Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Türkiye
Türkiye: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 28,352 million SLC in 2023. ▲ Rising
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Türkiye, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
In 2023, gross fixed capital formation (agriculture, forestry and fishing) in Türkiye stood at 28,352 million SLC.
That represents a change of down 13.0% on the previous year and up 9.8% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Türkiye peaked at 32,606 million SLC in 2022 and was at its lowest, 12,643 million SLC, in 2001.
The long-run direction has been consistently rising across the 29 years of available data.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Türkiye, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 13,720 million SLC | — |
| 1996 | 16,204 million SLC | +18.1% |
| 1997 | 15,574 million SLC | -3.9% |
| 1998 | 18,863 million SLC | +21.1% |
| 1999 | 15,456 million SLC | -18.1% |
| 2000 | 16,580 million SLC | +7.3% |
| 2001 | 12,643 million SLC | -23.7% |
| 2002 | 16,096 million SLC | +27.3% |
| 2003 | 17,819 million SLC | +10.7% |
| 2004 | 19,407 million SLC | +8.9% |
| 2005 | 21,947 million SLC | +13.1% |
| 2006 | 21,316 million SLC | -2.9% |
| 2007 | 21,390 million SLC | +0.3% |
| 2008 | 21,284 million SLC | -0.5% |
| 2009 | 20,788 million SLC | -2.3% |
| 2010 | 26,005 million SLC | +25.1% |
| 2011 | 27,074 million SLC | +4.1% |
| 2012 | 26,754 million SLC | -1.2% |
| 2013 | 25,814 million SLC | -3.5% |
| 2014 | 26,522 million SLC | +2.7% |
| 2015 | 29,583 million SLC | +11.5% |
| 2016 | 27,632 million SLC | -6.6% |
| 2017 | 28,917 million SLC | +4.7% |
| 2018 | 27,866 million SLC | -3.6% |
| 2019 | 29,617 million SLC | +6.3% |
| 2020 | 31,538 million SLC | +6.5% |
| 2021 | 28,086 million SLC | -10.9% |
| 2022 | 32,606 million SLC | +16.1% |
| 2023 | 28,352 million SLC | -13.0% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 15,963 million SLC | 13,720 million SLC | 18,863 million SLC | 5 |
| 2000s | 18,927 million SLC | 12,643 million SLC | 21,947 million SLC | 10 |
| 2010s | 27,578 million SLC | 25,814 million SLC | 29,617 million SLC | 10 |
| 2020s | 30,145 million SLC | 28,086 million SLC | 32,606 million SLC | 4 |
Countries ranked near Türkiye
- 4 Somalia 7.00 million million SLC compare
- 5 Democratic Republic of the Congo 1.77 million million SLC compare
- 5 Republic of Korea 5.32 million million SLC compare
- 6 India 4.98 million million SLC compare
- 7 Guinea 4.43 million million SLC compare
- 8 Uganda 3.79 million million SLC compare
- 9 Paraguay 2.67 million million SLC compare
- 9 Timor-Leste 24.48 million SLC compare
- 10 Cambodia 2.11 million 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)
- Sawnwood, non-coniferous — Export quantity, annual growth rate 30.38 % change on previous year (2024)
- Sawnwood, non-coniferous — Export value, annual growth rate 10.07 % change on previous year (2024)
- Wood-based panels — Import quantity, annual growth rate -36.67 % change on previous year (2024)
- Wood-based panels — Import value, annual growth rate -38.1 % change on previous year (2024)
- Plywood and LVL — Import quantity, annual growth rate -28.66 % change on previous year (2024)
Frequently asked questions
- What is gross fixed capital formation (agriculture, forestry and fishing) in Türkiye?
- Gross fixed capital formation (agriculture, forestry and fishing) in Türkiye was 28,352 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 Türkiye?
- The highest recorded value was 32,606 million SLC in 2022.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Türkiye?
- The lowest recorded value was 12,643 million SLC in 2001.
- How does Türkiye rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Türkiye ranks 7th out of 10 countries with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Türkiye?
- Over the last ten years it is up 9.8%. 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 Gross Fixed Capital Formation (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.