PROJECTS FINALISED  |  Research Report 2018/2019
  1. Projects financed 2018-2019

    1. Grain Yield Competition for canola 2018

      C Cumming
      Contractor, Protein Research Foundation

      Due to more favourable climatic conditions in April and May in the Swartland most canola was planted in moist soil. This was a marked improvement on the previous two seasons, where first seasonal rains only fell later. Above average rain was measured in June, resulting in good wetting of the sub-soil. Although rainfall for July, August and September was slightly below average, the lower than average temperatures experienced was favourable for canola production.

      Due to more favourable climatic conditions in April and May in the Swartland most canola was planted in moist soil. This was a marked improvement on the previous two seasons, where first seasonal rains only fell later. Above average rain was measured in June, resulting in good wetting of the sub-soil. Although rainfall for July, August and September was slightly below average, the lower than average temperatures experienced was favourable for canola production.

      Two other contestants, Koos Blanckenberg from Klipheuwel and Sakkie Rust from Tulbagh also had yields of over 3 ton/ha. The average yield of the 15 contestants from the Swartland was 2,893 ton/ha.

      The Southern Cape had very little summer rain and only received the long term average rain in March. April and May were relatively dry and early indications were that an unfavourable season was on the cards. For this reason, only five (5) contestants entered for the canola competition in the Southern Cape. June, July and August received near average rainfall and good rains fell in September. Coupled with this, temperatures remained below average for August and September, resulting in a remarkable recovery of the canola generally. The winner in the Southern Cape for the third year in a row, Pieter Beukes from Caledon, averaged 2,96 ton/ha. Four of the five contestants averaged over 2 tons/ha with an average yield for the five (5) Southern Cape contestants of 2,49 ton/ha despite an unfavourable first half of the season.

    2. Canola technology transfer

      C Cumming
      Contractor, Protein Research Foundation

      The Protein Research Foundation (PRF) recognises the importance of empowering their partners in the latest and most important technology regarding canola production so as to convey uniform and meaningful information to canola producers. These partners include agri-business representatives (co-op technical personnel, agro-chemical agents, seed and fertiliser representatives and Department of Agriculture personnel). For this reason canola information days are organised annually by the PRF. At the end of February 2019 the fifth annual information day was presented in Moorreesburg. Attendees requested that canola producers, especially newer entrants to canola production, be allowed to attend the day. In total sixty one (61) personnel and twenty five (25) canola producers attended the day.

      Subjects covered included cultivar choices, planting techniques, economy of crop rotation and correct determination of harvest timing. Herbicide residue problems, projections for oilseed futures as well as bee friendly production practices were also covered.

    3. An evaluation of continuous cash crop production (including small grains, canola and other alternative broadleaf crops) under conservation agriculture principles on the high potential soils of the Riversdale Flats

      JA Strauss and W Langenhoven
      Western Cape Department of Agriculture

      2018 was the 7th year of production on the new trial. Six cash crop systems were tested including shortened canola rotations and cover crops. A total of 60 plots were planted. The 6 systems tested are replicated 3 times and all crops within each system are represented on the field each year.

      All protocols developed during the annual technical committee meeting in February 2018 were followed and the integrity of the trial layout was upheld.

      Riversdale received very little summer rainfall, which resulted in a very dry start to the 2017 production season. Only 65mm fell from January to the end of April. In 2017 a new weather station was installed at the research site which is managed by the Department of Agriculture. A total of 128mm was received from April to the end of September.

      Wheat production

      SST0127 was planted at Riversdale at 65kg/ha. A total of 38kg N/ha was applied to each plot (8kg N/ha at planting and 30kg N/ha top-dressing). Wheat yields at Riversdale averaged 1 885ha. This was 441kg/ha more than in 2017.

      Canola production

      Diamond was planted at Riversdale at 3,5kg/ha. A total of 38kg N/ha was applied to each plot (8kg N/ha at planting and 30kg N/ha top-dressing). Canola yields at Riversdale averaged 1 662kg/ha with all plots showing oil yield of over 40%.

      Barley production

      Hessekwa was planted at Riversdale at 65kg/ha. Barley yields at Riversdale averaged 2 515kg/ha. This average yield was 1 304kg/ha more than in 2017. All plots were classified as malting grade.

      Lupin production

      A bitter lupine mixture was planted at a rate of 100kg/ha. No lupine plots were harvested due to poor germination and weed problems in the very low rainfall year.

      Cover crops

      Saia oats and field peas were planted at Riversdale at 25kg/ha and 80kg/ha, respectively. No other input cost was incurred during the season except the herbicide cost to kill the cover crop following the information day.


      Although 2018 proved to be a poor production year, all systems tested show a positive gross margin above directly allocated production costs.

    4. Performance of a dual disc and tine planter, soil quality, residue management and rate of nitrogen placement with seed for canola production

      PA Swanepoel, PJG le Roux and GA Agenbag
      Stellenbosch University

      Handling crop residue during plant operations is a challenge to conservation agriculture (CA) farmers worldwide. It remains unclear which tools are most effective in which conditions. Canola (Brassica napus L.), an oilseed crop widely used in rotation with cereals, is particularly sensitive to seedbed conditions, and thus may be influenced by residue loads and the choice of seed-drill openers. To identify optimal planting practices, this study compared the performance of disc and tine openers on canola establishment, growth, and yield under differing residue loads in a Mediterranean-type climate region. Soil disturbance caused by disc and tine openers was evaluated to assess their effect on seedbed conditions; and the interacting effects of the openers with different residue loads were investigated. Tine openers and low crop residue loads resulted in the best (P>0.05) canola establishment. However, canola at reduced plant populations compensate in both biomass and gain yield, so that no yield differences resulted from different opener types, and only small yield differences occurred between residue loads (P>0.05).

