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

Summary

This comprehensive review traces the evolution of soil health assessment from a narrowly agricultural productivity focus towards an integrated evaluation of soil biota, biotic processes, and their feedback mechanisms. The authors argue that despite proliferation of soil health indicators across physical, chemical, and biological domains, mechanistic linkages between indicators and soil functions remain incomplete. They propose the soil-plant digital twin approach—integrating process-based models, Earth Observation, data assimilation, and physics-informed machine learning—as a pathway toward systematic, nuanced understanding of intricate interactions between soil properties, hydrology, plant physiology, microbiome dynamics, and landscape genomics.

UK applicability

The review's framework for integrating multiple soil health indicators and digital twin modelling is directly relevant to UK soil management and policy, particularly for achieving soil health targets under the Environmental Land Management schemes. However, the review synthesises global approaches, so region-specific adaptation would be needed to address UK-specific soil types, climate conditions, and agricultural practices.

Key measures

Physical, chemical, and biological soil health indicators; soil microbial community properties; soil hydrological processes; soil-plant hydraulics; landscape genomics; Earth Observation data; data assimilation; physics-informed machine learning metrics

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 evaluates the soil-plant digital twin approach as an integrative framework for comprehensive soil health assessment across multiple spatiotemporal scales.

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