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 1.73 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
In 2023, consumption of fixed capital (agriculture, forestry and fishing) in United Republic of Tanzania stood at 1.73 million million SLC. That is the highest value across all 29 years on record.
The figure is up 24.1% on the previous year and up 219.2% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in United Republic of Tanzania peaked at 1.73 million million SLC in 2023 and was at its lowest, 160,110 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 | 160,110 million SLC | — |
| 1996 | 167,465 million SLC | +4.6% |
| 1997 | 176,736 million SLC | +5.5% |
| 1998 | 186,552 million SLC | +5.6% |
| 1999 | 198,186 million SLC | +6.2% |
| 2000 | 212,452 million SLC | +7.2% |
| 2001 | 228,040 million SLC | +7.3% |
| 2002 | 245,340 million SLC | +7.6% |
| 2003 | 263,855 million SLC | +7.5% |
| 2004 | 281,681 million SLC | +6.8% |
| 2005 | 298,644 million SLC | +6.0% |
| 2006 | 316,647 million SLC | +6.0% |
| 2007 | 334,909 million SLC | +5.8% |
| 2008 | 354,317 million SLC | +5.8% |
| 2009 | 380,002 million SLC | +7.2% |
| 2010 | 412,524 million SLC | +8.6% |
| 2011 | 448,824 million SLC | +8.8% |
| 2012 | 490,351 million SLC | +9.3% |
| 2013 | 541,096 million SLC | +10.3% |
| 2014 | 598,744 million SLC | +10.7% |
| 2015 | 664,231 million SLC | +10.9% |
| 2016 | 743,110 million SLC | +11.9% |
| 2017 | 835,414 million SLC | +12.4% |
| 2018 | 944,672 million SLC | +13.1% |
| 2019 | 1.07 million million SLC | +13.0% |
| 2020 | 1.19 million million SLC | +11.0% |
| 2021 | 1.29 million million SLC | +8.8% |
| 2022 | 1.39 million million SLC | +8.0% |
| 2023 | 1.73 million million SLC | +24.1% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 177,810 million SLC | 160,110 million SLC | 198,186 million SLC | 5 |
| 2000s | 291,589 million SLC | 212,452 million SLC | 380,002 million SLC | 10 |
| 2010s | 674,643 million SLC | 412,524 million SLC | 1.07 million million SLC | 10 |
| 2020s | 1.40 million million SLC | 1.19 million million SLC | 1.73 million million SLC | 4 |
Countries ranked near United Republic of Tanzania
- 1 Côte d'Ivoire 496,902 million SLC compare
- 1 Indonesia 205.59 million million SLC compare
- 1 Iran (Islamic Republic of) 97.19 million million SLC compare
- 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
- 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
- 5 Democratic Republic of the Congo 165,551 million SLC compare
- 6 Nigeria 2.28 million million SLC compare
- 6 Syrian Arab Republic 149,892 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 1.73 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 1.73 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 160,110 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 3rd 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 219.2%. 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, 2015 prices. 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.