Farms In Scotland Research Articles

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer-based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems.

Modeling of Blockage and Wake Effect: Comparison with Field data

We apply engineering approaches for estimating the induction zone in front of a wind turbine and escalating to the global blockage at wind farm and cluster of farms scale as well accounting for wind farm wake. The methods include the vortex cylinder model and the self-similar model for blockage, and Jensen and Eddy-viscosity models for the wake estimation. To calibrate each model, Reynold Average Navier-Stokes with Actuator disc simulations are employed. Every model’s efficacy is assessed in comparison to the validation data of an operated wind farm in Scotland with more than 100 turbines of 10 MW each. We also examine the widely used process within the wind industry for transferring the application of calibrated wake and blockage models from one wind farm. According to our simulation analysis, blockage causes sideways acceleration and upstream slowdown. This also result in a gradient in the power produced by each turbine on the farm, which, if not properly accounted for, appears to be a wake model constraint. Furthermore, the response of different turbine type in another environment is not effectively predicted by the blockage and wake model, which is based on a particular turbine type and environment.

Optimising seafood nutritional value and environmental sustainability in aquaculture through a novel integrated modelling tool applicable to IMTA and monoculture

Aquaculture policies that decrease the reliance on wild-caught fish and promote integrated multi-trophic aquaculture (IMTA) both aim to lessen the environmental footprint but often overlook economic-environmental trade-offs and neglect social, health, and broad-scale sustainability considerations. Here we present a new bio-physical model (FYNE) that bridges some of these gaps, by linking farm operational choices, such as feed choices and farm siting, with the nutritional value of the farm-gate seafood products and the resulting environmental eutrophication footprint at the farm level. The FYNE model operates both in conditions of monoculture or of IMTA production, and estimates feed composition effects on growth and omega-3 fatty acid content of Atlantic salmon (Salmo salar) and blue mussel (Mytilus edulis), and nitrogen and carbon concentrations, the latter being a measure of the environmental eutrophication footprint of the farm. The model here developed integrates for the first time (i) an ecosystem model of an IMTA and (ii) a fatty acid dynamics model, with new models for salmon feed digestibility and mussel ingestion. The outputs span (i) production analysis for simulation of biomass; (ii) farm mass-balance analysis on deposition analysis and environmental eutrophication footprint; and (iii) product nutritional quality, in terms of fatty acid content. An application of the model to an IMTA farm in Scotland (UK) demonstrates how fatty acid and environmental changes can be modelled in tandem and could be used to support the industry to optimize the nutritional value of cultured species and manage aquatic environmental eutrophication footprint at farm level, informing feed and siting decisions. The model effectively predicts changes in omega-3 fatty acids, particularly DHA and EPA, in response to variations in feed composition. Notably, our results confirm that salmon waste has limited influence on mussel growth due to the spatial separation within the IMTA system, which emphasizes the need for strategic siting to enhance system productivity through effective waste recycling. Additionally, results show that while increasing fishmeal in diets enhances environmental outcomes, it reduces levels of critical fatty acids like DHA and EPA, underscoring the need for balanced feed formulations. The method developed in this study described a novel integrated model (FYNE) that extend previous modelling work by incorporating the influence of farmed species feed composition on fatty acid dynamics as well as the farming environmental factors.

Biomarker and proteome analysis of milk from dairy cows with clinical mastitis: Determining the effect of different bacterial pathogens on the response to infection

Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49–61) common to CATHL 1–7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 μg/ml and LF ≥ 325 μg/ml and MAA < 16 μg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including β-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).

