Income and Cost Budgets for Winter Crops
2019 season

Area coverage

Table 1 and Table 2 indicates the area of coverage and include the dryland and irrigated crops. The source of data and collaborators are also presented. The collaborators are sincerely grateful for the interaction and assistance with data, knowledge and other inputs from each organisation, agribusiness and farmers.

Yield assumptions

Figure 1 and Figure 2 reports the yield assumptions for dryland and irrigated crops. The yield assumptions represents target yields which were determined in a round table discussion which are based on the crop potential in the respective regions, historic trends and expert opinions. It is important to note that intra-regional variations will occur and it is recommended that producers adjust their respective target yields based on their location and potential. Table 1.3 illustrates descriptive statistics for yield trends and projections over the period from 2005-2020.

Source: BFAP, 2019

Crop price assumptions

Annually, the Bureau for Food and Agricultural Policy (BFAP) presents an outlook of agricultural production, consumption, prices and trade in South Africa over a 10-year period. The information presented is based on assumptions about a range of economic, technological, environmental, political, institutional, and social factors. The outlook is generated by the BFAP system of models. A number of critical assumptions have to be made for baseline projections. One of the most important assumptions is that normal weather conditions will prevail in Southern Africa and around the world; therefore, yields grow constantly over the baseline as technology improves. Assumptions regarding the outlook of macroeconomic conditions are based on a combination of projections developed by the International Monetary Fund (IMF), the World Bank and the Bureau for Economic Research (BER) at Stellenbosch University. Baseline projections for world commodity markets were generated by FAPRI at the University of Missouri. Once the critical assumptions are captured in the BFAP system of models, the Outlook for all commodities is simulated within a closed system of equations. This implies that, for example, any shocks in the grain sector are transmitted to the livestock sector and vice versa. Therefore, for each commodity, important components of supply and demand are identified, after which an equilibrium is established through balance sheet principles by equalling total demand to total supply.

Figure 3 illustrates the commodity price assumptions for wheat, barley and canola that were used as base price for the winter crop budgets for the 2019 production season. Table 1.4 illustrates the standard deduction from the base SAFEX price as presented in Figure 1.3. The sensitivity analysis in the respective crop budgets makes provision for variation in price and yield and indicates the gross margin under each price and yield combination.

Key input cost trends

Figure 1.4 illustrates the cost trends for fuel, urea, MAP and potassium chloride over the period from January 2017 to February 2019. The cost for nitrogen, phosphates and potassium have reported a decreasing trend since roughly November 2018. The same trend was observed for fuel, however, the projection for 2019 (average) remains higher opposed to the February 2019 price. The average cost of MAP in 2019 is also projected higher compared to February 2019 levels. Persistent uncertainty in the macro-economic environment could entail continuous volatility in the Rand / USD exchange rate, which could impact the cost of imported inputs such as fuel, fertilisers, chemicals and machinery / implements.