Monitoring and Modeling the Soil‐Plant System Toward Understanding Soil Health

Summary

This comprehensive review documents the evolution of soil health assessment from productivity-focused physicochemical approaches towards integrated evaluation of soil biota, microbial communities, and ecological processes. The authors argue that despite expanding availability of diverse soil health indicators and datasets, mechanistic linkages between indicators and soil functions across multiple spatiotemporal scales remain incompletely established. The review proposes that digital twin technology—integrating process-based models, remote sensing, data assimilation, and machine learning—offers a promising framework for achieving holistic understanding of soil-plant-microbiome interactions and their responses to climate change and anthropogenic disturbance.

UK applicability

The frameworks and monitoring approaches reviewed are applicable to UK agricultural systems and climate contexts, though the review is global in scope and does not specifically address UK-specific soil types, regulations, or farming practices. UK researchers and policymakers may find the digital twin methodology and indicator synthesis relevant for implementing enhanced soil health monitoring under agricultural support schemes and environmental targets.

Key measures

Physical, chemical, and biological soil health indicators; soil microbial community properties; soil hydrological processes; soil-plant hydraulics; landscape genomics; Earth Observation data integration; digital twin modelling outputs

Outcomes reported

This review synthesises the state-of-the-art in soil health monitoring, examining how soil-microbiome-plant processes contribute to feedback mechanisms and soil property changes. It proposes the soil-plant digital twin approach as an integrative framework combining process-based models, Earth Observation data, data assimilation, and physics-informed machine learning for comprehensive soil health assessment, whilst identifying key research gaps and opportunities for enhanced understanding of soil system dynamics.

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