Actualizations of agroecology among Scottish farmers

ABSTRACT Transformative agroecology challenges industrialized food and farming systems, proposing an alternative vision in which farms are designed around ecological symbioses and embedded within socially just food networks. However, at a policy level, alternative conceptualizations of agroecology have emerged that emphasize on-farm efficiency gains but lack broader objectives of agroecosystem and food system transformation. This phenomenological inquiry explores the agroecological narrative among Scottish farmers and considers its impacts on agroecosystem and food system change. Interviews were conducted across 15 farms in Scotland (20 participants) following the trans positional cognition approach (TPCA). Actualizations of agroecology were found to be value-driven approaches to developing individualized, lower-input farming systems. All farms were attempting to influence agroecosystem change through the application of ecological principles, and most (11/15) were contributing to food system change directly through involvement in alternative food networks. Smaller-scale farmers appear to deliver the most authentic actualizations of transformative agroecology but emphasized more strongly their financial challenges. A key recommendation for policymakers is to strengthen the support mechanisms available for small-scale ecological agriculture.

Interactive effects of multiple stressors with significant wave height exposure on farmed Atlantic salmon (Salmo salar) welfare along an inshore-offshore gradient

Fish farming is the fastest growing food production sector worldwide and now accounts for most human fish consumption. Expansion of finfish aquaculture to exposed offshore marine environments is appealing where additional sheltered areas are unavailable. While more energetic environments may reduce waste accumulation and parasite exposure, effects on fish health and wellbeing are largely speculative. The multiple stressors faced by fish on offshore farms may interact synergistically and increase their cumulative impact. We used 20 months of health and welfare data from eight Atlantic salmon (Salmo salar) sea pen farms in Scotland along a wave exposure gradient to assess the effects of, and interactions between, environmental variables and management treatments on fish mortality and parasite loads. While farms showed high variability in mortality rate and sea lice infections, multi-level Bayesian modelling indicated that wave exposure primarily modulated effects of other variables. Higher exposure farms showed steeper increases in mortality with time and with extreme temperatures. Similarly, sea lice infections tended to increase with time, with higher exposure farms seeing steeper increases at higher Amoebic Gill Disease (AGD) scores and at high temperatures. The effect of AGD was greater at slow water speeds. Treatments against parasites were more frequent at low exposure farms, leading to uncertainty in their impact on welfare across farms. The support for interactive effects of wave exposure with other variables rather than strong direct effects suggests an accumulation of chronic and acute stressors. Expansion of aquaculture to more energetic offshore environments may have negative impacts on fish health in some circumstances, requiring adaptation of practices. In particular, the stronger increase in mortality over time may have implications for cycle length in different environments, and the more dramatic impacts of the warmest temperatures at high exposure farms call for consideration of the change in water temperature both inshore and offshore.

New models for feasibility assessment and electrolyser optimal sizing of hydrogen production from dedicated wind farms and solar photovoltaic farms, and case studies for Scotland and Vietnam

Although green hydrogen produced from dedicated wind and PV plants is considered an alternative route to achieve net zero targets, its assessment models are only being developed in recent years. Even though electrolyser is known to account for a large portion of the investment costs, there is still a lack of sizing models for this equipment, which can negatively affect the feasibility of plants. Moreover, the impacts of future technology maturity and cost reduction on the feasibility of hydrogen production plants are rarely analysed and reported. Realising the research gaps from previous studies, this paper proposes a comprehensive feasibility assessment model for green hydrogen production from dedicated offshore wind and PV plants, utilising net present value, discounted payback time, and levelised cost of energy and of hydrogen (LCOH) as economic evaluation criteria. Furthermore, a unique sizing model is presented, allowing the optimal electrolyser size with the lowest LCOH to be found. Two case studies investigating a hypothetical offshore wind farm in Scotland and a hypothetical PV farm in Vietnam to produce green hydrogen have been carried out, in which the impacts of technology maturity and cost reduction on the LCOH are analysed. It is projected that the LCOH in Scotland and Vietnam case studies, with specific conditions and assumptions of the two cases, would reduce from 3.10 and 3.05 $/kg in 2030 to the lowest values of 2.16 and 1.59 $/kg in 2050, respectively. Additionally, the optimal electrolyser capacity is found to equal 89.3% of the offshore wind plant capacity, and 57.7% of the PV plant capacity. Finally, a sensitivity analysis is conducted, which revealed the capital cost of renewable energy plants has the most significant effect on the final LCOH.

