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), founded in 2004, serves the agro-food, fibre and beverage sectors in South Africa and Africa. Our purpose is to inform better decisions-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 very 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 chain 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 beverage sectors.
   • 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 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 the farm-level, risk analysis via stochastic simulation, the impact of policy decisions, input- and market-related shocks on the farm-level, and the intermediate and long-term projection 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: 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	Western Cape: table grapes
   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

   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.

   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:

   Levels definitions

   • Level 1: Commodity enterprise budgets: updated annually;
   • Level 2: Actual cost of production (historic);
   • Level 3: Projections / Quarterly Updates.

   

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

   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 on 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 8kg/ha and a topdressing of 30kg/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 lupine.

   Cover crops

   A mixture of peas, lupine and barley were planted during 2020 at seeding rates of 80 kg/ha.

   Economics

   Although commodity prices were excellent the problems with the canola yields and rain during the harvesting period had a pronounced effect on the economics of the 2021 season.

3. Cultivar evaluation of soybeans in the western dryland production area of South Africa

   Mr GP De Beer
   Contractor, Protein Research Foundation

   The past season was certainly the wettest and best soybean seasons the west ever had.

   The trials were planted at Migdol (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 with the farmers planter. We planted one repetition from MG 4.7 to 7.1 and randomised the other two replications.

   The trial at Leeudoringstad were planted on the 30 November 2021, Baberspan on 1 December 2021 and Hoopstad was planted on 29 November 2021. These three trials were planted with the planter of the ARC. All these trials were randomised differently.

   The ARC spends a lot of money on the planter which we are planting the soybean trials. They installed the Delta Force which controls the down force and up force on the units. Which means the planter can plant the soybeans more evenly at the same depth.

   The trials consist 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 were determinate. We had six new cultivars in the trial P57T19R, RA 5921 R, PAN 1588 R, P62T16R and RA 6520RS.

   The trial at Leeudoringstad has a mean yield off 2490.9 kg/ha. The cultivar with the highest yield was DM 6.8i RR (MG 6.8) with 4020.8 kg/ha and the cultivar with the lowest yield was DM 5351 RSF (MG 5.1) with 1110.9 kg/ha. This trials yield isn't very high because of a lot of rain during the season and the soil was waterlogged.

   The trial at Schweizer Reneke (PD 1) had a mean yield of 3233.4 kg/ha. The cultivar with the highest yield was P64T39 (MG 6.4) with 4072.5 kg/ha and the cultivar with the lowest yield was DM 5953 RSF (MG 4.8) with 2109.9 kg/ha.

   The trial at Schweizer Reneke (PD 2) had a mean yield of 1749.7 kg/ha. This trial had a lot of rain after planting and were waterlogged for the first 7 weeks after planting. The cultivar with the highest yield was DM 6.8i RR (MG 5.5) with 2492.7 kg/ha and the cultivar with the lowest yield was PAN 1479 R (MG 4.7) with 1064.8 kg/ha.

   The trial at Hoopstad had a mean yield of 4484.9 kg/ha. The cultivar with the highest yield was RA 4618 R (MG 4.9) with 5383.3 kg/ha and the cultivar with the lowest yield was PAN 1588 R (MG 5.9) with 3358 kg/ha. This trial also had a lot of rain but it was spread more evenly.

4. The influence of potassium fertilisation on soybean yield with special reference to the amount of potassium removal from the soil by a certain harvest

   WF van Wyk
   Contractor, Protein Research Foundation

   The aim of the trial was to establish what influence potassium has on the yield of SOYBEANS. The amount of potassium removed from the soil by a 1 ton/ha yield of soybeans as well as the amount of potassium needed to increase the soil-K with 1 mg/kg. Potassium content in soil is high when figures are 80mg/kg K in sandy, 150mg/kg K in loamy and 200mg/kg K in clay soil.

   Treatments in the trial existed of 3 different rates of K - 100, 200 and 300kg K/ha, a control where no K was applied and one treatment where 100kg K/ha was applied but the plot was not planted with soybeans and was kept clean from weeds in order to determine how many K is needed to increase Soil-K with 1 mg/kg. A sixth treatment where 100 kg K/ha was applied in a band between every second pair of rows at a depth of 20cm was included in order to make an environment where the K is concentrated in a high amount.

   Rainfall was near optimum for only the first year of the trial and water was added by irrigation when drier conditions appeared. The rainfall of the second and third years were excessive for at least 4 weeks of every season where more than 150mm of rain was measured. This led to the possibility of leaching of potassium as can be seen in many scientific papers. For example – the treatment where no crop was planted but 100kg K was applied – showed a 25mg/kg increase in K in season 1 but in season 2 and 3 it stayed the same at about a soil-K of 104mg/kg. The conclusion was made that to increase soil-K with 1 mg/kg you need 4 kg K/ha.

   In the treatment where no K was applied the soil-K dropped from 79.3 mg/kg to 62 mg/kg with a yield of 3397kg/ha. Kg K used per 1 ton yield is therefore 79.3 – 62 = 17.3 x 4 = 69.2kg K/3.397 ton = 20.37 kg K. The lowest yield(3397kg/ha) was at the control where no K was applied and the highest (5274kg/ha) at the treatment where K was applied in a band between rows at a depth of 15cm, followed closely (5076kg/ha) by the treatment where 300kg of K/ha was applied. At all the treatments where K was applied, it was done with potassium chloride which contains 50% K.

   Keywords: Glycine max; Potassium; Soil-K, Deep placement.