The multiple crises (climate, biodiversity, austerity) facing our socio-ecological systems require ambitious responses; with much of the responsibility for protecting public goods and developing sustainably lying with public policy. To tackle these wicked problems, there are increasing calls for policy coherence: to use the levers of government in a more holistic and systemic manner. Land use transformation is crucial to achieving these ambitions. However, there is limited scholarship that takes a comprehensive approach to analysing policy coherence (both horizontal and vertical). Common to many nation-states, the Scottish Government has made ambitious pledges to address climate action (mitigation and adaptation) and nature, with an emphasis on leaving no one behind e.g., net zero by 2045 using Just Transitions. In this research we examine the policy coherence of 66 Scottish land use related policies in addressing land use transformation, as well as an in-depth coherence analysis of 11 agricultural policies. We address three research questions on the synergies and problems in policy coherence for land use transformation, as well as opportunities for improvement. Overall, we found that half of the 66 policies examined advanced land use transformation, but we query the possibility of hidden conflicts. The in-depth coherence analysis highlighted that when looking at the agricultural policies as a collective, coherence was clear, however, on the individual level it was not. Our paper shows that whilst challenging to implement, paying attention to multiple forms of policy coherence can highlight opportunities to consider when revising or designing policies for these pressing problems.

Agricultural support payments are a significant position in public budgets, and the legitimacy of such payments is subject to continuing debate. The legitimacy rests on the social acceptance of citizens for support payments to farmers, which is the focus of this study. Social acceptance is investigated using evaluations of farm and farmer descriptions in a factorial survey experiment. The results reveal higher acceptance of payments for farms demonstrating enhanced animal welfare, biodiversity, and a lower carbon footprint. The acceptance of support payments is negatively associated with payment amount, but payments to farmers who are financially struggling are more accepted than payments to profitable farmers; indicating respondent preferences that align with the need justice principle. Study findings can be used to inform priorities for legitimate policies of agricultural support schemes, to identify areas of consensus or disagreement regarding social acceptance of support, and to facilitate effective communication on agricultural support policy.

Climate smart farming requires food production to sit alongside practices which sequester greenhouse gas emissions. Given the requirement to meet net zero emissions by the middle of the century, agricultural policies are now seeking to embed climate smart approaches within future support schemes. Path dependency, the influence of past choices on decision making, has been found to constrain future growth pathways. We apply this concept within a survey of 2494 farmers in Scotland to understand their intentions towards uptake of two prominent climate smart approaches, namely forestry expansion and on-farm renewable energy. We employ a bivariate probit model to estimate the single and joint dependences of these two activities within a farm decision making framework. Factors such as succession planning, the level of agricultural diversification and risk seeking perceptions were found to be positively related to influencing uptake. However, the strongest predictors for uptake were past expansion of these activities and, conversely, a limiting factor for those who did not intend to increase activities. This provides some evidence that path dependencies will limit large scale adoption to meet a net zero target. We argue for a dual approach to intervention which differentiates between past adopters and those who are reluctant to adopt. More targetted support for these two cohorts would address these high level policy ambitions.

The UKs withdrawal from the European Union presents multiple uncertainties for farm management planning. Encouraging growth within the agricultural economy requires some acceptance of risk within farming decision making. This briefing note outlines the results of a survey of 2,494 farmers, crofters and smallholders, run during the summer of 2018, on their approaches towards farming and how this may affect business planning post-Brexit.

Epidemiological studies have consistently linked exposure to PM2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP; however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity campaign (APHH-Beijing), and PM2.5 OP was analysed using four acellular methods: ascorbic acid (AA), dithiothreitol (DTT), 2,7-dichlorofluorescin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Each assay reflects different oxidising properties of PM2.5, including particle-bound reactive oxygen species (DCFH), superoxide radical production (EPR) and catalytic redox chemistry (DTT/AA), and a combination of these four assays provided a detailed overall picture of the oxidising properties of PM2.5 at a central site in Beijing. Positive correlations of OP (normalised per volume of air) of all four assays with overall PM2.5 mass were observed, with stronger correlations in winter compared to summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM2.5 mass concentrations (μgm-3) were found to have lower mass-normalised OP values as measured by AA and DTT. This finding supports that total PM2.5 mass concentrations alone may not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM2.5 composition, gas-phase composition and meteorological data, provided detailed insight into the chemical components and atmospheric processes that determine PM2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM2.5 for both mass- and volume-normalised data. AA and DTT assays were well predicted by a small set of measurements in multiple linear regression (MLR) models and indicated fossil fuel combustion, vehicle emissions and biogenic secondary organic aerosol (SOA) as influential particle sources in the assay response. Mass MLR models of OP associated with compositional source profiles predicted OP almost as well as volume MLR models, illustrating the influence of mass composition on both particle-level OP and total volume OP. Univariate and multivariate analysis showed that different assays cover different chemical spaces, and through comparison of mass- and volume-normalised data we demonstrate that mass-normalised OP provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis. This study constitutes one of the most extensive and comprehensive composition datasets currently available and provides a unique opportunity to explore chemical variations in PM2.5 and how they affect both PM2.5 OP and the concentrations of particle-bound reactive oxygen species.

