Towards climate-Positive cRopping: developing mOdel-based approaches to idenTify pathways, and EvidenCe benefiTs (PROTECT)

该项目旨在确定减少谷物(大麦)生产的碳足迹的方法，将其作为符合净零目标的示范作物品种。农业是英国和全球温室气体(GHG)排放的重要贡献者，苏格兰承诺到2045年实现净零碳排放。虽然作物研究试验已经确定了减少大麦蒸馏碳足迹的潜在方法，但这些方法还没有得到严格的检查。因此，学生将分析价值链数据，以确定和计算环境影响指标，这些指标使用生命周期分析(LCA)评估大麦生产方法和下游价值链部分的可持续性。该方法将确定总体减排方面的协同“胜利”，最大限度地减少其他影响类别的负面权衡。初步文献回顾和专家利益相关者咨询将确定减少大麦生产碳足迹的创新。同时，将开发一个定制的从谷物到玻璃的生命周期评价模型，以描述Glenmorangie的大麦生产和加工的碳足迹和其他环境影响。LCA-情景将被考虑，以确定净零大麦生产和蒸馏的最佳情况。该方法将借鉴：Hutton的综合种植实践的广泛数据集；Abertay在蒸馏加工应用科学方面的专业知识；并包括Glenmorangie的行业定位。该项目为多学科技能和技术培训提供了极好的机会，包括利益相关者启发、生命周期评价建模、数据管理和统计分析。该项目将适合具有数据科学背景的候选人，他们热衷于应用自己的技能，使用可持续发展的量化工具来解决农业环境和价值链挑战。与Glenmorangie合作还在将研究转化为实际应用方面提供了宝贵的经验，因为基于生命周期评价的工具已经成为企业和整个行业的可持续发展和研究重点的关键。

The project aims to identify approaches to reduce the carbon (C) footprint of cereal (barley) production as a model crop species in-line with net-zero targets. Agriculture is a significant contributor to UK and global greenhouse gas (GHG) emissions, and Scotland has committed to reach net-zero C-emissions by 2045. While crop research trials have identified potential approaches to reduce barley C-footprint for distilling, these have not been critically examined. The studentship will therefore analyse value chain data to identify and account environmental impact indicators that assess the sustainability of barley production methods, and downstream value-chain segments, using life-cycle analysis (LCA). The approach will identify synergistic 'wins' in overall emissions reductions, minimising negative trade-offs in other impact-categories. An initial literature review and expert stakeholder consultation will identify innovations to reduce the C-footprint of barley production. In parallel, a bespoke 'grain-to-glass' LCA model will be developed to characterise barley production and processing at Glenmorangie in terms of C-footprint and other environmental impacts. LCA-scenarios will be considered to identify best-case approaches for net-zero barley production and distilling. The approach will draw-upon: Hutton's extensive datasets of integrated cropping practices; Abertay's expertise in applied-science of processing for distilling; and includes industry placements with Glenmorangie. The project presents an excellent opportunity for training in multi-disciplinary skills and techniques including stakeholder elicitation, LCA modelling, data management, and statistical analysis. The project will suit candidates with a data science background who are keen to apply their skills to solve agri-environmental and value-chain challenges using quantitative tools for sustainable development. Partnering with Glenmorangie also offers invaluable experience in translating research for practical application - since LCA-based tools which have emerged as critical to inform sustainable development and research priorities of the business, and the industry more broadly.