      Seed-drills are used in no-tillage systems to place seed directly in soil. Fertilisers are commonly placed in various regions of the seed-furrow, in a single pass with seeding. Various furrow-openers exist, all with unique seed and fertiliser placement characteristics, particularly positioning of fertiliser in relation to the position of the seed. Seedling damage may occur if seed and fertiliser placement is not appropriate for the particular crop. Canola (Brassica napus) seed is small, resulting in seedlings susceptible to injury by fertilisers. A study was conducted to evaluate the effect of in-row nitrogen placement with a seed-drill fitted with single and double chute tine openers, and disc openers. The study was carried out in a Mediterranean-type climate region in South Africa over two years. Single and double chute tine openers and disc openers were evaluated in terms of their influence on fertiliser placement in relation to seed. A control treatment was included where no N was placed with seed in the row, but rather broadcast. Plant establishment was reduced by 48% in both years when canola was established with disc openers with N placement with a tine opener (P<0.05). Tine openers were most successful in establishing an acceptable plant population. This was also supported by data collected for biomass production and leaf area index throughout the season. No yield differences were observed (P>0.05). It was concluded that the application of N in the band at the same position as the seed is a risk. Tine openers, either with a single or double seed chute, that separate seed and fertiliser, resulted in the best canola performance.

    5. Projected protein requirements for animal consumption in South Africa

      D Strydom¹, W de Jager¹ and E Briedenhann²
      ¹ University of the Free State / ² Protein Research Foundation


      The Protein Research Foundation (PRF) has as its main objective the replacement of imported protein with domestically produced protein. After many years of investigating numerous alternatives, the focus changed mainly to soybeans and canola, where the largest impact could be made.

      The growth in the domestic availability of oilcake is a good measure by which the PRF could ascertain if it was achieving its objectives, by way of supporting the industry with research, new technology and technology transfer. The targets that will need to be met in the future for the PRF to continue to emulate the great progress that has been made thus requires projections of future oilcake demands as well as projections of self-sufficiency requirements, and when this goal is likely to be met.

      To accurately measure this progress various models were developed and used over the years. A new model has been developed.

      The new model methodology

      Collaboration between the University of the Free State's Agricultural Economics department, the PRF's existing APR model and BFAP, created a successful new model that can accurately calculate current protein requirements and project future requirements under various scenarios.

      The model considers changes in per capita consumption of meat, milk and eggs as projected by BFAP, as well as population growth. The quantity of meat, milk and eggs that are predicted to be imported and exported are also considered. Projected future prices of major raw materials are incorporated as well as the availability of raw materials, mainly those that are derived as by-products from various agricultural processing industries.

      The genetic improvement of animals has a substantial impact on productivity, therefore changes in animal performance is an important factor that the model incorporates. The model calculates the quantity of feed required as well as raw material breakdown for these feeds.

      There are several animals that are not producers of meat, milk and eggs that consume a substantial amount of animal feed including protein. The feed consumption of these animals, including protein materials, also needs to be accounted for.

      The new model offers this ability by making use of least cost linear programming, and considering transport costs of raw materials across various regions of the country to formulate the actual feeds required by all animals in South Africa, given the constraints of which quantity of raw materials will be domestically available. The result is an accurate prediction of protein requirements and projection of protein requirements both domestically and imported.

      The growth in the domestic availability of oilcake is a good measure by which the PRF could ascertain if it was achieving its objectives, by way of supporting the industry with research, new technology and technology transfer. The targets that will need to be met in the future for the PRF to continue to emulate the great progress that has been made thus requires projections of future oilcake demands and what will be required to obtain self-sufficiency, as well as when this goal is likely to be met. The APR model in collaboration with BFAP data is used to calculate these projections.


      Current scenario

      Based on the current per capita consumption of animal products it is estimated (using the APR Model) that the requirement for animal feed in South Africa is as follows:

      Table 1: National animal feed production 2018
      Feed type National feed consumption (ton)
      Dairy 2 421 756
      Beef and Sheep 3 433 951
      Pigs 880 623
      Layers 1 053 808
      Broilers 3 258 449
      Pet foods 343 952
      Horses 138 303
      Ostriches 112 117
      Aquaculture 5 314
      Total 11 648 273

      In terms of oilcake consumption, the most consumed oilcake is still soya oilcake followed by sunflower oilcake.

      Table 2: Oilcake usage for 2018
      Oilcake type National consumption (ton)
      Soya oilcake 1 150 521
      Sunflower oilcake 429 375
      Canola oilcake 63 000
      Palm kernel 33 075
      Soya full fat 212 662
      Cotton full fat 36 000
      Canola full fat 3 246
      Total 1 927 879

      On the local market, South Africa progressed in terms of substituting imported soya oilcake with local oilcake. South Africa produced 69% of the total requirement in 2018, in 2008 this was only at a 20% level. The projection for 2021 is at 89% and will increase to 95% in 2027.

