The Scottish Government’s Carbon Calculator for wind farms on Scottish peatlands was developed in 2008, to calculate the impact of wind farm development on peatland carbon stocks in Scotland and thereby support decision making. Electricity generation emission factors are updated annually, but no major revisions have been made to the Carbon Calculator since 2014.

Aims

The increased focus on the transition to net zero might affect the suitability of the Carbon Calculator for future use. This research conducted a detailed review of the latest spreadsheet version of the Carbon Calculator (v2.14), which mirrors the web version (v1.8.1). It provides an evidence base for future considerations and recommendations.

This review has initiated further discussions and highlighted the need for ongoing engagement, which will be instrumental in the development of the Carbon Calculator.

Key findings

Based on the findings of a technical assessment, evidence review and quality control mechanisms, the report recommends that when considered against recent policy updates and advancements in science, the Carbon Calculator, in its current form, should be updated. Each area of the Carbon Calculator was assessed for scientific accuracy and data availability:

• The ‘payback time and CO₂ emissions’ are not relevant/consistent with the findings of the technical assessment and literature review. It is important to consider whether emissions due to turbine life and back up are required, given new planning policy and the applicability of whole lifecycle carbon assessments.
• For all peat-related areas of the Carbon Calculator, as well as the forestry area, accuracy is lacking in one or more methodologies, use of emission factors and assumptions.
• While some data are accessible to users, it is not clear if they are able to accurately obtain some of that data – in particular, for variables that drive the results (the water table depth and extent of drainage), which could affect the accuracy of outputs.

This study is the first phase of a review of the Carbon Calculator. The findings of the report will be used to inform the next phase.

For further details, please read the report.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Research completed: July 2024

DOI: http://dx.doi.org/10.7488/era/5506

Executive summary

The Scottish Government’s Carbon Calculator for wind farms on Scottish peatlands was developed in 2008, to calculate the impact of wind farm development on peatland carbon stocks in Scotland and thereby support decision making. Electricity generation emission factors are updated annually, but no major revisions have been made to the Carbon Calculator since 2014.

Aims

The increased focus on the transition to net zero might affect the suitability of the Carbon Calculator for future use. This research conducted a detailed review of the latest spreadsheet version of the Carbon Calculator (v2.14), which mirrors the web version (v1.8.1). It provides an evidence base for future considerations and recommendations.

This review has initiated further discussions and highlighted the need for ongoing engagement, which will be instrumental in the development of the Carbon Calculator.

Key findings

Based on the findings of a technical assessment, evidence review and quality control mechanisms, we recommend that when considered against recent policy updates and advancements in science, the Carbon Calculator, in its current form, should be updated. Each area of the Carbon Calculator was assessed for scientific accuracy and data availability:

• The ‘payback time and CO₂ emissions’ are not relevant/consistent with the findings of the technical assessment and literature review. It is important to consider whether emissions due to turbine life and back up are required, given new planning policy and the applicability of whole lifecycle carbon assessments.
• For all peat-related areas of the Carbon Calculator, as well as the forestry area, accuracy is lacking in one or more methodologies, use of emission factors and assumptions.
• While some data are accessible to users, it is not clear if they are able to accurately obtain some of that data – in particular, for variables that drive the results (the water table depth and extent of drainage), which could affect the accuracy of outputs.

In addition to the technical assessment, the research has triggered the need to examine the wider planning and consenting context through the following questions:

Does the calculator need to consider the lifecycle emissions of the wind farm, or could the focus be purely on the impact of development on peat?

Well established methods and tools are available to undertake Whole Life Carbon Assessments (e.g. PAS2080), including forthcoming offshore wind carbon footprinting guidance. This aspect of the Carbon Calculator might not be necessary as it replicates these approaches. Instead, it may be more beneficial to concentrate efforts on analysing the specific impacts of development on peatlands/habitat carbon emissions.

Is the output of the Carbon Calculator useful as a decision-making tool?

Since the inception of the Carbon Calculator, it has become clearer that improving and restoring biodiversity is important to tackling climate change. This progress is reflected the National Planning Framework 4’s mitigation hierarchy.

As the UK transitions to net zero, the current ‘carbon payback’ approach becomes less relevant, as it compares development emissions to the counterfactual of electricity generated by fossil fuels. The focus should shift to evaluating the impact of the developments on the natural environment, specifically, whether it improves the environment and sequesters CO₂ effectively.

To better assess the development’s impact on peatland carbon emissions, the timeline for achieving ‘carbon payback’ or ‘carbon neutrality’ should consider land-based emissions. For example, ‘payback time’ could be defined as the period needed to restore peatland to a ‘near pristine’ condition from a reported baseline, compared to the site’s baseline emissions without development and counterfactual scenarios for non-peaty sites, and Scotland’s widespread peatland restoration efforts.

Should the Carbon Calculator incorporate other land use types?

This would offer a more comprehensive view of the carbon impact on other land use types, as compared to the carbon impact on peatland. This aspect should be evaluated considering Scotland’s evolving biodiversity net gain requirements, current Peatland Management Plans (PMP), Habitat Management Plans (HMP), and their anticipated updates.

Are the quality controls sufficient?

There are no in-built quality control mechanisms within the Carbon Calculator. Due to its complexity and skillsets required to review the data outputs, the Carbon Calculator is not used as a decision-making tool in the capacity it is intended. Additional quality controls would be beneficial.

The future of the Carbon Calculator

In addition to the technical review, the report also considers the future of the Carbon Calculator in terms of a review of incorporating high-resolution spatial data (HRSD) and/or peatland condition categories (from the Peatland Carbon Code), and applicability of the Carbon Calculator to other developments.

Integrating HRSD into the Carbon Calculator would enable an understanding of land cover types, providing proxies for peat condition and water table depth. This could reduce the need for manual site surveying for data collection and enable wider evaluation of the site.

We recommend that the integration of HRSD is explored for future versions of the Carbon Calculator, to ascertain the level of accuracy these enhancements could bring (i.e. through reduced manual inputs and/or quality controls). This can be done in conjunction with the findings from Scottish Government’s exploration of a national LiDAR mapping scheme.

The Peatland Code’s emission calculator provides emission factors to calculate the average net emissions from peatland in various conditions, based on the UK inventory. Whilst not Scotland-specific, integration of the peatland condition categories could provide a recognised approach to quantifying the benefits of peatland restoration activities.

There is potential for the Carbon Calculator to be adapted and applied to grid infrastructure and other development types on peatland and carbon rich soils, even though it is currently employed solely for wind farm developments. There are no concerns on the Carbon Calculator’s ability to be used on projects of all sizes. However, to be applied to different infrastructure types, consideration would need to be given to their unique spatial aspects, e.g. the effects of shading and effect of excess heat for solar farms. Further research is needed to understand the implications of other infrastructure developments on peatland and carbon rich soils prior to extending the applicability of the Carbon Calculator.

Glossary / Abbreviations

Introduction

Background

The Scottish Government’s Carbon Calculator for wind farms on Scottish peatlands (hereafter referred to as ‘the Carbon Calculator’) was developed in 2008 and updated in 2011 and 2014. It was developed due to concerns raised about the reliability of methods used to calculate the time taken for these facilities to reduce greenhouse gas emissions, combined with an increasing public policy demand for renewable energy following Scotland’s commitments at the time to reduce greenhouse gas emissions by reducing the use of fossil fuels for energy generation, principally; Scottish Planning Policy 6: Renewable Energy to deliver renewable energy in a way that “affords appropriate protection to the natural and historic environment without unreasonably restricting the potential for renewable energy development” (Scottish Government, 2007).

The Carbon Calculator was developed to ‘support the process of determining wind farm developments in Scotland. The tool’s purpose is to assess, in a comprehensive and consistent way, the carbon impact of wind farm developments. This is done by comparing the carbon costs of wind farm developments with the carbon savings attributable to the wind farm.’ (Nayak et al, 2008). The output of the Carbon Calculator compares the carbon costs of a wind farm development with the carbon savings attributable to the production of renewable energy (when compared to a counterfactual alternative). Electricity generation emission factors are updated annually, but no major revisions have been made to the Carbon Calculator since 2014.

The Scottish Environment Protection Agency (SEPA) developed the Carbon Calculator into a web Carbon Calculator (C-CalcWebV1.0), which has been available since 2016. The calculator is currently owned by the Scottish Government and is hosted and maintained by SEPA. The Carbon Calculator is currently used by developers to submit project carbon assessments. These submissions are then evaluated by the Energy Consents Unit (ECU) as part of the application for consent.

An evolving legislative, policy, science, and technology landscape

In the 16 years since the Carbon Calculator’s inception, there has been an increased focus on the transition to net zero, with updates to Scottish legislation and policy reflecting this shift. Key legislation and policy drivers include:

• The Climate Change (Emissions Reduction Targets) (Scotland) Act 2019 (updated): sets a key driver for Scotland to deliver and meet its carbon reduction targets.
• Scotland’s National Planning Framework 4 (NPF4) (adopted in February 2023): sets the framework for development across Scotland, including renewable energy. NPF4 includes national planning policies which set out ‘to protect carbon-rich soils, restore peatlands and minimise disturbance to soils from development’. Policy 5 sets out a mitigation hierarchy^[1], and new development proposals on peatlands, carbon-rich soils, and priority peatland habitat are only supported in certain limited circumstances, including renewable energy generation. The policy also outlines the need for a site-specific assessment (such assessments may include peat depth surveys, Peat Landslide Hazard Risk Assessment, and detailed habitat and condition surveys) to identify the likely net effects of the development on climate emissions and loss of carbon. The mitigation hierarchy can be achieved through the Construction Environmental Management Plan, Habitat Management Plan (HMP), and Peat Management Plan (PMP), developed at the application stage.

There have also been significant advancements in science and technology during this period. The collective understanding of peatland science has evolved, and research, technology, and collaborative groups have fostered a greater understanding of the science, with the likes of the Peatland Code and NatureScot National Peatland Plan emerging as a result. This new legislative, policy and science landscape highlight the need for a comprehensive review of the Carbon Calculator’s original design and purpose.

Aim of the report

This report provides the findings of a technical assessment of the latest spreadsheet version of the Carbon Calculator (v2.14), which mirrors the web-version (v1.8.1) to determine if in its current form it remains fit for purpose, considering recent policy updates, the ongoing transition to net zero, and advancements in science. Furthermore, the report provides an evidence base for future considerations and explores how the Carbon Calculator could be improved via Peatland Code category integration, use of High-Resolution Spatial Data (HRSD), and improved quality controls.

Carbon Calculator Technical Assessment

Overview

The Carbon Calculator features numerous components used to assess the carbon impact of wind farm developments on Scottish peatland. The Carbon Calculator is split into the areas shown in Table 1. Appendix 11.3 provides a detailed breakdown of each section, including their specific calculations and assumptions.

Table 1: Carbon Calculator Section

The assessment provides a review of each area of the Carbon Calculator as outlined in Table 1. Each section consists of the following:

• Assessment findings – narrative summarising the findings from the technical assessment and evidence review. For the technical areas of the Carbon Calculator a Red, Amber, Green (RAG) rating has been provided to illustrate the technical accuracy and data availability of each area. It uses the colour rating system presented in Table 2.
• Key considerations and questions – considers the key takeaways from the assessment, and outlines questions for policy decision makers when considering revisions to the current Carbon Calculator.

Table 2. RAG Ratings

Assessment findings: Data inputs

Scientific accuracy

The scientific accuracy of the data inputs is provided as part of the narrative within the assessment findings for the corresponding technical areas of the Carbon Calculator (Sections 3.3-3.11). Therefore, no RAG rating has been provided.

Usability

The following commentary applies to the Carbon Calculator’s core input data. Specific commentary relating to data inputs of the technical areas of the Carbon Calculator are covered within the corresponding sections of this report (Sections 3.3-3.11).

• The user is required to input a high number of variables (i.e. for the core input data, 70 input variables are required).
• Each input variable requires an expected value, as well as a minimum and maximum range, therefore over ~200 input variables are required in total for core inputs.
• For infrastructure design related inputs (wind farm characteristics, borrow pits, foundations, access tracks, cable trenches and peat excavated) the values are well defined based on the wind farm design, therefore the minimum and maximum ranges could represent unnecessary data requirements for design related inputs given their level of certainty. If still viewed as necessary in some instances, a minimum and maximum range could be automated, and/or an optional requirement for users.

Key consideration: Minimum and maximum data inputs

Wind farm characteristics – consider removal/option to ‘opt out’ of minimum and maximum variables where site specific data is known and can be evidenced by the user.

Peat variables – Review the minimum and maximum parameters for peat variables and explore replacing with individual infrastructure specific inputs (i.e. Turbine 1, 2 etc). Industry feedback indicated that prior to completing the Carbon Calculator, users proactively aim to reduce the impact of development on peat through the design process. If there is large variation in peat parameters around the site, should more detailed site-specific data be captured (to reflect the construction and forestry ‘areas’, and/or align with the PMP reporting where individual infrastructure outputs are provided) as an alternative?

Assessment findings: Payback time and CO₂ emissions

Scientific accuracy

• Although the calculations that produce the payback time and CO₂ emissions are accurate (i.e. there are no errors in them), the carbon payback time that is generated (measured against the current fossil-mix of electricity generation) is a significant simplification which does not present an accurate representation of future payback. This is because the payback calculations assume a consistent counterfactual for the lifetime of the wind farm. However, as we transition to net zero, the National Grid is rapidly decarbonising and forecast to be near net zero by 2035 (DESNZ, 2023).

Usability

• Payback combines infrastructure emissions (embodied carbon from wind turbines and their construction) with site-specific factors associated with peatland disturbance, and/or management. Emissions from the wind turbine manufacturing make up the largest proportion of the emissions, and so in this context, the overall carbon impact on peat (i.e. all peat related carbon calculations) appears to the user as a small proportion.
• Currently there are no official guidelines about what constitutes an acceptable or unacceptable payback time, which would benefit both users and decision makers in determining ‘what good looks like’ for land based emissions.

Key consideration: Is the output of the Carbon Calculator useful as a decision-making tool?

As the National Grid transitions to net zero, the presented ‘savings’ (comparison to fossil generated electricity) become less relevant. It may be more appropriate to consider the ‘payback time’ as the time taken to restore the peatland condition to ‘near pristine’ from a reported baseline. To inform this, the sources of emissions could be split out and reported separately:

• Emissions resulting from land use change (the impact on land carbon emissions as a result of the development including all peatland and other carbon rich soil related carbon sources), should be compared against the project site’s baseline emissions.
• Emissions associated with the construction, operation, and decommissioning (Whole Lifecycle Carbon Assessment (WLCA)) of the wind farm. To aid decision making, this should be benchmarked against industry best practice, and/or compared against the whole life carbon impact of the counterfactual (e.g. gas turbine plant). Although this may be included within a WLCA, in which case this function is not required.
• The carbon intensity of electricity generated could primarily be compared against i) the current back-up energy source of natural gas and ii) against the UK average (considering future decarbonisation) if not done so via a WLCA.

Key consideration: Is the focus of the Carbon Calculator correct?

Currently, the main use within decision making is the payback period. However, this is based on the counterfactual of electricity generated by fossil fuels. Focusing on land-based emissions and the impact of development on peatland, an alternative would be to consider the baseline site conditions and ‘payback’ time to a restored site (see 3.3.3 for suggested approach). There is widespread action to restore degraded peatland across Scotland (Scottish Government, 2024), it could be expected that if a wind farm is not developed, the sites would be restored through a variety of financial mechanisms such as the Peatland Code, and Scottish Government funding (ibid). Another relevant counterfactual could include the land-based emissions from a non-peaty site. Whether a counterfactual payback period should be updated to reflect this context is an important consideration.

Key consideration: Does the Carbon Calculator need to consider the lifecycle emissions of the wind farm, or could the focus be purely on the impact of development on peat and other carbon rich soils?

In order to demonstrate a minimisation of emissions, established methods and tools are available to undertake WLCA (e.g. PAS2080), which will include materials, construction, operational and decommissioning emissions of the entire wind farm. NPF4 Policy 2 (climate mitigation and adaptation) states that all proposals will be ‘be sited and designed to minimise lifecycle greenhouse gas emissions as far as possible.’ Given the new policy context in combination with the Carbon Calculator’s core aim (to determine the impact of development on peatland carbon emissions), key considerations include:

• Whether the lifecycle emissions of a wind farm need to be included in the Carbon Calculator?
• Could the calculations in the Carbon Calculator solely be focused on the impact of the development on peatland emissions?
• Is the presentation of the current payback output necessary or appropriate for decision making?

Assessment findings: Wind farm CO₂ emission savings

Scientific accuracy

• The UK grid average is forecast to be broadly decarbonised by 2035 (BEIS, 2020). Using the current grid average (DESNZ, 2023) across the lifetime of the wind farm project represents a ‘static’ coefficient which is not representative of long-term UK grid decarbonisation over time. Additionally, over time as the grid average decarbonises this comparison will not show an operational benefit of using renewable energy.
• The UK generates ca. 1% of electricity from coal (Statista, 2024). The emissions factors in the Carbon Calculator are updated annually. If users apply the current (optional) coal factor, this factor is also a ‘static’ coefficient. Coal is due to be phased out completely by the end of September 2024 (BEIS, 2021), and therefore the ‘coal-fired electricity generation’ comparison should be removed as it is not a representative comparison.
• Renewable energy from wind and solar is not guaranteed and therefore a backup is required. Currently, where back up for renewables is required, gas peaking plants provide additional capacity. As we transition to a zero-carbon grid, natural gas will continue to be used to support both renewable back-up and additional demand (BEIS, 2020). There is also work ongoing nationally (Great Grid Upgrade, (National Grid, 2024)) to improve infrastructure and connectivity which will reduce the reliance on back-up energy requirements.
• Most of Scotland’s electricity demand is already met by renewables (Scottish Government, 2024). There is an opportunity to increase renewables across the UK and for exports, however, this will require appropriate infrastructure.
• The counterfactual emission factors only include electricity generation (i.e. the emissions associated with burning fossil fuels to generate electricity). They exclude the development of the infrastructure (i.e. the power station). Therefore, savings are based on operational energy efficiency, there is no consideration to the embodied carbon or operational maintenance of the alternative power.
• Noting the transition to net zero, consideration needs to be given to the appropriateness of represented savings.

Usability

• This section of the Carbon Calculator is used to calculate the Wind farm CO₂ emissions. The input variables which inform it are acceptable in terms of usability.

See Section 3.3.4 Key consideration: Is the focus of the Carbon Calculator correct?

Assessment findings: Emissions due to turbine life

Scientific accuracy

• The methodology for estimating emissions is based on turbine capacity derived from the regression analysis of data points found within a selection of papers dated between 2002 and 2006. The wind industry has evolved in the last 20 years and these assumptions are outdated, for the following reasons:
• The average onshore wind turbine has increased over recent years to 2.5-3MW (National Grid, n.d.). the references within the current Carbon Calculator are based on studies around 1MW (Lenzen and Munksgaard, 2002; Ardente et al., 2006; Vestas, 2005) and have a direct correlation between turbine MW and embodied carbon (i.e. the greater the power, the higher the embodied carbon), however due to technology advancements (i.e. lightweighting), increased power may not require increased materials. The methodology should be updated to consider more recent manufacturer lifecycle assessments.
• The physical size of UK wind turbines (i.e. height and turbine span) have increased.
• The Carbon Calculator uses an emissions factor for reinforced concrete taken from The Concrete Centre (2013). This reference has been superseded with the most recent market data being available for 2023 (Concrete Centre, 2023) and should be updated.
• Estimations only account for lifetime emissions attributed to turbine structures and concrete hard standings. The methodology disregards emissions from the manufacture, construction, and disassembly of other wind farm assets (e.g., site fences, access tracks, battery storage, etc) (Appendix 10.1). Carbon emissions resulting from the transport of labour and materials to the construction-site is also excluded. This underestimates emissions and does not align to common WLCA practice (e.g., PAS 2080).
• Emissions exclude decommissioning; due to the uncertainty in this area this would be difficult to estimate, however it should be recognised that decommissioning activities would result in additional disruption to peat. With the net zero transition and increasing energy demand it is likely that sites will be repowered rather than decommissioned. However, as wind farm developments are only provided with consent to operate for fixed period (and should be followed by decommissioning), it may not be appropriate to include this functionality.

Usability

• Many lifecycle assessments for wind turbines include foundations (e.g. Vesta, n.d.). Therefore the ‘carbon dioxide emissions from turbine life’ variable may result in double counting of construction emissions when using the ‘direct input of total emissions’ option if not split out by the turbine provider and/or Carbon Calculator user, when paired with foundations and hardstanding emissions, and/or the construction input data tab.
• As this is a significant part of the assessment, lifecycle emissions should be modelled on site specific data.
• Depending on the size of the development, developers may be required to submit an Environmental Impact Assessment (EIA), including a WLCA. Scottish Government is preparing Planning and Climate Change guidance, which includes consideration of information sources, tools, methods and approaches (including WLCAs) that can be used to demonstrate whether and how lifecycle greenhouse gas emissions of development proposals have been minimised. For reference, there is currently an industry standard approach for wind farm LCA being developed for offshore wind developments through the Offshore Wind Sustainability JIP (anticipated to be released by the end of 2024) (The Carbon Trust, 2022).

See Section 3.3.4 Key consideration: Is the focus of the Carbon Calculator correct?

See Section 3.3.5 Key consideration: Does the Carbon Calculator need to consider the lifecycle emissions of the wind farm, or could the focus be purely on the impact of development on peat?

Assessment findings: Emissions due to back up power generation

Scientific accuracy

• Back up requirements are typically modelled using the guidance note assumption of 5% of the wind farm capacity following guidance within the Carbon Calculator (Dales et al, 2004). The wind industry has evolved in the last 20 years. From a review of literature and current policy, there are no specific requirements for back-up in planning applications for renewable energy. As the National Grid decarbonises (DESNZ, 2023) back-up will increasing be supplied by other renewable energy. Therefore, this area of the Carbon Calculator could be redundant.
• Emissions associated with back up are calculated based on a grid connection. See Section 3.4 regarding selection of counterfactual emission factors. There are other options such as interconnections, energy storage solutions and nuclear that provide alternatives (National Grid, 2024).

Usability

• The input variable is acceptable in terms of usability.

Key consideration: Should the Carbon Calculator include ‘Back-up requirements’?

From a review of literature and current policy, there are no specific requirements for back-up in planning applications for renewable energy, As the National Grid decarbonises (DESNZ, 2023) back-up will increasing be supplied by other renewable energy. Where back-up requirements are specified, it’s anticipated that these would be included within an WLCA. Therefore, this area of the Carbon Calculator could be redundant.

Assessment findings: Loss of CO₂ fixing potential

Scientific accuracy

• This section of the Carbon Calculator quantifies the annual carbon sequestration from bog plant fixation (without the wind farm). The loss of carbon fixing potential is calculated from user inputs for the area which peat is removed (m²) as well as the area affected due to drainage (m²). Loss of CO₂ fixing potential has a low significance within the outputs of the Carbon Calculator (typically 1-2% of the total lifetime emissions), most land-based CO₂ losses due to wind farm development are associated with soil organic matter (see Appendix 11.3).
• Loss of carbon fixation is calculated based on the lifetime of the wind farm and time required until full peatland functioning is restored. No consideration is given to the condition the peatland will be restored to.
• The Carbon Calculator currently assumes that peatland is in a pristine condition and therefore is a net carbon sink. However, 80% of UK peatland is already degraded (NatureScot, 2015). Degraded peatland is likely to be a net source of emissions rather than a sink (NatureScot, 2015).
• The Carbon Calculator assumes a constant rate of carbon fixation over time, failing to take account for the impact of changing climatic conditions e.g. increased frequency of drought. See key consideration 3.7.4 on the impacts of climate change.
• The condition of the peatland is influenced by vegetation composition (Marshall et al, 2021), and degraded peat is associated with changes to vegetation structure with scrubbier species to the disadvantage of characteristic peatland species (NatureScot, n.d.). Literature was located which described the known link between ecosystem resilience and peatland vegetation (Speranskaya et al, 2024), and highlighted that the interactions between temperature, precipitation, nitrogen deposition, and atmospheric CO₂ and their effects can be a result of vegetation composition (Heijmans et al, 2008).
• The literature review indicates that the Carbon Calculator’s current output for ‘loss of carbon fixation potential’ may not be accurate, because: i) the current condition of peatland may not be pristine, and may therefore have a lower carbon fixation rate, and ii) there is considerable uncertainty in the ability to restore peatland to its fully functioning ‘pristine’ state so the future fixation rate may be overestimated.
• However, no research was located which presented the relationship between peatland condition and bog fixing potential, or updated fixation emission factor rates. This is anticipated to be because other methodologies (e.g. Evans et al, 2023) do not explicitly assess the loss of bog fixing potential, but instead assess the ‘Net Ecosystem Production of the peatland’. There was also no literature located to explain how the interaction between vegetation and hydrology impacts carbon fixing potential, and so the degree to which peatland condition impacts the carbon fixation value in the Carbon Calculator is uncertain and represents an evidence gap.
• This review is unable to conclusively determine the accuracy of this area of the Carbon Calculator and whether carbon fixation is accurately represented. Although carbon fixation represents a very small proportion of the total emissions, the current assumption is likely to represent a worst case (in terms of emissions) and may be suitable in the absence of other literature to inform it. This area of the Carbon Calculator could be superseded through the integration of the Peatland Code which uses the UK inventory and includes carbon sequestration (e.g. carbon fixation from bog plants) within its net emission factors.

Usability

• Carbon fixed by bog plants is a user input (a guidance note within the Carbon Calculator states ‘the Scottish National Heritage use a value of 0.25tC/ha/yr.’ however the guidance which informs this is no longer available, and this is highlighted as an evidence gap.

Key consideration: Should the baseline condition of peatland be incorporated in the Carbon Calculator?

Whilst the loss of CO2 fixing potential will remain the same, degraded peatland is likely to be a net source of emissions rather than a sink (ibid) and there is no consideration of these emissions within the Carbon Calculator. Other reasons for incorporating the baseline condition and replication of the Peatland Code’s calculation methodology are provided within this report (see Section 3.11.1). The use of HRSD could support the identification of peatland condition.

Key consideration: Impacts of climate change

Carbon fixing potential of blanket bogs (which make up 90% of Scotland’s peatland) is anticipated to decline/be under threat by 2050-80 when considering the impact of climate change (Ferretto et al, 2019). The impact of climate change on peat has not previously been considered, however is of growing concern. Degraded peatlands are less resilient to the impacts of climate change, so the emissions will change proportionally more in degraded versus pristine peatland. Climate change is also likely to make successful restoration more challenging Norby et al (2019), although it has also been indicated that successful restoration of degraded/actively eroded sites could see the greatest CO2 improvements (Evans et al, 2023), there is variation in results of the impacts of climate change on carbon fluxes following restoration (see Section 3.11 for more information).

