Value Added (Agriculture, Forestry and Fishing) — Value Standard in Singapore
Singapore: Value Added (Agriculture, Forestry and Fishing) — Value Standard was 162.39 million SLC in 2024. ▼ Falling
Value Added (Agriculture, Forestry and Fishing) — Value Standard in Singapore, 1970–2024
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
The most recent figure for value added (agriculture, forestry and fishing) — value standard in Singapore is 162.39 million SLC, measured in 2024.
That represents a change of up 3.0% on the previous year and up 17.2% over ten years.
Over the whole period, value added (agriculture, forestry and fishing) — value standard in Singapore peaked at 444.5 million SLC in 1984 and was at its lowest, 109.1 million SLC, in 2003.
Singapore ranks 166th of 191 countries on this measure, in the bottom quarter.
The long-run direction has been consistently falling across the 55 years of available data.
Value Added (Agriculture, Forestry and Fishing) — Value Standard in Singapore, year by year
| Year | million SLC | Change |
|---|---|---|
| 1970 | 299.4 million SLC | — |
| 1971 | 331.2 million SLC | +10.6% |
| 1972 | 358.6 million SLC | +8.3% |
| 1973 | 339.3 million SLC | -5.4% |
| 1974 | 325.8 million SLC | -4.0% |
| 1975 | 353.7 million SLC | +8.6% |
| 1976 | 388.7 million SLC | +9.9% |
| 1977 | 392.8 million SLC | +1.1% |
| 1978 | 378.6 million SLC | -3.6% |
| 1979 | 385.9 million SLC | +1.9% |
| 1980 | 390.9 million SLC | +1.3% |
| 1981 | 402.2 million SLC | +2.9% |
| 1982 | 406 million SLC | +0.9% |
| 1983 | 431.3 million SLC | +6.2% |
| 1984 | 444.5 million SLC | +3.1% |
| 1985 | 399.7 million SLC | -10.1% |
| 1986 | 348.9 million SLC | -12.7% |
| 1987 | 319.4 million SLC | -8.5% |
| 1988 | 271.4 million SLC | -15.0% |
| 1989 | 258.7 million SLC | -4.7% |
| 1990 | 237.5 million SLC | -8.2% |
| 1991 | 217.7 million SLC | -8.3% |
| 1992 | 207.8 million SLC | -4.5% |
| 1993 | 198.9 million SLC | -4.3% |
| 1994 | 205.3 million SLC | +3.2% |
| 1995 | 195.2 million SLC | -4.9% |
| 1996 | 201.3 million SLC | +3.1% |
| 1997 | 196.4 million SLC | -2.4% |
| 1998 | 177.2 million SLC | -9.8% |
| 1999 | 175.4 million SLC | -1.0% |
| 2000 | 164.9 million SLC | -6.0% |
| 2001 | 143.7 million SLC | -12.9% |
| 2002 | 119 million SLC | -17.2% |
| 2003 | 109.1 million SLC | -8.3% |
| 2004 | 109.1 million SLC | +0.0% |
| 2005 | 116.6 million SLC | +6.9% |
| 2006 | 118.6 million SLC | +1.7% |
| 2007 | 118.6 million SLC | +0.0% |
| 2008 | 111.7 million SLC | -5.8% |
| 2009 | 116 million SLC | +3.8% |
| 2010 | 119.2 million SLC | +2.8% |
| 2011 | 124.2 million SLC | +4.2% |
| 2012 | 129.6 million SLC | +4.3% |
| 2013 | 134.2 million SLC | +3.5% |
| 2014 | 138.6 million SLC | +3.3% |
| 2015 | 138.1 million SLC | -0.4% |
| 2016 | 137.5 million SLC | -0.4% |
| 2017 | 141.5 million SLC | +2.9% |
| 2018 | 145.9 million SLC | +3.1% |
| 2019 | 156 million SLC | +6.9% |
| 2020 | 149.4 million SLC | -4.2% |
| 2021 | 166.3 million SLC | +11.3% |
| 2022 | 153.6 million SLC | -7.6% |
| 2023 | 157.6 million SLC | +2.6% |
| 2024 | 162.39 million SLC | +3.0% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1970s | 355.4 million SLC | 299.4 million SLC | 392.8 million SLC | 10 |
| 1980s | 367.3 million SLC | 258.7 million SLC | 444.5 million SLC | 10 |
| 1990s | 201.27 million SLC | 175.4 million SLC | 237.5 million SLC | 10 |
| 2000s | 122.73 million SLC | 109.1 million SLC | 164.9 million SLC | 10 |
| 2010s | 136.48 million SLC | 119.2 million SLC | 156 million SLC | 10 |
| 2020s | 157.86 million SLC | 149.4 million SLC | 166.3 million SLC | 5 |
Countries ranked near Singapore
More environment data for Singapore
- Historical exposure to drought — Land soil moisture anomaly -1.71 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -0.9965 Percentage change (2025)
- Standard Deviation, annual growth rate 42.94 % change on previous year (1990)
- Standard Deviation 0.253 °C (1990)
- Temperature change 0.647 °C (1990)