      Table 3: Historical usages of soya oilcake (Local and imported soybeans processed in South Africa)
      Year Local soya oilcake (ton) Total soya oilcake (ton) Local %
      2001 121 140 598 070 20
      2002 141 520 616 593 23
      2003 120 000 705 352 17
      2004 119 280 740 558 19
      2005 92 080 740 558 12
      2006 210 000 849 678 25
      2007 303 280 1 115 280 27
      2008 253 200 1 261 791 20
      2009 181 600 1 111 172 16
      2010 251 840 1 083 640 23
      2011 301 600 1 291 069 23
      2012 347 760 1 271 341 27
      2013 469 360 1 197 978 39
      2014 565 280 1 232 687 46
      2015 765 287 1 254 120 61
      2016 768 800 1 218 001 63
      2017 871 913 1 267 098 69
      2018 766 795 1 150 521 69

      In terms of total oilcake the local share in consumption increased from 37% in 2007 to 82% in 2018. It is projected that the local share will increase to 82% in 2021 and 94% in 2027.

      Table 4: Historical usages of total oilcake (Local and imported oilcake)
      Year Local oilcake (ton) Total oilcake (ton) Local %
      2001 454 192 1 021 862 44
      2002 482 448 1 149 224 42
      2003 472 312 1 210 396 39
      2004 489 413 1 121 460 44
      2005 416 736 1 212 593 34
      2006 572 231 1 414 338 40
      2007 608 370 1 635 525 37
      2008 494 557 1 758 185 28
      2009 565 181 1 664 927 34
      2010 701 030 1 743 137 49
      2011 624 912 1 857 391 34
      2012 766 927 1 856 360 41
      2013 760 321 1 877 671 40
      2014 913 356 1 889 979 48
      2015 1 197 604 1 914 330 63
      2016 1 238 120 1 965 291 63
      2017 1 300 865 1 798 372 72
      2018 1 441 527 1 649 498 87

      Growth in requirements for animal products

      In order to calculate the consumption figures of the different species it is very important to determine the demand. The demand was calculated by means of using the following macro variables in combination with animal feed conversion ratios growth figures:

      • Population growth
      • Per capita consumption growth
      • Imported animal products
      • Exported animal products
      Table 5: Projections of feed and oilcake requirements to the year 2021 and 2027
      Year Feed (ton) Oilcake (ton) Soya oilcake (ton)
      2018 11 628 789 1 649 498 1 150 521
      2021 12 561 132 1 764 946 1 307 338
      2027 13 372 018 1 826 894 1 287 638

      As explained earlier, soya oilcake remains the dominant protein source in South Africa. This dominance has increased over time and will continue to do so. Soya oilcake in 2010 made up 40% of oilcake requirements, this increased to 70% in 2018 and is predicted to stabilise at 71 % in 2021. Table 5 indicates that soya oilcake consumption will decrease from 2021 to 2027, mainly due to BFAP's projection that poultry imports will increase with 63% from 2021 up to 2027.

      Poultry feeds make up only 39% of total feed consumed in South Africa. This market share of total feed is predicted to remain constant until 2020. Most oilcake is however used in this sector with the share of 84% of soya oilcake usage currently in this sector expected to remain relatively stable up to the year 2027.

      Local Soya Oilcake Production

      The increase in local oilcake production from locally produced soybeans will make South Africa increasingly self-sufficient in protein requirements.

      Table 6: Local vs imported soya oilcake
      Year Local soya oilcake Local soybean Total soya oilcake Local soya % Local
      (From local soybeans) ton Production (required) ton Requirements ton Production (required) ton self-sufficiency
      2018 980 860 1 226 075 1 150 521 1 436 151 85
      2021 1 172 228 1 465 285 1 307 338 1 634 173 89
      2027 1 228 798 1 535 998 1 243 233 1 554 041 98

      Oilcake requirements in South Africa are estimated at 1 649 498 tons in 2018 versus a local production of 1 441 527 tons locally produced, or 87% of requirements (Table 4). The soybean requirement of 1,2 million tons excludes the 238 000 ton full fat soybeans and 30 000 ton for human consumption required.

      Soya oilcake produced in South Africa in 2018 provided 85% of the country's soya oilcake requirements (Table 6).

      According to the model, feed requirements will increase to 13 372 018 tons in 2027 and 12 561 132 tons in 2021. The soya oilcake requirement will be 1 307 338 tons by the year 2021 and 1 243 233 by 2027 (Table 5). There is a decrease in requirements which can be attributed to an increase in feed conversion ratios.

      Estimates indicate 98% self-sufficiency by 2027 and 89% by 2021. In terms of soybeans, this can be attributed to an increase in production of soybeans as estimated by BFAP (Table 5).

      Figure 1: Growth in self-sufficiency in terms of soya oilcake
      Growth in self-sufficiency in terms of soya oilcake

      Although beef and sheep combined is the largest consumer of animal feed, the poultry sector plays a major role in oilcake and particularly soya oilcake usage. Growth and sustainability in the poultry industry will play a major role in oilcake requirements.

