Stimulants — Losses 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...

Countries reporting
96
Highest
237 1000 t
Brazil
Lowest
0 1000 t
Russian Federation
Median
1 1000 t
Years covered
14
2010–2023
Data points
1,658

What the numbers show

Stimulants — Losses is currently reported for 96 countries. The highest value is 237 1000 t in Brazil; the lowest is 0 1000 t in Russian Federation.

The median across all reporting countries is 1 1000 t, and the mean is 15.93 1000 t.

Over the past decade 32 countries rose and 14 fell. The largest increase was in Guinea (up 1,000.0%), and the largest decrease in Cuba (down 100.0%).

Stimulants — Losses: full country ranking

#Country LatestYear 10-year changeTrend
1 Brazil 237 1000 t 2023 down 4.8% flat
2 China 199 1000 t 2023 up 80.9% rising
3 China, mainland 198 1000 t 2023 up 81.7% rising
4 Viet Nam 140 1000 t 2023 up 68.7% rising
5 Indonesia 110 1000 t 2023 up 37.5% rising
6 Côte d'Ivoire 86 1000 t 2023 up 59.3% rising
7 Kenya 73 1000 t 2023 up 192.0% volatile
8 India 69 1000 t 2023 up 245.0% volatile
9 Uganda 41 1000 t 2023 up 156.2% rising
10 Spain 31 1000 t 2023 up 19.2% rising
11 Peru 26 1000 t 2023 up 73.3% rising
12 Ecuador 22 1000 t 2023 up 214.3% rising
13 Argentina 20 1000 t 2023 down 23.1% volatile
14 Bangladesh 19 1000 t 2023 up 375.0% volatile
15 Cameroon 17 1000 t 2023 up 6.2% rising
15 Ethiopia 17 1000 t 2023 down 22.7% falling
15 Türkiye 17 1000 t 2023 up 183.3% volatile
18 Nigeria 16 1000 t 2023 down 11.1% falling
19 Sri Lanka 15 1000 t 2023 down 21.1% falling
20 Mexico 12 1000 t 2023 down 7.7% falling
21 Colombia 11 1000 t 2023 up 22.2% rising
21 Guinea 11 1000 t 2023 up 1,000.0% volatile
21 Malawi 11 1000 t 2023 up 266.7% volatile
21 Lao People's Democratic Republic 11 1000 t 2023 up 120.0% rising
25 Honduras 8 1000 t 2023 down 46.7% falling
25 Nicaragua 8 1000 t 2023 up 60.0% rising
27 Myanmar 7 1000 t 2023 up 16.7% rising
27 Papua New Guinea 7 1000 t 2023 up 40.0% rising
27 Thailand 7 1000 t 2023 up 16.7% rising
27 Melanesia 7 1000 t 2023 up 40.0% flat
27 United Republic of Tanzania 7 1000 t 2023 up 16.7% rising
32 Guatemala 6 1000 t 2023 up 20.0% rising
32 Rwanda 6 1000 t 2023 up 200.0% rising
32 Caribbean 6 1000 t 2023 unchanged rising
35 United Arab Emirates 5 1000 t 2023 unchanged rising
35 Pakistan 5 1000 t 2023 up 150.0% rising
37 Costa Rica 4 1000 t 2023 unchanged falling
37 Dominican Republic 4 1000 t 2023 up 300.0% volatile
37 Madagascar 4 1000 t 2023 up 33.3% rising
37 Philippines 4 1000 t 2023 unchanged falling
37 Democratic Republic of the Congo 4 1000 t 2023 up 100.0% rising
37 Iran (Islamic Republic of) 4 1000 t 2023 down 20.0% falling
43 Nepal 3 1000 t 2023 up 200.0% volatile
44 Yemen 2 1000 t 2023 up 100.0% rising
44 Bolivia (Plurinational State of) 2 1000 t 2023 unchanged rising
46 Angola 1 1000 t 2023 unchanged flat
46 Congo 1 1000 t 2023 volatile
46 Liberia 1 1000 t 2023 volatile
46 Malaysia 1 1000 t 2023 unchanged falling
46 Panama 1 1000 t 2023 volatile
46 Sierra Leone 1 1000 t 2023 down 66.7% falling
46 El Salvador 1 1000 t 2023 unchanged falling
46 Zambia 1 1000 t 2023 volatile
46 Venezuela (Bolivarian Republic of) 1 1000 t 2023 down 66.7% falling
46 China, Taiwan Province of 1 1000 t 2023 unchanged flat
56 Azerbaijan 0 1000 t 2023 flat
56 Bahrain 0 1000 t 2023 flat
56 Belize 0 1000 t 2023 flat
56 Bhutan 0 1000 t 2023 flat
56 Comoros 0 1000 t 2023 flat
56 Cuba 0 1000 t 2019 down 100.0% volatile
56 Fiji 0 1000 t 2023 flat
56 Gabon 0 1000 t 2023 flat
56 Georgia 0 1000 t 2023 flat
56 Ghana 0 1000 t 2023 flat
56 Grenada 0 1000 t 2023 flat
56 Guyana 0 1000 t 2023 flat
56 Haiti 0 1000 t 2023 down 100.0% volatile
56 Jamaica 0 1000 t 2023 flat
56 Cambodia 0 1000 t 2023 flat
56 Saint Lucia 0 1000 t 2023 flat
56 Montenegro 0 1000 t 2023 flat
56 Mozambique 0 1000 t 2023 down 100.0% volatile
56 Mauritius 0 1000 t 2023 flat
56 New Caledonia 0 1000 t 2023 flat
56 Portugal 0 1000 t 2023 flat
56 Paraguay 0 1000 t 2023 down 100.0% volatile
56 French Polynesia 0 1000 t 2023 flat
56 Qatar 0 1000 t 2023 flat
56 Saudi Arabia 0 1000 t 2019 flat
56 Solomon Islands 0 1000 t 2023 flat
56 Sao Tome and Principe 0 1000 t 2023 flat
56 Suriname 0 1000 t 2023 flat
56 Seychelles 0 1000 t 2023 flat
56 Tonga 0 1000 t 2023 flat
56 Trinidad and Tobago 0 1000 t 2023 flat
56 Saint Vincent and the Grenadines 0 1000 t 2023 flat
56 Vanuatu 0 1000 t 2023 flat
56 Samoa 0 1000 t 2023 flat
56 Zimbabwe 0 1000 t 2023 volatile
56 Timor-Leste 0 1000 t 2023 volatile
56 Cabo Verde 0 1000 t 2023 flat
56 Polynesia 0 1000 t 2023 flat
56 China, Hong Kong SAR 0 1000 t 2023 flat
56 Republic of Korea 0 1000 t 2023 flat
56 Russian Federation 0 1000 t 2023 flat

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
Stimulants — Losses
Unit
1000 t
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
123 places, 1,658 data points, 2010–2023
Last refreshed

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.