Climate change effects on northern Spanish grassland-based dairy livestock systems

Background Understanding the effects of climate change on agro-ecosystems is fundamental in order to select the optimum management practices to mitigate environmental pressures. There is a need to forecast greenhouse gas emissions (GHG) emissions of grassland systems under climate change scenarios whilst also accounting for SOC sequestration. The objective of this study is to assess the net GHG emissions over > 405,000 hectares (ha) of moist temperate Northern Spanish grasslands utilised for dairy production, under climate change conditions (i.e., RCP 4.5, and RCP 8.5), compared to a reference baseline scenario. It is hypothesised that net GHG will increase under climate change conditions and that implementing specific manure management practices (namely the anaerobic digestion (AD)) may mitigate the global warming effect. Methods We used an integrated modelling framework comprising: (i) geographic information systems (GIS); (ii) a modified RothC version to simulate SOC changes in managed grasslands under moist temperate conditions; and (iii) Tier 2 recent IPCC methods to estimate GHG emissions. Results Average net GHG emissions contributed to global warming potential with average emissions of 5.8 and 6.2 Mg CO2-e ha−1 year−1, under RCP 4.5 and RCP 8.5, respectively. Anaerobic digestion allowed net GHG under both climate change scenarios to equal net GHG under the baseline reference scenario. Conclusion Under climate change conditions, implementing specific manure management practices, namely AD, will likely reduce the net GHG emissions of the grassland systems associated with dairy production in Northern Spain

Impacts of reduced synthetic fertiliser use under current and future climates Exploration using integrated agroecosystem modelling in the upper River Taw observatory, UK

The intensification of farming and increased nitrogen fertiliser use, to satisfy the growing population demand, contributed to the extant climate change crisis. Use of synthetic fertilisers in agriculture is a significant source of anthropogenic Greenhouse Gas (GHG) emissions, especially potent nitrous oxide (N2O). To achieve the ambitious policy target for net zero by 2050 in the UK, it is crucial to understand the impacts of potential reductions in fertiliser use on multiple ecosystem services, including crop production, GHG emissions and soil organic carbon (SOC) storage. A novel integrated modelling approach using three established agroecosystem models (SPACSYS, CSM and RothC) was implemented to evaluate the associated impacts of fertiliser reduction (10%, 30% and 50%) under current and projected climate scenarios (RCP2.6, RCP4.5 and RCP8.5) in a study catchment in Southwest England. 48 unique combinations of soil types, climate conditions and fertiliser inputs were evaluated for five major arable crops plus improved grassland. With a 30% reduction in fertiliser inputs, the estimated yield loss under current climate ranged between 11% and 30% for arable crops compared with a 20–24% and 6–22% reduction in N2O and methane emissions, respectively. Biomass was reduced by 10–25% aboveground and by <12% for the root system. Relative to the baseline scenario, soil type dependent reductions in SOC sequestration rates are predicted under future climate with reductions in fertiliser inputs. Losses in SOC were more than doubled under the RCP4.5 scenario. The emissions from energy use, including embedded emissions from fertiliser manufacture, was a significant source (14–48%) for all arable crops and the associated GWP20

Superconducting properties of very high quality NbN thin films grown by high temperature chemical vapor deposition

Niobium nitride (NbN) is widely used in high-frequency superconducting electronics circuits because it has one of the highest superconducting transition temperatures ($T_c$ $\sim$ 16.5 K) and largest gap among conventional superconductors. In its thin-film form, the $T_c$ of NbN is very sensitive to growth conditions and it still remains a challenge to grow NbN thin film (below 50 nm) with high $T_c$. Here, we report on the superconducting properties of NbN thin films grown by high-temperature chemical vapor deposition (HTCVD). Transport measurements reveal significantly lower disorder than previously reported, characterized by a Ioffe-Regel ($k_F$$\ell$) parameter of $\sim$ 14. Accordingly we observe $T_c$ $\sim$ 17.06 K (point of 50% of normal state resistance), the highest value reported so far for films of thickness below 50 nm, indicating that HTCVD could be particularly useful for growing high quality NbN thin films

Estimating soil organic carbon changes in managed temperate moist grasslands with RothC

Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model''s performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions. © 2021 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Feasibility of mitigation measures for agricultural greenhouse gas emissions in the UK. A systematic review