      Figure 2: Specie feed consumption
      Specie feed consumption
      Table 7: National animal feed production 2020 and 2026
      Feed type National feed consumption
      2021 (ton) 2027 (ton)
      Dairy 2 538 634 2 762 097
      Beef and sheep 3 770 587 4 131 534
      Pigs 981 688 1 138 128
      Layers 1 116 578 1 163 747
      Broilers 3 498 788 3 393 667
      Other 654 857 782 845
      Total 12 561 132 13 372 018
      Table 8: Oilcake usage projection 2021 and 2027
      Oilcake 2021 (ton) 2027 (ton)
      Soya oilcake 1 307 388 1 287 638
      Sunflower oilcake 356 299 362 300
      Canola oilcake 63 000 125 064
      Palm kernel 37 710 42 993
      Soya full fat 147 302 159 000
      Cotton full fat 36 000 65 086
      Canola full fat 3 246 5 345
      Total 1 950 945 2 047 426


      Animal feed consumption in South Africa decreased drastically in 2017. This was mainly due to the lag effect of the drought but more importantly to the outbreak of bird flu within the borders of South Africa. In 2018 there was a slight recovery on feed consumption. However, given the major increase in production of local soybeans, self-sufficiency increased drastically. In terms of total oilcake consumption, South Africa is at a level of 87% self-sufficiency. This is expected to increase towards 2027, indicating the progress South Africa is making in substituting imports.

      Table 9: Self-sufficiency of total oilcake and oilcake
      2018 2021 2027
      Total oilcake 87% 90% 94%
      Soya oilcake 85% 89% 98%
    6. Research, remedial measures technique, soya plantings, Bothaville and Rustenburg, South Africa

      SA Oosthuyse
      Hort Research SA

      The Remedial Measures Technique was applied to a soya section (stand) on each of two farms - one farm in the Rustenburg region, and the other in the Bothaville region. The farms in question are owned or managed by:

      • Jan Botma, Bothaville (, contact number: 0823880700, in the vicinity of Bothaville.
      • Ferdi Meyer, Rustenburg (, contact number: 0827776892, in the vicinity of Rustenburg.

      Sampling was completed in April 2019. In applying the method on the analysis results obtained from the samples, remedial measures for the coming season (2019-2020) are to be determined.

      Plant and Soil sampling

      From each stand, of which there are two, twenty soil samples and twenty plant samples were taken from 20 sites well distributed throughout the identified stands sections. This was done just prior to harvest, to assess soil nutrient concentrations and other soil variables, as well as variables relating to plant performance (growth and yield).

      Figure 1: Plant and soil samples being taken from the stand near Rustenburg
      Plant and soil samples being taken from the stand near Rustenburg

      The soil and tissue samples are to be sent to SGS laboratory in Somerset West for a comprehensive analysis. From each soil sample a number of parameters are to be analysed for, these relating to soil fertility, chemistry and texture. Tissue nutrient concentrations are also be determined.

      Plant parameters to be quantified

      From the plants at each site, the following are to be quantified after the plants had been sectioned and dried: number of pods, pod weight, bean weight, bean shell weight, root weight, leaf weight, stem weight, weight of 20 beans, number of beans and whole plant weight.

      Data analysis

      The data are to be subjected to multivariate analysis, where the soil and leaf analysis variables were stipulated as independent variables, and the plant quantifications as dependent variables.

      Activities of the prior season: 2018-2019

      The following was found after data analysis in 2018:

      The technique was carried out on the farms of Ferdi Meyer (Rustenburg) and Jan Botma (Bothaville). There was a difference in method, in that plants and soil were removed from the plantings once the beans were close to maturity. Groups of plants differing in size were removed along a line through each stand. Soil occupied by the roots was also removed. This was done at 20 sites per stand. The plants were analysed for performance parameters, including bean yield, pod yield, root dry weight and, plant dry weight. Application of the technique yielded the following results:

      • Ferdi Meyer, Rustenburg

        Here bean yield was strongly related to and positively correlated with exchangeable soil potassium levels. Cation exchange capacity and soil manganese levels also correlated with bean yield. The indication was that magnesium levels in the soil were negatively affecting performance. High soil magnesium concentration is an inherent problem of the growing region. The effect of increasing soil K to 200 kg/mg is to be assessed next season, as well as the application of micro-nutrient adherents to the potassium granules applied.

      • Jan Botma, Bothaville

        The soil at this stand is practically sand. In considering bean yield, the analyses indicated a relationship with cation exchange capacity and soil zinc levels. Coated (adherent) micro element granule fertilizer application is to be recommended, as well as spreading fertilisation over the growing period, in view of poor nutrient retention by the soil (sand). The effect of these adjustments is to be assessed next season.

      Problems encountered regarding application and valuation of effects during 2019

      • Jan Botma

        Severe drought and adverse weather conditions gave rise to Jan Botma's inability to carry out the recommendations for the 2018-2019 season – to provide a stand that could be evaluated for a yield increase. We recommend that we proceed again in taking data during 2019 to determine the yield enhancing measures required for the 2020 crop.

      • Ferdi Meyer

        Problems relating to the time of obtaining soil lab analysis results (those reflecting soil nutrient status levels after winter cropping of wheat) prevented timeous pre-plant application of potassium. We recommend that we proceed again in taking data during 2019 to determine the yield enhancing measures for the 2020 crop.

      In both instances the results for the Remedial Measures Technique obtained during 2018 were clear in their indication of measures to be taken to increase bean yield. Moreover, the results made clear sense in the consideration of soil nutrient balance and textural analysis research findings already established in the literature.

