OPDT   OIL & PROTEIN SEEDS DEVELOPMENT TRUST
OAC   OILSEEDS ADVISORY COMMITTEE

OPDT
OIL & PROTEIN SEEDS DEVELOPMENT TRUST

OAC
OILSEEDS ADVISORY COMMITTEE


RESEARCH PROJECTS  //  Research Report 2018/2019

Joint Research Projects

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

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

    Background

    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.

      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.

      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: Crop budgets: updated annually
      • Level 2: Actual cost of production (historic)
      • Level 3: Projections / Quarterly updates
      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
  2. Evaluation of shortened canola production periods and the use of alternative crops on the sustainability of winter grain production under conservation agricultural practices in the Riversdale Flats

    JA Strauss
    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 1304 kg/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.

    Economics

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

  3. 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.

    Procedures

    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.

    Trial 3: SW Graaff

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

    Summary

    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).

  4. The influence of potassium on the yield of soybeans and the amount of patassium removed by 1 ton soybeans/ha

    Mr WF van Wyk
    Protein Research Foundation

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

    • Potassium trial

      Treatments: Four (4) levels of K (0, 100, 200 and 300kh/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 how many (kg/ha K) is necessary 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.

      All 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:

    • 25 cm 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