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. However, soils are complex ecosystems – with even a teaspoon of soil containing billions of living organisms. Plus soils are continuously interacting with plant roots which exude carbon substrates into the soil, feeding soil microbes.
In short, greenhouse gas emission and carbon sequestration in soils 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 representing complex root foraging strategies and the dynamics of 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 arable soils with annual cropping in which soil disturbance is typically frequent, while the second will study soils with a permanent crop such as a vineyard.
The project would be ideal for a graduate student with computational or mathematical skills or a keen interest in developing these skills, as well as an interest in applying these to understanding soil processes using new technologies.
The position is part of the Quantitative Methods in Ecology and Evolution (QMEE) CDT which provides training along with a cohort of other students working in this area.
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 algorithm development for the sensors.
To apply please send your CV, cover letter and academic references to Dr Lindsay Todman, [Email Address Removed] by 5pm on 7 July 2019. The cover letter should explain your interest in and suitability for the project and the aims of 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 essential.