The value of decision support models for farmer learning

Abstract

Advances in computer technologies and mathematical modelling have enabled technology providers to develop decision support tools. Studies with farmer groups showed that the value of these tools to farmers may be as much for learning as for decision support. Stockpol was used in one farmer group to support decisions around farm systems design. Quickfeed was developed with another group that were interested in pasture quality, and Bestbreed with a group whose target was lamb growth rate from birth to weaning. The ultimate aim in all three studies was to improve farm profitability. When evaluating the studies, farmers ranked the overall study as being highly effective (72 to 86 %) in achieving the goal of improved profitability yet the value of the models was ranked much lower (40 to 44%). This result may be explained by the perception value relates to hands on use, rather than the learning associated with using the model and interaction with the information encapsulated in the model. Here the learning environment included collecting information to use in the model and comparing the model output with the actual changes that had occurred on farms. In other words, farmers were learning through participation. The value to farmers of computer models or tools may be better measured by their success in improving farm profitability. In these studies, this lay directly in what the farmers learnt and how their behaviour changed as a result of participation in the overall study rather than in continued use of the models per se. This paper discusses this aspect of farmer learning and the benefits of packaging technology in the form of decision support tools. Keywords: computer technologies, decision support, farmer study groups, learning, mathematical modelling, technology providers