Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Djibouti
Djibouti: Net Capital Stocks (Agriculture, Forestry and Fishing) — Value was 5,270 million SLC in 2023. ◆ Volatile
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Djibouti, 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 Djibouti is 5,270 million SLC, measured in 2023. That is the highest value across all 29 years on record.
The figure is up 6.7% on the previous year and up 107.2% over ten years.
Over the whole period, net capital stocks (agriculture, forestry and fishing) — value in Djibouti peaked at 5,270 million SLC in 2023 and was at its lowest, 951.17 million SLC, in 2000.
That places Djibouti 125th out of 179 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.
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Djibouti, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 977 million SLC | — |
| 1996 | 968.97 million SLC | -0.8% |
| 1997 | 971.1 million SLC | +0.2% |
| 1998 | 972.13 million SLC | +0.1% |
| 1999 | 957.49 million SLC | -1.5% |
| 2000 | 951.17 million SLC | -0.7% |
| 2001 | 981.22 million SLC | +3.2% |
| 2002 | 1,030 million SLC | +5.0% |
| 2003 | 1,106 million SLC | +7.4% |
| 2004 | 1,163 million SLC | +5.2% |
| 2005 | 1,289 million SLC | +10.8% |
| 2006 | 1,492 million SLC | +15.8% |
| 2007 | 1,747 million SLC | +17.1% |
| 2008 | 1,986 million SLC | +13.7% |
| 2009 | 2,156 million SLC | +8.6% |
| 2010 | 2,211 million SLC | +2.5% |
| 2011 | 2,308 million SLC | +4.4% |
| 2012 | 2,372 million SLC | +2.8% |
| 2013 | 2,543 million SLC | +7.2% |
| 2014 | 2,733 million SLC | +7.5% |
| 2015 | 2,953 million SLC | +8.1% |
| 2016 | 3,264 million SLC | +10.5% |
| 2017 | 3,622 million SLC | +11.0% |
| 2018 | 3,899 million SLC | +7.7% |
| 2019 | 4,169 million SLC | +6.9% |
| 2020 | 4,432 million SLC | +6.3% |
| 2021 | 4,657 million SLC | +5.1% |
| 2022 | 4,938 million SLC | +6.0% |
| 2023 | 5,270 million SLC | +6.7% |
Djibouti compared with similar countries
- Djibouti's 5,270 million SLC is below the median for lower middle income countries, which is 149,162 million SLC, 4% of the median. (41 countries reporting)
- Djibouti's 5,270 million SLC is below the median for Middle East, North Africa, Afghanistan & Pakistan, which is 20,327 million SLC, 26% of the median. (20 countries reporting)
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 969.34 million SLC | 957.49 million SLC | 977 million SLC | 5 |
| 2000s | 1,390 million SLC | 951.17 million SLC | 2,156 million SLC | 10 |
| 2010s | 3,007 million SLC | 2,211 million SLC | 4,169 million SLC | 10 |
| 2020s | 4,824 million SLC | 4,432 million SLC | 5,270 million SLC | 4 |
Countries ranked near Djibouti
- 122 Sierra Leone 6,914 million SLC compare
- 123 Azerbaijan 6,793 million SLC compare
- 124 Ecuador 6,188 million SLC compare
- 126 Jordan 4,151 million SLC compare
- 127 Zimbabwe 4,085 million SLC compare
- 128 Solomon Islands 3,995 million SLC compare
More environment data for Djibouti
- Historical exposure to drought — Land soil moisture anomaly -57.06 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.289 °C (2025)
- Temperature change 1.25 °C (2025)
- Nutrient potash K2O (total) — Agricultural Use, gaps filled 8 t (2024)
- Nutrient potash K2O (total) — Agricultural Use, annual growth rate 0 % change on previous year (2024)
- Country area — Area, annual growth rate 0 % change on previous year (2024)
- Country area — Area, per unit of GDP 0 1000 ha per US$ of GDP (2024)
- Country area — Area, per capita 0.002 1000 ha per person (2024)
- Land area — Area, annual growth rate 0 % change on previous year (2024)
Frequently asked questions
- What is net capital stocks (agriculture, forestry and fishing) — value in Djibouti?
- Net capital stocks (agriculture, forestry and fishing) — value in Djibouti was 5,270 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 Djibouti?
- The highest recorded value was 5,270 million SLC in 2023.
- What is the lowest net capital stocks (agriculture, forestry and fishing) — value recorded in Djibouti?
- The lowest recorded value was 951.17 million SLC in 2000.
- How does Djibouti rank for net capital stocks (agriculture, forestry and fishing) — value?
- Djibouti ranks 125th out of 179 countries with data for 2023.
- Is net capital stocks (agriculture, forestry and fishing) — value rising or falling in Djibouti?
- Over the last ten years it is up 107.2%. The long-run trend across the full record is volatile.
- Where does this Djibouti 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, 2015 prices. 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.