      The original proposition was that the research be carried out for at least 3 seasons. In terms of this, we still have one season for evaluation (2019-2020).

    7. The influence of planting date and row width on recommended planting density and yield of soya beans in the North Eastern Free State

      JP van Zyl
      Department of Agricultural Development, VKB

      Aim of the research project

      The aim of the project is to develop guidelines for adapting planting density according to varying planting dates and differential maturity classes for soya beans. It can be seen as an aspect of precision farming in the North Eastern Free State that was ignored in the past and will thus have to be fine-tuned.


      Detailed information was provided with the detailed progress report.

      Participating farmers and trial localities:

      • Trial 1: Izak Dreyer (Vrede Ascent)
      • Trial 2: Jaco van Dyk (Vrede/Memel)
      • Trial 3: Jan Nell (trial was abandoned))
      • Trial 4: SW Graaff (Frankfort/Jim Fouché)

      The following trials were planted:

      Unfortunately, only one trial could be planted due to low rainfall during planting time.

      Experimental site summary

      • Trial 4: SW Graaff
        • 2 x row widths: 0.30m and 0.60m
        • 1 x planting date: late (27 November 2018))
        • 4 x plant densities: 150 000, 250 000, 350 000, 400 000 plant/ha
        • 4 x cultivars
          • SSS 4945 (MG 4.5)
          • SSS 5449 (MG 5)
          • SSS 6560 (MG 6)
          • DM 5953 RSF (MG 4.7)


      Plant population

      Plant populations of 150 000 plants/ha produced satisfactory yields. For all four maturity classes, 350 000 plants/ha produced the highest yields. There is a suggestion that later planting dates with higher plant populations result in higher yields than at a lower plant population.

      Row width

      Narrow rows in general produced higher yields than wide rows, yet for maturity class 4.5 the wider rows yielded higher. It seemed that the effect of narrow rows was better at 400 000 plants/ha for all maturity classes, except for the 4.5 maturity class.

      Planting date and maturity class

      The effect of planting date and maturity class could not be compared to an early planting date, but due to the late planting date the shorter maturity classes (4.5 and 4.7) yielded higher than the medium (5) and long maturity class (6).

    8. Income and cost budgets for summer and winter crops in South Africa

      D van der Westhuizen
      The Bureau for Food en Agricultural Policy (BFAP)


      The Bureau for Food and Agricultural Policy (BFAP) was founded in 2004 with the purpose of informing decision-making by stakeholders in the agro-food, fibre and beverage complex through independent, research-based policy and market analyses. BFAP is a non-profit company with an advisory board consisting of representatives from the affiliated universities as well as the private and public sector. The company is managed by a Board of Directors. BFAP has a distinguished history of partnerships in the South African agricultural sector, providing exclusive advanced analysis and insights of both primary and secondary agricultural markets to public and private sector. In addition to publication of the annual baseline outlook, its integrated analytical framework has been applied in a number of research projects supporting the agricultural sector at large. Such projects include an evaluation of the possible contribution of the agro-industrial complex to employment creation for the National Planning Commission, an analysis of the long term impact of mining on food security in South Africa and an assessment of the impact of proposed minimum wages for farm workers in South Africa. Furthermore, the training of individuals in specialized strategic decision-making and analytical techniques remains a key priority, ensuring the provision of high quality human capital to support the greater South African agricultural industry.

      Over the past decade BFAP has developed into a well-positioned global virtual network linking individuals with multi-disciplinary backgrounds to a coordinated research system that informs decision making within the Food and Beverage sector. The core analytical team consists of independent analysts and researchers who are affiliated with the Department of Agricultural Economics, Extension and Rural Development at the University of Pretoria, the Department of Agricultural Economics at the University of Stellenbosch, or the Directorate of Agricultural Economics at the Provincial Department of Agriculture, Western Cape. Recently, BFAP also signed a Memorandum of Understanding to increase collaborative research with the University of Fort Hare. This proposal motivates a partnership that will enable the PRF to benefit not only from the expertise and information systems of a diverse local group, but also gain access to a much broader international network including institutions such as the Food and Agricultural Policy Research Institute (FAPRI), the Food and Agricultural Organization (FAO) of the United Nations, the Organisation for Economic Cooperation and Development (OECD), the agri benchmark group at the Thünen Institute in Germany and the Regional Network of Agricultural Policy Research Institutes (ReNAPRI) in Eastern and Southern Africa.

      The BFAP Farming Systems Analysis Program

      The program: The BFAP farming systems analysis program was established with the main objective to assist agribusinesses and farm businesses with strategic decision-making under changing and uncertain market conditions. This is done by means of advanced quantitative analyses of how different policy options, macroeconomic variables, and volatile commodity market conditions could impact upon farm businesses in selected production regions in South Africa. The BFAP Farming Systems Analysis Program includes economic analysis of the production of grain, oilseed, livestock, wine, fruit, sugar and vegetables. Proto-type farms across South Africa's key producing regions are constructed according to a standard operating procedure (SOP) defined by the agri benchmark methodology and are presented in the Table 1.

      The models and methodology: The farm-level activity of BFAP consists of two key components on which services to individual clients are based. These include the system of linked models between the sector and the FinSim farm-level models and the agri benchmark international network.

