• Hochman, Zvi
• Carberry, Peter

Abstract

The prospect that decision support systems (DSS) can help farmers adjust their management to suit seasonal conditions by putting scientific knowledge and rational risk management algorithms at farmers¿ fingertips has been a powerful promise. This promise stands in contrast to evidence that farmers do not adopt the vast majority of such systems; a problem identified as the ¿implementation problem¿. A number of reviews have called for a re-appraisal of the field, for the need to reflect on past mistakes and to learn from the broader history of DSS and from social and management theory. This paper deals with this issue, in the context of Australian agriculture, in three parts. In the first part, past reviews are summarised in order to inform future developers and funders of DSS projects of the current state of knowledge about the ¿implementation problem¿, whether it can be overcome and if so how. In the second part, five current DSS development and delivery efforts in Australia were investigated to determine what could be learned from recent attempts to overcome the ¿implementation problem¿. Lessons from the literature and recent experience were then combined to determine a set of conditions that must be satisfied before commencing a DSS project and a set of factors that might contribute to success in developing and delivering a DSS that is both useful and used to benefit a significant number of farmers.In the third part, 23 DSS stakeholders completed a questionnaire that tested the current level of support for the lessons drawn from the first and second parts.In conclusion, it is clear that DSS development should not commence unless it is backed by marketing information and a plan for delivery of the DSS beyond the initial funding period. To be credible such a plan must involve a private-sector partner that is well linked into the agricultural advisory system. Beyond satisfying these pre-conditions, a key requirement is that a DSS needs to better match farmers¿ naturalistic decision making processes; it should aim to educate farmers¿ intuition rather than replace it with optimised recommendations. DSS should enable users to experiment with options that satisfy their needs rather than attempt to present ¿optimised¿ solutions. DSS need to be embedded in a support network consisting of farmers, consultants and researchers so that they fit better into farmers¿ knowledge networks.Finally, DSS tools stand on the quality and authority of their underlying science and require ongoing improvement, testing and validation. Achieving all of these criteria requires the commitment of a critical mass of appropriately skilled people involved in the development of a DSS.

Topics

• impedance spectroscopy
• theory
• experiment