A Field Experiment for Tracing Lateral Subsurface Flow in a Post-Glacial Hummocky Arable Soil Landscape

Lateral subsurface flow (LSF) is a phenomenon that is widely occurring including the hummocky ground moraine landscape. Due to the heterogeneous structure of the subsurface, transport times of pesticides and nutrients from agricultural areas to adjacent water bodies are difficult to assess. Here, LSF at Luvisol and Regosol plots of an experimental field were studied by applying potassium bromide along a 10 m trench below the plow pan in October 2019. The soil solution was collected in suction cups 3 m downslope of the trench and in April 2021, the soil was sampled down to 1 m depth. Almost no bromide was found in the soil solution except for the 160 cm depth of the Regosol plot after a 541 day period. After the same time, bromide was observed in the 90 cm soil depth directly underneath the application trench of the Luvisol plot. A 3D reconstruction of the subsurface horizon boundaries of the Regosol revealed subsurface heterogeneities such as sand lenses that might have been attributed to the heterogeneous subsurface flow pattern

Understanding the role of water and tillage erosion from 239+240Pu tracer measurements using inverse modelling

Soil redistribution on arable land is a major threat for a sustainable use of soil resources. The majority of soil redistribution studies focus on water erosion, while wind and tillage erosion also induce pronounced redistribution of soil materials. Tillage erosion especially is understudied, as it does not lead to visible off-site damages. The analysis of on-site/in-field soil redistribution is mostly based on tracer studies, where radionuclide tracers (e.g. 137Cs, 239C240Pu) from nuclear weapon tests are commonly used to derive the erosion history over the past 50-60 years. Tracer studies allow us to determine soil redistribution patterns but integrate all types of soil redistribution processes and hence do not allow us to unravel the contribution of individual erosion processes. The aim of this study is to understand the contribution of water and tillage erosion leading to soil patterns found in a small hummocky ground moraine kettle hole catchment under intensive agricultural use. Therefore, 239C240Pu-derived soil redistribution patterns were analysed using an inverse modelling approach accounting for water and tillage erosion processes. The results of this analysis clearly point out that tillage erosion is the dominant process of soil redistribution in the study catchment, which also affects the hydrological and sedimentological connectivity between arable land and the kettle hole. A topographic change up to 17 cm (53 yr)1 in the eroded parts of the catchment is not able to explain the current soil profile truncation that exceeds the 239C240Pu-derived topographic change substantially. Hence, tillage erosion already started before the onset of intense mechanisation since the 1960s. In general, the study stresses the urgent need to consider tillage erosion as a major soil degradation process that can be the dominant soil redistribution process in sloped arable landscapes. © 2020 Georg Thieme Verlag. All rights reserved.ISSN:2199-3971ISSN:2199-398

Understanding the role of water and tillage erosion from 239+240Pu tracer measurements using inverse modelling

Soil redistribution on arable land is a major threat for a sustainable use of soil resources. The majority of soil redistribution studies focus on water erosion, while wind and tillage erosion also induce pronounced redistribution of soil materials. Tillage erosion especially is understudied, as it does not lead to visible off-site damages. The analysis of on-site/in-field soil redistribution is mostly based on tracer studies, where radionuclide tracers (e.g. 137Cs, 239C240Pu) from nuclear weapon tests are commonly used to derive the erosion history over the past 50-60 years. Tracer studies allow us to determine soil redistribution patterns but integrate all types of soil redistribution processes and hence do not allow us to unravel the contribution of individual erosion processes. The aim of this study is to understand the contribution of water and tillage erosion leading to soil patterns found in a small hummocky ground moraine kettle hole catchment under intensive agricultural use. Therefore, 239C240Pu-derived soil redistribution patterns were analysed using an inverse modelling approach accounting for water and tillage erosion processes. The results of this analysis clearly point out that tillage erosion is the dominant process of soil redistribution in the study catchment, which also affects the hydrological and sedimentological connectivity between arable land and the kettle hole. A topographic change up to 17 cm (53 yr)1 in the eroded parts of the catchment is not able to explain the current soil profile truncation that exceeds the 239C240Pu-derived topographic change substantially. Hence, tillage erosion already started before the onset of intense mechanisation since the 1960s. In general, the study stresses the urgent need to consider tillage erosion as a major soil degradation process that can be the dominant soil redistribution process in sloped arable landscapes. © 2020 Georg Thieme Verlag. All rights reserved.ISSN:2199-3971ISSN:2199-398

