Agriculture Crops

The map below shows the potential in groundnut yield estimates across South Africa*. Click here for an interactive map:

*Data shows groundnut yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). *Going by Smith’s (1994; 1998) criteria, the climatically most suitable areas for dryland groundnut production are the northeastern parts of the Eastern Cape, much of KwaZulu Natal, and parts of Mpumalanga and Limpopo, with some areas having the potential to produce more than 3.5 t/ha. This is in stark contrast with the actual production areas of North West, the Northern Cape and the Free State.

Source: Schulze, R.E, and Maharaj, M. 2007. “Groundnut Yield Estimation.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/cc0ff4c7-ec54-44bb-b3be-842774895feb

The map below shows the feasible extraction of wheat residues in the Western Cape*. Click here for an interactive map:

*Data shows the feasible extraction of wheat residues allocated to mesozones in the Western Cape. The extent of commercial wheat farming in Western Cape was obtained from the Department of Agriculture of the Western Cape (2014). On such land, production of wheat was estimated from scaled net primary productivity (Schulze et. al.) with yield estimates applied to recent average (10 year) wheat production in the province, derived from Department of Agriculture Annual Statistics. Residue production was calculated based on a fixed percentage of the residue 15%, allowing for soil conditioning (50%) and animal feed (35%).

Source: Hugo, W. 2015. “Feasible Extraction of Wheat Residues in the Western Cape.” In South African BioEnergy Atlas. Pretoria, Republic of South Africa: DST. https://catalogue.saeon.ac.za/records/e34fcffc-c581-4382-8d90-4957df9dd500

The map below shows the potential in maize yield estimates across South Africa*. Click here for an interactive map:

*This dataset contains Maize, Zea mays L. in South Africa and is the country’s most important field and grain crop. Objectives of the study was to simulate maize yields, and their inter-annual, at a spatial resolution of Quaternary Catchments for 12 different combinations of three plant dates, viz. 15 October, 15 November and 15 December. This was done to evaluate which hybrid lengths and plant dates give the highest yields irrespective of plant dates and hybrid lengths respectively and also which hybrid lengths and plant dates give the lowest coefficients of variation (%) irrespective of plant dates and hybrid lengths respectively.

Source: Schulze, R.E, and Walker, N.J. 2007. “Maize Yield Estimation.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/4ff505d9-3270-4c5c-9974-59a6f5a1f3e5

The map below shows the potential in sorghum yield estimates across South Africa*. Click here for an interactive map:

*Data shows sorghum yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). Using Smith’s (1998) climatic criteria, yields of sorghum are estimated using the effective rainfall for October to March and heat units (base 10 degree Celsius) for the same period, with modifications to yield made for soil properties and management levels. Rainfall values were derived from the 1 arc minute (1′ x 1′ latitude x longitude) median monthly rainfalls generated for South Africa by Lynch (2004).

Source: Schulze, R.E, and M. Maharaj. 2007. “Sorghum Yield Estimates per mesozone.” In South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/74e14b7e-6a6b-4d02-8ee6-715dbcda99c3

The map below shows the potential in Soybean Yield Estimates across South Africa*. Click here for an interactive map:

*The dataset shows soybean yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). *Based on Smith’s (1994; 1998) climatic criteria, the highest soybean yields should be attained in the northeastern parts of the Eastern Cape, the moister inland areas of KwaZulu-Natal, and eastern Mpumalanga, with potential yields decreasing towards the west.

Source: Schulze, R.E, and Maharaj, M. 2007. “Soybean Yield Estimation.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/1a812a1c-1e90-488b-9f14-1451cd13aab9

The map below shows the potential in sugar yields across South Africa*. Click here for an interactive map:

*Data shows sugarcane yield estimates for South Africa allocated to mesozones. South Africa is ranked 13th in the world (SA Yearbook, 2005) as a producer of sugarcane. Average sucrose content of the South African crop is approx 13.5%, varying from 11.9 – 13.8% and inversely related to the year’s rainfall. It takes approximately 8.5 tonne sugarcane to produce 1 t sugar, varying from 8.3 – 10.0 t (SASA, 2005). Of the cane under irrigation, 72% is in KwaZulu-Natal (mainly Pongola and Mfolozi flats) and the remaining 28% in Mpumalanga (Statistics SA, 2002). Estimation of Sugarcane Yield was with the ACRU-Thompson Model equation which related sugarcane water use (total evaporation) to yield as Ysc = 9.53(Ean / 100) – 2.36 and where Ysc = annual sugarcane yield (t/ha), and Ean = annual total evaporation (mm).

