Seeing the unseen: application of novel sensor networks to measure the potential of soils for greenhouse gas mitigation.
Soils can store carbon but also emit greenhouse gases such as nitrous oxide. As such, there is global interest in understanding the role that soils could play in mitigating net greenhouse gas emissions.
Greenhouse gas sequestration in soils, however, is complex. It is regulated by a number of processes, such as photosynthesis, organic matter input by root turnover and exudation, and finally a series of transformations by microbial communities in the soil. All of these processes vary both temporarily and spatially, and understanding them is made even more challenging by one critical factor: we cannot see them because they are happening underground.
This project aims to develop and improve tools to quantify carbon storage in and the emission of greenhouse gases from soils by developing and using a new sensor technology to ‘see’ below the soil. The objectives are to: (i) develop and test new signal processing algorithms to be used with sensing techniques to ‘see’ and quantify below ground conditions and processes and (ii) use datasets generated by sensor networks to test and improve process-based models of soil greenhouse gas emissions, including representation of root foraging and soil microbial communities.
The project will focus on two case studies, in which the sensing techniques will be deployed to quantify the net flux of greenhouse gasses in these cases. The first study will focus on an annual crop such as wheat or maize for which soil disturbance is typically frequent, while the second will study a permanent crop such as a vineyard.
The project would be ideal for a graduate student with existing computational or mathematical skills, and who has an interest in applying these to understanding soil processes using new technologies. When applying for this position please provide evidence of the following: Interest in the aims of the QMEE CDT, Research experience/potential, Academic training (degree class obtained or expected in BSc/MSc; at least two academic references).
The supervisory team include Dr Lindsay Todman and Prof Martin Lukac at the University of Reading who provide expertise on soil processes and modelling and Prof Julie McCann at Imperial College London who will supervise the sensor development.
To apply please send your CV, cover letter and academic references to Dr Lindsay Todman by 5pm on 16 January 2019. The cover letter should explain your interest in and suitability for the project and the QMEE College of Doctoral Training. Informal enquiries are welcome.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent or a Masters degree in a relevant subject, such as Computer Science, (Environmental) Engineering, Ecology, Environmental Science, Mathematics, Physics. Programming skills are
This project is in competition for funding from the NERC QMEE CDT View Website. Commencing autumn 2019 if successful. Full studentships (fees and stipend) are available to UK and other EU nationals who have resided in the UK for three years prior to commencing the studentship. Citizens of an EU member state are eligible for a fees-only award, and must be able to support themselves for the duration of the studentship at the RCUK level.