Academic Advisory Panel - Potential delivery models for agricultural payments: advisory note

The Academic Advisory Panel (AAP) preopared for this advisory note in response to a request from Scottish Government officials proposals for possivle delivery mechanisms of agricultural support that align with the strategic outcomes of the Vision for Agriculture and the Agriculture and Rural Communities (Scotland) Act 2024 . This note summarises discussion held during the AAP meeting on 3 February 2025.

Introduction and request for advice

The current delivery framework for agricultural payments deploys relatively weak eligibility criteria and adherence to prescribed or proscribed measures (i.e. management practices or actions) as conditions for receipt of funding. While this ensures compliance, the existing delivery model is more aligned with the drivers of the past EU CAP and post-Brexit arrangements than with the Vision for Agriculture’s goals of high-quality food production, biodiversity, climate change, thriving rural communities and Just Transition, and the Agriculture and Rural Communities (Scotland) Act 2024 objectives of the adoption and use of sustainable and regenerative agricultural practices, the production of high-quality food, the promotion and support of agricultural practices that protect and improve animal health and welfare, the facilitation of on-farm nature restoration, climate mitigation and adaptation, and enabling rural communities to thrive.

In the next phase of the Agriculture Reform Programme (ARP), possible future delivery models for the future Agricultural Support Framework are being explored, and initial discussions were held with the ARIOB in November 2024 and February 2025. A delivery model is the framework of mechanisms and methods used to provide specific services, products, or outcomes to customers or stakeholders. It is primarily concerned with "how" services or initiatives are delivered. The AAP received a short paper exploring potential operational delivery models for implementing ARP outcomes under the Vision for Agriculture and the Agriculture and Rural Communities Act.  This will help enable the creation of more detailed, fully costed future Service and Target Operating Models.

Alternative models that could potentially better align with those strategic outcomes were presented to the panel by Scottish Government officials. Payment criteria can be structured in three main ways: eligibility-focused, measure/action-focused and outcome-focused. Additionally, hybrid variants combine elements of all three.

The panel was invited to provide their professional opinion on the extent to which these options could deliver on the strategic outcomes.

Recommendation

AAP recommends that the Hybrid C model (which combines different forms of conditions - eligibility, measures, and outcomes) is the most effective approach for achieving strategic goals and driving meaningful progress. However, the AAP did express concerns about the ability and costs to deliver outcome-based policy effectively across Scotland’s varied land-based sector. Outcome based policy may not be deliverable in the short term but should not be excluded from longer-term thinking of the SG. The panel agreed that the main focus is likely required to be on eligibility conditions and measures/actions.

A balanced mix of all three models benefits from greater flexibility, but its effectiveness will depend on priority-setting and the practicalities of monitoring different conditionality criteria. Caution is needed as finding the right balance remains a challenge. Moreover, conditionality criteria can be attached to different types of payment mechanisms, meaning that debates about policy design and delivery need to be wider than just about what conditionality to apply.

Key Summary

Effective policy requires clear, verified outcomes or measures/actions directly linked to payments. These outcomes should be achievable, scientifically measured, and transparently reported to ensure accountability. It is generally accepted that measures/actions can act as a proxy for outcomes which are notoriously difficult to monitor. The intervention logic should be clearly outlined for all outcomes, including those that are difficult to measure or attribute to specific causes. A theory of change or logic chain should connect actions, measures or intermediate results to outcomes using structured monitoring frameworks.

Proper integration of data is also crucial to ensure that tools and measurements provide meaningful insights rather than being used without clear understanding. Scientific evidence and robust data collection is essential to support the model’s implementation. Addressing knowledge gaps will refine future strategies and ensure successful delivery.

It is important to recognise that different forms of conditionality (e.g. eligibility criteria, adherence to measures, achievement of specified outcomes) can be, and already are, attached to different forms of payment (e.g. area payments, agri-environmental payments). That is, payment criteria need to be distinguished from payment mechanisms, with policy design and delivery needing to consider both dimensions.

Key discussion points

AAP recommends that when designing a future delivery model, policy officials should:

• structure policies to efficiently incentivise desired outcomes. Public payments are always conditional on something—whether eligibility criteria (e.g. age requirements), actions (e.g. attending training, avoiding hedge cutting), achievements (e.g. meeting biodiversity targets), or a combination of these. Therefore, “eligibility criteria”, "measures/actions" and "outcomes" are simply different forms of conditionality. Logic Chains (should) transparently link different conditionalities

Importantly, conditionality can be attached to various payment mechanisms, not just decoupled area payments. For example, agri-environmental schemes, whether fixed-rate or variable-rate (e.g. via auctions) also impose conditions – differing from area payments in their degree of competitiveness rather than in terms of conditionality per se. It is therefore essential to distinguish between payment criteria and payment mechanisms, as they are separate issues with different operational and equity considerations (previous AAP advice has noted the various challenges around reward systems for public goods, and the challenges facing carbon and nature markets).

Budget considerations must be integrated into the planning process, as financial constraints can limit adoption of some practical solutions. However, by adjusting conditions, actions, and budgets, the right outcomes can be achieved.

A realistic assessment of the level of change needed to deliver strategic outcomes for nature restoration will help determine the most suitable model, or combination of models for initial implementation.

Beyond financial incentives, policy delivery can also occur through regulation or direct public provision, with the latter effectively integrating conditions into employee job roles and performance reviews.

