An agrogeophysical modelling framework for the detection of soil compaction spatial variability due to grazing using field-scale electromagnetic induction data.

A - Papers appearing in refereed journals

Romero-Ruiz, A., O'Leary, D., Daly, E., Tuohy, P., Milne, A. E., Coleman, K. and Whitmore, A. P. 2024. An agrogeophysical modelling framework for the detection of soil compaction spatial variability due to grazing using field-scale electromagnetic induction data. Soil Use and Management. 40 (2), p. e13039. https://doi.org/10.1111/sum.13039

AuthorsRomero-Ruiz, A., O'Leary, D., Daly, E., Tuohy, P., Milne, A. E., Coleman, K. and Whitmore, A. P.
Abstract

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.

KeywordsAnimal treading ; Agrogeophysics; Grazing strategies; Hydrogeophysics; Soil structure; Water dynamics
Year of Publication2024
JournalSoil Use and Management
Journal citation40 (2), p. e13039
Digital Object Identifier (DOI)https://doi.org/10.1111/sum.13039
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Teagasc
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Publisher's version
Copyright license
CC BY 4.0
Output statusPublished
Publication dates
Online08 Apr 2024
Publication process dates
Accepted23 Feb 2024
PublisherBritish Society of Soil Science (BSSS)
ISSN0266-0032

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