Analysis of Escherichia coli O157 strains in cattle and humans between Scotland and England & Wales: implications for human health.

For the last two decades, the human infection frequency of Escherichia coli O157 (O157) in Scotland has been 2.5-fold higher than in England and Wales. Results from national cattle surveys conducted in Scotland and England and Wales in 2014/2015 were combined with data on reported human clinical cases from the same time frame to determine if strain differences in national populations of O157 in cattle could be associated with higher human infection rates in Scotland. Shiga toxin subtype (Stx) and phage type (PT) were examined within and between host (cattle vs human) and nation (Scotland vs England and Wales). For a subset of the strains, whole genome sequencing (WGS) provided further insights into geographical and host association. All three major O157 lineages (I, II, I/II) and most sub-lineages (Ia, Ib, Ic, IIa, IIb, IIc) were represented in cattle and humans in both nations. While the relative contribution of different reservoir hosts to human infection is unknown, WGS analysis indicated that the majority of O157 diversity in human cases was captured by isolates from cattle. Despite comparable cattle O157 prevalence between nations, strain types were localized. PT21/28 (sub-lineage Ic, Stx2a+) was significantly more prevalent in Scottish cattle [odds ratio (OR) 8.7 (2.3-33.7; P<0.001] and humans [OR 2.2 (1.5-3.2); P<0.001]. In England and Wales, cattle had a significantly higher association with sub-lineage IIa strains [PT54, Stx2c; OR 5.6 (1.27-33.3); P=0.011] while humans were significantly more closely associated with sub-lineage IIb [PT8, Stx1 and Stx2c; OR 29 (4.9-1161); P<0.001]. Therefore, cattle farms in Scotland were more likely to harbour Stx2a+O157 strains compared to farms in E and W (P<0.001). There was evidence of limited cattle strain migration between nations and clinical isolates from one nation were more similar to cattle isolates from the same nation, with sub-lineage Ic (mainly PT21/28) exhibiting clear national association and evidence of local transmission in Scotland. While we propose the higher rate of O157 clinical cases in Scotland, compared to England and Wales, is a consequence of the nationally higher level of Stx2a+O157 strains in Scottish cattle, we discuss the multiple additional factors that may also contribute to the different infection rates between these nations.

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations.

Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.

Identifying best practice in Less Favoured Area mixed livestock systems

CONTEXTThe ruminant livestock sector is under scrutiny for its contribution to greenhouse gas emissions. Less Favoured Areas (LFA) carry a large proportion of Europe's cattle and sheep ruminant population and is also characterised by variable economic and biophysical constraints. OBJECTIVEThis study aims to assess the heterogeneity in carbon emissions, resource usage and financial performance in a sample of mixed LFA livestock farms in Scotland. METHODSIndividual farm account data for 263 LFA cattle and sheep producers were augmented with emissions data over the period 2019–2020. Environmental impacts were estimated for each farm using emissions intensity and carbon productivity. Technical efficiency at the farm level (the rate of output to total inputs) was estimated using non-parametric data envelopment analysis. Financial resilience was measured through returns on assets to meet short-term liquidities. To manage the heterogeneity in these indicators Latent Profile Analysis (LPA) was employed. This allocates farms into separate groups, or profiles, representing different levels of environmental, resource efficiency and financial dimensions. To gain understanding of the drivers on membership of profiles we assess the impact of intensity and enterprise mix using multinomial logistic regression (MLN). RESULTS AND CONCLUSIONSThree profiles were identified within our sample which reflect different levels of performance; i) a best practice profile (23% of farms) have relatively low mean emissions intensities (22.3 kg CO2-eq./kg CW), high carbon productivity (£0.17 GVP/ kg CO2-eq.), high technical efficiencies and strong financial resilience; ii) a low resilience profile (31% of farms) which are the most economically fragile and susceptible to economic shocks, and iii) a high emissions profile (46% of farms) which have the lowest technical efficiencies and also the highest mean emissions intensities (31.9 kg CO2-eq./kg CW) and lowest carbon productivity (£0.10 GVP/ / kg CO2-eq.). The MLN identified that farms within the best practice profile will be more extensive compared to the other profiles. SIGNIFICANCELess Favoured Areas dominate global agricultural systems and they offer a more heterogenous picture compared to more intensive lowland or feedlot cattle systems. Agricultural support payments are changing to reward public goods. Our approach sets realistic thresholds for higher level performance across multiple dimensions that may merit higher payments if an outcomes-based payment scheme were adopted.

