2.2 million tonnes of topsoil is eroded annually in the UK and over 17% of arable land show signs of degradation (Fig.1). Reversing soil degradation and restoring fertility by 2030 is an aim of the government’s 25 Year Environment Plan but there are insufficient data on the health of UK soils. A sound scientific basis to inform strategic land management that helps us to understand how the land changes over time and identifies events that result in soil impoverishment at the earliest possible stage are therefore urgently needed. Previous studies have already highlighted the key role of microbial communities in biogeochemical transformation as well as their use as both excellent bioindicators of the health of the environment and tools to detect environmental changes. Microbial metabolism could therefore act as a valuable tool to monitor degradation processes.
This multidisciplinary project aims to develop an integrated model system that enable us to predict early signs of land impoverishment. Preliminary data obtained through the Big Soil Community project led by FERA will be used to determine land areas showing different levels of degradation. Those will be sampled and analysed for physical, chemical and biological features. Physical and chemical analysis will be carried out by using LiDAR, GIS, and standardised techniques. Specific metabolic markers associated to early stages of land degradation will be determined by metagenomics (Illumina/Nanopore technology) and by the use of specific R packages in combination with reference databases. Data will be integrated into complex mathematical models able to interpret the role of microbial ecosystems at the macro-scale of land degradation. Finally, the biological system will be validated by testing new samples. A similar approach has been already applied in a project, led by Dr. Montero-Calasanz and funded by IAFRI-FERA, to develop a detection system for agricultural bioinoculants based on in situ metatranscriptomics.
The student will receive an invaluable multidisciplinary training in SIGs, LiDAR and high-throughput sequencing and acquire a strong background in bioinformatics, statistics, functional genomics, and modelling systems. Additionally, it is expected that the student gains experience in a range of teaching related activities.
Biological background and statistics and numerical skills are essential. Knowledge in genomics is desired.
This project is part of the ONE Planet DTP. Find out more here: View Website