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

   he 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 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: Irrigtion	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) historically had their own cost of production efforts which focused on key summer- and winter crops produced in South Africa's key agro-ecological zones. Given ongoing activities associated with the organisations and the extent of the coverage of South African agricultural production, the PRF, GSA and BFAP together with agri businesses agreed to collaborate on the compilation of enterprise budgets.

   The main objective of this collaboration and project is to consolidate these efforts and to generate comprehensive crop income and cost budgets for key summer- and winter growing regions and generate sensitivity analysis for these crops. The compilation of these enterprise budgets is underpinned by the latest macroeconomic trends, BFAP sector model underlying assumptions and international- and domestic market updates. BFAP together with the PRF, Grain SA, agri businesses and industry experts will continue to engage with the objective of refining model assumptions and to ensure alignment.

   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

   Figures 2-11 illustrate the existing coverage between the GSA, BFAP and agri businesses. It is proposed to continue with the below listed regions and crops covered by GSA and BFAP.

   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

   2022 was the 11th 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 2022 were followed, and the integrity of the trial layout was upheld.

   Canola production

   44Y90 was planted at Riversdale at 2.5 kg/ha. A total of 38 kg N/ha was applied to each plot. Canola yields at Riversdale averaged 1364 kg/ha. All plots had oil yields above 40%,

   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 3122 kg/ha. This was 80 kg/ha more than in 2021.

   Barley production

   Kadie was planted at Riversdale at 50 kg/ha. Barley yields at Riversdale averaged 1882 kg/ha. This average yield was 2319 kg/ha less than in 2021.

   Lupin production

   Lupin plots were planted to bitter lupine 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.

   Cover crops

   A mixture of peas, lupine and barley was planted in 2022 at 80 kg/ha seeding rate.

   Economics

   Commodity prices were excellent, slug problems with the canola lowered yields and low protein in a number of the wheat plots lowered the income of these two crops. All systems tested show a positive gross margin above directly allocated production.

3. 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

   Mr WF van Wyk
   Contractor, Protein Research Foundation

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   2021/2022
   Trials conducted on the UP-Experimental farm in Hatfield, Pretoria

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   Demonstration trial in a wheel design

   Treatments: Four (3) Cultivars from an 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 get inside the trial. In one half of the trial, the plant density was kept constant at a planted 250 000 plants/ha while row width went from 1.5m at the outside of the circle to 30cm at the inside of the circle. See Fig 1 below.

   

   On the other halve of the demonstration trial the distance between plants was constant at 8cm from each other which gives 87 719 plants/ha in 1.425m rows and 308 025 plants/ha in .405m rows when planted.

   

   The arrangement of the rows can be seen in Photos 1 and 2 in the wheel design.

   In the centre of the "wheel" a circle with a diameter of 2 meters was unplanted to enable one to move from one side of the wheel to the other side by using the two unplanted entrances. The rows were 8m long but only 7 meters were harvested as 7 different treatments because the row width changed from 1.5m at the outside to 0.3m at 1m from the centre. See the Table below for 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	1.25m
   T3	115-104cm	1.095m
   T4	104-85cm	0.945m
   T5	85-71cm	0.78m
   T6	71-51cm	0.61m
   T7	51-30cm	0.405m

   Soybeans were harvested, and threshed and all the yield and other data were taken. 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 the number of seeds planted in order to find an explanation for the big differences that sometimes occur between planting and harvesting. The three cultivars used were C1 = DM 5953 (MG 4.4), C2 = PAN 1521 R (MG 5) and C3 = PAN 1644 (MG 6.4).

   At one side of the circle, these four cultivars were planted at a rate of 250 000 plants/ha over all treatments. The yield of the MG 4 and 5 cultivars were influenced by the large amount of rain we had from Nov to Dec and the average yield was in the order of 2.4 ton/ha for MG4 and 2.5 ton/ha for MG5. The best yields of 4132kg, 3891kg and 3703kg/ha were achieved with PAN 1644 R at respectively plant densities of 224 000, 234 000 and 233 000 plants /ha. The row widths at these plant densities were 78, 61 and 94.5cm, respectively.

