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Monitoring and Modeling the Soil-Plant System Toward Understanding Soil Health
Reviews of Geophysics ( IF 25.2 ) Pub Date : 2025-01-25 , DOI: 10.1029/2024rg000836
Yijian Zeng, Anne Verhoef, Harry Vereecken, Eyal Ben-Dor, Tom Veldkamp, Liz Shaw, Martine Van Der Ploeg, Yunfei Wang, Zhongbo Su

The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology indicators. This shift enables a comprehensive exploration of soil microbial community properties and their responses to environmental changes arising from climate change and anthropogenic disturbances. Despite the increasing availability of soil health indicators (physical, chemical, and biological) and data, a holistic mechanistic linkage has not yet been fully established between indicators and soil functions across multiple spatiotemporal scales. This article reviews the state-of-the-art of soil health monitoring, focusing on understanding how soil-microbiome-plant processes contribute to feedback mechanisms and causes of changes in soil properties, as well as the impact these changes have on soil functions. Furthermore, we survey the opportunities afforded by the soil-plant digital twin approach, an integrative framework that amalgamates process-based models, Earth Observation data, data assimilation, and physics-informed machine learning, to achieve a nuanced comprehension of soil health. This review delineates the prospective trajectory for monitoring soil health by embracing a digital twin approach to systematically observe and model the soil-plant system. We further identify gaps and opportunities, and provide perspectives for future research for an enhanced understanding of the intricate interplay between soil properties, soil hydrological processes, soil-plant hydraulics, soil microbiome, and landscape genomics.

中文翻译:


监测和建模土壤-植物系统以了解土壤健康



土壤健康评估已经从主要关注农业生产力发展为对影响土壤特性的土壤生物群和生物过程的综合评估。因此,土壤健康评估已从主要的物理化学方法转变为结合生态、生物和分子微生物学指标。这种转变使人们能够全面探索土壤微生物群落特性及其对气候变化和人为干扰引起的环境变化的响应。尽管土壤健康指标(物理、化学和生物)和数据的可用性不断增加,但跨多个时空尺度的指标和土壤功能之间尚未完全建立整体机制联系。本文回顾了土壤健康监测的最新进展,重点了解土壤-微生物组-植物过程如何促进土壤特性变化的反馈机制和原因,以及这些变化对土壤功能的影响。此外,我们还调查了土壤-植物数字孪生方法提供的机会,这是一个综合框架,融合了基于过程的模型、地球观测数据、数据同化和基于物理的机器学习,以实现对土壤健康的细致入微的理解。本综述通过采用数字孪生方法来系统地观察和建模土壤-植物系统,描绘了监测土壤健康的前瞻性轨迹。我们进一步确定了差距和机会,并为未来的研究提供了视角,以增强对土壤特性、土壤水文过程、土壤-植物水力学、土壤微生物组和景观基因组学之间错综复杂的相互作用的理解。
更新日期:2025-01-25
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