SYSTEMS ANALYSIS AND MODELLING IN SUGAR RESEARCH: REFLECTIONS ON THE CRC SUGAR EXPERIENCE

The Systems Analysis and Modelling (SAM) Cross Program, a unique feature of CRC Sugar, was established in recognition of the need for more integrated approaches to address the many complex production and environmental challenges faced by the sugar industry. The SAM Cross Program addressed this need by fostering multi-disciplinary, multi-organisational, participatory and collaborative approaches to improve understanding of how farm enterprises and their components work, and how they fit within broader sugar producing catchments. The SAM Cross Program worked with and through the other CRC Sugar programs and in partnership with industry and others, including community and environmental groups. It introduced new capabilities in soils, economics, spatial analysis, modelling and scenario analysis to help research, develop and implement management practices that address both profitable and environmental goals at the paddock, farm, and regional scale. In this paper, we highlight some of the successes of the SAM Cross Program, discuss approaches that worked well, some that could have been done differently, and offer suggestions of areas that need particular attention in future. It is clear from progress made to date that it is critical that future research work be placed in a systems context so that we know how various components of the system contribute to behaviour of the whole. This will allow knowledge from interacting and potentially contradictory findings and experiences to be captured and used to help make the whole sugar production system work more effectively and efficiently. While models are important tools in these tasks, progress will be limited without full impact of systems research on sugar industry practice, which will require continued effort at achieving effective partnerships between growers, millers, marketers, advisers, agribusiness and researchers. The human dimensions of sugarcane production systems and of systems approaches remain the most challenging.