Soil compaction is a regarded as a major environmental and economical hazard, degrading soils across the world. Changes in soil properties due to compaction are known to lead to decrease in biomass and increase in greenhouse gas emissions, nutrient leaching and soil erosion. Quantifying adverse impacts of soil compaction and developing strategies for amelioration relies on an understanding of soil compaction extent and temporal variability. The main indicators of soil compaction (i.e., reduction of pore space, increase in bulk density and decrease in soil transport properties) are relatively easy to quantify in laboratory conditions but such traditional point‐based methods offer little information on soil compaction extent at the field scale. Recently, geophysical methods have been proposed to provide non‐invasive information about soil compaction. In this work, we developed an agrogeophysical modelling framework to help address the challenges of characterizing soil compaction across grazing paddocks using electromagnetic induction (EMI) data. By integrative modelling of grazing, soil compaction, soil processes and EMI resistivity anomalies, we demonstrate how spatial patterns of EMI observations can be linked to management leading to soil compaction and concurrent modifications of soil functions. The model was tested in a dairy farm in the midlands of Ireland that has been grazed for decades and shows clear signatures of grazing‐induced compaction. EMI data were collected in the summer of 2021 and autumn of 2022 under dry and wet soil moisture conditions, respectively. For both years, we observed decreases of apparent electrical resistivity at locations that with visible signatures of compaction such as decreased vegetation and water ponding (e.g., near the water troughs and gates). A machine learning algorithm was used to cluster EMI data with three unique cluster signatures assumed to be representative of heavy, moderately, and non‐compacted field zones. We conducted 1D process‐based simulations corresponding to non‐compacted and compacted soils. The modelled EMI signatures agree qualitatively and quantitatively with the measured EMI data, linking decreased electrical resistivities to zones that were visibly compacted. By providing a theoretical framework based on mechanistic modelling of soil management and compaction, our work may provide a strategy for utilizing EMI data for detection of soil degradation due to compaction.

中文翻译：

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使用田间电磁感应数据检测放牧引起的土壤压实空间变化的农业地球物理建模框架

土壤板结被认为是一种主要的环境和经济危害，导致世界各地的土壤退化。众所周知，压实导致的土壤性质变化会导致生物量减少，温室气体排放、养分淋溶和土壤侵蚀增加。量化土壤压实的不利影响并制定改善策略依赖于对土壤压实程度和时间变化的了解。土壤压实的主要指标（即孔隙空间的减少、容重的增加和土壤迁移特性的降低）在实验室条件下相对容易量化，但这种传统的基于点的方法很少提供有关现场土壤压实程度的信息规模。最近，人们提出了地球物理方法来提供有关土壤压实的非侵入性信息。在这项工作中，我们开发了一个农业地球物理建模框架，以帮助解决使用电磁感应 (EMI) 数据表征整个放牧围场土壤压实的挑战。通过放牧、土壤压实、土壤过程和 EMI 电阻率异常的综合建模，我们演示了 EMI 观测的空间模式如何与导致土壤压实和土壤功能同时改变的管理联系起来。该模型在爱尔兰中部的一个奶牛场进行了测试，该奶牛场已经放牧了数十年，并显示出放牧引起的压实的明显特征。 EMI 数据分别于 2021 年夏季和 2022 年秋季在干燥和潮湿土壤湿度条件下收集。两年来，我们观察到具有明显压实特征的位置的表观电阻率下降，例如植被减少和积水（例如，水槽和大门附近）。使用机器学习算法对具有三个独特聚类特征的 EMI 数据进行聚类，这些聚类特征假定代表重度、中度和非压实场区域。我们对非压实和压实土壤进行了基于一维过程的模拟。建模的 EMI 特征在定性和定量上与测量的 EMI 数据一致，将降低的电阻率与明显压实的区域联系起来。通过提供基于土壤管理和压实机械模型的理论框架，我们的工作可以提供一种利用 EMI 数据检测压实引起的土壤退化的策略。