Projects financed 2021/2022
Index of project titles
- PRF Canola Yield Competition
- Canola technology transfer
- 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
- Projected protein requirements for animal consumption in South Africa
- Income and cost budgets for summer and winter crops in South Africa
- Research on soybeans to study new preliminary treatments with different biological leaf applications as well as chemical applications and a demonstration trial in a wagon wheel design
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PRF Canola Yield Competition
The format of the competition underwent a further change for the 2021 season. The whole farm/farming unit concept which had worked well for the previous season was retained, but all canola producers in the Western Cape were automatically incorporated as participants.
By doing away with the requirement to enter the competition individually all producers were eligible to win the competition, thereby increasing interest in the competition and promoting the concept of maximizing yields by adopting best practices. To facilitate the monitoring of results of all canola producers, the PRF joined forces with SOILL, who have access to all the relevant information required.
2021 proved to be a favourable year for canola production in both the Southern Cape and Swartland. The Swartland received slightly lower rainfall than the long term average for the region in June, July and September, but good rain in August and October together with cool conditions throughout the flowering and pod filling stages resulted in above average yields being realised in most of the region.
The Southern Cape season started with torrential rains being experienced in large parts of the region in May. In Tygerhoek, for example, 397mm of rain was recorded for the month and some lands suffered severe soil erosion. In the western part of the Southern Cape some lands could only be planted in June due to saturated soils. Although slightly lower than average rainfall was experienced for the rest of the season, with the exception of July, the below average temperatures recorded during flowering and seed set resulted in excellent yields being achieved.
Recognition was given to the top three growers from each region for the categories producers planting 50 to 150 ha, those planting more than 150 ha, while an additional category was added for the best new canola producer in each region.
The top producers for 2021 were as follows:
SOUTHERN CAPE
CATEGORY 50-150 ha PLANTED
Danie du Toit
Springerskuil Bdry, Caledon
2,72 ton/haChris Swart
Witklip Malanskraal Bdry, Bredasdorp
2,63 ton/haJan de Kock
Kograh Bdry, Caledon
2,57 ton/ha
CATEGORY >150 ha PLANTED
Jan Paul Kempen
Amerika Landgoed, Klipdale
3,03 ton/haPhilip Morkel
Quarrie Bdry, Klipdale
2,72 ton/haJohan Fourie
Broodkas Bdry, Heidelberg
2,41 ton/ha
NEW PRODUCER
Wynand Wessels
Wynarend Bdry, Napier
2,95 ton/haSWARTLAND
CATEGORY 50-150 ha PLANTED
Thys Louw
Diemersdal Bdry, Klipheuwel
2,71 ton/haMelt van der Westhuizen
Moorreesburg
2,56 ton/haDirk Lesch
DJ Lesch Bdry, Malmesbury
2,50 ton/ha
CATEGORY >150 ha PLANTED
Hennie Walters
Walters Groep, Malmesbury
2,67 ton/haDolfie Walters
Rustfontein Bdry, Malmesbury
2,28 ton/haAJ Louw
AJ Louw Bdry, Malmesbury
2,34 ton/ha
NEW PRODUCER
Ewan van Heerden
Bakensig Bdry, Moorreesburg
2,32 ton/ha
Thanks to SOILL and specifically Zander Spammer, Agricultural Resource Manager at SOILL, for providing the information required and their co-operation in making the competition a success.
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Canola technology transfer
The canola information days were originally initiated to train agricultural chemical company employees and other personnel who were involved in advising producers on issues related to the production of canola. Over the years, persons from the Department of Agriculture, Universities, Agri-business and other institutions that provide inputs to canola production, as well as producers of canola themselves, have changed the configuration of attendees.
Due to Covid 19 restrictions prohibiting gatherings, the information day planned for early 2021 could only be presented in May 2021 in the Hopefield Sports club, Hopefield. Presentations on climate change and related risks, cultivar evaluation feedback, choices for cover crops, and the latest research findings on Sclerotinia and Black Leg were made. Issues related to yield loss at harvesting and the oilseed market were also covered.
A total of 73 attendees were present and the event was concluded with a sumptuous lunch provided by the sports club personnel.
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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
Summary of results and outputs during the 2021 production year.
2021 was the 10th year of production on the new trial. Six cash crop systems are 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 in the field each year.
All protocols developed during the annual technical committee meeting in February 20201 were followed and the integrity of the trial layout was upheld.
