Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Africa
Africa: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 43,097 million USD in 2023. ▲ Rising
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Africa, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million USD.
Analysis
In 2023, gross fixed capital formation (agriculture, forestry and fishing) in Africa stood at 43,097 million USD. That is the highest value across all 29 years on record.
Compared with earlier readings it is up 1.2% on the previous year and up 16.7% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Africa peaked at 43,097 million USD in 2023 and was at its lowest, 14,002 million USD, in 1995.
That places Africa 11th out of 29 groups with data for 2023, putting it in the middle of the range.
The long-run direction has been consistently rising across the 29 years of available data.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Africa, year by year
| Year | million USD | Change |
|---|---|---|
| 1995 | 14,002 million USD | — |
| 1996 | 16,861 million USD | +20.4% |
| 1997 | 17,371 million USD | +3.0% |
| 1998 | 18,636 million USD | +7.3% |
| 1999 | 18,249 million USD | -2.1% |
| 2000 | 18,023 million USD | -1.2% |
| 2001 | 18,060 million USD | +0.2% |
| 2002 | 23,249 million USD | +28.7% |
| 2003 | 24,020 million USD | +3.3% |
| 2004 | 20,209 million USD | -15.9% |
| 2005 | 22,491 million USD | +11.3% |
| 2006 | 24,156 million USD | +7.4% |
| 2007 | 24,295 million USD | +0.6% |
| 2008 | 27,513 million USD | +13.2% |
| 2009 | 28,818 million USD | +4.7% |
| 2010 | 31,293 million USD | +8.6% |
| 2011 | 30,536 million USD | -2.4% |
| 2012 | 35,502 million USD | +16.3% |
| 2013 | 36,928 million USD | +4.0% |
| 2014 | 37,937 million USD | +2.7% |
| 2015 | 41,320 million USD | +8.9% |
| 2016 | 41,424 million USD | +0.3% |
| 2017 | 41,030 million USD | -1.0% |
| 2018 | 38,784 million USD | -5.5% |
| 2019 | 38,200 million USD | -1.5% |
| 2020 | 37,432 million USD | -2.0% |
| 2021 | 38,335 million USD | +2.4% |
| 2022 | 42,600 million USD | +11.1% |
| 2023 | 43,097 million USD | +1.2% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 17,024 million USD | 14,002 million USD | 18,636 million USD | 5 |
| 2000s | 23,083 million USD | 18,023 million USD | 28,818 million USD | 10 |
| 2010s | 37,295 million USD | 30,536 million USD | 41,424 million USD | 10 |
| 2020s | 40,366 million USD | 37,432 million USD | 43,097 million USD | 4 |
Countries ranked near Africa
- 8 Australia 12,467 million USD compare
- 8 Democratic Republic of the Congo 1,907 million USD compare
- 9 United Kingdom of Great Britain and Northern Ireland 11,332 million USD compare
- 9 United Republic of Tanzania 1,813 million USD compare
- 10 Germany 11,082 million USD compare
- 11 Bolivia (Plurinational State of) 698.09 million USD compare
- 11 Japan 10,400 million USD compare
- 12 Nigeria 9,663 million USD compare
- 12 Melanesia 567.17 million USD compare
- 13 Lao People's Democratic Republic 528.72 million USD compare
- 13 Pakistan 8,919 million USD compare
- 14 Brazil 8,865 million USD compare
- 14 Timor-Leste 24.48 million USD compare
More environment data for Africa
- Historical exposure to drought — Land soil moisture anomaly -7.93 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -7.12 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Temperature change 1.32 °C (2025)
- Standard Deviation 0.213 °C (2025)
- Sawlogs and veneer logs, non-coniferous — Production 29.81 million m3 (2024)
- Industrial roundwood — Export value 652,409 1000 USD (2024)
- Industrial roundwood, non-coniferous — Production 66.41 million m3 (2024)
- Wood charcoal — Production 38.75 million t (2024)
- Other industrial roundwood, non-coniferous (production) — Production 26.92 million m3 (2024)
Frequently asked questions
- What is gross fixed capital formation (agriculture, forestry and fishing) in Africa?
- Gross fixed capital formation (agriculture, forestry and fishing) in Africa was 43,097 million USD 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 Africa?
- The highest recorded value was 43,097 million USD in 2023.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Africa?
- The lowest recorded value was 14,002 million USD in 1995.
- How does Africa rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Africa ranks 11th out of 29 groups with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Africa?
- Over the last ten years it is up 16.7%. The long-run trend across the full record is rising.
- Where does this Africa 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 US$, 2015 prices. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 29 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.