   The treatment which had the least reduced plant density at harvest was at PAN 1644 R in 61cm rows where planting density decreased only 6.55% from the original 250 000 plants/ha to 233 606 plants/ha and a yield of 3.891 ton/ha. The treatment which had the most reduced plant density at harvest was at DM 5953 in 150cm rows where planting density decreased by 34% from the original 250 000 plants/ha to 164 192 plants/ha and a yield of 1.333 ton/ha.

   At the other side of the circle, the four cultivars were planted with a constant distance between plants of 8cm - in other words, 12.5 plants per running meter. In the wide rows (1.425m) of DM 5953 the harvest plant density should be 87 719 plants/ha but there were only 70 175 plants/ha – a drop of 20% in plant density from planting to harvesting. For the narrow rows (0.405cm) 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 19.2% and 18 and 25%. The 3 top yields were achieved at PAN 1521 R (4617kg/ha at 259 259 plants/ha), PAN 1644 R (4123kg/ha at 249 152 plants/ha) and PAN 1521 R (3801kg/ha at 131 410 plants/ha).

   Additional preliminary research with different treatments that were used are:

   • Application of kraal manure at 20 tons/ha, sheep manure at 15 tons/ha and poultry manure at 12 tons/ha;
   • 25 cm row spacing with two seeds/position planted every 33.3 cm in the row - gives a density of 240 000 plants/ha;
   • Application of Spoor and 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 x dosage on Cultivar PAN 1644 R and application at 1 x dosage on Cultivar DM 5953 R;
   • Two controls in 45cm rows on cultivar DM 5953 R and one on cultivars PAN 1644 R and PAN 1521 R respectively;
   • Application of LAN at R2 at rates of 0, 200 and 300kg/ha on DM 5953;
   • Application of Ammonium Sulphate at rates of 0, 100 and 200kg/ha at R2 on DM 5953 R;
   • Application of Brandt Smart Quatro containing Molybdenum, Cobalt, Copper, Magnesium and Boron as foliar on PAN 1521 R;
   • Application of Green Liquid(Product of Elim Kusmis) containing a broad range of macro- and trace-elements, plant stimulating hormones and enzymes and a stimulant for natural soil microflora. This application was on PAN 1644 R.

   The treatments were harvested, threshed and post-harvest data were taken. The best yields were at treatment 4 (Rolfes at double dosage on PAN 1644 R with 5485 kg/ha), second best was treatment 8 (Brandt Smart Quatro on PAN 1521 R with 5263 kg/ha) and third best was treatment 4 (Rolfes at single dosage on PAN 1644 R with 4833 kg/ha).

4. Research on Sclerotinia with emphasis on cultivation practices and treatment with biological products to reduce its occurrence in soybean

   Mr WF van Wyk
   Contractor, Protein Research Foundation

   • TRIALS AT FARMERS;
   • SCLEROTINIA TRIALS

   Planting of 2 trials at Wonderfontein and Stoffberg

   • Ploughed trial — Stoffberg

     A section of 50m to 100m 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 soil was used for data purposes as the remains of the surrounding soybeans that are exposed to sclerotinia will not be spread on the data soil. It also creates room for the farmer to move the planting direction with a few degrees every year as is standard cultivation practice for no-tillage farmers. The cultivar DM 6.8i R was used.

     There was no sclerotinia this season and the expectation was that the yields of the plough treatment and control would be the same but the treatment outyielded the control by 500kg/ha. The yield of the control was 3000kg/ha while that of the treatment was 3500kg/ha. The reason for this strange outcome can be found in the fact that there was better drainage and less compaction in the soil.

   • Trial with biological treatments — Wonderfontein

     A biological treatment (Brandt Smart Quatro) active ingredient Bacillus methylotrophicus was used as a foliar application when the first sclerotinia was observed. Two weeks later a second application was done and although the amount of sclerotinia was less in the sprayed section the yield was only 180kg/ha more than the control at 3124 kg/ha.

5. 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 not the wettest season, but the rain was spread more evenly during the season. It was the best soybean season the West ever had. This is due to good rainfall and better cultivars. The new Intacta cultivars were some of the best performers the past season. In the past season, the hectares in the North West jumped from 100 000 ha to 155 000ha due to economics and better adapt cultivars for the region. 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 twelve new cultivars in the trial which consist of 2 new RR1 cultivars (PAN 1502 R and PAN 1507 R) and 10 RR2 (Intacta) cultivars (RA 5022 BR, RA 5722 BR, RA 5821R(CT233R), LG602601PR, DM 59160 RSF IPRO, LG 60261PR, RA 6422BR, Y651 RR PRO, RA 6521BR and DM 61163 RSF IPRO).