The negative effects of air pollution on human health has been subject to a number of epidemiological studies that consistently link respiratory and cardiovascular diseases to exposure to particulate matter (PM) (Englert, 2004). It is estimated that up to 0.3 million premature deaths per year in Europe and 2.1 million deaths worldwide are the result of exposure to particles with an aerodynamic diameter less than 2.5 μm (PM2.5) (Andersson, 2009). However, identifying the specific particle properties responsible for these health effects, such as their physical and physicochemical characteristics, as well as their chemical composition, remains a challenge. One of the leading hypotheses for how particles cause harm is by inducing oxidative stress and inflammation, which can subsequently lead to disease (Øvrevik, 2015). In particular, reactive oxygen species (ROS), which typically refer to a range of species including hydrogen peroxide (H2O2) possibly including organic peroxides, the hydroxyl radical (.OH) and superoxide radical (O2.-), may substantially contribute to the oxidative potential (OP) of PM and hence influence their toxicity. An excess of ROS in the lung, introduced or generated via particle exposure, leads to an imbalance of the oxidant-antioxidant ratio in favour of the former, which can subsequently promote oxidative stress. There are a number of acellular methods used routinely to measure aerosol OP, including the dithiothreitol assay (DTT), ascorbic acid assay (AA), 2,7-dichlorofluoroscein/hydrogen peroxidase assay (DCFH/HRP), and electron paramagnetic resonance (EPR) spectroscopy. In this work, the OP of aerosol collected in Beijing, China, in the winter 2016 and summer 2017 during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH) campaign is quantified, with 30 24-hr aerosol filter samples analysed for each season. We use the four aforementioned methods to measure OPAA, OPDTT, OPDCFH and OPEPR, and to extensively characterise the seasonal variation of aerosol OP in a megacity. All OP measurements show a significantly stronger correlation with PM2.5 mass in the winter compared to summer. Furthermore, the OPAA, OPDTT, OPDCFH and OPEPR were correlated using univariate and multivariate analysis with a variety of other measurements such as meteorological data, trace gas measurements and aerosol composition measurements including organic aerosol components and x-ray fluorescence elemental analysis. These results emphasise that the four OP methods applied in this study capture different aspects of aerosol OP between the seasons. As an example, OPAA normalised to account for aerosol mass show that aerosol OPAA in the winter is higher on average and more variable compared to the summer, whereas OPDCFH is more consistent between the winter and summer seasons. OPAA also showed a strong correlation with PM2.5 mass in the winter (r2 = 0.91) but correlated poorly in the summer months (r2 = 0.09), suggesting different aerosol components affect OPAA in summer and winter. Englert, N. Toxicol. Lett. 149, 235–242 (2004). Andersson, C., et al., Atmos. Environ. 43, 3614–3620 (2009). Øvrevik, J., et al., Biomolecules 5, 1399–1440 (2015).

Emma Congreve is joined by Professor Andrew Barnes and Steven Thomson from Scotland’s Rural College (SRUC) to discuss how the current pandemic is affecting the rural economy. Covid-19 has provided farmers and the domestic food supply chain with many challenges, and these differ by sector and within sector. Another key plank of the rural economy, tourism, has seen demand disappear. Whilst many of the effects of Covid-19 will be the same in both rural and urban sectors, the predominance of these sectors in these areas presents particular challenges and perhaps opportunities. Covid-19 has emerged at the same time as uncertainty over future trade relations and financial support for agriculture post-Brexit. In the final part of the podcast, we discuss some of the challenges coming up in the medium to long term. For those who would like to find out more, SRUC has a dedicated Rural Brexit Business webpage. Timestamps(2.17) Overview of the rural economy, agriculture & the domestic food supply chain in Scotland(8.22) Impact of Covid-19 so far(14.25) What support is there for the sector(20.16) Impact on tourism and remote rural areas(28.14) Medium to long term prospects, including Brexit

Less Favoured Areas (LFA) were designated to support farming activity on land with limited productive potential. However, progressive land abandonment in these areas questions the rationale and targeting of support payments to maintain viable farming enterprises. Using micro level data on farm businesses over the period 2003-2016 matched to land capability and spatial data we identify the distribution of viable and vulnerable enterprises in Less Favoured Areas. We find five categories of household based on progressive quality of life thresholds, namely i. vulnerable, ii. sustainable, iii. viable, iv. resilient, and v. robust. A proportional odds model measured the effect of biophysical and remote disadvantage on predicting these states of viability, along with farm family lifecycle factors. Whilst we would expect higher proportions of disadvantaged farmland to be negatively related to viability, when combined with rural remoteness this increases the magnitude of the effect. However, clear succession planning and tenancy arrangements suggest that approaches to management of the business and the farm family life-cycle may overcome some of these disadvantages. These results have to be considered against the UK's planned withdrawal from the Common Agricultural Policy. This offers opportunities to provide a more nuanced approach to targeting and supporting disadvantaged regions beyond current criteria. However, there would seem to be dissonance between the proposed payment for public goods agenda, which is results orientated, and support for correcting natural disadvantages where opportunities for delivery of these public goods will be limited.

