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 standar operating procedure (SOP) defined by the agri benchmark methodology and are present 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: Southern coastal dryland	Limpopo: Irrigation	Western Cape: Pears	KwaZulu-Natal integrated farm
   Eastern Free State: Maize	Northern Cape: Wheat	Northern Free State: Sunflower		KwaZulu-Natal: Midlands	KwaZulu-Natal: Seed		North West integrated farm
   Northern Cape: Maize	Northern Cape: Barley	North West: Sunflower		Mpumalanga: Irrigation	Sandveld: Irrigtion
   Mpumalanga: Maize (budgets)	Swartland: Wheat, barley and canola (2019)	Mpumalanga: Soybeans (budgets)		KwaZulu-Natal: Northern coastal dryland (small-scale)
   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.

   

   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.

   

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

   2019 was the 8th 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 on the field each year.

   Riversdale received very little summer rainfall which resulted again in a dry start to the 2019 production season. Only 72mm 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 100mm was received from April to the end of September.

   Wheat production

   SST0127 was planted at Riversdale at 60kg/ha. Wheat yields at Riversdale averaged 2 772kg/ha. This was 887kg/ha more than in 2018 (also a dry year).

   Canola production

   Alpha was planted at Riversdale at 3kg/ha. Canola yields at Riversdale averaged 1 897kg/ha which was 235kg/ha more than 2018.

   Barley production

   Hessekwa was planted at Riversdale at 52kg/ha. Barley yields at Riversdale averaged 3 287kg/ha. This average yield was 772kg/ha more than in 2018. Yields varied between 2 888kg/ha and 3 604kg/ha. All but one plot was classified as malting grade.

   Lupin production

   Peas was planted at a rate of 80kg/ha. No plots were harvested due to poor germination and weed problems in the very low rainfall year.

   Cover crops

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

   Economics

   Although it proved to be a low rainfall year in the 2019 year all systems tested show a positive gross margin above direct allocated production costs.

   Technology transfer

   The following summarises presentations, reports and publications based on the crop production trials being conducted in the Swartland and the southern Cape during 2019.

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

   In South Africa soya beans are mainly produced in the Mpumalanga and Free State provinces, while within the Free State, production is concentrated in the North Eastern Free State. It is widely known that soya bean yield is influenced by agronomic inputs such as, maturity group, plant density, row width as well as planting date. Extensive research was done globally on these agronomic inputs. However, very little, if any research was done in South Africa, especially in the North Eastern Free State.

   In an attempt to evaluate the yield response to different maturity groups, plant densities, row widths and planting dates, three trials were conducted on farmer's fields over two seasons (2016/17 and 2017/18) in the North Eastern Free State at three agro-ecologically different experimental sites. The same maturity groups (MG 4.5, MG 5 and MG 6) was planted at different plant densities, row widths and planting dates at each trial. Phenological development, plant height, pod height, number of pods per plant, number of seeds per pod, hundred-seed weight and grain yield were measured.

   At trial 1 the three maturity groups were planted at four plant densities (200 000, 300 000, 400 000 and 500 000 plants ha^-1), one row width (0.76m) and one planting date. Maturity group had the greatest effect on phenological development, plant height, pod height, hundred-seed weight, and grain yield. Plant density had the greatest effect on plant height, number of pods per plant, while also affecting yield only during the 2016/17 season.

   At trial 2 the three maturity groups were planted at four plant densities (150 000, 200 000, 300 000 and 400 000 plants ha^-1), two row widths (0.38m and 0.76m) and one planting date. Similar to trial 1, maturity group had the greatest effect on phenological development, plant height, pod height, hundred-seed weight and grain yield. Plant density had the greatest effect on plant height and number of pods per plant, while also affecting grain yield slightly during the 2017/18 season. Row width had the greatest effect on hundred-seed weight and grain yield.

   At trail 3 the three maturity groups were planted at four plant densities (150 000, 300 000, 400 000 and 600 000 plants ha^-1), two row widths (0.30m and 0.60m) and at two planting dates (early/normal and late). Similar to trial 1 and 2 only, maturity group had an effect on phenological development for both planting dates. Plant height was affected by maturity group, plant density and row width at both planting dates. Plant heights for the late planting date were shorter compared to the early/normal planting date. Pod height was affected most by maturity group, while the effect of plant density and row width was not as profound. Between planting dates, pod height was slightly higher during the early/normal planting date, but this was negligible. Number of pods per plant was only affected by plant density for the early/normal planting date, while for the late planting date maturity group and row width also produced an effect. Between planting dates there were no significant difference in number of pods per plant. Hundred-seed weight was affected by both maturity group and plant density for both planting dates, while row width only had an effect during the late planting date. Hundred-seed weight was considerably higher during the early/normal planting date compared to the late planting date. Grain yield was mostly affected by maturity group during the early/normal planting date, while row width also had an effect. During the late planting date grain yield was affected by maturity group, plant density and row width. Grain yield was considerably higher during the early/normal planting date compared to the late planting date.

   It can therefore be concluded that maturity group and planting dates have a great effect on grain yield. The grain yield of a late planting date is considerably lower compared to early/normal planting dates. Plant density also affects grain yield, but the effect is not as profound for an early/normal planting date, while for a late planting date the effect is greater with grain yield increasing slightly with increased plant density. Grain yield is also affected by row width with narrower rows producing greater grain yields compared to wider rows.

4. The influence of weed and herbicides on the growth and yield of soybeans

   Mr WF van Wyk, Contractor
   Protein Research Foundation

   Increasing soybean production on the Highveld

   2019/2020: 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. Where Potassium was applied at 300kg/ha the yield was 3 054kg/ha, while where no K was applied it was 2 609kg/ha, 2 956kg/ha where 100kg K/ha was applied and 3 263kg/ha where 200kg K/ha was applied. At the treatment where K was applied at a depth of 20cm between rows the yield was 3 441kg/ha.

     Potassium-analysis of the soil after harvesting was not done yet due to the Covid 19 pandemic and therefore no comparison with pre-planting analysis is possible.

     Seedling diseases like Rhizoctonia and Pythium were found at the trial this year. (It is possible from the heavy rains during in December as well as drainage problems).

     All the results will be given in the progress report.

   • Weed control trial

     Five (5) treatments were applied namely, no weed control, only pre-emerge control with diclosulam and alachlor, pre-emerge control with diclosulam and alachlor followed by a glyphosate application at 3-4 weeks, no pre-emerge control with glyphosate applied at 6 weeks and no pre-emerge control with glyphosate applied at 10 weeks. The yields were 2232, 3370, 3483 and 4 322kg/ha respectively.

     (Additional or Demonstration-trials as started in 2018/2019.)

   Additional treatments that were used are:

   • 25cm row spacing with two seeds/position planted every 16.65cm in the row – gives a density of 240 000 plants/ha.
   • 45cm row spacing with two seeds/position planted every 33.3cm in the row – gives a density of 265 000 plants/ha.
   • 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).
   • Urea(46) applied at 100g N, 200kg N and 300kg N at R2-R6.
   • LAN(28) applied at 100g N, 200kg N and 300kg N at R2-R6.
   • Controls(2) in 45cm rows and plant density of 290 000 plants/ha.
   • No inoculation.

   The treatments were harvested, threshed and post-harvest data were taken. Where LAN was applied at 200 and 300kg/ha the yield was better than 5 ton/ha. The yields of treatments 1 and 3 were respectively 4 989 and 4 724kg/ha. The yields of the other treatments were under 4 500kg/ha with the controls and urea treatments the lowest at 3 600-3 800kg/ha.