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

IF 25.2 1区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Yijian Zeng, Anne Verhoef, Harry Vereecken, Eyal Ben-Dor, Tom Veldkamp, Liz Shaw, Martine Van Der Ploeg, Yunfei Wang, Zhongbo Su
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Abstract

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.

Abstract Image

土壤-植物系统监测与建模对土壤健康的认识
土壤健康评估已从主要关注农业生产力发展到对影响土壤特性的土壤生物群和生物过程进行综合评价。因此,土壤健康评估已从主要采用物理化学方法转向纳入生态、生物和分子微生物学指标。这一转变使我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,但在多个时空尺度上,指标与土壤功能之间的整体机制联系尚未完全建立。本文综述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性质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,我们调查了土壤-植物数字孪生方法提供的机会,这是一个整合基于过程的模型、地球观测数据、数据同化和物理信息机器学习的综合框架,以实现对土壤健康的细致理解。本文概述了采用数字孪生方法系统地观察和模拟土壤-植物系统来监测土壤健康的未来轨迹。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复杂相互作用的理解。
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来源期刊
Reviews of Geophysics
Reviews of Geophysics 地学-地球化学与地球物理
CiteScore
50.30
自引率
0.80%
发文量
28
审稿时长
12 months
期刊介绍: Geophysics Reviews (ROG) offers comprehensive overviews and syntheses of current research across various domains of the Earth and space sciences. Our goal is to present accessible and engaging reviews that cater to the diverse AGU community. While authorship is typically by invitation, we warmly encourage readers and potential authors to share their suggestions with our editors.
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