portugal | -2 1000 t | 2023 | up 60.0% | volatile |
| 157 | China, mainland | -2 1000 t | 2023 | up 50.0% | volatile |
| 163 | Moldova | -3 1000 t | 2023 | — | volatile |
| 163 | Peru | -3 1000 t | 2023 | down 50.0% | volatile |
| 163 | Syria | -3 1000 t | 2023 | unchanged | volatile |
| 163 | Republic of Moldova | -3 1000 t | 2023 | — | volatile |
| 163 | Syrian Arab Republic | -3 1000 t | 2023 | unchanged | volatile |
| 168 | Spain | -4 1000 t | 2023 | — | volatile |
| 168 | Hungary | -4 1000 t | 2023 | — | volatile |
| 168 | Myanmar | -4 1000 t | 2023 | — | volatile |
| 171 | Afghanistan | -5 1000 t | 2023 | — | volatile |
| 172 | Bolivia (Plurinational State of) | -6 1000 t | 2023 | up 40.0% | volatile |
| 173 | Ghana | -7 1000 t | 2023 | up 91.9% | volatile |
| 173 | Zambia | -7 1000 t | 2023 | — | volatile |
| 175 | Austria | -9 1000 t | 2023 | — | volatile |
| 176 | United States | -10 1000 t | 2023 | down 11.1% | volatile |
| 176 | Democratic People's Republic of Korea | -10 1000 t | 2018 | — | volatile |
| 178 | Cote d'Ivoire | -11 1000 t | 2023 | — | volatile |
| 178 | Ethiopia | -11 1000 t | 2023 | down 1,000.0% | volatile |
| 178 | Côte d'Ivoire | -11 1000 t | 2023 | — | volatile |
| 181 | France | -12 1000 t | 2023 | down 9.1% | volatile |
| 182 | Egypt | -14 1000 t | 2023 | — | volatile |
| 183 | Mozambique | -16 1000 t | 2023 | — | volatile |
| 184 | Czechia | -18 1000 t | 2023 | — | volatile |
| 185 | Azerbaijan | -41 1000 t | 2023 | — | volatile |
| 186 | Paraguay | -49 1000 t | 2023 | down 880.0% | volatile |
| 187 | Italy | -53 1000 t | 2023 | — | volatile |
| 188 | United Republic of Tanzania | -66 1000 t | 2023 | — | volatile |
| 189 | Burkina Faso | -67 1000 t | 2023 | down 2,133.3% | volatile |
| 190 | India | -257 1000 t | 2023 | down 12,950.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Oceania 0 1000 t
- Small island developing States (SIDS) 0 1000 t
- Central Asia 0 1000 t
- Southern Africa 0 1000 t
- Northern Europe -2 1000 t
- Eastern Asia -2 1000 t
- Central America -3 1000 t
- South-Eastern Asia -6 1000 t
- Northern America -10 1000 t
- United States of America -10 1000 t
- Northern Africa -18 1000 t
- Western Europe -22 1000 t
- Eastern Europe -27 1000 t
- Western Asia -46 1000 t
- South America -57 1000 t
- Southern Europe -60 1000 t
- Americas -70 1000 t
- European Union (27) -105 1000 t
- Eastern Africa -106 1000 t
- Europe -110 1000 t
- Western Africa -163 1000 t
- Land Locked Developing Countries (LLDCs) -204 1000 t
- Low Income Food Deficit Countries (LIFDCs) -256 1000 t
- Least Developed Countries (LDCs) -264 1000 t
- Southern Asia -264 1000 t
- Africa -288 1000 t
- Net Food Importing Developing Countries (NFIDCs) -293 1000 t
- Asia -318 1000 t
- World -786 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.