Statizoid

Soft-Fibres, Other — Export Quantity by country

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...

Countries reporting
96
Highest
217,353 t
France
Lowest
0 t
China, Macao SAR
Median
7.5 t
Years covered
53
1961–2013
Data points
5,938

What the numbers show

Soft-Fibres, Other — Export Quantity is currently reported for 96 countries. The highest value is 217,353 t in France; the lowest is 0 t in China, Macao SAR.

The median across all reporting countries is 7.5 t, and the mean is 5,783 t.

Over the past decade 17 countries rose and 37 fell. The largest increase was in Canada (up 10,194.9%), and the largest decrease in Estonia (down 100.0%).

Soft-Fibres, Other — Export Quantity: full country ranking

#Country LatestYear 10-year changeTrend
1 France 217,353 t 2013 up 10.9% rising
2 Belgium 142,790 t 2013 up 6.6% falling
3 Belgium-Luxembourg 111,816 t 1999 up 50.9% falling
4 Belarus 23,532 t 2013 up 59.7% volatile
5 Canada 14,001 t 2013 up 10,194.9% volatile
6 Netherlands (Kingdom of the) 9,034 t 2013 down 35.2% volatile
7 Lithuania 7,176 t 2013 down 60.5% volatile
8 China, mainland 6,782 t 2013 up 30.5% volatile
9 Czechoslovakia 6,300 t 1992 up 152.0% volatile
10 Egypt 5,027 t 2013 down 69.7% rising
11 USSR 3,000 t 1991 down 55.3% falling
12 United Kingdom of Great Britain and Northern Ireland 1,514 t 2013 down 31.0% falling
13 Poland 1,146 t 2013 down 67.8% volatile
14 Ukraine 756 t 2013 down 89.8% volatile
15 India 602 t 2013 up 256.2% volatile
16 Germany 565 t 2013 down 47.2% volatile
17 Italy 502 t 2013 down 91.3% volatile
18 Mauritius 457 t 2013 — volatile
19 Yugoslav SFR 400 t 1991 down 66.4% volatile
20 Russian Federation 391 t 2013 down 81.9% volatile
21 China, Taiwan Province of 245 t 2013 up 40.8% volatile
22 Latvia 207 t 2013 down 80.8% volatile
23 Tunisia 181 t 2013 up 57.4% volatile
24 Jordan 154 t 2013 — volatile
25 United Republic of Tanzania 145 t 2013 — volatile
26 Hungary 135 t 2013 down 88.5% volatile
27 Czechia 119 t 2013 down 90.1% volatile
27 Denmark 119 t 2013 up 41.7% volatile
29 Spain 108 t 2013 up 248.4% volatile
30 Brazil 73 t 2013 down 24.7% volatile
31 Bangladesh 69 t 2013 — volatile
32 Switzerland 66 t 2013 up 46.7% volatile
33 T�rkiye 61 t 2013 down 87.7% volatile
34 Republic of Korea 40 t 2013 — volatile
35 Romania 37 t 2013 down 96.7% volatile
36 Australia 32 t 2013 — volatile
36 Australia and New Zealand 32 t 2013 — volatile
38 Pakistan 31 t 2013 — volatile
39 Philippines 28 t 2013 down 79.6% volatile
40 Kyrgyzstan 19 t 2013 — volatile
41 Nepal 18 t 2013 down 94.0% volatile
42 Austria 16 t 2013 down 96.5% volatile
43 Portugal 15 t 2013 up 114.3% volatile
43 Iran (Islamic Republic of) 15 t 2013 — volatile
45 Japan 14 t 2013 down 50.0% volatile
46 Slovakia 13 t 2013 down 98.3% volatile
47 Slovenia 10 t 2013 up 900.0% volatile
48 Sweden 8 t 2013 up 700.0% volatile
49 Croatia 7 t 2013 — volatile
50 Norway 5 t 2013 up 400.0% volatile
51 Ethiopia 4 t 2013 — volatile
51 Serbia 4 t 2013 — volatile
53 United Arab Emirates 2 t 2013 — volatile
53 Bulgaria 2 t 2013 down 97.8% volatile
53 Greece 2 t 2013 — volatile
56 Peru 1 t 2013 — volatile
56 Thailand 1 t 2013 down 87.5% volatile
58 Argentina 0 t 2013 — volatile
58 Botswana 0 t 2013 — volatile
58 Chile 0 t 2013 — volatile
58 Colombia 0 t 2013 — volatile
58 Cyprus 0 t 2013 — volatile
58 Ecuador 0 t 2013 — volatile
58 Estonia 0 t 2013 down 100.0% volatile
58 Finland 0 t 2013 down 100.0% volatile
58 Georgia 0 t 2013 — volatile
58 Honduras 0 t 2013 — volatile
58 Indonesia 0 t 2013 down 100.0% volatile
58 Ireland 0 t 2013 down 100.0% volatile
58 Israel 0 t 2013 down 100.0% volatile
58 Jamaica 0 t 2013 — volatile
58 Kazakhstan 0 t 2013 — volatile
58 Kenya 0 t 2013 down 100.0% volatile
58 Cambodia 0 t 2013 — volatile
58 Lebanon 0 t 2013 — volatile
58 Sri Lanka 0 t 2013 down 100.0% volatile
58 Luxembourg 0 t 2013 down 100.0% volatile
58 Madagascar 0 t 2013 — volatile
58 Mexico 0 t 2013 down 100.0% volatile
58 Malta 0 t 2013 — volatile
58 Myanmar 0 t 2013 — volatile
58 Malaysia 0 t 2013 down 100.0% volatile
58 Namibia 0 t 2013 — volatile
58 Nigeria 0 t 2013 — volatile
58 New Zealand 0 t 2013 — volatile
58 Saudi Arabia 0 t 2013 down 100.0% volatile
58 El Salvador 0 t 2013 — volatile
58 Togo 0 t 2013 — volatile
58 Uganda 0 t 2013 down 100.0% volatile
58 Zambia 0 t 2013 — volatile
58 Serbia and Montenegro 0 t 2005 — volatile
58 Caribbean 0 t 2013 — volatile
58 China, Hong Kong SAR 0 t 2013 down 100.0% volatile
58 Republic of Moldova 0 t 2013 — volatile
58 Venezuela (Bolivarian Republic of) 0 t 2013 — volatile
58 China, Macao SAR 0 t 2013 — volatile

Regions and income groups

Aggregates are excluded from the country ranking above so that a region can never outrank a country.

About this data

Indicator
Soft-Fibres, Other — Export Quantity
Unit
t
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
126 places, 5,938 data points, 1961–2013
Last refreshed

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 caput 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.