      • Farm-level modelling

        The BFAP farm-level model (FinSim) is a total budgeting model capable of simulating a (representative) farm comprising various enterprises, e.g. grain, oilseeds, and livestock. Apart from the enterprise specifics, the model captures business specifics, such as the asset structure and financing method(s). The output of the farm-level model is presented through various financial performance indicators. The BFAP FinSim model is utilised in various ways, which include whole-farm planning (capital and operational expenditure), financial and economic feasibility on farm-level, risk analysis via stochastic simulation, impact of policy decisions, input- and market-related shocks on farm-level, and the intermediate and long-term projections based on the BFAP sector model output.

        Table 1: BFAP existing network of prototype farms
        Summer Grains Winter Grains Oilseeds Small-scale Sugarcane Potatoes Horticulture Pig Network
        Western Free State: Maize Overberg: Wheat Eastern Free State: Soybeans KwaZulu-Natal: Traditional producers KwaZulu-Natal: Coastal dryland Eastern Free State: Dryland Western Cape: Apples Western Cape integrated farm
        Northern Free State: Maize Overberg: Barley Eastern Free State: Sunflower KwaZulu-Natal: Grain development program KwaZulu-Natal: Midlands Limpopo: Irrigation Western Cape: Pears KwaZulu-Natal integrated farm
        Eastern Free State: Maize Northern Cape: Wheat Northern Free State: Sunflower Mpumalanga: Irrigation KwaZulu-Natal: Seed North West integrated farm
        Northern Cape: Maize Northern Cape: Barley North West: Sunflower Sandveld: Irrigtion
        Mpumalanga: Maize (budgets) Overberg: Canola
        North West: Maize Overberg: Canola
      • Agri benchmark

        The agri benchmark network is an international network of agricultural research and advisory economists aiming to create a better understanding of global cash crop farming and the economics thereof. The objective of the agri benchmark initiative is to create a national and international database on farm information through collaboration between the public sector, agribusinesses and producer organisations. The link between the local and international network provides the means to benchmark South African agriculture with worldwide farming systems. More specifically, the national farm information database that is linked to the international information system provides decision makers and stakeholders in South African agriculture with a useful tool to obtain business intelligence information, to obtain updates on local and international agriculture, to make financial and managerial strategies for profitable and sustainable farming, and finally, it provides a platform to compare farming businesses and production systems of 16 cash crop enterprises all over the world. The map below illustrates the major countries and crops in the agri benchmark network.

        Figure 1: Agri benchmark cash crop network
        Figure 1: Agri benchmark cash crop network

      Objectives and key deliverables

      The Protein Research Foundation (PRF), Grain South Africa (GSA) and the Bureau for Food and Agricultural Policy (BFAP) currently have their individual cost of production programs which focusses on the key summer- and winter crops produced in South Africa's key agro-ecological zones. Given the existing activities associated within the organisations and the extent of the coverage of South African agricultural production, it is envisaged that by collaboration and integration of existing activities by PRF, GSA and BFAP will add immense value to the individual organisations' annual output. The main objective is hence to consolidate the three programs, generate comprehensive crop income and cost budgets for the key summer- and winter growing regions and lastly to generate sensitivity analysis for these crops based on the latest macroeconomic trends, BFAP Baseline underlying assumptions and international and domestic updates. Please refer to annexure of this proposal for detailed regions and proposed crops.

      Figure 2: Mpumalanga / Eastern Highveld
      Figure 2: Mpumalanga / Eastern Highveld
      Figure 3: Eastern Free State
      Figure 3: Eastern Free State
      Figure 4: Northern and Western Free State
      Figure 4: Northern and Western Free State
      Figure 5: North West
      Figure 5: North West
      Figure 6: KwaZulu-Natal
      Figure 6: KwaZulu-Natal
      Figure 7: Summer irrigation – Northern Cape, Brits, Limpopo and Bergville
      Figure 7: Summer irrigation – Northern Cape, Brits, Limpopo and Bergville
      Figure 8: Winter irrigation – Northern Cape, Brits, Limpopo and Bergville
      Figure 8: Winter irrigation – Northern Cape, Brits, Limpopo and Bergville
      Figure 9: Free State – Winter
      Figure 9: Free State – Winter
      Figure 10: Southern Cape – Winter
      Figure 10: Southern Cape – Winter
      Figure 11: Western Cape – Winter
      Figure 11: Western Cape – Winter
    9. The influence of weed and herbicides on the growth and yield of soybeans

      WF van Wyk
      Contractor, Protein Research Foundation

      Increasing soybean production on the Highveld

      2018/2019: Trials conducted on the UP Experimental farm in Hatfield, Pretoria.

      • Potassium trial

        Treatments: four (4) levels of K (0, 100, 200 and 300kg/ha) were administered and incorporated into the soil pre-planting. Potassium was also applied at 100kg/ha in the middle of every second pair of rows at a depth of 20cm concentrated in a band. A sixth treatment was where 100kg K/ha was applied, but the plots were not planted with soybeans. The plots were kept free of weeds throughout the growing season.

        The trial was harvested, threshed and all post-harvest data were taken.

        Potassium analysis was done before planting and after harvesting to compare removal figures as well as the necessary quantity (kg/ha K) to increase soil K with 1mg/kg.

        All the results will be given in the progress report.