Wheat production
SST0166 was planted at Riversdale at 60 kg/ha. A total of 38 kg N/ha was applied to each plot (8 kg N/ha at planting and 30 kg N/ha top-dressing).
Wheat yields at Riversdale averaged 3042 kg/ha. This was 1463 kg/ha less than in 2020.
Canola production
44Y90 was planted at Riversdale at 2.5 kg/ha. A total of 38 kg N/ha was applied to each plot. Nitrogen at plant was 8 kg/ha and a topdressing of 30 kg/ha was applied at the end of July. Canola yields at Riversdale averaged 1095 kg/ha which was 1055 kg/ha less than the 2020 average.
Barley production
Kadie was planted at Riversdale at 50 kg/ha. Barley yields at Riversdale averaged 4201 kg/ha. This average yield was 70 kg/ha more than in 2020.
Lupin production
Lupin plots were planted to bitter lupin SSL10 at a rate of 80 kg/ha. No plots were harvested. Good growth but poor seed set and poor weed control led to the termination of the lupin trial.
Cover crops
A mixture of peas, lupins and barley were planted during 2020 at seeding rates of 80 kg/ha.
Economics
Although commodity prices were excellent the problems with canola yields and rain during the harvesting period had a pronounced effect on the economics of the 2021 season.
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Projected protein requirements for animal consumption in South Africa
Introduction
The Protein Research Foundation (PRF) has as its main objective the replacement of imported protein with domestically produced protein for animal feed. After many years of investigating numerous protein sources, the PRF decided to focus its research on soybeans and canola, believing that these would make the largest impact on its objective.
Growth in the domestic production of oilcake is the best measure by which the PRF can ascertain the extent to which it is achieving its objectives, by way of supporting the industry with research, new technology and technology transfer. For the PRF to continue to emulate the great progress that has been made to date, targets need to be defined, for which projections need to be made of future oilcake demands, what will be required to obtain self-sufficiency, and when this goal is likely to be met.
To measure this progress accurately, various models have been developed and used over the years. Recently, a new model has been developed which 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. Incorporated into the model are projected future prices and availability of major raw materials, mainly those that are derived as by-products from various agricultural processing industries. The model calculates the quantity of feed required as well as the raw material breakdown for these feeds.
The genetic improvement of animals has a substantial impact on productivity, therefore the change in animal performance is an important factor considered by the model.
There are several animal species, such as dogs and cats, that are not producers of meat, milk and eggs but which nevertheless consume substantial amounts of animal feed, including protein. The feed and protein consumption of these animals also needs to be accounted for.
Making use of least cost linear programming and considering transport costs of raw materials across various regions of the country, the model formulates the actual feed required by all animals in South Africa given the constraint of the quantity of raw materials that are domestically available. The result is an accurate prediction of current and projected protein requirements both domestically and imported.
Results
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 shown in Table 1:
Table 1: Historical usages of soya oilcake (Local and imported soybeans processed in South Africa) Feed type National feed consumption (ton) Aquaculture 5 125 Broiler 3 326 392 Cattle Beef 2 920 558 Cattle Dairy 2 600 809 Horses 127 310 Layer 1 305 565 Ostriches 88 014 Pets 368 287 Pigs 1 086 174 Sheep 258 888 Various 10 Grand Total 12 087 124 Soya oilcake remains the most consumed oilcake in South Africa, followed by sunflower oilcake (Table 2).
Table 2: Oilcake usage for 2021 Oilcake type National consumption (ton) Cotton Full Fat 9 000 Soya Full Fat 155 000 Canola Oilcake 74 000 Soya Oilcake 1 444 193 Sunflower Oilcake 381 804 Palm Kernel Meal 5 700 Total 2 069 697 On the local market, South Africa progressed in terms of substituting imported soya oilcake with local oilcake. Currently South Africa produces 73% of the total requirement in 2022, whereas in 2009 this was only 16% (Table 3). The projection for 2024 is 81% and will increase to 100% in 2030. However, the substitution is highly dependent on efficient infrastructure and logistical support, providing internal raw materials to coastal areas at competitive prices. The usage of oilcake is also sensitive in terms of price and competition for raw materials containing protein. For instance, an increase in lucerne production or wet-milled by-products directly affects the usage of oilcakes.