Assessment findings: Loss of soil CO₂

Scientific accuracy: Peat removed

• Calculating volume of peat removed:
• The Carbon Calculator uses an appropriate methodology for calculating the volume of peat removed for borrow pits, turbine foundations, hard-standing and access tracks, as well as any additional peat.
• However, the use of averages may be producing a less accurate result than if actual numbers for each infrastructure feature (i.e. turbine foundation #1,2,3 etc) were inputted, as carried out in PMPs. This was reflected in industry feedback where it was highlighted that excavation volumes shown in the PMP are more realistic than what is shown in the Carbon Calculator.
• Calculating CO₂ loss from removed peat:
• This is the largest source of peatland related carbon emissions because of development.
• The carbon content of dry peat and dry soil bulk density are important parameters which drive the outputs of the Carbon Calculator. Sensitivity analysis (Appendix 10.2) demonstrates the correlation between carbon content of dry peat and dry soil bulk density and carbon losses from soil organic matter. Halving the data input values of either independent variable has the impact of a 60% reduction on emissions associated with carbon losses from soil organic matter.
• Literature review findings indicate that carbon content of dry peat has a typical range of 50% to 55% and dry soil bulk density a range of 0.06 to 0.25 gcm³ (e.g., Chapman et al., 2009; Ratcliffe et al., 2018; Heinemeyer et al., 2018; Howson, 2021, Lindsay, 2010; Parry and Charman, 2013; Levy and Gray, 2015; Carless et al., 2021; Howson et al., 2022).
• The calculation methodology is appropriate.
• The Carbon Calculator assumes a worse-case scenario that all peat removed is destroyed and the carbon content is lost. Although in practice peat is often relocated, which should be more favourable, subject to it being sensitively relocated (SEPA, 2012; IUCN, 2023), there is an evidence gap in literature which illustrates successful peat relocation (i.e. via emissions rates from relocated excavated peat). In the absence of evidence, the assumption that the carbon content will be lost over time is an appropriate worst-case conclusion.

Usability: (Peat removed)

• Calculating volume of peat removed:
• The ‘average depth of peat at site’ input variable in the ‘characteristics of peatland before wind farm development’ is not applied to any of the calculations in the Carbon Calculator. However, the ‘average depth of peat removed’ from each development feature (i.e. ‘average depth of peat removed from borrow pit, hard standing, turbine foundations’) is applied to calculate the quantity of peat removed. This provides greater accuracy than the singular ‘average depth of peat at site’ variable which could be removed from the Carbon Calculator.
• Mirroring the assessment findings from 3.8.1, the data inputs for peat depth provide an average peat depth for each development feature type (e.g. ‘average depth of peat removed from turbine foundations’) they are not specific to each individual feature on which the average is may up of. For example, there will be multiple turbine foundations. The use of an average in this context may be a poor representation of the spatial variability in peat cover, as well as the positioning of infrastructure within that peat cover. This is particularly relevant where there are different peat conditions, depths and land use types across a site. Peat depth is not uniform and varies over short distances due to the underlying topography (Parry et al., 2014). Under blanket peat thickness is typically 0.4–6 m; it can be up to ten metres and often more in raised bogs, and in fens is 0.4–5m. Peat soil is defined as requiring a depth of 0.5m and a surface peat layer containing more that 60% organic matter (NatureScot, 2023). A more detailed data input, like the ‘construction and forestry input data’ sheets and/or reflecting how peat is reported in the PMP (i.e. by turbine, borrow pit etc.) could allow for a more accurate assessment of the quantity of peat removed.
• NPF4 requires consideration of peaty soils, peat soil and peatland. Whilst the Carbon Calculator can be used in its current form on any peatland and responds appropriately to shallow peat depths (inputted as averages for each infrastructure type) a more specific data input for peat depth from each area where peat is removed would allow for better differentiation between different depths.
• Calculating CO₂ loss from removed peat:
• Carbon content of dry peat and dry soil bulk density are user inputs. Whilst the exact metrics will be site specific, industry feedback indicated that these data inputs were difficult to obtain due to the lab analysis requirements (to obtain accurate data peat samples requiring drying out for long periods of time) and are therefore often based on assumptions, with one user utilising the von post scale. The ranges identified from the literature review could be incorporated into the Carbon Calculator as recognised minimum and maximum parameters to inform an inbuilt quality control measure.

Key consideration: replace the use of averages with infrastructure specific inputs

This approach would provide more accurate outputs and replicate how peat is reported in the PMP.

Key consideration: Reuse of removed peat

Feedback from industry indicated that where possible projects seek to relocate peat (excavate peat for development and then reuse it where there is a need e.g. due to cut and fill balance) rather than remove from site. There were concerns the Carbon Calculator assumes a worse-case scenario. Consideration of whether the Carbon Calculator should incorporate an option to include peat reuse needs to be weighed up against whether this would be appropriate, as the reuse of peat is site specific, i.e. there will be limited sites with options appropriate for peat reuse, and unless peat for reuse is handled carefully it is likely to oxidise over time and lose carbon to the atmosphere. Options for positive reuse are highlighted as an evidence gap and would require additional research prior to updating the Carbon Calculator.

Key consideration: Incorporate minimum and maximum parameters into the Carbon Calculator for the carbon content of dry peat and dry soil bulk density variables

These two variables have a significant impact on the Carbon Calculator output. The literature review has identified an acceptable range for both variables which could act as parameters and inform quality control.

Key consideration: the use of HRSD

A recent study from JHI explored the mapping of soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates (Aitkenhead and Coull, 2020), Although further research is required to determine the appropriateness of this approach, in relation to bias in datasets, model complexity and comparison, model performance, and separate models for interrelated properties, and further engagement with JHI and NatureScot on the role of HRSD in this context is recommended as a next step. 

Scientific accuracy: Peat drained

• Calculating volume of peat drained:
  • Volume of peat drained is calculated based on the depth of the drain and the extent of drainage. However, accurately establishing drainage efficacy is complicated as it affected by other parameters which are not well documented, and the changes brought about by drainage are expressed over a long period of time (IUCN, 2014).
    • In pristine peatland the water table is typically close to the surface. As a result of excavation, drainage causes a drop in the water table (Irish Peatland Conservation Council, n.d.). This stimulates soil respiration and the release of carbon (Ma et al., 2022).
    • Drainage also leads to subsidence (Ma et al., 2022) (IUCN, 2014). Subsidence should be measured alongside the water table depth to fully inform the likely extent of drainage.
    • Drainage can be influenced by distance between ditches, hydraulic conductivity, and slopes (Price et al, 2023).
    • There is a linear relationship between age of a drain and the cumulative carbon lost (Evans et al, 2021).
    • Within degraded peat, the local formation of drainage ‘pipes’ is common, therefore possibly enhancing the extent of drainage.
  • Despite research in the area there is an evidence gap in understanding what a suitable average is, and the methodologies to define the extent of drainage are difficult to apply.
• Calculating CO₂ loss from drained peat:
  • In flooded soils, CO₂ emissions are equalled or exceeded by fixation leading to near-zero emissions or net carbon sequestration, whilst in drained soils CO₂ emissions exceed fixation leading to net emissions. The carbon emissions associated with peat drainage are calculated based on the difference between emissions from drained land and emissions from undrained land.
  • If site is not restored after decommissioning: The Carbon Calculator assumes a worse-case scenario that all carbon is lost (i.e. full drainage) following the same approach as removed peat. Due to the uncertainty in the parameter of the extent of drainage, this approach provides an appropriate worst-case scenario.
  • If site is restored after decommissioning: The Carbon Calculator calculates emissions from drained land against the lifetime of the wind farm, restoration period (as defined by the user) and considers the number of flooded days per year based on IPCC (1997) assumptions, which should be updated to reflect more recent literature (see below ‘calculating emission rates from soils’). Due to the uncertainty around end-of-life and decommissioning it may be more appropriate to assume a worse-case scenario (i.e. assume site is not restored after decommissioning), and separately account for the benefits from restoration within the ‘CO₂ gain – site improvement’ tab so that it is reported separately to the impact during the lifetime of the wind farm.
  • See Section 3.8.1 for commentary on ‘carbon content of dry peat’ and ‘dry soil bulk density’ data inputs.
• Calculating emission rates from soils:
  • The purpose of this calculation is to determine the loss of soil carbon in the peatland as a result of a wind farm development. This is calculated from the total carbon loss from physically removed peat, and total carbon loss from peat drainage.
  • There are two approaches included within the Carbon Calculator – the IPCC methodology is a default approach and excludes any site detail; the model used by Nayak et al, 2008 is provided as a site-specific option. Users have the option to use either the IPCC (1997) methodology or the site-specific methodology. However, the Carbon Calculator states the site-specific method must be used for planning applications. If the IPCC (1997) methodology is redundant, it should be removed from the Carbon Calculator.
  • IPCC 1997:
    • This has been superseded by the 2014 Wetland Supplement.
    • Whilst the Carbon Calculator does not include N₂O (as it uses IPCC (1997) emission factors), the implications of this are small, and further updates could be made to include this. Whilst not expected to be a significant emission (ca. 2%) and dependent on the nutrient content of soils, it could be incorporated based on nitrogen content of soil samples. Where relevant (in the instance of intensive farming) N₂O emissions could be comparable to CH₄ .
    • The IPCC emission factors referenced are Tier 1, and therefore not representative of Scotland’s peatlands. The factors are mainly based on warm season data, and peatlands in colder climates are likely to emit less (Hongxing and Roulet, 2023).
    • Although these Tier 1 emissions factors could be updated by those represented by Evans et al, 2023 (Tier 2) and used within the 2021 update to the Emissions Inventory for UK Peatlands, they may not be fully representative of Scotland (which is wetter, and agriculture is predominantly less intensive). Furthermore, the Carbon Calculator states the site-specific method must be used for planning applications. It is therefore recommended that the IPCC (1997) methodology is removed due to the greater accuracy that the site-specific methodology can provide.
  • Nayak et al, 2008:
    • Calculates emissions factors via a bespoke methodology. Two options for type of peatland provided: acid bog, and fen (core data inputs). This covers the four main peatland habitats in Scotland; blanket bog (acid bog), raised bog (acid bog), fen (fen) and bog woodland (acid bog).
    • The methodology equations for CO₂ and CH₄ emissions are derived by regression analysis, considering the average annual air temperature and average water table depth. Whilst the methodology does not directly refer to peatland condition, it incorporates air temperature and water table depth which is a good proxy in establishing emission rates (Tiemeyer et al., 2020) (Ma et al, 2022), as the water table has a significant influence on peatland CO₂ and CH₄ emissions (Huissteden et al, 2016, Evans et al, 2021). Empirical relationships between water table depth and CH₄ and CO₂ emissions defined by Evans et al (ibid) enable it to be used to calculate carbon emissions, as illustrated by Evans et al (2023).
    • The evidence base for the methodology uses multiple peer reviewed studies (Bubier et al. 1993, Martikainen et al. 1995, Silvola et al. 1996, MacDonald et al. 1998, Nykänen et al, 1998, Alm et al. 1999), the analysis includes a robust sensitivity analysis which supports accuracy. However, the studies referenced reflect boreal peatland, and this element of the Carbon Calculator could be updated to reflect more recent literature ( (Evans et al, 2021), (Evans et al, 2023), (Ojanen and Minkkinen, 2019), (Wilson et al, 2016), (Tieymer et al, 2016)) which reflects a temperate climate and/or accounts for land use type.

Usability: Peat drained

• Calculating volume of peat drained:
  • The volume of peat drained is highly sensitive to the user input for the ‘average depth of peat removed’ from each development feature (i.e. ‘average depth of peat removed from borrow pit, hard standing, turbine foundations’); increasing the depth and/or extent of drainage directly correlates with the volume of peat effected by drainage. This volume feeds into the calculations for CO₂ loss from drained peat.
  • The average water table depth and extent of drainage is a user input. These parameters vary depending on the specific site, and within the site itself. Authors of the Carbon Calculator, Nayak et al (2008) underline the importance of accuracy in the choice of these inputs. However, the cost of correctly following the methodologies presented in the Carbon Calculator were highlighted by industry stakeholders as ‘prohibitively high’ for projects that may not obtain planning consent.
    • Average water table depth variable: The Carbon Calculator describes this variable as the upper boundary of the groundwater. Considerable variety in the method used to obtain the ‘average water table depth’ by users was observed – from obtaining an average depth via hydrologists, to using the water table depth from a previous similar site. Evidence of the hydrology calculations to inform user inputs were not assessed as part of this research, and could merit further research in conjunction with a review of other EIA deliverables and their applicability to the Carbon Calculator’s data inputs. The narrow timescales associated with the preparation of planning documents (i.e. EIA) present a challenge in obtaining reliable information, and the current approach does not account for the temporal changes of the water table. The Carbon Calculator output likely only represents a ‘snapshot’ which consequently, in combination with the variety in approaches to obtaining the variable, may be inaccurate.
    • Average extent of drainage around drainage features at site’ variable: Industry feedback on this variable’s method was resolute in it being impractical to collect this data (due to both time requirements and associated cost) during planning timescales. Despite reviewing available evidence, a practical methodology (i.e. within planning timescales) to inform this variable could not be identified.
• Calculating CO₂ loss from drained peat:
  • See Section 3.8.2 for commentary regarding carbon content of dry peat and dry soil bulk density.
• Emission rates from soils:
  • See Section 3.8.2 for commentary regarding emission rates from soils.

Key consideration: update the methodology for emissions rates from soils

The methodology should incorporate recent literature and a temperate peatland that reflects the Scottish context, it should also acknowledge the role of the mean annual water table depth, which has been identified as the overwhelmingly dominant control on CO₂ fluxes (Evans et al, 2021). The literature review identified papers which should be reviewed when undertaking this update:

• Tiemeyer et al (2020)’s ‘A new methodology for organic soils in national greenhouse gas inventories: Data synthesis, derivation and application’ incorporates HRSD and uses water table data to determine Germany’s GHG estimate for organic soils at a National level, which it states could be applied at a project level.
• Evans et al (2023) ‘Aligning the Peatland Code with the UK peatland inventory’ provides an overview of low-cost methodologies to obtain site data to inform peat-carbon variables, including water table depth and reference to ‘Eyes on the bog’ methodologies (Lindsey et al, 2019).

Key consideration: should the Carbon Calculator account for emissions from drainage ditches?

Although the extent of drainage is captured in the Carbon Calculator, drainage ditches represent an additional source of CH₄ emissions from drained organic soils (Peacock et al, 2021) which are not currently included in the calculations. Emissions from ditches are captured in the IPCC’s 2014 Wetlands supplement and could be applied to developments if the Carbon Calculator were to specify to peat condition, to replicate the approach used in the Peatland Code (Evans et al, 2023). The inclusion of drainage ditches could also be informed by the use of HRSD (see 3.8.12).

Key consideration: Investigate the use of HRSD in measuring water table depth

HRSD can be utilised to ascertain water table depth and provide historic trends. This could enhance the accuracy of Carbon Calculator when combined with ground truthing. For more information, please see Section 5. This could also inform Quality Control Mechanisms.

Key consideration: to what extent can assumptions/parameters, and HRSD be used to inform ‘Average extent of drainage around drainage features at site’?

The current methodology to obtain the extent of drainage is viewed as being impractical within planning timescales. Whether this variable (using an indicative assumption) should be automated, and/or include parameters, requires careful consideration, particularly as it is a highly sensitive input. The IUCN classifies drained peatland as that which lies within 30m of an active drain, (IUCN, 2022). The literature review was unable to determine a range to inform parameters on this variable, although it did identify a paper where GIS was utilised to establish surrounding drainage areas (Sallinen et al, 2019). The role of HRSD in informing this input variable should be considered in conjunction with other efforts being undertaken to establish better accuracy in quantifying drainage impacts. This includes work undertaken (and ongoing) at the James Hutton Institute (e.g. Aitkenhead et al, 2016, the Peat Mothership Project (2024)) to inform the best approach. Discussion of the draft report highlighted an additional study utilising HRSD to provide a national scale map of Scotland’s individual drainage channels and erosion features (Macfarlane et al, 2024) which would further inform the role of HRSD in this context and Section 3.8.10.

Key consideration: what quality control mechanisms are needed to enable a consistent (and accurate) approach to obtaining WTD and extent of drainage?

Industry feedback consistently highlighted concerns around the time and cost in obtaining the input variables required for extent of drainage and water table. These variables have a significant bearing on the carbon outputs, and so the approach to obtaining them should be uniform and feasible within planning timescales. This could be remedied through further engagement, the subsequent development/updating of guidelines (i.e. Guidance on Developments on Peatland, 2017), and/or the provision of training (to users and decision makers) and reinforced through the appropriate use of quality controls. This data could then go on to inform a national dataset of measurements.

Assessment findings: CO₂ loss by Dissolved Organic Carbon (DOC) and Particulate Organic Carbon (POC) loss

Scientific accuracy

• This area of the Carbon Calculator determines the gross loss of soil carbon from both DOC and POC loss following peat drainage. Only restored formerly drained land is included in this calculation because if land is not restored, the carbon lost has already been counted as carbon dioxide via ‘CO₂ loss from drained peat’ (Section 3.8.7). CO₂ loss by DOC and POC has a low significance within the outputs of the Carbon Calculator, most CO₂ losses due to wind farm development is associated with soil organic matter (see Appendix 10.2).
• The Carbon Calculator advises that “No POC losses for bare soil included yet. If extensive areas of bare soil is present at site need modified calculation (Birnie et al, 1991)”.
• Assuming site restoration, DOC and POC are calculated for the period (years) of site restoration (i.e. the time between the year of site improvement and the year of the sites habitat and hydrology being restored).
• Emissions are calculated based on a percentage of the total gaseous losses of carbon from improved/restored land, these are based on averages from Worrall (2009) which provide the following:
• DOC – 26% (7-40%)
• POC – 8% (4-10%)
• These assumptions (including the minimum and maximum) are tied into the Carbon Calculator (i.e. not editable by the user). DOC has a broad range, which could be causing some inaccuracy in the results. The Carbon Calculator’s assumption that DOC and POC loss is only applied to restored formerly drained sites may be underestimating DOC and POC emissions for sites which have eroding peatland.
• The Peatland Code methodology Smyth et al. (2015) uses DOC and POC emission factors (reflecting condition type) which follow Tier 1 default values for drained and rewetted temperate peatlands developed for the IPCC Wetland Supplement (IPCC, 2014). Evans et al (2023) note for DOC that few limited UK studies have been published, and other studies fall outside the UK-relevant climatic region; and similar for POC; few additional POC flux estimates exist to enable refinement. Although some recent UK evidence indicates DOC increases may be larger or smaller depending on the peatland type, there is insufficient DOC flux data across the range of UK peat types and condition classes to support a full country specific approach (ibid).
• Pickard et al (2022) found that increased DOC concentrations were detected in areas of drained peatland relative to non-drained peatland from the UK’s largest tract of blanket bog in the Flow Country of northern Scotland. These findings could be incorporated into the Carbon Calculator, however, as they represent one study based on a unique area of pristine peatland, a more conservative approach is recommended until further research is available.
• Discussion of the draft report raised an additional study from the Whitlee wind farm development exploring the effect of development phasing in relation to DOC and POC loss over a ten-year timespan, we suggest that further review incorporates the findings from this study.

Usability

• DOC and POC calculations require no inputs from the user.

Key consideration: align DOC and POC with the 2014 IPCC Wetland Supplement

For the purposes of the Carbon Calculator, emissions factors for DOC and POC could be applied to projects based on the peat condition, utilising the IPCC 2014 methodology, replicating the Peatland Code (Evans et al, 2023) which uses the UK inventory emissions factors. This would replace the current methodology but is more robust as the studies used to inform these default factors were based partly on a small number of UK studies (including two from Worrall), rather than a single study as currently used. This approach would have the added benefit of capturing DOC and POC emissions that are already occurring on eroding peatland and provide greater accuracy. The literature review highlighted an evidence gap where additional research is required to provide more specific DOC and POC estimations, building on the findings from Pickard et al (2022).

Assessment findings: CO₂ losses associated with loss of forest

Scientific accuracy (simple)

• The simple methodology for forestry CO₂ loss uses figures obtained from a single source (Cannell, 1999). Loss of future carbon sequestration is calculated by multiplying an emission factor by the area of forestry and lifetime of the wind farm. In the simple methodology this is a user input, “estimated carbon sequestered (t C ha^-1 yr^-1)”. The guidance note provides an assumption of 3.6 tC ha^-1 yr^-1 for yield class 16 m³ ha^-1 y^-1 (Cannell, 1999). Whilst this is comparable with an average (over 200 years) from the Woodland Carbon Code (Yield 16, 1.7m spacing, thinned) Woodland Carbon Code, 2024) it doesn’t consider aspects such as species, age, density etc of the site-specific parameters. Therefore, a level of uncertainty/ error can be inferred for users with differing site characteristics (tree species).
• There is no consideration of emissions associated with the felling activities. Whilst this is likely to be insignificant, it could be incorporated into the Carbon Calculator for completeness.
• There is no consideration of emissions associated with the loss of carbon stock (i.e. if the felled forest wood is destroyed), which depending on the use of the wood could be relevant (e.g. if the timber is burnt).
• There is no consideration of the impact on the peatland of removing the trees (where forestry is located on peatland). Whilst expected to have a positive impact over time on peatland restoration, it is acknowledged that further research is required in this area (Howson et al, 2021; IUCN, 2020).
• Based on our sensitivity analysis results (Table 3) from the simple and detailed methodology vary significantly based on similar parameters:

Table 3: Forest methodologies sensitivity analysis

• This is due to the simple methodology not accounting for/underestimating the following:
• Tree species and age.
• cleared forest emissions (currently labelled ‘carbon sequestration in soil under trees’ in the detailed methodology).
• Underestimating the amount of carbon lost due to felling in comparison to the detailed methodology (likely because of the additional variables that inform the detailed methodology – light interception and primary production).

Usability (simple)

• The input variables are acceptable in terms of usability. However, there is the potential for error with the current input variables guidance. The Carbon Calculator notes that sequestration rate is dependent on the yield class of the forestry. The guidance note provides an assumption of 3.6 tC ha^-1 yr^-1 for yield class 16 m³ ha^-1 y^-1. No guidance is provided as to how the species of tree influences yield class, although poplar, Sitka, and beech CO₂ sequestration rates are provided in the separate user Guidance document, they are not visible in the Carbon Calculator. Enhanced user guidance and/or reference to sources of information (e.g. The Woodland Carbon Code) could be provided.

Scientific accuracy (detailed)

• The detailed method uses similar principles to the simple method, however, differs in its calculation of ‘the average carbon sequestered per year’, it requires additional user input (‘forestry input data tab’) to account for carbon loss based on soil type, species, and age of forestry, and provides a more complete account of the emissions from forestry in comparison to the simple methodology (see Table 3) .
• The method which informs these calculations (Xenakis et al, 2008) is comprehensive in calculating emissions from forestry. It uses the uses 3-PG (Landsberg, J.J., Waring, R.H., 1997). A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning, and builds on this to incorporate a soil organic matter decomposition mode, incorporating differences due to age of forestry at felling. The model has been calibrated and tested for commercial plantations of Scots pine in Scotland.
• ‘Carbon sequestration in soil under trees’: is noted within the Carbon Calculator as ‘more data needed’. ‘ It states that the aim of this calculation is to ‘account for the respiration from newly felled and disturbed soil, so as to include respiration from fresh plant inputs, from background soil organic matter decomposition, and from the disturbance of soil resulting in the release of additional carbon from soil aggregates. Different types of management disturbance should be considered’. This is labelled as ‘Cleared Forest Floor Emissions’ within the Carbon Calculator. It later states that this information is not yet available, so as an interim measure, carbon sequestration in soil under trees (including background respiration from soil organic matter decomposition and respiration from fresh plant input) is used.
• The two emissions factors currently used for the ‘Carbon sequestration in soil under trees’ are based on two studies located in Scotland which is appropriate. However, both studies assumes that forestry is on peaty soils, which may not be the case for all forestry inputs. Given that this element of the Carbon Calculator was originally planned to account for the ‘Cleared Forest Floor Emissions’ only (see previous paragraph), the emissions factors used in lieu of this are possibly overestimating the carbon sequestration associated with soil under trees. Since this literature was published, there has been further research to understand the relationship between carbon emissions and newly felled/disturbed soils (West, 2011) (Matthews et al. 2012), these studies have informed the development of the Woodland Carbon Code (2024).
• The detailed methodology also provides a calculation to determine the capacity factor for the turbines at the site. This is dependent on tree height, forest width and distance of the forest from the turbine. Although this methodology appears scientifically correct in terms of the measurements being used, none of the references provide justification of the overarching rationale/purpose of this calculation. Some of the references used for wind speed calculations are over 20 years old and it’s unclear whether these factor in the impacts of climate change on wind speeds. The technological advances in turbine functionality (and the extent to which they are impacted by forestry) needs to be considered. It is also reasonable to assume that the potential capacity of the wind turbines and influence of forestry on a wind turbine’s power curve will be considered by developers when establishing the Levelised Cost of Electricity (i.e. site feasibility) for a development. Overall, the appropriateness of this calculation in the context of the Carbon Calculator’s purpose is questionable and should be removed (see 3.3.4 Key consideration: Is the focus of the Carbon Calculator, correct?).

Usability (detailed)

• Feedback from industry engagement highlighted that the detailed methodology is not used as the number of input variables required is perceived as onerous/requiring specialist support.
• The forestry input data tab provides two options for soil types provided: peaty gley and deep peat. This appropriately covers both peat (organic) soils and peaty (organo-mineral) soils.
• The forestry input data tab provides two options for species: Scots pine and Sitka spruce. Scots pine is the main species in bog forests (NatureScot, n.d) the inclusion of other species may be beneficial in providing a more accurate output.
• The separate user Guidance document states the following: ‘Loss from soils of non-forested land is given by the estimated rate of carbon loss for two peat depths taken from Zerva et al (2005) for peaty gley (peat depth 5 to 50cm = 3.98 t C ha-1yr-1), and Hargreaves et al (2003) for deep peat (peat depth>50cm = 5.00 t C ha-1 yr-1)’. The reference to ‘non-forested land’ in the Guidance may be an error given the references used.
• Emissions from felling and transportation are a user input; these could be estimated based on assumptions and utilisation of UK Government emission factors. The existing guidance notes provide outdated references (Morison et al, 2011). The most up-to-date UK Government emission factors should be used and could be automated within the Carbon Calculator.

Key consideration: Replace the simple and detailed methodologies with one approach, informed by Woodland Carbon Code calculations

Although the detailed forestry methodology is comprehensive, it is perceived as onerous/requiring specialist support by users, and so in many applications the simple methodology is used. The simple methodology is likely to be underestimating carbon impacts. In turn, the detailed methodology may be providing inaccuracies in relation to ‘Carbon sequestration in soil under trees’. The comprehensive nature of the detailed approach also has implications for the ability to ‘futureproof’ the Carbon Calculator. The equations which inform it and the formula within the Calculator, are complicated and difficult to interpret without advanced excel skills. This presents a risk when undertaking future updates to the Carbon Calculator.

Having one option in the Carbon Calculator which strikes a balance between inputs required and the generation of an accurate output is an important consideration. The Woodland Carbon Code’s (WCC) (Woodland Carbon Code, 2024) calculator includes a wider range of tree species with rates based on spacing (m), yield class, management type and age. The WCC is supported by Scottish Forestry and has undergone independent validation and verification. It provides a credible dataset that is reviewed and updated regularly. To enable a more robust output, the sequestration rates ‘Biomass Carbon Lookup Table’ could be replicated in the Carbon Calculator and aligned with the WCC to enable consistency in reporting methods.

Key consideration: Remove the option to affect the wind turbine’s capacity factor via the forestry inputs tab

The calculations that inform this appear to go beyond the remit of this Section’s purpose in calculating the CO₂ losses associated with forestry. More rationale on why this is not appropriate and should be removed is provided in Section 3.10.3 bullet point 6.

