Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in India
India: Net Capital Stocks (Agriculture, Forestry and Fishing) — Value was 50.65 million million SLC in 2023. ▲ Rising
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in India, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
India recorded 50.65 million million SLC for net capital stocks (agriculture, forestry and fishing) — value in 2023. That is the highest value across all 29 years on record.
The figure is up 7.0% on the previous year and up 67.5% over ten years.
Over the whole period, net capital stocks (agriculture, forestry and fishing) — value in India peaked at 50.65 million million SLC in 2023 and was at its lowest, 11.28 million million SLC, in 1995.
That places India 6th out of 179 countries with data for 2023, putting it in the top 10%.
The long-run direction has been consistently rising across the 29 years of available data.
Net Capital Stocks (Agriculture, Forestry and Fishing) — Value in India, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 11.28 million million SLC | — |
| 1996 | 11.90 million million SLC | +5.4% |
| 1997 | 12.48 million million SLC | +4.9% |
| 1998 | 13.05 million million SLC | +4.6% |
| 1999 | 13.70 million million SLC | +5.0% |
| 2000 | 14.27 million million SLC | +4.1% |
| 2001 | 15.10 million million SLC | +5.8% |
| 2002 | 15.85 million million SLC | +5.0% |
| 2003 | 16.50 million million SLC | +4.1% |
| 2004 | 15.34 million million SLC | -7.1% |
| 2005 | 16.02 million million SLC | +4.4% |
| 2006 | 16.95 million million SLC | +5.9% |
| 2007 | 17.95 million million SLC | +5.9% |
| 2008 | 19.68 million million SLC | +9.6% |
| 2009 | 22.34 million million SLC | +13.5% |
| 2010 | 24.37 million million SLC | +9.1% |
| 2011 | 25.90 million million SLC | +6.3% |
| 2012 | 28.28 million million SLC | +9.2% |
| 2013 | 30.25 million million SLC | +6.9% |
| 2014 | 31.99 million million SLC | +5.8% |
| 2015 | 33.29 million million SLC | +4.1% |
| 2016 | 34.92 million million SLC | +4.9% |
| 2017 | 36.55 million million SLC | +4.7% |
| 2018 | 38.39 million million SLC | +5.0% |
| 2019 | 40.22 million million SLC | +4.8% |
| 2020 | 42.30 million million SLC | +5.2% |
| 2021 | 44.57 million million SLC | +5.4% |
| 2022 | 47.33 million million SLC | +6.2% |
| 2023 | 50.65 million million SLC | +7.0% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 12.48 million million SLC | 11.28 million million SLC | 13.70 million million SLC | 5 |
| 2000s | 17.00 million million SLC | 14.27 million million SLC | 22.34 million million SLC | 10 |
| 2010s | 32.41 million million SLC | 24.37 million million SLC | 40.22 million million SLC | 10 |
| 2020s | 46.21 million million SLC | 42.30 million million SLC | 50.65 million million SLC | 4 |
Countries ranked near India
- 3 Cabo Verde 17,574 million SLC compare
- 3 Lao People's Democratic Republic 29.32 million million SLC compare
- 3 Uzbekistan 59.43 million million SLC compare
- 4 Somalia 57.79 million million SLC compare
- 4 United Republic of Tanzania 25.25 million million SLC compare
- 5 Colombia 56.60 million million SLC compare
- 5 Democratic Republic of the Congo 11.79 million million SLC compare
- 6 Syrian Arab Republic 5.00 million million SLC compare
- 7 Paraguay 44.22 million million SLC compare
- 7 Türkiye 375,176 million SLC compare
- 8 Bolivia (Plurinational State of) 44,643 million SLC compare
- 8 Nigeria 33.50 million million SLC compare
- 9 Timor-Leste 431.78 million SLC compare
- 9 Uganda 28.33 million million SLC compare
More environment data for India
- Historical exposure to drought — Land soil moisture anomaly 11.05 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly 12.52 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.254 °C (2025)
- Temperature change 0.954 °C (2025)
- Roundwood, non-coniferous — Production, annual growth rate -0.3116 % change on previous year (2024)
- Wood fuel — Production, annual growth rate -0.3646 % change on previous year (2024)
- Wood fuel, non-coniferous — Production, annual growth rate -0.3648 % change on previous year (2024)
- Industrial roundwood — Production, annual growth rate 0 % change on previous year (2024)
- Industrial roundwood — Import quantity, per unit of GDP 0 m3 per US$ of GDP (2024)
Frequently asked questions
- What is net capital stocks (agriculture, forestry and fishing) — value in India?
- Net capital stocks (agriculture, forestry and fishing) — value in India was 50.65 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 India?
- The highest recorded value was 50.65 million million SLC in 2023.
- What is the lowest net capital stocks (agriculture, forestry and fishing) — value recorded in India?
- The lowest recorded value was 11.28 million million SLC in 1995.
- How does India rank for net capital stocks (agriculture, forestry and fishing) — value?
- India ranks 6th out of 179 countries with data for 2023.
- Is net capital stocks (agriculture, forestry and fishing) — value rising or falling in India?
- Over the last ten years it is up 67.5%. The long-run trend across the full record is rising.
- Where does this India 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.