Effect of Soil Water on Apparent Soil Electrical Conductivity and Texture Relationships in a Dryland Field

McCutcheon, M.C.; Farahani, Hamid J.; Stednick, John D.; Buchleiter, Gerald W.; Green, Timothy R.

doi:10.1016/j.biosystemseng.2006.01.002

Cited by 77 publications

(56 citation statements)

References 19 publications

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“…Relationships between averaged ECa and SWC values followed the exponential model; this type of relationship was observed in earlier works (Celano et al, 2011;McCutcheon et al, 2006;Mishra et al, 2014). This exponential relationship held at SWCs below 0.11 kg kg -1 , but was not applicable at higher water contents, possibly because of the presence of complex vertical distributions of soil water content in the wet soil.…”

Section: Discussionsupporting

confidence: 72%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Section: Discussionsupporting

confidence: 72%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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“…In particular, the transient nature of soil water content and soil temperature was found to complicate the characterization of ECa variability by altering its response to a given soil property during a given mapping event (McCutcheon et al, 2006), such that the spatial and temporal variance of θ explained by EMI-ECa data is strongly unstable (Calamita et al, 2015). Repeated EMI measure-ments at one site (which require accounting for temperature changes between different dates) allow for inference of the dynamic component of the signal, based on the assumption that changes of ECa are related to changes in the volume of water in the soil pores and/or changes in the concentration of ions in the soil solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Although the use of non-invasive geophysical techniques for soil mapping is very attractive, the dependence of the measured ECa on a number of parameters complicates any interpretation to determine soil properties or states (Robinson et al, 2012). A firm understanding of the spatial and temporal variability of soil electrical conductivity (EC) and an appreciation for its highly complex interactions with static soil properties and dynamic state variables, particularly at low-salt concentrations, is needed (Sudduth et al, 2001(Sudduth et al, , 2005McCutcheon et al, 2006), and it is helpful for understanding when EMI 496 E. Martini et al: Repeated electromagnetic induction measurements for mapping soil moisture can be applied, as it is not applicable under all circumstances (Robinson et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Martini

Werban

Zacharias

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ , with particular focus on the temporal variability of the spatial patterns of ECa and θ . To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ , and (iii) the ECa-θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ , and the use of EMI-based ECa data with respect to hydrological applications.

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“…The σ properties of the soil are also influenced by organic matter content, water content, and clay content [11,12]. The more organic contents in the peat, the higher the σ [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Identifying Peat Soil Layers in the South Kalimantan, Indonesia Using K-Means Clustering

Iryanti¹,

Aminudin²,

Agustine³

et al. 2018

Preprint

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Various type of soils have been identified based on their electrical and magnetic properties, especially with regards to peat soils. Peat soils are commonly considered as partly decomposed vegetation. In this study, electrical and magnetic properties have been used in K-means clustering to identify layers of peat soils. K-means clustering is a partitioning method that treats observations in the data. Data cores were obtained at every centimeter and examined for their electrical conductivity (σ) and magnetic susceptibility (χm) properties. A 291 cm core was obtained at Tegal Arum Village in South Kalimantan, Indonesia. The K-means clustering results indicate two different layers at 148 cm, and this is supported by loss on ignition (LOI) measurements. In the first layers, a 87.65% LOI was found associated with peat soils (above 248 cm). Whereas, in the second layers, there was a 26.11% LOI associated with mineral soils (below 248 cm). The results of this study using K-means clustering can be used to delineate soil layers.

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Effect of Soil Water on Apparent Soil Electrical Conductivity and Texture Relationships in a Dryland Field

Cited by 77 publications

References 19 publications

Temporal stability of electrical conductivity in a sandy soil

Temporal stability of electrical conductivity in a sandy soil

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Identifying Peat Soil Layers in the South Kalimantan, Indonesia Using K-Means Clustering

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