Key consideration: Use of HRSD in determining forestry inputs

The role of HRSD and whether it could be utilised to determine key input variables for forestry and/or estimated carbon stocks (see Tolan et al, 2024, Cheng et al, 2024, which use cutting edge technology to estimate carbon stocks) should be explored in collaboration with forestry organisations (i.e. Scottish Forestry, NatureScot, Forestry and Land Scotland, Forest Research). There are several open resources that could inform this (i.e. Scottish Forestry Map viewer (Scottish Forestry, n.d.), Habitat Land Cover Map of Scotland (2024), Scottish Remote Sensing Portal (Scottish Government, n.d.)). Process-based modelling, data assimilation and remote sensing has been applied by the University of Edinburgh to quantify carbon stock changes, and remote sensing is used by Forest Research to accurately map woodland.

Assessment Findings: CO₂ gains from site improvement

Scientific accuracy

• This area of the Carbon Calculator estimates the reduction in GHG emissions due to restoration of the site. The calculation for this area of the tab replicates the calculation used to ascertain loss of soil CO₂ (peat drained) (Nayak et al, 2008), and so the findings from 3.8.7 and 3.8.8 are also relevant to this section.
• The current calculations assume that restoration will be successful, and that peatland will be restored to pristine condition. The UK Inventory and Peatland Code transitions land from degraded condition categories to ‘modified bog’ upon restoration, it does not apply the ‘near-natural’ emission factor to restored peatland, recognising the difficulty in fully restoring peatland to the full sequestration potential.
• It is difficult to accurately model emission reductions associated with restoration at pre-planning phases – in particular, the ‘depth of peat above the water table after restoration.’ There are several restoration activities (hydrology and habitat ‘yes/not applicable’ inputs) within the Carbon Calculator are assumed to occur post wind farm operation (>20 years in the future), although these are not linked to any calculations.
• Undisputed, is that the restoration of degraded sites should be a priority, and the benefits of such activities are well documented. However, there is variation in understanding the impact of restoration on carbon savings. How restoration affects carbon fluxes and storage on degraded sites shows variety in the potential results. Peatland recovery is not instantaneous (Gatis et al.,2023, Alderson et al, 2019), with interventions taking at least 5 years or more for ecosystems changes to stabilise (Gregg et al., 2021). Artz et al. (2012) note that carbon savings are dependent on the starting condition prior to restoration with some research indicating that severely degraded sites take longer to achieve emissions reduction than less affected peatlands. Restoring the carbon ‘sink’ functionality of a degraded peatland is possible, however this may take decades, and be dependent on the initial level of site degradation (Gregg et al., Ibid). Lindsay (2010) notes that peat accumulation in blanket bogs can be half that of raised bog due to warmer climatic conditions and suggests a timeframe of around four decades before restoration to a fully functional bog can achieve net carbon gain, although emissions reduction will occur much earlier. Although there can be short term CH₄ fluxes because of restoration the long-term carbon savings can negate this short-term effect (Emsens et al., 2021– note this study relates to fen bogs, but also highlights the important role of vegetation establishment). Evans et al. (2022) note that independent modelling studies by Heinemeyer et al. (2019) for the Defra Peatland-ES-UK (Defra BD5104) project, and Simon et al. (2021) for the BEIS review of UK GGR potential both suggested that degraded peatlands have the potential to accumulate carbon rapidly, and therefore that the CO₂ sequestration potential of peat restoration may have been significantly underestimated. The current methodology does not take these considerations into account.
• Future climate conditions (e.g. rising temperatures, extreme weather events) could affect the ‘success’ of peat restoration (i.e., carbon accumulation). Climate change is noted to exacerbate ecological stresses on less resilient, managed peatlands over the next 60 years, leading to more rapid losses of stored peat carbon (Worral et al, 2010) (Ferretto et al, 2019) (Natural England, 2020). Any estimates made have a high level of uncertainty, given the relatively short timeframe of restoration in the context of a wind farm’s lifespan.
• The calculations for site restoration are sensitive to water table depth changes, pre- and post-restoration (Appendix 11.3). Water table has a significant influence on peatland CO₂ and CH₄ emissions (see section 3.8.7). However, there is limited empirical data to provide a high level of certainty in relation to future carbon stocks and carbon flux; carbon benefits can be difficult to quantify and affected by environmental conditions on a site-by-site basis (Wille et al, 2023), Gregg et al. (2021) state in relation to blanket bogs, raised bogs and fens that ‘large spatial variability has been shown and studies have often been carried out at the same sites or regions’, blanket bogs are less responsive to drainage and rewetting alone, but can be beneficial when coupled with peatland stabilisation and re-establishment of vegetation cover, the role of vegetation as well as hydrology in site restoration should therefore be taken into account. Further research is required in the context of restoration, including blanket bog rewetting (Evans et al., 2014; Williamson et al., 2017), and restoration of plantations to semi-natural peatland.
• See also the commentary on ‘emission rates from soils’ within Section 203.8.

Usability

• Calculations within this tab are based on the changes to water table depth pre- and post-restoration of peat (inputted by the user) and the calculated emission rates from soils. It has been noted that small changes to the figures for water table depth can significantly increase the value of carbon gains due to peat restoration. Although the methodology for ‘Water table depth after improvement’ variables indicate an optimal water table level is ‘probably just below the surface (-10 to -6 cm)’, within planning timescales the future water table depth (and other associated variables) can only be approximated. When accounting for the high level of uncertainty regarding restoration, the question of whether this element of the Carbon Calculator should be conventionalised to replicate the Peatland Code’s calculations and guidance requires consideration.
• See also commentary on ‘emission rates from soils’ within Section 203.8.

See 3.3.3 key consideration: is the output of the Carbon Calculator useful as a decision-making tool?

The timeframe for achieving a ‘carbon payback’ or ‘carbon neutrality’ should be considered on a land for land basis (e.g. restoration gains vs construction losses) instead of relying on savings from generation. More information on how this should be presented is provided in 3.3.3.

Key consideration: the Carbon Calculator should be updated to replicate the Peatland Code

Site restoration should explore the option to replicate elements of the Peatland Code’s approach, including its requirements around restoration success. In particular, the Peatland Code utilises up-to-date emissions factors (aligned with the UK inventory), and includes a 15% sensitivity buffer to accommodate the risk of future carbon losses (e.g., restoration failure) (see Section 4 on the Peatland Code). Establishing a baseline condition that reflects the Peatland Code’s classification, would simplify the input required for site restoration (by then selecting the appropriate condition post-restoration). Considering the degree of uncertainty, this is appropriate and could prevent the risk of inaccuracy and/or ‘fixing’ of the current variables. This would negate the use of ‘carbon fixing’, ‘loss of DOC and POC’, and ‘peat drainage after restoration’ calculations. By bringing different funding mechanisms together, this alignment could also support data collection at a national restoration level. Through our engagement with the Peatland Expert Advisory Panel, it was determined that the full implementation of the Peatland Code on development sites is not suitable. Further dialogue with the Peatland Code representatives is recommended to identify the optimal approach for this consideration.

Key consideration: Quality control should review the Carbon Calculator in conjunction with the Peat Management Plan (PMP) and Habitat Management Plan (HMP)

In determining whether a development should be built on peatland, a key decision factor should be the extent to which the developer is able to illustrate site restoration post installation, reflecting the requirements of NPF4 (mitigation hierarchy) and Good Practice restoration Guidance (e.g. NatureScot, Peatland Code). Resilient restoration through credible restoration techniques which prioritise vegetation establishment and a return to high water tables are critical components of this. The remit of the Carbon Calculator is to determine whether the carbon impact of the development on peatland is acceptable, any carbon savings from site restoration should be reviewed holistically in conjunction with a robust PMP and HMP that evidences credible restoration techniques. To inform this, a review of the requirements for key EIA deliverables (i.e. PMP, HMP, Carbon Calculator) could be undertaken, to enable a streamlined decision-making process. 

Summary

Based on the findings from the technical assessment and evidence review, Table 4 presents a summary of the Carbon Calculator’s scientific accuracy and data usability ratings.

Table 4. Carbon Calculator areas summary

In summary, the ‘payback time and CO₂ emissions’ is not relevant/consistent with the findings of the technical assessment and literature review. The focus of the Carbon Calculator (3.4) requires revisiting, with consideration of whether 3.5. and 3.6. are required considering new planning policy and applicability of WLCAs.

Accuracy is lacking in one or more of the following: methodologies, use of emission factors and assumptions, for all peat-related areas of the Carbon Calculator, as well as the forestry area. The usability of the Carbon Calculator presents a more varied picture, with some data accessible to the user. However, there was uncertainty in the ability to accurately access some of the data required for the Carbon Calculator – in particular, for variables that drive the results, which could have a material bearing on the accuracy of outputs.

Further commentary is provided in 7. Conclusion and recommendations.

SWOT analysis

Table 5 presents the strengths, weaknesses, opportunities, and threats of the current Carbon Calculator identified from this Report’s findings:

Table 5: SWOT analysis

Evaluation of Peatland Code

The IUCN Peatland Code is a voluntary certification standard for UK peatland (fens and bogs) projects seeking financial benefits from restoration activities through ‘carbon units.’ The code provides a framework for the validation and verification of greenhouse gas reductions.

The principle of the Peatland Code is classification of land use or peatland condition pre-restoration and post-restoration. In the following subsections we explore the value add of integrating this categorisation into the Carbon Calculator, focusing on bog peatland.

The Carbon Calculator does not currently fully align with the Peatland Code; there are opportunities to replicate elements of the Peatland Code within the Carbon Calculator, as well as aligning emission factors.

Overview of the Peatland Code

The Peatland Code encompasses a simplified methodology to quantify the effect of peatland restoration on land emissions, for the purpose of verification for ‘carbon units.’ The Peatland Code considers accuracy and reliability when quantifying the climatic benefits of peatland restoration. As such key requirements on projects include:

• Validation and Verification: There is a requirement for restoration projects to undertake third-party validations and verifications to ensure climate benefits are quantifiable, additional, and permanent.
• Management and monitoring plan: all projects are required to have a restoration management plan for the duration of the project. The monitoring plan should track the peatland condition over time.
• Management of Permanence: to manage the risk of project permanence, a 15% risk buffer is applied to emission reduction calculations. This acknowledges the risk of future carbon losses; either from emissions associated with restoration activities (e.g. fuel use) or to future peatland restoration failure.

Bog emissions calculator

The bog emissions calculator requires four inputs (area, project duration, pre-restoration condition and post-restoration condition) (Table 6) from which emission reductions (tCO₂e) are calculated from a ‘emissions lookup table’ across 100-year period (Table 7). The emission factors have been developed to align with the UK Greenhouse Gas Inventory, based on recent research from the UK Centre for Ecology & Hydrology, and the JHI (Evans et al, 2023). The difference between the pre- and post-restoration emission factors provides the carbon reductions achieved through restoration.

Table 6: Peatland Code Condition Categories (bogs)

Table 7: Peatland Code Bog Emission Factors

Fen emissions calculator

The fens emissions calculator requires three inputs for both the pre- and post-restoration scenarios (land use classification, average annual water table depth and average peat depth) (Table 8), from which emissions from peat are calculated. Unlike the bogs emission calculator the emission factors are locked, however are understood to be a combination of Tier 1 and 2 emission factors (IPCC), and emission estimated derived from the site’s effective water table depth (Evans et al. 2021).

Table 8: Fen Land Uses

Benefits and drawbacks

Based on our findings of the Carbon Calculator’s technical assessment (see Section 3) and review of the Peatland Code, Table 9 provides a high-level summary of the benefits and drawbacks of integrating the Peatland Code’s methodology and emission factors within the Carbon Calculator.

Table 9: Peatland Code Summary

Recommendations for the Carbon Calculator

The Peatland Code provides an established methodology to quantify GHG benefits across the UK. Aligning with this methodology could improve the accuracy of baseline carbon flux and consistency in reporting the benefits of restoration activities. However, through our engagement with the Peatland Expert Advisory Panel, it was determined that the full implementation of the Peatland Code on development sites is not suitable. Further dialogue with the Peatland Code representatives is recommended to identify the optimal approach for the following opportunities for the Carbon Calculator:

• The condition categories could be replicated to establish a more representative baseline and subsequent restoration status. The Carbon Calculator currently assumes peatland is pristine and presents a worse-case scenario in terms of carbon lost, however lost carbon may not be fairly attributed to the wind farm development.
• Whilst the emission factors may not be wholly representative of Scotland (based on a UK average) they are widely recognised as best practice. Integration of the peatland condition categories could provide a recognised approach to quantifying the benefits of peatland restoration activities (site improvements tab).
• Use of a risk buffer (measure of uncertainty) within the site improvements tab.
• If building on degraded peatland, the Carbon Calculator could include a requirement on developers to improve condition of the site through the project’s lifespan. The principles of the Peatland Code could be used to inform guidance on this.

High Resolution Spatial Data (HRSD)

A literature review (Appendix 11.4) of eight data sources was conducted to identify HRSD measures that could indicate the presence and condition of peat. The following subsections provide analysis of the benefits and drawbacks of HRSD, and how it might improve the Carbon Calculator’s accuracy.

Summary of HRSD methodologies

To date, multiple types of imagery have been used to varying degrees of success (Table 10).

Table 10: HRSD summary of findings

Summary of literature review findings

For optical based imagery (#1 and #2) cloud cover often limits the number of temporal snapshots captured, although it has not been successful in providing a good measure of condition, it can provide an understanding of landcover, including vegetation.

Active based sensing (#3, #4 and #5) can be coupled with landcover information provided from optical based imagery to provide a holistic understanding of peat condition and water table depth proxies. LiDAR data, as demonstrated by #5, is very useful for mapping topographical features such as draining channels and flow paths in high resolution but is expensive to obtain in real-time, given these features are relatively stable, LiDAR surveys commissioned over a wide area (i.e. a National Scheme) would be a useful dataset for identifying hydrological features that could inform the Carbon Calculator inputs. Our findings indicate that SAR data, coupled with the methodologies referenced in #3 and #4 appears to be the most promising in both its ability to capture hydrological condition of peat (including water depth) and the ability to obtain temporal imagery. More information on ESA’s Sentinel 1 platform is provided in Appendix 11.4. The limiting resolution of this approach may reduce the accuracy for small and/or spatially varying sites, but is advantageous over the deployment of ground-based sensors in that:

• It provides continual mapping across the whole site, compared to a sparse deployment of specific ground-based sensors.
• Archival data and repeated visits provide a longer temporal dataset from which to establish condition compared to ground-based sensors placed for a discrete time interval.

Future trends show a rise in popularity for SAR data products, with companies like Umbra offering high-resolution (1m) options, mitigating some of the current limitations. However, as SAR is unable provide landcover information, combining it with optical imaging could yield the most informative and accurate maps.

Although not assessed as part of this review, it is understood that Scottish Government is exploring a national LiDAR scheme with repeat collections every few years, which could track the stability, loss, and/or growth of peatlands. LiDAR alongside optical SAR and InSAR data could provide key data to inform the Carbon Calculator.

Recommendations for the Carbon Calculator

Scottish Government is exploring a national LiDAR scheme with repeat collections every few years, the results of this could be integrated into the Carbon Calculator, and reviewed to understand whether any further use of HRSD would provide additional transparency and support accuracy, over and above the following:

• Integrating HRSD into the Carbon Calculator, through a model which combines #1, #3 & #4 HRSD types, would enable an understanding of i) land cover types, providing proxies for ii) peat condition, and iii) water table depth, as well as the provision to understand the history of prospective sites to better inform peat condition. It could therefore also be used to inform subsequent monitoring activities. The condition of peat is causally related to the emission and sequestration of carbon sequestration and since this not currently considered by the Carbon Calculator, adding this capability would provide a step change in improving the accuracy of the Carbon Calculator. The water table depth is currently considered in the Carbon Calculator but requires manual surveying. Adopting the remote sensing approach would be advantageous in providing consistent and temporal measurements that would improve the accuracy between sites and support quality control.
• Integrating remote sensing into the Carbon Calculator will depend on having data products that are deemed accurate enough and are readily available at little or no cost. The products from TerraMotion (#4) would appear to be the most promising for peat condition but further stakeholder engagement would be needed to determine whether their offering suffices both in accuracy and cost, over and above the nationwide LiDAR scheme being explored by Scottish Government.
• An additional piece of work could be carried out to explore a proof-of-concept data product that brings together the surface motion, water table depth and vegetation cover measures identified in the review. Combining all three types of data is likely to provide the most informative and accurate measure of presence and condition of peat. The output should be validated against a typical ground-based survey carried out by an organisation using the Carbon Calculator.

Quality Control Mechanisms

Decision makers that utilise the outputs of the Carbon Calculator include the Energy Consents Unit (ECU) and local planning authorities. ECU review applications for consent for the construction, extension and operation of electricity generating stations with capacity more than 50MW. Applications below this threshold are reviewed by the relevant local planning authority. Following engagement with ECU, it has been ascertained that the existing quality assurance processes undertaken to evaluate and support decision-making would benefit from significant enhancement. Due to the Carbon Calculator’s complexity and the skillsets required to review the data outputs, it is ascertained that the Carbon Calculator is not currently used as a decision-making tool in the capacity it was intended but is used to check the credibility of the ‘payback period.’

Recommendations for the Carbon Calculator

The following actions are recommended to improve the utility of the Carbon Calculator as a decision-making instrument:

• The Carbon Calculator should have automated mechanisms for input variables that exceed acceptable error margins or contradict other variables.
• A guidance document should be produced to support developers, ECU, and local planning authorities on the key drivers of peat-related carbon emissions and potential variances (i.e. carbon fluxes), this could be done through the updating of existing guidelines (i.e. Guidance on Developments on Peatland, 2017).
• The decision to build on peatland should consider the developer’s ability to demonstrate post-installation site restoration, in line with NPF4 and Good Practice restoration Guidance (e.g. NatureScot, Peatland Code). Resilient restoration through credible restoration techniques which prioritise vegetation establishment and a return to high water tables are critical components of this. The Carbon Calculator’s purpose is to assess the carbon impact of the development on peatland. Carbon savings from site restoration should be reviewed holistically alongside a robust PMP and HMP. A review of the requirements for key EIA deliverables in terms of the inputs they require could benefit quality control and streamline the decision-making process.

A further consideration is that through the implementation of the above recommendations, Quality-controlled application data could contribute to a national database.

Carbon Calculator applicability

Based on our findings, this section explores the Carbon Calculator’s applicability as a decision-making Carbon Calculator across proposals for alternative infrastructure (e.g., transmission and distribution, battery storage options) and renewable energy development (e.g., solar) on peatland and carbon rich soils within Scotland. Whilst the Carbon Calculator, in its current form, would not be fully applicable to alternative development proposals, modifications can be made to increase transferability. Table 12Table provides some considerations against each area of the Carbon Calculator.

Table 11. RAG Ratings

Table 12: Increasing Carbon Calculator applicability (Note Section 3 recommendations apply to the below).

Recommendations for the Carbon Calculator

In summary, although amendments would be required to the data inputs, wind turbine related emissions, and the presentation of ‘payback’ and carbon emission savings, the majority of methodologies for the peatland related calculations are relevant to any development on peatland. Whilst currently employed solely for wind farm developments, there is potential for the Carbon Calculator to be adapted to apply to grid infrastructure and other development types on peatland and carbon rich soils. There are no concerns on the Carbon Calculator’s ability to be used on projects of all sizes. However, to be applied to different infrastructure types, it is essential to consider their unique spatial characteristics, such as the shading effects and excess heat generated by solar farms. Further research and engagement are necessary to thoroughly understand how these factors impact peatland and carbon-rich soils before extending the Carbon Calculator to other development types.

Conclusion and recommendations

Conclusion

This report concludes that, based on the findings of a technical assessment, evidence review and quality control mechanisms, we recommend updating the Carbon Calculator in its current form to align with recent policy updates and advancements in science.

Our conclusions and recommendations set out how the Carbon Calculator could be updated through:

• Section 8.2: Addressing ‘big picture’ questions regarding the Carbon Calculator’s current remit to inform future decision making.
• Section 8.3: Making a series of updates to the current Carbon Calculator to bring it in line with scientific understanding and improve its accuracy.

Further areas of research due to evidence gaps identified during the literature review are summarised in Section 8.4.

Overarching considerations to inform future decision making

Key consideration: Does the calculator need to consider the lifecycle emissions of the wind farm, or could the focus be purely on the impact of development on peat? (Section 3.3.5)

Well-established methods and tools are available to undertake Whole Life Carbon Assessments (e.g. PAS2080). NPF4 Policy 2 (climate mitigation and adaptation) states that all proposals will be “be sited and designed to minimise lifecycle greenhouse gas emissions as far as possible.” Given this context, it is pertinent to question the necessity of the Carbon Calculator in replicating these existing approaches. Instead, it may be more beneficial to concentrate efforts on analysing the specific impacts of development on peatland/habitat carbon emissions. Key considerations include:

• Whether the lifecycle emissions of a wind farm need to be included in the Carbon Calculator?
• Could the calculations in the Carbon Calculator solely be focused on the impact of the development on peatland/habitat carbon emissions?
• Is the presentation of the current payback output necessary or appropriate for decision making?

Key consideration: Is the output of the Carbon Calculator useful as a decision-making tool? (Section 3.3.3)

Since the inception of the Carbon Calculator, scientific advancements have deepened our understanding of the interplay between nature and climate change. This progress is reflected in NPF4’s mitigation hierarchy and Policy 3b, which require substantial biodiversity improvements alongside restoration and offsetting requirements. In this context, it is important to acknowledge that carbon emissions sources should be segregated and reported separately to facilitate informed decision-making.

As the UK transitions to net zero, the current carbon payback’ approach (comparing development emissions to the counterfactual of electricity generated by fossil fuels) becomes less relevant. The focus should shift to evaluating the developments on the natural environment, specifically, whether it improves the environment and sequesters CO₂ effectively. This method is more insightful than balancing combined wind farm and peatland emissions against ‘carbon payback,’ which does not provide significant insights.

To better assess the carbon impact on peatland, the timeline for achieving ‘carbon payback’ or ‘carbon neutrality’ should consider land-based emissions. For example, ‘payback time’ could be defined as the period needed to restore peatland to a ‘near pristine’ condition from a reported baseline, compared to the site’s baseline emissions without development and counterfactual scenarios for non-peaty sites, considering Scotland’s widespread peatland restoration efforts (refer to Section 3.3.3 for more details).

Key consideration: Should the Carbon Calculator incorporate other land use types?

Considering the previous point, it’s important to consider whether the Carbon Calculator should be updated to account for various land use and habitat types. This would offer a more comprehensive view of the carbon impact on other land use types, as compared to the carbon impact on peatland. This aspect should be evaluated considering Scotland’s evolving Biodiversity Net Gain requirements, current PMPs, HMPs, and their anticipated updates.

Key consideration: The current quality control mechanisms are insufficient

The scope of this report was to identify the key updates or improvements which would bring the tool in line with current scientific understanding and improve the accuracy to better inform decision making. However, this report concludes that due to its complexity and skill sets required to review the data outputs, the Carbon Calculator is not currently used as a decision-making tool. Section 6 on Quality Controls provides more detail on the rationale behind this, and provides recommendations to improve the current approach, which should be considered ahead of updating the Carbon Calculator.

Key updates to bring the Carbon Calculator in line with scientific understanding and improve accuracy

Updates to the current Carbon Calculator

This report concludes that the current Carbon Calculator is no longer up to date following advancements in science, but it could be brought in line with scientific understanding and improved accuracy through the updates to the following:

3.2 Data inputs:

To improve data usability, explore options to integrate the Carbon Calculator and/or allowance for easy transfer from/to input variables that align with/can be obtained directly from other sources, i.e. Peatland Management Plan, Hydrological Assessment, HMP, and (in future) WLCA.

• 3.3 Payback time and CO₂ emissions:

Section 8.2 concludes that this area requires a significant update to accurately reflect a carbon ‘payback time’ in relation to land use emissions, and so updating the technical elements of its current calculation approach (Section 3.3.1) would not be appropriate.

• 3.4 Wind farm CO₂ emission savings, 3.5 Emissions due to turbine life and 3.6 Loss of carbon due to back up power generation:

Section 8.2 concludes that these areas of the Carbon Calculator are not required. Updating the respective technical elements of each where inaccuracies have been identified would not be appropriate.

• 3.7 Loss of carbon fixing potential of peatlands:

To improve both scientific accuracy and data usability the baseline condition of peatland should be incorporated into the Carbon Calculator, the inclusion of the Peatland Code’s calculation methodology may make this area of the Carbon Calculator redundant (Section 3.7.3).

• 3.8 Loss of soil CO₂:
• To significantly improve the scientific accuracy and data usability of this area:
• Incorporate minimum and maximum parameters into the Carbon Calculator for the carbon content of dry peat and dry soil bulk density variables (Section 3.8.5).
• Update the methodology for emissions rates from soils to reflect more recent literature and Scottish context (see Section 3.8.9 for more information).
• Account for emissions from drainage ditches (Section 3.8.10).
• Replace the use of averages with infrastructure specific inputs to replicate how peat is reported on in the PMP.

3.9 CO₂ loss by DOC and POC loss:

To improve scientific accuracy, align DOC and POC with the 2014 IPCC Wetland Supplement, replicating the Peatland Code’s calculation methodology (Section 3.9.3).

3.10 Loss of carbon due to forestry loss:

To improve both scientific accuracy and data usability:

• Replace the simple and detailed methodologies with one approach, informed by Woodland Carbon Code calculations (Section 3.10.5) and HRSD.
• Remove the option to affect the wind turbine’s capacity factor via the forestry inputs tab (Section 3.10.6).

3.11 Carbon saving due to improvement of peatland habitat:

To significantly improve scientific accuracy and data usability, Update the Carbon Calculator to replicate the Peatland Code’s principles (Section 3.11.3).

5. High Resolution Spatial Data (HRSD):

HRSD has the potential to improve and enhance the data usability of the Carbon Calculator and could support quality control mechanisms. Recommendations include:

• Consider options to integrate HRSD into the Carbon Calculator to enable an understanding of i) land cover types, providing proxies for ii) peat condition, and iii) water table depth, as well as the provision to understand the history of prospective sites to better inform peat condition, drainage variables, and subsequent monitoring activities. This could act as a quality control measure against inputted variables.
• Further engagement with JHI and other key stakeholders involved in HRSD within Scotland (i.e. Nature Scot, CivTech) is recommended to enable a joined-up and effective approach to the solution developed.

Further research

This review has identified the following evidence gaps that necessitate further research and/or engagement:

• Further research is required to understand the impacts of climate change on the carbon fixing potential of peatlands.
• Further research is required to understand whether the option to reuse peat elsewhere would be appropriate.
• Further research required into the link between peatland condition and bog plant fixing potential, or on updated fixation emission factor rates (if appropriate).
• Further research is required to identify a suitable ‘average extent of drainage.’
• Further research is required to provide more specific DOC and POC estimations.
• Further research is required to understand whether HRSD could inform the carbon content of dry peat and dry soil bulk density variables.
• Further research on the impact on peatland from the removal of trees (where located on peatland and other carbon rich soils).
• Further research is necessary to understand how the spatial variability of different development types could impact peatland and carbon-rich soils.

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Appendices

The following appendices open a download link to each of the spreadsheets.

Appendix 1 Technical assessment (opens spreadsheet)

Appendix 2 Sensitivity Analysis (opens spreadsheet)

Appendix 3 – High Resolution Spatial Data (HRSD) Assessment (opens spreadsheet)

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While every effort is made to ensure the information in this report is accurate, no legal responsibility is accepted for any errors, omissions or misleading statements. The views expressed represent those of the author(s), and do not necessarily represent those of the host institutions or funders.

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1. 
   

