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Apparent magnetic susceptibility (MSa) as recorded by electromagnetic induction (EMI) instruments could offer relevant information about non-soil subsurface features. It is less affected by natural soil properties than its prominent counterpart, i.e., apparent electrical conductivity (ECa). Hence, MSa is generally a promising approach to investigate artificial inclusions and structures in soil. However, while the origin depth of EMI based ECa is widely accepted, the depth sensitivity (DS) of MSa measurements remains poorly understood. The depth interpretation of MSa is particularly challenging due to negative values especially for objects that are randomly distributed over different depths. Here we assessed the performance of both multi-coil (MC) and multi-frequency (MF) EMI sensors for identifying and determining the DS of MSa measurements in shallow soils through detection of buried small targets of known conductivity. Two experiments were conducted in a sandy loam podzolic soil in western Newfoundland, Canada. Materials of different conductivities, including metal and plastic targets, were buried at depths between 20 and 80 cm. Three inter-coil separations (32, 71 and 118 cm) of the MC sensor and four factory-calibrated frequencies (18, 38, 49 and 80 kHz) of the MF sensor were tested in both horizontal and vertical coil orientations. The MC sensor clearly detected all four metal targets from three coil separations in both coil orientations while the MF sensor identified more anomalies than targets limiting its information value. Based on the measurements from MC and the theoretical DS function, a criterion was developed and validated to assess the potential depth origin of MSa. We found that negative or less than the background values occur, if the depth of the target is shallower than 0.36 times the coil distance of the employed EMI sensor. According to this criterion, the depth origins of metallic targets were correctly identified under the assumption of low induction numbers, even if values were negative. [ABSTRACT FROM AUTHOR]

The spatial variability in soil physical and hydraulic properties for a managed podzol was assessed using soil apparent electrical conductivity (ECa). Two EMI sensors, the multi-coil (MC) and multi-frequency (MF), were adopted for measurement of ECa on a silage- corn experimental plot in western Newfoundland, Canada. Results demonstrated a significant relationship between the ECa mean relative differences (MRD) and the soil moisture content MRD (R2 = 0.33 to 0.70) for both MC and MF sensors. The difference in depth sensitivity between MC and MF sensors accounted for the variation (0.015 to 0.09) in ECa standard deviation of the relative differences. A significant linear relationship was found between the ECa MRD and sand (R2 = 0.35 and 0.53) or silt (R2 = 0.43), but not with clay (R2 = 0.06 and 0.16). The spatial variability of the ECa-based predictions (CV = 3.26 to 27.61) of soil properties was lower than the measured values (CV = 5.56 to 41.77). These results inferred that the temporal stability of ECa might be a suitable proxy to understand the spatial variability of soil physical and hydraulic properties in agricultural podzols. [ABSTRACT FROM AUTHOR]

Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (ECa) of soil as a proxy for its physicochemical properties. Multi-frequency (MF) and multi-coil (MC) are the two types of commercially available EMI sensors. Although the working principles are similar, their theoretical and effective depth of investigation and their resolution capacity can vary. Given the recent emphasis on non-invasive mapping of soil properties, the selection of the most appropriate instrument is critical to support robust relationships between ECa and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between ECa (i.e., MF–ECa and MC–ECa) and shallow soil properties. Field experiments were conducted under wet and dry conditions on a silage-corn field in western Newfoundland, Canada. Relationships between temporally stable properties, such as texture and bulk density, and temporally variable properties, such as soil water content (SWC), cation exchange capacity (CEC) and pore water electrical conductivity (ECw) were investigated. Results revealed significant (p < 0.05) positive correlations of ECa to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–ECa. Under wet conditions, correlation of MF–ECa to temporally variable properties decreased, particularly to SWC, while the correlations to sand and silt increased. We concluded that the MF sensor is more sensitive to changes in SWC which influenced its ability to map temporally variable properties. The performance of the MC sensor was less affected by variable weather conditions, providing overall stronger correlations to both, temporally stable or variable soil properties for the tested Podzol and hence the more suitable sensor toward various precision agricultural practices. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn's Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale. [ABSTRACT FROM AUTHOR]

Electromagnetic induction (EMI) is an established method for mapping field-scale soil water content (SWC). However, the correlation between the recorded apparent electrical conductivity (ECa) and SWC is affected by several factors that can vary across test sites and with environmental conditions. As agricultural practices affect both, ECa and SWC, it is likely that the mismatch in SWC predictions using ECa can be directly or indirectly attributed to agronomic treatment effects. Hence, EMI based SWC predictions are often limited to sites with one soil amendment and strong ECa – SWC correlations. However, non-invasive SWC mapping is particularly desirable for larger agricultural fields, covering different soil amendments. We hypothesized that different agronomic treatments altered the ECa – SWC correlations and consequently the EMI based SWC prediction accuracy. We further hypothesized that a model established on areas with high positive ECa – SWC correlation could be used to predict SWC for areas with unsuitable ECa – SWC correlations. A field-scale experiment was conducted to investigate the effects of six agronomic treatments (including biochar, BC) on SWC, ECa, and ECa – SWC correlation in a silage corn field. We tested the accuracy of three models to predict the SWC of independent data sets using data from i) all treatments (T all ), ii) plots with lowest and iii) plots with highest ECa – SWC correlations. We found statistically significant treatment effects on both, ECa and SWC, although overlapping data ranges were given. Furthermore, the correlations between ECa and SWC were affected by the treatments. Correlations were found to be lowest on nutrient-rich dairy manure plots (T2) and highest on the control plots (T6), likely due to differences in the ionic strength of pore water. BC mitigated the effect of ionic strength for T2 while it showed no measurable effects on ECa on plots receiving inorganic fertilizers. Most accurate SWC predictions were reached by employing T all data (RMSEP 1.40–3.13% vol.). However, models based on T6 data provided similar accuracies (RMSEP 1.46–3.96% vol.) using only 12.5% of the area. The T2 based model performance failed (RMSEP 3.02–7.21% vol.). Results suggest that ECa – SWC models established on non-manured areas could provide best possible SWC predictions and are recommended as training areas if soil texture and mineralogical composition can be expected to be relatively homogeneous. [ABSTRACT FROM AUTHOR]