Sunflower seed — 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
Sunflower seed — Stock Variation is currently reported for 89 countries. The highest value is 387 1000 t in Turkey; the lowest is -629 1000 t in Romania.
The median across all reporting countries is 0 1000 t, and the mean is 7.67 1000 t.
Over the past decade 33 countries rose and 27 fell. The largest increase was in Bosnia and Herzegovina (up 1,600.0%), and the largest decrease in Poland (down 1,050.0%).
Sunflower seed — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Turkey | 387 1000 t | 2023 | up 141.9% | volatile |
| 1 | Türkiye | 387 1000 t | 2023 | up 141.9% | volatile |
| 3 | Russia | 316 1000 t | 2023 | down 75.7% | volatile |
| 3 | Russian Federation | 316 1000 t | 2023 | down 75.7% | volatile |
| 5 | Hungary | 217 1000 t | 2023 | down 19.3% | volatile |
| 6 | France | 141 1000 t | 2023 | — | volatile |
| 7 | Kazakhstan | 130 1000 t | 2023 | up 44.4% | volatile |
| 8 | Netherlands (Kingdom of the) | 108 1000 t | 2023 | up 285.7% | volatile |
| 9 | India | 97 1000 t | 2023 | up 283.0% | volatile |
| 10 | United States | 95 1000 t | 2023 | up 151.9% | volatile |
| 11 | Spain | 73 1000 t | 2023 | down 27.0% | volatile |
| 12 | Pakistan | 44 1000 t | 2023 | up 113.8% | volatile |
| 13 | Myanmar | 41 1000 t | 2023 | up 241.7% | volatile |
| 14 | Argentina | 40 1000 t | 2023 | down 89.1% | volatile |
| 15 | Serbia | 32 1000 t | 2023 | up 140.0% | volatile |
| 16 | Zimbabwe | 25 1000 t | 2023 | up 457.1% | volatile |
| 17 | Bosnia and Herzegovina | 17 1000 t | 2023 | up 1,600.0% | volatile |
| 18 | Uzbekistan | 15 1000 t | 2023 | down 11.8% | volatile |
| 19 | Canada | 11 1000 t | 2023 | up 134.4% | volatile |
| 20 | Italy | 9 1000 t | 2023 | down 93.3% | volatile |
| 21 | Morocco | 6 1000 t | 2023 | — | volatile |
| 22 | Iran (Islamic Republic of) | 4 1000 t | 2023 | up 300.0% | volatile |
| 23 | Algeria | 3 1000 t | 2023 | up 200.0% | volatile |
| 24 | Australia | 2 1000 t | 2023 | up 200.0% | volatile |
| 24 | Finland | 2 1000 t | 2023 | up 200.0% | volatile |
| 24 | Lebanon | 2 1000 t | 2023 | up 100.0% | volatile |
| 24 | Australia and New Zealand | 2 1000 t | 2023 | up 200.0% | volatile |
| 28 | Mexico | 1 1000 t | 2023 | down 50.0% | flat |
| 28 | Philippines | 1 1000 t | 2023 | — | volatile |
| 28 | China, Taiwan Province of | 1 1000 t | 2023 | — | volatile |
| 31 | Armenia | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Azerbaijan | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Bangladesh | 0 1000 t | 2023 | — | volatile |
| 31 | Belarus | 0 1000 t | 2023 | down 100.0% | volatile |
| 31 | Botswana | 0 1000 t | 2023 | — | volatile |
| 31 | Chile | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | China | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Colombia | 0 1000 t | 2023 | — | volatile |
| 31 | Georgia | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Ireland | 0 1000 t | 2023 | — | volatile |
| 31 | Iraq | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Israel | 0 1000 t | 2023 | — | volatile |
| 31 | Jordan | 0 1000 t | 2023 | — | volatile |
| 31 | Kenya | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Kyrgyzstan | 0 1000 t | 2023 | down 100.0% | volatile |
| 31 | Kuwait | 0 1000 t | 2023 | — | volatile |
| 31 | Lithuania | 0 1000 t | 2023 | — | flat |
| 31 | Mozambique | 0 1000 t | 2023 | — | volatile |