   Avoid – by removing the impact at the outset, Minimise – by reducing the impact, Restore – by repairing damaged habitats, Offset – by compensating for residual impact that remains, with preference to on-site over off-site measures. ↑

   
   

The Scottish Government’s Climate Change Plan Update sets out an ambition for the agriculture sector to reduce emissions by 31% from 2019 levels by 2032. It also sets a commitment to “work with the agriculture and science sectors regarding the feasibility and development of a SMART target for reducing Scotland’s emissions from nitrogen (N) fertiliser.”

The agricultural sector is dependent on N inputs, both organic and inorganic. The inefficient use of these inputs creates N wastage, impacting air and water quality and the climate.

The global nature of the issue provides an opportunity for Scottish agriculture to learn from other countries on how to improve Nitrogen Use Efficiency (NUE), i.e. taking action to reduce agricultural N losses while maintaining and supporting the sector in terms of income and yield.

This report explores the potential for setting a NUE target for agriculture in Scotland. It examines N flows found in Scottish agriculture as shown in the Scottish Nitrogen Balance Sheet (SNBS). It provided a clear analysis of the opportunities and barriers.

Conclusions

While this research identified opportunities for setting a NUE target for Scottish agriculture, more work is needed to fully understand the following elements:

• differential flows for each sector
• make appropriate changes to the SNBS
• ensure that the role of legumes in emissions reduction is fully integrated and
• carefully plan communication to achieve support from the farming sector.

A NUE target is not currently the most appropriate option for Scotland, the report concludes. This is partially due to the methodology in the current SNBS.

For further information on the findings, barriers and opportunities, please download the report.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Research completed March 2024

DOI: http://dx.doi.org/10.7488/era/5316

Executive summary

Background

The Scottish Government’s Climate Change Plan Update (CCPu) sets out an ambition for the agriculture sector to reduce emissions by 31% from 2019 levels by 2032, and a commitment to “work with the agriculture and science sectors regarding the feasibility and development of a SMART target for reducing Scotland’s emissions from nitrogen (N) fertiliser.”

The agricultural sector is dependent on N inputs, both organic and inorganic. The inefficient use of these inputs creates N wastage, impacting air and water quality and the climate. The global nature of the issue provides an opportunity for Scottish agriculture to learn from other countries on how to improve Nitrogen Use Efficiency (NUE), i.e. taking action to reduce agricultural N losses while maintaining and supporting the sector in terms of income and yield.

This report explores the potential for setting a NUE target for agriculture in Scotland. It examines N flows found in Scottish agriculture as shown in the Scottish Nitrogen Balance Sheet (SNBS), providing a clear analysis of the opportunities and barriers.

Key findings

Whilst there is theoretical potential for setting a NUE target for Scotland, there are practical obstacles that policy makers would need to overcome for the target to be implemented.

This research argues sector specific NUE values are not currently feasible due to the calculation set-up in the SNBS and the assumption that production will remain stable, with only inputs decreasing.

• We suggest that the SNBS calculations need refinement to attribute flows of N to the different measures and sectors. In the current version of the SNBS, the NUE calculations do not align directly with what happens in practice because there are overlaps and movements of N flows between the different agricultural sectors.
• These are not easily viewed in isolation and not necessarily attributed to the correct sector. For example, mitigation measures around manure management will, in practice, be mainly implemented by the livestock sector but will, in the current calculations, be attributed to the arable sector because they are linked to reduced emissions from spreading of organic matter to soils.

Opportunities

• The SNBS would offer an effective data source for setting and monitoring progress towards a single nationwide NUE target that covers all sectors.
• Many mitigation measures with known impacts on reducing N waste and improving N use are already in use in Scotland. Measures with the greatest potential improvement on NUE are
• nitrification inhibitors
• improving livestock nutrition, and
• improving livestock health.
• Note – that the improvement reflects implementing the relevant measure individually and does not consider any combination effects or interactions with other measures.
• The lowering of N-related emissions through reaching a NUE target will positively contribute to other emission reduction targets and the potential for an increase in farm business profitability.

Barriers

• Since a sector specific NUE target is currently not feasible, the remaining option is a single nationwide target.
• However, the arable, horticultural and livestock sectors would need to implement distinct mitigation measures, start from differing baselines, and will react inconsistently to implemented changes. This is partially due to the current limitations in the SNBS, but also due to the much lower baseline of current NUE values, setting a nationwide NUE target might cause the livestock sector to feel unfairly targeted.
• Some mitigation measures require significant capital expenditure to implement.
• The concept of NUE is complex and clear communication is required to ensure that targets and measures are clearly understandable and achievable to generate support from the farming sector.
• We examined different scenarios to model a potential target. The table below shows an achievable target and one that is more ambitious. The 2045 (Ambitious) scenario is based on transformational change across the sector.

• No country currently uses a standalone NUE target. Several countries have set N-related targets, some of which include information on NUE. Notably, the Colombo Declaration represents the first time that governments are collaborating on a global N management target on N waste.

Conclusions and recommendations

While this research identified opportunities for setting a NUE target for Scottish agriculture, more work is needed to fully understand the following elements:

• differential flows for each sector
• make appropriate changes to the SNBS
• ensure that the role of legumes in emissions reduction is fully integrated and
• carefully plan communication to achieve support from the farming sector.

A NUE target is not currently the most appropriate option for Scotland. This is partially due to the methodology in the current SNBS.

Recommendations

• Explore the potential for a more granular breakdown, and accurate representation of N flows in the SNBS. This may be difficult but would significantly help both monitoring and setting of a SMART NUE target.
• Creating a NUE target requires considering several criteria including mitigation measures, current uptake, applicability, expected future uptake, timescales, and sector breakdown. It is important to understand that other agricultural practices may impact N flows, as will changes in the size of agricultural sectors, and achieving these targets in practice will require supporting instruments to encourage the uptake of these measures. This research recommends that:
• N waste be considered as a target instead of a NUE target and that a SMART analysis is carried out to explore a N waste target further. Opportunities for setting a N waste reduction target include:
• It is an easier concept to communicate to the farming community.
• It values any N as a resource until it is lost as waste, creating options for greater collaboration between the arable, horticulture and livestock sectors. Any potential bias towards a sector will be avoided.
• A N waste target would achieve reductions in national NUE thereby achieving the same objectives without the current issues around NUE targets.
• Experience of the United Nations Environment Assembly and the Green Deal’s Farm to Fork targets has shown more potential in successfully reducing N pollution when focusing on reducing N waste over NUE targets as a policy option.
• If a decision is made to set a NUE target, the underlying assumptions should first be updated based on latest available evidence, for example using the updated Up to date Farm Census data. would strengthen any underlying assumptions and may directly influence the potential for the mitigation measures, particularly relating to slurry and manure management.
• The SNBS be improved by assigning distinct N flows to N waste and N re-use. A SMART target analysis for N waste will be beneficial to set a challenging and realistic target.

Glossary / Abbreviations table

Table 1: Glossary/ abbreviations table

Introduction

Nitrogen and its relevance to agriculture

Crops require nitrogen (N) to maximise growth. Most crops take up N from the soil in the form of nitrate (NO₃-). NO₃– in soils come from three major sources, the application of organic livestock manures, the application of inorganic N fertilisers and nitrogen fixing plants such as legumes. During harvest and through grazing, N is removed from the system. N can also be lost from soil through NO₃– leaching and through emissions of ammonia (NH₃) and nitrous oxide (N₂O). The N cycle (Figure 1) shows how different forms of N flow through the agricultural system.

Figure 1: The Nitrogen Cycle

An excess of N can both directly and indirectly lead to soil, water and air quality deterioration which is detrimental to human and ecosystem health (e.g., affecting respiratory systems and reducing oxygen in water). According to the IPCC AR5 Synthesis Report, N₂O has a global warming potential (GWP) 273 times that of carbon dioxide (CO₂) over a 100-year timescale. In Scotland N₂O is responsible for a quarter of the agriculture sector’s total GHG emissions.

More detail can be found in Appendix A on the process of leaching, the effects of eutrophication and how N₂O and NH₃ are emitted from agricultural sources and in Appendix B on the chemical processes of N conversion.

Nitrogen Use Efficiency

Nitrogen use efficiency (NUE) describes the ratio between total N input (e.g., fertiliser) and total N output (e.g., harvested product) expressed as a percentage (%)_. Figure 2 presents a visual example of NUE.

Figure 2. NUE diagram. Source: Udvardi et al., 2021

NUE gives an indication of the efficiency of crop utilisation of N. Generally, the higher the percentage NUE the better as this means less loss of N to air and water and indicates the crop is efficient in the uptake of N. However, pushing the ratio too high (for example over 90% in a cereal crop) can indicate ‘soil nutrient mining’ leaving not enough available N to maintain healthy crop growth and soil ecosystems (Sanchez, 2002). When NUE is too low (less than 50% in cereal crops), a large amount of N is likely being lost to the water and air. An ideal NUE would therefore be between 50% and 90%. NUE efficiency is also greatly impacted by climatic conditions, with changes in microbial activity in drought and frozen soils, along with increased risk of denitrification or leaching when soils are waterlogged.

NUE values are therefore both indicators of resource efficiency and markers for improvement. Key factors influencing NUE include crop type and rotation, soil pH and texture, climate, ammonia, leaching, biological utilisation of N and N management amongst others. As such, an absolute NUE reference value cannot be universally applied and will need to be understood and optimised for specific systems.

Nitrogen and NUE targets in other countries

Introduction

A Rapid Evidence Assessment (REA) seeking evidence relating to the setting and use of nitrogen and NUE targets was undertaken and identified peer-reviewed academic literature as well as government policies and websites. The review also identified grey literature sources such as farming and industry press reports. This search included, but was not limited to, targets for NUE, N emissions and N fertiliser use. The methodology can be found in Appendix C. The review focussed on identifying:

• Relevant scientific research on NUE target setting (4.2)
• Countries with N-related target/s, including types, values and timeframes (4.3)
• Relevance to Scottish agriculture, agricultural sectors and N flows (4.4).

Research on NUE target setting

This section includes information found through the REA on global NUE trends and relevant scientific research on the possibility of setting a NUE target including the necessary considerations (e.g., differences in farming sectors). 95 sources of literature were reviewed through the REA, 38 of which were from the UK, 32 from European countries and the remaining from other countries from around the world. Search strings used to gather this data can be found in Appendix C.

The NUE trend in the UK shows an increase from 1961 to 2014 (Lassaletta, L et al., 2014) which is likely a response to both regulation and market forces (for example the Nitrates Directive and changes in farm incomes). A full list of country-specific changes (%) in NUE values from 1961 to 2014 can be found in Appendix D. Following on from these observations, the research discussed below highlights the requirements and considerations for setting a NUE target.

Studies such as Quemada et al., 2020 collected farm-level data from 1240 farms across Europe and through statistical analysis, present NUE targets for different agricultural systems (e.g., 23% for a pig farm and 61% for an arable farm) which demonstrates the possibility of setting farm-level NUE targets. However, the study also highlights the importance of how differences in farming sectors will impact target setting.

A study conducted by Antille et al., 2021 states that there is no universal method for the calculation and reporting of NUE across all agricultural sectors. Furthermore, research projects which provide recommendations for NUE targets also suggest that such targets could be dependent on the agricultural system and its management, as well taking the ‘4R nutrient stewardship’ approach (right fertilizer type, right amount, right placement and right time) (Waqas et al., 2023). These approaches are country and region specific, dependent on climate, farmer knowledge, technological advancement and availability.

The EU Nitrogen Experts Panel (EUNEP) (initiated by an industry-based organisation ‘Fertilizers Europe’) recommends a maximum NUE of 90% (Duncombe, 2021), with an ‘ideal range’ of 50% to 90%. This range has been set to reflect that a NUE value below 50% is likely to result in N lost to the environment, while a value above 90% could result in soil N mining. Further detail is given in section 3.2. Whilst it is important to note that values will vary according to context (soil, climate, crop etc), the identification of this ‘ideal range’ by the EUNEP helps us to understand the opportunity and potential for setting a NUE target.

The research has highlighted that whilst it is possible to set NUE targets, there are a number of variables which impact upon setting a NUE target. These variables include the differences in farming sectors, differences in farming management, a lack of universal calculation and reporting of NUE, country / region specificity and climate.

N targets by country – types and policy context

There are currently no standalone country level NUE targets. Several countries, however, have set N targets through various means, some of which include information or actions on NUE. The review of approaches and literature can be summarised as having three main reasons/drivers for introducing N targets, these are all focused on responding to environment and climate impacts of N emissions:

• To lower GHG emissions
• To improve water quality
• To improve air quality

The underlying impact of N-related targets all seek to reduce N waste^[1], however, the two primary mechanisms differ in their points of measurement. Some targets are set to reduce N emissions whilst others are set to improved water or air quality. Table 2 gives an overview of existing initiatives across the world and their main N target with relation to agriculture. Many are relatively vague and reflect the difficulty in setting firm policy across regions or countries. No set value was found for the targets in table 2 that do not include a percentage or numeric change. These initiatives or legislation are described in further detail below.

Table 2. Overview of existing initiatives on N targets.

International action

The UN Environment Program (UNEP) previously considered ‘an aspirational goal for a 20% relative improvement in full-chain NUE by 2020’ (Sutton et al., 2014). However, Sutton et al., (2021) found that this could lead to an unfair distribution of effort whereby everyone had to increase their NUE by a relative amount. If this was the case a farm currently operating with high efficiency, e.g., 60% NUE, would have to increase by 12% to reach this 20% target. Whereas a farm operating with low efficiency e.g., 10% NUE, would have to increase by 2% to reach the same 20% target.

To overcome this unfair distribution, a target to halve N waste was seen as a more equitable approach as less waste means less action is needed. For example, to reduce N waste by 50%, a farm with higher N waste e.g., 100t N/yr would have to reduce by 50 t N/yr and a farm with less N waste e.g., 10 t N/yr would have to reduce by 5t N/yr. Therefore, the largest effort needed is placed on farms with higher N waste (low NUE) as opposed to farms already operating with high efficiency (high NUE).

Alongside the support from the UNEP and the technical support of the International Nitrogen Management System (INMS), the Colombo Declaration represents the first-time that governments are collaborating on an ambitious, quantitative, and global N management target by seeking to cut N waste by 50% across the world.

Outside Europe

New Zealand’s Climate Change Response (Zero Carbon) Amendment Act 2019 includes a target to reduce N₂O emissions to net zero by 2050. Canada (which has set a target to reduce fertiliser emissions by 30% by 2030) applies a region-specific approach due to the vast expanse of the country having variable meteorological conditions.

The European Union

The Nitrates Directive (1991) aims to protect water quality across Europe by preventing nitrate losses from agricultural sources through the promotion of good farming practices and includes limitations on N application from manures. Nitrate Vulnerable Zones (NVZs) are areas where the water bodies, such as lakes or rivers, are considered ‘at risk’ because there they have more than 50 mg/l of NO₃^– or are eutrophic. Farmers in these areas must comply with rules set out in the Member States’s action programmes to reduce the risk and the Managing Authorities need to report on NO₃^– concentrations in ground and surface waters. The Directive does not focus on N emissions other than NO₃^–. While the Nitrates Directive has driven a reduction in nutrient application over the last 30 years, targets have failed to improve NUE in many areas with reported high levels of N surplus (N remaining beyond plant and soil requirements) found in the Netherlands, Belgium, north-west Germany, Luxembourg and Brittany in France.

The National Emissions reduction Commitment (NEC) Directive (2016) is the current primary European regulation requiring actions to improve air quality and sets targets for reduction in the emissions of key air pollutants. This is important in an agricultural context due to the inclusion of setting reduction targets for NH₃. Target reductions are specific to each Member State and vary significantly with the target NH₃ reduction for 2030 ranging from 1% for Estonia and 32% for Hungary.

The European Green Deal (2019) is the EU’s holistic plan to achieve net zero GHG emissions across the EU, while improving biodiversity and human health. The Farm to Fork strategy (2020) includes targets to reduce the use of N fertilisers and losses of N to the environment to support improvements in air and water quality and to reduce emissions of GHGs. The strategy sets a target to reduce nutrient losses by at least 50%, while ensuring that there is no deterioration in soil fertility. The European Commission expect this to reduce the use of fertilisers by at least 20% by 2030.

Considering the European wide scope of the directives and strategies to reduce N pollution, our study findings were surprising in that examples of nationwide NUE targets are limited. Whilst no country has a standalone NUE target, some countries such as Ireland and Denmark have incorporated NUE as an ‘action’ as part of a programme or another target (e.g., GHG target).

The Danish example relates to the historic, 1980 ‘Good Agricultural Practice Program’ where increasing NUE was part of a suite of actions to reduce N use. This program was unsuccessful in limiting emission effects and as such ‘harmony rules’ were introduced, which, along with other measures, increased the Danish national NUE to an average of 40%. The Danish harmony rules prescribe the minimum area that a livestock farm must have for spreading livestock manure from their livestock production, thus limiting N inputs to land from livestock manure (Sommer and Knudsen., 2021).

Ireland’s Climate Action Plan 2021 put forward a suite of actions to deliver their GHG target that includes N. Action 359 details the implementation of ‘a suite of measures to improve NUE’. Teagasc, who is leading this action, sees that there is room for improvement across Irish dairy farms with an industry target of 35% NUE “set for farmers to achieve in the coming years” – an improvement of 10% from the current NUE of 25%.

Also in 2021, Denmark introduced the ‘Green transition of Danish agriculture’ which has set an agricultural target to reduce GHG emissions by 55-60% by 2030, including a reduction of N emissions by 10,800 tonnes by 2027. The specific impacts on the aquatic environment are further covered through their Action Plan on the Aquatic Environment III which has targets to reduce N leaching.

France, through the French Climate and Resilience Law 2021, have set targets for reduction of N₂O emissions by 15% of 2015 levels and NH₃ emissions by 13% of 2005 levels by 2030 (Hawley., 2022). This law includes measures to reduce the use of mineral N fertilisers.

The United Kingdom

In the UK, there are N relevant targets at both UK-wide and devolved levels. Nitrate vulnerable zones (NVZ), designated as part of the Nitrates Directive (1991), aim to reduce nitrate water pollution by encouraging good farming practice. Areas where the concentration of nitrate in water exceed 50 mg/l in ground and/or surface waters have been designated as NVZs. There are at least 70 NVZs in England and Wales, covering 55% of agricultural land in England and 2.3% of Wales. Five areas of Scotland (Lower Nithsdale, Lothian and Borders, Strathmore and Fife (including Finavon), Moray, Aberdeenshire / Banff and Buchan, and Stranraer Lowlands) have been designated as NVZs.

The National Emissions Ceilings Regulations (NECR) (2018) commits the UK to reduce NH₃ of 8% by 2020 and 16% by 2030, both relative to 2005 levels. The 2020 target was not met, but there has been a 12% reduction since 2005^[2]. The NECR also contains reduction targets for nitrogen oxides (NO_x), of 55% by 2020 (which was met) and 73% by 2030 but agriculture is a less important source.

Wales have set a target of reducing its total agriculture specific GHG emissions by 28% by 2030 compared to 1990. There are currently no UK-wide agriculture specific GHG emissions reduction targets, however, there is a UK-wide target of net zero by 2050, and agriculture will play an important role in achieving this target. For example, Defra has implemented new regulations on the use of urea fertilisers from 2023, which means that only urease-inhibitor treated or protected urea fertilisers may be used throughout the year, while untreated/unprotected urea fertilisers may only to be used from 15^th January to 31^st March each year. This regulation is expected to deliver an 11kt reduction in ammonia emissions by 2024/2025.

Why set a NUE target in Scotland?

It is important to consider the size and balance of the different Scottish agricultural sectors to understand the NUE potential of each sector. This section provides detail on the different forms of N found in Scottish agriculture, their impact on flows of N and how they can be targeted to improve NUE. A list of mitigation measures to improve NUE can be found in Appendix E and the impacts of these measures on NUE in Scotland are discussed in section 6.2.

The most recent Scottish GHG Statistics (2021) states that 2MtCO₂e of N₂O was emitted from the agricultural sector, which is a quarter of Scotland’s agriculture sector’s total GHG emissions and 2/3rds of total N₂O emissions. N₂O is emitted from soils after the application of N-fertilisers and manures (Brown, 2021). In addition, 90% of Scotland’s total NH₃ emissions are attributed to the agricultural sector. Tackling the emissions of these pollutants will directly contribute to the following Scottish Government policies and ambitions:

• The Nitrates Directive is the basis of Scotland’s five NVZs under the Nitrate Vulnerable Zones (Scotland) Regulations 2008^[3],
• the Scottish Government’s Biodiversity strategy to 2045: tackling the nature emergency, has the ambition of “restored and regenerated biodiversity across the country by 2045”,
• the Scottish Government’s Cleaner Air for Scotland 2 delivery plan
• the Pollution Prevention and Control (Scotland) Regulations 2012,
• target 7 of the Kunming-Montreal Global Biodiversity Framework to ‘reduce excess nutrients lost to the environment by at least half including through more efficient nutrient cycling and use’. The UK is a signatory to this framework and Scotland signed the associated Edinburgh Declaration,
• National GHG targets set by the Climate Change (Emissions Reduction Targets) (Scotland) Act 2019
• the CCPu sets out an ambition for the Scottish agriculture sector to reduce emissions by 31% from 2019 levels by 2032, and a commitment to “work with the agriculture and science sectors regarding the feasibility and development of a SMART target for reducing Scotland’s emissions from nitrogen (N) fertiliser.”

Understanding N flows in Scotland

In recognition of the potential for reducing N to reduce total GHG emissions, the Climate Change (Emissions Reduction Targets) (Scotland) Act 2019 set requirements for Scottish Ministers to create a Scottish Nitrogen Balance Sheet (SNBS) from 2022 (Figure 3). The N flows in the SNBS combine data across all sectors of the economy and environment forming an evidence base to support the optimal use of N across all economic sectors to achieve optimal economic and environmental outcomes. While the SNBS was published in 2022, the data within it relates to 2019. Scotland is currently the only country to have planned to regularly update a cross-economy and cross-environment N balance sheet.

Figure 3. Scottish Nitrogen Balance Sheet (baseline data (mainly 2019)). Source: 3. Results from the initial version of the Scottish Nitrogen Balance Sheet – Establishing a Scottish Nitrogen Balance Sheet – gov.scot (www.gov.scot)

The annual SNBS report to the Scottish Parliament presents an assessment of:

• progress towards implementing proposals and policies relevant to improving NUE in Scotland,
• any future opportunities for improving NUE in Scotland, and
• how NUE is expected to contribute to the achievement of future emissions reduction targets (as per section 98 of the Climate Change (Emissions Reduction Targets) (Scotland) Act 2019)

In 2022, the SNBS report published NUE values for agriculture as a whole sector (27%) with more granular figures of 65% for crop production NUE and 10% for livestock feed conversion. This valuable baseline shows NUE’s potential for improvement which can reduce emissions from all forms of N to support improvements in air and water quality with positive implications to both human (Pozzer et al., 2017) and biodiversity health (Houlton et al., 2019). While the SNBS is a valuable baseline for improving N management it is important to note the specificities of its set-up particularly on how different quantities of N are attributed to different sectors and how this relates to what happens in practice (more detail on this can be found in Section 6.5).

Research has found that the global arable NUE is 35%. When we do not consider all the variables which impact NUE and NUE target setting, as discussed in sections 3.2 and 4.2, the Scottish arable NUE of 65% appears to compare well to international data, however, some EU countries have arable NUEs of up to 77%, showing there may be room for improvement. The 2022 SNBS report states total N losses from agriculture to the environment amount to 30.2 kt N/yr as air pollutants (NH_3, nitrogen dioxide (NO₂) and N₂O) and 104 kt N/yr from runoff and leaching from agricultural soils.

Targeting different forms of N

Figure 4 Scottish agricultural sectors (Scottish Agricultural Census June 2021)

The different N inputs and outputs of Scottish agriculture are described below (also see Figure 3). Most of Scotland’s 5.64 million ha of agricultural area is best suited to livestock farming with a significant proportion occupied by cattle and sheep in Less Favoured Areas (LFAs) (55% or 3,159,137 ha) followed by crops and grass (1,885,701 ha), shown in Figure 4. Non-LFA cattle and sheep (107,712 ha) and specialist dairy (106,935 ha) are large sources of N in manure. More intensive sectors such as pigs and poultry do not have a direct correlation between NUE and land area, however they are significant sources of manures and contribute to N inputs. These areas are used to track N flows from the SNBS against sectors of particular potential in section 4.4.2. Note that forestry and aquaculture are out of scope of this project but will have impacts on Scottish N flows.

NUE varies between different Scottish farm types as the biological utilisation of N influences the potential NUE. The SNBS shows that livestock farms currently have a lower NUE (10%) than arable farms (65%). This is partly due to the relative inefficiency in the conversion of ingested N in feed converting to stable N within livestock products (milk and meat).

N Inputs

Fertiliser as the N input

The SNBS details that one of the largest flows of N in Scotland (143.8 kt N/y) is the use of inorganic fertiliser on arable crops and grass, with 62.1kt of this inorganic N applied to crops per year and 81.7kt going to grass ^[4]. The British Survey of Fertiliser practice states that in 2022, 63 kg N/ha were applied on average to all crops and grass in Scotland.

There is little information on N use in Scottish horticulture and permanent crops. Nonetheless, N fertiliser recommendations for vegetables, minority arable crops, bulbs, soft fruit and rhubarb crops exist. The high value of many of these crops and the technological advances taking place in this sector facilitate a higher degree of precision in management (e.g., GPS use for N application, leaf N monitoring, fertiliser application within irrigation water etc), which allows a better understanding of N flows in these systems. Targeted N applications could lead to reductions in inputs and waste thereby improving overall NUE for these crops. However, to date there are no recommended NUE levels for these specialist crops, thus more research is needed to understand the impact of reduced N applications on crop health and yield.

The evidence relating to the N requirements for the majority of crop and grass areas in Scotland is well described within the technical notes, and recommendations for NUE targets could build upon the evidence supporting these recommendations. Like specialist crops, improvements in fertiliser practices and technology can support improvements in N applications which will help matching of N inputs to crop requirements with greater precision and thus improves NUE.

Livestock Feed intake as the N input

The optimum levels for dietary crude protein are often exceeded to ensure that N intake does not limit either growth or welfare. This excess of N supply in the diet results in surplus N being excreted through manure and urine leading to N losses. Cattle cannot efficiently convert dietary N (efficiency ranging between 22-33%) and therefore, on average, 75% of consumed N is wasted, mainly through excretion. Matching N supply in feed with livestock requirements is part of ‘precision livestock feeding’ which can increase farm profitability, reduce emission intensity of methane (Rooke et al., 2016) and reduce N intake and excretion. Reductions to NH₃ and N₂O emissions from livestock sources due to precision feeding vary widely. However, studies have found that a reduction in crude protein of 2% leads to a 24% reduction in NH₃ emissions in broilers, and a 1% crude protein reduction in pig feed results in a 10% reduction in NH₃ emissions (Santonja, 2017).

The SNBS found one of the largest N flows is N excreted by livestock (142.9 kt N/y). The control of N levels added to soil from livestock directly impacts the input part of the livestock NUE calculation. A NUE target aimed at the livestock sector may be most impactful as it currently has the lowest NUE (10%) whilst also covering the largest amount of agricultural land (combined total of 3.3 million ha) meaning even a small, targeted improvement in NUE for livestock could have a significant impact on the overall N budget.