The power of integrating proximal and high-resolution remote sensing for mapping SOC stocks in agricultural peatlands

Soil electrical conductivity (ECa) data derived from electromagnetic induction (EMI) is valuable for estimating peat thickness and soil organic carbon stocks (SOCstocks). However, generating ECa maps locally using geostatistics limits the coverage area. This study explores the use of digital soil mapping (DSM) with random forest (RF) and universal kriging (UK) to create high-resolution ECa maps from field survey EMI data. The objective is to enhance the predictive accuracy of SOCstocks models in peatlands by incorporating these ECa maps as environmental variables

Digital mapping of buried soil horizons using 2D and pseudo-3D geoelectrical measurements in a ground moraine landscape

The identification of buried soil horizons in agricultural landscapes helps to quantify sediment budgets and erosion-related carbon dynamics. High-resolution mapping of buried horizons using conventional soil surveys is destructive and time consuming. Geoelectrical sensors can offer a fast and non-destructive alternative for determining horizon positions and properties. In this paper, we compare the suitability of several geoelectrical methods for measuring the depth to buried horizons (Apb, Ahb and Hab) in the hummocky ground moraine landscape of northeastern Germany. Soil profile descriptions were developed for 269 locations within a 6-ha experimental field “CarboZALF-D”. A stepwise linear discriminant analysis (LDA) estimated the lateral position of the buried horizons using electromagnetic induction data and terrain attributes. To predict the depth of a buried horizon, multiple linear regression (MLR) was used for both a 120-m transect and a 0.2-ha pseudo-three-dimensional (3D) area. At these scales, apparent electrical conductivity (ECa), electrical resistivity (ER) and terrain attributes were used as independent variables. The LDA accurately predicted Apb- and Ahb-horizons (a correct classification of 93%). The LDA of the Hab-horizon had a misclassification of 24%, which was probably related to the smaller test set and the higher depth of this horizon. The MLR predicted the depth of the Apb-, Ahb- and Hab-horizons with relative root mean square errors (RMSEs) of 7, 3 and 13%, respectively, in the pseudo-3D area. MLR had a lower accuracy for the 2D transect compared to the pseudo-3D area. Overall, the use of LDA and MLR has been an efficient methodological approach for predicting buried horizon positions. Highlights: The suitability of geoelectrical measurements for digital modelling of diagnostic buried soil horizons was determined. LDA and MLR were used to detect multiple horizons with geoelectrical devices and terrain attributes. Geoelectrical variables were significant predictors of the position of the target soil horizons. The use of these tested digital technologies gives an opportunity to develop high-resolution soil mapping procedures.</p

Towards mapping soil carbon landscapes: Issues of sampling scale and transferability

The conversion of point observations to a geographic field is a necessary step in soil mapping. For pursuing goals of mapping soil carbon at the landscape scale, the relationships between sampling scale, representation of spatial variation, and accuracy of estimated error need to be considered. This study examines the spatial patterns and accuracy of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging approaches excelled at internal spatial prediction with more densely spaced sample points. Because kriging depends on spatial autocorrelation, kriging performance was naturally poor in areas of spatial extrapolation. In contrast, the spatial regression approaches tested could continue to perform well in spatial extrapolation areas. However, the problem of induction allowed the potential for problems in some areas, which was less predictable. This problem also existed for the kriging approaches. Spatial phenomena occurring between sampling points could also be missed by kriging models. Use of covariates with kriging can help, but the requirement of capturing the full feature space in the map remains. Methods that utilize spatial association, such as spatial regression, can map soil properties for landscape scales at a high resolution, but are highly dependent on the inclusion of the full attribute space in the calibration of the model and the availability of transferable covariates

