Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Libya
Libya: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 12.14 % in 2023. ▬ Flat
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Libya, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in %.
Analysis
Libya recorded 12.14 % for gross fixed capital formation (agriculture, forestry and fishing) in 2023.
Compared with earlier readings it is up 29.7% on the previous year and up 69.8% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Libya peaked at 15.31 % in 2002 and was at its lowest, 6.99 %, in 1998.
That places Libya 85th out of 181 countries with data for 2023, putting it in the middle of the range.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Libya, year by year
| Year | % | Change |
|---|---|---|
| 1995 | 7.24 % | — |
| 1996 | 7.65 % | +5.6% |
| 1997 | 7.26 % | -5.1% |
| 1998 | 6.99 % | -3.8% |
| 1999 | 7.16 % | +2.5% |
| 2000 | 7.48 % | +4.5% |
| 2001 | 14.41 % | +92.6% |
| 2002 | 15.31 % | +6.3% |
| 2003 | 13.7 % | -10.5% |
| 2004 | 11.16 % | -18.6% |
| 2005 | 10.95 % | -1.9% |
| 2006 | 10.48 % | -4.3% |
| 2007 | 11.25 % | +7.4% |
| 2008 | 11.36 % | +1.0% |
| 2009 | 11.66 % | +2.6% |
| 2010 | 10.89 % | -6.5% |
| 2011 | 7.64 % | -29.9% |
| 2012 | 7.68 % | +0.5% |
| 2013 | 7.15 % | -6.9% |
| 2014 | 7.65 % | +6.9% |
| 2015 | 7 % | -8.4% |
| 2016 | 8.5 % | +21.4% |
| 2017 | 9.51 % | +11.8% |
| 2018 | 9.58 % | +0.8% |
| 2019 | 8.38 % | -12.6% |
| 2020 | 9.69 % | +15.7% |
| 2021 | 9.28 % | -4.3% |
| 2022 | 9.36 % | +0.9% |
| 2023 | 12.14 % | +29.7% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 7.26 % | 6.99 % | 7.65 % | 5 |
| 2000s | 11.78 % | 7.48 % | 15.31 % | 10 |
| 2010s | 8.4 % | 7 % | 10.89 % | 10 |
| 2020s | 10.12 % | 9.28 % | 12.14 % | 4 |
Countries ranked near Libya
More environment data for Libya
- Historical exposure to drought — Land soil moisture anomaly -35.17 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -10.37 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.386 °C (2025)
- Temperature change 1.4 °C (2025)
- Wood-based panels — Import quantity, annual growth rate 14.59 % change on previous year (2024)
- Wood-based panels — Import quantity, per unit of GDP 0 m3 per US$ of GDP (2024)
- Wood-based panels — Import quantity, per capita 0.0372 m3 per person (2024)
- Wood-based panels — Import value, annual growth rate 14.39 % change on previous year (2024)
- Wood-based panels — Import value, per unit of GDP 0 1000 USD per US$ of GDP (2024)
Frequently asked questions
- What is gross fixed capital formation (agriculture, forestry and fishing) in Libya?
- Gross fixed capital formation (agriculture, forestry and fishing) in Libya was 12.14 % 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 Libya?
- The highest recorded value was 15.31 % in 2002.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Libya?
- The lowest recorded value was 6.99 % in 1998.
- How does Libya rank for gross fixed capital formation (agriculture, forestry and fishing)?
- Libya ranks 85th out of 181 countries with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Libya?
- Over the last ten years it is up 69.8%. The long-run trend across the full record is flat.
- Where does this Libya 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) — Share of Value Added Standard Local Currency. 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.