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

Türkiye: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 19,283 million SLC in 2023. ▲ Rising

Latest (2023)
19,283 million SLC
Change on year
down 13.6%
World rank
7th
of 10 countries
All-time high
22,328 million SLC
in 2022
All-time low
10,157 million SLC
in 1995
Years of data
29
1995–2023

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

05.0k10.0k15.0k20.0k1995200920231995: 10.2k million SLC1996: 10.4k million SLC1997: 10.8k million SLC1998: 11.2k million SLC1999: 11.5k million SLC2000: 11.8k million SLC2001: 12.0k million SLC2002: 12.1k million SLC2003: 12.4k million SLC2004: 12.8k million SLC2005: 13.2k million SLC2006: 13.7k million SLC2007: 14.2k million SLC2008: 14.6k million SLC2009: 15.0k million SLC2010: 15.5k million SLC2011: 16.2k million SLC2012: 16.8k million SLC2013: 17.4k million SLC2014: 17.9k million SLC2015: 18.5k million SLC2016: 19.1k million SLC2017: 19.7k million SLC2018: 20.2k million SLC2019: 20.7k million SLC2020: 21.3k million SLC2021: 21.8k million SLC2022: 22.3k million SLC2023: 19.3k million SLC

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

Analysis

The most recent figure for consumption of fixed capital (agriculture, forestry and fishing) in Türkiye is 19,283 million SLC, measured in 2023.

The figure is down 13.6% on the previous year and up 10.9% over ten years.

Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Türkiye peaked at 22,328 million SLC in 2022 and was at its lowest, 10,157 million SLC, in 1995.

The long-run direction has been consistently rising across the 29 years of available data.

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, 2015 prices in Türkiye, 1995 to 2023.
Year million SLC Change
1995 10,157 million SLC
1996 10,446 million SLC +2.8%
1997 10,772 million SLC +3.1%
1998 11,159 million SLC +3.6%
1999 11,519 million SLC +3.2%
2000 11,789 million SLC +2.3%
2001 11,958 million SLC +1.4%
2002 12,103 million SLC +1.2%
2003 12,394 million SLC +2.4%
2004 12,767 million SLC +3.0%
2005 13,242 million SLC +3.7%
2006 13,745 million SLC +3.8%
2007 14,202 million SLC +3.3%
2008 14,630 million SLC +3.0%
2009 15,014 million SLC +2.6%
2010 15,517 million SLC +3.3%
2011 16,179 million SLC +4.3%
2012 16,823 million SLC +4.0%
2013 17,390 million SLC +3.4%
2014 17,917 million SLC +3.0%
2015 18,525 million SLC +3.4%
2016 19,130 million SLC +3.3%
2017 19,679 million SLC +2.9%
2018 20,202 million SLC +2.7%
2019 20,714 million SLC +2.5%
2020 21,306 million SLC +2.9%
2021 21,816 million SLC +2.4%
2022 22,328 million SLC +2.3%
2023 19,283 million SLC -13.6%

Averages by decade

DecadeAverage LowestHighest Years
1990s 10,811 million SLC 10,157 million SLC 11,519 million SLC 5
2000s 13,184 million SLC 11,789 million SLC 15,014 million SLC 10
2010s 18,208 million SLC 15,517 million SLC 20,714 million SLC 10
2020s 21,183 million SLC 19,283 million SLC 22,328 million SLC 4

Countries ranked near Türkiye

  1. 4 Lao People's Democratic Republic 1.58 million million SLC compare
  2. 4 Uzbekistan 3.95 million million SLC compare
  3. 5 Colombia 3.45 million million SLC compare
  4. 5 Democratic Republic of the Congo 165,551 million SLC compare
  5. 6 Nigeria 2.28 million million SLC compare
  6. 6 Syrian Arab Republic 149,892 million SLC compare
  7. 7 Guinea 2.00 million million SLC compare
  8. 8 Bolivia (Plurinational State of) 2,963 million SLC compare
  9. 8 Uganda 1.66 million million SLC compare
  10. 9 India 1.66 million million SLC compare
  11. 9 Timor-Leste 27.19 million SLC compare
  12. 10 Japan 1.63 million million SLC compare
  13. 10 Micronesia (Federated States of) 6.95 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 19,283 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 22,328 million SLC in 2022.
What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Türkiye?
The lowest recorded value was 10,157 million SLC in 1995.
How does Türkiye rank for consumption of fixed capital (agriculture, forestry and fishing)?
Türkiye ranks 7th 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 10.9%. The long-run trend across the full record is rising.
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, 2015 prices. 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 09 September 2026, from https://environment.statizoid.com/stat/consumption-of-fixed-capital-agriculture-forestry-and-fishing-value-standard-local-2/turkiye-2/

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About this data

Indicator
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices
Unit
million SLC
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
194 places, 5,511 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.