Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Türkiye

Türkiye: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 191,619 million SLC in 2023. ◆ Volatile

Latest (2023)
191,619 million SLC
Change on year
up 54.0%
World rank
6th
of 10 countries
All-time high
191,619 million SLC
in 2023
All-time low
144.52 million SLC
in 1995
Years of data
29
1995–2023

Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Türkiye, 1995–2023

050.0k100.0k150.0k200.0k1995200920231995: 144.5 million SLC1996: 261 million SLC1997: 481.8 million SLC1998: 875.2 million SLC1999: 1.3k million SLC2000: 2.0k million SLC2001: 3.2k million SLC2002: 4.4k million SLC2003: 5.3k million SLC2004: 6.2k million SLC2005: 6.5k million SLC2006: 7.4k million SLC2007: 7.9k million SLC2008: 9.1k million SLC2009: 9.8k million SLC2010: 10.7k million SLC2011: 12.2k million SLC2012: 13.6k million SLC2013: 14.8k million SLC2014: 16.6k million SLC2015: 18.5k million SLC2016: 20.6k million SLC2017: 24.0k million SLC2018: 29.4k million SLC2019: 34.5k million SLC2020: 40.9k million SLC2021: 57.5k million SLC2022: 124.5k million SLC2023: 191.6k million SLC

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

Analysis

Türkiye recorded 191,619 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 54.0% on the previous year and up 1,192.0% over ten years.

Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Türkiye peaked at 191,619 million SLC in 2023 and was at its lowest, 144.52 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 Türkiye, year by year

Annual values for Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency in Türkiye, 1995 to 2023.
Year million SLC Change
1995 144.52 million SLC
1996 261 million SLC +80.6%
1997 481.77 million SLC +84.6%
1998 875.18 million SLC +81.7%
1999 1,336 million SLC +52.6%
2000 2,001 million SLC +49.8%
2001 3,241 million SLC +61.9%
2002 4,423 million SLC +36.5%
2003 5,252 million SLC +18.8%
2004 6,164 million SLC +17.4%
2005 6,546 million SLC +6.2%
2006 7,426 million SLC +13.4%
2007 7,947 million SLC +7.0%
2008 9,085 million SLC +14.3%
2009 9,824 million SLC +8.1%
2010 10,704 million SLC +9.0%
2011 12,228 million SLC +14.2%
2012 13,570 million SLC +11.0%
2013 14,832 million SLC +9.3%
2014 16,646 million SLC +12.2%
2015 18,525 million SLC +11.3%
2016 20,596 million SLC +11.2%
2017 23,950 million SLC +16.3%
2018 29,378 million SLC +22.7%
2019 34,479 million SLC +17.4%
2020 40,933 million SLC +18.7%
2021 57,506 million SLC +40.5%
2022 124,465 million SLC +116.4%
2023 191,619 million SLC +54.0%

Averages by decade

DecadeAverage LowestHighest Years
1990s 619.66 million SLC 144.52 million SLC 1,336 million SLC 5
2000s 6,191 million SLC 2,001 million SLC 9,824 million SLC 10
2010s 19,491 million SLC 10,704 million SLC 34,479 million SLC 10
2020s 103,631 million SLC 40,933 million SLC 191,619 million SLC 4

Countries ranked near Türkiye

  1. 3 Cabo Verde 1,375 million SLC compare
  2. 3 Lao People's Democratic Republic 2.30 million million SLC compare
  3. 3 Uzbekistan 11.17 million million SLC compare
  4. 4 Republic of Korea 8.80 million million SLC compare
  5. 4 United Republic of Tanzania 1.97 million million SLC compare
  6. 5 Somalia 5.64 million million SLC compare
  7. 5 Syrian Arab Republic 1.04 million million SLC compare
  8. 6 Lebanon 5.51 million million SLC compare
  9. 7 Colombia 5.48 million million SLC compare
  10. 7 Democratic Republic of the Congo 154,505 million SLC compare
  11. 8 Bolivia (Plurinational State of) 3,107 million SLC compare
  12. 8 Guinea 3.24 million million SLC compare
  13. 9 India 2.29 million million SLC compare
  14. 9 Timor-Leste 28.96 million SLC compare

See the full ranking of 194 places →

More environment data for Türkiye

All data for Türkiye →

Frequently asked questions

What is consumption of fixed capital (agriculture, forestry and fishing) in Türkiye?
Consumption of fixed capital (agriculture, forestry and fishing) in Türkiye was 191,619 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 Türkiye?
The highest recorded value was 191,619 million SLC in 2023.
What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Türkiye?
The lowest recorded value was 144.52 million SLC in 1995.
How does Türkiye rank for consumption of fixed capital (agriculture, forestry and fishing)?
Türkiye ranks 6th out of 10 countries with data for 2023.
Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Türkiye?
Over the last ten years it is up 1,192.0%. The long-run trend across the full record is volatile.
Where does this Türkiye 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.

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Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Türkiye. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 08 September 2026, from https://environment.statizoid.com/stat/consumption-of-fixed-capital-agriculture-forestry-and-fishing-value-standard-local/turkiye-2/

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About this data

Indicator
Consumption of Fixed Capital (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, 5,516 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.