Environment, Natural Resources and Agriculture Research: Strategy 2027 to 2032

Annex B: ENRA Strategy for Decision Support

Introduction

The ENRA Decision Support Strategy sets a clear direction for enhancing the analytical capabilities of the 2027-2032 ENRA Research Programme. It focuses on strengthening the modelling and analysis activities delivered through the Strategic Research Programme, including supporting Underpinning National Capacity activities, to ensure a more integrated approach across the programme.

This strategy addresses current challenges and explores alternative approaches to meet future demands. Grounded in the principles of evidence-based decision-making, innovation, and alignment with national guidance and standards, it sets a vision for the development of Scotland’s modelling and data analysis capabilities within the ENRA Research Programme over time. It aims to deliver long-term improvements by addressing current barriers and enabling more consistent, collaborative, and policy-relevant use of data and models.

This strategy focuses on modelling for decision support, and the underpinning data activities that support modelling, within the ENRA Research Programme 2027-2032. The following activities are within the scope of this strategy:

• Modelling activity funded and delivered through the 2027-2032 ENRA Research Programme’s Strategic Research Programme (SRP).
• Underpinning data and related processes that support modelling and analysis within the SRP, including data sharing, processing, quality assurance, and analytical workflows.

This Annex concludes with an outline of potential priority areas for delivery of decision support system, for biodiversity, water resources, food systems, land use, and Just Transition.

Purpose and Guiding Principles

The guiding principles of this strategy are:

• Working for the Public Good: data and models are treated as shared assets that inform public policy and maximise societal benefit, delivering good value for public money. This includes ensuring that work with new and emerging technologies is ethical and transparent.
• Co-Design and Collaboration: researchers, policymakers, and stakeholders will be engaged through consultations and workshops to co-create feasible and ambitious solutions.
• Delivering Impact: data analysis and modelling activity underpin much of the wider research activity in the ENRA Research Programme 2027-2032. This strategy will ensure analysis and modelling outputs support better policy outcomes and deliver cross-programme impact.

The desired modelling and underpinning data analysis outcomes for the ENRA Research Programme 2027-2032, informed by these principles, are:

1. Ensuring traceability and transparency in how data, analysis, and models inform decisions.

2. Enabling timely, evidence-based policy decisions through accessible, high-quality analysis and modelling.

3. Fostering a culture of reuse, collaboration and active engagement across research and policy communities.

By delivering on these outcomes, the ENRA research programme will be better positioned for long-term success. Greater consistency and coordination in how underpinning data analysis and models are managed could help minimise duplication, encourage collaboration, and increase the overall utility of research outputs.

Vision for Modelling in the Research Programme

Ensuring a more integrated and accessible approach to modelling, and underpinning data, is a key priority for the next research programme. This annex sets out a series of guiding principles that reflect the Scottish Government’s vision to improve how data, analysis and modelling are used across the programme and deliver decision support tools for government. These principles are intended to foster greater transparency, timeliness, and collaboration in research and policy contexts. By promoting integration, collaboration, and improving access to data and models, the vision seeks to support more coherent, responsive, and evidence-informed decision-making.

Ensuring traceability and transparency in how data, analysis, and models inform decisions.

1. Building consistent approaches to definitions and standards, including metadata and documentation for models and data.

2. Improving cross-programme visibility of actively used datasets and models, for example through registries.

3. Facilitating data linkage by identifying and sharing work that addresses common linkage challenges.

4. Ensuring open sharing of code and workflows using effective platforms, with clarity on model assumptions, inputs, and outputs.

5. Considering accessible formats for outputs, such as scenario tools, disaggregated data, and policy summaries, to improve usability for diverse stakeholders.

Enabling timely, evidence-based policy decisions through accessible, high-quality analysis and modelling.

1.Emphasising interoperability, collaboration, and coherent messaging in the delivery of models and analyses.

2.Empowering analysts and modellers to experiment with new approaches, data collections, and advance the state of the art, such as LiDAR.

3.Committing to the role of clear, traceable quality assurance practices in delivering robust, reliable outputs in line with standards, such as the Aqua Book, with clarity on data and model uncertainty and their causes.

4.Prioritising data curation as an essential quality control method to identify gaps, enable integration, and strengthen the pathway from data to decision making.

Fostering a culture of reuse, collaboration and active engagement across research and policy communities.

1. Building stronger relationships between modellers, analysts and their policy partners to increase the relevance and impact of modelling.

2. Creating new channels for policymakers to engage with the modelling community and raising awareness to help improve the understanding of modelling activity across government.

3. Addressing barriers to the effective sharing of data, methods, and insights that limit the policy impact of modelling and analysis, including effective communication of uncertainty.

4. Prioritising inclusivity through real collaboration and co-design with end users.

5. Supporting training and capacity building, such as mentoring and skills development, particularly to enable wider participation in data, analysis, and modelling.

Recognising the diversity of modelling needs, data types, and organisational contexts within the programme, a ‘one size fits all’ approach is not appropriate. Instead, the research programme must encourage flexibility and innovation providing a framework that can evolve as technology, policy priorities, and user needs change. This includes supporting models that can evolve with changing data availability, policy requirements, and climate conditions, ensuring they remain relevant and robust over time. Where helpful, the strategy will draw on relevant public sector guidance on data and reform to support consistency and collaboration. Established frameworks like the Green Book, Magenta Book, Aqua Book, and the Scottish AI Playbook offer useful principles for ensuring quality, transparency, and relevance in analysis and modelling.

