Analysing Data from Open Sources to Manage Risks in Food Production

Agri-food supply chains pose unique challenges. They are inherently complex due to natural processes, random processes, and unpredictability. In modern agriculture and food production, the supply chains necessary for operations are wider because of more components involved and longer because of more complex products and services that themselves depend on supply chains. From a primary producer’s perspective, risk-sensitive components of the supply chain are not always fully identified. While the most important direct risks—such as energy, seeds, and fertilizers—are usually quite well understood, other deeper dependencies in the supply chain are not easily recognized. On the other hand, with data ‘revolution’ it has become easier to monitor processes. Advances in big data and analytics have made extracting information easier. Open data are also increasingly available and organized to the point where large amounts of data are shared and hosted through international collaboration. In this chapter, we seek to detail a producer’s risks and map available sources of relevant data. Through relevant example cases we illustrate the utility of using big data to increase information content that can help alleviate risks. We foresee this strategy as a tool for producers to customize their own supply chains and gain a deeper understanding of their vulnerabilities.