The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

Seeing the unseen: application of novel sensor networks to measure the potential of soils for greenhouse gas mitigation.
The potential role of agricultural soils to reduce the concentration of greenhouse gases in the atmosphere was recently highlighted by the 4 per mille initiative. The capacity of soils to store carbon and nitrogen was given further impetus by the latest 1.5C report by IPCC. However, there is much debate as to whether large-scale sequestration is achievable and whether this process might eventually lead to increased greenhouse gas emissions from soils. Greenhouse gas sequestration in soils 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. A vast majority of soil dwelling organisms are essentially aquatic, making soil moisture availability a key determinant of their activity and thus an important driver of greenhouse gas emission from soils. This project aims to develop and improve tools to quantify carbon storage in and the emission of greenhouse gases from soils. The objectives are to: (i) develop and test new sensing techniques to 'see' and quantify below ground conditions and processes relevant to greenhouse gas cycling via soils and (ii) use datasets generated by sensor networks to test and improve process-based models of soil greenhouse gas emissions to include 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 a vineyard in Sussex. Additional experiments may be conducted to validate the techniques if necessary. At these sites, next generation sensor networks will be used to monitor soil moisture content. A new set of algorithms will be developed to use the signals from these sensors to monitor root growth and activity. Additional sensors will be newly developed and tested for their ability to detect nitrous oxide emissions from soils, with a verification by gas emission chambers. Acquired data will then be used to test and inform development of process-based models able to utilise description of root system behaviour, organic compound transformation and greenhouse gas emissions from soils. The respiration of plant roots and the dynamics of carbon inputs from roots into the soil are often neglected in existing models. The project will make use of existing models of individual roots that represent root growth traits and carbon dynamics combined with a model of rhizosphere carbon cycling. A novel scaling method will then be developed in order to model root growth at the plant scale and between interacting plants. This scaling methodology will use a clustering approach to group roots with common characteristics in similar soil conditions (e.g. soil moisture, density of surrounding roots). Each root type will be simulated with the detailed model. A recursive programming approach will then be used to simulate the spatial variability within the root zone. The root model will then be integrated with a spatially explicit biogeochemical model in order to simulate nutrient cycling and greenhouse gas emissions. High resolution data on the spatial and temporal variability in soil moisture generated in this project will allow these models to be tested thoroughly.
Lindsay Todman
Julie McCann
Martin Lukac
Computing, Quantitative data analysis
Lindsay Todman
The project requires high-frequency data processing to integrate signals from a range of different sensors employed to monitor soil condition and gas emissions. Also, the scaling approach developed here will make use of statistical clustering approaches as well as dynamic programming to improve computation efficiency.
The project will utilise low-power radio RF signals to detect plant roots, something not attempted to date, requiring the development of novel algorithms. This research would, for the first time, attempt to create a spatially explicit model of root activity in the soil and link it to soil gas emission, requiring the development of a novel scaling approach.
Plant root traits such as depth, order, branching, turnover, predisposition to mycorrhizas or exudation play a key role in soil carbon inputs. The timing, quantity and quality of this carbon input affects the functioning of soil organisms. This project will contribute to knowledge of root system development, generating new tools to investigate the development and functioning of soil communities.
This project aims to improve understanding of root development and thus improve knowledge of below-ground carbon input to soils. This will improve our understanding of soil carbon sequestration, and the processes that drive greenhouse gas emission from soils. The project also stands to contribute to knowledge of in-situ root development for plant nutrient acquisition.
New tools for monitoring and modelling roots would transform studies of root development in field conditions, providing potential for studies on carbon cycling, the availability of water and nutrients to plants and as such their resilience to extreme conditions.
The PhD requires integration of expertise on novel sensing technologies, wireless data transmission, data analytics from computing and engineering to optimise deployment of sensor networks, as well as understanding of plant growth and soil processes. It will thus bring together knowledge of electronics, soil physics and chemistry, plant physiology, data analytics and process-based modelling.
Ecosystem-scale processes and land use, Environmental physiology, Ecological/Evolutionary tools, technology & methods
The student will attend the research development programme at the University of Reading. On-job training on the high frequency processing of radio RF signals will be based at Imperial College within the AESE sensor group. Training on the using of models of soils and roots will be based at Reading. Training in additional data analysis skills via online courses (e.g. Coursera) will be encouraged.
University of Reading and Imperial College
2019-05-20 16:12:47