• clearly define policy outcomes associated with support payments. It is desirable to establish measurable outcomes / actions / eligibility criteria linked to support payments that can be scientifically verified. Outcomes should be SMART, and can be associated with meeting eligibility criteria or delivering specified measures

However, it is important to acknowledge that SMART outcomes are rarely set by policy for individual recipients (e.g. because outcomes are often difficult to measure and/or attribute to particular causes). Many high-level goals, such as those outlined in the Programme for Government (PfG) or the Agricultural Vision, or the Agriculture and Rural Communities (Scotland) Act, tend to be operating at broader scales, across multiple businesses and supply chains, making them aspirational and difficult to measure directly.

Future support programmes must improve the measurement, monitoring, and evaluation of current baselines. Existing tools for measuring carbon sequestration, and emissions from peatlands and wetlands are insufficient as the emissions record only high-level CO2 equivalent (CO2e) results while overlooking crucial additional input data such as local conditions, soil characteristics, or local management and history. This gap could lead to policy manipulation aimed at securing rewards rather than achieving the intended environmental goals.

Clear, measurable outcomes are essential for assessing the success of policies and programmes. They also ensure the efficient use of public funds and promote transparency in financial allocations. Clearly defined outcomes facilitate the adoption of actions aligned with desired goals. Evidence is not being routinely collected by Scottish Government (e.g. soil tests, carbon-footprint entry and emissions intensity data, biodiversity audits as part of AECS, animal health and welfare plans). This gap makes baselining national performance impossible, let alone at a recipient level.

Reporting outcomes publicly, even negative ones, provides valuable learning opportunities linked to improvements, when shared transparently and promotes accountability.

• improve agricultural support with a logical framework or theory of change. Introduce a clear intervention logic or theory of change framework that links specific actions / eligibility criteria to observed effects and decision-making process. Without these there is a risk of continued disconnect between high-level assessments and the ability to identify the most effective levers and targets for action on the ground. Assessing the relationship between management practices and outcomes can be challenging and may result in probabilistic estimates that are difficult to communicate. The best available science and expertise should be used in considering outcomes – including a thorough appraisal of unintended outcomes associated with some policy incentives

Policy makers must better identify points within the intervention logic chain where monitoring of progress is more reliable - whether at the level of actions, intermediate results, or outputs. While these measurable indicators may not directly capture how the specific policy ambition is being delivered in the long-term, they serve as essential proxy-evidence, providing a scientifically grounded basis for tracking progress.

A well-articulated Logic Chain, built on robust science, models, and available data, is crucial for ensuring that what can be monitored with confidence can be linked to and contribute effectively to overarching policy goals. By refining how outcomes are defined and monitored, policymakers can enhance the credibility, efficiency, and impact of environmental programmes.

The “Monitoring Maps” process in the third Scottish National Adaptation Plan (SNAP3) monitoring and evaluation framework, is a good example of intervention logic that identifies data gaps, determines necessary actions, assigns responsibilities, and integrates science into future framework. This approach should be adopted to ensure that evidence is effectively used across government sectors.

• prioritise interdependent outcomes and assess risks. Some outcomes are interdependent and the success of one (e.g. improving soil health) may impact others, such as pest control or economic stability. A failure in one area can disrupt the entire system. Evidence for some of these connections is still lacking, so future assessments should take a probabilistic approach to account for uncertainties
• acknowledge external factors influencing outcomes and uncertainties. Recognise that factors beyond the control of farmers and crofters, such as weather, economic conditions, environmental changes, pollution, or political factors can influence outcomes. Plant and animal diseases, which can spread through air or water, also pose external challenges, making it difficult to link actions to outcomes directly
• range of rates of change. It may not be possible to achieve the same range of outcomes across similar agricultural businesses in different locations. Additionally, achieving immediate (measurable) results within the next few years will be challenging

AAP recommends integrating future policy outcomes by rewarding the meeting of thresholds rather than requiring continuous improvement, to avoid incentivising counterproductive behaviour and constant pressure on farmers and crofters.

• consider data provision as an important outcome in itself. Accurate data provision is essential for assessing the effectiveness of agricultural policies. There are significant challenges in recording and monitoring robust agricultural data, particularly regarding the scale of outcomes, and time variability of outcomes. It is crucial to determine whether outcomes are measured at the farm, or catchment scale. Multi-year variability should also be accounted for to ensure that short-term setbacks (e.g. poor pollinator season) do not undermine long-term progress

Measuring outcomes from local to the national scale presents complexities, such as accessing farm records and managing audit schemes. High costs associated with detailed analysis, establishing baselines in a dynamic climate-changing environment, and balancing scientific rigour with practical, farmer-led data collection also pose challenges.

Data provision can be costly for farmers and crofters, but it plays a crucial role in assessing policy outcomes. Improved data capture is needed when public funds support improved agricultural practices such as through soil sampling, airborne LiDAR, and carbon calculators. Data should be collected through publicly available platforms and it is desirable to ensure that data is audited. Additionally, data sharing should be considered as a condition for receiving public funding.

AAP recommends expanding data collection efforts. A system similar to LANDMAP, the Welsh mapping system that organises data into a nationally consistent dataset, could serve as a useful model. However, to be effective, the system would need a method for linking landscape- or catchment-scale data to individual recipients. Additionally, remote sensing should also be used to verify data supporting outcome-based approaches and payment verification systems.

• identify evidence and knowledge gaps related to agricultural goals. Gaps in evidence and knowledge must be addressed to support Vision for Agriculture’s strategic goals and Agriculture and Rural Communities (Scotland) Act 2024 objectives. While some links between animal health and welfare, food production, climate change, and nature restoration are somewhat understood, further research is needed in areas such as like mental health, workforce diversity, and the impact of a living wage on farming viability. Research gaps also exist in understanding farmers’ and crofters’ perceptions of the environment, their engagement with data, and their role in data collection. Identifying these gaps will help refine agricultural policies and their broader impact. A review of existing measures/actions and outcomes could help pinpoint these gaps and guide future improvements