Environment, Natural Resources and Agriculture research strategy 2027-2032: consultation analysis

12. Question 10

Theme: Impact Routes – Decision Support, Living Labs and innovation

Question 10: Which principles relating to the delivery of analysis and modelling are most important (for example, collaboration, innovation, impact)?

Introduction

The majority (43, 61%) of consultation respondents answered question 10.

All respondent types typically identified a consistent set of interconnected principles to help deliver credible, high-impact evidence, as described further below.

Theme 1: Genuine co-design and collaboration

The vast majority of respondents who answered question 10 said that genuine co-design and collaboration of scientific research was essential to deliver impactful analysis and modelling. Respondents said it would be vitally important to adopt approaches that aim to engage with all relevant stakeholders from the outset and on an ongoing basis. They emphasised the importance of bringing together researchers, policymakers, industry, and communities to ensure that research/ modelling/evidence was directly shaped and informed by a diversity of views, perspectives, and expertise.

They added that a meaningful co-design approach that fostered longer-term partnership working relationships, would help ensure relevance, legitimacy, the uptake and use of research findings, as well as encourage the sharing of lessons. Respondents said continuing efforts were needed to address barriers to collaboration – for example, breaking down silos across different disciplines, research institutions, sectors, and geographic borders. They also identified a need for investment to encourage co-design and collaboration, including skills development and capacity building support.

“Ensuring stakeholders are invested in the idea of research is crucial, involving them as creators of research (as opposed to merely participants) can help with this.” Lantra Scotland

“To operationalise these principles, it is important to ensure a longer-term baseline understanding of analytical processes and long-term partnership working across all parties (policymakers, stakeholders, researchers) and within institutions. This requires investment in improving skills and capacity building to ensure research resilience across the Portfolio.” SEFARI Directors Executive Committee

Theme 2: Impact driven and practicality

Ensuring the delivery of analysis and modelling is impact driven and results in practical and relevant actions was considered important by a majority of respondents. These respondents said it was important to use data and analysis to address real-world problems and challenges and to directly inform decision-making and policy development. They suggested that impact be measured by the extent to which it has supported better outcomes for rural businesses, the environment, and communities.

Respondents also advocated for long-term data collection to track and evaluate impact (for example, environmental uplift) – as well as to help inform scalable approaches and future interventions (alongside mechanisms that facilitate targeted efforts to inform an emerging policy issues).

“Practical relevance and impact: analysis and modelling should generate actionable insights for farmers and policymakers, supporting credible measurement of outcomes and evidence-based decision-making.” Agriculture and Horticulture Development Board

“The potential for impact will be determined by how productive and positive the collaborations between modellers and how innovations have been developed.” The International Barley Hub, based at The James Hutton Institute

Theme 3: Transparency

Transparency was identified by some respondents as a key principle relating to the delivery of analysis and modelling. Respondents said that transparency was built through trust, maintained through clear governance, and underpinned by open communication. Respondents said that a co-design and collaborative approach would help ensure transparency and build trust. They added that ensuring clear and open communication of assumptions, uncertainties, approaches, methodologies, data sources, and limitations were important so that users can trust and understand the results and how research findings can be used.

Respondents called for approaches to encourage easier data linkages and steps to ensure models and data have open access. They said this would help to maintain confidence among data providers and end-users and would support the credibility of research approach and results. Ensuring results and outputs are published in clear, practical, and accessible formats to policymakers and end-users, including non-specialists, was considered important in this regard.

Other points raised by respondents included the need for continuing efforts to address barriers (such as time and cost) to accessing datasets and sharing resultant data products. They suggested that this could help to facilitate collaboration and the delivery of responsive and impactful analysis to policymakers.

“Analyses must make clear the assumptions, data sources, and uncertainties involved. This openness builds trust in results and gives policymakers, industry, and civil society the confidence to act on the evidence.” SRUC

“This is likely to require cross-government initiatives to create a comprehensive and integrated secure data environment or portal, for use by analysts in the Scottish Government and the research community. The proposals in the draft Strategy provide an opportunity for such an initiative to be taken forward as a model for other non-Open and secure datasets held by the Scottish Government. This approach could directly facilitate collaboration and the delivery of responsive and impactful analysis to policymakers. The Administrative Data Agricultural Research Collection (AD|ARC) provides an example of a model of such an environment.” SEFARI Directors Executive Committee

“Models and data should be open and understandable, allowing users to trust and scrutinise results.” Merman Conservation Expeditions Ltd

Theme 4: Innovation

Innovation was identified as a key principle relating to the delivery of analysis and modelling by some respondents. In summary, the main points raised included the importance of supporting innovation in data gathering, analysis and modelling such as embracing and adopting new technologies and allowing space to experiment with new approaches. Respondents also emphasised the importance of making effective use of real-time data, ensuring new tools or metrics were tested in real-world settings and refined based on feedback from those who will use them, and developing strong global collaborations.

Theme 5: Flexibility and adaptability

A common theme that underpins the delivery of analysis and modelling was considered to be flexibility and adaptability. Here, respondents mentioned that it would be important that research undertaken was flexible and responsive to emerging challenges and evolving priorities. They emphasised the value of adaptive and iterative learning and action as well as the design of data models that can evolve with changing data, policy needs, and climate conditions. They added that models must be capable of being easily updated and tailored to different scales and user needs.

Theme 6: Other principles identified

Consultation responses identified other principles relating to the delivery of analysis and modelling. These respondents mentioned:

• awareness, understanding and adoption of best practice approaches to the design and conduct of scientific research
• clarity of purpose from the outset – analysis should begin with an agreed definition of the policy problem, the scope of work, and the expectations of the outputs
• the need for research integrity and developing clear and transparent new research approaches – whilst maintaining data quality checks and curation processes
• efficiency and proportionality – analysis should be proportionate to the policy challenge, avoiding unnecessary complexity where a simpler approach could provide clear and timely answers. Applying a ‘collect once, use many times’ principle could reduce duplication and minimise the burden on data providers ensuring resources are used effectively
• data governance and system design considerations – for example: promoting interoperability, open standards, and secure data environments; ensuring FAIR data principles; and joined-up data systems and shared methodologies to enhance consistency and scalability
• maximising the benefits of analysis and modelling for the public good
• policy and research coherence – alignment with Defra and other UK/GB research programmes could strengthen policy cohesion across related domains