Table 3: Historical usages of soya oilcake (Local and imported soybeans processed in South Africa) Year Local soybean oilcake (ton) Total soybean oilcake (ton) Local % 2001 121 140 598 070 20 2002 141 520 616 593 23 2003 120 000 705 352 17 2004 119 280 616 596 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 836 285 1 267 098 66 2018 766 795 1 150 521 66 2019 820 000 1 218 000 67 2020 849 700 1 213 700 70 2021 1 073 682 1 444 193 73 In terms of total oilcake, the local share in consumption increased from 34% in 2009 to 79% in 2020, and kept constant at 79% in 2021 (Table 4). It is projected that the local share will increase to 85% in 2024 and 99,7% in 2030.
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 2019 1 434 660 1 875 738 76 2020 1 485 183 1 885 663 79 2021 1 643 916 2 069 697 79 To calculate the consumption figures of the different species it is important to determine the demand for their products. This was calculated using the following macro variables in combination with animal feed conversion ratios and growth projections:
- Population growth
- Per Capita consumption growth
- Imported animal products
- Exported animal products
Table 5: Projected feed and oilcake requirements for the year 2024 and 2030 Year Feed (ton) Oilcake (ton) Soya Oilcake (ton) 2021 12 087 124 2 069 697 1 444 193 2024 12 803 849 2 147 953 1 515 659 2030 13 631 093 2 202 461 1 537 560 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. Future consumption of soya oilcake is highly influenced by feed conversion ratios (FCR), which, if these continue to improve, will stabilise future consumption. However, exports of beef, sheep and the prospect of chicken being exported will increase the consumption of animal feed and will have an impact on the end consumption figures for 2030.
Local Soya Oilcake Production
Conclusion
The prospect of South Africa in the future becoming independent of imported protein for animal feed looks increasingly promising. However, this is dependent on several factors. With increasing efficiencies in FCR, if animal numbers remain the same the demand for animal protein may stabilise, in which case it would be important for the soybean industry to increase demand elsewhere. This additional demand could occur in exports of animal products. Currently this is the focus of the different Masterplans being discussed which, if successful, will play an important role in sustainability. However, the outcome and positive outlook is yet to be seen.
Another factor of importance is the price and competition for raw materials. To sustain the projected local consumption levels, raw material prices tend towards export parity. This implies that much work will be needed to increase the feasibility of producing the raw materials at these levels. Sensitivity is specifically related to the price of protein sources, one of these ratios being the maize to soybean price ratio.
Given the production of raw materials, the question is whether there will be opportunities in the future for exports. Some of the commodities can be exported but there may be better opportunities to export processed product.
The protein raw material basket is complex, and any change in price or availability of one raw material will affect the utilisation of oilcake, specifically soya oilcake. If the production or importation of an alternative protein source increases, this will have a direct impact on the consumption of soya oilcake.
Logistics and biosecurity will play an important role in the future. If inland products cannot be delivered price-competitively to coastal consumers, imports will continue. If the exports of animal products do not increase, due, for example, to biosecurity lapses, there will be pressure on the utilisation of soybean oilcake in South Africa and this will have a direct impact on production.
If all of the above fall into place, South Africa could soon become totally self-sufficient in oilcake production (Table 8) and there may even be some space to increase soyabean production.
Table 8: Projected self-sufficiency in total oilcake and soya oilcake 2021 2024 2030 Total oilcake 73% 85% 99.7% Soya oilcake 66% 82% 100% -
Income and cost budgets for summer and winter crops in South Africa
Background
The Bureau for Food and Agricultural Policy (BFAP), founded in 2004, serves the agro-food, fibre and beverage sectors in South Africa and Africa. Our purpose is to inform better decision-making by providing unique insights gained through rigorous analyses, supported by credible databases, a combination of integrated models and considerable experience. Over more than 15 years, the Bureau has developed a distinct value proposition to deliver a holistic solution to public sector and private clients active in the agricultural sector and related value chains. This offering is complemented through BFAP's investment in the Integrated Value Information System (IVIS), a geo-spatial platform which further enhances BFAP's product offering by providing enhanced systems-solutions to the integration of data and insights visualisation to support strategic-decision-making along multi-dimensional value chains.
The BFAP Group consist of a team of experienced private and public sector experts with a range of multi-disciplinary skills including agricultural economics, food science, mathematics and data science, engineering, supply chain management, socio-economic impact assessment, systems technology, and geo-informatics. In addition, we fundamentally believe that a competitive and thriving agricultural sector with its related value chains is built on long-run partnerships. Hence, BFAP has developed a well-established network of local and international collaborators and partners in the public and private sector. This includes long-standing partnerships with private sector clients for more than a decade, research partners like the Food and Agricultural Policy Research Institute (FAPRI) at the University of Missouri in the USA and the Food and Agricultural Organization of the United Nations (FAO). BFAP is also one of the founding members and partners of the Regional Network of Agricultural Policy Research Institutes (ReNAPRI) in Eastern and Southern Africa. As a team and as a network, we pool our knowledge and experience to offer the best possible insights and access to a unique high value network.
