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Designing cropping systems that are simultaneously profitable, resilient, and sustainable remains a key challenge for agricultural producers, particularly under increased climate variability. While digital agriculture tools have advanced rapidly, most applications remain limited to single-season, sub-field precision management rather than supporting multi-year, system-level decisions. Here we present RotationARM, an interactive, simulation-based tool that assists growers and advisors in evaluating and comparing multi-year cropping strategies. The tool is underpinned by a database of 22.9 million crop simulations using the Agricultural Production Systems sIMulator (APSIM) integrated with financial cost-benefit data, spanning 24 sites across Queensland and New South Wales in Australia, 60 years of weather records, three soil profile depths, 88 rotations, nine sowing rules, and three fertiliser intensity levels. It provides an accessible user interface requiring minimal input data. A core novelty is the representation of inter-seasonal water and nutrient carryover, enabling users to evaluate the performance of continuous cropping trajectories rather than single-year outcomes. Multi-criteria outputs – covering agronomic performance, financial returns, risk metrics, and resource-use efficiency – are communicated through intuitive visualisations tailored for non-scientific audiences. Twelve participatory workshops with 65 farmers, industry and government stakeholders informed the tool’s design, prioritising iterative reflection over prescriptive recommendations. Scenario analyses demonstrate how adjustments to cropping strategies, such as changes in sowing rules or cropping intensity, can improve agronomic, financial, and environmental outcomes while mitigating risk. RotationARM fills a critical gap in decision support for cropping system design. The tool represents a practical step toward applying dynamic crop models in farm-level tactical and strategic decision making. The APSIM tool has been validated using extensive observed field trial datasets, with strong representation of eastern Australia.
Grewer et al. (Fri,) studied this question.