Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Liberia
Liberia: Net Capital Stocks (Agriculture, Forestry and Fishing) — Value was 1,007 million SLC in 2023. ◆ Volatile
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Liberia, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
The most recent figure for net capital stocks (agriculture, forestry and fishing) — value in Liberia is 1,007 million SLC, measured in 2023. That is the highest value across all 29 years on record.
That represents a change of up 4.0% on the previous year and up 202.5% over ten years.
Over the whole period, net capital stocks (agriculture, forestry and fishing) — value in Liberia peaked at 1,007 million SLC in 2023 and was at its lowest, 111.16 million SLC, in 2000.
That places Liberia 149th out of 179 countries with data for 2023, putting it in the bottom quarter.
The series is highly variable year to year, so single readings are best treated with caution.
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Liberia, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 138.81 million SLC | — |
| 1996 | 148.43 million SLC | +6.9% |
| 1997 | 127.08 million SLC | -14.4% |
| 1998 | 127.06 million SLC | -0.0% |
| 1999 | 135.05 million SLC | +6.3% |
| 2000 | 111.16 million SLC | -17.7% |
| 2001 | 113.81 million SLC | +2.4% |
| 2002 | 126 million SLC | +10.7% |
| 2003 | 142.28 million SLC | +12.9% |
| 2004 | 172 million SLC | +20.9% |
| 2005 | 181.24 million SLC | +5.4% |
| 2006 | 187.47 million SLC | +3.4% |
| 2007 | 165.92 million SLC | -11.5% |
| 2008 | 181.65 million SLC | +9.5% |
| 2009 | 183.65 million SLC | +1.1% |
| 2010 | 197.74 million SLC | +7.7% |
| 2011 | 232.68 million SLC | +17.7% |
| 2012 | 264.02 million SLC | +13.5% |
| 2013 | 332.83 million SLC | +26.1% |
| 2014 | 366.86 million SLC | +10.2% |
| 2015 | 481.24 million SLC | +31.2% |
| 2016 | 550.25 million SLC | +14.3% |
| 2017 | 584.44 million SLC | +6.2% |
| 2018 | 636.34 million SLC | +8.9% |
| 2019 | 810.64 million SLC | +27.4% |
| 2020 | 716.47 million SLC | -11.6% |
| 2021 | 775.64 million SLC | +8.3% |
| 2022 | 967.69 million SLC | +24.8% |
| 2023 | 1,007 million SLC | +4.0% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 135.28 million SLC | 127.06 million SLC | 148.43 million SLC | 5 |
| 2000s | 156.52 million SLC | 111.16 million SLC | 187.47 million SLC | 10 |
| 2010s | 445.7 million SLC | 197.74 million SLC | 810.64 million SLC | 10 |
| 2020s | 866.66 million SLC | 716.47 million SLC | 1,007 million SLC | 4 |
Countries ranked near Liberia
- 146 Lesotho 1,707 million SLC compare
- 147 Puerto Rico 1,636 million SLC compare
- 148 Singapore 1,336 million SLC compare
- 150 Fiji 894.46 million SLC compare
- 151 Sao Tome and Principe 824.24 million SLC compare
- 152 Montenegro 543.59 million SLC compare
More environment data for Liberia
- Historical exposure to drought — Land soil moisture anomaly -4.21 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -4.28 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.221 °C (2025)
- Temperature change 1.42 °C (2025)
- Value Added (Agriculture, Forestry and Fishing) — Value US$, 2015 3.53 % change on previous year (2024)
- Value Added (Agriculture, Forestry and Fishing) — Value US$, 2015 0 million USD per US$ of GDP (2024)
- Value Added (Agriculture, Forestry and Fishing) — Value US$, 2015 0.0001 million USD per person (2024)
- Primary wood and paper products (export/import) — Import value 7.67 % change on previous year (2024)
- Primary wood and paper products (export/import) — Import value, per 0 1000 USD per US$ of GDP (2024)
Frequently asked questions
- What is net capital stocks (agriculture, forestry and fishing) — value in Liberia?
- Net capital stocks (agriculture, forestry and fishing) — value in Liberia was 1,007 million SLC in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest net capital stocks (agriculture, forestry and fishing) — value recorded in Liberia?
- The highest recorded value was 1,007 million SLC in 2023.
- What is the lowest net capital stocks (agriculture, forestry and fishing) — value recorded in Liberia?
- The lowest recorded value was 111.16 million SLC in 2000.
- How does Liberia rank for net capital stocks (agriculture, forestry and fishing) — value?
- Liberia ranks 149th out of 179 countries with data for 2023.
- Is net capital stocks (agriculture, forestry and fishing) — value rising or falling in Liberia?
- Over the last ten years it is up 202.5%. The long-run trend across the full record is volatile.
- Where does this Liberia data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Net Capital Stocks (Agriculture, Forestry and Fishing) — Value 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.