Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo
Democratic Republic of the Congo: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 165,551 million SLC in 2023. ▼ Falling
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo, 2000–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
In 2023, consumption of fixed capital (agriculture, forestry and fishing) in Democratic Republic of the Congo stood at 165,551 million SLC. That is the lowest value across all 24 years on record.
Compared with earlier readings it is down 70.1% on the previous year and down 60.8% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Democratic Republic of the Congo peaked at 650,268 million SLC in 2000 and was at its lowest, 165,551 million SLC, in 2023.
The long-run direction has been consistently falling across the 24 years of available data.
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo, year by year
| Year | million SLC | Change |
|---|---|---|
| 2000 | 650,268 million SLC | — |
| 2001 | 619,640 million SLC | -4.7% |
| 2002 | 594,762 million SLC | -4.0% |
| 2003 | 570,700 million SLC | -4.0% |
| 2004 | 545,227 million SLC | -4.5% |
| 2005 | 520,071 million SLC | -4.6% |
| 2006 | 496,643 million SLC | -4.5% |
| 2007 | 475,346 million SLC | -4.3% |
| 2008 | 458,385 million SLC | -3.6% |
| 2009 | 446,503 million SLC | -2.6% |
| 2010 | 433,120 million SLC | -3.0% |
| 2011 | 420,752 million SLC | -2.9% |
| 2012 | 420,805 million SLC | +0.0% |
| 2013 | 422,108 million SLC | +0.3% |
| 2014 | 417,140 million SLC | -1.2% |
| 2015 | 413,266 million SLC | -0.9% |
| 2016 | 412,570 million SLC | -0.2% |
| 2017 | 415,820 million SLC | +0.8% |
| 2018 | 421,802 million SLC | +1.4% |
| 2019 | 424,438 million SLC | +0.6% |
| 2020 | 428,278 million SLC | +0.9% |
| 2021 | 455,944 million SLC | +6.5% |
| 2022 | 554,527 million SLC | +21.6% |
| 2023 | 165,551 million SLC | -70.1% |
Biggest year-on-year movements
Years where Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo changed far more than this series normally does. A large move can be a real event or a change in how the figure was measured — the source note below says who published it.
| Year | Change | From | To |
|---|---|---|---|
| 2023 | -70.1% | 554,527 million SLC | 165,551 million SLC |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2000s | 537,754 million SLC | 446,503 million SLC | 650,268 million SLC | 10 |
| 2010s | 420,182 million SLC | 412,570 million SLC | 433,120 million SLC | 10 |
| 2020s | 401,075 million SLC | 165,551 million SLC | 554,527 million SLC | 4 |
Countries ranked near Democratic Republic of the Congo
- 2 Republic of Korea 7.09 million million SLC compare
- 2 Viet Nam 84.70 million million SLC compare
- 3 Cabo Verde 736.63 million SLC compare
- 3 Somalia 4.40 million million SLC compare
- 3 United Republic of Tanzania 1.73 million million SLC compare
- 4 Lao People's Democratic Republic 1.58 million million SLC compare
- 4 Uzbekistan 3.95 million million SLC compare
- 5 Colombia 3.45 million million SLC compare
- 6 Nigeria 2.28 million million SLC compare
- 6 Syrian Arab Republic 149,892 million SLC compare
- 7 Guinea 2.00 million million SLC compare
- 7 Türkiye 19,283 million SLC compare
- 8 Bolivia (Plurinational State of) 2,963 million SLC compare
- 8 Uganda 1.66 million million SLC compare
More environment data for Democratic Republic of the Congo
- Historical exposure to drought — Land soil moisture anomaly -10.22 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -10.46 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.193 °C (2025)
- Temperature change 1.17 °C (2025)
- Permanent meadows and pastures — Share in Land area 8.03 % (2024)
- Agriculture — Area 36,710 1000 ha (2024)
- Land area — Area 226,705 1000 ha (2024)
- Country area — Area 234,541 1000 ha (2024)
- Permanent crops — Share in Agricultural land 5.43 % (2024)
Frequently asked questions
- What is consumption of fixed capital (agriculture, forestry and fishing) in Democratic Republic of the Congo?
- Consumption of fixed capital (agriculture, forestry and fishing) in Democratic Republic of the Congo was 165,551 million SLC in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest consumption of fixed capital (agriculture, forestry and fishing) recorded in Democratic Republic of the Congo?
- The highest recorded value was 650,268 million SLC in 2000.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Democratic Republic of the Congo?
- The lowest recorded value was 165,551 million SLC in 2023.
- How does Democratic Republic of the Congo rank for consumption of fixed capital (agriculture, forestry and fishing)?
- Democratic Republic of the Congo ranks 5th out of 10 countries with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Democratic Republic of the Congo?
- Over the last ten years it is down 60.8%. The long-run trend across the full record is falling.
- Where does this Democratic Republic of the Congo data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 24 observations, free to reuse under CC BY-NC-SA 3.0 IGO (FAO).
About this data
As part of the FAO Agriculture Capital Stock (ACS) database, the Statistics Division of FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). The database is further supplemented by OECD Structural Analysis database (OECD STAN) and, in a few cases, data from country’s statistics websites. If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official submissions by Member Countries. With a view of producing internationally comparable net capital stock estimates, the Perpetual Inventory Method (PIM) with a constant geometric depreciation rate is employed to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required.An attempt is made to rely as much as possible on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, econometric techniques to impute missing observations are employed. Depending on the pattern of data missingness for the countries, different imputation methods are applied (from among the ARIMAX, PANEL regression, and OLS approaches) for the data series from 1995 to 2022. The values of Agriculture Capital Stock related indicators for 2023, including Agriculture Investment Ratio, Agriculture Orientation Index, Net Capital Stock, Gross Fixed Capital Formation and Consumption of Fixed Capital, are estimated using the Holt-Winters (HW) method (Cipra et al., 1995). The HW method is an exponential smoothing method for forecasting the annual values of economic variables. In this context, the HW method relies on existing (historical) values of the Agriculture Capital Stock. The predicted value is an extrapolation of the historical values to the specified target date, which extends the timeline without considering seasonality in the annual series.All data series in the database are provided both in national currencies and in US dollars as well as in current prices and constant prices with base year 2015.