SOILS RESEARCH TO DELIVER GREENHOUSE GAS REMOVALS AND ABATEMENT TECHNOLOGIES (SOILS-R-GGREAT)

土壤研究提供温室气体清除和减排技术 (SOILS-R-GGREAT)

• 批准号: 2358656
• 金额: --
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2358656
• 关键词: SOILS; RESEARCH; DELIVER; GREENHOUSE; GAS

The studentship will focus on modelling soil greenhouse gas (GGR) removal options. The student will be based at University of Aberdeen and will partner with CCAFS. Dr Lini Wollenberg (CCAFS) will be a co-supervisor. The student will work with process-based models and with CCAFS data and tools. The project will involve running process-based models and decision support tools and comparing to soil carbon change data from meta-analyses. Through CCAFS, the student will also have access to a vast dataset of field trials carried out at CGIAR institutions, and will visit the AfSIS team at ICRAF, the TSBF team at CIAT Nairobi, the soil carbon team at IITA, and the pasture team at CIAT Colombia. The studentship will allow further exploration of the models and data than is necessary for simply assessing GGR potential - it will for example allow ecosystem service trade-offs to be fully explored with the models / datasets, it will allow the temporal dynamics of GGR to be explored and will allow trade-offs between alternative uses of biomass to be fully explored. The studentship will allow the student to pursue curiosity-driven research within this broad area of research, and will allow previously unexploited synergies between the models and data to be harnessed, leading to high impact publications.

该项目将重点研究土壤温室气体（GGR）去除方案的建模。该学生将在阿伯丁大学工作，并将与CCAFS合作。Lini Wollenberg博士（CCAFS）将担任联合导师。学生将使用基于过程的模型和CCAFS数据和工具。该项目将包括运行基于过程的模型和决策支持工具，并与来自元分析的土壤碳变化数据进行比较。通过CCAFS，该学生还可以访问CGIAR各机构开展的大量田间试验数据集，并将访问ICRAF的AfSIS团队、内罗毕CIAT的TSBF团队、IITA的土壤碳团队和哥伦比亚CIAT的牧场团队。该项目将允许对模型和数据进行进一步的探索，而不是简单地评估GGR潜力——例如，它将允许利用模型/数据集充分探索生态系统服务的权衡，它将允许探索GGR的时间动态，并允许充分探索生物质替代用途之间的权衡。该奖学金将允许学生在这一广泛的研究领域内追求好奇心驱动的研究，并将允许利用模型和数据之间以前未开发的协同作用，从而产生高影响力的出版物。