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The cumulative sensitivity forward model is limited in some cases.EMagPy is an open-source Python API and GUI for 1D EMI modeling/inversion. Application of EMagPy is illustrated through cases studies with real and synthetic data. Both Maxwell-based and cumulative sensitivity forward models are implemented. Inversion algorithms include deterministic and stochastic methods.

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GB and SS contributed to the GUI GB, SS, JB and PM contributed to the API GB specifically contributed to the R2 and Survey classes JB specifically contributed to the mesh generation and handling SS specifically contributed to the IP part of the API and GUI PM specifically contributed to the sequence generation of the API and the testing of the GUI GB, SS, JB and PM wrote the paper AB wrote all the Fortran executables and provided feedback on the manuscript Code availabilityThe open-source code (GPL license) is available on GitLab: https://gitlab.com/hkex/pyr2. Highlights• Geophysics is more frequently used in interdisciplinary projects by non-specialists.• ResIPy is a simple to use, intuitive, open source graphical user interface and API.• ResIPy is a good teaching tool to learn how to invert and model geoelectrical data.• Data filtering and error modeling of resistivity and IP data improve inversion.• Field applications and survey design with ResIPy is demonstrated.

Geophysical surveys are now commonly used in agriculture for mapping applications. High-throughput collection of geophysical properties such as electrical conductivity (inverse of resistivity) can be used as a proxy for soil properties of interest (e.g., moisture, texture, salinity). Most applications only rely on a single geophysical survey at a given time. However, time-lapse geophysical surveys have greater capabilities to characterize the dynamics of the system, which is the focus of this work. Assessing the impact of agricultural practices through the growth season can reveal important information for the crop production. In this work, we demonstrate the use of time-lapse electrical resistivity tomography (ERT) and electromagnetic induction (EMI) surveys through a series of three case studies illustrating common agricultural practices (cover crops, compaction with irrigation, and tillage with N fertilization). In the first case study, time-lapse EMI reveals the initial effect of cover crops on soil drying and the absence of effect on the subsequent main crop. In the second case study, compaction leading to a shallower drying depth for potatoes (Solanum tuberosum L.) was imaged by timelapse ERT. In the third case study, larger changes in electrical conductivity over time were observed in conventional tillage compared with direct drill using timelapse EMI. In addition, different N application rates had a significant effect on the yield and leaf area index but only ephemeral effects on the dynamics of electrical conductivity, mainly after the first application. Overall, time-lapse geophysical surveys show great potential for monitoring the impact of different agricultural practices that can influence crop yield.

monitoring under winter wheat . Near Surface Geophysics.

Geophysical methods, such as electromagnetic induction (EMI), can be effective for monitoring changes in soil moisture at the field scale, particularly in agricultural applications. The electrical conductivity (σ) inferred from EMI needs to be converted to soil moisture content (θ) using an appropriate relationship. Typically, a single global relationship is applied to an entire agricultural field; however, soil heterogeneity at the field scale may limit the effectiveness of such an approach. One application area that may suffer from such an effect is crop phenotyping. Selecting crop varieties based on their root traits is important for crop breeding and maximizing yield. Hence, highthroughput tools for phenotyping the root system architecture and activity at the field scale are needed. Water uptake is a major root activity and, under appropriate conditions, can be approximated by measuring changes in soil moisture from time-lapse geophysical surveys. We examine here the effect of heterogeneity in the θ-σ relationship using a crop phenotyping study for illustration. In this study, the θ-σ relationship was found to vary substantially across a field site. To account for this, we propose a range of local (plot specific) θ-σ models. We show that the large number of parameters required for these models can be estimated from baseline σ and θ measurements. Finally, we compare the use of global (field scale) and local (plot scale) models with respect to ranking varieties based on the estimated soil moisture content change.

Abstract. Saturated and near-saturated soil hydraulic conductivities Kh (mm h−1) determine the partitioning of precipitation into surface runoff and infiltration and are fundamental to soils' susceptibility to preferential flow. Recent studies found indications that climate factors influence Kh, which is highly relevant in the face of climate change. In this study, we investigated relationships between pedoclimatic factors and Kh and also evaluated effects of land use and soil management. To this end, we collated the Open Tension-disk Infiltrometer Meta-database (OTIM), which contains 1297 individual data entries from 172 different publication sources. We analysed a spectrum of saturated and near-saturated hydraulic conductivities at matric potentials between 0 and 100 mm. We found that methodological details like the direction of the wetting sequence or the choice of method for calculating infiltration rates to hydraulic conductivities had a large impact on the results. We therefore restricted ourselves to a subset of 466 of the 1297 data entries with similar methodological approaches. Correlations between Ks and Kh at higher supply tensions decreased especially close to saturation, indicating a different flow mechanism at and very close to saturation than towards the dry end of the investigated tension range. Climate factors were better correlated with topsoil near-saturated hydraulic conductivities at supply tensions ≥ 30 mm than soil texture, bulk density and organic carbon content. We find it most likely that the climate variables are proxies for soil macropore networks created by the respective biological activity, pedogenesis and climate-specific land use and management choices. Due to incomplete documentation in the source publications of OTIM, we were able to investigate only a few land use types and agricultural management practices. Land use, tillage system and soil compaction significantly influenced Kh, with effect sizes appearing comparable to the ones of soil texture and soil organic carbon. The data in OTIM show that experimental bias is present, introduced by the choice of measurement time relative to soil tillage, experimental design or data evaluation procedures. The establishment of best-practice rules for tension-disk infiltrometer measurements would therefore be helpful. Future studies are needed to investigate how climate shapes soil macropore networks and how land use and management can be adapted to improve soil hydraulic properties. Both tasks require large numbers of new measurement data with improved documentation on soil biology and land use and management history.