A return to decision support: How can decision support tools be used at the science-policy interface?

This paper presents the outcomes and reflections from a policy-led application of the Quantitative Story Telling (QST) methodology within the Land Use Transformation project, part of the Scottish Government’s 2022–2027 Strategic Research Programme. The focus of this second QST cycle was the regionalisation of farm support payments in Scotland, exploring how different configurations of payment regions and schemes could better align with policy objectives such as Net Zero and environmental sustainability. The work was conducted in close collaboration with Scottish Government analysts and policy officials and aimed to support deliberation and decision-making on future agricultural support mechanisms.
The QST process, which integrates both formal (quantitative) and semantic (interpretive) phases, was used to co-develop and assess a series of regionalisation scenarios. These included updates to existing schemes, new configurations of Basic Payment Scheme (BPS) regions, and alternative implementations of Less Favoured Area Support Scheme (LFASS) and Voluntary Coupled Support (VCS). The analysis was underpinned by integrated datasets from administrative and research sources and supported by a Scenario Builder tool developed in R Shiny, and Power-BI dashboards. This tool enabled rapid generation, testing, and visualisation of payment scenarios, facilitating iterative engagement with the project steering group.
The research was structured into five phases: baseline characterisation of farm businesses; development and assessment of BPS options; evaluation of LFASS alternatives; exploration of VCS adjustments; and synthesis, reporting, and stakeholder engagement. A key innovation in the context of conducting QST with Scottish Government was the use of dashboards and interactive visualisations to support interpretation and communication of complex results. The Scenario Builder and Dashboards allowed analysts to explore the (re)distributional impacts of policy options in terms of funding per business across farm types, regions, and size classes, and to link payment outcomes to environmental and socio-economic indicators such as peatland extent and land capability.
Despite the technical sophistication and collaborative nature of the work, the study highlights persistent challenges in translating research outputs into policy-relevant insights. The “funnel” effect—where only a small subset of analyses can be communicated effectively within policy processes—remains a significant barrier. While decision support tools (DSTs) like the Scenario Builder can enhance capacity and responsiveness on the research side, their uptake and utility within government are constrained by institutional, technical, and resource limitations.
While DSTs can play a valuable role in capacity building and scenario analysis, their impact on policy processes is mediated by the quality of the underlying QST process and the strength of relationships between researchers and policy actors. Greater investment in DST literacy and infrastructure within government could enhance the shared use of such tools, but this requires sustained collaboration and advocacy. The findings contribute to broader discussions on the role of modelling and decision support in transdisciplinary research and science-policy interfaces.