Determining AI-driven Soil Health Benchmarks for UK Agricultural Systems at Cranfield University

This is an exciting NERC PhD opportunity in collaboration with NIAB and BGS to develop, test, and refine, soil health metrics that can offer actionable insights for sustainable land management. By integrating long-term monitoring with AI-driven approaches, this research looks to deliver scalable, data-driven solutions for assessing and improving soil health in arable agriculture.

Type of opportunity

1. Fully-funded studentship – Opportunities which are fully funded (e.g. covers all fees and stipend)

Main Copy (advised structure)

The project aims to develop, test, and refine, soil health metrics that offer actionable insights for sustainable land management. By integrating long-term monitoring with AI-driven approaches, this research looks to deliver scalable, data-driven solutions for assessing and improving soil health in agriculture.

This cross-industry study, based at Cranfield University and NIAB, aims to advance the evaluation of soil health benchmarks using diverse, large-scale datasets and cutting-edge analytical techniques. The research will leverage NIAB’s and other industry long-term experiments and soil datasets, alongside Cranfield University’s national LandIS soil datasets, and BGS data, to develop a comprehensive understanding of soil health and how is measured across various arable agricultural systems and scales. Such metrics should account for productivity and hyper local environmental risk priorities (field level), advancing us from current regional or climate driven benchmarks.

The student will work closely with NIAB’s Soils and Farming Systems team, using these datasets (e.g. Fig 1) to assess existing soil health metrics and identify areas for targeted improvement. Where necessary, additional data will be collected to fill gaps and strengthen the analysis. Building on NIAB’s current AI-driven work in agriculture and Cranfield’s Environmental Informatics, the student will apply advanced methods such as machine learning, predictive modeling, and remote sensing to maximise the potential of these datasets.

Entry requirements

Applicants should have at least a 2:1 at UK BSc level or at least a pass at UK MSc level or equivalent in a related discipline.

Funding              

Sponsored by NERC through CENTA DTP, Cranfield University. Successful home-fees-eligible candidates will receive an annual stipend, set at £19,237 per year (or pro rata if part time), paid directly to the student in monthly increments, plus full university fees and a research training support grant (RTSG) of £8,000.

How to apply

For further information please contact:
Name: Professor Stephen Hallett
Email: s.hallett@cranfield.ac.uk
T: (0) 1234 754287

Application deadline: 8/01/2025