Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Syrian Arab Republic
Syrian Arab Republic: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 457,122 million SLC in 2023. ◆ Volatile
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Syrian Arab Republic, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
Syrian Arab Republic recorded 457,122 million SLC for gross fixed capital formation (agriculture, forestry and fishing) in 2023.
That represents a change of down 46.8% on the previous year and up 272.1% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Syrian Arab Republic peaked at 926,497 million SLC in 2020 and was at its lowest, 48,151 million SLC, in 2008.
The series is highly variable year to year, so single readings are best treated with caution.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Syrian Arab Republic, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 58,112 million SLC | — |
| 1996 | 57,496 million SLC | -1.1% |
| 1997 | 58,721 million SLC | +2.1% |
| 1998 | 56,815 million SLC | -3.2% |
| 1999 | 52,443 million SLC | -7.7% |
| 2000 | 56,585 million SLC | +7.9% |
| 2001 | 52,601 million SLC | -7.0% |
| 2002 | 73,931 million SLC | +40.6% |
| 2003 | 71,439 million SLC | -3.4% |
| 2004 | 84,057 million SLC | +17.7% |
| 2005 | 89,025 million SLC | +5.9% |
| 2006 | 81,135 million SLC | -8.9% |
| 2007 | 60,140 million SLC | -25.9% |
| 2008 | 48,151 million SLC | -19.9% |
| 2009 | 60,146 million SLC | +24.9% |
| 2010 | 76,348 million SLC | +26.9% |
| 2011 | 88,436 million SLC | +15.8% |
| 2012 | 112,909 million SLC | +27.7% |
| 2013 | 122,835 million SLC | +8.8% |
| 2014 | 143,771 million SLC | +17.0% |
| 2015 | 221,579 million SLC | +54.1% |
| 2016 | 382,963 million SLC | +72.8% |
| 2017 | 515,273 million SLC | +34.5% |
| 2018 | 583,361 million SLC | +13.2% |
| 2019 | 706,572 million SLC | +21.1% |
| 2020 | 926,497 million SLC | +31.1% |
| 2021 | 681,266 million SLC | -26.5% |
| 2022 | 859,958 million SLC | +26.2% |
| 2023 | 457,122 million SLC | -46.8% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 56,717 million SLC | 52,443 million SLC | 58,721 million SLC | 5 |
| 2000s | 67,721 million SLC | 48,151 million SLC | 89,025 million SLC | 10 |
| 2010s | 295,405 million SLC | 76,348 million SLC | 706,572 million SLC | 10 |
| 2020s | 731,211 million SLC | 457,122 million SLC | 926,497 million SLC | 4 |
Countries ranked near Syrian Arab Republic
- 3 Cabo Verde 835.18 million SLC compare
- 3 Colombia 7.78 million million SLC compare
- 3 Lao People's Democratic Republic 4.30 million million SLC compare
- 4 Somalia 7.00 million million SLC compare
- 4 United Republic of Tanzania 3.61 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
- 7 Türkiye 28,352 million SLC compare
- 8 Bolivia (Plurinational State of) 4,824 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
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 gross fixed capital formation (agriculture, forestry and fishing) in Syrian Arab Republic?
- Gross fixed capital formation (agriculture, forestry and fishing) in Syrian Arab Republic was 457,122 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 Syrian Arab Republic?
- The highest recorded value was 926,497 million SLC in 2020.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Syrian Arab Republic?
- The lowest recorded value was 48,151 million SLC in 2008.
- How does Syrian Arab Republic rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Syrian Arab Republic ranks 6th out of 10 countries with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Syrian Arab Republic?
- Over the last ten years it is up 272.1%. 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 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.