N Outputs

Ammonia as the output

NH₃ from agricultural sources produces particulate matter which can impact human health, causing diseases such as cardiovascular and respiratory disease. In addition, NH₃ emissions can result in the long-range transport of N compounds and this N deposition can cause acidification and eutrophication. Scottish agriculture accounts for 90% of total NH₃ emissions, which have decreased by 12% over the last 30 years. NH₃ is tied specifically to the (housed) livestock sector, with most emissions (35% of NH₃ emissions) coming from cattle manure management. Livestock housing and storage of manure is responsible for 10.5kt N/y in the form of NH₃ emissions, therefore improvements targeted at this sector would directly improve NUE. Examples of mitigation measures which can be introduced to lower the NH₃ emissions in this sector are detailed in Table 3 under section 6.2.1 and include slurry store covers and slurry acidification.

Use of urea based inorganic fertilisers can lead to significant losses of NH₃. High temperatures and winds at the time of fertiliser application or very dry conditions can lead to high levels of NH₃ volatilisation (the conversion of NH₄+ to NH₃ gas) with a significant proportion of the N being lost and unavailable to the plants. A useful mitigation measure is the use of urease inhibitors with urea fertilisers to reduce these emissions.

Nitrate leaching as the output

Excessive leaching of N from agricultural activity can lead to water pollution and eutrophication which can then result in the loss of aquatic biodiversity and GHG emissions. The SNBS shows N run-off and leaching from crops and arable land as 45.5 kt N/yr and from grass as 58.5 kt N/yr. This N is lost as NO₃^–, which is readily mobile in soil water or runoff. Any N that is lost from the soil is no longer available to plants thereby lowering the potential NUE and increasing agricultural pollution.

According to Adaptation Scotland, Scotland is predicted to experience an increase in rainfall, with intense, heavy rainfall events increasing in both winter and summer. This has the potential to increase N leaching as soil moisture controls both crop N uptake and N leaching (McKay Fletcher et al., 2022). In addition, Scotland’s topography affects the rate of run-off as steep slopes promote surface run-off. When considering Scotland’s topography and the predicted change in rainfall, the potential for leaching will increase and continue to negatively affect water quality. Those areas currently most at risk are classified as NVZs.

Nitrous oxide emissions as the output

N₂O is a GHG that accumulates in the atmosphere and directly contributes to climate change. The SNBS shows 5.9kt N₂O per year is emitted from the agriculture sector. This includes 0.9kt from livestock (including manure management), 3.8kt from soil management (including mineral fertiliser use), and 1.2kt of indirect emissions (from N deposition and NO₃^– leaching). N₂O is produced in the process of denitrification, where denitrifying bacteria under conditions where oxygen is limited (for example waterlogged soils) use the NO₃^– available in soil. By using the NO₃^–in soil, these bacteria reduce the NO₃^– available by plants potentially negatively impacting yield. In conditions where NO₃^– is available in excess denitrification can reduce NO₃^– losses through leaching. However, since N₂O is produced in the process, negative impacts on climate are the result. Total elimination of N₂O emissions from agriculture is not possible; however, some mitigation is possible through improvements in soil conditions and avoidance of N fertiliser application under wet conditions (Munch and Velthof, 2007).

Crop and livestock outputs

Crop and livestock products are the useful outputs of N from agriculture. In Scotland these account for 54.5kt N per year. This value includes livestock products, including meat, milk, eggs, and wool, and harvested crops used for food for human consumption (but excludes crops for animal feed or fodder). Useful crop outputs also include seed, feed and straw, but these are retained in the agricultural system and so are not final outputs.

Cereals, explicitly for alcohol production, accounts for the largest useful output flow in Scotland at 20.5kt N, followed by livestock products at 19.6kt N and crop product for human consumption at 12.2kt N (all values per year).

Optimising the quantity of N recovered in these outputs i.e. the N is taken up by the plant or animal and used to increase growth, relative to the quantity of inputs (feed and fertiliser) is key to reducing N waste and improving NUE. Managing the quantity of N application to meet crop and livestock requirements alongside the soil conditions will improve the overall NUE.

Viability of a SMART Target for NUE in Scotland

This section looks at the viability of setting a NUE target for Scotland and provides a summary of the risks and benefits of setting a Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART) NUE target in Scotland and presents how a range of influences can support or hinder the achievement of a NUE target. Information on N targets in other countries was considered and analysed for their applicability to Scotland. Since no other country has a standalone NUE target, we had to rely on information on other N targets for our analysis and transfer these finding to a NUE target for Scotland. The methodology can be found in Appendix F.

Analysis Tools

SWOT analysis

Strengths, weaknesses, opportunities, and threats (SWOT) of setting N-related targets were analysed based on the information gathered on N targets in other countries. We also included analysis of GHG and climate related targets where relevant to increase the body of information. This information was then used to assess applicability of setting a NUE target for Scottish agriculture with the limitation that the analysis was based on N, GHG and climate related, rather than NUE specific targets. The SWOT analysis shows a range of influences which can support or hinder the achievement of a NUE target. The full SWOT analysis can be found in Appendix F.

PESTLE analysis

Setting NUE and other N targets are subject to a range of enablers and barriers. Therefore, a political, economic, social, technical, legal, and environmental (PESTLE) analysis was undertaken to assess the feasibility of setting a NUE target for Scottish agriculture, again, with the limitation that the analysis was based on N, GHG and climate related rather than NUE specific targets. The PESTLE assessment took place following the SWOT analysis to ensure the findings from the SWOT were assessed and, if relevant, included into the PESTLE categories. The full PESTLE analysis can be found in Appendix F.

Discussion

Supporting a SMART NUE target

The SNBS is reviewed and updated annually and provides a source of data for measuring and monitoring the changes in NUE and thus the progression of a NUE target. In addition, all mitigation measures identified in section 6.2 and analysed for their effect on Scottish agriculture NUE are captured by the SNBS. The use of the SNBS enables a measurable target. This was identified as a strength and technical enabler in the analysis of setting a NUE target.

Another strength and technical enabler identified through the analysis includes the mitigation measures required to achieve a NUE target. N-related mitigation measures are well understood, and many are relatively low cost and already practiced in Scottish agriculture (e.g., use of catch and cover crops) which makes reduction in N losses achievable. Furthermore, measures continue to be developed through additional research e.g. in Canada to understand the emission reduction potential, costs and benefits of different measures at farm level.

Section 6.4 recommends years 2030, 2040 and 2045 as deadlines which would ensure a NUE target is time-bound. These years align with other emission targets set in Scottish Government which may affect agriculture and therefore complement a new, potential NUE target. Including three timed steps into a binding target would also help measure the progression of the NUE target whilst also encouraging the delivery of high reductions.

A NUE target would be relevant in meeting statutory emission reduction targets. Introducing a NUE target would lower N-related emissions and would therefore contribute to other emissions reduction targets, for example the CCPu which aims to reduce agricultural GHG emissions by 31% from 2019 levels by 2032. Similarly, a NUE target would be relevant to several other environmental issues as the implementation and success of a NUE target would have multiple benefits for example, improvements to water quality, air quality (Sutton et al., 2014), human health and biodiversity (Houlton et al., 2019).

The SWOT and PESTLE analysis identified influences needed to support a specific and achievable NUE target by detailing opportunities which could assist with the implementation of such a target. Regulatory instruments include BAT/mitigation measures and fertiliser use limits, economic instruments include taxes and subsidies, and communicative instruments include extension services and awareness (Oenema et al., 2011).

Other positive influences include an increase in farm profitability following the implementation of mitigation measures such as precision livestock feeding and matching N supply to demand) which was found as a strength and economic enabler through the analysis. Moreover, through the introduction of a NUE target, there would be an opportunity to involve advisors and consultants which may also lead to the implementation of better advice and practice regarding N use in Scottish agriculture.

Hindering a SMART NUE target

All analysis was based on N targets rather than NUE targets due to the lack of any NUE specific targets in other countries. Therefore, clear evidence on NUE targets is lacking and the analysis of a NUE target for Scotland is based on assumptions through transferring information from N-related targets to NUE.

To achieve any potential NUE targets a range of new techniques, technologies and systems would be required. These are referred to as mitigation measures. There is already a good body of evidence and supporting examples of the implementation of mitigations. These have been identified as a strength and enabler as some examples such as variable rate N application (precision farming) can save farmers money on inputs by only purchasing and applying N as needed. Others, however, require significant capital expenditure with upfront investment of time and money required to implement some of the mitigation measures (for example, low emission slurry application equipment). This has also been identified as a weakness and economic barrier which may be experienced by Scottish farmers. This could directly impact upon the achievability of a NUE target. Similarly, several barriers to uptake of mitigation measures were identified as a threat through the SWOT analysis. Barriers include lack of awareness and knowledge of why and how to improve N use, and farmer’s personal beliefs, both of which may lead to Scottish farm managers finding it difficult to quantify the benefits to their business and understand the relevance of a NUE target. These barriers would generally hinder the achievability of a NUE target.

In trying to make a NUE target relevant in terms of meeting statutory emission reduction targets, there is a risk when reducing N-related emissions, through mitigation measures, that pollution-swapping takes place. An example of this is the decrease in NH₃ emissions and an increase in N₂O emissions (due to nitrification/denitrification processes) when using slurry injection (a type of low emission slurry application) compared to surface application. Pollution-swapping as an unintended consequence of some mitigation measures was identified as a threat and environmental barrier in introducing a NUE target.

Farmers’ perception of a national NUE target for Scotland may limit target achievability. Scottish farmers may not understand how their practices impact NUE and how introducing on-farm mitigation measures may impact on a general NUE target for Scottish agriculture. For example, questions may arise on how many and at what frequency the relevant mitigation measures need to be introduced by each farmer to achieve this overarching target. To overcome this, some farmers may respond more positively to several more specific targets, for example a reduction of fertiliser input (by a certain amount and by a certain date). Alternatively, ensuring a NUE target is accompanied with very specific and relevant action points on how this NUE target would be achieved so that farmers have a clear understanding on what is expected of them and their farming system to contribute to a national NUE target.

The time taken to create and process the appropriate legislation for a NUE target can be uncertain and longwinded. This process has the potential to directly impact the time-bound element of a SMART NUE target.

In the Netherlands, an ambitious target led to civil unrest where more than 10,000 Dutch farmers have been protesting following government plans to reduce N emissions. Similarly, when targets or limits are seen to be a barrier to economic performance, implementation of new regulation can become challenging, as is seen in the case of revising the approach towards Nutrient Neutrality in England. The use of a SMART target is therefore critical to avoid the implementation of a policy which is neither appropriate nor achievable.

In the main, these examples relate to current exceedances of regulations under the Habitats or Nitrates directives, follow a long period of previous actions and constraints on the farming sector and relate to farming systems which are very different to those present within Scotland. In addition, these regulations are not focused on NUE but rather on the achievement of environmental targets and so do not consider the productive potential of the sector. Notwithstanding these differences, these risks do indicate the importance of well formulated targets, based on sound scientific understanding and with a clear plan for consultation and implementation on their achievement and delivery.

The political and legal barriers identified include the potential for pushback on mitigation measures which are seen to reduce productive output and a concern that Scottish farmers may not comply with regulatory requirements. This could directly impact upon the achievability of a NUE target.

Development of a NUE target for Scotland

Assessment using the Scottish Nitrogen Balance Sheet

The SNBS has been used as a baseline to assess how practices that influence N pools or flows may impact the agricultural NUE value. This dataset contains values for key sectors, pools (stores of N within parts of the N cycle e.g. in manure, in soils or in livestock/crops), and flows of N (movement of N into different pools as the N form changes or is taken up by plant or animals). These flows include inputs to the system (e.g. fertilisers, animal feed), useful outputs (e.g. meat, cereals), and waste (e.g. NO₃^– leaching, NH₃ emissions). Each of these flows have a value in kt N/yr assigned. The NUE is improved by either increasing the output flow values or reducing input and waste flow values. This can be modelled by estimating the impact of a mitigation measure (e.g. improved nutrient planning or reduced protein livestock feed) and applying these values to the relevant N flow in the SNBS (for improved nutrient planning this would be reduced inputs of fertiliser and reduced N emissions to atmosphere). This produces estimates for N flows that can then be summarised in NUE calculations as currently setup in the SNBS, resulting in estimates of improved NUE values (see Appendix E for a detailed methodology and all assumptions).

Mitigation measures

The effect of mitigation measures on Scottish agriculture’s NUE

This section presents and discusses the effects of 18 different mitigation measures on the current NUE of Scottish agriculture.

The table below presents modelled estimates for the NUE of Scottish agriculture, by individual measure and at each future projected target year. The values reflect implementing the relevant measure individually and compared to the current whole-agriculture NUE of 27.2% (i.e. preventing soil compaction may improve total NUE by 0.1% by 2030). The results show the impact for the relevant measure in isolation and do not reflect any combination effects for interactions with other measures. Further detail on the assumptions and methodology can be found in Appendix E. The 2030, 2040 and 2045 scenarios are based on minimal change, continuing recent trends of recent changes in uptake, but including greater increases where there is precedent to, e.g. low emissions spreading techniques all increasing to 95% by 2030 as this will be required under the New General Binding Rules on Silage and Slurry. However, the 2045 Ambitious scenario is based on a transformational change across the sector where there is greater effort to improve NUE to meet a legally binding target. Therefore, the improved NUE in the 2045 and 2045 (Ambitious) scenarios may be viewed as the range where a target may be set, where the lower bound of the range (2045 scenario) is more achievable, while the higher bound (2045 (Ambitious) scenario) would require more effort across stakeholders to be achieved but is a better value.

Table 3 List of mitigation measures and their effect on Scottish agriculture NUE (%) compared to the current whole agriculture NUE of 27.2%. The two 2045 scenarios can be viewed as an ideal range for NUE; where the lower bound (2045 scenario) reflects changes to agriculture planned to come in (current and upcoming legislation, expert judgement on technological developments etc.); while the upper bound (2045 ambitious scenario) reflects the possibility for a greater push from industry and government to improve NUE (financial incentives, increased awareness of N management, etc.).

There are potential interactions/overlaps between several of these measures. Where this occurs, measures cannot be applied on the same unit (area of land/head of livestock) at the same time as they are mutually exclusive. We have avoided double counting these effects by resolving the total maximum applicability across overlapping measures i.e. the combination of measures cannot exceed the total land available to apply the measure to.

The key outcomes are:

• The measures with the greatest potential improvement on NUE are nitrification inhibitors, improving livestock nutrition, and improving livestock health.
• Nitrification inhibitors are more effective at improving NUE than urease inhibitors as they can be applied to a greater proportion of fertiliser products used in Scotland (both NO₃^– and urea-based products, while urease inhibitor can only be applied to urea-based products).
• Improving livestock nutrition will improve NUE by reducing the overall quantity of N being fed to livestock while maintaining liveweight yield.

Measures that are based on the use of legume crops were not included in the modelling of the new NUE values, as the reduced requirement for inorganic fertiliser input will be offset by increased biological fixation of N from the atmosphere. Both flows are included in the N input values when calculating NUE in the SNBS. Therefore, the total N inputs levels will stay constant, as will the outputs, and so there is no impact on NUE. However, there are benefits of legume crops beyond an improvement to NUE, which should be considered, namely the effects of reduced requirement for inorganic fertiliser inputs (lower GHG emissions), improved soil health and soil function, and reduced costs. This is likely to be economically beneficial to the farmer, as soil health benefits the local ecosystem and improves resilience, reduced fertiliser use avoids emissions from manufacture and transportation of inorganic fertiliser; all of which are benefits from moving to a circular economy.

Potential N savings through implementation

The table below summarises the potential savings of N inputs of mineral fertiliser in both absolute values in kt N yr^-1, and relative to the quantity in the current SNBS as a %. The values presented here include fertiliser use savings due to legume-based measures (legume-grass mixtures, and legumes in crop rotations). The effect of these measures is not included in calculations of NUE due to the assumption that the saved fertiliser N application will be replaced by increased N deposition from the atmosphere.

Table 4 Absolute values of N inputs of mineral fertiliser saved in kt N per year and as % of the quantity in the current SNBS when all modelled measures are included. The two 2045 scenarios can be viewed as an ideal range for NUE; where the lower bound (2045 scenario) reflects changes to agriculture planned to come in (current and upcoming legislation, expert judgement on technological developments etc.); while the upper bound (2045 ambitious scenario) reflects the possibility for a greater push from industry and government to improve NUE (financial incentives, increased awareness of N management, etc.).

Recommended criteria for target(s) setting for Scotland

When modelling the NUE improvements and the establishment of potential targets, the key criteria for consideration are listed below.

Mitigation measures

The measures/farming practices that have been included for modelling are the result of literature searches and expert judgement. Measures that impact N flows in agricultural systems, and the relevant data, were extracted from literature. These were then reviewed to ensure applicability to Scotland, and any other measures that were identified by experts as being important were also researched.

Current uptake

The current uptake provides a basis from which to estimate what future uptake may be possible and the likely rate of additional implementation. It also supports the calculation of a baseline or counterfactual against which change can be measured. These values come from the same sources which have provided the NUE impact values (see Appendix E for detail on current uptake for each measure).

Applicability

The applicability values refer to the portion of a SNBS N flow that a measure’s impact value can apply to. Expected future uptake

The expected future uptake values are estimates based on expert judgment and consultation within the project team. The values for each measure can be found in Appendix E and are additional to the current uptake levels. These values increase over time to reflect increasing commitment to NUE improvements. The two 2045 scenarios can be viewed as an ideal range for NUE; where the lower bound (2045 scenario) reflects changes to agriculture planned to come in (current and upcoming legislation, expert judgement on technological developments etc.); while the upper bound (2045 ambitious scenario) reflects the possibility for a greater push from industry and government to improve NUE (financial incentives, increased awareness of N management, etc.). The expected future uptake ranges from 1% to 100% depending on the measure and scenario. For example, soil compaction was only expected to increase by 2% even in the 2045 (Ambitious) scenario as it was assumed that where soil compaction is occurring most farmers will already be taking steps to improve it. While low emission spreading techniques increased to 95% by 2030 to reflect the New General Binding Rules on Silage and Slurry. A full example is provided in Appendix G.

Timescales

We modelled potential NUE targets for Scottish agriculture for 2030, 2040, and 2045. These were chosen to align with Scotland’s Climate Change Act 2019 with a target date of 2045 for reaching net zero GHG emissions.

One NUE target for Scottish agriculture or per sector?

Currently, the arable sector is more N efficient than the livestock sector (65% and 10% respectively). This difference is due to inherent qualities of livestock systems with animals unable to process N as protein as efficiently as plants uptake N. The current NUE should, however, be seen as a baseline, and the scale of improvements from this should be the focus rather than an absolute target applicable to all sectors and systems. The majority of measures included in the modelling of NUE improvements target the soil N pools (arable and grass land), therefore separate targets for each sector are advisable.

Analysis of recommendations

The table below presents the estimated NUE values in 2030, 2040, and 2045 based on increased uptake of on-farm measures. As well as an additional value for the year 2045 where increased ambition has been included in the projected uptake values.

Table 5. Potentially achievable NUE estimates in 2030, 2040 and 2054 based on increased uptake of on-farm measures. The two 2045 scenarios can be viewed as an ideal range for NUE; where the lower bound (2045 scenario) reflects changes to agriculture planned to come in (current and upcoming legislation, expert judgement on technological developments etc.); while the upper bound (2045 ambitious scenario) reflects the possibility for a greater push from industry and government to improve NUE (financial incentives, increased awareness of N management, etc.).

The NUE values that are modelled in this study are based on the selected measures, and the achievement of these NUE targets rely on their implementation. Other agricultural practices may impact N flows, as will changes in the size of agricultural sectors.

Similarly, the NUE values that have been calculated are based on the levels of implementation that have been included in the modelling. Achieving these targets in practice will require supporting instruments to encourage the uptake of these measures. As stated in Section 6.2.1, the NUE values in the above table for 2030 and 2040 reflect assumptions on uptake based on minimal change and not a transformational change to the sector (such as the setting of a target). Therefore, these values should not be viewed as potential targets for these years, but as indicators of the feasibility of improvements to NUE in Scottish agriculture.

Sector specific NUE values are not currently feasible due to the calculation set-up in the current SNBS (which flows are considered as inputs/outputs for arable and livestock), and the assumption made in the modelling that production will not increase and only inputs will decrease. This set-up leads to results that make it seem that the arable sector is mining N, which is not the case. Improvements to the set-up of calculations to overcome this barrier are outlined in Section 6.5 below.

Guidance for future implementation

In the current version of the SNBS, the NUE calculations do not align directly with what happens in practice in the different agricultural sectors because there are overlaps and movements of N flows between the different agricultural sectors that are not easily viewed in isolation. For example, in practice, improvements to NUE due to implementation of manure management measures will largely be implemented by the livestock sector. However, given the current set-up of the calculations in the SNBS, N flows related to manure management may not be attributed to the livestock sector NUE values as they will reduce emissions from spreading of organic matter to soils, which would be reported in the arable sector calculation. This would make it more difficult to use the SNBS to set and measure sectoral targets. Therefore, accurately monitoring the changes in NUE and attributing these changes to the correct sector would be important if considering sectoral targets. Accurately representing N flows in the SNBS to the relevant sector may be difficult, due to, for example, data availability, different ways data is collected across mitigation measures and sectors and difficulties in correctly separating overlaps and movements of N flows between the different agricultural sectors, however, could significantly help the feasibility of achieving and monitoring NUE targets.

When reflecting the potential impacts of mitigation measures on the values in the SNBS, certain hurdles resulting from the disaggregation of flows make it more difficult and possibly less accurate. More details of these hurdles, and how they were overcome, can be found in Appendix E, but a key example here is the use of slurry acidification on livestock slurry. In the SNBS there is one flow of N from manure management to atmosphere which includes all manure storage types and all livestock types. However, the implementation potential and mitigation impact potential will vary between storage and livestock types. This required an assumption to be made on the breakdown of this manure management N flow so that the appropriate uptake levels and impact values can be applied to the correct portion of the total N value (in this instance the Scottish Agricultural Census was used). This can be considered a sound approach to reflect the mitigation measures in the current SNBS, however going forward, to improve the ease and accuracy with which targets can be projected and improvements can be measured, a more granular breakdown on the N flows in the agricultural sector in the SNBS are required.

Conclusions

A NUE target for Scotland

The rationale behind setting a NUE target for Scotland is to reduce the impacts of N wastages to the environment to lower GHG emissions and improve water and air quality. NUE values can be used as indicators for N resource use efficiency and as markers for improvement. Scotland is in the unique position to use and regularly update a cross-economy and cross-environment N Balance Sheet (SNBS). The SNBS provides a valuable baseline in the current performance of Scottish agriculture and provides a tool to tackle all forms of N pollution.

However, setting a NUE target is not without challenges and nowhere in the world has yet set a NUE target. NUE values are impacted by various factors (soil type, climate, crop type, livestock type, etc). Whilst research shows that the ideal range for NUE is between 50-90%, it is crucial to understand the different forms of N inputs and outputs and to allocate these correctly to the different farming sectors.

As no other country has yet set a standalone NUE target, we had to solely rely on other N-related targets for our evidence base. Our analysis of the viability of setting a NUE target for Scotland is therefore based on assumptions through transferring information from N-related targets to NUE.

The SWOT and PESTLE analysis carried out in this study highlighted several factors which can influence the success of a SMART NUE target for Scottish agriculture. Importantly, the use of the SNBS would make the target measurable and the fact that many N-related mitigation measures are well understood and already practiced in Scottish agriculture would make the target achievable. However, some mitigation measures require significant capital expenditure, such as slurry management equipment, or increased ongoing investment, such as nitrification inhibitors, or a change in focus, such as better-balanced protein in livestock feed. These changes would need support from the farming sector. Using NVZ regulations as an example, a small study conducted in 2016 (Macgregor and Warren 2016) showed that some farmers regarded the NVZ regulations as “burdensome and costly”. To avoid similar responses to setting NUE targets, farmers would need to be able to quantify the benefits to their business and understand the relevance of a NUE target for climate and the environment. It is therefore important to accompany NUE targets with specific actions points expected by farming businesses. Providing funding to farmers to help implement mitigation measures and share knowledge on the impact to their businesses, the climate, water quality, air quality and biodiversity is likely to aid faster and easier uptake of these measures.

Looking at initiatives worldwide, we know that N use can be targeted in many different forms (fertiliser use, livestock diet, reduction of N waste, reduction of emission of air pollutants, etc.) and alongside the proven mitigation measures discussed above, it is clear that improvements to NUE are achievable.

Modelling NUE improvements using the SNBS

In this study, the SNBS has been used to model NUE improvements by estimating the impact of a mitigation measure and applying these values to the relevant N flow in the SNBS. It is important to note that these results show the impact for the relevant measure in isolation so do not reflect any combination effects for interactions with other measures. In the arable sector, the mitigation measures with the greatest potential to improve NUE are the use of variable rate N application (precision farming) and the use of nitrification inhibitors potentially increasing NUE to 28.8% and 29.7%, respectively, by 2045. In the livestock sector, improving nutrition and improving livestock health (NUE of 31.7% and 29.4% respectively by 2045) have the greatest potential. Overall, the modelling suggests that total NUE of Scottish agriculture could be increased to 38.2-40.9% by 2045, depending on the level of implementation of mitigation measures.

Sector specific NUE values are not presently feasible due to the calculation set-up in the SNBS and the assumptions that production will remain stable, with only inputs decreasing. In the current version of the SNBS, the NUE calculations do not align directly with what happens in practice in the different agricultural sectors because there are overlaps and movements of N flows between the different agricultural sectors that are not easily viewed in isolation and not necessarily attributed to the correct sector. For example, mitigation measures around manure management will, in practice, be mainly implemented by the livestock sector but will, in the current calculations, be attributed to the arable sector because they are linked to reduced emissions from spreading of organic matter to soils.

The feasibility of a NUE target for Scotland

This research indicates that a NUE target for Scotland is not currently feasible. We see potential for such a target in the future but recommend to first consider several points for improvement.

• The SNBS. Improvements to the calculations and attributions of flows of N to the different measures and sectors are required. The modelling for this report depends on assumptions and figures from another CXC report (Eory, et al., 2023). We recommend updating this data with real on farm data to better inform assumptions that follow from it.
• The sectors. Currently, the arable sector is more N efficient than the livestock sector (65% and 10% respectively). Sector specific targets would be helpful due to differences in current NUE, N inputs and N wastages but this is presently not possible due to the current limitations in the SNBS.
• The mitigation measures. More data on the impacts of mitigation measures under Scottish conditions would increase the accuracy of modelling achievable aims. Since NUE values are both indicators of resource efficiency and markers for improvement, it is possible to focus on mitigation measures with the most potential to improve NUE values.
• Farmers. It is highly important to ensure that targets and measures are clearly understandable and achievable for farmers to create support from the farming sector.

A potential target figure?

If a NUE target was set, this could be in line with the modelled potential NUE estimates of 38.2-40.9% by 2045, depending on mitigation measure implementation. To achieve greater improvement, a combined push from industry and government (financial incentives, increased awareness of N management, etc.) is required. This additional push is reflected in our ‘Ambitious scenario’.

However, based on our research findings, the barriers identified to implementing an achievable and successful NUE target and the need for farmer and industry support to achieve changes in practices and expectations, we conclude that focusing on reducing N waste is likely to have more success than NUE targets as a policy option. Experience from the United Nations Environment Assembly’s discussions on N and the Green Deal’s Farm to Fork targets, has shown more success in including the reduction of N pollution in policy when focusing on N waste over NUE targets. NUE can instead be used as a technical tool to mark improvements, with the SNBS key to setting a baseline and providing a visualisation of the combined impacts of implemented mitigations measures over time. We therefore recommend setting a target for N waste.

An alternative – a N waste target?

