Optimisation and Enhanced Rock Weathering (ERW) in Rainfed Agriculture: AI for monitoring reporting and verification (MRV) Frameworks under Uncertainty
This project aims to scale Enhanced Rock Weathering (ERW) for carbon removal and soil health by reducing the cost and improving the accuracy of MRV. It explores optimisation in monitoring, field trials, sensor placement, and supply chain logistics for rainfed agriculture.
Project Outline:
Enhanced Rock Weathering (ERW) has the potential to deliver large-scale and permanent carbon removal, while also improving soil fertility in rainfed farming systems. But for this potential to turn into practice, we need to solve two big challenges: how to measure and verify the amount of carbon stored (MRV), and how to deploy ERW at scale in a way that is cost-effective and practical. At the moment, MRV is the most expensive part of ERW projects, and supply chains for quarrying, grinding, and transport are complex. This project will first look at current MRV methods, understand how they work, and identify where they can be improved or made cheaper. From there, it will explore how optimisation can help in different parts of the system.
Optimisation is crucial and can be applied in areas such as: Designing monitoring and sampling strategies that give confidence in the results but keep costs down; Planning trials and field studies in a way that learns the most from limited experiments; Deciding where sensors or measurements should be placed, and how often; Modelling supply chains for rock material — from quarry to farm — and planning routes and logistics.
By bringing these pieces together, the project aims to reduce the cost of MRV, improve its robustness, and provide practical strategies for scaling ERW in rainfed agriculture.
From engaging in this project the student will learn how to:
o Gain an understanding of how current MRV frameworks for ERW are designed, their strengths, and their limitations.
o Learn how to apply optimisation methods under uncertainty to real agricultural and climate problems.
o Develop skills in designing monitoring and sampling strategies, trial planning, and logistics modelling.
o Build experience in linking measurement, modelling, and deployment strategies to create workable, cost-effective solutions.
o Contribute to both the academic understanding of optimisation and the practical development of scalable carbon removal methods.
To register an expression of interest, click here. You will need to outline why you have selected the research project and how your skills, experience and/or knowledge meet the project requirements.