Maize is the most important grain crop produced in South Africa, serving as a food source for humans and animals, an input provider to other sectors, a source of job creation, a contributor of value added to the national economy, and an earner of foreign exchange. The South African maize industry plays an important role in the South African economy and consequently its role players should be supported to promote the industry. However, since the abolishment of the agricultural marketing boards and the deregulation of South African agriculture, farmers have suddenly found themselves exposed to global competition and a liberalised economy. Maize prices are uncertain and volatile, leading to increased risk. In addition, input prices have increased more rapidly than maize prices in some instances, and since no government protection exists, the cost squeeze effect places many farmers in a financial predicament. In order to mitigate the cost squeeze effect, farmers have started exploring farming methods and strategies that can improve their financial position. Precision farming (PF) is identified as a technological tool that can improve the profitability of a maize farm through higher yields and lower input costs, and can also indirectly assist in the general farm management and financial functions on the farm. The literature indicates that PF has been successfully implemented on various occasions with subsequent benefits, whether financial or qualitative. It could also be a useful tool to improve the profitability of South African maize farmers. Despite its various benefits, PF is associated with high capital expenditures and therefore farmers are reluctant to implement this technology on their farms. However, a PF service system that requires little capital expenditure is implemented by an agribusiness (Griekwaland-Wes Koöperasie) in the Northern Cape Province. Farmers who are part of this program only pay PF service fees that are charged on a perhectare basis. Most of the PF technologies and knowledge are provided by GWK and/or affiliated fertilizer companies, which subsequently mitigate the burden of high capital expenditures. The general objective of the study was to investigate the impact of PF on the profitability of selected maize irrigation farms in the Northern Cape Province. This was achieved by comparing the profitability and risk position of selected farms under a conventional farming (CF) system with the profitability of the same farms when converting to a PF system. The specific objectives of the study were to determine whether PF would generate better profits than CF; to determine whether PF would improve the farmer’s ability to repay his debt and generate an income (thereby improving the financial survivability of the farm); to determine whether PF would improve the debt-to-asset position of the farmer; and to determine whether PF is less risky than CF with respect to net farm income and cash position. The Bureau for Food and Agricultural Policy (BFAP) farm-level model developed by Strauss (2005) proved to be a useful tool to achieve the set objectives, since the BFAP farm-level model is linked with the BFAP sector model, which enables it to accurately analyse the impact of changes in policies and markets at both farm and sector level in South Africa. A positivistic approach was followed in order to answer the question, “What will the likely outcome be?” The model has the capacity to do simulations in both deterministic and stochastic modes. Three maize irrigation farms in the Northern Cape Province were chosen by a panel of agricultural specialists who are accustomed with the irrigation farms and PF system in this province. The farms were analysed by means of the BFAP farm-level model in order to determine the impact of PF on the profitability of each farm. The BFAP baseline of 2008 was used for this purpose. Key input variables were identified and simulated based on the BFAP baseline of 2008, as well as actual data, assumptions regarding PF and CF farming, and reported features and benefits associated with PF. In order to simulate the risk associated with CF and PF through stochastic modelling, correlated probability distributions were assigned to the relevant key input variables by de-trending the historical data of the key input variables. A correlation matrix based on the absolute deviation of a specific variable from its trend was subsequently constructed. Each variable was then simulated by means of a correlated empirical distribution, with 500 model iterations being run for each simulation in order to obtain stable probability distributions. From the results obtained in the study, the conclusion can be drawn that PF not only improves profit margins, but indirectly contributes to improved financial management. Considering the higher profit margins, more cash is at the disposal of the farmer. When this extra cash is again reinvested in the farming business, debt (in terms of production loans and medium- and long term loans) can be repaid more quickly and/or less debt has to be incurred, leading to lower interest payments that in turn further increase profit margins, ultimately improving the debt and cash position of the farm. The results also indicate that the risk position of the participating farms improved significantly with the implementation of PF. It can therefore be concluded that PF could also serve as a valuable risk management tool. From the discussions with the farmers it also became apparent that their overall farm management abilities were improved significantly, due to the informative nature of PF. Based on the results of this study, it can be concluded that the hypothesis as stated in Chapter 1 cannot be rejected. In addition, several other aspects pertaining to PF should be considered. Firstly, the results are applicable to the specific participating farms in the study only, and cannot be attributed to all maize farms in general. Secondly, despite a meticulous process of data verification and validation, the conclusions drawn in the study are based on the quality of the data provided by the stakeholders. Thirdly, factors such as farming operations, management decisions, market, weather and disease conditions might divert from the assumptions made in the study and thereby affect the actual results in future. Fourthly, since the study focuses solely on irrigation farming, a similar study can be conducted on dryland maize farming, since the majority of maize is produced under dryland conditions. Fifthly, the study could serve as a starting point for a comprehensive study on the impact of PF on maize farming throughout South Africa. Sixthly, the study could pave the way for an investigation into using PF as a tool to negotiate lower crop insurance premiums for farmers. Lastly, it would be useful to conduct a similar study on the impact of PF on maize farming where farmers are responsible for the acquisition of their own PF equipment, unlike on the participating farms where no extra capital expenditures were required. This could enable researchers to provide a better answer on the question of costs involved when converting to a PF system, as well as the ideal farm size in terms of economies of scale. Copyright
Dissertation (MScAgric)--University of Pretoria, 2010.