Fats, Animals, Raw — Residuals in Low Income Food Deficit Countries (LIFDCs)
Low Income Food Deficit Countries (LIFDCs): Fats, Animals, Raw — Residuals was 1 1000 t in 2023. ◆ Volatile
Fats, Animals, Raw — Residuals in Low Income Food Deficit Countries (LIFDCs), 2010–2023
Source: Food and Agriculture Organization of the United Nations. Measured in 1000 t.
Analysis
Low Income Food Deficit Countries (LIFDCs) recorded 1 1000 t for fats, animals, raw — residuals in 2023.
That represents a change of down 50.0% over ten years.
Over the whole period, fats, animals, raw — residuals in Low Income Food Deficit Countries (LIFDCs) peaked at 3 1000 t in 2012 and was at its lowest, -3 1000 t, in 2019.
Low Income Food Deficit Countries (LIFDCs) ranks 6th of 29 groups on this measure, in the top quarter.
The series is highly variable year to year, so single readings are best treated with caution.
Fats, Animals, Raw — Residuals in Low Income Food Deficit Countries (LIFDCs), year by year
| Year | 1000 t | Change |
|---|---|---|
| 2010 | 1 1000 t | — |
| 2011 | 1 1000 t | +0.0% |
| 2012 | 3 1000 t | +200.0% |
| 2013 | 2 1000 t | -33.3% |
| 2014 | 0 1000 t | -100.0% |
| 2015 | 1 1000 t | — |
| 2016 | 2 1000 t | +100.0% |
| 2017 | 0 1000 t | -100.0% |
| 2018 | 0 1000 t | — |
| 2019 | -3 1000 t | — |
| 2020 | 0 1000 t | -100.0% |
| 2021 | 1 1000 t | — |
| 2022 | 1 1000 t | +0.0% |
| 2023 | 1 1000 t | +0.0% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2010s | 0.7 1000 t | -3 1000 t | 3 1000 t | 10 |
| 2020s | 0.75 1000 t | 0 1000 t | 1 1000 t | 4 |
Countries ranked near Low Income Food Deficit Countries (LIFDCs)
- 3 Ecuador 5 1000 t compare
- 4 Ireland 3 1000 t compare
- 5 Uzbekistan 2 1000 t compare
- 6 Bulgaria 1 1000 t compare
- 6 Mexico 1 1000 t compare
- 7 Cabo Verde 0 1000 t
- 7 Côte d'Ivoire 0 1000 t compare
- 8 Tuvalu 0 1000 t
- 8 Naoero 0 1000 t
- 8 Tonga 0 1000 t
- 8 Marshall Islands 0 1000 t
- 8 Bhutan 0 1000 t
- 8 Qatar 0 1000 t
- 8 Bahrain 0 1000 t
- 8 Cuba 0 1000 t
- 8 Turkmenistan 0 1000 t
- 8 Lesotho 0 1000 t
- 8 Kiribati 0 1000 t
- 8 Guinea-Bissau 0 1000 t
- 8 Comoros 0 1000 t
- 8 Djibouti 0 1000 t compare
- 8 Sao Tome and Principe 0 1000 t
- 8 Liberia 0 1000 t
- 8 Mauritania 0 1000 t
- 8 Tajikistan 0 1000 t
- 8 Afghanistan 0 1000 t compare
- 8 China, Macao SAR 0 1000 t
- 8 Gambia 0 1000 t compare
- 8 Saint Kitts and Nevis 0 1000 t
- 8 Solomon Islands 0 1000 t
- 8 Libya 0 1000 t
- 8 Albania 0 1000 t
- 8 Sierra Leone 0 1000 t
- 8 Kuwait 0 1000 t compare
- 8 Suriname 0 1000 t
- 8 Montenegro 0 1000 t
- 8 Vanuatu 0 1000 t
- 8 Maldives 0 1000 t
- 8 Armenia 0 1000 t
- 8 Saint Vincent and the Grenadines 0 1000 t
- 8 Gabon 0 1000 t
- 8 Eswatini 0 1000 t
- 8 Saudi Arabia 0 1000 t compare
- 8 Guinea 0 1000 t
- 8 Haiti 0 1000 t
- 8 Iraq 0 1000 t
- 8 Guyana 0 1000 t
- 8 Kyrgyzstan 0 1000 t
- 8 Papua New Guinea 0 1000 t
- 8 Oman 0 1000 t compare
- 8 Samoa 0 1000 t
- 8 Angola 0 1000 t
- 8 French Polynesia 0 1000 t
- 8 Seychelles 0 1000 t
- 8 Grenada 0 1000 t
- 8 North Macedonia 0 1000 t
- 8 Jordan 0 1000 t
- 8 Congo 0 1000 t
- 8 Estonia 0 1000 t compare
- 8 Niger 0 1000 t
- 8 Algeria 0 1000 t
- 8 Namibia 0 1000 t compare
- 8 Georgia 0 1000 t compare
- 8 Paraguay 0 1000 t compare
- 8 Lithuania 0 1000 t compare
- 8 Bahamas 0 1000 t
- 8 Burkina Faso 0 1000 t compare
- 8 Saint Lucia 0 1000 t
- 8 New Caledonia 0 1000 t
- 8 Luxembourg 0 1000 t
- 8 Belarus 0 1000 t compare
- 8 Slovakia 0 1000 t compare
- 8 Chile 0 1000 t compare
- 8 Ukraine 0 1000 t compare
- 8 Israel 0 1000 t