The BFAP Group utilises globally recognised techniques and modelling systems to analyse the food, fibre and beverage sectors.
The current BFAP modelling system covers more than 50 commodities each supported by:
- In-depth study of agro-resources and input-output markets, production systems and farming business operations, offering the ability to evaluate the competitiveness and sustainability of farming systems.
- End-to-end value chains analysis, tracking product flow, efficiencies, and margins along the chain.
- Commodity markets scenario modelling and forecasting to quantify future outcomes, evaluate risk, identify growth opportunities and assess impacts of changes in the macro-economic, business and policy environment.
- Analysis of the consumer and retail space to provide insights on food price impacts and food security.
- Credible analysis, monitoring and evaluation of rural and socio-economic development related to the food, fibre and beverage industries.
The extensive integrated database and modelling frameworks enable BFAP to analyse and generate long-run projections and unpack alternative future scenarios for agricultural commodity markets and within the main sub-sectors (grains, livestock, and horticulture).
The BFAP Farm & Production Analytics Division
The program
The BFAP Farm & Production Analytics 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 Farm & Production Analytics Division 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.
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: northern 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: southern coastal dryland Limpopo: irrigation Western Cape: pears KwaZulu-Natal: integrated farm Eastern Free State: maize Northern Cape: wheat Northern Free State: sunflower and cotton KwaZulu-Natal: midlands KwaZulu-Natal: seed Citrus North West: integrated farm Northern Cape: maize Northern Cape: barley North West: sunflower and cotton Mpumalanga: irrigation Sandveld: irrigation Mpumalanga: maize (budgets) Swartland: wheat, barley and canola (2019) Mpumalanga: soybeans (budgets) KwaZulu-Natal: northern coastal dryland (small-scale) North West: maize Overberg: canola Northern Cape: cotton Limpopo: cotton 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 output.
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 Africa agriculture with a useful tool top 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 
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.
Specific objectives
- Generate crop income and cost budgets for key summer grains and oilseeds in selective regions in South Africa: Dryland: Mpumalanga / Eastern Highveld, Eastern Free State, Northern and Western Free State, North West and KwaZulu-Natal. Irrigation: Northern Cape, Brits, Limpopo and Bergville.
- Generate crop income and cost budgets for key winter grains- and oilseeds in selective regions in South Africa: Dryland: Eastern Free State, Southern Cape and Western Cape. Irrigation: Northern Cape, Brits, Limpopo and Bergville.
- Generate sensitivity analysis for the above identified crops based on the latest market trends and projections. The identified regions and proposed crop coverage is presented in the annexure of this proposal.
- Generate a bi-annual report on crop budgets for the subsequent season.
Proposed schedule of reports
- February/March – Planning and analysis for subsequent winter crop;
- August/September – Planning and analysis for subsequent summer crop.
Annexure: Proposed regions and crops covered
Figures 2-11 illustrate the existing coverage between the GSA and BFAP. It is proposed to continue with the below listed regions and crops covered by GSA and BFAP which will cover and also add to the scope of work and objectives from the PRF. Lastly, the existing needs from the PRF will focus on level 1 of the program: crop budgets updated annually.
Levels definitions
- Level 1: Commodity enterprise budgets: updated annually;
- Level 2: Actual cost of production (historic);
- Level 3: Projections / Quarterly Updates.
Figure 2: Mpumalanga / Eastern Highveld 
Figure 3: Eastern Free State 
Figure 4: Northern and Western Free State 
Figure 5: North West 
Figure 6: KwaZulu-Natal 
Figure 7: Summer irrigation - Northern Cape, Brits, Limpopo and Bergville 
Figure 8: Winter irrigation - Northern Cape, Brits, Limpopo and Bergville 
Figure 9: Free State - Winter 
Figure 10: Southern Cape - Winter 
Figure 11: Western Cape - Winter 
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Research on soybeans to study new preliminary treatments with different biological leaf applications as well as chemical applications and a demonstration trial in a wagon wheel design
2021/2022: Trials conducted on the UP-Experimental farm in Hatfield, Pretoria
Demonstration trial in a wheel design
Treatments: Four (4) Cultivars from a MG 4-7 were used. There were 4 rows of each cultivar and 2 entrances to the middle of the trial were kept clean in order to access the trial. Plant density was kept constant, at 250 000 plants/ha, in half of the trial, while row width in this half varied from 1.5m at the outside of the circle to 30cm at the inside. See Figure 1 below.
