Improved management of key northern region weeds: diverse problems, diverse solutions

The cropping region of northern Australia has a diverse range of cropping systems and weed flora. A fallow phase is commonly required between crops to enable the accumulation of stored soil water in these farming systems dominated by reduced tillage. During the fallow phase, weed control is important and is heavily reliant on herbicides. The most commonly used herbicide has been glyphosate. As a result of over-reliance on glyphosate, there are now seven confirmed glyphosate-resistant weeds and several glyphosate-tolerant species common in the region. As a result, the control of summer fallow weeds is become more complex. This paper outlines project work investigating improved weed control for summer fallows in the northern cropping region. Areas of research include weed ecology, chemical and non-chemical tactics, glyphosate resistance and resistance surveys. The project also has an economic and extension component. As a result of our research we have a better understanding of the ecology of major northern weeds and spread of glyphosate resistance in the region. We have identified and defined alternative herbicide and non-chemical approaches for the effective control of summer fallow weeds and have extended our research effectively to industry.

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.