Value Added (Agriculture, Forestry and Fishing) — Value US$ in South Africa
South Africa: Value Added (Agriculture, Forestry and Fishing) — Value US$ was 11,206 million USD in 2024. ▲ Rising
Value Added (Agriculture, Forestry and Fishing) — Value US$ in South Africa, 1970–2024
Source: Food and Agriculture Organization of the United Nations. Measured in million USD.
Analysis
The most recent figure for value added (agriculture, forestry and fishing) — value us$ in South Africa is 11,206 million USD, measured in 2024.
The figure is up 17.4% on the previous year and up 38.4% over ten years.
Over the whole period, value added (agriculture, forestry and fishing) — value us$ in South Africa peaked at 11,470 million USD in 2022 and was at its lowest, 1,230 million USD, in 1970.
South Africa ranks 46th of 200 countries on this measure, in the top quarter.
The long-run direction has been consistently rising across the 55 years of available data.
Value Added (Agriculture, Forestry and Fishing) — Value US$ in South Africa, year by year
| Year | million USD | Change |
|---|---|---|
| 1970 | 1,230 million USD | — |
| 1971 | 1,474 million USD | +19.8% |
| 1972 | 1,523 million USD | +3.3% |
| 1973 | 1,989 million USD | +30.6% |
| 1974 | 2,997 million USD | +50.7% |
| 1975 | 2,740 million USD | -8.6% |
| 1976 | 2,310 million USD | -15.7% |
| 1977 | 2,664 million USD | +15.3% |
| 1978 | 2,909 million USD | +9.2% |
| 1979 | 3,183 million USD | +9.4% |
| 1980 | 4,789 million USD | +50.4% |
| 1981 | 5,109 million USD | +6.7% |
| 1982 | 4,079 million USD | -20.2% |
| 1983 | 3,549 million USD | -13.0% |
| 1984 | 3,392 million USD | -4.4% |
| 1985 | 2,790 million USD | -17.7% |
| 1986 | 3,052 million USD | +9.4% |
| 1987 | 4,509 million USD | +47.8% |
| 1988 | 5,006 million USD | +11.0% |
| 1989 | 4,800 million USD | -4.1% |
| 1990 | 4,807 million USD | +0.2% |
| 1991 | 5,222 million USD | +8.6% |
| 1992 | 4,753 million USD | -9.0% |
| 1993 | 5,038 million USD | +6.0% |
| 1994 | 5,765 million USD | +14.4% |
| 1995 | 5,361 million USD | -7.0% |
| 1996 | 5,577 million USD | +4.0% |
| 1997 | 5,519 million USD | -1.0% |
| 1998 | 4,642 million USD | -15.9% |
| 1999 | 4,313 million USD | -7.1% |
| 2000 | 3,968 million USD | -8.0% |
| 2001 | 3,809 million USD | -4.0% |
| 2002 | 3,817 million USD | +0.2% |
| 2003 | 5,229 million USD | +37.0% |
| 2004 | 6,169 million USD | +18.0% |
| 2005 | 6,023 million USD | -2.4% |
| 2006 | 6,232 million USD | +3.5% |
| 2007 | 7,847 million USD | +25.9% |
| 2008 | 8,132 million USD | +3.6% |
| 2009 | 7,912 million USD | -2.7% |
| 2010 | 8,797 million USD | +11.2% |
| 2011 | 9,365 million USD | +6.5% |
| 2012 | 8,587 million USD | -8.3% |
| 2013 | 7,721 million USD | -10.1% |
| 2014 | 8,097 million USD | +4.9% |
| 2015 | 7,740 million USD | -4.4% |
| 2016 | 7,812 million USD | +0.9% |
| 2017 | 9,505 million USD | +21.7% |
| 2018 | 9,162 million USD | -3.6% |
| 2019 | 7,593 million USD | -17.1% |
| 2020 | 8,736 million USD | +15.1% |
| 2021 | 10,744 million USD | +23.0% |
| 2022 | 11,470 million USD | +6.8% |
| 2023 | 9,546 million USD | -16.8% |
| 2024 | 11,206 million USD | +17.4% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1970s | 2,302 million USD | 1,230 million USD | 3,183 million USD | 10 |
| 1980s | 4,107 million USD | 2,790 million USD | 5,109 million USD | 10 |
| 1990s | 5,100 million USD | 4,313 million USD | 5,765 million USD | 10 |
| 2000s | 5,914 million USD | 3,809 million USD | 8,132 million USD | 10 |
| 2010s | 8,438 million USD | 7,593 million USD | 9,505 million USD | 10 |
| 2020s | 10,340 million USD | 8,736 million USD | 11,470 million USD | 5 |
Countries ranked near South Africa
More environment data for South Africa
- Historical exposure to drought — Land soil moisture anomaly 5.46 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly 8.52 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.275 °C (2025)
- Temperature change 1.05 °C (2025)
- Paper and paperboard — Import value, per unit of GDP 0 1000 USD per US$ of GDP (2024)
- Paper and paperboard — Import value, per capita 0.0126 1000 USD per person (2024)
- Graphic papers — Import quantity, annual growth rate 42.65 % change on previous year (2024)
- Graphic papers — Import quantity, per unit of GDP 0 t per US$ of GDP (2024)
- Graphic papers — Import quantity, per capita 0.0047 t per person (2024)
Frequently asked questions
- What is value added (agriculture, forestry and fishing) — value us$ in South Africa?
- Value added (agriculture, forestry and fishing) — value us$ in South Africa was 11,206 million USD in 2024, according to Food and Agriculture Organization of the United Nations.
- What is the highest value added (agriculture, forestry and fishing) — value us$ recorded in South Africa?
- The highest recorded value was 11,470 million USD in 2022.
- What is the lowest value added (agriculture, forestry and fishing) — value us$ recorded in South Africa?
- The lowest recorded value was 1,230 million USD in 1970.
- How does South Africa rank for value added (agriculture, forestry and fishing) — value us$?
- South Africa ranks 46th out of 200 countries with data for 2024.
- Is value added (agriculture, forestry and fishing) — value us$ rising or falling in South Africa?
- Over the last ten years it is up 38.4%. The long-run trend across the full record is rising.
- Where does this South Africa 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 US$. Statizoid updates them automatically from the source API.
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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).