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

Kyrgyzstan: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 0.1673 in 2023. ◆ Volatile

Latest (2023)
0.1673
Change on year
down 27.5%
World rank
173rd
of 181 countries
All-time high
0.4562
in 2002
All-time low
0.0118
in 2016
Years of data
24
2000–2023

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

00.10.20.30.40.52000201120232000: 0.1282001: 0.4472002: 0.4562003: 0.0932004: 0.12005: 0.3052006: 0.1752007: 0.1592008: 0.1322009: 0.1492010: 0.1612011: 0.3112012: 0.1952013: 0.1712014: 0.1232015: 0.0162016: 0.0122017: 0.1192018: 0.2062019: 0.2412020: 0.1892021: 0.2192022: 0.2312023: 0.167

Source: Food and Agriculture Organization of the United Nations.

Analysis

In 2023, gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan stood at 0.1673.

The figure is down 27.5% on the previous year and down 2.4% over ten years.

Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Kyrgyzstan peaked at 0.4562 in 2002 and was at its lowest, 0.0118, in 2016.

Kyrgyzstan ranks 173rd of 181 countries on this measure, in the bottom quarter.

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) — Agriculture orientation index Standard Local Currency in Kyrgyzstan, 2000 to 2023.
Year Value Change
2000 0.1277
2001 0.4474 +250.3%
2002 0.4562 +2.0%
2003 0.0931 -79.6%
2004 0.1004 +7.8%
2005 0.3053 +204.2%
2006 0.1751 -42.7%
2007 0.1592 -9.1%
2008 0.1319 -17.1%
2009 0.1489 +12.8%
2010 0.1607 +7.9%
2011 0.3106 +93.3%
2012 0.195 -37.2%
2013 0.1713 -12.2%
2014 0.1233 -28.0%
2015 0.0163 -86.8%
2016 0.0118 -27.7%
2017 0.119 +907.3%
2018 0.2057 +72.8%
2019 0.2415 +17.4%
2020 0.1888 -21.8%
2021 0.2194 +16.2%
2022 0.2306 +5.1%
2023 0.1673 -27.5%

Kyrgyzstan compared with similar countries

  • Kyrgyzstan's 0.1673 is below the median for lower middle income countries, which is 0.4508, 37% of the median. (41 countries reporting)
  • Kyrgyzstan's 0.1673 is below the median for Europe & Central Asia, which is 1.38, 12% of the median. (44 countries reporting)

Biggest year-on-year movements

Years where Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Kyrgyzstan changed far more than this series normally does. A large move can be a real event or a change in how the figure was measured — the source note below says who published it.

YearChange FromTo
2017 +907.3% 0.0118 0.119

Averages by decade

DecadeAverage LowestHighest Years
2000s 0.2145 0.0931 0.4562 10
2010s 0.1555 0.0118 0.3106 10
2020s 0.2015 0.1673 0.2306 4

Countries ranked near Kyrgyzstan

  1. 170 Maldives 0.1888 compare
  2. 171 Saint Vincent and the Grenadines 0.177 compare
  3. 172 Suriname 0.1744 compare
  4. 174 New Caledonia 0.1668 compare
  5. 175 Dominica 0.1666 compare
  6. 176 Palau 0.148 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 0.1673 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 0.4562 in 2002.
What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Kyrgyzstan?
The lowest recorded value was 0.0118 in 2016.
How does Kyrgyzstan rank for gross fixed capital formation (agriculture, forestry and fishing)?
Kyrgyzstan ranks 173rd 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 down 2.4%. 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) — Agriculture orientation index Standard Local Currency. 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 13 September 2026, from https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-agriculture-orientation/kyrgyz-republic/

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About this data

Indicator
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Agriculture orientation index Standard Local Currency
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.