In addition, the strategy will be impact driven, ensuring that data, analysis, and modelling are focused on addressing real-world problems and policy challenges, and delivering tangible benefits for communities, stakeholders, and the environment. To maintain long-term relevance, the strategy will also include a commitment to horizon scanning in an international context, aligning Scottish approaches with emerging UK and global standards, technologies, and best practices. This will help anticipate future data needs, modelling innovations, and policy developments, supporting comparability and collaboration.

Governance Support for this Vision

As the research programme is developed, we will explore how enhanced governance can help align modelling activity with strategic aims. Potential areas for a modelling governance framework to explore are:

• Facilitating clearer communication between researchers and policymakers.
• Keeping the wider research community informed of changes to key underpinning datasets that could impact their work.
• Giving researchers avenues to highlight challenges, opportunities, and limitations of data, analysis and modelling to policymakers.
• Providing policymakers with greater opportunities to have regular, responsive engagement with models and analyses.

The overarching objective of the governance framework will be to foster collaboration and integration between different areas of modelling and analysis in the program.

This will be a proportionate approach and will not be, for example, a forum for approving changes to individual models or for directing the development of models.

As part of any governance, it will be important to understand how mechanisms are supporting better integration, collaboration, and use of modelling across the programme. This could involve identifying broad indicators of progress, embedding regular opportunities for reflection, and creating inclusive ways to gather feedback from those involved in research and policy. These activities can help ensure that governance remains adaptive, learning-focused, and responsive to the evolving needs of the programme.

In addition, agreed formats and data standards will be essential to ensure greater consistency across datasets, facilitate collaboration, and avoid duplication of effort. However, any approach to data governance should remain proportionate and must not undermine existing mechanisms for accessing data or create unnecessary barriers for researchers.

Enablers

As well as timely and effective governance, several enablers are key to delivering on the vision detailed above. These include:

Skills and Capacity Development

Identifying and supporting opportunities for engagement and knowledge exchange between the research community and the Scottish Government to build long-term capacity and alignment. This includes fostering a shared appreciation of research lifecycles, analytical best practices, and the contexts in which evidence is used.

The strategy will also address capacity gaps in advanced analytical and modelling expertise, which have constrained progress in areas such as soil monitoring, where high costs and a limited pool of specialists have restricted the volume of data that can be collected and analysed.

Priority actions could include:

• Targeted training and mentoring.
• Investment in skills development to expand expertise and reduce bottlenecks.

Innovation and Use of Emerging Technologies

To ensure the research programme remains forward-looking, we will encourage innovation in data gathering, analysis, and modelling, and embrace emerging technologies such as AI, machine learning, LiDAR, and high-resolution remote sensing.

Key priorities could include:

• Building strong global collaborations to access and apply the best available technologies.
• Testing new tools and metrics in real-world settings and refining them based on user feedback.
• Making effective use of real-time data to support timely and responsive decisions.
• Creating space for experimentation and the development of new approaches.

Best Practice for Model and Data Integration

Establishing shared principles for integrating models and data is essential to delivering the vision. This requires collaboration across the research and stakeholder communities and alignment with international standards and exemplars.

Example Areas for Decision Support

Better decisions start with better evidence. To address complex challenges, we need to gather and organise data, develop models (or frameworks) that reflect real-world interactions, and apply systems thinking to understand how changes ripple across sectors and communities. This integrated approach – linking data and interoperable modelling – will enhance decision making and be essential for value for money. Consultation has highlighted priority areas where this can deliver the greatest impact.

1. Biodiversity: the Scottish Biodiversity Strategy aims to deliver thriving biodiversity by 2045. Its delivery will require systemic understanding of how biodiversity is related to climate, land use and land management, and its response to changes in these factors. This could involve developing a national biodiversity model or integrating existing research and models to provide a more comprehensive view for policy development (Natural Environment Bill) and NatureScot’s activities.

2. Catchment water resources: the Scottish National Adaptation Plan suggests a catchment-based approach for water management. Scotland lacks modelling capacity to understand the interactive risks for water scarcity, excess and quality generated by climate change and land use. Catchment models could support SEPA and Scottish Water and strengthen links with land managers and communities for shared climate resilience, with the ENRA research programme working closely with these agencies to improve existing modelling or develop new approaches where needed.

3. Food system resilience: the food system is complex, linking across supply chains and vulnerable to climate and trade shocks. Scotland lacks a model to explore risks and resilience options. A food system model could support government priorities on food security, drawing together existing data and modelling approaches into a coherent framework to explore risks and resilience options across the wider system.

4. Land use: Scotland has a finite land resource and multiple demands for land to deliver against goals for food, biodiversity, energy, housing, and forestry. Climate change will alter land capability in coming decades. Scotland lacks a model that can provide insights into how policy, climate and trade will influence land use and how changes in use impact the economy, well-being, and nature. This could be achieved by a combination of integrating existing data and modelling approaches, and filling in gaps with new modelling, into a more connected framework.

5. Just transition: rural policy changes, such as agricultural support adjustments, impact communities, sectors and regions in complex ways. Without care, changes might imperil the Just Transition. Modelling has helped to explore how farm size, type, and location affect outcomes. Further model enhancement could support more robust policy development.

6. Other areas may also benefit from improved modelling, such as deer management and natural capital. Future work should consider how best to strengthen these capabilities, whether through new development or by building on existing evidence and tools.

These examples highlight the need for not only advanced modelling but also strong data linkage and collaboration. Linking data across research institutes and agencies will be essential to create interoperable systems that underpin modelling and decision support. The ENRA research programme, working in partnership with stakeholders, can play a key role in delivering this integrated evidence base. In the longer term, these models and datasets share the potential to be linked together to deliver deeper understanding of feedbacks, generating impact through supporting government and agency decision making, and hence supporting public service reform.