   The trials were planted at Schweizer Reneke (2 planting dates), Hoopstad, Leeudoringstad and Sannieshof.

   The trials at Schweizer Reneke were planted on 28 October 2022 and 1 December 2022 with the farmer's planter. We planted one repetition from MG 4.7 to 7.1 and randomised the other two replications.

   The trial at Schweizer Reneke (PD 1) had a mean yield of 4558.5 kg/ha. The cultivar with the highest yield was Y 657 (MG 6.5) with 5505.8 kg/ha and the cultivar with the lowest yield was RA 4918 R (MG 4.9) with 3606.5 kg/ha.

   The trial at Schweizer Reneke (PD 2) had a mean yield of 4350.1 kg/ha. The cultivar with the highest yield was DM 59R03 (MG 6.0) with 5116.4 kg/ha and the cultivar with the lowest yield was DM 6.8 i RR (MG 6.8) with 3450.0 kg/ha.

   The trial at Leeudoringstad was planted on 31 October 2022 and was planted with the planter of the ARC. All these trials were randomised differently.

   The trial at Leeudoringstad has a mean yield of 3739.7 kg/ha. The cultivar with the highest yield was RA 5722 BR (MG 5.7) with 4806.5 kg/ha and the cultivar with the lowest yield was DM 53154 RSF (MG 5.1) with 2960.3 kg/ha.

   The Trial at Baberspan was planted on 17 November 2022 and was planted with the planter of the ARC. All these trials were randomised differently.

   The trial at Baberspan has a mean yield of 2689.7 kg/ha. The cultivar with the highest yield was DM 6163 RSF IPRO (MG 6.7) with 4003.6 kg/ha and the cultivar with the lowest yield was PAN 1644 R (MG 6.7) with 2034.3 kg/ha.

   The trial at Hoopstad was planted on 29 October 2022 and was planted with the planter of the ARC. All these trials were randomised differently.

   The trial at Hoopstad had a mean yield of 5520.6 kg/ha. The cultivar with the highest yield was DM 53154 RSF (MG 5.1) with 6580.4 kg/ha and the cultivar with the lowest yield was PAN 1555 R (MG 5.7) with 4183.2 kg/ha. This trial also had a lot of rain but it was spread more evenly.

6. Is additional nitrogen beneficial to soybeans (Glycine max. L) in different agro-ecologies of the Eastern Highveld?

   Mr B Nkutha
   University of the Free State

   Problem description

   The fertilization of soybeans with nitrogen as an additional/supplemental application is gradually becoming an integral part of the management practices of most farmers within the Eastern Highveld. Producers began this management practice with the aim of enhancing their yield by supplying the plant with sufficient nitrogen to finish its growth cycle without experiencing any deficiencies and compromising quality. Most have successfully obtained their target yield, but there is a lot of uncertainty related to the application rate as well as the source that effectively enhances the quality of soybeans while ensuring substantial returns on investment. This research project seeks to determine the optimum application rate and nitrogen source for the successful improvement of soybean production in the Eastern Highveld.

   Objectives

   The main objective of the project is to determine the benefits of additional nitrogen on soybean yield and quality under different agro-ecological conditions of the Eastern Highveld. Sub-objectives include statistical identification of the optimum rate of nitrogen application that will ensure a substantial return on investment. The inclusion of a nitrogen source consisting of 5% Sulphur will help identify whether Sulphur has a beneficial role on the soybean yield and quality.

   Methodology

   Agronomic field trials were planted at two locations within the Eastern Highveld, namely Clarens and Grootvlei. A randomized complete block design (RCBD) with four replicates was used for the statistical layout of each of the respective field trials. There were two treatment factors, namely nitrogen source and application rate. The nitrogen sources were Limestone Ammonium Nitrate (LAN) and Greensulf 35 from Omnia Nutriology. The application rates were an untreated control (0) together with incremental rates of nitrogen at 15, 30, 45, and 60 kg N ha^-1. The fertilizer was broadcast using a handheld fertilizer spreader at flowering for uptake at pod filling when nitrogen demands are at their highest.

