Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in South-Eastern Asia
South-Eastern Asia: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 51,296 million USD in 2023. ▲ Rising
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in South-Eastern Asia, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million USD.
Analysis
South-Eastern Asia recorded 51,296 million USD for gross fixed capital formation (agriculture, forestry and fishing) in 2023. That is the highest value across all 29 years on record.
That represents a change of up 8.4% on the previous year and up 46.0% over ten years.
Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in South-Eastern Asia peaked at 51,296 million USD in 2023 and was at its lowest, 16,648 million USD, in 2000.
South-Eastern Asia ranks 9th of 29 groups on this measure, in the middle of the range.
The long-run direction has been consistently rising across the 29 years of available data.
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in South-Eastern Asia, year by year
| Year | million USD | Change |
|---|---|---|
| 1995 | 18,330 million USD | — |
| 1996 | 19,645 million USD | +7.2% |
| 1997 | 20,139 million USD | +2.5% |
| 1998 | 17,000 million USD | -15.6% |
| 1999 | 17,232 million USD | +1.4% |
| 2000 | 16,648 million USD | -3.4% |
| 2001 | 17,052 million USD | +2.4% |
| 2002 | 18,569 million USD | +8.9% |
| 2003 | 19,262 million USD | +3.7% |
| 2004 | 19,951 million USD | +3.6% |
| 2005 | 20,190 million USD | +1.2% |
| 2006 | 21,121 million USD | +4.6% |
| 2007 | 23,761 million USD | +12.5% |
| 2008 | 26,574 million USD | +11.8% |
| 2009 | 26,567 million USD | -0.0% |
| 2010 | 29,489 million USD | +11.0% |
| 2011 | 31,564 million USD | +7.0% |
| 2012 | 33,501 million USD | +6.1% |
| 2013 | 35,143 million USD | +4.9% |
| 2014 | 36,005 million USD | +2.5% |
| 2015 | 37,287 million USD | +3.6% |
| 2016 | 38,895 million USD | +4.3% |
| 2017 | 40,905 million USD | +5.2% |
| 2018 | 42,490 million USD | +3.9% |
| 2019 | 43,619 million USD | +2.7% |
| 2020 | 44,620 million USD | +2.3% |
| 2021 | 46,060 million USD | +3.2% |
| 2022 | 47,338 million USD | +2.8% |
| 2023 | 51,296 million USD | +8.4% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 18,469 million USD | 17,000 million USD | 20,139 million USD | 5 |
| 2000s | 20,970 million USD | 16,648 million USD | 26,574 million USD | 10 |
| 2010s | 36,890 million USD | 29,489 million USD | 43,619 million USD | 10 |
| 2020s | 47,328 million USD | 44,620 million USD | 51,296 million USD | 4 |
Countries ranked near South-Eastern Asia
- 6 Iran (Islamic Republic of) 4,542 million USD compare
- 6 Italy 14,009 million USD compare
- 7 Russian Federation 13,388 million USD compare
- 7 Syrian Arab Republic 1,929 million USD compare
- 8 Australia 12,467 million USD compare
- 8 Democratic Republic of the Congo 1,907 million USD compare
- 9 United Kingdom of Great Britain and Northern Ireland 11,332 million USD compare
- 9 United Republic of Tanzania 1,813 million USD compare
- 10 Germany 11,082 million USD compare
- 11 Bolivia (Plurinational State of) 698.09 million USD compare
- 11 Japan 10,400 million USD compare
- 12 Nigeria 9,663 million USD compare
- 12 Melanesia 567.17 million USD compare
More environment data for South-Eastern Asia
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Historical exposure to drought — Land soil moisture anomaly 1.01 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly 1.59 Percentage change (2025)
- Temperature change 1.1 °C (2025)
- Standard Deviation 0.195 °C (2025)
- Recovered paper — Production 10.49 million t (2024)
- Total fibre furnish — Production 22.16 million t (2024)
- Agricultural land — Value of agricultural production (Int. $) per Area 2,246 USD_PPP/ha (2024)
- Nutrient potash K2O (total) — Use per capita 6.08 kg/cap (2024)
- Nutrient potash K2O (total) — Use per value of agricultural production 14.29 g/Int$ (2024)
Frequently asked questions
- What is gross fixed capital formation (agriculture, forestry and fishing) in South-Eastern Asia?
- Gross fixed capital formation (agriculture, forestry and fishing) in South-Eastern Asia was 51,296 million USD 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 South-Eastern Asia?
- The highest recorded value was 51,296 million USD in 2023.
- What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in South-Eastern Asia?
- The lowest recorded value was 16,648 million USD in 2000.
- How does South-Eastern Asia rank for gross fixed capital formation (agriculture, forestry and fishing)?
- South-Eastern Asia ranks 9th out of 29 groups with data for 2023.
- Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in South-Eastern Asia?
- Over the last ten years it is up 46.0%. The long-run trend across the full record is rising.
- Where does this South-Eastern Asia 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 US$, 2015 prices. Statizoid updates them automatically from the source API.
Download this data
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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.