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A vast literature on market integration and price transmission has accumulated over the last half century. This literature has received major impetus, first from the introduction of increasingly sophisticated co-integration methods since the late 1980s, and second from the so-called food price crisis of 2007–2008, which heightened interest in the transmission of food price spikes in space and along the supply chain. In this paper I review the literature on price transmission and market integration, highlighting applications in an African setting. I begin by reviewing the basic methods that are commonly used in price transmission analysis, as well as several extensions that have gained prevalence in recent years. I then discuss several important limitations and challenges that remain to be addressed in future research on price transmission and market integration. [ABSTRACT FROM AUTHOR]

The prices of agricultural grain commodities are known to be volatile due to several factors that influence these prices. Moreover, different combinations of these factors, such as demand, supply and macroeconomic indicators are responsible for the price volatility at different times. Big Data presents opportunities to collect and integrate datasets from several sources for the purpose of discovering useful patterns and extracting actionable insights that can be used to gain competitive advantage or improve decision making. Neural Networks presents research opportunities for training computer algorithms to model linear and non-linear patterns that might exist in datasets for the purpose of extracting actionable insights such as making predictions. This article proposes a Big Data and Neural Networks approach for predicting prices of grain commodities in South Africa. It was identified that disparate data that influence the grain commodities market can be acquired, integrated and analysed in real-time to predict future prices of grain commodities. By utilising SAP HANA as the enabling Big Data technology, data acquired from several sources was used to create an integrated dataset, and a predictive model was developed using Backpropagation Neural Network algorithms. This model was used to predict the daily spot prices of white maize on the Johannesburg Stock Exchange (JSE) at the end of each trading day. The initial results indicate that the approach can be scientifically used to predict future prices of grain commodities in a real-time environment. [ABSTRACT FROM PUBLISHER]

Maize is the most important crop in South Africa, being both the major feed grain for livestock and the primary staple food crop for the majority of the population. Farmers’ production decisions regarding maize and their response to policy incentives are critical for national food security and regional trade. The aim of the study was to estimate the supply response of the South African maize sector to price and non-price incentives. A Nerlovian partial adjustment model was applied to historical time series data of area under maize cultivation, measured in hectares spanning from 1980-2012 to determine the supply response. Results indicate a short-run price elasticity of 0.24 and a long-run price elasticity of 0.36, signifying that maize farmers are less sensitive to price changes than non-price incentives. The results confirm that non-price incentives such as rainfall and technology seem to have more effect on maize supply than price incentives in South Africa. Given the findings, the study recommends policies and programmes that focus more on non- price incentives, such as technology and infrastructure development, investment in irrigation and research services, as a means of stabilising maize production in South Africa. [ABSTRACT FROM AUTHOR]

The study uses a partial equilibrium model of Tanzania’s informal dairy value chain to determine the benefits to milk producers and consumers from three policy interventions proposed in the Tanzania Livestock Modernization Initiative (TLMI). Using aggregate time series data to simulate the model, the study finds that from an individual policy perspective, expanding the national herd would produce the largest benefits for producers and consumers; producers would gain over 550 million shillings annually and consumers would gain about 35 shillings per capita. Comparing the other two interventions, consumers would benefit more from a better regulatory environment than from a reduction in cow prices, while producers would benefit more from a reduction in cow prices than from better regulations. If all policies are implemented simultaneously, even greater benefits would be realised; consumer surplus would increase by 85 shillings annually and producer surplus would increase by over one billion shillings in real terms. The results suggest that policy interventions outlined in the TLMI that address supply constraints are critical to developing the country’s dairy industry. [ABSTRACT FROM PUBLISHER]