Opportunities for setting a N waste reduction target include:

• It is an easier concept to communicate to the farming community and other N producing sectors.
• It gives the opportunity to value any N as a resource until it is lost as waste, creating options for greater collaboration between the arable, horticulture and livestock sectors. Any potential bias towards a sector will be avoided.
• Each individual farmer and land manager would be encouraged to reduce N waste for the economic and environmentally beneficial outcomes. The positive messages around a N waste target would be likely to create support from the farming sector.
• Achievements towards an N waste target would achieve reductions in national NUE thereby achieving the same objectives without the current issues around NUE targets.

Following the Colombo Declaration of 50% reduction of N waste and the Green Deal target of reducing nutrient waste by 2030, a reduction of 50% of N waste in Scottish agriculture would align with other examples. However, we recommend further research to determine a realistic N waste target for Scotland.

Research gaps for setting a N waste target

In the SNBS, N flows would need to be properly assigned to N waste and N re-use. Legumes would need to be included in the SNBS because N waste is likely to be lower than N input. A SMART target analysis for N waste would be beneficial to set a challenging and realistic target. It would be helpful to closer investigate the relationship between N waste and NUE targets if a NUE target is the long-term aim.

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Appendix / Appendices

Appendix A: Nitrogen and its relevance to agriculture

Leaching and the effects on eutrophication

Leaching is the loss of N (as nitrate) as water drains through the soil moving nitrate away from the root zone. Both organic forms of N (such as slurry and manures) and inorganic fertilisers are liable to leaching. When nitrate is leached from soils, it can enter watercourses contributing to environmental problems such as eutrophication. Eutrophication is an accumulation of nutrients in watercourses causing excessive plant and algal growth resulting in reduced water quality and impacts upon fish, invertebrates and plant diversity. The extent of leaching is determined by factors such as soil type, crop cover, land management methods, geological characteristics and meteorological conditions prior to, during and following the application of the nutrients.

How NH₃ is emitted from agricultural sources

Loss of ammonia which is a significant air pollutant impacting upon both human health and biodiversity (respiratory harms and nutrient enrichment of sensitive habitats) is common from agricultural systems. Ammonia is lost through volatilisation of ammonium (NH₄+).

How N₂O is emitted from agricultural sources

Nitrous oxide is emitted in the process of denitrification, a bacterial process in waterlogged soils that converts nitrate to nitrous oxide and N₂ (for more explanation regarding the chemical processes involved please see Annex F). N₂O is a potent greenhouse gas and forms a significant contribution to agriculture’s impact on climate warming.

Appendix B: Chemical processes of Nitrogen

Appendix C: Rapid evidence assessment methodology

The Rapid Evidence Assessment (REA) methodology used for this project aligns with NERC methodology and comprised of the following steps.

• Define the search strategy protocol, identify key search words or terms, define inclusion/exclusion criteria. A list of key words, terms and search strings were created and reviewed by the project steering group to direct the REA review to the most relevant sources.
• Searching for evidence and recording findings. Literature was searched using Google Scholar, utilising our accounts with Science Direct and Research Gate to access restricted PDF’s where required. When searching through Government websites (to find policy initiatives and associated targets), the search engine Google was used. Searches were divided into academic literature and government websites (including farming press and industry). A unique search reference was assigned for each individual search, and the date, search string used, total number of results found, and the total number of relevant papers found were recorded. Examples of search strings include:
• “Nitrogen” “target” “Europe”
• NH₃ target agriculture
• Nitrate leaching target
• Emission reduction target Denmark

All results were recorded in an excel spreadsheet with information extracted on the following:

• Country
• Target
• Target timeframe
• Benefits and risks/challenges of proposed target
• Mitigation measures (introduced, planned and proposed/suggested)

A RAG (red, amber, green) rating was also assigned for each source, based on the following criteria:

• Screening. Sources of evidence were then screened initially by title and then accepted papers were screened again using the summary or abstract. Literature was screened for information on the following inclusion criteria:
• Nitrogen target (including but not limited to target for NUE or nitrogen emissions, or nitrogen fertiliser use, or nitrogen deposition)
• Benefits and risks of introducing a target
• Mitigation methods that improve NUE, or decrease nitrogen inputs
• Extract and appraise the evidence. The screening provided an organised list of papers which enabled evidence to be extracted directly from the literature into the report. Literature extracted also guided the internal workshop and supported information included in the SWOT and PESTLE tables.

How was the evidence found used. Evidence gathered from the REA was used to identify the different types of N targets used in other countries and provided a discussion following examples of the relevance of these targets to Scottish agriculture (section 4). The evidence was also used to identify the benefits and risks of setting a NUE target for Scotland and assisted the SWOT and PESTLE analysis (section 5) and to inform criteria and underpin recommendations for setting an appropriate target/s for Scotland.

Appendix D: Country-specific changes (%) in NUE values from 1961 to 2014

Table 6: Country-specific changes (%) in NUE values from 1961 to 2014 (Our World in Data)

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Appendix E: Analysis & recommendation development Methodology

• Data collection

Relevant measures were collated from results of the REA. The impact factors for these measures on N flows was extracted into an excel file.

• Data extraction

All relevant data points were extracted from the papers into an Excel spreadsheet manually.

• Data appraisal

A RAG rating was applied to all data sources based on the quality of the data (including publishing date, assumptions made, applicability etc.). Where data was considered to be very poor quality, alternative sources to fill or improve this data point were sourced.

• Mapping

Relevant mitigation measures were mapped on to the SNBS according to what nitrogen flows they impacted. This allowed for accurate modelling of the change in nitrogen flow, and subsequently nitrogen use efficiency, if the measures are implemented at the estimated uptake rates in the given years.

• Calculations

Once the impact values had been mapped on to relevant N flows, they were evaluated to ensure that the theory behind these values relate and can therefore be applied to the values in the SNBS. This involved ensuring that each measure had a relative impact value as percentage, and that the baseline is applicable to that in the SNBS.

Applicability: the portion of the relevant flow in the SNBS that the measure/impact value applies to e.g. emissions from livestock are grouped in one flow in the SNBS, and so a measure/impact value relevant to only dairy animals can only be applied to a portion of the N flow value. There were several sources used to determine this granularity of application. For livestock sectors the Scottish agricultural census data was used. The total livestock number in heads was divided by the total number of livestock from all sectors and reported as a percentage. Fertiliser use was determined from data within the Agricultural SMT produced by ADAS.

Some measures that include N fixation may not improve NUE but will reduce mineral N inputs.

Current uptake: An estimation of the portion of the relevant N flow that is subject to the impact of the measures. This is subtracted from the overall applicability as the impact is already considered in the current NUE values. In this way double counting of impacts is avoided.

Maximum future impact: Calculated as applicability minus current uptake, multiplied by the impact value. This calculates the impact the measure may have if implemented on all remaining applicable units. The value is then multiplied by the projected future uptake value in each of the time points to produce an estimate for the impact that could be expected.

• Quality Assessment

All data inputs, calculations, and outputs of this task were reviewed internally by the sector experts to ensure robustness and validity. Where possible the results were also compared to peer reviewed literature to ensure that they were consistent with the current scientific understanding.

• Assumptions around SNBS

There is no flow that relates to N from soil into grass, so impacts on this could not be quantified in the SNBS.

Crop residue N is recycled within the system. Therefore, this flow is not considered in the NUE calculations in the SNBS, and so any impacts to crop residue N due to implementation of measures will not be reflected in an improvement to NUE. To compensate for this, improvements to crop residue N was modelled as a reduction in N inputs from fertilisers.

There is around 30kt N unaccounted for through livestock flows. This is perhaps accounted for in what is considered in the report as ‘stocks’ – i.e. an amount of N in living livestock at any one time.

Appendix F: Description of measures and assumptions

In the following tables, where the source is given as “other CXC paper” this is referring to the paper Eory, V., et al. (2023) and “MACC Update” refers to the paper Eory V., et al. (2015).

• Preventing soil compaction

Approximately 20% of arable land in Scotland is susceptible to soil compaction and is therefore eligible to have compaction prevention applied. This measure is expected to increase yields and crop residue N, and so is assumed to reduce mineral N requirements.

• Optimal soil pH

This measure involves applying lime to soils to ensure that soil pH is in the optimal range for N availability. This means that when applying N fertilisers there will be less excess N as it will be more bioavailable and taken up by crops. This has been found to increase crop residue N and yield, both by 6%, while reducing the emission of N₂O by 3%, in arable and grassland. It has previously been assumed that approximately 9% of arable land and 22% grassland are applicable to have pH optimised.

• Use of catch/cover crops

Catch/cover crops are non-productive plants cultivated between catch crops with the effect of taking up excess N that was left in soil, having not been taken up by the preceding cash crop. This reduces the amount of N (in the form of NO₃^– ) that is lost in leaching by 45%. The applicability of this measure to crops has previously been set to 34%.

• Variable rate nitrogen application

Variable rate nitrogen application (VRNT) is where a digital map or real-time sensors supports a decision tool that calculates the N needs of the plants, transfers the information to a controller, which adjusts the spreading rate (Barnes et al. 2017). This measure is applicable to all land that receives fertiliser. 2-22% of farms use precision farming technologies and 16% used variable rate application, though only 11% use yield mapping (25% cereal farms, 18% other crop farms, 5% pig/poultry and dairy farms, 2% grazing livestock farms, 11% mixed farms). This measure can increase yield, reduce fertiliser use rates, and increase crop residue N. As with all measures yield is kept constant with current levels, and crop residue N is considered through a decrease in N fertilisation. Therefore, this measure is modelled as a decrease to N inputs through three mechanisms.

• Urease Inhibitors

Urease inhibitors slow down the hydrolysis of urea to ammonia when urea-based fertilisers are applied to soils, reducing ammonia emissions and increasing the N available to plants.

• Nitrification Inhibitor

• Improved Nutrition

Improving the nutrition of livestock can involve matching N in feed to the needs of the animal, improving the availability of N in the feed to animal, improving the digestibility of the feed so that more N is utilised by the animal and converted to liveweight. This can reduce N inputs and/or reduce N losses while keeping useful N outputs constant, and so increases NUE. From previous modelling of this measure in Scotland it was found that the N content of feed could be reduced by 2% in beef, poultry, and dairy, while excreted N could be reduced by 5% in pigs and 2% in sheep. The applicability of this measure for each livestock type is based on the proportion of total livestock units of each livestock type based off the Scottish Agricultural Census. The current uptake is based off data from previous reports modelling this measure in Scotland.

• Improved health

This measure includes eliminating issues including worms, liver fluke, and lameness, increasing the productivity/efficiency of the animals. While in theory 100% of the herd could have improved health (the stance taken in CXC A scenario), an 80% applicability value was chosen, following the assumption in CXC marginal abatement. This will produce a slightly more conservative estimate of the impact on NUE, to allow for not all diseases/health issues that contribute to lower productivity being treatable/eradicated, and a portion of the herd that may already be achieving higher health. Previous studies focusing on improving livestock health to mitigate nutrient loss, greenhouse gas loss etc. focused on the mechanism of increased productivity. Therefore, as we are keeping yields constant in this model the increased productivity is factored in as a reduction in feed inputs. 

• Livestock Genetics

Livestock genetics techniques can be used with various goals including increasing productivity, climate resilience, or reducing emissions. For improving NUE of livestock systems the key goal is increasing efficiency i.e. increasing the utilisation of N and yield of livestock products, compared to the feed N intake levels. The uptake of using better genetic material is only around 20-25% in the dairy herd, and still lower in the beef herd (Defra 2018). The outcomes of this measure will depend on the breeding tools used and the breeding goal chosen. Three more specific measures have been gathered from the literature, and their potential impact on NUE has been modelled. These are:

• Increased uptake of the current approach in the dairy herd,
• Using the current breeding goals but enhancing the selection process by using genomic tools, in dairy and beef,
• New breeding goals to include lower GHG emissions, using genomic tools.

In 2018 usage of improved genetic material was reported as 20-25% in the dairy herd, and less in the beef herd. However, several previous projects modelling similar measures set the current uptake at 0% of both dairy and beef herds.

• Slurry acidification

Livestock excreta is susceptible to N volatilization, leading to losses to the atmosphere using storage, and leaching during spreading. Acidification of slurry can immobilize the N and reduce these losses. The impact of acidification is largely measured and reported in reductions to emissions, however, as the emissions values are not considered in the NUE calculations this has to be transformed to an impact on inputs. Higher N in slurry will increase yields/maintain yields with lower inputs. Therefore, in this model we include the impact of slurry acidification as a reduced input of N to land receiving fertiliser.

• Slurry store cover

Based on an impermeable slurry cover. Impact and uptake values taken from previous CXC paper. The flow in the SNBS does not distinguish between NH₃ emissions from housing and spreading and N₂O emissions from animal husbandry in general. The portion of each of these gaseous emissions was then extrapolated from the SMT. An impermeable cover is applicable to 100% of slurry tanks and lagoons as there is no available uptake data.

• Low Emission Housing

Acid air scrubbers can remove nitrogen from air, reducing NH3 emissions, which can then be applied to soils as N fertiliser, and essentially recovering more N in useful outputs by reducing waste N in emissions. Approximately 90% of recovered N can be reinput into the soil. The removal efficiency depends on the specific machinery used and approximately 90% can be expected for acid air scrubbers.

• Novel Crops

Novel crops (crops with improved NUE) is designed to reflect the impact of growing new cultivars of crops that can maintain (or improve yields) with a lower requirement for N inputs as fertiliser. Previous

• Rapid Incorporation

• General Assumptions:
• Take the total inputs and subtract the total loss to atmosphere as NH₃ and loss to run off and leaching
• Maybe assume that N₂ and NOx stay constant, NH₃ and N₂O, estimate the losses and subtract from inputs
• Ignore crop residue N, check how this impacts flow
• Increased N fixation will lead to reduced mineral fertiliser inputs, balance out
• Reduced losses (N₂O, NH₃, leaching) will reduce inputs in equal amounts (may need to apply a percentage to this, as farmers may only reduce inputs by 80%, may have to look into the literature)
• Maintain yield (useful outputs), and so any change to output will be modelled as a change to inputs. This is based on the principle that there will be economic drivers at play that will mean on a Scotland wide scale production levels will be maintained, and so if there is a yield increase/decrease on one farm this will be balanced out by the converse on a different farm. Any yield increase/decrease will be felt as the converse in inputs – feed, fertiliser etc. will be reduced in line with the estimated increase of milk, liveweight, crop, etc.
• All legume measures will not impact NUE as any saving in N fertilisation will be balanced by increased biological fixation.
• Assumed that legumes are included once in every five years. Therefore, a fertiliser saving is felt in two of every five years and so impacts 40% of the mineral fertiliser input to crops flow (one year (20%) will be saved from the legume cycle, and one year (20%) from the subsequent crop year due to residual soil N).
• Within the SNBS, nitrogen flows to or from livestock pools were given as a single value for all livestock, rather than by type. However, the measures relating to livestock were species-specific (e.g. slurry acidification in dairy slurry and pig slurry). To compensate for this the number of heads of each livestock type (from the Scottish agricultural census) was converted to livestock units, and then the proportion of total livestock amount of each type was calculated and applied to the relevant measures.
• A single flow value is provided in the SNBS for all mineral fertiliser to crops and all mineral fertilisers to grass, however several of the measures only impact a certain type of fertiliser or may have a different impact depending on the type of fertiliser.

Appendix G: SWOT and PESTLE Analysis

The risks and benefits to Scotland from determining a NUE target were determined through giving consideration to numerous avenues of information and data. Evidence gathered following the completion of Task 1 (evidence review) focusing upon risks and benefits of setting NUE targets in other countries were collated and analysed. This was followed by an internal workshop, led by key experts within the agricultural field, to determine the applicability of the information to Scotland, during which time additional risks and benefits were identified. Following the internal Workshop, a more detailed study of the aspirations and trends in agricultural practices set by the Scottish Government was undertaken. The SWOT (strengths, weaknesses, opportunities, threats) and PESTLE (political, economic, sociological, technological, legal and environmental) tables were populated to better understand the complexities of the information gathered by Ricardo, with the analysis tools providing a summary of the risks and benefits of setting a NUE target in Scotland and demonstrating how a range of influences can support or hinder the achievement of a NUE target. The points presented in both the SWOT and PESTLE analysis have varying degrees of severity therefore a judgment on overall supporting and hindering influences cannot be made on the number of points alone.

SWOT

Strengths, weaknesses, opportunities, and threats (SWOT) of setting N-related targets were analysed based on the information gathered on N targets in other countries. We also included analysis of GHG and climate related targets where relevant to increase the body of information. This information was then used to assess applicability of setting a NUE target for Scottish agriculture with the limitation that the analysis was based on N, GHG and climate related rather than NUE specific targets. The SWOT analysis shows a range of influences which can support or hinder the achievement of a NUE target.

PESTLE

Setting NUE and other N targets are subject to a range of enablers and barriers. Therefore, a political, economic, social, technical, legal, and environmental (PESTLE) analysis was undertaken to assess the feasibility of setting a NUE target for Scottish agriculture, again, with the limitation that the analysis was based on N, GHG and climate related rather than NUE specific targets. The PESTLE assessment took place following the SWOT analysis to ensure the findings from the SWOT were assessed and, if relevant, included into the PESTLE categories.

Appendix H: Worked example

To aid in understanding the approach taken to calculate the impact of each measure on the NUE worked example, for slurry acidification has been presented below.

Slurry acidification can reduce the NH₃ volatilisation at the storage stage by 75% for dairy, beef and pigs. It will also reduce N₂O at the spreading stage by 23%. This measure cannot be applied on all managed livestock manure, and can be applied only where slurry is stored in tanks. Approximately, 41%, 4%, and 38% of dairy, beef, and pig excreta is on a slurry system, respectively, and approximately 50% is in slurry tanks rather than lagoons, for each livestock type. Therefore, this measure can be applied to approximately, 21%, 2%, and 19% of all dairy, beef, and pig excreta.

The relevant flows with the SNBS for these two impact values are N2O emissions from animal husbandry (including manure management), with a value of 0.92 kt N yr^-1, and NH3 from housing and storage of manure, with a value of 10.5 kt N yr^-1.

These flow values represent the absolute quantity of N transferring from the excreta pool to the atmosphere, for all livestock and storage types. Of total livestock units in Scotland, approximately 42% are beef, 11% are dairy, and 12% are pigs.

The uptake levels of this measure in 2030 is estimated to be 7%.

The current uptake is assumed to 0%.

The applicability of this measure on dairy is:

Portion of livestock that are dairy animals * portion of dairy excreta suitable for acidification

0.11 * 0.21

= 0.0228

Therefore, the impact of slurry acidification on the dairy sector is:

Applicability * (1-Current Uptake) * Impact Value * 2030 Uptake

0.0228 * (1-0.00) * -0.75 * 0.07

= -0.12%

Apply this to the absolute value for dairy from the SNBS:

10.5 * 0.0012

= 0.0126 kt N yr^-1

This calculation is carried out for all three livestock types, and for the N2O value. The total N saved is 0.03 kt N yr^-1, which is subtracted from the quantity of mineral fertiliser applied to soils:

143.78 – 0.03

= 143.74 kt N yr^-1

The NUE is recalculated taking into account the new mineral fertiliser quantity:

(Inputs / Outputs) * 100

(200.08 / 54.48) * 100

= 27.23%

© The University of Edinburgh, 2024
Prepared by Ricardo PLC on behalf of ClimateXChange, The University of Edinburgh. All rights reserved.

While every effort is made to ensure the information in this report is accurate, no legal responsibility is accepted for any errors, omissions or misleading statements. The views expressed represent those of the author(s), and do not necessarily represent those of the host institutions or funders.

ClimateXChange

Edinburgh Climate Change Institute

High School Yards

Edinburgh EH1 1LZ

+44 (0) 131 651 4783

info@climatexchange.org.uk

www.climatexchange.org.uk

1. 
   

   N waste is reactive nitrogen (Nr) that is not used in the nitrogen cycle. Higher N waste reduces NUE. ↑

   
   
2. 
   

   The 2021 total is an adjusted total to consider compliance, meaning the contribution of emissions from non-manure digestate spreading is removed ↑

   
   
3. 
   

   A NVZ designation limits the total amount of N (from livestock manure) that can be applied to agricultural land in that area. Scottish NVZ designation is reviewed every four years and nitrate concentrations in surface and ground water are measured by The Scottish Environment Protection Agency (SEPA). ↑

   
   
4. 
   

   N fertilisers are used most commonly in the forms of ammonium nitrate and to a lesser extent urea both as a solid prill (pellet) which is spread using a broadcast spreader. ↑

   
   

Land use transformation, and related reductions in greenhouse gas emissions, will be necessary to achieve Scotland’s ambitions to reach net zero emissions by 2045, as well as biodiversity and climate change targets.

A variety of support systems for land use transformation, such as financial support and advice, are already in place.

This study aims to understand how and why land managers engage, or don’t engage, with these support systems, to inform how policy could be best deployed to accelerate the process of change.

Findings

There is substantial evidence for land manager behaviour and decision making that influences engagement with support systems. Their decisions are determined by a range of interacting internal and external factors, primarily related to financial, practical and cultural influences.

• Overall, the public sector grant-giving support network is logical and straightforward to use.
• The administrative burden associated with applying to schemes is a barrier to engagement.
• Land managers often decide whether to engage with support and advice based on confidence in its source. For example, farmers are more likely to trust advisers that have a practical farming background over those from a consulting or academic background.
• Land managers in Scotland primarily access public funding support. Some access private finance to supplement their income or achieve specific goals.
• The breadth of support sources is confusing for some land managers.
• Applicants would prefer administrative simplicity and greater flexibility.
• Improved accessibility and flexibility will not, by themselves, increase overall engagement with land use change. Other measures, such as attractive payment rates, sufficient technical advice and training and management flexibility, will also be needed.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Research completed October 2023

DOI: http://dx.doi.org/10.7488/era/5005

Executive summary

Introduction

Land use transformation (and related reductions in greenhouse gas emissions) will be necessary to achieve Scotland’s ambitions to reach net zero emissions by 2045 as well as biodiversity and climate change targets. A variety of support systems for land use transformation, such as financial support and advice, are already in place. This study aims to understand how and why land managers engage, or not, with these support systems. This helps inform how policy could be best deployed to accelerate the process of change.

Influences on land manager decision making

We found substantial evidence for land manager behaviour and decision making that influences engagement with support systems. Their decisions are determined by a range of interacting internal and external factors, primarily related to financial, practical and cultural influences, which can be enabling or restricting, such as:

• personal values and knowledge
• perceived loss of control
• social norms/pressures
• trust in sources of information and advice e.g. land agents
• administrative burdens/transaction costs
• financial incentives
• awareness and understanding
• clarity of the benefits of change.

Restrictive barriers are compounded by context specific factors that vary across individual businesses, such as tenure, business scale and biophysical constraints.

Findings

Overall, the public sector grant-giving support network is logical to use. Most schemes are accessed through the Rural Payments and Inspections Division (RPID) portal. Other schemes are straightforward with regard to procedures. The RPID portal only requires one set of login credentials to access a wide range of support systems. Support systems under this umbrella are easy to access and do not require additional login credentials.

The administrative burden associated with applying to schemes, i.e. form filling, is a barrier to engagement. Procedural support (i.e. form filling by an adviser) is widely available from both public and private advisory sources but requires additional resource to procure. This is distinct from practical support, such as site-specific implementation advice, which was frequently mentioned by stakeholders as key to facilitating the uptake of environmental management practices and yet less readily available.

Land managers often decide whether to engage with support and advice based on confidence in its source. For example, farmers are more likely to trust advisers or organisations that have a background in practical farming over those from a consulting or academic background.

Land managers in Scotland primarily access public funding support. Some access private finance to supplement their income or achieve specific goals. Those accessing private finance generally do it to avoid the conditionality of public funding support and retain operational control over the management of their land. Combining Agri-Environment Schemes and e.g. the Peatland Code is perceived as overly cumbersome, with interactions between schemes, different application dates and the need to demonstrate additionality proving complex.

The breadth of support sources is confusing for some land managers. Better alignment, or at least signposting between sources, would be helpful. Ideally this needs to be via people as well as (rather than just) an online portal. This will enable land managers to choose the correct support more readily, according to their own circumstances.

Applicants would prefer administrative simplicity and greater flexibility. Therefore, efforts to streamline application and monitoring processes, reduce information burdens, widen application windows and vary contract lengths, are justifiable.

Administrative touch points and contractual constraints are only one influence on land manager behaviour. Improved accessibility and flexibility will not, by themselves, increase overall engagement with land use change. Other measures will also be needed such as attractive payment rates, sufficient technical advice and training, and management flexibility. Further research from workshops with potential support recipients, ideally out of peak summer work season, would help understand how future engagement can be maximised.

Abbreviations table

Introduction

Rural land use in Scotland directly supports the national economy, rural communities, and local businesses. Sustainable land use holds a key role delivering Scotland’s biodiversity goals and response to climate change. Agriculture is the second largest source of greenhouse gas emissions in Scotland, behind the transport sector, with emissions largely coming from livestock and soils.^[1] In order to achieve biodiversity recovery and climate mitigation and adaptation, agricultural transformation is required to reduce emissions, and capture carbon in vegetation and soils. A continued, long-term expansion and integration of regenerative agriculture, afforestation and peatland restoration will be necessary and is currently underway as part of the plan to achieve Scotland’s net zero targets.

This research was undertaken to gain a better understanding of the key influences that have a bearing on land manager decision making, including their motivations, what they want to achieve for their operation and their appetite for change.

The aims of the project were to map current support services across different land use sectors to inform our understanding of a land manager’s ability to make decisions and access funding and advice for different land uses. One of the key influences on land manager decision making is their awareness and engagement with support systems. “Support systems”, for the purpose of this report, refers to all sources of support that a land manager in Scotland could access to aid their management of their operation. This includes the following sources:

• Public funding support (e.g. Agri-Environment Climate Scheme (AECS))
• Private funding support (e.g. Woodland Carbon Code (WCC))
• Procedural and practical support from advisors, both public and private (e.g. Farm Advisory Service (FAS))
• Informal networks (Family, friends, and peers)

We looked at availability and links between existing and relevant land use information systems, support services, and current incentives for land use transformation which are directly related to achieving Net Zero and/or nature restoration.

Through stakeholder interviews and other evidence, we established where, when and how different rural land managers interact with the systems and services; we then collated the evidence for issues and barriers to access them. The results are presented using SWOT and PESTLES analysis, conclusions, and visualisations.

When we defined “land manager” we focussed our research on managers of agricultural land, including moorland, peatland and forestry, whether that be farmers, crofters, large estates or organisations such as NGOs.

Understanding land manager behaviour in relation to their awareness of, and drivers of actions that support (or not) environmental outcomes is complex. Decisions and outcomes in this area are a result of multiple interactions between agronomic, cultural, social and psychological factors, all of which sit within the national, regional and specific site context (Mills et al, 2016). Therefore, understanding land manager engagement with current support systems will prove equally complex.

To further our understanding, we carried out an evidence review of the literature. This informed the design of typical land manger archetypes to facilitate the analysis of how specific sectors in Scotland are engaging and accessing support systems. Please see Table 6 in Appendix B for the longlist of archetypes. The long list was used to gather further data, through stakeholder interviews, from both support providers and receivers, across the spectrum of land manager sectors in Scotland. Twenty-five stakeholder interviews were conducted, with participants ranging from support recipients such as crofters and farmers, to support providers and academics. Views from the agriculture, forestry and peatland sectors were captured. Attitudes relating to land managers’ ability and willingness to engage with support systems as well as what determines the level of engagement with these systems were explored. This included the types of support available, their pros and cons, as well as whether they were felt to be accessible, credible and available.

