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A rapid increase in demand and severe droughts in recent years has increased the pressure on water supplies throughout most parts of Australia. This has resulted in the need for tools to allocate limited water across users in different regions, and explore scenarios so as to achieve economic, social and environmental benefits. A major challenge in water resource allocation is dealing with the uncertainty in the system, particularly with respect to reservoir inflow. Stochastic non-linear programming is applied to water resource allocation to accommodate this uncertainty across the time periods of the planning horizon. A large range of solutions is produced representing the distributions of uncertainty in reservoir inflow. These solutions are used in a Monte Carlo simulation to estimate the trade-off in amounts of water allocated versus risk of not achieving minimal reservoir levels. The methodology is applied to a case study in South East Queensland in Australia, a region which is currently facing a severe water shortage over the next 3 years. A new water supply initiative that the Queensland State Government is considering to overcome the water crisis is assessed using the methodology.

Success stories for applying supply chain methods to enhance agricultural industries are limited, despite their great potential. One key reason is that agricultural chains are subjected to the considerable managerial, social and biophysical complexity, which often leads to the inappropriate use of different methods. We capture supply chain complexity by formulating a matrix of biophysical by management factors. This is used to comprehend supply chain complexity and show how participants in agricultural chains can implement adaptation strategies that add value to their industry. Through various case studies we illustrate how adaptation strategies adopted by chain participants relate to different quadrants of the complexity matrix. An analysis of the literature based on this matrix also shows the suitability of different types of technical methods when used within adaptation strategies of each matrix quadrant. The complexity matrix aids the identification of the right strategies to use for the right problem through engagement with the right people.

Making strategic innovations to agricultural value chains can be difficult. Complex biophysical and logistical interactions make a priori estimates of chain-wide impacts difficult. This paper gives the results of applying an agent-based, value chain modelling framework, developed and applied in participation with stakeholders in three sugar mill case studies, which evaluated the viability of maximising the co-generation of electricity by harvesting the whole crop (i.e. cane leaves as well as stalk). The existing practice of harvesting predominantly cane stalk (as in the 2003 production year) was the base case for comparing a scenario where electricity co-generation (from crop residues) was an added revenue stream. Increased revenues were compared to costs within sectors as per changes in biophysical flows for the grower, harvesting, transport and milling segments in the chain. In general, predicted impacts on the farming and milling sectors were greater than anticipated by stakeholders, while the impacts on the harvesting and transport sectors were less than anticipated because of increased logistical efficiency possible in the sectors. A virtual experience of their supply chain facilitated more complete knowledge transfer about possible impacts of whole-crop harvesting to managers across the chain and thus increased their capacity to evaluate innovative business models involving the co-generation of electricity.