Explainable AI for UK agricultural land use decision-making

农业土地利用动态及其相关驱动因素代表了高度复杂的流动系统，这些系统在时空尺度上具有非线性、敏感性和不确定性。因此，使用传统的统计建模方法来表示它们是具有挑战性的。现有的基于过程的建模使人们能够进一步了解支撑农业土地利用系统的个别生物物理过程（例如作物、牲畜和生物地球化学模型）。然而，这些方法往往侧重于个别过程的细节，或在大尺度上将有限数量的过程联系起来，从而大多忽略了土地利用系统中多种相互作用的生物物理和社会经济组成部分之间复杂的相互依赖关系。人工智能（AI）技术通过挖掘来自地面传感器（例如经常用于精准农业）和地球观测卫星的“大”数据中包含的深度知识（例如耕作模式和行为），为补充这种建模方法提供了巨大的潜力。这将有助于深入了解农业土地利用系统流动的过去、现状和时空动态，以及它们如何受到农业政策决定和相关农场管理实践的影响。我们的建议旨在通过使用概率推理和明确的“如果-那么”规则，开发一种新的可解释的人工智能框架，该框架是透明的、数据驱动的和空间明确的。我们将在英国的两个试点地区（牛津郡和林肯郡）进行概念验证，并以一种可以随时扩展到整个英国的方式建立框架。具体而言，我们将利用农业用地利用和生产数据集的时间序列（由行业项目合作伙伴SOYL提供实物支持）来确定导致试验区农业用地利用历史变化的关键社会经济和环境驱动因素。然后，我们将为这些农业用地变化的特征建立可解释的人工智能规则，并通过机器学习和参数优化在框架内对其进行完善。我们将展示和测试可解释的人工智能框架的潜力，为预测两个试验区农业用地变化模式提供一种新的、强大的方法。这将包括测试人工智能框架的能力，以从“大”数据中提高对过去和现在跨多个时空尺度的农业土地利用动态的理解。它还将评估利用传感器的数据不断实时更新土地利用动态预测的潜力。这可以在触发某些驾驶条件时提供早期预警，或用于反复改进土地利用变化的短期预测及其对不确定性的估计。

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.