Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Myanmar
Myanmar: Net Capital Stocks (Agriculture, Forestry and Fishing) — Value was 25.70 million million SLC in 2023. ◆ Volatile
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in Myanmar, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
In 2023, net capital stocks (agriculture, forestry and fishing) — value in Myanmar stood at 25.70 million million SLC. That is the highest value across all 29 years on record.
That represents a change of up 4.2% on the previous year and up 202.4% over ten years.
Over the whole period, net capital stocks (agriculture, forestry and fishing) — value in Myanmar peaked at 25.70 million million SLC in 2023 and was at its lowest, 86,936 million SLC, in 1995.
That places Myanmar 12th out of 179 countries with data for 2023, putting it in the top 10%.
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 Myanmar, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 86,936 million SLC | — |
| 1996 | 111,583 million SLC | +28.4% |
| 1997 | 146,486 million SLC | +31.3% |
| 1998 | 205,738 million SLC | +40.4% |
| 1999 | 262,733 million SLC | +27.7% |
| 2000 | 317,164 million SLC | +20.7% |
| 2001 | 454,886 million SLC | +43.4% |
| 2002 | 666,015 million SLC | +46.4% |
| 2003 | 921,704 million SLC | +38.4% |
| 2004 | 1.10 million million SLC | +19.3% |
| 2005 | 1.45 million million SLC | +31.9% |
| 2006 | 1.99 million million SLC | +37.0% |
| 2007 | 2.74 million million SLC | +38.1% |
| 2008 | 3.61 million million SLC | +31.8% |
| 2009 | 4.33 million million SLC | +19.7% |
| 2010 | 5.30 million million SLC | +22.6% |
| 2011 | 6.51 million million SLC | +22.7% |
| 2012 | 7.39 million million SLC | +13.6% |
| 2013 | 8.50 million million SLC | +14.9% |
| 2014 | 9.76 million million SLC | +14.9% |
| 2015 | 11.08 million million SLC | +13.5% |
| 2016 | 12.45 million million SLC | +12.4% |
| 2017 | 14.02 million million SLC | +12.6% |
| 2018 | 15.82 million million SLC | +12.9% |
| 2019 | 17.72 million million SLC | +12.0% |
| 2020 | 19.32 million million SLC | +9.0% |
| 2021 | 21.71 million million SLC | +12.3% |
| 2022 | 24.66 million million SLC | +13.6% |
| 2023 | 25.70 million million SLC | +4.2% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 162,695 million SLC | 86,936 million SLC | 262,733 million SLC | 5 |
| 2000s | 1.76 million million SLC | 317,164 million SLC | 4.33 million million SLC | 10 |
| 2010s | 10.86 million million SLC | 5.30 million million SLC | 17.72 million million SLC | 10 |
| 2020s | 22.85 million million SLC | 19.32 million million SLC | 25.70 million million SLC | 4 |
Countries ranked near Myanmar
- 9 Lebanon 49.95 million million SLC compare
- 9 Timor-Leste 449.49 million SLC compare
- 10 Guinea 45.19 million million SLC compare
- 10 Micronesia (Federated States of) 142.11 million SLC compare
- 11 Uganda 34.70 million million SLC compare
- 13 Pakistan 22.55 million million SLC compare
- 14 Cambodia 19.02 million million SLC compare
- 15 Japan 17.07 million million SLC compare
More environment data for Myanmar
- Historical exposure to drought — Land soil moisture anomaly -0.016 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly 0.9805 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.243 °C (2025)
- Temperature change 1.43 °C (2025)
- Industrial roundwood — Import quantity, per unit of GDP 0 m3 per US$ of GDP (2024)
- Industrial roundwood — Import quantity, per capita 0 m3 per person (2024)
- Industrial roundwood — Import value, per unit of GDP 0 1000 USD per US$ of GDP (2024)
- Industrial roundwood — Import value, per capita 0 1000 USD per person (2024)
- Industrial roundwood — Export quantity, annual growth rate 25.35 % change on previous year (2024)
Frequently asked questions
- What is net capital stocks (agriculture, forestry and fishing) — value in Myanmar?
- Net capital stocks (agriculture, forestry and fishing) — value in Myanmar was 25.70 million 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 Myanmar?
- The highest recorded value was 25.70 million million SLC in 2023.
- What is the lowest net capital stocks (agriculture, forestry and fishing) — value recorded in Myanmar?
- The lowest recorded value was 86,936 million SLC in 1995.
- How does Myanmar rank for net capital stocks (agriculture, forestry and fishing) — value?
- Myanmar ranks 12th out of 179 countries with data for 2023.
- Is net capital stocks (agriculture, forestry and fishing) — value rising or falling in Myanmar?
- Over the last ten years it is up 202.4%. The long-run trend across the full record is volatile.
- Where does this Myanmar 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.