Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Kyrgyzstan

Kyrgyzstan: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 3,345 million SLC in 2023. ◆ Volatile

Latest (2023)
3,345 million SLC
Change on year
down 33.7%
World rank
99th
of 181 countries
All-time high
7,013 million SLC
in 2001
All-time low
232.18 million SLC
in 2016
Years of data
24
2000–2023

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Kyrgyzstan, 2000–2023

02.0k4.0k6.0k8.0k2000201120232000: 2.0k million SLC2001: 7.0k million SLC2002: 6.6k million SLC2003: 1.2k million SLC2004: 1.3k million SLC2005: 4.1k million SLC2006: 3.5k million SLC2007: 3.3k million SLC2008: 2.7k million SLC2009: 2.7k million SLC2010: 2.5k million SLC2011: 4.3k million SLC2012: 3.7k million SLC2013: 2.9k million SLC2014: 2.5k million SLC2015: 326 million SLC2016: 232.2 million SLC2017: 2.5k million SLC2018: 4.3k million SLC2019: 5.4k million SLC2020: 4.1k million SLC2021: 5.2k million SLC2022: 5.0k million SLC2023: 3.3k million SLC

Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.

Analysis

The most recent figure for gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan is 3,345 million SLC, measured in 2023.

That represents a change of down 33.7% on the previous year and up 14.7% over ten years.

Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan peaked at 7,013 million SLC in 2001 and was at its lowest, 232.18 million SLC, in 2016.

That places Kyrgyzstan 99th out of 181 countries with data for 2023, putting it in the middle of the range.

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 Kyrgyzstan, year by year

Annual values for Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices in Kyrgyzstan, 2000 to 2023.
Year million SLC Change
2000 2,019 million SLC
2001 7,013 million SLC +247.3%
2002 6,607 million SLC -5.8%
2003 1,235 million SLC -81.3%
2004 1,273 million SLC +3.0%
2005 4,067 million SLC +219.6%
2006 3,522 million SLC -13.4%
2007 3,315 million SLC -5.9%
2008 2,725 million SLC -17.8%
2009 2,652 million SLC -2.7%
2010 2,470 million SLC -6.9%
2011 4,332 million SLC +75.4%
2012 3,729 million SLC -13.9%
2013 2,918 million SLC -21.8%
2014 2,478 million SLC -15.1%
2015 326 million SLC -86.8%
2016 232.18 million SLC -28.8%
2017 2,490 million SLC +972.3%
2018 4,292 million SLC +72.4%
2019 5,393 million SLC +25.6%
2020 4,107 million SLC -23.8%
2021 5,241 million SLC +27.6%
2022 5,049 million SLC -3.7%
2023 3,345 million SLC -33.7%

Averages by decade

DecadeAverage LowestHighest Years
2000s 3,443 million SLC 1,235 million SLC 7,013 million SLC 10
2010s 2,866 million SLC 232.18 million SLC 5,393 million SLC 10
2020s 4,436 million SLC 3,345 million SLC 5,241 million SLC 4

Countries ranked near Kyrgyzstan

  1. 96 Belarus 3,742 million SLC compare
  2. 97 Jamaica 3,575 million SLC compare
  3. 98 Israel 3,548 million SLC compare
  4. 100 North Macedonia 3,068 million SLC compare
  5. 101 Greece 2,745 million SLC compare
  6. 102 Tajikistan 2,709 million SLC compare

See the full ranking of 194 places →

More environment data for Kyrgyzstan

All data for Kyrgyzstan →

Frequently asked questions

What is gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan?
Gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan was 3,345 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 Kyrgyzstan?
The highest recorded value was 7,013 million SLC in 2001.
What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Kyrgyzstan?
The lowest recorded value was 232.18 million SLC in 2016.
How does Kyrgyzstan rank for gross fixed capital formation (agriculture, forestry and fishing)?
Kyrgyzstan ranks 99th out of 181 countries with data for 2023.
Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Kyrgyzstan?
Over the last ten years it is up 14.7%. The long-run trend across the full record is volatile.
Where does this Kyrgyzstan 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 Kyrgyzstan. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 10 September 2026, from https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local-2/kyrgyz-republic/

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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.