Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic
Syrian Arab Republic: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 149,892 million SLC in 2023. ◆ Volatile
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic, 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 Syrian Arab Republic is 149,892 million SLC, measured in 2023.
The figure is down 44.9% on the previous year and up 146.2% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic peaked at 271,900 million SLC in 2022 and was at its lowest, 32,978 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 Syrian Arab Republic, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 32,978 million SLC | — |
| 1996 | 34,468 million SLC | +4.5% |
| 1997 | 35,886 million SLC | +4.1% |
| 1998 | 37,199 million SLC | +3.7% |
| 1999 | 38,245 million SLC | +2.8% |
| 2000 | 39,221 million SLC | +2.6% |
| 2001 | 40,143 million SLC | +2.4% |
| 2002 | 41,531 million SLC | +3.5% |
| 2003 | 43,400 million SLC | +4.5% |
| 2004 | 45,461 million SLC | +4.7% |
| 2005 | 47,926 million SLC | +5.4% |
| 2006 | 50,155 million SLC | +4.7% |
| 2007 | 51,384 million SLC | +2.5% |
| 2008 | 51,550 million SLC | +0.3% |
| 2009 | 51,706 million SLC | +0.3% |
| 2010 | 52,698 million SLC | +1.9% |
| 2011 | 54,480 million SLC | +3.4% |
| 2012 | 57,251 million SLC | +5.1% |
| 2013 | 60,888 million SLC | +6.4% |
| 2014 | 65,233 million SLC | +7.1% |
| 2015 | 72,280 million SLC | +10.8% |
| 2016 | 86,079 million SLC | +19.1% |
| 2017 | 107,862 million SLC | +25.3% |
| 2018 | 134,349 million SLC | +24.6% |
| 2019 | 164,986 million SLC | +22.8% |
| 2020 | 204,079 million SLC | +23.7% |
| 2021 | 240,067 million SLC | +17.6% |
| 2022 | 271,900 million SLC | +13.3% |
| 2023 | 149,892 million SLC | -44.9% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 35,755 million SLC | 32,978 million SLC | 38,245 million SLC | 5 |
| 2000s | 46,248 million SLC | 39,221 million SLC | 51,706 million SLC | 10 |
| 2010s | 85,611 million SLC | 52,698 million SLC | 164,986 million SLC | 10 |
| 2020s | 216,484 million SLC | 149,892 million SLC | 271,900 million SLC | 4 |
Countries ranked near Syrian Arab Republic
- 3 Cabo Verde 736.63 million SLC compare
- 3 Somalia 4.40 million million SLC compare
- 3 United Republic of Tanzania 1.73 million million SLC compare
- 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
- 7 Guinea 2.00 million million SLC compare
- 7 Türkiye 19,283 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
More environment data for Syrian Arab Republic
- Historical exposure to drought — Land soil moisture anomaly -33.54 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -24.28 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.561 °C (2025)
- Temperature change 2.09 °C (2025)
- Cropland — Share in Land area 29.67 % (2024)
- Cropland — Area per capita 0.22 ha/cap (2024)
- Cropland — Share in Agricultural land 40.03 % (2024)
- Cropland — Area 5,449 1000 ha (2024)
- Agricultural land — Value of agricultural production (Int. $) per Area 588.84 USD_PPP/ha (2024)
Frequently asked questions
- What is consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic?
- Consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic was 149,892 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 Syrian Arab Republic?
- The highest recorded value was 271,900 million SLC in 2022.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Syrian Arab Republic?
- The lowest recorded value was 32,978 million SLC in 1995.
- How does Syrian Arab Republic rank for consumption of fixed capital (agriculture, forestry and fishing)?
- Syrian Arab Republic ranks 6th out of 10 countries with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Syrian Arab Republic?
- Over the last ten years it is up 146.2%. The long-run trend across the full record is volatile.
- Where does this Syrian Arab Republic 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.