- Wood charcoal — Production, annual growth rate -1.54 % change on previous year (2024)
- Sawnwood — Production, annual growth rate 0 % change on previous year (2024)
- Sawnwood — Production, per unit of GDP 0 m3 per US$ of GDP (2024)
- Sawnwood — Production, per capita 0.0002 m3 per person (2024)
- Sawnwood — Import quantity, annual growth rate -15.19 % change on previous year (2024)
Frequently asked questions
- What is value added (agriculture, forestry and fishing) — value standard in Singapore?
- Value added (agriculture, forestry and fishing) — value standard in Singapore was 162.39 million SLC in 2024, according to Food and Agriculture Organization of the United Nations.
- What is the highest value added (agriculture, forestry and fishing) — value standard recorded in Singapore?
- The highest recorded value was 444.5 million SLC in 1984.
- What is the lowest value added (agriculture, forestry and fishing) — value standard recorded in Singapore?
- The lowest recorded value was 109.1 million SLC in 2003.
- How does Singapore rank for value added (agriculture, forestry and fishing) — value standard?
- Singapore ranks 166th out of 191 countries with data for 2024.
- Is value added (agriculture, forestry and fishing) — value standard rising or falling in Singapore?
- Over the last ten years it is up 17.2%. The long-run trend across the full record is falling.
- Where does this Singapore data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Value Added (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 55 observations, free to reuse under CC BY-NC-SA 3.0 IGO (FAO).
About this data
The FAOSTAT Macro Indicators database provides a selection of country-level macro indicators relating to total economy (Gross Domestic Product, Gross Fixed Capital Formation); agriculture activity; agriculture, forestry and fishing activity; total manufacturing activity; manufacturing of food products and beverages activity; manufacturing activity of tobacco products; and manufacturing activity of food, beverage and tobacco products.It releases time series for a selection of National Accounts variables, including gross domestic product, gross fixed capital formation, industry-level value added and gross output. The database also proposes additional indicators such as gross domestic product per capita, year-on-year growth rates and measures of industry contribution to gross domestic product. All data relating to Gross Domestic Product, Gross Fixed Capital Formation, agriculture, forestry and fishing activity, and to total manufacturing activity originates from the United Nations Statistics Division (UNSD) National Accounts Estimates of Main Aggregates database, which consists of a complete and consistent set of time series of the main National Accounts aggregates of all UN Members States and other territories in the world for which National Accounts information is available. The UNSD database's content is based on the countries' official National Accounts data reported to UNSD through the annual National Accounts Questionnaire, supplemented with data estimates for any years and countries with incomplete or inconsistent information (See http://unstats.un.org/unsd/snaama/Introduction.asp). Data series relating to the sub-industry Agriculture activity are obtained from the UNSD national accounts Official Country Data databases while series on the Manufacturing activity of food and beverages products, manufacturing activity of tobacco products and manufacturing activity of food, beverages and tobacco products originates from the United Nations Industrial Development Organization (UNIDO) INDSTAT2 database. In order to ensure that sub-industry series are consistent in levels with National Accounts based series, which is needed to support comparability across industries (agriculture vs. agro-industry and sub-industries), UNIDO originating series are rescaled on UNSD National Accounts Estimates of Main Aggregates data series (See Section 17.5 for a more detailed description of the data processing steps).