The proposed research focuses on the crop rotation component of the Robust Temporal Optimisation Model (RTOM) inputs.

Crop rotations contribute to Australian agricultural production systems by ameliorating pest and weed loads; fumigation of the soil profile to control soil pathogens and invertebrate organisms and diversify farm income streams.

One tool to assist primary producers in their decision -making process for income optimisation is computational models. Currently models range in sophistication, with a trade-off between ease of use, depth of information relevance, and usefulness as a long-range scheduling tool. A key feature omitted from current models is the lack of memory to enhance resource optimisation.

RTOM offers a new approach which optimises farm net revenue by selecting most productive crop options from within the resource constraints of the farm business informed by climatic modelling data.

The proposed research examines how current best practice crop rotations for conventional and non-conventional operating systems may be incorporated into RTOM. Crop rotation data will be complied from industry specific best practice options, cross referenced to farm operations. Model outputs will be evaluated for feasibility, validated by primary producers.

The potential benefit to schedule crop rotations informed by active climatic data is minimisation of resource wastage and maximisation of activity revenue. This information will assist in preparedness for predicted climatic conditions.