Explainable AI for UK agricultural land use decision-making

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 therefore challenging to represent using traditional statistical modelling approaches. Existing process-based modelling has enabled advances in 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 or link a limited number of processes at large scales, thereby mostly ignoring the complex interdependencies between the multiple interacting biophysical and socio-economic components of land use systems. Artificial intelligence (AI) techniques offer great potential to complement such modelling approaches by mining the deep knowledge (e.g. farming patterns and behaviours) encapsulated in 'big' data from ground-based sensors (such as frequently used for precision farming) and Earth Observation satellites. This will deliver enhanced insight on the past and current state and spatio-temporal dynamics of agricultural land use system flows and how they can be influenced by decisions on agricultural policies and related farm management practices.

Our proposal aims to develop a novel explainable AI framework that is transparent, data-driven and spatially-explicit by using probabilistic inference and explicit "if-then" rules. We will demonstrate proof-of-concept for two pilot regions of the UK (Oxfordshire and Lincolnshire), and the framework will be set up in a way that can be readily expanded to the whole UK. Specifically, we will draw on time-series of agricultural land use and production datasets (in-kind support from industry project partner SOYL) to identify the key socio-economic and environmental driving factors that have led to historic agricultural land use changes in the pilot regions. We will then establish explainable AI-rules for the characterisation of these agricultural land use changes and refine them within the framework through machine learning and parameter optimisation.

We will demonstrate and test the potential of the explainable AI framework for providing a new and robust method for predicting changing patterns of agricultural land use in the two pilot regions. This will include testing the ability of the AI framework for improving understanding of past and present agricultural land use dynamics across multiple temporal and spatial scales from 'big' data. It will also assess the potential for continually updating the predictions of land use dynamics in real-time using data from sensors. This could provide early warning when certain driving conditions are triggered or used to repeatedly refine short-term projections of land use change and their estimates of uncertainty.

We will engage with four major types of beneficiaries to maximise the societal impact of this project:

1. UK Government, Devolved Administrations and Policy-makers:
Our project will provide decision-makers with an innovative and integrated knowledge base supporting agricultural land use decision-making. We will engage with policy-makers including Defra, Natural England (a project partner), the Environment Agency, and the devolved administrations through consultations on the development of the explainable AI system to ensure it meets a broad set of decision-making needs. We will also demonstrate the final AI framework at a 1-day policy workshop that explores the potential of AI approaches for supporting landscape decision-making alongside other modelling and information products, including defining future needs beyond the remit of this proposal. This will include exploring how the framework can help inform the objective in the 25 Year Environment Plan to deliver a clear evidence base to promote precision agriculture and land management.

2. Farming Industries and Agri-business:
Through our strategic partnership with SOYL, the leading precision crop production service provider in the UK (see letter of support), we will have access to a unique 'big' dataset of in-situ and EO-based agricultural land use statistics to establish the explainable AI framework. The methodological innovation in the project will be co-created with SOYL (and our other project partner, Natural England) through four meetings throughout the one-year project lifetime. This will ensure that our outcomes, disseminated through a one-week training event at SOYL, are fit-for-purpose in informing the decision-making needs of agri-businesses in order to catalyse business change and innovation.

3. AI Industries and the Economy:
The AI innovations developed in this project will support the socio-economic development of the UK by promoting automation as part of the Industry 4.0 revolution. The AI framework is fully transformative and scalable, such that it can be elaborated or enhanced as required to support decision-making within a growing UK economy that aims to balance economic, environmental and societal aspects of the UK agricultural sector. Continued technological innovations in the agricultural sector are expected through precision farming and Earth Observation, creating an increasing demand for AI and big data-driven models.

4. Land owners and Trusts:
The knowledge and explainable AI rules derived in the system will support the decision-making of land owners and national trusts, by reducing environmental hazards (e.g. pesticides), enhancing ecosystem services and natural capital, and promoting smart and profitable agriculture, as well as building an inclusive society that allows people and nature to thrive. We will engage with relevant land owners and trusts through Natural England (see letter of support) via dissemination meetings and communications (e.g. media).
The 'Pathways to Impact' document details the activities we will undertake to deliver these impacts.