Predicting agricultural Greenhouse gas emissions through improved model representation of soil biogeochemical processes

Description

Agriculture accounts for approximately 10% of anthropogenic greenhouse gas (GHG) emissions globally, and as a sector is the largest contributor of non-CO2 related GHGs. UK agriculture accounts for around 50 and 70% of methane and nitrous oxide (N2O) emissions respectively. The UK’s pledge to reach net zero emissions by 2050 and policies such as the Environmental Land Management scheme highlight the requirement to reduce GHG emissions from agriculture. There is a need to accurately quantify emissions from agricultural land at a national level to inform implementation of policy changes, whilst at a local level farmers require effective management options to reduce emissions. Current models, particularly those estimating N2O, are limited in their accuracy of GHG emissions.

Agricultural management practices have altered the natural composition of soil biological communities. Microbial activity can result in soils acting as a source of GHGs or a sink of carbon and associated nutrients. Despite their significance, quantification of soil biological processes and the impacts of agricultural management is incomplete.

This project will provide a multivariate insight into the behaviour of GHG emissions from soils and the influence of agricultural practices on these fluxes. The project will link to NERC and BBSRC funded research that aims to provide high temporal resolution (hourly) data of CO2, CH4 and N2O emissions from agricultural plots under varying management practices. Combined with measures of soil microbial communities (e.g. nitrifiers and denitrifiers), this project will further scientific understanding of the processes driving emissions, the links to soil biological processes, and influence of agricultural management practices. The project will assess the ability of existing process-based models and new data-driven methods (statistical and machine learning) to represent these processes, capture variability in GHG emissions and provide improved model capability. The student will gain training in applications of machine learning and process-based environmental models.

HOW TO APPLY

Notes and details of how to apply are available here: https://accedtp.ac.uk/acce-dtp-phd-opportunities-at-university-of-liverpool/

All applicants to ACCE must complete the ACCE personal statement proforma. This is instead of a normal personal/supporting statement/cover letter. The proforma is designed to standardise this part of the application to minimise the difference between those who are given support and those who are not.

The ACCE DTP is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

Informal enquiries may be made to v.janes-bassett@liverpool.ac.uk