Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution.

Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σ_a), and σ_a represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σ_a, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σ_a. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (EC_a) 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 EC_a and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between EC_a (i.e., MF–EC_a and MC–EC_a) 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 (EC_w) were investigated. Results revealed significant (p < 0.05) positive correlations of EC_a to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–EC_a. Under wet conditions, correlation of MF–EC_a 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.

Conversion of boreal forest into agricultural land is likely to occur due to the shift of climatic zones and increasing food demand. However, any land conversion will affect the water balance and hence solute fluxes within the soil column and connected ecosystems. Understanding the consequences of land conversion on soil hydrology is essential to support an economically viable agriculture while minimizing its environmental footprint. Hydrological models can simulate these effects based on regionally adjusted climate scenarios. Here, we combined a local climate analysis with hydrological simulations (Hydrus-1D) of boreal soils before and after agricultural conversion. Historical climate analysis showed increasing temperatures and growing degree days while precipitation remains stable. Hydrological simulations revealed lower saturation and higher infiltration rates for unconverted soils, indicating lower runoff and increased infiltration and deep percolation. In contrast, agricultural soils have slower infiltration rates, particularly in the upper horizon. Over the long term, agricultural conversion consequently increases erosion risk and nutrient loss by runoff. This might further progressively limit groundwater recharge, affect hydrological processes and functions and future drought/flood conditions at catchment levels. Hence, conversion of boreal soils demands a primary identification of suitable areas to minimize its impacts.

Boreal agriculture struggles with soils of lower agronomic value, most of which are sandy with a low water holding capacity (WHC) and prone to nutrient leaching. Biochar amendments are associated with positive effects on soil hydraulic properties and enhanced nutrient retention. However, these effects are strongly related to feedstock type and pyrolysis parameters and depend on biochar application rates and soil types. While biochar could increase the productivity of boreal agriculture by improving water and nutrient use efficiency, little is known about its effects on hydraulic processes in podzol. In this study, we investigated the effects of biochar rates (10, 20, 40, 80 Mg carbon ha⁻¹) and maturity on soil hydrology for an agriculturally used Podzol in Labrador, Canada. The in-situ soil water content (SWC) and weather data over an entire growing season were analysed. Hydrus 1D simulations were used to estimate changes in water fluxes. SWC showed clear differentiation among storage parameters (i.e., initial, peak and final SWC) and kinetic parameters (i.e., rate of SWC change). Storage parameters and soil wetting and drying rates were significantly affected by biochar rates and its maturity. The magnitude of the changes in SWC after either wetting or drying events was statistically not affected by the biochar rate. This confirms that biochar mostly affected the WHC. Nevertheless, reductions in cumulative lower boundary fluxes were directly related to biochar incorporation rates. Overall, biochar had positive effects on hydrological properties. The biochar rate of 40 Mg C ha⁻¹ was the most beneficial to agriculturally relevant hydraulic conditions for the tested Podzol.