Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in United Republic of Tanzania

United Republic of Tanzania: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 4.12 million million SLC in 2023. ◆ Volatile

Latest (2023)
4.12 million million SLC
Change on year
up 14.2%
World rank
4th
of 10 countries
All-time high
4.12 million million SLC
in 2023
All-time low
58,562 million SLC
in 1995
Years of data
29
1995–2023

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in United Republic of Tanzania, 1995–2023

01.0M2.0M3.0M4.0M1995200920231995: 58.6k million SLC1996: 75.0k million SLC1997: 91.6k million SLC1998: 105.6k million SLC1999: 122.7k million SLC2000: 137.1k million SLC2001: 153.0k million SLC2002: 178.0k million SLC2003: 210.8k million SLC2004: 255.2k million SLC2005: 280.8k million SLC2006: 353.4k million SLC2007: 388.3k million SLC2008: 513.0k million SLC2009: 631.3k million SLC2010: 742.9k million SLC2011: 905.3k million SLC2012: 1.1M million SLC2013: 1.4M million SLC2014: 1.5M million SLC2015: 1.8M million SLC2016: 2.2M million SLC2017: 2.6M million SLC2018: 2.6M million SLC2019: 2.9M million SLC2020: 3.1M million SLC2021: 3.3M million SLC2022: 3.6M million SLC2023: 4.1M million SLC

Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.

Analysis

United Republic of Tanzania recorded 4.12 million million SLC for gross fixed capital formation (agriculture, forestry and fishing) in 2023. That is the highest value across all 29 years on record.

The figure is up 14.2% on the previous year and up 201.7% over ten years.

Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in United Republic of Tanzania peaked at 4.12 million million SLC in 2023 and was at its lowest, 58,562 million SLC, in 1995.

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 United Republic of Tanzania, year by year

Annual values for Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency in United Republic of Tanzania, 1995 to 2023.
Year million SLC Change
1995 58,562 million SLC
1996 75,035 million SLC +28.1%
1997 91,628 million SLC +22.1%
1998 105,578 million SLC +15.2%
1999 122,665 million SLC +16.2%
2000 137,060 million SLC +11.7%
2001 152,996 million SLC +11.6%
2002 177,953 million SLC +16.3%
2003 210,754 million SLC +18.4%
2004 255,216 million SLC +21.1%
2005 280,791 million SLC +10.0%
2006 353,421 million SLC +25.9%
2007 388,313 million SLC +9.9%
2008 513,041 million SLC +32.1%
2009 631,312 million SLC +23.1%
2010 742,867 million SLC +17.7%
2011 905,263 million SLC +21.9%
2012 1.13 million million SLC +24.9%
2013 1.37 million million SLC +20.9%
2014 1.52 million million SLC +11.0%
2015 1.82 million million SLC +20.1%
2016 2.19 million million SLC +20.1%
2017 2.55 million million SLC +16.7%
2018 2.60 million million SLC +1.7%
2019 2.85 million million SLC +9.9%
2020 3.06 million million SLC +7.2%
2021 3.34 million million SLC +9.1%
2022 3.61 million million SLC +8.2%
2023 4.12 million million SLC +14.2%

Averages by decade

DecadeAverage LowestHighest Years
1990s 90,694 million SLC 58,562 million SLC 122,665 million SLC 5
2000s 310,086 million SLC 137,060 million SLC 631,312 million SLC 10
2010s 1.77 million million SLC 742,867 million SLC 2.85 million million SLC 10
2020s 3.53 million million SLC 3.06 million million SLC 4.12 million million SLC 4

Countries ranked near United Republic of Tanzania

  1. 1 Côte d'Ivoire 895,346 million SLC compare
  2. 1 Indonesia 387.06 million million SLC compare
  3. 1 Iran (Islamic Republic of) 1.81 billion million SLC compare
  4. 2 Uzbekistan 23.90 million million SLC compare
  5. 2 Viet Nam 210.06 million million SLC compare
  6. 3 Cabo Verde 1,559 million SLC compare
  7. 3 Colombia 12.37 million million SLC compare
  8. 3 Lao People's Democratic Republic 6.24 million million SLC compare
  9. 4 Nigeria 10.33 million million SLC compare
  10. 5 Somalia 8.98 million million SLC compare
  11. 5 Syrian Arab Republic 3.18 million million SLC compare
  12. 6 Democratic Republic of the Congo 1.65 million million SLC compare
  13. 6 Guinea 7.18 million million SLC compare
  14. 7 India 6.88 million million SLC compare
  15. 7 Türkiye 281,738 million SLC compare

See the full ranking of 194 places →

More environment data for United Republic of Tanzania

All data for United Republic of Tanzania →

Frequently asked questions

What is gross fixed capital formation (agriculture, forestry and fishing) in United Republic of Tanzania?
Gross fixed capital formation (agriculture, forestry and fishing) in United Republic of Tanzania was 4.12 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 United Republic of Tanzania?
The highest recorded value was 4.12 million million SLC in 2023.
What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in United Republic of Tanzania?
The lowest recorded value was 58,562 million SLC in 1995.
How does United Republic of Tanzania rank for gross fixed capital formation (agriculture, forestry and fishing)?
United Republic of Tanzania ranks 4th out of 10 countries with data for 2023.
Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in United Republic of Tanzania?
Over the last ten years it is up 201.7%. 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 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).

Share, cite or embed this page

Cite this page

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in United Republic of Tanzania. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 17 September 2026, from https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local/united-republic-of-tanzania/

Embed or link this data

Paste this into a page to link back to these figures. The data itself is free to reuse under CC BY-NC-SA 3.0 IGO (FAO); please keep the attribution.

<a href="https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local/united-republic-of-tanzania/">Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in United Republic of Tanzania</a> — Statizoid

About this data

Indicator
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency
Unit
million SLC
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
194 places, 5,531 data points, 1995–2023
Last refreshed

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.