Using a Choice Experiment to Improve Decision Support Tool Design

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Abstract

The potential for computer-based decision support tools (DSTs) to better inform farm management decisions is well-recognised. However, despite considerable investment in a wide range of tools, uptake by advisers and farmers remains low. A greater understanding of the demand and the most valued features of decision support tools could improve the uptake and impact of DSTs. Using a choice experiment, we estimated the values that Australian farm advisers attach to specific attributes of decision support tools, in this case relating to weed and herbicide resistance management. Results from discrete choice models showed that advisers' preferences differ between private fee-charging consultants, those attached to retail outlets for cropping inputs, and advisers from the public sector. Advisers valued ‘reliable accurate results', and also placed a consistently high value on models with an initial input time of three hours or less, compared to models that are more time demanding. Results from latent class models revealed a large degree of preference heterogeneity across advisers. Although the majority of advisers valued DST output that is specific to individual paddocks, approximately one-quarter of the respondents preferred generic predictions for the district rather than predictions with greater specificity. The choice experiments helped to identify the attributes most valued by advisers in different market segments. This has implications for those seeking to influence decision-making by allowing DST development to be better targeted towards the preferences of potential users.

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