Wheel Design in Demonstration Trial
Figure 1: The wheel design which was used to compare different parameters and yield of four cultivars at different row widths and plant densities In the other half of the demonstration trial the distance between plants was constant at 8 cm (i.e.12.5 plants per running meter) which resulted in 87 719 plants/ha in 1.425m rows and 308 025 plants/ha in 0.405m rows when planted.
Photo 1. Wheel design trial at 5 weeks 
Photo 2. Wheel design trial at 15 weeks The arrangement of the rows can be seen in Photo's 1 and 2 in the wheel design.
In the centre of the 'wheel' a circle with a diameter of 2 meters remained unplanted, enabling access from one side to the other. The rows were 8m long but only 7m were harvested resulting in seven treatments. The table below describes these treatments, which are based on the differences in row width for every 1m distance from the outside to the inside as well as the average row width per meter.
Treatment from outside to inside Range Average row width T1 150-135cm 1.425m T2 135-115cm 1.250m T3 115-104cm 1.095m T4 104-85cm 0.945m T5 85-71cm 0.780m T6 71-51cm 0.610m T7 51-30cm 0.405m Soybeans were harvested, threshed and the yield and other data recorded. The aim of this demonstration trial was to establish the correlation between plant density and yield as well as branching and pod height. Plants at harvest were also compared to number of seeds planted in order to find an explanation for the differences that sometimes occur between planting and harvesting. The four cultivars used were C1 = DM 5953 (MG 4.4), C2 = PAN 1521 R (MG 5), C3 = PAN 1644 (MG 6.4) and C4 = DM 6.8i (MG 6.8).
Yield of the MG 4 and 5 cultivars was influenced by the large amount of rain that fell during Nov to Dec, the average yield being around 2.4 ton/ha for MG4 and 2.5 ton/ha for MG5. The best yields, of 4132 kg, 3891 kg and 3703 kg/ha, were achieved with PAN 1644R at plant densities of 224 000, 234 000 and 233 000 plants/ha, respectively. The row widths at these plant densities were 78.6 and 94.5 cm respectively.
The treatment which had the least reduced plant density at harvest was PAN 1644 R in 61 cm rows, where planting density decreased by only 6.55% from the original 250 000 plants/ha to 233 606 plants/ha with a yield of 3891 kg/ha. The treatment which had the most reduced plant density at harvest was DM 5953 in 150 cm rows where planting density decreased by 34% to164 192 plants/ha with a yield of 1333 kg/ha.
On the other side of the wheel, in the wide rows (1.425 m), the harvest plant density of DM 5953 dropped by 20% from planting to harvesting. For the narrow rows (0.405 cm) the drop in density was 40%. The drop in density for the wide and narrow rows for PAN 1521 R, PAN 1644 and DM 6.8i R was respectively 12 and 16%, 20 and 19% and 18 and 25%. The three top yields were achieved using PAN 1521R (4617 kg/ha at 259 259 plants/ha), PAN 1644 R (4123 kg/ha at 249 152 plants/ha) and PAN 1521 R (3801 kg/ha at 131 410 plants/ha).
Treatments involving biological leaf applications and/or chemical applications
- Application of kraal manure at 20 tons/ha, sheep manure at 15 tons/ha and poultry manure at 12 ton/ha.
- 25cm Row spacing with two seeds/position planted every 33.3cm in the row – giving a density of 240 000 plants/ha.
- Application of Spoor en Boor on the leaves of soybeans in 45cm rows at R2 (only micro-elements). CULTIVAR – PAN 1521.
- Application of product of Rolfes on the leaves of soybeans in 45cm rows at dosages 0.1 x dosage and 2.0 x dosage on Cultivar PAN 1644 R and application at 1.0 x dosage on Cultivar DM 5953 R.
- Two controls in 45cm rows on cultivar DM 5953R and one on cultivars PAN 1644R and on PAN 1521R respectively.
- Application of LAN at R2 at rates of 0, 200 and 300 kg/ha on DM 5953.
- Application of Ammonium Sulphate at rates of 0, 100 and 200 kg/ha at R2 on DM 5953R.
