Agri-food chains are complex systems involving multiple multifaceted firms usually working together within specific industry sectors (e.g. grains, beef, wool, dairy) to satisfy an increasingly globalised market demand for high value food products. In so doing, the groupings of companies involved in an agri-food chain undertake activities that require multidimensional inter-organisational and cross organisational decision-making in the process of adding value to a raw commodity product through the production, manufacturing and distribution stages of the chain. Additional complexity is added by climate variability which impacts randomly and unpredictably on decision making in every component of the chain. The work outlined in this paper is a pilot investigation looking at a number of approaches to conceptualising and modelling an agri-food chain and its related decision making processes to better evaluate the impact and effects of that decision making and associated information flows across the components of the agri-food chains. The modelling approaches were (i) a multimedia model initially explored as an opportunity to visualise supply and value chain issues for educational purposes; (ii) an agent based model (ABM) using deterministic rules to architecturally synthesise a supply chain, and (iii) a baysian belief network (BBN) which we discuss as an approach for looking at the likelihood of certain decisions being made under certain scenarios.