Reflecting its relative prominence within public expenditure and land-based businesses in rural areas, agriculture dominates much of published literature on land-use support. This evidence was supplemented by feedback from stakeholder interviewees, including individuals representing other sectors. The final step was to map the experience of six chosen, prioritised, archetypes in more detail. These are presented in section 6.2.

Full details of our methodology can be found in Appendix A-D.

This study included:

• Carrying out a rapid literature review. (methodology in Appendix D)
• Identifying and mapping the most prominent existing and relevant land use information systems, support services and the current incentives for land use transformation directly related to achieving Net Zero and/or nature restoration. (Appendix A)
• Developing typologies for land managers who might engage with these systems. (Appendix B)
• Agreeing a discussion guide (see Appendix C) for semi-structured interviews.
• Identifying a list of target candidate interviewees who were chosen to represent recipients of support, providers of information and advice, and academic experts. (Appendix C)
• Analysis of where, when, and how land managers interact with the systems and services.
• Presentation of evidence for issues and barriers to access these systems and services from the stakeholder interviews.

Introduction to land manager decision making

The literature is consistent in reporting that land manager decision making, regarding the use and management of their land, and therefore support system engagement, is influenced by both internal and external factors which combine to create individual circumstances. (Buamgart-Getz et al. 2012; Mills et al. 2016; Barnes et al. 2021; Conti et al. 2021; Thompson et al. 2021a).

These factors affect a land manager’s willingness and ability to adopt environmental management practices. The importance of this is underlined by the fact that climate is the most important element of agricultural productivity in many instances (Scottish Government, 2012). Therefore, once bio-physical conditions (an external factor in itself) have determined what management measures are suitable for a land manager, the wider range of internal/external factors will influence engagement with specific support systems offering funding, information, advice, and training. Table 1 below displays the different internal and external factors that influence land manager decision making, as identified by Thompson et al. (2021a).

Table 1 – Internal and external factors influencing land manager decision making – (adapted from Thompson et al. (2021a)).

The way these factors affect and interplay with land manager willingness – and their ability to adopt environmental practices – are shown in Figure 1 (after Mills et al. (2016)). For example, a land manager with limited resources, reliant on informal networks of support, with a strong anti-change personal attitude is unlikely to engage with environmental practices and support systems. Another land manager with higher access to finance, human and social capital, more formalised support networks and a positive outlook on environmental practices would be more likely to engage.

Figure 1 – Factors influencing land manager engagement, willingness and ability to adopt (from Mills et al. 2016).

These examples are clearly extreme ends of the spectrum. Landowners will all have a unique set of factors that influence their decision making when it comes to adopting environmental practices and engaging with specific support systems. It is for this reason that understanding and predicting land manager environmental behaviour and engagement with support systems is complex.

It is important to note that most of the literature on the subject of land manager engagement/motivations with support systems focuses on farmers. For example, (Sutherland et al. 2011) who state “research into actor influences on land use change (attitudes, motivations and objectives held by individuals and groups) has traditionally focused on single sectors, particularly farming. Neither is the range of landholding entities addressed, as emphasis is typically on private owners.”

Some studies (Ambrose-Oji, 2019; Tyllianakis et al. 2023) have explored wider land manager engagement with support systems in detail, however the focus in the academic literature remains centred on farmers. The reasons behind this focus are not currently clear, but it may be due to the large engagement of the agricultural industry with support systems, particularly financial support.

We have attempted to fill this gap in the literature through targeted stakeholder interviews with individuals representing land managers outside, as well as within, the agricultural industry.

Our evidence review has suggested that engagement with current support systems is primarily influenced by certain personal values and knowledge, perceived loss of control, excessive administrative burdens/transaction costs, a lack of credible financial incentives, a lack of awareness, understanding and clarity of the benefits of certain support schemes and social norms/pressures. These barriers are then further compounded by context specific factors that vary across individual businesses, such as tenure, business scale and biophysical constraints.

Land manager engagement with support systems is discussed in more detail in Section 6

Review of support systems

The next stage of this study attempted to identify the current land use support systems that land managers are engaging with in Scotland. This allowed us to map current support services across sectors in Scotland. Once we established the variety of support systems, we could begin to understand how land managers are interacting and engaging with these systems, whilst identifying key barriers and opportunities that could be used to inform future policy support.

We achieved this by firstly identifying a range of typical land manager archetypes in Scotland, followed by a review of all visible support systems identified through academic and grey literature review.

More detail on the types of support available is given in Appendix A Support in terms of funding is available from Government and the Private sector. Advice and information can be sought from direct Government sources plus third-party sources funded by Government (e.g. the Farm Advisory Service) but also independent third-party provision. Third sector, charities and Non-Governmental Organisations also provide landowners with advice and funding to undertake measures that align with their objectives.

Initial land use support system mapping

The infographic on the following page (Figure 2) displays a high-level mapping overview of the current land use support systems in Scotland and the extent to which land managers are engaging with each. Most land managers engage with government agency support and funding, with agricultural land managers doing this to a greater extent. This is mostly limited to schemes such as BPS and LFASS as these offer large rewards for less administrative actions compared to other schemes, such as AECS. Other land managers are more likely to be engaging with corporate buyers and private sector sources of support, such as emerging natural capital opportunities.

Figure 2 demonstrates clearly that the land manager support network in Scotland is a complex entity, with different land managers drawing from a wide range of support sources. Whilst it has not been possible to quantify the exact support flows between support providers and support receivers, we have provided an indication of the overall network and flow of support in Scottish Agriculture, helping us map current land manager engagement with support systems.

Figure 2 – Land use support system providers in Scotland. Source: Adapted from Sutherland et al. (2023)

Stakeholder views on engagement with support systems

It was recognised from the outset that the results of the evidence review must be calibrated against the lived experience of key stakeholders. We were able to conduct 25 interviews, and had scheduled to supplement this with additional workshops, but it proved very difficult to gain substantive input from planned workshops due to the timing overlap with the peak summer workload alongside harvesting.

We have captured the results of the stakeholder feedback below. This should be read alongside the review of the literature which is presented in section 7. Whilst there are significant similarities between the evidence from the literature review and stakeholder perceptions from the interviews, we recognise that this evidence would be usefully supplemented by a more in-depth form of action research with a wider stakeholder group, in particular potential support recipients, which would help to deliver more substantive results.

Factors influencing land managers’ decisions.

Stakeholder interviewees identified many factors influencing the ability and willingness of land managers to change management practices and/or land use patterns. Although varying in terms of emphasis and specific examples offered, there was a high degree of agreement across stakeholders (and consistency with the literature) regarding the main categories of (interacting) influences, which can be summarized as follows:

Confidence and understanding

Land management involves a range of tasks requiring both practical skills (e.g. handling livestock and machinery) but also organizational (e.g. resource allocation) and strategic (e.g. business planning). Changing land management practices and/or land use patterns requires expanding this skill set. However, not all land managers currently have the necessary skills, leading to many having a low understanding of how to change and low confidence in abilities to change successfully. Conflicting messages about the definitions, relative merits and compatibility of different practices (e.g. afforestation, regenerative agriculture) cause significant confusion, reinforcing an underlying wariness of changing unnecessarily.

Indeed, stakeholders were concerned that basic awareness amongst many land managers of requirements for change under both future agricultural policy, but also private supply-chain pressures, is still very low. Clearer and more consistent messaging from government and industry leaders would help, particularly if it was accompanied by more detail on practical support measures, including funding levels, the provision of information, advice and training, and any implications for future eligibility for land-related tax breaks and other public funding sources.

Resource constraints

Although any given parcel of land can be used for a variety of purposes, its underlying natural capital and biophysical characteristics (e.g. climate, topography, soils) exert a significant influence over its inherent suitability for different uses. Consequently, land managers do not all face the same land use possibilities to deliver particular ecosystem services. The Less Favoured Area (LFA) designation recognizes this in agricultural production terms but variation in suitability to deliver other ecosystem services is also recognized through various environmental designations (and indeed spatial targeting of agri-environment measures).

Farm type provides a convenient, albeit crude, indicator of likely flexibility in agricultural land use, with many hill and upland livestock farms being particularly constrained. The JHI Agricultural Land Capability Map (and equally the forestry suitability map) offers a more refined indication, but greater use of maps to categorise potential to deliver wider, environmental services would be helpful. For example, High Nature Value (HNV) farming.

Beyond biophysical constraints, farm businesses are also constrained by the availability and quality of other resources – in particular, working capital, equipment and labour. Stakeholders stressed that many farm businesses operate on very slim margins and are risk averse, limiting the scope for experimentation and investment in new management practices or forms of land use. Financial support can help to overcome this, as can support scheme contracts’ length and flexibility. However, labour scarcity and the relentless nature of farming often leave little spare time to devote to engaging with the process of change.

Geographical remoteness and/or poor communications connectivity can add further challenges. So can small scale – smaller businesses with fewer resources (especially labour) typically lack both the economies of scale and flexibility available to larger businesses to accommodate/experiment with change. This limits their ability to be creative and do something different. Some larger businesses have recruited in-house expertise and/or they directly commission academic and other consultants, particularly in relation to emerging nature-based solutions and rewilding exercises.

Transaction costs

The transaction costs of seeking information, advice, training, and external funding to implement change can be significant. To make it easy for all applicants, sources of information, advice, training and funding should be easy to locate. Administrative processes for applications, monitoring and reporting should be simple and accessible, including in their choice of language and terminology.

Stakeholders acknowledged that accountability for public expenditure necessarily requires a degree of bureaucratic oversight. However, they expressed concern that the complexity of some funding schemes^[2] was a deterrent to some applicants, including those with little spare time and/or an unfamiliarity with administrative processes. This phenomenon was described as ‘form anxiety’. The difficulties of coordinating across multiple sources of information, advice and training were recognized, and it was suggested that clearer signposting and the use of one-stop-shops would be welcome.

Smaller businesses lacking the staff and/or finance to hire specialist advisors may be particularly affected by transaction costs, facing a proportionately greater burden than larger businesses. For example, there is often a fixed cost element to application processes regardless of the level of funding sought and having to seek information directly rather than being able to delegate to staff can have a high opportunity cost.

Tenure

Farm tenure exerts a direct influence over land managers’ ability to undertake change, particularly between different land uses. Specifically, whilst owner-occupiers have the freedom to choose how they manage their land, tenants are constrained by the terms of their lease. The degree of restriction varies across different types (e.g. length) of tenancy, with crofting tenure adding some further complexities, particularly in relation to common grazing.

In most cases, agricultural tenancies restrict the range of land use activities permitted. For example, afforestation and non-agricultural enterprises are typically precluded from leases by default (although may be agreed via negotiation). Moreover, non-agriculturally productive parcels of land (e.g. pre-existing woodland, riparian habitats) are often excluded from the area covered by a lease. Consequently, the ability of many tenants to implement and benefit from land use change is currently constrained.

However, some stakeholders believed that the issues around tenure constraints had become better understood in recent years and were hopeful that the forthcoming Agriculture Bill would address many of them.

Motivations and norms

Beyond the practical constraints suggested above that influence a land manager’s ability to change, willingness to change is also affected by various factors. In particular, by an individual land manager’s attitude towards and motivation for land management and by cultural norms held by family, friends and peer groups.

Land managers need to perceive how change fits with business viability and continuity. Some land managers (e.g. rewilding estates, NGOs) may be motivated to undertake change primarily by seeking environmental improvements. Others may be more motivated by the traditional farming values centred around food production, and they be more fundamentally opposed to activities perceived as incompatible with growing or rearing consumable produce. The latter is particularly relevant to debates around afforestation and (to a lesser extent) peatland restoration.

Many land managers are starting from a mainstream farming perspective, although not all are; other groups are perhaps more open to change such as community groups, foresters and horticultural producers. Stakeholders suggested that variation in willingness to change was likely to be significant across the full population of land managers and would complicate any targeting of encouragement to change.

Stakeholders also noted that willingness to change could ultimately be influenced by financial pressures, whether via public finding or market signals, but that sustainable change would require cultural shifts – winning hearts and minds. This implies a need for clear industry leadership backed-up by the provision of information, advice and training plus (probably) encouragement for generational renewal. Negative perceptions of bureaucracy and of support payments simply flowing to advisers (a ‘consultants charter’) are widespread.

Types and sources of support

Stakeholders identified different types of support for land managers, distinguishing funding from other forms of support.^[3]

Funding

Funding was further divided into public and private, although the emphasis was very much upon public funding. Public funding for land management is dominated by agricultural support, notably decoupled area payments plus limited voluntary coupled support. Significant funding is also available for forestry and peatland restoration, plus wider agri-environmental schemes, innovation funds and various capital grant schemes. Public funding is also available to land-based businesses from other sources, such as the Enterprise Networks (see Table 2 for listing).

Stakeholders regarded public funding as essential to achieving management and land use change; in particular to offer financial incentives (or at least reduce disincentives) to make change worthwhile and to encourage any necessary capital investments. However, it was noted that inflation continues to erode the real terms value of public funding, decreasing the leverage that it has over management decisions.

Private funding for changing land management is also available. For example, there are high-profile cases of new and large landowners essentially self-funding and/or harnessing emerging environmental funding mechanisms. The latter include the Woodland Carbon Code and the Peatland Code.

However, the accessibility of such mechanisms to all land managers (e.g. tenants, common grazing, smaller holdings, community owners) is imperfect. Moreover, considerable uncertainty exists over the future value of carbon credits, and the possibility of claims over them by downstream supply-chain partners. Consequently, notwithstanding Scottish Government aspirations to increase private funding, stakeholders expressed some scepticism about the potential of private funding to replace public funding.

Non-funding support

Stakeholders also sub-divided non-funding support, into procedural support to help land managers navigate bureaucratic processes (e.g. advice on how to complete application forms, enrol in training programmes) and support to help with actual activities on-the-ground (e.g. training in new management practices). Both were regarded as necessary, but the degree of procedural support required relates back to concerns about transaction costs.

Procedural support tends to either take the form of information and general advice provided by the source of any funding, or the form of professional assistance to comply with application and reporting processes. For example, public funding is accompanied by online (and sometimes print) public guidance material plus online, phone and (sometimes) face-to-face advice on (e.g.) eligibility criteria, payment rates and evidence requirements. Private sources (e.g. land agents, consultants) often mirror this, but also offer further hands-on assistance to gather necessary data and complete paperwork plus more bespoke advice for individual land managers.

Practical support is similarly available in different forms from a variety of sources. Indeed, stakeholders emphasized the huge variety of forms and sources (see Table 2 for listing). For example, information is available via print and social media from public (e.g. Scottish Government, NatureScot, SEPA, Universities), private (e.g., levy bodies, consultants, input suppliers) and third-sector (e.g. NGOs) providers and advice can be offered one-to-one or one-to-many^[4] either online or face-to-face. Moreover, face-to-face may involve a simple meeting or a site visit or demonstration. Vocational training (e.g. via Lantra or colleges) tends to involve face-to-face events, but online training can suit some strategic and planning type skills development. Stakeholders suggested that the breadth of support sources was confusing for some land managers and better alignment or at least signposting between sources would be helpful, although signposting ideally needs to be via people as well as (rather than just) an online portal, for land managers to define the correct source of support for their own individual circumstances.

Importantly, stakeholders also stressed the role of informal sources of information and advice. For example, family and friends plus unrelated business professionals (e.g. accountants, vets). Peer group networks (local but also international) of like-minded people can also be important – indeed some stakeholders identified these as particularly relevant for emerging practices such regenerative agriculture and agro-forestry which some stakeholders regarded as not well-served by more formal support mechanisms. Peer networking can be encouraged through trained facilitators and funding.

Availability, accessibility and relevance

Uptake of information, advice and training requires land managers to trust the source and to see the relevance of what is being offered. This poses a demand-side challenge in persuading land managers of the need for change and relates back to points made above regarding the need for clear, consistent messaging from government and industry leaders to set the tone – particularly in relation to strategic business skills and new technical skills.

However, it also poses supply-side challenges in terms of the availability and accessibility of information, advice and training. Government only has leverage of this through either direct provision itself, or funding of third parties to provide support. Stakeholders noted that availability was already patchy geographically and in terms of specialist topics. Moreover, they were not confident that public funding levels would be sufficient to cover all future requirements – implying a need to prioritise particular topics or groups of land managers, and/or to rely more upon online and one-to-many methods (despite experiential, hands-on learning being viewed as more effective).

Citing diminishing returns and the 80/20 rule^[5], some questioned the merits of trying to accommodate all ‘hard to reach’ groups (e.g. smaller producers, new entrants, women, the very young, those with poor mental health). However, the Women in Agriculture initiative was cited as a good example of targeting.

Furthermore, even if future funding was sufficient, stakeholders were not confident that sufficient appropriate advisors would be available in the short-term. Trust depends on perceived credibility and, rightly or wrongly, in many cases this requires advisors to have agricultural backgrounds – yet the types of management and land use changes required extend beyond agriculture. This implies a need to upskill existing advisors but also to recruit advisors from different backgrounds – either to work in teams or (hopefully) to be accepted as credible by land managers.

Stakeholders offered a variety of solutions to this problem, including allowing the Farm Advisory Service (FAS) to evolve in terms of its modes of operation and topic overage but also to sub-contract other independent and/or specialist advisers (including existing land managers) as appropriate. Deployment of RPID staff to offer advice as well as conducting inspections was also suggested, reminiscent of previous policy eras and also, to some extent, emulating more recent practice in forestry and catchment management.

The use of facilitators rather than advisors was supported by some stakeholders, reflecting (possibly) easier recruitment (technical expertise is less essential than people skills) and perceived advantages of facilitated experiential learning rather than expert instruction.

It was also suggested that advisors should be included more formally in policy design and monitoring processes since they are well placed to offer insights into how ideas will be received and implemented on-the-ground. It was noted that total formal advisory capacity includes those working for input (e.g. seed, feed, fertiliser) suppliers as well as those aligned with FAS or working independently.^[6]

Table 2 – Cited examples of support

Land manager experiences of support systems

As part of this research project, we attempted to identify and map all existing and relevant land use information systems, support services and the current incentives for land use transformation directly related to achieving Net Zero and/or nature restoration. An outline of all the support schemes identified can be found in Appendix A. We then collected additional information on a sub-set of current support systems administered by the Scottish Government, to explore specific touch points for land managers. To frame this exercise, we firstly mapped the main agencies within the Scottish government that are responsible for the relevant land manager support systems (Figure 3).

Figure 3 underlines that multiple agencies are responsible for providing and administrating support to land managers in Scotland. This has the effect of increasing administrative burdens for land managers if systems across agencies are not in sync in terms of data collection and system operation.

Figure 3 – Agencies responsible for land manager support in Scottish Government

Insights from the literature

We can gain significant insight from published grey literature about where, when, and how land managers interact with support systems and services. There are three highly relevant published pieces of work. The first is the RPID customer satisfaction survey (RPID, 2021), where RPID customers gave their views on the application process and how it could be improved. 2147 customers filled in this survey, providing a robust sample size to gather insights from. The second piece of work is the NatureScot Research Report 1254 (NatureScot, 2021), where biodiversity outcomes were evaluated. This included a quick survey of applicants’ views on the application process. The third piece of work is ‘Doing Better Initiative to Reduce Red Tape for Farmers & Rural Land Managers’ (SRUC, 2014) where regulations (or their implementation) that impinge on business decisions were identified and solutions were put forward to address these.

Administrative burdens

The general literature review (reported in Section 7) and Stakeholder views (reported in Section 5) revealed that the administrative burden and ‘form anxiety’ associated with support schemes can significantly affect land manager engagement with support systems.

We can relate this to the RPID survey responses, in particular the question ‘Applications made to other schemes in the last twelve months’. Interestingly, 77% of RPID customers stated that they did not make another application to another non-SAF (Outside BPS, LFASS, AECS, FGS) scheme in the last 12 months.

Groups who had not made another scheme application are compared below:

• More owners (80%) than tenants (74%) and business partners (70%);
• More other businesses (84%) and farms (79%) than crofts (73%);
• More older (84%) than younger (66%) customers; and
• More customers that completed their SAF with support (81%) than those that completed it on their own (74%).

This would suggest that for the majority of RPID customers, the main support systems they are engaging with fall within the bracket of the SAF administrative process. It appears that many land managers are only engaging with SAF and not applying for schemes outwith this (e.g. AECS, Peatland Action etc.). Although it is difficult to draw conclusions from this question alone, the supporting evidence from this report would suggest that the administrative burdens are a considerable factor in preventing land managers from engaging with other support systems outside their SAF application.

For instance, the RPID survey found that a substantial number of RPID customers felt that application processes were too complicated, or the application forms were too long or complicated. When asked what customers’ main reasons for dissatisfaction with information from RPID, the main two reasons given were:

• The application process is too complicated (53%)
• Application forms are too long/complicated (52%)

Furthermore, in the 2013 RPID customer satisfaction survey, the most common reason for dissatisfaction with information from RPID was ‘not enough information being available’ (29%). This suggests that the administrative burden involved with applying for rural funding schemes has become a more significant influence on farmer decisions in the period between 2013-2021.

The challenges of administrative burdens are further reinforced when customers were asked about the ‘aspects of RPID’s performance customers would like to see improved’ where the most popular answer was ‘application forms are easy to complete’ (42%). One respondent was quoted:

“Website and all forms etc. need to be rewritten and simplified. They need to be clear and concise and user friendly. Use words not acronyms. Use far fewer words.”

We find further evidence to support this in SRUC (2014) where a list of recommendations is provided to the Scottish Government on how to reduce red tape burdens placed on farmers and land managers. Recommendation 5 states that an IT system should be developed that reduces the form filling burden for farmers and land managers – reducing administration costs. This recommendation also suggests that a full review of data requests from farmers and land managers is undertaken to ensure that duplication is minimised.

Despite this point being raised in 2014, the findings from the RPID survey suggest that from 2013 to 2021 administrative burdens on land managers applying for government support schemes have increased.

Support required to access funding

There is also substantial evidence that suggests that many land managers in Scotland require support to submit applications to financial support systems. Evidence for this is provided by the RPID survey, where the following three points were cited as the reasons why customers needed some support with their Single Application Form submission:

• Personal (e.g., first time completing form, learning disability) – 43%
• Mistakes (e.g., want to avoid mistakes) – 41%
• Forms (e.g., difficulty accessing forms, take too long to complete) – 34%

This would suggest that many land managers find the current administrative processes involved with submitting applications to support systems a significant barrier to engagement and require support to ensure that they can access these. The response to this question suggests that the current complexity is leading landowners to obtain procedural support to complete their applications.

Of those that are using procedural support to complete applications, SRUC agents are the most common support agents being used (48% of cases). Interestingly, other business (not farmers) used commercial agents to support applications 51% of the time.

Land manager support system mapping

This section presents three infographics (drawn from RPID survey data and our findings from the previous sections of this study) representing the typical land manager pathways to access agricultural support systems in Scotland. Each infographic is broken down into four main sections (from left to right). The first section, motivations, highlights the broad overarching motivations that a land manager is looking to achieve within their business objectives. This includes motivations such as ‘business support’ and ‘woodland establishment’. The following section highlights the agency touchpoints that a land manager will engage with if they decide to follow one or multiple of the previous motivations. This includes both the agency (such as RPID) and the specific scheme that relates to that motivation (such as the Forestry Grant Scheme for Woodland establishment). The third section shows the administrative actions that are associated with engaging with each different support scheme, including information such as what IT system is used (e.g. RPID portal) and if support is generally needed by a third party. The final section details what kind of login credentials are needed for each administrative action and if these are shared or unique for each scheme.

Figure 4 represents all the pathways open to land managers, providing an overview of the support system landscape. Figure 5 highlights the pathways that a typical farming land manager could take. Figure 6 highlights the pathway that a non-farming land manager, such as an estate, could take. The following sub-sections draw out some of the key findings and help understand where, when and how land managers interact with support systems and services.

Figure 4 – land manager support system map

This figure presents an overview of all the motivations, touchpoints and administrative actions that a land manager could undertake if they were to take certain pathways. Key points from this infographic include:

• It appears that land managers only need to have one login credential to access all support services via RPID (Rural Payments and Inspections Division) in Scotland. This is the RPID portal login, where land managers can access the SAF, AECS application, SSBSS & SUSSS form and FGS application. For those schemes not under the umbrella of the RPID portal (Peatland Action), online submissions are required that do not require login credentials (FAS applications still require RPID Business Reference Number however). This would suggest that login credentials do not pose a significant barrier to land manager engagement with support systems.
• Regarding touch points, RPID is the agency that land managers are most likely to be engaging with for funding. This is because the most popular support schemes (BPS, LFASS, AECS etc.) are administrated through this agency. Other support schemes that are not administrated by RPID, such as the Forestry Grant Scheme, are still accessed through the RPID portal. FAS and Peatland Action support schemes are accessed outwith the RPID portal, but require relatively simple administrative inputs to complete.
• Overall, the RPID public sector support system network is administratively logical from a high-level perspective. The majority of schemes are accessed through the RPID portal, and those that are not are procedurally straightforward in terms of required steps. However, the level of detailed information needed by certain schemes makes accessing a wide range of these extremely challenging for some land managers in Scotland (recalling from section 5 that land managers differ widely with respect to skills and confidence to tackle administrative processes and implement management changes). For example, AECS applications are considered very complex due to the level of information that needs to be provided along with the lengthy application form/process. Furthermore, Forestry Grant Scheme applications require a level of detail that is beyond most typical land managers’ (farmers etc.) knowledge, leading to a reliance on external specialists to complete applications.
• On the whole, this would suggest that the complexities in land manager support systems, including the level of detail needed for specific applications are reducing engagement with systems that could encourage improved environmental management practices. This does not take into account private schemes, such as the Woodland Carbon Code, which would only add to this complexity.
• All other things being equal, administrative simplicity is preferable to complexity and (for applicants) greater flexibility is preferred. Hence efforts to, for example, streamline application and monitoring processes, reduce information burdens, widen application windows and vary contract lengths, are justifiable. However, accountability for public expenditure requires a degree of bureaucracy to ensure that funds are disbursed and used as intended, and simplicity and flexibility for applicants may impose additional complexity for administrators. Consequently, there are trade-offs, and the scope for improvements in process design alone will typically be limited.
• This implies that other steps need to be taken to improve accessibility, including the provision of additional procedural information and advice – which necessarily incurs additional public administrative costs, raising familiar questions regarding the appropriate degree of such assistance and whether it should be universal or targeted at specific groups.
• Moreover, administrative touch-points and contractual constraints are only one influence on land manager behaviour, implying that improved accessibility and flexibility will not by itself increase overall engagement with land use change. Other measures will also be needed. For example, attractive payment rates, sufficient technical advice and training, and support for capital investments.

Figure 5 – farmer decision pathway map

This figure presents an indicative pathway through the support systems that would be taken by a land manager (farmer) who does not have any specific environmental goals (woodland establishment, peatland restoration) but would like to improve the efficiency of their operation and reduce their overall impact on the environment. It is important to stress that this pathway is indicative, and it is not intended to represent all farmers in all locations. In reality, as explained in the literature review in section 7 later, all land managers will have a unique set of motivations, barriers and opportunities regarding land management practices that will affect their engagement with support systems. The findings from this infographic are summarised below:

• The majority of farming land managers will be engaging with support systems that are accessed through the SAF process (BPS etc.) as these are familiar and provide a high level of financial support for a relatively small administrative and practical input.
• Land managers of this type could also be engaging with AECS. This provides the land manager with an opportunity to improve the economic performance of their operations, whilst also benefitting the environment. Land managers will often choose options that require the smallest practical/administrative inputs compared to financial returns. Many land managers will require support from a third party to complete their AECS application due to the complexity of information required.
• Many land managers of this type will rely on FAS and other agents, along with informal networks, to provide procedural support to their applications to support systems. This is because farming land managers are often time-poor due to their focus on practical activities on farm, relying on others to assist with the administrative processes of applying to support schemes.

