Soil organic C (SOC) and soil moisture (SM) affect the agricultural productivity of soils. For sustainable food production, knowledge of the horizontal as well as vertical variability of SOC and SM at field scale is crucial. Machine learning models using depth‐related data from multiple electromagnetic induction (EMI) sensors and a gamma‐ray spectrometer can provide insights into this variability of SOC and SM. In this work, we applied weighted conditioned Latin hypercube sampling to calculate 25 representative soil profile locations based on geophysical measurements on the surveyed agricultural field, for sampling and modeling. Ten additional random profiles were used for independent model validation. Soil samples were taken from four equal depth increments of 15 cm each. These were used to approximate polynomial and exponential functions to reproduce the vertical trends of SOC and SM as soil depth functions. We modeled the function coefficients of the soil depth functions spatially with Cubist and random forests with the geophysical measurements as environmental covariates. The spatial prediction of the depth functions provides three‐dimensional (3D) maps of the field scale. The main findings are (a) the 3D models of SOC and SM had low errors; (b) the polynomial function provided better results than the exponential function, as the vertical trends of SOC and SM did not decrease uniformly; and (c) the spatial prediction of SOC and SM with Cubist provided slightly lower error than with random forests. Hence, we recommend modeling the second‐degree polynomial with Cubist for 3D prediction of SOC and SM at field scale.

Biochar amendment changes chemical and physical properties of soils and influences soil biota. It is, thus, assumed that it can also affect soil erosion and erosion-related processes. In this study, we investigated how biochar particles instantly change erodibility by rain splash and the initial movement of soil water in a small-scale experiment. Hydrothermal carbonization (HTC)-char and Pyrochar were admixed to two soil substrates. Soil erodibility was determined with Tübingen splash cups under simulated rainfall, soil hydraulic conductivity was calculated from texture and bulk soil density, and soil water retention was measured using the negative and the excess pressure methods. Results showed that the addition of biochar significantly reduced initial soil erosion in coarse sand and silt loam immediately after biochar application. Furthermore, biochar particles were not preferentially removed from the substrate surface, but increasing biochar particle sizes partly showed decreasing erodibility of substrates. Moreover, biochar amendment led to improved hydraulic conductivity and soil water retention, regarding soil erosion control. In conclusion, this study provided evidence that biochar amendments reduce soil degradation by water erosion. Furthermore, this effect is detectable in a very early stage, and without long-term incorporation of biochar into soils.

Teuber, S., J. J. Ahlrichs, J. Henkner, T. Knopf, P. Kühn, and T. Scholten. 2017. Soil cultures – the adaptive cycle of agrarian soil use in Central Europe: an interdisciplinary study using soil scientific and archaeological research. Ecology and Society 22(4):13. https://doi.org/10.5751/ES-09729-220413

Multi-scale contextual modelling is an important toolset for environmental mapping. It accounts for spatial dependence by using covariates on multiple spatial scales and incorporates spatial context and structural dependence to environmental properties into machine learning models. For spatial soil modelling, three relevant scales or ranges of scale exist: quasi-local soil formation processes that are independent of the spatial context, short-range catenary processes, and long-range processes related to climate and large-scale terrain settings. Recent studies investigated the spatial dependence of topsoil properties only. We hypothesize that soil properties within a soil profile were formed due to specific interactions between different features and scales of the spatial context, and that there are depth gradients in spatial and structural dependencies. The results showed that for topsoil, features at small to intermediate scales do not increase model accuracy, whereas large scales increase model accuracy. In contrast, subsoil models benefit from all scales—small, intermediate, and large. Based on the differences in relevance, we conclude that the relevant ranges of scales do not only differ in the horizontal domain, but also in the vertical domain across the soil profile. This clearly demonstrates the impact of contextual spatial modelling on 3D soil mapping.