Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in South-Eastern Asia
South-Eastern Asia: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 34,177 million USD in 2023. ◆ Volatile
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in South-Eastern Asia, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million USD.
Analysis
The most recent figure for consumption of fixed capital (agriculture, forestry and fishing) in South-Eastern Asia is 34,177 million USD, measured in 2023. That is the highest value across all 29 years on record.
Compared with earlier readings it is up 10.4% on the previous year and up 54.1% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in South-Eastern Asia peaked at 34,177 million USD in 2023 and was at its lowest, 4,488 million USD, in 1998.
South-Eastern Asia ranks 11th of 29 groups on this measure, in the middle of the range.
The series is highly variable year to year, so single readings are best treated with caution.
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in South-Eastern Asia, year by year
| Year | million USD | Change |
|---|---|---|
| 1995 | 6,273 million USD | — |
| 1996 | 6,641 million USD | +5.9% |
| 1997 | 5,997 million USD | -9.7% |
| 1998 | 4,488 million USD | -25.2% |
| 1999 | 5,360 million USD | +19.4% |
| 2000 | 5,471 million USD | +2.1% |
| 2001 | 5,244 million USD | -4.1% |
| 2002 | 5,756 million USD | +9.8% |
| 2003 | 6,509 million USD | +13.1% |
| 2004 | 7,171 million USD | +10.2% |
| 2005 | 7,886 million USD | +10.0% |
| 2006 | 9,483 million USD | +20.3% |
| 2007 | 10,894 million USD | +14.9% |
| 2008 | 12,977 million USD | +19.1% |
| 2009 | 15,064 million USD | +16.1% |
| 2010 | 18,410 million USD | +22.2% |
| 2011 | 21,157 million USD | +14.9% |
| 2012 | 22,072 million USD | +4.3% |
| 2013 | 22,175 million USD | +0.5% |
| 2014 | 22,697 million USD | +2.4% |
| 2015 | 22,009 million USD | -3.0% |
| 2016 | 23,006 million USD | +4.5% |
| 2017 | 24,472 million USD | +6.4% |
| 2018 | 25,579 million USD | +4.5% |
| 2019 | 27,230 million USD | +6.5% |
| 2020 | 27,797 million USD | +2.1% |
| 2021 | 29,997 million USD | +7.9% |
| 2022 | 30,956 million USD | +3.2% |
| 2023 | 34,177 million USD | +10.4% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 5,752 million USD | 4,488 million USD | 6,641 million USD | 5 |
| 2000s | 8,645 million USD | 5,244 million USD | 15,064 million USD | 10 |
| 2010s | 22,881 million USD | 18,410 million USD | 27,230 million USD | 10 |
| 2020s | 30,732 million USD | 27,797 million USD | 34,177 million USD | 4 |
Countries ranked near South-Eastern Asia
- 8 Australia 14,523 million USD compare
- 8 United Republic of Tanzania 819.59 million USD compare
- 9 Bolivia (Plurinational State of) 449.63 million USD compare
- 9 Italy 13,652 million USD compare
- 10 Japan 13,058 million USD compare
- 10 Melanesia 174.88 million USD compare
- 11 Russian Federation 11,106 million USD compare
- 11 Syrian Arab Republic 140.53 million USD compare
- 12 Lao People's Democratic Republic 130.01 million USD compare
- 12 Spain 7,352 million USD compare
- 13 Democratic Republic of the Congo 66.03 million USD compare
- 13 Republic of Korea 6,736 million USD compare
- 14 Brazil 6,462 million USD compare
- 14 Timor-Leste 28.96 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 consumption of fixed capital (agriculture, forestry and fishing) in South-Eastern Asia?
- Consumption of fixed capital (agriculture, forestry and fishing) in South-Eastern Asia was 34,177 million USD in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest consumption of fixed capital (agriculture, forestry and fishing) recorded in South-Eastern Asia?
- The highest recorded value was 34,177 million USD in 2023.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in South-Eastern Asia?
- The lowest recorded value was 4,488 million USD in 1998.
- How does South-Eastern Asia rank for consumption of fixed capital (agriculture, forestry and fishing)?
- South-Eastern Asia ranks 11th out of 29 groups with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in South-Eastern Asia?
- Over the last ten years it is up 54.1%. The long-run trend across the full record is volatile.
- 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 Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value US$. 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.