Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in United Republic of Tanzania
United Republic of Tanzania: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 1.97 million million SLC in 2023. ◆ Volatile
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in United Republic of Tanzania, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
United Republic of Tanzania recorded 1.97 million million SLC for consumption of fixed capital (agriculture, forestry and fishing) in 2023. That is the highest value across all 29 years on record.
The figure is up 22.4% on the previous year and up 285.5% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in United Republic of Tanzania peaked at 1.97 million million SLC in 2023 and was at its lowest, 36,294 million SLC, in 1995.
The series is highly variable year to year, so single readings are best treated with caution.
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in United Republic of Tanzania, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 36,294 million SLC | — |
| 1996 | 40,923 million SLC | +12.8% |
| 1997 | 48,066 million SLC | +17.5% |
| 1998 | 57,294 million SLC | +19.2% |
| 1999 | 58,283 million SLC | +1.7% |
| 2000 | 64,023 million SLC | +9.8% |
| 2001 | 71,248 million SLC | +11.3% |
| 2002 | 80,396 million SLC | +12.8% |
| 2003 | 98,458 million SLC | +22.5% |
| 2004 | 129,039 million SLC | +31.1% |
| 2005 | 146,682 million SLC | +13.7% |
| 2006 | 178,857 million SLC | +21.9% |
| 2007 | 211,008 million SLC | +18.0% |
| 2008 | 259,524 million SLC | +23.0% |
| 2009 | 277,542 million SLC | +6.9% |
| 2010 | 312,801 million SLC | +12.7% |
| 2011 | 384,999 million SLC | +23.1% |
| 2012 | 452,125 million SLC | +17.4% |
| 2013 | 511,752 million SLC | +13.2% |
| 2014 | 583,184 million SLC | +14.0% |
| 2015 | 664,231 million SLC | +13.9% |
| 2016 | 761,428 million SLC | +14.6% |
| 2017 | 878,475 million SLC | +15.4% |
| 2018 | 850,364 million SLC | -3.2% |
| 2019 | 983,420 million SLC | +15.6% |
| 2020 | 1.22 million million SLC | +24.4% |
| 2021 | 1.49 million million SLC | +22.1% |
| 2022 | 1.61 million million SLC | +7.8% |
| 2023 | 1.97 million million SLC | +22.4% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 48,172 million SLC | 36,294 million SLC | 58,283 million SLC | 5 |
| 2000s | 151,678 million SLC | 64,023 million SLC | 277,542 million SLC | 10 |
| 2010s | 638,278 million SLC | 312,801 million SLC | 983,420 million SLC | 10 |
| 2020s | 1.58 million million SLC | 1.22 million million SLC | 1.97 million million SLC | 4 |
Countries ranked near United Republic of Tanzania
- 1 Côte d'Ivoire 506,428 million SLC compare
- 1 Indonesia 251.97 million million SLC compare
- 1 Iran (Islamic Republic of) 1.34 billion million SLC compare
- 2 Nigeria 12.65 million million SLC compare
- 2 Viet Nam 100.34 million million SLC compare
- 3 Cabo Verde 1,375 million SLC compare
- 3 Lao People's Democratic Republic 2.30 million million SLC compare
- 3 Uzbekistan 11.17 million million SLC compare
- 4 Republic of Korea 8.80 million million SLC compare
- 5 Somalia 5.64 million million SLC compare
- 5 Syrian Arab Republic 1.04 million million SLC compare
- 6 Lebanon 5.51 million million SLC compare
- 6 Türkiye 191,619 million SLC compare
- 7 Colombia 5.48 million million SLC compare
- 7 Democratic Republic of the Congo 154,505 million SLC compare
More environment data for United Republic of Tanzania
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.253 °C (2025)
- Temperature change 1.06 °C (2025)
- Newsprint — Import quantity, gaps filled 16,168 t (2024)
- Total fibre furnish — Production, annual growth rate 0 % change on previous year (2024)
- Other paper and paperboard, not elsewhere specified — Import value 23.24 % change on previous year (2024)
- Other paper and paperboard, not elsewhere specified — Import 13.1 % change on previous year (2024)
- Other paper and paperboard — Import value, annual growth rate 23.17 % change on previous year (2024)
- Other paper and paperboard — Import quantity, annual growth rate 26.75 % change on previous year (2024)
- Printing and writing papers — Import quantity, annual growth rate 24.67 % change on previous year (2024)
Frequently asked questions
- What is consumption of fixed capital (agriculture, forestry and fishing) in United Republic of Tanzania?
- Consumption of fixed capital (agriculture, forestry and fishing) in United Republic of Tanzania was 1.97 million 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 United Republic of Tanzania?
- The highest recorded value was 1.97 million million SLC in 2023.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in United Republic of Tanzania?
- The lowest recorded value was 36,294 million SLC in 1995.
- How does United Republic of Tanzania rank for consumption of fixed capital (agriculture, forestry and fishing)?
- United Republic of Tanzania ranks 4th out of 10 countries with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in United Republic of Tanzania?
- Over the last ten years it is up 285.5%. The long-run trend across the full record is volatile.
- Where does this United Republic of Tanzania 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. Statizoid updates them automatically from the source API.
Download this data
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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.