Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Uganda
Uganda: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 5.05 million million SLC in 2023. ◆ Volatile
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Uganda, 1995–2023
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 Uganda stood at 5.05 million million SLC. That is the highest value across all 29 years on record.
Compared with earlier readings it is up 20.9% on the previous year and up 192.7% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Uganda peaked at 5.05 million million SLC in 2023 and was at its lowest, 186,555 million SLC, in 1996.
That places Uganda 9th out of 181 countries with data for 2023, putting it in the top 10%.
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 Uganda, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 190,203 million SLC | — |
| 1996 | 186,555 million SLC | -1.9% |
| 1997 | 205,176 million SLC | +10.0% |
| 1998 | 222,495 million SLC | +8.4% |
| 1999 | 242,627 million SLC | +9.0% |
| 2000 | 253,328 million SLC | +4.4% |
| 2001 | 266,865 million SLC | +5.3% |
| 2002 | 262,039 million SLC | -1.8% |
| 2003 | 325,393 million SLC | +24.2% |
| 2004 | 345,627 million SLC | +6.2% |
| 2005 | 426,631 million SLC | +23.4% |
| 2006 | 455,953 million SLC | +6.9% |
| 2007 | 488,722 million SLC | +7.2% |
| 2008 | 628,438 million SLC | +28.6% |
| 2009 | 1.58 million million SLC | +150.8% |
| 2010 | 1.64 million million SLC | +4.0% |
| 2011 | 1.77 million million SLC | +7.9% |
| 2012 | 1.79 million million SLC | +1.2% |
| 2013 | 1.73 million million SLC | -3.7% |
| 2014 | 1.91 million million SLC | +10.5% |
| 2015 | 1.94 million million SLC | +2.0% |
| 2016 | 2.28 million million SLC | +17.1% |
| 2017 | 2.39 million million SLC | +4.8% |
| 2018 | 2.68 million million SLC | +12.3% |
| 2019 | 3.07 million million SLC | +14.6% |
| 2020 | 3.21 million million SLC | +4.5% |
| 2021 | 3.37 million million SLC | +5.0% |
| 2022 | 4.18 million million SLC | +23.9% |
| 2023 | 5.05 million million SLC | +20.9% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 209,411 million SLC | 186,555 million SLC | 242,627 million SLC | 5 |
| 2000s | 502,890 million SLC | 253,328 million SLC | 1.58 million million SLC | 10 |
| 2010s | 2.12 million million SLC | 1.64 million million SLC | 3.07 million million SLC | 10 |
| 2020s | 3.95 million million SLC | 3.21 million million SLC | 5.05 million million SLC | 4 |
Countries ranked near Uganda
- 6 Democratic Republic of the Congo 1.65 million million SLC compare
- 6 Guinea 7.18 million million SLC compare
- 7 India 6.88 million million SLC compare
- 7 Türkiye 281,738 million SLC compare
- 8 Republic of Korea 6.60 million million SLC compare
- 9 Timor-Leste 26.08 million SLC compare
- 10 Paraguay 3.68 million million SLC compare
- 11 Myanmar 2.83 million million SLC compare
- 12 Lebanon 2.77 million million SLC compare
More environment data for Uganda
- Historical exposure to drought — Land soil moisture anomaly -11.7 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -12.54 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.255 °C (2025)
- Temperature change 1.23 °C (2025)
- Paper and paperboard — Import quantity, annual growth rate 47.51 % change on previous year (2024)
- Paper and paperboard — Import quantity, per unit of GDP 0 t per US$ of GDP (2024)
- Paper and paperboard — Import quantity, per capita 0.003 t per person (2024)
- Paper and paperboard — Import value, annual growth rate 63.49 % change on previous year (2024)
- Paper and paperboard — Import value, per unit of GDP 0 1000 USD per US$ of GDP (2024)
Frequently asked questions
- What is gross fixed capital formation (agriculture, forestry and fishing) in Uganda?
- Gross fixed capital formation (agriculture, forestry and fishing) in Uganda was 5.05 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 Uganda?
- The highest recorded value was 5.05 million million SLC in 2023.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Uganda?
- The lowest recorded value was 186,555 million SLC in 1996.
- How does Uganda rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Uganda ranks 9th out of 181 countries with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Uganda?
- Over the last ten years it is up 192.7%. The long-run trend across the full record is volatile.
- Where does this Uganda 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. 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.