- 8 Myanmar 0 1000 t
- 8 Serbia 0 1000 t
- 8 Norway 0 1000 t compare
- 8 Yemen 0 1000 t
- 8 Antigua and Barbuda 0 1000 t
- 8 Barbados 0 1000 t
- 8 Panama 0 1000 t compare
- 8 Rwanda 0 1000 t
- 8 Nicaragua 0 1000 t
- 8 Dominican Republic 0 1000 t compare
- 8 El Salvador 0 1000 t
- 8 Malawi 0 1000 t
- 8 Belize 0 1000 t
- 8 Mozambique 0 1000 t
- 8 Zambia 0 1000 t
- 8 Mauritius 0 1000 t
- 8 Lebanon 0 1000 t compare
- 8 Madagascar 0 1000 t
- 8 United Arab Emirates 0 1000 t
- 8 Jamaica 0 1000 t
- 8 New Zealand 0 1000 t compare
- 8 Ghana 0 1000 t
- 8 China, Hong Kong SAR 0 1000 t compare
- 8 Senegal 0 1000 t
- 8 Trinidad and Tobago 0 1000 t
- 8 Malta 0 1000 t
- 8 Morocco 0 1000 t
- 8 Kazakhstan 0 1000 t compare
- 8 China, Taiwan Province of 0 1000 t
- 8 Zimbabwe 0 1000 t
- 8 Ethiopia 0 1000 t
- 8 Bangladesh 0 1000 t compare
- 8 Fiji 0 1000 t
- 8 Cameroon 0 1000 t
- 8 Pakistan 0 1000 t compare
- 8 Cambodia 0 1000 t
- 8 Nigeria 0 1000 t
- 8 Germany 0 1000 t compare
- 8 Republic of Korea 0 1000 t compare
- 8 Uganda 0 1000 t
- 8 Philippines 0 1000 t
- 8 Cyprus 0 1000 t
- 8 Sri Lanka 0 1000 t compare
- 8 India 0 1000 t compare
- 8 Honduras 0 1000 t
- 8 Peru 0 1000 t
- 8 Egypt 0 1000 t compare
- 8 Nepal 0 1000 t
- 8 Kenya 0 1000 t
More agriculture & rural data for Low Income Food Deficit Countries (LIFDCs)
- Sugar Crops Primary — Area harvested, annual growth rate 5.09 % change on previous year (2024)
- Cabbages — Area harvested, annual growth rate 1.82 % change on previous year (2024)
- Vegetables Primary — Production, annual growth rate 1.66 % change on previous year (2024)
- Vegetables Primary — Yield, annual growth rate -1.51 % change on previous year (2024)
- Vegetables Primary — Area harvested, annual growth rate 3.21 % change on previous year (2024)
- Sugar Crops Primary — Production, annual growth rate 7.15 % change on previous year (2024)
- Sugar Crops Primary — Yield, annual growth rate 1.96 % change on previous year (2024)
- Sheep and Goat Meat — Production, annual growth rate 3.62 % change on previous year (2024)
- Roots and Tubers, Total — Production, annual growth rate 2.26 % change on previous year (2024)
- Sheep and Goat Meat — Yield/Carcass Weight, annual growth rate 7.14 % change on previous year (2024)
Frequently asked questions
- What is fats, animals, raw — residuals in Low Income Food Deficit Countries (LIFDCs)?
- Fats, animals, raw — residuals in Low Income Food Deficit Countries (LIFDCs) was 1 1000 t in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest fats, animals, raw — residuals recorded in Low Income Food Deficit Countries (LIFDCs)?
- The highest recorded value was 3 1000 t in 2012.
- What is the lowest fats, animals, raw — residuals recorded in Low Income Food Deficit Countries (LIFDCs)?
- The lowest recorded value was -3 1000 t in 2019.
- How does Low Income Food Deficit Countries (LIFDCs) rank for fats, animals, raw — residuals?
- Low Income Food Deficit Countries (LIFDCs) ranks 6th out of 29 groups with data for 2023.
- Is fats, animals, raw — residuals rising or falling in Low Income Food Deficit Countries (LIFDCs)?
- Over the last ten years it is down 50.0%. The long-run trend across the full record is volatile.
- Where does this Low Income Food Deficit Countries (LIFDCs) data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Fats, Animals, Raw — Residuals. Statizoid updates them automatically from the source API.
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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.