Breaking Patriarchal Succession Cycles: How Land Relations Influence Women's Roles in Farming☆

AbstractIn this paper we assess the relationship between how women access the land they farm, and their farming identities, roles, and future succession plans. Utilizing a Bourdieusian approach, we conceptualize inheritance of farmland as a long‐term process of symbolic and economic investment. We conducted a cohort analysis of women living or working on farms in Scotland, UK who (a) were or were not raised on farms; and (b) bought into or inherited agricultural land (directly or through their spouses). Data comprised on‐line survey responses, qualitative interviews and focus groups. Analysis demonstrated that women who were raised on farms and either inherited farms or established new farms, were more likely to identify themselves as farmers, engage in machinery work and identify female successors. Women who accessed farmland through marriage tended to engage in more home‐making tasks, to identify themselves as farmers' wives, and to occupy larger farms. These largest, potentially most viable farms thus continue to be inherited by men. However, when women inherit farms, a step change occurs, where daughters are much more likely to be identified as future successors. Enabling women raised on farms to inherit and/or establish new farms is thus critical to altering patriarchal succession cycles.

Circular and Networked Bioeconomies for Net-Zero Food Production: There is Nothing Magic About Circles

Future food production will need to deliver a healthy diet to a growing world population whilst also contributing to policy objectives such as achieving Net Zero emissions and addressing biodiversity loss. This article looks at circular economy solutions to this challenge, comparing McArthur Foundation and other approaches to both the circular material economy (e.g. focusing on steel, plastics, aluminium, and cement) and the circular bioeconomy (operating in sectors that include agriculture, food production and industrial biotechnology). A case study based on salmon farming in Scotland considers the roles of innovation from a range of technology sectors in contributing to these objectives. The concept of circularity, and the closed-loop thinking that it encourages, could attract attention towards less optimal production options just because they can be accommodated within a circular model. A ‘networked bioeconomy’ model, guided by cascading principles and fast-tracked using innovative technologies, may be more powerful than one based on rigid closed-loop circularity, in enabling policy makers and producers to understand how they can best contribute both to the conservation of biodiversity and to mitigating climate change.

Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

Navigating the nexus: The role of intermediaries in charting a new frontier of policy integration for agrifood and energy systems transformation

The effects of climate change are instigating a discourse shift towards net-zero emissions agriculture. However, a sectoral silo paradigm of policy design is incompatible with a systems approach to address complex global environmental challenges. This study introduces a social cartography approach that amalgamates network theory with a nexus perspective to identify social relation changes in the making. The study examines the role of an advisor-led initiative promoting the discourse of climate-smart farming in Scotland and identifies the impacts upon farmer knowledge networks. The social cartography comprises of a survey, farm meeting observations, and social network analysis of the agriculture, climate change, and energy (ACE) policyscape. As a visualization method, the social cartography provides a conceptual compass to navigate social relation changes of farmer knowledge networks amidst discursive transition. The results demonstrate that the advisor-led initiative acts an intermediary of discursive transition by introducing new knowledge frames and practices of farming that facilitate network shifts. However, voluntary measures fall short of instigating cross-sectoral interplay to actualize transformation towards systems integration. Furthermore, the study presents policy design elements to chart a new frontier of integrated agrifood and energy systems governance in Scotland.

