The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.

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All correct reasoning is a grand system of tautologies, but only God can make direct use of that fact. The rest of us must painstakingly and fallibly tease out the consequences of our assumptions. (Herbert Simon in 'The Sciences of the Artificial', p.15)Decision support systems (DSS), like other information systems (IS) before them, were designed to serve functions deemed by 'management scientists' to be potentially useful to managers. But the unwelcome fact is that the use of agricultural DSSs by managers of farms has been low. This paper probes possible reasons for this through interpretation of agricultural DSS case histories and several strands of relevant social theory. From nine cases of DSS development effort and 14 products interpreted comparatively, a number of generalisations are made that serve as reference points in the following search for explanation in theory.First, the nature of management practice of family farms is explored and differences between the internal structure governing personal action and the scientific approach to practice are contrasted. Next, the interaction between the nature of the particular action/practice and the nature of the DSS is explored. A DSS designed to provide integrated, optimal recommendations for management typifies the DSS as a proxy for a manager's decision process. Examples of elaborate expert systems that simply were not used dramatically illustrate the resistance of family farmers to have their decision processes by-passed. On the other hand, the DSS designed to serve as a tool in a modified decision process is shown to have experienced higher use, by deriving and exploiting 'deep,' abstract information about the system, by introducing a powerful 'logic,' or a combination of both. A number of the referenced case stories demonstrate the resurgence of the decision support mode whereby the simulator is in the hands of an expert intermediary as an alternative to easy-to-use software in the hands of a farmer. This is the mode of operational research/ management science, which preceded the DSS.In comparison with hierarchical organizations, available options for overcoming the persistent 'problem of implementation' of the DSS in family farms are inherently weak. This focuses attention on the importance of the relationship between the DSS developer and the potential user. Drawing on a classic typology of possible configurations of 'understanding' between the scientist and the manager, four approaches to intervention are discussed. Three entail a degree of engagement that qualifies them as 'participative.' But one of these constitutes a departure from the DSS and broader IS traditions that places it in another paradigm. In this 'mutual understanding' relationship, intervention intent shifts from educating and persuading to recognition of and respect for other ways of viewing the world. This opens up the opportunities for co-creating information systems that utilise the comparative advantages of both practical and scientific knowledge. Intervention emphasis shi...

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