The ability of particulate matter (PM) to generate reactive oxygen species and induce oxidative stress in human body is known as oxidative potential (OP). OP is considered an important indicator of the toxicity of PM, which is associated with adverse health impacts. Linking the predicted health impacts of aerosols to OP may be more relevant than considering PM mass only. In this study, we determined the OP of PM2.5 (PM with aerodynamic diameter less than 2.5 µm) in Dammam, Saudi Arabia, in order to understand the relationship of OP to PM mass and composition in the present and absent of dust storm. PM2.5 was collected from two locations in Dammam city in the winter and summer of 2018. The first location was the city centre as an urban area while the second one was in the campus of Imam Abdulrahman Bin Faisal University as an urban background area. OP was quantified using dithiothreitol (DTT) assay. The mean PM2.5 mass in the summer (120.5 µg/m3) was nearly twice that in the winter (62.6 µg/m3). The average OP activity per air volume (DDTv) in the winter was 1.14 nmol min-1 m-3 while in the summer it was 1.77 nmol min-1 m-3. Conversely, the mean OP activity per PM mass (DDTm) in the winter was 24.56 pmol min-1 µg-3 while it was lower in the summer at 17.3 pmol min-1 µg-3. Results showed an inverse correlation between PM mass and DDTm, while there was a positive correlation between PM mass and DDTv. Even though the average mass of PM2.5 in the summer was almost twice that in the winter, the average DDTm was lower in the summer compared to winter. This is due to the much lower oxidative potential in dust storm particles, which contribute significantly to the summertime PM2.5. Our results suggest that OP is driven by PM composition rather than mass.

Citation for pulished version (APA): Brooker, R. W., Thomson, SG., Matthews, K. B., Hester, A. J., Newey, S., Pakeman, R. J., Miller, D., Mell, V., Aalders, I., McMorran, R., & Glass, J. (2019). Socioeconomic and biodiversity impacts of driven grouse moors in Scotland: Summary Report. SEFARI. https://sefari.scot/document/socioeconomic-and-biodiversity-impacts-ofdriven-grouse-moors-in-scotland-summary-report

Johne's disease is an endemic contagious bacterial infection of ruminants which is prevalent in the United Kingdom and elsewhere. It can lower financial returns on infected farms by reducing farm productivity through output losses and control expenditures. A farm-level analysis of the economics of the disease was conducted taking account of farm variability and different disease prevalence levels. The aim was to assess the financial impacts of a livestock disease on farms and determine their financial vulnerability if farm support payments were to be removed under future policy reforms. A farm-level optimization model, ScotFarm, was used on 50 Scottish dairy farms taken from the Farm Business Survey to determine the impacts of the disease. A counterfactual comparison of five alternative "disease" scenarios with a "no-disease" scenario was carried out to evaluate economic impact of the disease. The extent of a farm's reliance on direct support payments was considered to be an indicator of their financial vulnerability. Under this definition, farms were grouped into three financial vulnerability risk categories; "low risk," "medium risk," and "high risk" farms. Results show that farms are estimated to incur a loss of 32% on average of their net profit under a standard disease prevalence level. Farms in the "low risk" and "medium risk" categories were estimated to have a lower financial impact of the disease (22 and 28% reduction on farm net profit, respectively) which, along with their lower reliance on farm direct support payments, indicate they would be more resilient to the disease under future changes in farm payment support. On the contrary, farms in the "high risk" category were estimated to have a reduction of 50% on their farm net profit. A majority of these farms (61%) in the "high risk" category move from being profitable to loss making under the standard disease scenario when farm support payments are removed. Of these, 15% do so because of the impact of the disease. These farms will be more vulnerable if changes were to be made in farm support payments under future agricultural policy reforms.

Dataset to accompany work on the impact of hill farming in Scotland commissioned by RESAS (Scottish Government). Data resolution is agricultural parishes, spatial data defining these can be downloaded from . Data used to define the hill farming score is derived from the following datasets: Ordnance Survey Terrain 50; Scottish Natural Heritage, landscape character assessment, carbon and peatland map; James Hutton Institute land capability for agriculture; RESAS agricultural census common and rough grazing areas. Licence statements for input data are: Derived from or contains: Scottish Government and SNH information licensed under the Open Government Licence v3.0; James Hutton Institute materials licensed under the Open Government Licence v.2.0; and Ordnance Survey data Crown copyright and database right 2018. Generation code can be found here: https://doi.org/10.5281/zenodo.1887477