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Concept: Soil classification


The sorption of Ni(II) on a calcareous aridisol (CA) soil, one of the major soil types in northwestern China, was investigated using batch and extended X-ray absorption fine structure (EXAFS) approaches in a 0.01 mol/L NaClO4 solution at different pH values (6.0-10.0), temperatures (25-60 °C) and contact times (2-15 days). Under alkaline conditions, EXAFS analysis showed that the interatomic distances between Ni and O atoms (RNi-O) were approximately 2.04 Å with a typical coordination number (CN) of ~6.0 O atoms in the contact time range from 2 to 15 days. The RNi-Ni (~3.07 Å) suggested that the structure of the Ni(II) adsorbed on the CA soil was basically the same as that of Ni(OH)2(s), while the Ni-Al shell (RNi-Al ~3.16 Å) gradually formed and grew with the increasing contact time. Under weakly acidic conditions, the sorption mechanism of Ni(II) on the CA soil possibly included at least two processes: (i) a fast accumulation dominated by ion exchange and surface complexation and (ii) the formation of a Ni-Al LDH phase over the long term. A high temperature is beneficial to the fixation of Ni(II) on the CA soil and the formation of a Ni-Al LDH.

Concepts: Time, Electron, Hydrogen, Chemistry, Adsorption, PH, Term, Soil classification


This study identifies factors affecting the fate of buried objects in soil and develops a method for assessing where preservation of different materials and stratigraphic evidence is more or less likely in the landscape. The results inform the extent of the cultural service that soil supports by preserving artefacts from and information about past societies. They are also relevant to predicting the state of existing and planned buried infrastructure and the persistence of materials spread on land. Soils are variable and preserve different materials and stratigraphic evidence differently. This study identifies the material and soil properties that affect preservation and relates these to soil types; it assesses their preservation capacities for bones, teeth and shells, organic materials, metals (Au, Ag, Cu, Fe, Pb and bronze), ceramics, glass and stratigraphic evidence. Preservation of Au, Pb and ceramics, glass and phytoliths is good in most soils but degradation rates of other materials (e.g. Fe and organic materials) is strongly influenced by soil type. A method is proposed for using data on the distribution of soil types to map the variable preservation capacities of soil for different materials. This is applied at a continental scale across the EU for bones, teeth and shells, organic materials, metals (Cu, bronze and Fe) and stratigraphic evidence. The maps produced demonstrate how soil provides an extensive but variable preservation of buried objects.

Concepts: Soil, Zinc, Copper, Humus, Organic matter, Soil classification


The aim of this study is to investigate how the presence of Cu influences tebuconazole (Teb) sorption onto contrasting soil types and two important constituents of the soil sorption complex: hydrated Fe oxide and humic substances. Tebuconazole was used in commercial form and as an analytical-grade chemical at different Teb/Cu molar ratios (1:4, 1:1, 4:1, and Teb alone). Increased Cu concentrations had a positive effect on tebuconazole sorption onto most soils and humic substances, probably as a result of Cu-Teb tertiary complexes on the soil surfaces. Tebuconazole sorption increased in the following order of different Teb/Cu ratios 1:4 > 1:1 > 4:1 > without Cu addition, with the only exception for the Leptosol and ferrihydrite. The highest K ( f ) value was observed for humic substances followed by ferrihydrite, the Cambisol, the Arenosol, and the Leptosol. The sorption of analytical-grade tebuconazole onto all matrices was lower, but the addition of Cu supported again tebuconazole sorption. The Teb/Cu ratio with the highest Cu addition (1:4) exhibited the highest K ( f ) values in all matrices with the exception of ferrihydrite. The differences in tebuconazole sorption can be attributed to the additives present in the commercial product. This work proved the importance of soil characteristics and composition of the commercially available pesticides together with the presence of Cu on the behavior of tebuconazole in soils.

Concepts: Water purification, Soil, Ratio, Copper, Chelation, Humus, Humic acid, Soil classification


Biostimulants offer great potential in improving phytoremediation of contaminated soils. In the current greenhouse-based study, Brassica juncea seedlings grown on soils collected from Krugersdorp Goldmine and the adjourning areas (a Game Reserve and private farmland) were supplemented with different biostimulants (Kelpak® = KEL, vermicompost leachate = VCL, smoke-water = SW). Indole-3-butyric acid (IBA) was included in the study for comparative purposes because these biostimulants are known to enhance rooting. Prior to the pot trial, concentrations of elements in the three soil types were determined using Inductively Coupled Plasma-Optical Emission Spectroscopy. Plants were harvested after 105 days and the growth and concentrations of elements in the various plant organs were determined. The B. juncea seedlings with and without biostimulants did not survive when growing in soil from the Krugersdorp Goldmine. The Game Reserve and private farmland soils supplemented with KEL produced the highest plant biomass and the lowest accumulation of metals in the organs of B. juncea. High concentrations (> 13 000 mg kg(-1)) of zinc and aluminium were quantified in the roots of IBA-supplemented soils from the Game Reserve. Generally, IBA and SW enhanced the phytoremediation of B. juncea due to elevated levels of elements that accumulated in their different organs.