| 31 | Malaysia | 0 1000 t | 2023 | — | volatile |
| 31 | Nigeria | 0 1000 t | 2023 | — | volatile |
| 31 | New Zealand | 0 1000 t | 2023 | — | volatile |
| 31 | Panama | 0 1000 t | 2023 | — | volatile |
| 31 | Paraguay | 0 1000 t | 2023 | down 100.0% | volatile |
| 31 | Senegal | 0 1000 t | 2023 | — | volatile |
| 31 | Slovakia | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Slovenia | 0 1000 t | 2023 | — | volatile |
| 31 | Sweden | 0 1000 t | 2023 | — | volatile |
| 31 | Syria | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Thailand | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Tajikistan | 0 1000 t | 2023 | — | volatile |
| 31 | Tunisia | 0 1000 t | 2023 | — | volatile |
| 31 | Uganda | 0 1000 t | 2023 | — | volatile |
| 31 | Zambia | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Republic of Korea | 0 1000 t | 2023 | — | volatile |
| 31 | Syrian Arab Republic | 0 1000 t | 2023 | up 100.0% | volatile |
| 31 | Venezuela (Bolivarian Republic of) | 0 1000 t | 2023 | up 100.0% | volatile |
| 143 | United Arab Emirates | -1 1000 t | 2023 | down 120.0% | volatile |
| 143 | North Macedonia | -1 1000 t | 2023 | — | volatile |
| 143 | Malawi | -1 1000 t | 2023 | — | volatile |
| 143 | China, mainland | -1 1000 t | 2023 | up 99.8% | volatile |
| 147 | Brazil | -2 1000 t | 2023 | up 88.2% | volatile |
| 147 | Bolivia (Plurinational State of) | -2 1000 t | 2023 | — | volatile |
| 149 | Belgium | -3 1000 t | 2023 | — | volatile |
| 150 | South Africa | -4 1000 t | 2023 | down 300.0% | volatile |
| 151 | Czechia | -6 1000 t | 2023 | down 20.0% | volatile |
| 151 | Portugal | -6 1000 t | 2023 | down 114.3% | volatile |
| 151 | Uruguay | -6 1000 t | 2023 | down 100.0% | volatile |
| 154 | Germany | -8 1000 t | 2023 | down 108.4% | volatile |
| 155 | Egypt | -11 1000 t | 2023 | down 283.3% | volatile |
| 156 | Austria | -18 1000 t | 2023 | down 800.0% | volatile |
| 157 | Poland | -19 1000 t | 2023 | down 1,050.0% | volatile |
| 158 | Croatia | -22 1000 t | 2023 | down 375.0% | volatile |
| 159 | United Republic of Tanzania | -53 1000 t | 2023 | down 133.3% | volatile |
| 160 | Greece | -72 1000 t | 2023 | — | volatile |
| 161 | Moldova | -104 1000 t | 2023 | down 240.5% | volatile |
| 161 | Republic of Moldova | -104 1000 t | 2023 | down 240.5% | volatile |
| 163 | Ukraine | -370 1000 t | 2023 | down 374.1% | volatile |
| 164 | Bulgaria | -399 1000 t | 2023 | down 625.0% | volatile |
| 165 | Romania | -629 1000 t | 2023 | down 984.5% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- Asia 722 1000 t
- Western Asia 389 1000 t
- Western Europe 220 1000 t
- Central Asia 146 1000 t
- Southern Asia 145 1000 t
- Americas 138 1000 t
- Northern America 106 1000 t
- United States of America 95 1000 t
- World 83 1000 t
- Land Locked Developing Countries (LLDCs) 63 1000 t
- South-Eastern Asia 42 1000 t
- Southern Europe 31 1000 t
- South America 30 1000 t
- Net Food Importing Developing Countries (NFIDCs) 24 1000 t
- Oceania 2 1000 t
- Northern Europe 2 1000 t
- Central America 1 1000 t
- Eastern Asia 1 1000 t
- Western Africa 0 1000 t
- Southern Africa -4 1000 t
- Northern Africa -5 1000 t
- Least Developed Countries (LDCs) -16 1000 t
- Low Income Food Deficit Countries (LIFDCs) -16 1000 t
- Eastern Africa -29 1000 t
- Africa -38 1000 t
- European Union (27) -632 1000 t
- Europe -741 1000 t
- Eastern Europe -993 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.