The role of family life‐cycle events on persistent and transient inefficiencies in less favoured areas

AbstractFamily farms dominate less favoured areas (LFAs) within Europe, and family life‐cycle conditions, such as succession and retirement, affects how these farms adapt to changing circumstances. Past studies of on‐farm technical efficiency have not directly addressed these conditions, but they may explain why some farms are more efficient than others, especially as the farm family model dominates most farming systems. Motivated by the UK's withdrawal from the EU and the debate around establishing replacement support policies, we apply a multi‐step model to measure both transient and persistent inefficiencies using a panel of LFA cattle and sheep farms in Scotland over the period 2003–2020. We find a greater prevalence of persistent compared to transient inefficiency, which suggests that structural problems still exist. Farms with planned succession are found to have higher persistent efficiencies, whereas farmers nearing retirement have lower levels. Other factors, such as dependence on subsidy, off‐farm activity and classification as severely disadvantaged tend to compound these lower efficiencies. We argue that life‐cycle conditions should not be ignored in studies of farm technical efficiency. Within the scope of framing a new agricultural policy for UK administrations, these results inform the debate on support for LFAs, as well as the promotion of support towards generational renewal to ease transition across farm family life‐cycle events.

Prospective Longitudinal Study of Putative Agents Involved in Complex Gill Disorder in Atlantic salmon (Salmo salar).

Complex gill disorder (CGD) is an important condition in Atlantic salmon aquaculture, but the roles of the putative aetiological agents in the pathogenesis are uncertain. A longitudinal study was undertaken on two salmon farms in Scotland to determine the variations in loads of CGD-associated pathogens (Desmozoon lepeophtherii, Candidatus Branchiomonas cysticola, salmon gill pox virus (SGPV) and Neoparamoeba perurans) estimated by quantitative PCR. In freshwater, Ca. B. cysticola and SGPV were detected in both populations, but all four pathogens were detected on both farms during the marine stage. Candidatus B. cysticola and D. lepeophtherii were detected frequently, with SGPV detected sporadically. In the marine phase, increased N. perurans loads associated significantly (p < 0.05) with increases in semi-quantitative histological gill-score (HGS). Increased Ca. B. cysticola load associated significantly (p < 0.05) with increased HGS when only Farm B was analysed. Higher loads of D. lepeophtherii were associated significantly (p < 0.05) with increased HGS on Farm B despite the absence of D. lepeophtherii-type microvesicles. Variations in SGPV were not associated significantly (p > 0.05) with changes in HSG. This study also showed that water temperature (season) and certain management factors were associated with higher HGS. This increase in histological gill lesions will have a deleterious impact on fish health and welfare, and production performance.

Possible Neolithic ard marks and field boundaries at Wellhill and Cranberry, Perth and Kinross, and an evaluation of current physical evidence for Neolithic farming in Scotland

Although the Neolithic period is defined by farming, physical evidence for processes associated with farming are rare, with agricultural practices usually indicated by environmental and biomolecular proxies for domesticates such as pollen evidence, ceramic residues and lipids, animal bones, plant remains and stable isotope studies. This paper will, we hope, invigorate discussion on the recognition, interpretation and significance of physical traces of farming in Scotland. The starting point will be the summary of two excavations, Wellhill and Cranberry, both Perth and Kinross, in 2014 and 2016 respectively, part of the Strathearn Environs and Royal Forteviot (SERF) project. These cropmark sites revealed evidence for possible Neolithic farming in the form of possible ard marks and field ditches. There follows a synthesis of physical evidence for Neolithic farming in Scotland, drawing together evidence for ard marks, field boundaries, cultivation ridges, cultivated middens, and soils. Recommendations are made for recognising and interpreting such features on excavations, and the potential benefits of giving a higher profile to the act of farming in our narratives about Neolithic lifeways in Scotland and beyond are briefly explored.

