Exploring profit - Sustainability trade-offs in cropping systems using evolutionary algorithms

Models that implement the bio-physical components of agro-ecosystems are ideally suited for exploring sustainability issues in cropping systems. Sustainability may be represented as a number of objectives to be maximised or minimised. However, the full decision space of these objectives is usually very large and simplifications are necessary to safeguard computational feasibility. Different optimisation approaches have been proposed in the literature, usually based on mathematical programming techniques. Here, we present a search approach based on a multiobjective evaluation technique within an evolutionary algorithm (EA), linked to the APSIM cropping systems model. A simple case study addressing crop choice and sowing rules in North-East Australian cropping systems is used to illustrate the methodology. Sustainability of these systems is evaluated in terms of economic performance and resource use. Due to the limited size of this sample problem, the quality of the EA optimisation can be assessed by comparison to the full problem domain. Results demonstrate that the EA procedure, parameterised with generic parameters from the literature, converges to a useable solution set within a reasonable amount of time. Frontier ‘‘peels’’ or Pareto-optimal solutions as described by the multiobjective evaluation procedure provide useful information for discussion on trade-offs between conflicting objectives.

Understanding the cropping behaviour of Riberry (Syzygium leuhmannii)

This study highlights the complexity of flowering biology in Syzygium and demonstrates how a basic understanding of a species’ fundamental biology is necessary for successful commercial cultivation. This report brings together useful information from previous international research on Syzygium as well as providing a basic understanding of flower biology, the nature of fruit set and seediness in riberry. Much of these findings have implications for the cultural management of riberry orchards to optimise fruit set and minimise seed set. It raises the possibility of avenues for genetic improvement.