- Application of Brandt Smart Quatro containing Molybdenum, Cobalt, Copper, Magnesium and Boron as foliar on PAN 1521R.
- Application of Green Liquid (Product of Elim Kunsmis) containing a broad range of macro- and trace-elements, plant stimulating hormones and enzymes and a stimulant for natural soil micro-flora. This application was on PAN 1644R.
The treatments were harvested, threshed and post-harvest data were recorded. The best yield was on Rolfes at double dosage on PAN 1644R with a yield of 5485 kg/ha, second was Brandt Smart Quatro on PAN 1521R with 5263 kg/ha, and third was Rolfes at single dosage on PAN 1644R with a yield of 4833 kg/ha. All the results are to be included in the progress report.
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Research on Sclerotinia with emphasis on cultivation practices and treatment with biological products to reduce its occurrence in soybean
Ploughed trial – Mr Piet Prinsloo, Stoffberg
A section of 50 m to 100 m in length and at least 40m wide was ploughed in a chosen field in August/September at a depth of at least 25cm. A reversed harrow was used to prepare the seed bed without disturbing the soil. Only the 20m at the centre of the ploughed field was used for data collection. A similar field, unploughed, served as the control treatment. The cultivar used was DM 6.8i R.
No sclerotinia infection occurred on the farm and the expectation was that the yields of the ploughed treatment and control would be the same. However, the ploughed treatment outyielded the control by 500 kg/ha (3000 vs. 3500 kg/ha) possibly because of better drainage and lower compaction of the soil on the ploughed treatment.
Trial with biological treatment – Wonderfontein
A biological treatment (Brandt Smart Quatro) active ingredient Bacillus methylotrophicus was used as a foliar application when the first signs of sclerotinia were observed. Two weeks later a second application was used and, although the sclerotinia count was less in the sprayed section, the yield was only 180 kg/ha more than the control at 3124 kg/ha.
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Cultivar evaluation of soybeans in the western dryland production area of South Africa
The past season was certainly the wettest and best soybean seasons the west has ever experienced.
The trials were planted at Migdol, Schweizer-Reneke (2 planting dates), Hoopstad, Leeudoringstad and Baberspan (Between Delaryville and Sannieshof).
The trials at Schweizer-Reneke were planted on the 5 November and 30 November 2021 using the farmer's planter.
The trial at Leeudoringstad was planted on the 30 November 2021, Hoopstad on the 29 November 2021 and Baberspan on 1 December 2021. These three trials were planted using a new planter belonging to the ARC. This is a Delta Force planter which controls both the down and up forces on the units, with the result that the soybeans are planted more evenly at the same depth.
The trials consisted of 32 cultivars from a MG 4.7 to MG 7.1. All the cultivars in the trials were indeterminate except for LS 6851 R which was determinate. Six new cultivars were included in the trial, namely, P57T19R, RA 5921 R, PAN 1588 R, P62T16R and RA 6520RS.
The trial at Schweizer-Reneke (PD 1) had a mean yield of 3233 kg/ha. The cultivar with the highest yield was P64T39 (MG 6.4) with 4073 kg/ha and the cultivar with the lowest yield was DM 5953 RSF (MG 4.8) with 2110 kg/ha.
The second trial at Schweizer-Reneke (PD 2) had a mean yield of 1750 kg/ha. This was because the land was waterlogged for the first 7 weeks after planting because of heavy rains. The cultivar with the highest yield was DM 6.8i RR (MG 5.5) with 2493 kg/ha and that with the lowest yield was PAN 1479 R (MG 4.7) with 1065 kg/ha.
The trial at Leeudoringstad had a mean yield of 2491 kg/ha. The cultivar with the highest yield was DM 6.8i RR (MG 6.8) with 4021 kg/ha and the cultivar with the lowest yield was DM 5351 RSF (MG 5.1) with 1111 kg/ha. Yields in this trial were low because of the large amount of rain that fell during the season making the soil waterlogged.
The trial at Hoopstad had a mean yield of 4485 kg/ha. The cultivar with the highest yield was RA 4618 R (MG 4.9) with 5383 kg/ha and the cultivar with the lowest yield was PAN 1588 R (MG 5.9) with 3358 kg/ha. A lot of rain fell during this trial but it was spread more evenly than in the other localities.