Concepts: Plant, Soil, Zinc, Landfill, Game, Phytoremediation, Brassica juncea, Soil classification


80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management-organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

Concepts: Africa, Prediction, Soil, Mean squared error, Errors and residuals in statistics, Soil classification, Pedology, USDA soil taxonomy


This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Concepts: Scientific method, Regression analysis, Soil, Learning, Prediction interval, Conditional probability, Cation exchange capacity, Soil classification


Current research on the influence of environmental and physicochemical factors in shaping the soil bacterial structure has seldom been approached from a pedological perspective. We studied the bacterial communities of eight soils selected along a pedogenic gradient at the local scale in a Mediterranean calcareous mountain (Sierra de María, SE Spain). The results showed that the relative abundance of Acidobacteria, Canditate division WPS-1, and Armatimonadetes decreased whereas that of Actinobacteria, Bacteroidetes, and Proteobacteria increased from the less-developed soils (Leptosol) to more-developed soils (Luvisol). This bacterial distribution pattern was also positively correlated with soil-quality parameters such as organic C, water-stable aggregates, porosity, moisture, and acidity. In addition, at a lower taxonomic level, the abundance of Acidobacteria Gp4, Armatimonadetes_gp4, Solirubrobacter, Microvirga, Terrimonas, and Nocardioides paralleled soil development and quality. Therefore, our work indicates that the composition of bacterial populations changes with pedogenesis, which could be considered a factor influencing the communities according to the environmental and physicochemical conditions during the soil formation.

Concepts: Oxygen, Bacteria, Microbiology, Soil, Weathering, Soil classification, Pedogenesis, Pedology


Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they’re collected from the “Land Use/Cover Area frame Statistical Survey” (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960-1990 and 2000-2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%.

Concepts: Agriculture, Soil, Ecosystem, Sweden, Humus, Soil classification, Pedology, World Reference Base for Soil Resources


This study considers gamma ray attenuation in relation to the soils and bedrock of Northern Ireland using simple theory and data from a high resolution airborne survey. The bedrock is considered as a source of radiogenic material acting as parent to the soil. Attenuation in the near-surface is then controlled by water content in conjunction with the porosity and density of the soil cover. The Total Count radiometric data together with 1:250 k mapping of the soils and bedrock of Northern Ireland are used to perform statistical analyses emphasising the nature of the low count behaviour. Estimations of the bedrock response characteristics are improved by excluding areas covered by low count soils (organic/humic). Equally, estimations of soil response characteristics are improved by excluding areas underlain by low count bedrock (basalt). When the spatial characteristics of the soil-classified data are examined in detail, the low values form spatially-coherent zones (natural clusters) that can potentially be interpreted as areas of increased water content for each soil type. As predicted by theory, the highest attenuation factors are associated with the three organic soil types studied here. Peat, in particular, is remarkably skewed to low count behaviour in its radiometric response. Two detailed studies of blanket bogs reveal the extent to which peat may be mapped by its radiometric response while the intra-peat variations in the observed response may indicate areas of thin cover together with areas of increased water content.

Concepts: Scientific method, Soil, Ireland, Surface runoff, Peat, Regolith, Weathering, Soil classification


Intake of soil by children and adults is a major exposure pathway to contaminants including potentially toxic elements (PTEs). However, only the fraction of PTEs released in stomach and intestine are considered as bioaccessible and results from routine analyses of the total PTE content in soils, therefore, are not necessarily related to the degree of bioaccessibility. Experimental methods to determine bioaccessibility usually are time-consuming and relatively complicated in terms of analytical procedures which limits application in first tier assessments. In this study we evaluated the potential suitability of a recently developed single extract method (ISO-17586:2016) using dilute (0.43M) nitric acid (HNO3) to mimic the bioaccessible fraction of PTEs in soils. Results from 204 soils from Portugal, Brazil and the Netherlands including all major soil types and a wide range of PTEs' concentrations showed that the extraction efficiency using 0.43M HNO3 of Ba, Cd, Cu, Ni, Pb and Zn in soils is related to that of in vitro methods including the Simple Bioaccessibility Extraction Test (SBET) and Unified BARGE Method (UBM). Also, differences in the degree of bioaccessibility resulting from differences in parent material, geology and climate conditions did not affect the response of the 0.43M HNO3 extraction which is a prerequisite to be able to compare results from different soils. The use of 0.43M HNO3 as a first screening of bioaccessibility therefore offers a robust and representative way to be included in first tier standard soil tests to estimate the oral bioaccessibility.

Concepts: Evaluation, Assessment, Zinc, Nitric oxide, Geology, Nitric acid, Soil horizon, Soil classification