Source: Schulze, R.E, Hull, P.J, and Maharaj, M. 2007. “Sugarcane Yield Estimation.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/ac0bb82b-5941-4b68-b4b8-0e916078cfbc

The map below shows the potential in banana (irrigated plant crop) yield estimates across South Africa*. Click here for a larger image:

*This dataset shows banana (irrigated plant crop) yield estimates for South Africa. Bananas are stress sensitive plants which grow satisfactorily with a MAP of 1 200 mm, ideally well distributed throughout the year (Smith, 1998). Dry spells of more than 7 days are detrimental to yields. Preferring a relative humidity > 60 percent (especially during fruit setting), the mid-day RH should generally exceed 50 percent. The optimum mean monthly temperatures for bananas are 25 – 28 degree Celsius, with a range of 20 – 35 degree Celsius. Growth ceases below 12 degree Celsius and the July mean of daily minima should be at least 8 degree Celsius. Of the 1 200 ha under bananas, 64 percent are in Mpumalanga, 18 percent are in KwaZulu-Natal and a further 18 percent in Limpopo (Statistics SA, 2002). Highest average yields, around 26 t/ha/an, are attained in Mpumalanga, while those in KwaZulu-Natal and Limpopo are 20 t/ha/an, i.e. on the low side by international standards (Statistics SA, 2002).

Source: Schulze, R.E, and Maharaj, M. 2007. “Banana Yield Estimation.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/eea84133-211d-4371-a0da-4fc42350491d

The map below shows the climatically optimum growth areas for pineapple across South Africa*. Click here for a larger image:

*South Africa accounts for 6 percent of the world`s pineapple production in a market dominated by Thailand, Indonesia, the Phillipines and Kenya (SA Yearbook, 2005). Of the 6 350 ha planted to pineapple in South Africa, 69 percent is in the Eastern Cape and 30 percent in KwaZulu-Natal (Statistics SA, 2002). For pineapple production potential, precipitation is evaluated as follows: Monthly precipitation is divided by 10, with any monthly amount in excess of 100 mm scoring 10. The largest possible annual precipitation score would thus be 120, areas with MAP less than 500 mm are considered unsuitable for pineapple production, except under irrigation, areas with more than 3 consecutive months less than 15 mm or 4 consecutive months less than 25 mm or 5 consecutive months less than 40 mm precipitation per month are also considered unsuitable without supplementary irrigation.

Source: Schulze, R.E, and Maharaj, M. 2007. “Pineapples: Optimum Growth Areas.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/cff59779-fd44-4565-98cc-fe4bbb9e70a6

The map below shows the climatically optimum growth areas for papaya across South Africa*. Click here for a larger image:

*Papaya (or “pawpaw”) needs relatively little water in the rainy summer season in South Africa, but if irrigated every 2 weeks in the dry season it is well adapted to hot, dry areas. Papaya has high heat requirements, with average daily temperatures for optimum growth between 20 and 30 degree Celsius. Determination of climatically optimum growth areas for Papaya in South Africa is based on the expert knowledge of Bower (2005) and Moll (2005), climatically optimum growth areas were determined according to the four basic criteria: Criterion 1: Heat units (base 12 degree Celsius) should exceed 2 000 days per annum, Criterion 2: Optimum areas should have a low frequency of 4 consecutive days with maximum temperatures > 36 degree Celsius, Criterion 3: Monthly means of daily average temperatures in December and January should be 23 degree Celsius – 30 degree Celsius and Criterion 4: Optimum growth areas should have a low frequency of 4 consecutive days with minimum temperatures < 17 degree Celsius. Using the 50 year time series of quality controlled daily maximum and minimum temperatures generated by Schulze and Maharaj (2004) at a spatial resolution of 1 arc minute (i.e. 1` x 1` of a degree latitude/longitude), the above four temperature based criteria were first mapped individually and then superimposed to determine the climatically optimum growth areas of papaya in South Africa.

Source: Schulze, R.E, and Maharaj, M. 2007. “Papaya: Optimum Growth Areas.” In: South African Atlas of Climatology and Agrohydrology. Vol. 1489/1/06. Water Research Commission. https://catalogue.saeon.ac.za/records/e4416616-7c55-43e8-a830-430df7b2f235

The map below shows the commercial forests in South Africa*. Click here for a larger image:

*A South African, Lesotho and Swaziland forestry genera map was received from the Institute for Commercial Forestry Research at the University of KwaZulu-Natal. The forests in the Northern Cape and North-West provinces of South Africa were removed from the map. In South Africa there are no commercial plantations in the Northern Cape and North-West provinces thus these were excluded. Only the Acacia, Pine and Eucalyptus map locations in South Africa were analysed as these were the species that were cultivated for afforestation in the country. The other species, specified as other/mixed were also removed from the map and a South African Commercial Plantations Genera map was drawn.

De Lange, B. 2013. “Eskom Internal Report RES/RR/12/35052: Commercial Forests in South Africa.” Eskom. https://catalogue.saeon.ac.za/records/634772a0-cbf2-4ec1-8c8f-8a21eeeccd76