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
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Türkiye, 1995–2023
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
| 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
| Decade | Average | Lowest | Highest | 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
- 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
- 7 Guinea 2.00 million million SLC compare
- 8 Bolivia (Plurinational State of) 2,963 million SLC compare
- 8 Uganda 1.66 million million SLC compare
- 9 India 1.66 million million SLC compare
- 9 Timor-Leste 27.19 million SLC compare
- 10 Japan 1.63 million million SLC compare
- 10 Micronesia (Federated States of) 6.95 million SLC compare
More environment data for Türkiye
- Historical exposure to drought — Land soil moisture anomaly -4.66 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -4.91 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.628 °C (2025)
- Temperature change 1.93 °C (2025)
- Industrial roundwood — Export quantity, annual growth rate 33.11 % change on previous year (2024)
- Industrial roundwood — Export value, annual growth rate 40.1 % change on previous year (2024)
- Industrial roundwood, non-coniferous — Production, annual growth rate -6.51 % change on previous year (2024)
- Sawlogs and veneer logs — Production, annual growth rate 4.73 % change on previous year (2024)
- Sawlogs and veneer logs, non-coniferous — Production, annual growth -9.33 % change on previous year (2024)
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.
Download this data
CSV · JSON — 29 observations, free to reuse under CC BY-NC-SA 3.0 IGO (FAO).
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.