Optimal management of agricultural systems

To remain competitive, many agricultural systems are now being run along business lines. Systems methodologies are being incorporated, and here evolutionary computation is a valuable tool for identifying more profitable or sustainable solutions. However, agricultural models typically pose some of the more challenging problems for optimisation. This chapter outlines these problems, and then presents a series of three case studies demonstrating how they can be overcome in practice. Firstly, increasingly complex models of Australian livestock enterprises show that evolutionary computation is the only viable optimisation method for these large and difficult problems. On-going research is taking a notably efficient and robust variant, differential evolution, out into real-world systems. Next, models of cropping systems in Australia demonstrate the challenge of dealing with competing objectives, namely maximising farm profit whilst minimising resource degradation. Pareto methods are used to illustrate this trade-off, and these results have proved to be most useful for farm managers in this industry. Finally, land-use planning in the Netherlands demonstrates the size and spatial complexity of real-world problems. Here, GIS-based optimisation techniques are integrated with Pareto methods, producing better solutions which were acceptable to the competing organizations. These three studies all show that evolutionary computation remains the only feasible method for the optimisation of large, complex agricultural problems. An extra benefit is that the resultant population of candidate solutions illustrates trade-offs, and this leads to more informed discussions and better education of the industry decision-makers.

Computational Modelling of the Vacuum Drying of Australian Hardwoods

Computational Modelling of the Vacuum Drying of Australian Hardwoods.

Complexity of coffee flavor: A compositional and sensory perspective

For the consumer, flavor is arguably the most important aspect of a good coffee. Coffee flavor is extremely complex and arises from numerous chemical, biological and physical influences of cultivar, coffee cherry maturity, geographical growing location, production, processing, roasting and cup preparation. Not surprisingly there is a large volume of research published detailing the volatile and non-volatile compounds in coffee and that are likely to be playing a role in coffee flavor. Further, there is much published on the sensory properties of coffee. Nevertheless, the link between flavor components and the sensory properties expressed in the complex matrix of coffee is yet to be fully understood. This paper provides an overview of the chemical components that are thought to be involved in the flavor and sensory quality of Arabica coffee.