Designing an expert-led Bayesian network to understand interactions between policy instruments for adoption of eco-friendly farming practices.

Governments employ a range of policy instruments to encourage landholders to adopt land management practices that reduce the environmental impacts of agriculture. While the impact of policy instruments may be well-theorised, their implementation in diverse contexts and landholders' complex behavioural responses, makes measurement and prediction of the resulting adoption rates difficult. This constrains the ability of governments to select the optimal combination of policy instruments. We used a participatory modelling approach to incorporate expert knowledge into a Bayesian network model exploring the effect of different policy combinations on the adoption of sustainable farming practices in the Great Barrier Reef catchment, Australia. The model integrates policy instruments including regulating farming practices, offering financial incentives, and facilitating extension programs to educate and assist farmers. Results showed that the effectiveness of a policy instrument on practice adoption was expected to vary depending on which other instruments are implemented, the characteristics of the land managers, the surrounding social context, and the practice itself. This approach demonstrates the utility of Bayesian networks in integrating high-level multi-disciplinary knowledge to address complex environmental policy decisions such as water quality management in the Great Barrier Reef. ● Impact of agriculture on the Great Barrier Reef can be reduced using policy instruments. ● Interactions between instruments for practice adoption are currently poorly understood. ● Mechanisms influencing the impact of policy instruments were explored using an expert-derived Bayesian network. ● Regulations, financial incentives, extension, governance and communication were considered. ● The model facilitates scenario analysis to help to inform policy decisions. [ABSTRACT FROM AUTHOR]