Figure 6 – Non-farmer decision pathway map

This figure presents an indicative pathway through the support systems that would be taken by a land manager (non-farming) who is looking to diversify the use of their land, improving economic and environmental performance simultaneously. Again, it is important to stress that this pathway is indicative, and it is not intended to represent all non-farming land managers in all locations. In reality, as explained in the literature review, all land managers will have a unique set of motivations, barriers and opportunities regarding land management practices that will affect their engagement with support systems. The findings from this infographic are presented below:

• Non-farming land managers are much more likely to engage with a wider range of support systems outwith those administered by RPID. This may be due to a mixture of different beliefs, fewer/different constraints on time and resources and more desire to diversify income streams to ensure financial resilience.
• These land managers still often rely on external specialists to assist with certain elements of the application process, such as external forestry consults when applying for the Forest Grant Scheme.

Figure 4. Land manager support system map

Figure 5 – farmer decision pathway map (N.B. this is indicative and not intended to represent all farmers in all locations.)

Figure 6 – Non-farmer decision pathway map (N.B. this is indicative and not intended to represent all non-farming land managers in all locations.

Land manager attitudes – a review of the literature

Factors affecting engagement with support schemes

The literature review highlighted that internal factors such as attitudes, beliefs and personal values can have a significant impact on engagement with support systems.

Values and knowledge

It was recognised as far back as the 1970’s (Gasson 1973) that farmers do not always make financially rational decisions and that a range of social and intrinsic factors may also be prioritised; risk perception, values and knowledge are particularly influential in business decision making.

Land managers, in particular farmers, generally have a strong sense of self and are often influenced by their intrinsic values. This theme can be explored when looking at land manager attitudes towards planting trees on their land. Historic literature suggests that land managers have a resistance to creating woodland and forests, due to traditional values surrounding the belief that measurable productivity and growth are their traditional core purpose. Burton et al. (2008) explores the importance of the ‘good farmer’ identity, where social status and personal validation is derived by the evidence of delivering a skilled role on landscapes, i.e. livestock farming. Burton (2004) concludes that planting woodland and forest (afforestation), as well as engagement with other non-farming activities, represents both a loss in productive output and a symbolic loss of the opportunity to demonstrate farming skill and knowledge.

Farmers often resist afforestation on this basis, with agriculture and forestry historically being viewed as competitors for land rather than complementary land management practices that could be adopted as a sustainable approach to single proprietary unit diversification (Nicholls, 1969; Hopkins et al. 2017). Therefore, as many farmers perceive themselves to be farmers only, they are unwilling to change their practices due to inherent values that are tied to their current activity. This trend is likely to be seen across most landowners, not just restricted to afforestation, who will possess their own objectives, values and knowledge. For example, Moxey et al. (2021) note that the willingness to participate in peatland restoration schemes is highly variable, and that cultural ties shape attitudes towards restoration activities.

On the other hand, some land managers have intrinsic values that prioritise attempting to balance the need for a productive enterprise and protecting/enhancing the environment. Mills et al., (2017) found that it was common to hear that farmers were attempting to find a balance between production and environmental management, which were not always seen as conflicting needs.

This is reflected by the well documented finding that farmers (and land managers as a whole) are often willing to adopt environmental measures if they are perceived to increase the efficiency of on farm activities and therefore prove cost effective (Feliciano et al. 2014). For example, Farsted et al. (2022) noted that climate mitigation measures are mainly perceived as, treated as, and appreciated for offering farm-beneficial functions other than climate change mitigation by Norwegian farmers. This is also reflected in the Farm Practices Survey (2022) where 44% of farmers thought that reducing emissions would improve farm profitability and that the main motivation for farmers to take action to reduce GHGs on farm was that it was considered good business practice (84%).

Unsurprisingly, those land managers that are personally concerned/motivated to address climate change are more likely to be undertaking environmental management measures on their land. Those who are less engaged are likewise less likely to be undertaking environmental management practices.

Ease of transition, control and risk perception

An important aspect of land manager engagement with support systems is the perceived degree of control afforded by the available schemes and the ease of operational transition.

Academic literature in this area has focused on exploring the barriers that prevent uptake of Agri-Environment Schemes (AES), specifically focusing on schemes that restrict land manager’s ability to control and own the final product that is being delivered. For example, Lampkin et al. (2021) suggest that a top-down prescriptive approach of some AESs has failed to engage farmers in a way that would give them ownership of the delivery of environmental goods. This view is supported by Daxini et al. (2019) who found that the intention to follow a Nutrient Management Plan is primarily driven by perceived behavioural control.

Thompson et al. (2021) further suggest that farmers are more likely to participate in AESs if they retain some control over implementation, which requires flexible terms and regular monitoring. Therefore, it appears an important element of how land managers engage with current support systems involves analysing the degree to which each support system will affect operational control.

Another key internal factor that will influence land manager engagement with support systems is risk perception. Multiple sources suggest that the clarity and certainty of the final objective of any support scheme is important to its uptake and success. Analysis from the James Hutton Institute (Rajagopalan and Kuhfuss, 2017) suggested that the uptake of the Agri-Environment Climate Scheme (AECS) was restricted by the lack of flexibility in options, along with the uncertainty on the environmental outcome due to the influence of external factors outside of the land managers’ control (climate, pests etc.)

Kuhfuss et al. (2018) also suggest the success of AES may vary depending on the clarity of the objectives and perceived challenges in achieving them. For example, afforestation is a relatively easy concept to understand and is generally low risk, however peatland restoration is much more difficult conceptually and is seen as a higher risk option. Indeed, peatland restoration may seem to be of high risk because UK peatlands are at the southern limit in the northern hemisphere and therefore at risk due to anticipated climatic changes.

The tolerance of land manager to the inherent risks that are involved with engaging with support schemes that require alterations in management practices is an important factor in determining uptake.

Socio-demographic, age and education

The traditional view within the literature is that older land managers are less willing to change land management practices and that younger and more educated farmers are more willing to adopt new practices and engage with environmental support schemes. Sutherland et al. 2016; Mills et al. 2016; Brown, 2019)

This is often supported by evidence that younger people have a higher degree of environmental concern, risk tolerance and openness to new practices (Dessart et al. 2019). Therefore, younger land managers may be more able to engage with support systems and understand the requirements and trade-offs involved. Benni et al. (2022) reported that the age and education of farmers was not found to affect time requirements to fill in administrative burdens. This suggests that the transaction costs associated with support systems does not interplay with age and education levels of applicants.

When analysing the factors behind farmers’ adoption of ecological practices, Thompson et al. (2023) found that socio-economic factors were insignificant more often than they were significant. Despite these findings, Tyllianakis and Martin-Ortega (2021) argue that the evidence base suggests that wealthier land managers stand to gain more than less wealthy land managers in enrolling in AESs. The impact of socio-economic and demographic factors on land manager engagement is therefore likely to vary considerably across different sectors and organisational structures.

Engagement and trust of official advice vs. informal networks

Due to the rise of information available (mainly through the expansion of digital services), answers can be found to many real-world and agricultural issues and questions online. Rust et al. (2021) suggest that farmers have previously often relied on in-person advice from traditional ‘experts’, such as agricultural advisors, to inform farm management practices. Sutherland et al. (2013) stress the importance of the perceived credibility of sources of advice. This view is supported by Daxini et al. (2019) who found that trust in technical sources of information (e.g. advisor and discussion group) is found to be a more influential determinant of farmers’ attitude, subjective norm and perceived behavioural control than trust in social information sources (e.g. family and the media).

Nonetheless, Birner et al. (2006) and Sutherland et al. (2022, 2023) highlight the breadth of sources of information, advice and training utilised by land managers, encompassing family and friends, peer groups, accountants, vets, input suppliers, private consultants, NGOs and public sector bodies, accessed in different modes including via print and social media, online, one-to-one meetings, group meetings and events/demonstrations.

This is discussed further by Rust et al. (2021), who suggest that farmers are now changing their information sources due to the rise of online sources of knowledge and advice, foregoing traditional ‘expert’ advice in preference for peer-generated information. They found that farmers regularly use online sources to access soil information and often changed practices based on information from social media. Results from their survey suggested that farmers placed more trust in other farmers and peer networks rather than traditional ‘experts’, particularly those from academic and government institutions, who they believed were not empathetic with the farmers’ needs.

This could be further compounded by many farmers deciding not to engage with advisory services at all. Dunne et al. (2019) found that almost one-third of farmers in Ireland were not using extension services and a further third had contracts with private sector and public sector advisors.

Research from the James Hutton Institute (Hopkins et al. 2020) also found that new entrants to farming are less likely to engage with subsidies and support systems than existing farmers in the sector. In particular, new entrants did not think that the ‘official’ Farm Advisory Service (FAS) and the Scottish Government were helpful when starting their enterprise. This finding is mirrored by Labarthe et al. (2022), who suggest that new entrants to agriculture are often disconnected from knowledge structures, as they often operate businesses that are not typically addressed by advisory services. Other ‘hard to reach’ or less engaged groups can include women farmers and those suffering from poor mental health (Hurley et al. 2022).

Understanding how land managers engage with knowledge networks and their trust of these networks is an important factor in determining their experience of support systems. By improving farmers’ awareness, it is expected that changes in behaviour would be reflected in the adoption of improved management practices. However, Okumah et al. (2021) argue that the limited research in this area so far has shown that the link between awareness and adoption exists. This link is indirect and is mediated and moderated by other factors. Nevertheless, on balance, it seems that hypothetically, with all factors being equal, more awareness is better than less awareness.

Summary

The willingness of land managers to engage with forms of support for changing management practices and land use patterns is influenced by a number of internal factors. These include the compatibility of change with land managers’ self-identify of what it means to be a land manager, particularly a farmer – something that is ingrained and often inter-generational, making it difficult to alter in the short-term. Similarly, inflexible management prescriptions are at odds with cherished decision-making autonomy and change can be perceived as incurring higher than acceptable levels of risk, although attitudes can be softened if prescriptions align with personal or business objectives.

Weak confidence and understanding regarding the purpose and practicalities of change reinforce business-as-usual, with a lack of trust in the credibility and relevance of available sources of information, advice and training further constraining engagement. Such internal factors vary across individual land managers, but there is some evidence that greater openness to change may be associated with (younger) age and (greater) education but also that some groups, including women, new entrants with no prior experience and people suffering from poor mental health, may be further disconnected from support systems.

External factors influencing land manager engagement with support schemes

Alongside the internal factors identified above, there are significant external factors that influence land manager behaviours, including the physical, environmental, business structure, financial, knowledge availability, social norms and time factors on land management.

Funding, costs and policy indicators

As with any business operation, the need to generate revenue to ensure the survival of the business is a high priority for any land manager. The majority of land managers, especially tenants, seek to make a profit from their land. Therefore, financial considerations are paramount to the landowners’ decision-making process, underlining the importance of support schemes and their potential to influence change.

Previous research has indicated that given the unpredictability of agricultural and land-based activities, only when economic conditions were stable could land managers focus on other activities – including environmental considerations (Scottish Government, 2012). Measures that do not guarantee financial benefits – e.g., that may have a negative impact on production or come at a cost to the farmer – are unlikely to be adopted in the absence of other tangible benefits.

In the latest Farm Practices Survey (2022), 32% of farmers who were already taking actions to reduce GHG emissions stated that environmental measures were too expensive to implement. This may explain why Ruto and Garrod (2009) found payment rates to be a key driver and Pineiro et al. (2021) conclude that interventions that lead to short term financial benefits have higher adoption rates than those that concentrate on delivering ecological service provision. This view is supported by Mills et al. (2016) who state that current financial incentives and regulatory approaches have had a degree of success in encouraging environmental practices, but these are ultimately transient drivers that have not led to long-term sustainability.

Within this, policy uncertainty may further hinder the uptake of environmental land management practices. Kuhfuss et al. (2018) describe these uncertainties as:

• differences in sources in funding (public vs private)
• eligibility rules
• financial uncertainty of prices in the carbon markets and
• potential emerging markets that may provide better results.

This is further compounded by whether a payment by results or an activity model is used. Moxey at al. (2021) reinforce this point by suggesting that peatland restoration work is hindered by the perceived ineligibility for agricultural support payments, tax breaks and concerns over future support arrangement and carbon market fluctuations.

Bio-physical constraints, tenure and structure

Environmental constraints often limit which environmental measures can be implemented on a spatial scale. Location, climate and environmental quality are key determinants of which support schemes are viable for a land manager’s piece of land as they affect what is implementable practically in local conditions in relation to opportunities. An example of this is the large amount of peatland and moorland that provides potential for peat bog restoration management practices: in these locations woodland planting should be discouraged (Lampkin et al. 2021). Paulus et al. (2022) provide further evidence to support this point by suggesting that environmental management practices are more likely to be implemented on sites with unfavourable agricultural conditions.

Two more important factors are the size of the enterprise and the tenure of the land. Regarding tenure, a meta-analysis of 46 studies (Baumgart-Getz et al. 2012) looking at the adoption of best-management practices found secure tenure to be a positive indicator of adoption, and the findings are likely to apply to climate friendly measures as well. This suggests that land managers who either own their land or are on secure tenancies with a good relationship with their landlord are more likely to adopt environmental measures due to the long-term security that their tenure status affords them.

Multiple sources within the literature also suggest that larger enterprises may be more willing and able to engage with support systems, particularly those with environmental outcomes (Mills et al. 2013; Paulus et al. 2022). Smaller enterprises are likely to have fewer opportunities to take elements out of production and fewer resources to apply without impacting their net income.

Ease of access to support

A key determinant of engagement with support systems is the perceived and actual accessibility of these schemes.

If a scheme is considered to be straightforward and easy to apply for, there is likely to be high engagement. The opposite is true of a scheme that is considered complex and time consuming. For example, for land managers the administrative load (transaction costs) and time commitment is often the determining factor on whether to participate or not. A common criticism of AESs is that they often carry high transaction costs, especially in comparison to more traditional support schemes (Kuhfuss et al. 2018).

Lampkin et al. (2021) suggest that schemes have become increasingly complex, partially in response to regulatory, audit and compliance issues. The administrative burden can also vary across enterprise type, with Benni et al. (2022) finding that dairy producers face substantially higher transaction costs than arable producers. Furthermore, once schemes are in place, the ongoing maintenance requirement for many AES (reporting etc.) can prove a further barrier to uptake (MacKay & Prager, 2021).

The Peatland Code can be used to understand some of the accessibility issues found in the Scottish agricultural sector. Moxey et al. (2021) suggest that the administrative burden associated with applying for joint funding via AESs and via the Peatland Code is perceived as overly complex, with interactions between them further increasing this. The study notes that the issue of interacting schemes occurs when having to demonstrate additionality, aligning funding cycles between different sources and coordinating across multiple land managers and investors.

Novo et al. (2021) also found that challenges in understanding the application process and funding mechanism were a barrier mentioned by interviewees in their study regarding the peatland carbon code.

Therefore, the perceived and actual transaction costs associated with support systems are a barrier to uptake. When looking to address this, Westway et al. (2023) caution that simplicity is important to encourage uptake, however oversimplification of schemes can lead to unintended consequences and needs to be balanced against public accountability for expenditure.

Knowledge availability, sharing and awareness

Engagement with support schemes and uptake of specific on farm measures is frequently linked with the knowledge and understanding of the individual land manager (Toma et al. 2018).

A lack of knowledge and understanding has been frequently cited as a key barrier to new management practices. This is further enhanced when new technological and informational processes are needed for alternative practices and if the costs/benefits are not clear or easy to judge. This finding is supported by results from the Farm Practitioner Survey (2022), where the most reported reason for not taking action was being unsure on what to do due to too many conflicting views (44%). These informational barriers are important as 30% responded that a lack of information was another key reason for not taking action.

This sentiment is echoed by two specific examples in Scotland. Firstly, Moxey et al. (2021) found that the awareness of the need for and benefits of peatland restoration is generally not well known amongst land managers, along with the voluntary market of the Peatland Code. Secondly, Lozada & Karley (2022) suggest that more evidence and greater awareness are needed amongst land managers about the financial and social outcomes of agroecological practices to facilitate uptake.

There is also evidence that land managers have a difference in ability to adopt new practices due to a variance in resources. Larger scale land management operations may have more resources and the ability to bring in consultants and agents for any new opportunities and land management practices. This is in comparison to smaller scale land managers who may not be able to approach new opportunities in the same manner due to (e.g.) a lack of time and cash plus higher overhead and transaction costs and less scope to cope with risk.

As an example, it has been suggested that small scale agroecological farmers might disproportionately suffer from a lack of access to incentives, despite delivering to environmental policy targets, or see incentive schemes as contrary to their farming ethos (Lozada & Karley 2022). This involves access to specialist advisors, where more profitable enterprises will be able to access specific advice on a more frequent basis compared to less profitable enterprises.

Social norms

As seen in section 4.2 above, farmers do not always make rational economic decisions and are influenced by societal goals and norms (Mills et al. 2017), the influence of a land manager’s peer group is likely to determine the extent to which they engage with specific support systems and management practices. This is observed in multiple studies (Kuhfuss et al. 2016: Cullen et al. 2020; Cusworth, 2020) where peer behaviour has been deemed to influence land manager uptake of environmental practices to a varying degree through framing of what it means to be a ‘good farmer’.

Howley et al. (2021) suggest that social norms can be harnessed to encourage pro-environmental behaviours in land managers. The researchers found that providing farmers with an opportunity to demonstrate their “green credentials” to their peer group can encourage conservation practices.

Summary

The ability of land managers to engage with changing management practices and land use patterns is influenced by a number of external factors. At a practical level, biophysical characteristics, and the area of land available will determine the suitability of alternative practices and land uses, but also the scope for experimentation and risk management. Equally, tenancy restrictions may impose legal constraints on freedom to change.

As businesses, the financial consequences of making changes matters. Funding needs to cover actual cash costs but also opportunity costs (time, income forgone) and transaction costs. The latter arise from application and reporting processes, both for funding and/or non-funding support, and can be disproportionately burdensome for smaller land managers. Separately, access to support can vary in terms of eligibility but also the availability of information, advice and training. Importantly, internal factors such as social norms and peer group pressure strongly influence land managers’ self-identity. This affects their perception of whether different management practices and land use patterns are compatible with their own values.

Discussion guide

The findings from the literature review suggested that we should focus on three main themes when we were drilling into the details with key stakeholders:

• identify the main determinants of ability and willingness to change land use and land management practices, to give us a clearer understanding of the key factors that influence land manager decision making, including their motivations, what they want to achieve for their business or organisation, and their appetite to change.
• focus on the existing support systems that land managers are engaging with and their experiences of doing so. This allowed us to identify and map all existing and relevant land use information systems, support services and the current incentives for land use transformation directly related to achieving Net Zero and/or nature restoration and understand some of the key barriers/opportunities regarding land manager engagement with these systems.
• explore how land managers are accessing these support systems, which allowed us to explore where, when and how the land managers interact with the systems and services.

The interview methodology and more detail on the interview questions can be found in Appendix C, and the findings are summarised above in section 5.

SWOT & PESTLE analysis

This section provides the details of a SWOT and PESTLE analysis on the current land manager support systems in Scotland and were informed by the literature review and stakeholder engagement exercises.

8.1 SWOT analysis

8.2 PESTLE analysis

Conclusions

Our research has reinforced existing findings in the literature surrounding land manager behaviour and decision making. Reflecting its relative prominence within public expenditure and land-based businesses in rural areas, agriculture dominates much of published literature on land use support and this was supplemented by stakeholder interviews, including with individuals representing other sectors.

The key message is that land manager engagement with support systems is determined by a range of interacting internal and external factors. These relate to financial, practical and cultural influences on both willingness and ability to engage. This is supported by the following conclusions:

• The administrative systems associated with land use support in Scotland are perceived as logical from a high-level perspective. Most interactions with the system are through the RPID portal, which only requires one set of login credentials to access a wide range of support systems. Those support systems not under this umbrella are easy to access.
• However, the administrative burden associated with applying to these schemes, i.e. form filling, is the main barrier to engagement. Some land managers have more resources available to absorb this administrative burden, such as large estates, investment owners and rewilding estates. If several schemes are appropriate this burden will increase.
• Procedural support (i.e. form filling by an advisor on behalf of a land manager) is widely available from both public (FAS, SAC) and private advisory sources. However, this is distinct from practical support, such as site-specific implementation advice, which was frequently mentioned by stakeholders as key to facilitating the uptake of environmental management practices, and yet less readily available, and can depend on location.
• We found that land managers often decide whether to engage with support and advice based on the credence of its source. For example, farmers are more likely to trust advisers/organisations that have a background in practical farming over those from a consulting/academic background.
• Another key determinant of engagement with support systems was the level of control associated with outcomes/management practices. Stakeholders mentioned that the perceived prescriptive nature of AECS and forestry related grants would prevent land managers from choosing to access these support services.
• Land managers in Scotland primarily access public funding support, with some accessing private finance to supplement their income or achieve specific goals. For those accessing private finance, this is generally done to avoid the conditionality of public funding support and retain operational control over the management of their land.
• A lack of knowledge and understanding has been frequently cited as a key barrier to new management practices. This is further enhanced when new technological and informational processes are needed for alternative practices and if the costs/benefits are not clear or easy to judge.

Going forwards, administrative simplicity is preferable to complexity and (for applicants) greater flexibility is preferred. Therefore, efforts to streamline application and monitoring processes, reduce information burdens, widen application windows and vary contract lengths, are justifiable. However, accountability for public expenditure requires a degree of bureaucracy to ensure that funds are disbursed and used as intended, and simplicity and flexibility for applicants may impose additional complexity for administrators. Consequently, there are trade-offs, and the scope for improvements in process design alone will typically be limited.

As our literature findings highlight, administrative touch points and contractual constraints are only one influence on land manager behaviour. This implies that improved accessibility and flexibility will not by itself increase overall engagement with land use change. Other measures will also be needed such as attractive payment rates, sufficient technical advice, training and management flexibility.

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Appendices

Appendix A – Support system overview

As part of the desk-based research element of this report, we attempted to discover as many of the existing official support systems available to land managers in Scotland as possible. This included visiting Scottish Government resources, such as the Rural Payments and Services website^[7], along with an internet trawl through other resources – such as NatureScot’s summary of the Agri-Environment and Climate Scheme^[8]. We used this information to compile Table 5 below, giving a summary of all the available sources of support and an indication, where possible, of how land managers are engaging with this support system.

To help understand how land managers are engaging with support systems, we identified and defined the key support system providers. These are outlined below:

Government – publicly funded support systems. These can come in the form of general funding support schemes (such as BPS) or more targeted schemes with environmental objectives (AECS). Government funding also underpins other forms of support, such as the Farm Advisory Service. Generic, rather than agricultural-specific business funding is also available from local and central government, but is generally regarded as less relevant to land managers.

Private sector – Land managers routinely access private sector funding in the form of overdrafts and loans offered by banks, plus calling upon personal networks (friends and family). Other sources of short-term credit include auction markets and input suppliers. More novel funding sources such as crowdfunding and impact bonds have emerged in recent years, as have voluntary carbon markets e.g. the Woodland Carbon Code and the Peatland Code.

Knowledge networks and advisory services – Land managers draw on a range of informational support when making decisions. This includes direct government sources plus third-party sources funded by government (e.g. the Farm Advisory Service) but also independent third-party provision. The latter includes advisory services tied to input suppliers as well as independent consultants but also, importantly, less formal reliance upon friends and family plus peer-to-peer networks.

Third sector, charities and NGOS – Certain groups with defined goals, such as nature protection and restoration, also provide landowners with advice and funding to undertake measures that align with their objectives. These groups are often landowners themselves.

Table 5: Support scheme overview

Appendix B – Archetype methodology

Archetype identification

The first priority was to define a baseline list of Scottish land manager archetypes^[16] in discussion with the project steering group.

Archetypes are a useful tool when trying to simplify the heterogeneity of land managers in Scotland and provide context to the following sections of analysis. The simplified archetypes were then used to underpin the mapping elements of this study. Firstly, archetypes were used to provide a high-level overview of how different land managers are engaging with support systems in Scotland. Secondly, the archetypes were used to identify potential climate change mitigation project providers in Table 6 below. Thirdly, archetypes were discussed with participants at the stakeholder workshop to explore the extent to which each archetype is interacting with support systems in the manner to which is expected based on stakeholder interviews and our literature review.

The following archetypes have been informed by Mills et al. (2017) (see Figure 1) where three main factors are defined that influence a land manager’s willingness and ability to undertake environmental management.

These are listed below:

• Willingness to adopt – willingness of land managers to undertake environmental land management practices and the intrinsic factors (e.g., motivations, beliefs, social norms) affecting land managers environmental behaviours.
• Farmer Engagement – where land managers enter into dialogue, discussion and collective problem framing with those who hold environmental knowledge and expertise.
• Ability to adopt – farm characteristics (e.g., tenancy, scale, skills and capital constraints), that influence land manager’s decision making in relation to environmental management and their ability to adopt new practices.

Mills et al. (2017) found that land managers tend to exhibit different sub-optimal positions within this conceptual framework. These positions are found below:

• Willing and engaged only – willingness to undertake environmental management activities on their land, but this has not translated into behaviour because the manager does not have the ability to do so.
• Able and engaged only – undertaking environmental management and has engaged with advice, but lacks sustained motivation to maximise environmental benefits.
• Willing and able only – actively undertaking environmental management, but has not engaged with any advice which means that land is not delivering its full environmental potential.
• Disengaged – not engaged with any environmental management, either because they were not willing, they do not have capacity, or they dislike outside interference or are concerned with loss of control or management flexibility.

Some characteristics are more readily observable than others. For example, farm type, size and tenure status are recorded routinely, levels of financial, human and social capital or personal attitudes less so. Nevertheless, it is possible to construct example archetypes that can be used to explore how different configurations may affect land use decisions.^[17] The Table on the following page is an attempt to illustrate a broad range of potential land manager archetypes in Scotland. This has been arranged primarily based on activity, as this is the most observable difference between land manager types. We have provided a hypothesis of the likely size, tenure and engagement along with a brief description of key characteristics and indication of location. Words in bold indicate that this characteristic applies to the archetype.

In further developing these archetypes, we hypothesized additional influences on ability and willingness to change land management/use:

• Tenure restrictions (particularly short-term leases and crofting tenure, notably common grazing) constrain automatic freedom to change (and reap rewards);
• Small scale incurs proportionally higher transaction (e.g., application) costs, although transaction costs also deter larger land managers. Small scale also constrains availability of labour/capital/land to make changes.
• Availability of advisers (particularly for non-traditional topics) perceived as credible and relevant is limited, especially/ in remoter areas.
• General lack of policy certainty also deters change.
• Biophysical conditions constrain land use options.
• Financial circumstances constrain ability to change – but also affect relative importance (leverage) of public funds e.g., market revenues and/or non-land income may matter more, making some land managers less responsive to policy (i.e., opportunity cost vary) even if public funding is generous.
• All of the previous influences are mediated through cultural identities, social norms and personal motivations – willingness to change will vary within any given category of activity, size, tenure, region, biophysical circumstances and financial circumstances.

Archetype table

Table 6 – Archetypes