Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo
Democratic Republic of the Congo: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 1.77 million million SLC in 2023. ◆ Volatile
Gross Fixed Capital Formation (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, gross fixed capital formation (agriculture, forestry and fishing) in Democratic Republic of the Congo stood at 1.77 million million SLC.
That represents a change of down 41.1% on the previous year and up 416.5% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Democratic Republic of the Congo peaked at 3.00 million million SLC in 2022 and was at its lowest, 83,198 million SLC, in 2000.
The series is highly variable year to year, so single readings are best treated with caution.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Democratic Republic of the Congo, year by year
| Year | million SLC | Change |
|---|---|---|
| 2000 | 83,198 million SLC | — |
| 2001 | 196,385 million SLC | +136.0% |
| 2002 | 213,639 million SLC | +8.8% |
| 2003 | 173,804 million SLC | -18.6% |
| 2004 | 118,501 million SLC | -31.8% |
| 2005 | 133,424 million SLC | +12.6% |
| 2006 | 125,801 million SLC | -5.7% |
| 2007 | 157,592 million SLC | +25.3% |
| 2008 | 227,729 million SLC | +44.5% |
| 2009 | 292,960 million SLC | +28.6% |
| 2010 | 153,935 million SLC | -47.5% |
| 2011 | 300,052 million SLC | +94.9% |
| 2012 | 543,212 million SLC | +81.0% |
| 2013 | 341,855 million SLC | -37.1% |
| 2014 | 336,761 million SLC | -1.5% |
| 2015 | 368,385 million SLC | +9.4% |
| 2016 | 434,918 million SLC | +18.1% |
| 2017 | 498,571 million SLC | +14.6% |
| 2018 | 532,484 million SLC | +6.8% |
| 2019 | 398,976 million SLC | -25.1% |
| 2020 | 577,887 million SLC | +44.8% |
| 2021 | 1.20 million million SLC | +107.8% |
| 2022 | 3.00 million million SLC | +149.6% |
| 2023 | 1.77 million million SLC | -41.1% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2000s | 172,303 million SLC | 83,198 million SLC | 292,960 million SLC | 10 |
| 2010s | 390,915 million SLC | 153,935 million SLC | 543,212 million SLC | 10 |
| 2020s | 1.64 million million SLC | 577,887 million SLC | 3.00 million million SLC | 4 |
Countries ranked near Democratic Republic of the Congo
- 2 Iran (Islamic Republic of) 131.77 million million SLC compare
- 2 Uzbekistan 8.44 million million SLC compare
- 3 Cabo Verde 835.18 million SLC compare
- 3 Colombia 7.78 million million SLC compare
- 3 Lao People's Democratic Republic 4.30 million million SLC compare
- 4 Somalia 7.00 million million SLC compare
- 4 United Republic of Tanzania 3.61 million million SLC compare
- 5 Republic of Korea 5.32 million million SLC compare
- 6 India 4.98 million million SLC compare
- 6 Syrian Arab Republic 457,122 million SLC compare
- 7 Guinea 4.43 million million SLC compare
- 7 Türkiye 28,352 million SLC compare
- 8 Bolivia (Plurinational State of) 4,824 million SLC compare
- 8 Uganda 3.79 million million SLC compare
More environment data for Democratic Republic of the Congo
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -10.46 Percentage change (2025)
- Historical exposure to drought — Land soil moisture anomaly -10.22 Percentage change (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 gross fixed capital formation (agriculture, forestry and fishing) in Democratic Republic of the Congo?
- Gross fixed capital formation (agriculture, forestry and fishing) in Democratic Republic of the Congo was 1.77 million million SLC in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest gross fixed capital formation (agriculture, forestry and fishing) recorded in Democratic Republic of the Congo?
- The highest recorded value was 3.00 million million SLC in 2022.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Democratic Republic of the Congo?
- The lowest recorded value was 83,198 million SLC in 2000.
- How does Democratic Republic of the Congo rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Democratic Republic of the Congo ranks 5th out of 10 countries with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Democratic Republic of the Congo?
- Over the last ten years it is up 416.5%. The long-run trend across the full record is volatile.
- 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 Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices. Statizoid updates them automatically from the source API.
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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.