      • Weed control trial

        Four (4) treatments were applied namely no weed control, only pre-emerge control with diclosulam and alachlor, no pre-emerge control with glyphosate applied at 4 weeks and no pre-emerge control with glyphosate applied at 8 weeks. The trial was harvested, threshed and all post-harvest data were taken.

        Al the results will be given in the progress report.

      Additional or Demonstration trials as started in 2018-2019

      The potassium and weed control trials needed only 14 sprinklers to irrigate but the minimum amount is 18 sprinklers because of a pressure issue on the pipe system at the UP farm. The researcher decided to plant more soybeans in order not to waste water on unplanted soil. Additional (demonstration) treatments could therefore be conducted without increasing the budget because all the trials were planted on one day and also harvested on one day.

      Additional treatments that were used are

      • 25cm row spacing with two seeds/position planted every 33,3cm in the row – gives a density of 240 000 plants/ha.
      • Plant density of 200 000 plants/ha in 45cm rows (soybeans were planted at 300 000 plants/ha and thinned to 200 000 plants/ha after emergence).
      • Plant density of 155 000 plants/ha in 45cm rows (soybeans were planted at 300 000 plants/ha and thinned to 155 000 plants/ha after emergence).
      • Plant density of 110 000 plants/ha in 45cm rows (soybeans were planted at 300 000 plants/ha and thinned to 110 000 plants/ha after emergence).
      • Growth points of soybeans removed with slasher at 15cm height above the soil when plants were 30cm tall (R1). Plant density of 290 000 pl/ha.
      • Application of Spoor en Boor on the leaves of soybeans in 45cm rows at R2 (only micro-elements).
      • Application of Groenwoema on the leaves of soybeans in 45cm rows at R2 (micro-elements and N, P and K).
      • Leaf application of MAP-tegnies at R5.
      • Liquid inoculant next to the row at R4 followed by irrigation.
      • 42 N applied as Ammonium Sulphate at R5.
      • 84 N applied as Ammonium Sulphate at R5.
      • 126 N applied as Ammonium Sulphate at R5.
      • Control in 45cm rows and plant density of 290 000 plants/ha.
      • Cultivar PAN 1623 R (MG – 6.2) planted on 11.1.2019.
      • Cultivar DM 5953 rsf R (MG – 4.2) planted at 11.1.2019.
    10. Cultivar evaluation of soybeans in the western dry land production area of South Africa

      GP De Beer and WF van Wyk
      Contractors, Protein Research Foundation

      The past season was one of the driest planting seasons that the west ever had.

      The plan was to plant cultivar trials at Schweizer Reneke, Hoopstad, Hertzogville and Bossies (Between Delaryville and Sannieshof). The first rain in this region fell in the beginning of January 2019, which is too late to plant soybeans.

      Only the trial at Schweizer Reneke was planted on 3 January 2019 but couldn't be used because of a poor plant population.

    11. The use of cultivation practices in soybean to avoid sclerotinia infection

      WF van Wyk
      Contractor, Protein Research Foundation

      Planting of 2 cultivars (DM 5953 – short season grower and PAN 1623 – long season grower) with 3 different planting dates at Wonderfontein and Stoffberg.

      • Trial at Mr Piet Prinsloo from Stoffberg

        Three (3) planting dates namely 5 October 2018, 12 November 2018 and 13 December 2018 were used. Planting was done with an Apache no-till planter in no-till conditions under irrigation. Trial was damaged by hail with 60 percent on 19 December but recovered well so that data could be used. No Sclerotinia was observed here. Rainfall data and temperatures were taken as well as the dates on which the different planting dates were in the different reproductive stages (R – values). These values still have to be incorporated into graphs and all the other data will also be included in the progress report.

      • Trial at Mr Gerrit Roos from Wonderfontein

        Three (3) planting dates namely 12 October 2018, 5 November 2018 and 12 December 2018 were used. Planting was done with a John-Deere minimum-till planter in minimum-till conditions under dryland. Sclerotinia developed in February and infections appeared on the long season grower (PAN 1623 R) at different degrees of severity. The short season grower (DM 5953) showed no infections at all the different planting dates. Rainfall data and temperatures were taken as well as the dates on which the different planting dates were in the different reproductive stages (R – values). These values still have to be incorporated into graphs and all the other data will also be included in the progress report.

    12. Determination of canola harvest yield losses

      C Cumming
      Contractor, Protein Research Foundation

      The PRF Canola Planning Committee approved efforts to determine losses incurred by producers during the harvesting process in the 2018 season. Co-operators monitoring the harvesting process in the Canola Yield Competition, primarily in the Swartland, would assist in the process.

      Ten plastic containers, 21,4cm² in size, were purchased and distributed to those identified to assist. Initially the idea was to determine losses volumetrically, which would have made it possible to calculate the losses (kg/ha) in the field, but it soon became apparent that the amount of grain collected in the containers was not sufficient to measure volumetrically. Samples were then placed in plastic bags to be weighed to determine actual yield losses.

      All the harvesting, with the exception of two producers in the Swartland, was done on windrowed canola at the combining of the windrow.