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Data-Intensive Farm Management (DIFM) project in South Africa
Background
Human society has managed to manipulate the nitrogen cycle to great benefit (Christensen, 2004), particularly for food and agricultural systems; but chronic inefficient use of nitrogen fertilizer continues to suppress farm income and degrade the environment. Reliable estimates of what optimal nitrogen application rates and timing might be, and of how these might differ as soil, topology, and weather conditions vary, are largely dependent on labour-intensive techniques, making it financially feasible only to run trials on small plots, at few locations, and for a few years. Reliable estimates of optimal seeding rates face similar challenges.
The past decade has witnessed a revolution in precision farming technology and access to the era of big data. In fact, it is argued that in the next decade agriculture's biggest efficiency gains and improvements will not come from improved seed varieties or advances in mechanisation, but from big data and the analytics and interpretation thereof. Since 2019, BFAP has led the development of a pilot project to introduce Data-Intensive Farm Management (DIFM) in South Africa in collaboration with the University of Illinois, Grain SA, the Protein Research Foundation (PRF) and the University of Pretoria. The DIFM pilot project has been successfully completed in South Africa for three seasons (2019/2020, 2020/2021 and 2021/2022). We believe that the DIFM approach provides a unique opportunity with respect to the application of mechanization technology to generate high-density and high-observation spatial data. Based on research findings during the pilot project and the potential impact that we believe can fundamentally change a wide range of decisions that farmers have to take at farm-level every season, this document puts forward a proposal to continue with the DIFM project by expanding it to further production regions and to repeat the applications over a 3-year period to incorporate a wider spectrum of climatic events.
DIFM presents a research methodology that generates vast quantities of high-quality agronomic field trial data (see Appendix 1 for more details on the methodology). Compared to trials on small plots, where only a few observations can be gathered, the DIFM approach gathers thousands of observations on commercial fields. e.g., in the DIFM pilot in South Africa, more than 2000 observations were gathered on a 57 ha maize field to develop a production function that estimates the relationship between yields, fertilizer applications and seeding rates. Preliminary findings of DIFM trials suggest that on most of the U.S. trial fields, farmers could have improved their financial outcomes by applying both nitrogen fertilizer and seed at lower rates. Preliminary findings from the South African DIFM trials suggest that farmers can improve their financial outcomes by employing site-specific management (as opposed to flat-rate applications). In most cases, higher seeding and fertiliser rate applications could have improved financial outcomes. However, we also need to take into consideration that the pilot study was implanted over the past three years with above-normal rainfall conditions in most of the summer production region. Hence, there is a need to continue with these trials to generate estimates under normal/below normal rainfall conditions.
During the first year of implementing the pilot project in South Africa (2019/20 summer crop season), three trials (2x Maize and 1x Soybeans) were conducted on two farms. During the second year (2020/21 summer crop season), 10 trials (5x Maize and 5x Soybeans) were conducted on five farms. During the third year (2021/22 summer crop season), 10 trials (3x Soybeans, 6x Maize and 1x Popcorn) were conducted on the same five farms, and crop and field combinations were repeated where rotation and farm management practices allowed.
Figure 1: Soybean and maize trial locations Results Summary
Results from the DIFM pilot phase are presented below.
The maize trial results presented in Table 1 were collated from various farms over the three seasons of the pilot project period. Note, that in most of these locations, the three pilot project seasons were exceptional with rainfall and the timing thereof. They can all be classified as above-average wet, and yielding seasons. This might be a key driver for the recommendations to be quite high. In all the trials it was found that a higher (sometimes the maximum included in the trial treatments) seeding rate and fertilizer rate would be recommended as a profit-maximising flat rate application. With profit differences / impact of between R1692 and R3749 per hectare.
Table 1: Summary of Maize Trial Results Province Production region Planting date Seeding rate (plants per ha) Fertiliser rate (kg/ha) Profit difference
(R/ha)Status quo Optimal Status quo Optimal¹ Mpumalanga Wonderfontein 13/11/2019 50 000 60 000 380 380 R3 749 Free State Hennenman 09/12/2019 18 000 28 147 200 239 R1 692 Mpumalanga Wonderfontein 11/2020 50 000 70 000 (max) 500 600 (max) KwaZulu-Natal Paulpietersburg 04/11/2020 55 000 65 000 (max) 250 380 R2 900 Free State Hennenman 14/11/2020 18 000 26 000 200 282 R3 500 North West Ottosdal 28 000 As-applied data could not be retrieved KwaZulu-Natal Paulpietersburg 16/10/2021 55 000 80 000 (max) 250 380 (max) Free State Hennenman 01/11/2021 18 000 50 000 (max) 200 300 (max) ¹ Optimal is defined as profit maximizing, given the in-season input and output prices.
