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

Summary

This review examines the evolution of soil health assessment from predominantly physicochemical approaches to integrated evaluation of soil biota and microbial processes. The authors identify a mechanistic gap: whilst multiple soil health indicators (physical, chemical, biological) are increasingly available, a holistic linkage between indicators and soil functions across spatiotemporal scales remains incomplete. The review proposes that soil-plant digital twin frameworks—integrating process-based models, Earth Observation, data assimilation, and physics-informed machine learning—offer a systematic pathway to understand the interplay between soil properties, hydrology, plant physiology, and microbiome dynamics.

UK applicability

The review's framework and identified research gaps are broadly applicable to UK agricultural and environmental contexts, where soil health monitoring informs land management policy and climate adaptation strategies. The emphasis on integrating microbial indicators and digital twin approaches aligns with UK priorities for precision agriculture and sustainable intensification, though specific UK field validation of the proposed methodologies would be required.

Key measures

Physical, chemical, and biological soil health indicators; soil microbial community properties; soil hydrological processes; soil-plant hydraulics; landscape genomics 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 potential of soil-plant digital twin approaches combining process-based models, Earth Observation data, and physics-informed machine learning to advance soil health understanding.

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