The UK Government has set an ambitious target of achieving a national “net-zero” greenhouse gas economy by 2050. Agriculture is arguably placed at the heart of achieving net zero, as it plays a unique role as both a producer of GHG emissions and a sector that has the capacity via land use to capture carbon (C) when managed appropriately, thus reducing the concentration of carbon dioxide (CO2) in the atmosphere. Agriculture’s importance, particularly in a UK-specific perspective, which is also applicable to many other temperate climate nations globally, is that the majority of land use nationwide is allocated to farming. Here, we present a systematic review based on peer-reviewed literature and relevant “grey” reports to address the question “how can the agricultural sector in the UK reduce, or offset, its direct agricultural emissions at the farm level?” We considered the implications of mitigation measures in terms of food security and import reliance, energy, environmental degradation, and value for money. We identified 52 relevant studies covering major foods produced and consumed in the UK. Our findings indicate that many mitigation measures can indeed contribute to net zero through GHG emissions reduction, offsetting, and bioenergy production, pending their uptake by farmers. While the environmental impacts of mitigation measures were covered well within the reviewed literature, corresponding implications regarding energy, food security, and farmer attitudes towards adoption received scant attention. We also provide an open-access, informative, and comprehensive dataset for agri-environment stakeholders and policymakers to identify the most promising mitigation measures. This research is of critical value to researchers, land managers, and policymakers as an interim guideline resource while more quantitative evidence becomes available through the ongoing lab-, field-, and farm-scale trials which will improve the reliability of agricultural sustainability modelling in the future

A commentary on key methodological developments related to nutritional life cycle assessment (nLCA) generated throughout a 6-year strategic scientific programme

Rothamsted Research (RRes) is the world's oldest agricultural research centre, notable for the development of the first synthetic fertilizer (superphosphate) and long-term farming experiments (LTEs) spanning over 170 years. In 2015, RRes recruited several life cycle assessment (LCA) experts and began adopting the method to utilize high resolution agronomical data covering livestock (primarily ruminants), grassland/forage productivity and quality, and arable systems established on its North Wyke Farm Platform (NWFP) and the LTEs. The NWFP is a UK ‘National Bioscience Research Infrastructure’ (NBRI) developed for informing and testing systems science utilising high-resolution data to determine whether it is possible to produce nutritious food sustainably. Thanks largely to the multidisciplinary knowledge at RRes, and its collaborators, its LCA Team has been at the forefront of methodological advances during a 6-year Institute Strategic Programme (ISP) ‘Soil-to-Nutrition’ (S2N). While S2N investigated the co-benefits and trade-offs of new mechanistic understanding of efficient nutrient use across scales from pot to landscape, this commentary specifically synthesizes progress in incorporating human nutrition in the context of environmental footprinting, known as ‘nutritional LCA’ (nLCA). We conclude our commentary with a brief discussion on future pathways of exploration and methodological developments covering various activities along entire agri-food supply-chains

MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants

Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of LDLr variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of LDLr missense variants with specificity of 92.5% and sensitivity of 91.6%. © 2021 The Author

Functional Characterization of p.(Arg160Gln) PCSK9 Variant Accidentally Found in a Hypercholesterolemic Subject

Familial hypercholesterolaemia (FH) is an autosomal dominant dyslipidaemia, characterised by elevated LDL cholesterol (LDL-C) levels in the blood. Three main genes are involved in FH diagnosis: LDL receptor (LDLr), Apolipoprotein B (APOB) and Protein convertase subtilisin/kexin type 9 (PCSK9) with genetic mutations that led to reduced plasma LDL-C clearance. To date, several PCSK9 gain-of-function (GOF) variants causing FH have been described based on their increased ability to degrade LDLr. On the other hand, mutations that reduce the activity of PCSK9 on LDLr degradation have been described as loss-of-function (LOF) variants. It is therefore important to functionally characterise PCSK9 variants in order to support the genetic diagnosis of FH. The aim of this work is to functionally characterise the p.(Arg160Gln) PCSK9 variant found in a subject suspected to have FH. Different techniques have been combined to determine efficiency of the autocatalytic cleavage, protein expression, effect of the variant on LDLr activity and affinity of the PCSK9 variant for the LDLr. Expression and processing of the p.(Arg160Gln) variant had a result similar to that of WT PCSK9. The effect of p.(Arg160Gln) PCSK9 on LDLr activity is lower than WT PCSK9, with higher values of LDL internalisation (13%) and p.(Arg160Gln) PCSK9 affinity for the LDLr is lower than WT, EC50 8.6 ± 0.8 and 25.9 ± 0.7, respectively. The p.(Arg160Gln) PCSK9 variant is a LOF PCSK9 whose loss of activity is caused by a displacement of the PCSK9 P’ helix, which reduces the stability of the LDLr-PCSK9 complex.This research was funded by Grupos Consolidados Gobierno Vasco 2021, grant number IT1720-22. A.L.-S. was supported by a grant PIF (2019–2020), Gobierno Vasco and partially supported by Fundación Biofísica Bizkaia. S.J-B. was supported by a Margarita Salas Grant 2022 from the University of the Basque Country