      The data realised at the end of the season is summarised below:

      Izanè Crous

      • Koos Blanckenberg

        Philadelphia. Harvested 11.10.2018
        John Deere S780   Cv. 44Y90
        Yield loss determined to be less than 0,02 ton/ha

      • Gert Claassen

        Moorreesburg. Harvested 29.10.2018
        John Deere S680   Cv. 44Y90
        Yield loss determined to be less than 0,02 ton/ha

      Johann Laubser

      • Jannie du Toit

        Hopefield Harvested 16.10.2018 (Harvested direct)
        Claas 580 Lexion   Cv. Hyola 559TT
        Yield loss 1,22g/container = 26,1g/m2 = 261kg/ha

      • John Slabber

        Moorreesburg. Harvested 18.10.2018
        Case 2388   Cv. Hyola555TT
        Yield loss 0,8g/container = 17,12g/m2 = 171,2kg/ha

      • Franco Koch

        Moorreesburg. Harvested 17.10.2018 (Harvested direct)
        Case 2388   Cv. 44Y89
        Yield loss 8,82g/container = 188,855g/m2 = 1 888kg/ha (sample 1) down to less than
        200kg/ha (sample 4) as producer kept adjusting settings between samples.

      • Flip van Schalkwyk

        Piketberg. Harvested 8.10.2018
        Case 2388   Cv. Diamond
        Yield loss 1,975g/container = 42,265g/m2 = 422,65kg/ha

      Chris Cumming

      • Franco le Roux

        Napier. Harvested 23.10.2018
        Case 11 8120 Cv. 44Y90

        Combine spreads chaff the same width as cutting width. First 3 samples taken on side of combine and final 2 taken under combine.

        • Yield loss 5,55g/m² = 55,5kg/ha
        • Yield loss 4,625g/m² = 46,25kg/ha
        • Yield loss 5,55g/m² = 55,5kg/ha
        • Yield loss 23,125g/m² = 231,25kg/ha
        • Yield loss 18,5g/m² = 185kg/ha
      • Pieter Beukes

        Caledon. Harvested 26.10.2018

        • Claas 480 Tucano (b), John Deere 9660STS (c)
          Claas Lexion.Cv. 44Y91
        • Yield loss 19,425g/m2 = 194,25kg/ha
        • Yield loss 56,425g/m2 = 542,25kg/ha
        • Yield loss 28,675g/m2 = 286,75kg/ha
      • Ruan Schutte

        Roodebloem Research Farm, Caledon.
        Harvested 25.10.2018
        Claas Lexion 470 Cv. 44Y90

        • Yield loss 1,12g/container = 23,968g/m² = 239,68kg/ha
        • 1,01     21,614     216,14
        • 1,31     28,034     280,34
        • 1,55     33,17     331,70


      Many of the evaluations conducted indicate that between 200 and 300kg/ha losses were incurred in the harvesting process. In some isolated cases the losses were substantial, indicating that producers would benefit from some assistance with information regarding limiting losses. Because small amounts of seed collected in the container (1g in container translates to 21,4g/m² in the containers used = 214kg/ha or over R1000/ha) give the impression of acceptable losses, it would be beneficial to producers to have some means of determining losses infield. Photos to illustrate the levels of losses in an ice cream container could give a rough indication which could be used for infield comparisons.

    13. PRF website

      M du Preez and Y Papadimitropoulos
      Protein Research Foundation and

      Period: 1 March 2018 to 28 February 2019

      This year - in addition to the normal page content updates - the focus was mainly on:

      • standardising the spelling of key terminology; and
      • implementing best practices using HTML markup

      across the 17 sections and 1,032 HTML pages (excluding dynamically created pages) that make up the website of the Protein Research Foundation.

      PHP 7.2

      The website utilises several programming languages to output page content to internet browsers, for example HTML, PHP, JavaScript and MySQL. In preparation for the world-wide PHP upgrade scheduled for September 2019, the PHP programming was audited to ensure compatibility when the server on which the PRF's website is hosted switches over to the new version of PHP.

      Because the website was first launched more than 10 years ago, there were several instances where older, deprecated PHP functions and variables had to be replaced with newer, more secure ones.

      Visitor statistics
      Reporting Year Unique Visitors
      Raw values Google values
      Visitors Pages Pages per visit
      2004 1 691
      2005 3 285
      2006 4 552
      2007 5 404 3 041 10 838 2.79
      2008 11 104 5 274 18 829 2.82
      2009 10 194 6 610 27 341 3.18
      2010 11 812 6 054 23 347 2.98
      2011 12 357 5 511 24 258 3.29
      2012 16 306 6 909 28 206 3.12
      2013 54 739 8 767 34 284 2.97
      2014 54 590 10 189 39 363 3.03
      2015 35 653 12 519 45 078 3.60
      2016 31 674 8 733 53 811 4.47
      2017 49 417 6 901 20 514 2.18
      2018 38 049 10 041 24 873 1.90

      Google values shows an increase in page views and a significant increase in unique visitors. Pages per visit decreased slightly. The most pageviews came from the following pages in order of percentage share:

      • Homepage, 15.66%
      • Soy oilcake price average, 6.3%
      • Guidelines for executive summaries, 3.54%
      • Biofuels government policy, 2.55%
      • Biobrandstof inleiding, 1.85%

      The future of the PRF website

      Its capacity as a provider of valuable information to both producers and researchers via the web, places the PRF in the perfect position to offer more interactive functionalities to visitors who are more and more immersed in 3D worlds where content is displayed in digitally created environs.

      The PRF is also in the process of developing an android application in respect of the income and cost budgets.