In several countries around the world, grain commodities are traded as assets on stock exchanges. This indicate that the market and effectively the prices of the grain commodities in such countries, are controlled by several local and international economic, political and social factors that are rapidly changing. As a result, the prices of some grain commodities are volatile and trading in such commodities are prone to price-related risks. There are different trading strategies for minimising price-related risks and maximising profits. But empirical research suggests that making the right decision for effective grain commodities trading has been a difficult task for stakeholders due to high volatility of grain commodities prices. Studies have shown that this is more challenging among grain commodities farmers because of their lack of skills and the time to sift through and make sense of the datasets on the plethora of factors that influence the grain commodities market. This thesis focused on providing an answer for the main research problem that grain farmers in South Africa do not take full advantage of all the available strategies for trading their grain commodities because of the complexities associated with monitoring the large datasets that influence the grain commodities market. The main objective set by this study is to design a framework that can be followed to collect, integrate and analyse datasets that influence trading decisions of grain farmers in South Africa about grain commodities. This study takes advantage of the developments in Big Data and Data Science to achieve the set objective using the Design Science Research (DSR) methodology. The prediction of future prices of grain commodities for the different trading strategies was identified as an important factor for making better decisions when trading grain commodities and the key factors that influence the prices were identified. This was followed by a critical review of the literature to determine how the concepts of Big Data and Data Science can be leveraged for an effective grain commodities trading decision support. This resulted in a proposed framework for grain commodities trading. The proposed framework suggested an investigation of the factors that influence the prices of grain commodities as the basis for acquiring the relevant datasets. The proposed framework suggested the adoption of the Big Data approach in acquiring, preparing and integrating relevant datasets from several sources. Furthermore, it was suggested that algorithmic models for predicting grain commodities prices can be developed on top of the data layer of the proposed framework to provide real-time decision support. The proposed framework suggests the need for a carefully designed visualisation of the result and the collected data that promotes user experience. Lastly, the proposed framework included a technology consideration component to support the Big Data and Data Science approach of the framework. To demonstrate that the proposed framework addressed the main problem of this research, datasets from several sources on trading white maize in South Africa and the factors that influence market were streamed, integrated and analysed. Backpropagation Neural Network algorithm was used for modelling the prices of white maize for spot and futures trading strategies were predicted. There are other modelling techniques such as the Box-Jenkins statistical time series analysis methodology. But, Neural Networks was identified as more suitable for time series data with complex patterns and relationships. A demonstration system was setup to provide effective decision support by using near real-time data to provide a dynamic predictive analytics for the spot and December futures contract prices of white maize in South Africa. Comparative analysis of predictions made using the model from the proposed framework to actual data indicated a significant degree of accuracy. A further evaluation was carried out by asking experienced traders to make predictions for the spot and December futures contract prices of white maize. The result of the exercise indicated that the predictions from the developed model were much closer to the actual prices. This indicated that the proposed framework is technically capable and generally useful. It also shows that the proposed framework can be used to provide decision support about trading grain commodities to stakeholders with lesser skills, experience and resources. The practical contribution of this thesis is that relevant datasets from several sources can be streamed into an integrated data source in real-time, which can be used as input for a real-time learning algorithmic model for predicting grain commodities prices. This will make it possible for a predictive analytics that responds to market volatility thereby providing an effective decision support for grain commodities trading. Another practical contribution of this thesis is a proposed framework that can be followed for developing a Decision Support System for trading in grain commodities. This thesis made theoretical contributions by building on the information processing theory and the decision making theory. The theoretical contribution of this thesis consists of the identification of Big Data approach, tools and techniques for eradicating uncertainty and equivocality in grain commodities trading decision making process.

