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

Latest (2023)
457,122 million SLC
Change on year
down 46.8%
World rank
6th
of 10 countries
All-time high
926,497 million SLC
in 2020
All-time low
48,151 million SLC
in 2008
Years of data
29
1995–2023

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Syrian Arab Republic, 1995–2023

0200.0k400.0k600.0k800.0k1.0M1995200920231995: 58.1k million SLC1996: 57.5k million SLC1997: 58.7k million SLC1998: 56.8k million SLC1999: 52.4k million SLC2000: 56.6k million SLC2001: 52.6k million SLC2002: 73.9k million SLC2003: 71.4k million SLC2004: 84.1k million SLC2005: 89.0k million SLC2006: 81.1k million SLC2007: 60.1k million SLC2008: 48.2k million SLC2009: 60.1k million SLC2010: 76.3k million SLC2011: 88.4k million SLC2012: 112.9k million SLC2013: 122.8k million SLC2014: 143.8k million SLC2015: 221.6k million SLC2016: 383.0k million SLC2017: 515.3k million SLC2018: 583.4k million SLC2019: 706.6k million SLC2020: 926.5k million SLC2021: 681.3k million SLC2022: 860.0k million SLC2023: 457.1k million SLC

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

Annual values for Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices in Syrian Arab Republic, 1995 to 2023.
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

DecadeAverage LowestHighest 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

  1. 3 Cabo Verde 835.18 million SLC compare
  2. 3 Colombia 7.78 million million SLC compare
  3. 3 Lao People's Democratic Republic 4.30 million million SLC compare
  4. 4 Somalia 7.00 million million SLC compare
  5. 4 United Republic of Tanzania 3.61 million million SLC compare
  6. 5 Democratic Republic of the Congo 1.77 million million SLC compare
  7. 5 Republic of Korea 5.32 million million SLC compare
  8. 6 India 4.98 million million SLC compare
  9. 7 Guinea 4.43 million million SLC compare
  10. 7 Türkiye 28,352 million SLC compare
  11. 8 Bolivia (Plurinational State of) 4,824 million SLC compare
  12. 8 Uganda 3.79 million million SLC compare
  13. 9 Paraguay 2.67 million million SLC compare
  14. 9 Timor-Leste 24.48 million SLC compare

See the full ranking of 194 places →

More environment data for Syrian Arab Republic

All data for Syrian Arab Republic →

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.

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Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Syrian Arab Republic. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 07 September 2026, from https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local-2/syrian-arab-republic-2/

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About this data

Indicator
Gross Fixed Capital Formation (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, 5,526 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.