Digital mapping of buried soil horizons using 2D and pseudo-3D geoelectrical measurements in a ground moraine landscape

The identification of buried soil horizons in agricultural landscapes helps to quantify sediment budgets and erosion-related carbon dynamics. High-resolution mapping of buried horizons using conventional soil surveys is destructive and time consuming. Geoelectrical sensors can offer a fast and non-destructive alternative for determining horizon positions and properties. In this paper, we compare the suitability of several geoelectrical methods for measuring the depth to buried horizons (Apb, Ahb and Hab) in the hummocky ground moraine landscape of northeastern Germany. Soil profile descriptions were developed for 269 locations within a 6-ha experimental field “CarboZALF-D”. A stepwise linear discriminant analysis (LDA) estimated the lateral position of the buried horizons using electromagnetic induction data and terrain attributes. To predict the depth of a buried horizon, multiple linear regression (MLR) was used for both a 120-m transect and a 0.2-ha pseudo-three-dimensional (3D) area. At these scales, apparent electrical conductivity (ECa), electrical resistivity (ER) and terrain attributes were used as independent variables. The LDA accurately predicted Apb- and Ahb-horizons (a correct classification of 93%). The LDA of the Hab-horizon had a misclassification of 24%, which was probably related to the smaller test set and the higher depth of this horizon. The MLR predicted the depth of the Apb-, Ahb- and Hab-horizons with relative root mean square errors (RMSEs) of 7, 3 and 13%, respectively, in the pseudo-3D area. MLR had a lower accuracy for the 2D transect compared to the pseudo-3D area. Overall, the use of LDA and MLR has been an efficient methodological approach for predicting buried horizon positions. Highlights: The suitability of geoelectrical measurements for digital modelling of diagnostic buried soil horizons was determined. LDA and MLR were used to detect multiple horizons with geoelectrical devices and terrain attributes. Geoelectrical variables were significant predictors of the position of the target soil horizons. The use of these tested digital technologies gives an opportunity to develop high-resolution soil mapping procedures.</p

Interactive effects of agricultural management on soil organic carbon accrual: A synthesis of long-term field experiments in Germany

Crop production often leads to soil organic carbon (SOC) losses. However, under good management practice it is possible to maintain and even re-accumulate SOC. We evaluated how different cropland management techniques affected SOC stocks in the topsoil (0–30 cm depth) of 10 long-term experiments (LTE) in Germany. We found that SOC stocks were particularly enhanced by mineral fertilization and organic amendments like straw incorporation and to a smaller degree by irrigation, but only slightly affected by the choice of preceding crops. In agreement with global meta-analyses, liming and reduced tillage had little or even negative effects on SOC storage, but effects also depended on fertilization. Management effects on SOC stocks were dependent on soil texture: sandy soils showed the lowest SOC stocks of 20.9 ± 2.3 (standard error of the mean) Mg ha−1, but exhibited the largest relative response to different management options. Annual changes in SOC stocks ranged from −3.0 ‰ with no mineral N fertilization, to + 6.1 ‰ with farmyard manure application, using the mineral-fertilized and limed treatment as reference. Even higher rates of up to + 10.6 ‰ yr−1 were reached with the combination of irrigation and straw incorporation. Note that the contribution of organic amendments to SOC accrual and thus to climate change mitigation must be adjusted for reduction in SOC at sites from which straw was removed. Overall, the potential of agricultural management to influence and enhance SOC stocks is significant. This potential is controlled by soil type and land-use duration, is largest for sandy soils with overall lowest SOC stocks, and is characterized by antagonistic and synergistic effects of different management practices