Understanding the economic barriers to the adoption of agroforestry: A Real Options analysis

Agroforestry has a potentially important role in helping agriculture address both the climate and biodiversity crises. It provides a means of producing additional marketable goods from agricultural land and enhancing biodiversity at the same time as increasing carbon sequestration and, in silvo-pastural systems, reducing carbon emissions if livestock stocking rates are reduced. However, the uptake of agroforestry in the UK has been limited. This paper adopts Real Options techniques to explore how the decision to adopt agroforestry is influenced by the relative levels of returns from agriculture, forestry and the price of carbon under the scenario where there are financial penalties from livestock Greenhouse Gas (GHG) emissions, financial benefits from carbon sequestration in trees and reversibility in land use decisions. The results are compared to the equivalent findings from a Land Equivalent Value capital budgeting approach to agroforestry adoption. Analysis is based on data from a case study upland livestock farm in Scotland, comparing the impacts of introducing agroforestry into the hill sheep enterprise or the low ground cattle and sheep enterprise. The results suggest that the adoption of agroforestry is far less likely than would be suggested by standard budgeting approaches, especially in more extensive upland enterprises (hill area) where sequestration benefits are low relative to more productive farmland areas (low ground area). Upfront support payments are shown to increase the likelihood of agroforestry adoption. They also have the effect of reducing the rotation length of forestry in such systems.

Evaluation of Potential Transfer of the Pathogen Saprolegnia parasitica between Farmed Salmonids and Wild Fish.

Saprolegnia infections are among the main parasitic diseases affecting farmed salmonids. The distribution and potential transfer of Saprolegnia spp. between farms and the natural environment has been scarcely investigated. Therefore, this work aimed to study the diversity and abundance of oomycete species in salmonid farms, tributary water, and effluent water systems. Four trout farms in Italy and two Atlantic salmon farms in Scotland were considered. In Italian farms, 532 isolates of oomycetes were obtained from fish and water, at upstream, inside, and downstream the farms. In Scottish farms, 201 oomycetes isolates were obtained from water outside the farm and from fish and water inside the farming units. Isolates were identified to the species level through amplification and sequencing of the ITS rDNA region. In Italy, S. parasitica was significantly more present in farmed than in wild fish, while in water it was more frequently isolated from the wild, particularly in effluent systems, not associated with more frequent isolation of S. parasitica in wild fish downstream the farm. In Scotland, S. parasitica was the most prevalent species isolated from fish, while isolates from water were mostly Pythium spp. with few S. parasitica isolates from upstream and downstream the farms.

Techno-Economic Analysis of On-Site Energy Storage Units to Mitigate Wind Energy Curtailment: A Case Study in Scotland

Wind energy plays a major role in decarbonisation of the electricity sector and supports achieving net-zero greenhouse gas emissions. Over the last decade, the wind energy deployments have grown steadily, accounting for more than one fourth of the annual electricity generation in countries like the United Kingdom, Denmark, and Germany. However, as the share of wind energy increases, system operators face challenges in managing excessive wind generation due to its nondispatchable nature. Currently, the most common practice is wind energy curtailment in which wind farm operators receive constraint payments to reduce their renewable energy production. This practice not only leads to wastage of large volumes of renewable energy, but also the associated financial cost is reflected to rate payers in the form of increased electricity bills. On-site energy storage technologies come to the forefront as a technology option to minimise wind energy curtailment and to harness wind energy in a more efficient way. To that end, this paper, first, systematically evaluates different energy storage options for wind energy farms. Second, a depth analysis of curtailment and constraint payments of major wind energy farms in Scotland are presented. Third, using actual wind and market datasets, a techno-economic analysis is conducted to examine the relationship between on-site energy storage size and the amount of curtailment. The results show that, similar to recent deployments, lithium-ion technology is best suited for on-site storage. As case studies, Whitelee and Gordon bush wind farms in Scotland are chosen. The most suitable storage capacities for 20 years payback period is calculated as follows: (i) the storage size for the Gordonbush wind farm is 100 MWh and almost 19% of total curtailment can be avoided and (ii) the storage size for the Whitlee farm is 125 MWh which can reduce the curtailment by 20.2%. The outcomes of this study will shed light into analysing curtailment reduction potential of future wind farms including floating islands, seaports, and other floating systems.