• Geosciences, Vol. 8, Pages 423: Hydrodynamic Shear-Induced Densification of Bacteriogenic Iron Oxides: Mechanisms and Implications

      Bacterial–mineral aggregates are the products of a tight biogeochemical coupling between microbes and geological media and play an outsized role in governing the composition of natural waters through biogeochemical cycling and mineral formation and dissolution processes. The results of combined batch column settling experiments, volumetric analyses, and microscopic investigations demonstrate that composite bacteriogenic iron oxide aggregates are sensitive to densification in response to hydrodynamic shear, a physical fluid phenomenon that introduces significant alterations to aggregate size and structure, permeability, and settling and transport behaviour. After exposing aggregate suspensions to varying degrees of shear stress, final solids volume fractions decreased by as much as 75% from initial data, while aggregate bulk density saw increases from 999 kg∙m–3 to as much as 1010 kg∙m–3. Inverse modelling of time course data yielded estimates for settling rate constants and initial settling velocities that increased with shear stress application. As well as having implications for aqueous contaminant transport and potential bacterial bioenergetic strategies, these results suggest the preservation potential of microfossils formed from bacterial–mineral aggregates may be significantly reduced with shear-induced alterations, leading to a possible underrepresentation of these microfossils in the sedimentary record and a gap in our understanding of early life on Earth.

    • Geosciences, Vol. 8, Pages 422: Precious Metal Enrichment at the Myra Falls VMS Deposit, British Columbia, Canada

      Gold, present as electrum, in the Battle Gap, Ridge North-West, HW, and Price deposits at the Myra Falls mine, occurs in late veinlets cutting the earlier volcanogenic massive sulphide (VMS) lithologies. The ore mineral assemblage containing the electrum comprises dominantly galena, tennantite, bornite, sphalerite, chalcopyrite, pyrite, and rarely stromeyerite, and is defined as an Au-Zn-Pb-As-Sb association. The gangue is comprised of barite, quartz, and minor feldspathic volcanogenic sedimentary rocks and clay, comprised predominantly of kaolinite with subordinate illite. The deposition of gold as electrum in the baritic upper portions of the sulphide lenses occurs at relatively shallow water depths beneath the sea floor. Primary, pseudosecondary, and secondary fluid inclusions, petrographically related to gold, show boiling fluid inclusion assemblages in the range of 123 to 173 °C, with compositions and eutectic melt temperatures consistent with seawater at approximately 3.2 wt % NaCl equivalent. The fluid inclusion homogenization temperatures are consistent with boiling seawater corresponding to water depths ranging from 15 to 125 m. Slightly more dilute brines corresponding to salinities of approximately 1 wt % NaCl indicate that there is input from very low-salinity brines, which could represent a transition from subaqueous VMS to epithermal-like conditions for precious metal enrichment, mixing with re-condensed vapor, or very low-salinity igneous fluids.

    • Geosciences, Vol. 8, Pages 421: Statistical Analysis of Displacement and Length Relation for Normal Faults in the Barents Sea

      This paper is devoted to the statistical analysis of dependence between fault length (L) and displacement (D). The main purpose of this work is to study the scaling relations between fault length and displacement using a database that includes datasets of 21 faults with geometric data extracted from 3D seismic coherence cubes of the Norwegian Barents Sea. Multiple linear regression and Bayesian and Akaike information criterions are applied to obtain optimal regression parameters. Our dataset is unique since it includes segment lengths of individual faults, unlike the previously published datasets. Hence, we studied both the dependence of fault segment length and accumulated fault length on displacement. The latter relation (accumulated fault length versus displacement) shows a general agreement (positive correlation and power-law relation) with the previously published results that are mainly obtained from outcrop studies, although the slopes vary for different lithologies. The differences could be attributed to the unique characteristics of our dataset that includes data of all segment lengths of individual faults.

    • Geosciences, Vol. 8, Pages 420: The Impact of Biochar Incorporation on Inorganic Nitrogen Fertilizer Plant Uptake; An Opportunity for Carbon Sequestration in Temperate Agriculture

      Field studies of biochar addition to soil and nutrient cycling using 15N fertilizers in temperate agriculture are scant. These data are required in order to make evidence based assessments. This study was conducted to test the hypothesis that biochar application can increase crop yields through improving the nitrogen uptake and utilization of added inorganic fertilizer, whilst sequestering significant quantities of carbon. Results showed that although biochar addition led to significant spring barley grain yield increases in the first year of biochar application, an unusually dry year; this was possibly not solely the result of improved nitrogen uptake, as total crop N was similar in both treatments. Results suggested it was improved water utilization, indicated by the crop carbon isotope values and soil moisture characteristics. In the second year, there were no significant effects of the previous year’s biochar addition on the sunflower yield, N status, fertilizer recovery or any signs of improved water utilization. These data add to a growing body of evidence, suggesting that biochar addition has only slightly positive or neutral effects on crop growth and fertilizer retention but has the potential to sequester vast amounts of carbon in the soil with minimal yield losses in temperate agriculture.

