Agricultural land-use dynamics and their associated driving factors represent highly complex systems of flows that are subject to non-linearities, sensitivities, and uncertainties across spatial and temporal scales. They are thus challenging to represent using traditional statistical modelling approaches. Existing process-based modelling has enabled advanced understanding of individual biophysical processes underpinning agricultural land-use systems (e.g. crop, livestock and biogeochemical models). However, these tend to focus on individual processes in detail and often ignore the complex interdependencies between the multiple interacting components of land use systems. To address the gap in modelling capability, this project aims to explore and test an explainable AI to learn the complex spatial and temporal relationships from agricultural land use and land management datasets. This exploratory study will demonstrate proof-of-concept for selected regions in the UK and provide greater understanding of the state and dynamics of agricultural land use systems and how they can be influenced by policy and management decisions.
The project will run from 1 December 2019 to 30 November 2020.
- Natural Environment Research Council
- Lancaster University