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

Latest (2023)
149,892 million SLC
Change on year
down 44.9%
World rank
6th
of 10 countries
All-time high
271,900 million SLC
in 2022
All-time low
32,978 million SLC
in 1995
Years of data
29
1995–2023

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

50.0k100.0k150.0k200.0k250.0k1995200920231995: 33.0k million SLC1996: 34.5k million SLC1997: 35.9k million SLC1998: 37.2k million SLC1999: 38.2k million SLC2000: 39.2k million SLC2001: 40.1k million SLC2002: 41.5k million SLC2003: 43.4k million SLC2004: 45.5k million SLC2005: 47.9k million SLC2006: 50.2k million SLC2007: 51.4k million SLC2008: 51.5k million SLC2009: 51.7k million SLC2010: 52.7k million SLC2011: 54.5k million SLC2012: 57.3k million SLC2013: 60.9k million SLC2014: 65.2k million SLC2015: 72.3k million SLC2016: 86.1k million SLC2017: 107.9k million SLC2018: 134.3k million SLC2019: 165.0k million SLC2020: 204.1k million SLC2021: 240.1k million SLC2022: 271.9k million SLC2023: 149.9k million SLC

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

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

DecadeAverage LowestHighest 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

  1. 3 Cabo Verde 736.63 million SLC compare
  2. 3 Somalia 4.40 million million SLC compare
  3. 3 United Republic of Tanzania 1.73 million million SLC compare
  4. 4 Lao People's Democratic Republic 1.58 million million SLC compare
  5. 4 Uzbekistan 3.95 million million SLC compare
  6. 5 Colombia 3.45 million million SLC compare
  7. 5 Democratic Republic of the Congo 165,551 million SLC compare
  8. 6 Nigeria 2.28 million million SLC compare
  9. 7 Guinea 2.00 million million SLC compare
  10. 7 Türkiye 19,283 million SLC compare
  11. 8 Bolivia (Plurinational State of) 2,963 million SLC compare
  12. 8 Uganda 1.66 million million SLC compare
  13. 9 India 1.66 million million SLC compare
  14. 9 Timor-Leste 27.19 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 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.

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Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 12 September 2026, from https://environment.statizoid.com/stat/consumption-of-fixed-capital-agriculture-forestry-and-fishing-value-standard-local-2/syrian-arab-republic-2/

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About this data

Indicator
Consumption of Fixed Capital (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, 5,511 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.