   Data Collection and Analysis

   The trials were monitored weekly to identify growth stages and observe any deficiencies or diseases. The primary parameters that were measured are the yield, yield components, as well as quality parameters. The two experimental sites have a GPRS mini weather station that measures different climatic parameters hourly. Leaf analysis was done before the treatments were applied to the experiment and after the treatments were applied to identify any differences that may occur in the concentration of the different nutrient elements, with special attention to nitrogen and Sulphur. The leaves that are sampled for analysis are the uppermost fully developed trifoliate leaves of the different treatments.

   Yield and Yield Components

   Grain yield
   The grain was harvested, weighed, and adjusted to 12,5% moisture content. The yield was then expressed in tons per hectare.

   Pods per plant
   The number of pods per plant was counted by sampling ten plants in each plot and calculating the average pods per plant.

   Number of kernels per pod
   The number of kernels per pod were counted from the same plants that are sampled for pods per plant. An average of kernels per pod was calculated by counting 1-kernel, 2-kernel-, 3-kernel and 4-kernel pods per plant and then dividing by the total number of pods on the specific plants.

   Thousand seed-weight
   A total of 1000 seeds were counted using a seed counting machine and then weighed. The weight of the seeds was recorded, and the moisture was adjusted to 12%.

   Quality Parameters

   Protein content
   The protein content of the seeds was measured using equipment from Free State Oil (a VKB-owned company). The extractable protein analysis is conducted by the Animal Science laboratory at the University of the Free State.

   Oil content
   The oil content of the seeds is measured using the same equipment as the latter. The extractable oil analysis is conducted by the Animal Science laboratory at the University of the Free State.

   Climate Data

   Relative humidity
   It is a ratio that is expressed in percent of the amount of atmospheric moisture present relative to the amount that would be present if the air were saturated. This data is not used for the project, but the weather station sends its readings too.

   Ambient temperature
   These temperature (°C) readings represent the actual temperature as it feels, which is an average of both the minimum and maximum temperature.

   Rainfall
   The amount of rainfall (mm) was recorded from planting until the crops reached maturity.

   Temperatures
   Minimum and maximum temperatures (°C) were recorded from planting until the crops reached maturity. These temperature readings were used for calculating growth degree days (GDDs) as well as heat units (HU).

   Results

   The results of the first production season will be discussed thoroughly in the annual report that will be sent to PRF by the end of August. These results cannot be used as recommendations before the trials for the second production season have been conducted and analysed statistically.

   Relevance and impact of proposed outcomes

   More farmers in the summer rainfall regions are expanding their soybean production. The area estimates and sixth production forecast of summer field crops shows that the total area where soybeans were planted for the 2020/21 production season was 827 100 hectares and for the 2021/22 season it was 915 300 hectares, which is an increment of more than 10% from the preceding production season (DALRRD, 2022). The production season after the latter realized an increment of more than 19% with the total area planted equal to 1 150 000 hectares for the 2022/23 production season. These increments and expansions promote the ever-growing interest in conducting scientific research on soybeans in the country.

   The high fertilizer costs play an important role in the financial viability of supplying additional nitrogen for yield improvement because producers spend a lot of money on large quantities of nitrogen fertilizer and apply it in high quantities but do not really make a good return from it. Regardless of whether supplemental nitrogen application is too high or not, the costs involved need to be evaluated and sound decisions should be taken to ensure that the money spent on fertilizer secures higher yields and produces a higher income. The results from this project will assist in the mitigation of risks and ultimately, help producers use their fertilizer wisely and possibly save a substantial amount of money on fertilizer. Agronomists, agriculturalists, and other crop-related research scientists and/or consultants will be able to use the information from this experiment to make recommendations.

   Recommendations

   The trial needs to be conducted for another production season before any recommendations can be made using the results above. Therefore, it is recommended that the project continues for another production year due to the high value it adds to the soybean industry. In future, a research trial testing the effectiveness of more than two nitrogen sources applied at every growth stage at different application rates needs to be conducted to ensure that the debate about the effective growth stage is investigated scientifically. A trial of that nature should most probably take place for four (4) production seasons or longer. Van Wyk (2016) conducted a trial almost like the one proposed above and he found that it is not economically beneficial to supplement soybeans with nitrogen fertilizer at any growth stage because it does not necessarily produce significant and economically viable yields.