An agent-based approach to QUICKly valuing the benefits of agricultural research and extension.

The complexity of farming and rural communities poses challenges to research and extension initiatives seeking industry-wide change. The effectiveness of these initiatives depends on factors ranging from individual psychology to the science of effects. Tools like intervention logic models, which visualise the causal chain of an intervention through to its impact, are useful for programme planning but are of limited usefulness for comparing the relative benefits of initiatives. Benefit-cost analysis can quantify relative benefits, but applications often oversimplify the causal chain and leave key impacts unquantified. This paper aims to develop a benefit-cost analysis modelling framework that both captures the important causal logic from intervention to impact as well as quantifying and monetising key benefit categories. We apply the framework to value the impacts of 'Hill Country Futures', a programme of research and extension designed to assist in future-proofing the environmental sustainability, profitability, and well-being of New Zealand's hill country farmers, their farm systems, and communities. The QUICK (Quantifying and Understanding the Impact of Capability and Knowledge) model uses an agent-based simulation to represent processes of skill development and practice improvement that result from both extension activities for management and research tools as well as the use of those tools. We calibrate the model using a combination of an external predictive adoption model, an expert workshop, and researcher judgment. We use a choice experiment to value simulated changes in financial, environmental, community, and well-being outcomes. We explore how optimising the programme of extension interventions through targeting could increase impact. Our results suggest that the benefits of the 'Hill Country Futures' programme outweigh the costs by a factor of 13.5. We find that targeting extension efforts towards building awareness could be slightly more beneficial than targeting both awareness and skill development. We find no evidence that targeting extension efforts towards the best resource would be beneficial. This paper combines desirable features from intervention logic models, benefit-cost analysis, choice modelling, and agent-based models to value the benefits of a large programme of research and extension. It highlights that such modelling can be useful for evaluating both planned and complete programmes. [Display omitted] • Programme evaluation is dominated by qualitative research and would often benefit from more quantitative analysis. • We evaluate Hill Country Futures, designed to improve the sustainability and profitability of hill country farmers. • We combine intervention logic models with benefit-cost analysis using an agent-based approach informed by choice modelling. • Our results suggest that the programme will deliver substantial benefits compared to costs, with a benefit-cost ratio of 13.5. • We show the potential of using intervention logic with benefit-cost analysis to value agricultural research and extension. [ABSTRACT FROM AUTHOR]