Thesis (M.Sc.Agric.)--StellenboschUniversity, 2000. / ENGLISH ABSTRACT: The protection that agriculture in South Africa enjoyed under the Marketing Act was terminated in 1996 when the controlled marketing of agricultural products was abolished. The grain industry and individual winter-grain producers in the Western Cape is now compelled to be competitive internationally. The ability of local producers to compete effectively can be improved by increased yields, a reduction in production costs, higher import levies, import control and the weakening in the exchange rate of the rand. This investigation focuses on strategies aimed at reducing production costs as a means of improving competitiveness. The field of this study is the winter-grain producing sub-regions of the Southern Cape which are severly affected by the deteriorating situation. In the course of this investigation typical farming units were identified that can be regarded as representative of farming activities in the specific sub-regions of the Southern Cape. Existing literature as well as the opinions of authorities (group of experts) on the subject were used as sources of information in the study of cost-saving methods of production. The emphasis was therefore on the identification and evaluation of appropriate alternative production practices. The practice of minimum tillage with accomanying reduced input costs proved to have merit. The current situation was evaluated with the assistance of a group of experts and possible alternative practices were discussed. A financial cost-benefit analysis was used to compare the current production practices with practices proposed in this study in order to ascertain its feasibility. Directives are offered to producers in order to reduce the production costs and thereby become more cost-effective. Based on the results of the case studies in the different sub-regions the conclusion was reached that the suggested alternative practices can result in a reduction in production costs and thereby make a marked contribution towards the ability of winter-grain farmers in the Southern Cape to improve their competitive position. / AFRIKAANSE OPSOMMING: Met die deregulering van die Suid-Afrikaanse landbou in 1996, is aile beskerming vanaf beheerrade aan produsente geskrap. Die graanbedryf in die Wes-Kaap is nou onder groot druk om meer mededingend te wees teenoor internasionale produsente en elke individuele produsent moet dus poog om so mededingend as moontlik te wees. Die mededingende posisie van plaaslike produsente kan verbeter word deur, onder andere, 'n verhoging in die eenheidsopbrengs, 'n verswakkende wisselkoerswaarde van die Rand, invoertariewe en -kwotas en deur die verlaging van insetkoste. Hierdie ondersoek fokus op strategiee gemik op die verlaging van insetkostes ten einde mededingendheid te verbeter. Die kleingraanproduserende sub-streke van die Suid-Kaap, wat ook deur hierdie verswakkende mededingendheid-situasie geraak word, dien as ondersoekgebied vir hierdie studie. In die ondersoek is daar gefokus op die identifisering van tipiese boerdery-eenhede wat as verteenwoordigend beskou kan word van die boerderybedrywighede in die ge'identifiseerde sub-streke van die Suid-Kaap. Bestaande literatuur en menings van bedryfskenners is gebruik as inligtingsbronne vir die ondersoek na koste-besparende produksiemetodes. Die klem val dus op die identifikasie en evaluasie van toepaslike alternatiewe produksiepraktyke. Die praktyk van verminderde bewerking met die gepaardgaande verlaagde bewerkings- en ander insetkoste het veral op die voorgrond getree. Die gedetailleerde ontleding van die produksiepraktyke en gepaardgaande winsgewendheid van die huidige situasiesketse van die verskillende gevallestudies dien as vertrekpunt vir die ontledings. Deur gebruik te maak van 'n groep bedryfskenners (ekspertgroep) word die huidige situasie beoordeel en moontlike alternatiewe praktyke wat gevolg kan word, word bespreek en beoordeel. Die huidige produksiepraktyke word vergelyk met die voorgestelde verbeterde produksiepraktyke met behulp van 'n tinansiele voordeel-koste ontleding ten einde die haalbaarheid daarvan te evalueer. Daar word in hierdie ondersoek dus riglyne aan produsente verskaf waarvolgens te werk gegaan kan word ten einde hul mededingende posisie deur middel van koste-besparende produksiemetodes te verbeter. Vanuit die evaluering van die gekose gevallestudies in die verskillende sub-streke is dit duidelik dat die alternatiewe praktyke, soos voorgestel deur die bedryfskenners, kan lei tot 'n verlaging van insetkoste en dus tot 'n verhoging in die mededingendheid van die Suid-Kaapse kleingraanprodusent.

Agricultural commodity derivatives were first introduced in South Africa in 1996 after the deregulation of the former marketing system. In the context of its proposed functions, namely price discovery and risk management, the question arose as to whether the futures market developed over time to performed its role efficiently. According to the Efficient Markets Hypothesis (EMH) an efficient market is one that accurately incorporates all information available at any point in time. The purpose of the research was to address the issue of price discovery efficiency, firstly, focusing on the weak-form methodology. Secondly, considering the behaviour of futures prices over time, the study addressed the concern of anomalies in daily returns – phenomena contradictory to the EMH by implication. Thirdly, as a means of defining the sources of inefficiency, the role of scheduled public information and its impact on futures prices was examined. Therefore, the primary objective of the research was to investigate and identify the main components of agricultural futures market inefficiency within the unique price formation structure of South African grain markets. The assessment of this problem is important in terms of evaluating the growth and development of the futures market for different grain commodities to date. The Exchange needs to review rules and regulations on a frequent basis in order to ensure proper functioning at all times especially in the case of a relatively new and fast growing market. The study contributed to the knowledge of understanding the price adjustment process and its implications for market efficiency in the context of the three grain markets considered. The weak-form efficiency was tested using a co-integration based model. Analysing daily spot and futures prices of white maize, yellow maize, and wheat, results indicated that all three markets were efficient and unbiased. Non-parametric tests revealed the significant presence of day-of-the-week and turn-of-the-month effects in the futures returns of the three commodities. Further non-parametric analyses suggested a high degree of uncertainty in futures returns around scheduled agricultural and macroeconomic information release dates also contributing significantly to the identified anomalies. It was concluded that (1) the markets’ ability to anticipate the contents of future information to be released, (2) the current skewed size distribution of broking members, (3) the significant role of the R/$ exchange rate in the price formation process of South African grains and, therefore, (4) the relationship to and influence of the broader economy enhanced the return effects (anomalies) creating opportunity for profitable arbitrage. This conclusion was mainly attributed to South Africa’s status as a price-taker in the world grain complex as well as the relatively short existence of the local agricultural futures markets.