    • Geosciences, Vol. 8, Pages 419: Estimating Groundwater Abstractions at the Aquifer Scale Using GRACE Observations

      Groundwater monitoring requires costly in situ networks, which are difficult to maintain over long time periods, especially in countries facing economic recession such as Greece. Our work aims at providing a methodology to estimate groundwater abstractions at the aquifer scale using publicly available remotely sensed data from the NASA’s Gravity Recovery and Climate Experiment (GRACE) together with publicly available meteorological observations that serve as input variables to an Artificial Neural Network (ANN) method. The methodology was demonstrated in an alluvial aquifer in NE Greece for a 10-year period (2005–2014), where irrigation agriculture poses a serious threat to both groundwater resources and their dependent ecosystems. To generalize the developed model, an ensemble of 100 ANNs was created by the initial weight randomization approach and output was computed by averaging the output of each individual model. Scaled Root Mean Square Error and Nash–Sutcliffe coefficient were used to test the model efficiency. Both of these performance metrics indicated that monthly groundwater abstractions can be estimated efficiently and that the developed methodology offers an inexpensive substitute for in situ groundwater monitoring when in situ networks are not available or cannot operate properly.

    • Multi-source data integration for soil mapping using deep learning

      Multi-source data integration for soil mapping using deep learning Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny SOIL Discuss., https//,2018 Manuscript under review for SOIL (discussion: open, 0 comments) With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual Digital Soil Mapping (DSM) using uncertain measurements of the soil property. The deep learning model, called Convolutional Neural Network (CNN), has the advantage that it uses as input a local representation of environmental covariates to leverage the spatial information contained in the vicinity of a location. Spatial non-linear relationships between covariate pixel values and measured soil properties are found by optimizing an objective function, which can be weighted with respect to a measurement error of soil observations. In addition, a single model can be trained to predict a soil property at different soil depths. This method is tested in mapping top- and subsoil organic carbon using laboratory analyzed and spectroscopically inferred measurements. Results show that CNNs significantly increased prediction accuracy as indicated by the coefficient of determination and concordance correlation coefficient, when compared to a conventional DSM technique. Deeper soil layer prediction error decreased, while preserving the interrelation between soil property and depths. The tests conducted using different window size of input covariates matrix to predict organic carbon suggest that CNN benefits from using local contextual information up to 260 to 360 metres. We conclude that CNN is a flexible, effective and promising model to predict soil properties at multiple depths while accounting for contextual covariates information and measurement error.

    • Spatial assessments of soil organic carbon for stakeholder decision-making – a case study from Kenya

      Spatial assessments of soil organic carbon for stakeholder decision-making – a case study from Kenya Tor-Gunnar Vågen, Leigh Ann Winowiecki, Constance Neely, Sabrina Chesterman, and Mieke Bourne SOIL, 4, 259-266,, 2018 Land degradation impacts the health and livelihoods of about 1.5 billion people worldwide. The state of the environment and food security are strongly interlinked in tropical landscapes. This paper demonstrates the integration of soil organic carbon (SOC) and land health maps with socioeconomic datasets into an online, open-access platform called the Resilience Diagnostic and Decision Support Tool for Turkana County in Kenya.

    • Effect of deforestation and subsequent land use management on soil carbon stocks in the South American Chaco

      Effect of deforestation and subsequent land use management on soil carbon stocks in the South American Chaco Natalia Andrea Osinaga, Carina Rosa Álvarez, and Miguel Angel Taboada SOIL, 4, 251-257,, 2018 The sub-humid Argentine Chaco, originally covered by forest, has been subjected to clearing since the end of the 1970s and replacement of the forest by no-till farming. The organic carbon stock content up to 1 m depth varied as follows: forest > pasture > continuous cropping, with no impact of the number of years under cropping. The incorporation of pastures of warm-season grasses was able to mitigate the decrease of C stocks caused by cropping and so could be considered sustainable management.

    • Soil lacquer peel DIY: simply capturing beauty

      Soil lacquer peel DIY: simply capturing beauty Cathelijne R. Stoof, Jasper H. J. Candel, Laszlo van der Wal, and Gert Peek SOIL Discuss., https//,2018 Manuscript under review for SOIL (discussion: open, 0 comments) Teaching and outreach of soils is often done with real life snapshots of soils and sediments in lacquer or glue peels. While it may seem hard, anyone can make such a peel. Illustrated with handmade drawings and instructional video, we explain how to capture soils in peels using readily available materials. A new twist to old methods makes this more safe, simple and successful, and thus a true DIY (do it yourself) activity, highlighting the value and beauty of the ground below our feet.

    • Aluminium and base cation chemistry in dynamic acidification models – need for a reappraisal?

      Aluminium and base cation chemistry in dynamic acidification models – need for a reappraisal? Jon Petter Gustafsson, Salim Belyazid, Eric McGivney, and Stefan Löfgren SOIL, 4, 237-250,, 2018 This paper investigates how different dynamic soil chemistry models describe the processes governing aluminium and base cations in acid soil waters. We find that traditional cation-exchange equations, which are still used in many models, diverge from state-of-the-art complexation submodels such as WHAM, SHM, and NICA-Donnan when large fluctuations in pH or ionic strength occur. In conclusion, the complexation models provide a better basis for the modelling of chemical dynamics in acid soils.