Mostly, the soybean trials focus on seeding rate treatments with only a few farmers adding fertilizer variations to the trial. The results vary significantly and in the Wonderfontein and Paulpietersburg cases, multiple profit-optimisation rates are observed from the fitted yield response curve leading to mixed conclusions. The Schweizer-Reneke case is particularly interesting where an average row width of 1.15 m is planted and an optimal seeding rate of 349 780 and 397 000 was recommended. This implies that soybeans are planted in very close proximity within the row – closer than is currently generally recommended by industry. The Bothaville trial underwent some severe water logging during the 2020/2021 season.
Again, one needs to take into account, that these findings are presented for exceptionally wet seasons and that these trials need to be repeated for average and drier conditions as well in order to make recommendations that take some climate risk into account.
Table 2: Summary of soybean trial results Province Production region Planting date Seeding rate (plants per ha) Fertiliser rate (kg/ha) Status quo Optimal Status quo Optimal¹ Mpumalanga Wonderfontein 26/10/2019 260 000 230 000 / 340 000 N/A N/A KwaZulu-Natal Paulpietersburg 16/10/2020 285 000 190 000 / 270 000 / 350 000 190 205 Mpumalanga Wonderfontein 26/10/2020 260 000 340 000 N/A N/A North West Schweizer-Reneke 12/11/2020 300 000 349 780 N/A N/A Free State Bothaville 18/11/2020 250 000 150 000 (min) Urea: 0
Superphosphate: 1200 (min)
0 (min)North West Schweizer-Reneke 23/11/2020 300 000 397 000 N/A N/A Free State Bothaville 12/11/2021 250 000 380 000 Urea: 0
Superphosphate: 12020
118 / 200North West Schweizer-Reneke 01/12/2021 300 000 200 000 (min) N/A N/A ¹ Optimal is defined as profit maximizing, given the in-season input and output prices.
The analysis for the 2021/2022 trial data was still in progress at the time of writing and can be reported on in a few months' time.Research outputs to date
- The results from the 2020 analysis were presented at the INFER1 Symposium on Agri-Tech Economics for Sustainable Futures in October 2020 and published in conference proceedings (see Appendix 3).
- Draft paper to be submitted to the International DIFM conference in Miami, January 2022, and to be submitted to a suitable international peer-reviewed journal.
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PRF website
This year was mainly uneventful in terms of major backend updates, and the focus was on keeping existing and new website content up to date. On 28 February 2022, the Protein Research Foundation website hosted a total of 1,258 HTML pages (excluding dynamically created pages).
POPIA
South Africa's POPI Act (Protection of Personal Information Act) came into effect on 1 July 2021 and the Privacy and Cookie Policies were reviewed in March 2021 to ensure that these are compliant. Visitors to the Protein Research Foundation's website are encouraged to read through the policies to see what personal information is collected, how it is used and how it is protected.
ICB Calculator App
During 2021/2022 the online browser web app received approx. 120 launch requests and the Google Play Store statistics show approximately 16 active installations (the number of downloads the desktop received cannot be calculated at this stage).
PHP 7.4 to PHP 8.0
In anticipation of the major PHP** version upgrade scheduled for October 2022 and due to the volume of the content on the PRF's website, we have already initiated validation of the website framework and content.
** PHP is the base programming language that is used to output website content to the browser. It is also used to process data stored in the MySQL database in order to create and display the dynamic content in the Crops, Research, Bursaries and Snippets sections.
Website 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 2019 45 787 10 444 23 628 1.78 2020 27 317 9 632 24 169 1.93 2021 25 905 12 096 24 176 1.66 Google values show an increase in page views and an increase in unique visitors. Pages per visit decreased slightly. The most page views came from the following pages in order of percentage share:
- Home page: 13.4%
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- Canola Snippets: 2.16%
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- Cassava Characteristics: 2.04%
YouTube visitor statistics Reporting Year YouTube statistics Views Watch time (hours) Subscriber gain 2014 611 28 +2 2015 1 900 74 +4 2016 8 400 256 +21 2017 23 000 568 +78 2018 35 000 716 +195 2019 27 900 660 +151 2020 27 600 670 +163 2021 19 400 570 +161 The Protein Research Foundation's YouTube channel has grown to almost 858 subscribers by the end of 2021.
REPORTS RESEARCH REPORTS 2021/2022 2021 PROJECTS FINANCED