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    About

Geoderma

Elsevier

1967-

ISSN: 0016-7061 (1872-6259)
Soil Science - Soil Science

Recent articles

1 show abstract
0016-7061 * 0016-7061 * 33245861
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Daniel Puppe

Abstract

Biogenic silicon (BSi) has been found to play a fundamental role in the link between global Si and carbon cycles, because it represents a key factor in the control of Si fluxes from terrestrial to aquatic ecosystems. Furthermore, various beneficial effects of Si accumulation in plants have been revealed, i.e., increased plant growth and resistance against abiotic and biotic stresses. Due to intensified land use humans directly influence Si cycling on a global scale. For example, Si exports through harvested crops and increased erosion rates generally lead to a Si loss in agricultural systems with implications for Si bioavailability in agricultural soils, which is controlled by BSi to a great extent. However, while corresponding research on phytogenic BSi (i.e., BSi synthesized by plants) has been established for decades now, studies dealing with protozoic BSi (i.e., BSi synthesized by testate amoebae) have been conducted just recently and in the current review I summarized the findings of these field and laboratory studies. My review clearly highlights the potential of testate amoebae for Si cycling in terrestrial ecosystems and identifies knowledge gaps that have to be filled by future studies. In this context, especially the importance of single idiosomes (i.e., the building blocks of testate amoeba shells) is emphasized as there are no data on total protozoic Si pool quantities (represented by intact shells and single idiosomes) available yet. The filling of these knowledge gaps will be crucial for a detailed understanding of the role of testate amoebae in the biogeochemistry of terrestrial ecosystems.

2 show abstract
0016-7061 * 0016-7061 * 33245862
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Cheng Ji, Shuqing Li, Yajun Geng, Yiming Yuan, Junzhang Zhi, Kai Yu, Zhaoqiang Han, Shuang Wu, Shuwei Liu, Jianwen Zou

Abstract

Acidic soils are hotspots of nitrous oxide (N2O) and nitric oxide (NO) and biochar is documented to have the potential for mitigating N2O and NO. The N2O and NO emissions associated with soil functional genes and physicochemical properties under biochar amendment remains unclear in acidic soils. Here, we carried out a two-year field study to examine the responses of soil N2O and NO emissions to biochar amendment in a subtropical tea plantation in China. Measurements of N2O and NO fluxes were taken from inter-row soils using the static chamber method. We also measured the seasonal changes in soil key nitrogen (N)-cycling functional genes and physicochemical properties. Annual N2O and NO emissions averaged 27.31 kg N2O-N ha−1 yr−1 and 8.75 kg NO-N ha−1 yr−1 for the N fertilizer applied plots, which were decreased by 24% and 16% due to biochar application, respectively. In addition, both potential nitrification (PNR) and denitrification (PDR) rates were stimulated by biochar amendment, which significantly increased the abundances of bacterial amoA (AOB), nirK and nosZ genes. Changes in the composition of the N2O-related microbial functional community were closely associated with soil PNR, pH, DOC, and NO3
−-N contents. The ratios of NO/N2O were mainly lower than 1, suggesting that N2O was produced mostly through denitrification rather than nitrification. There were negative correlations between soil N2O and NO emissions and soil PDR and pH, and soil N2O emissions were negatively correlated with nosZ gene abundances. Together, the decrease in N2O and NO emissions following biochar application could be largely attributed to the enhanced denitrification process, in which biochar enriched the nirK and nosZ genes abundance, resulting from the enhancement of soil DOC and pH in acidic soils.

3 show abstract
0016-7061 * 0016-7061 * 33245863
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Si-Yi Zhang, Muning Zhuo, Zhenye Xie, Zaijian Yuan, Yiting Wang, Bin Huang, Yishan Liao, Dingqiang Li, Yi Wang

Abstract

Vegetation restoration typically aims to control and restore collapsing gullies, which are prevalently and seriously eroded areas in South China. However, its effectiveness has rarely been evaluated, especially on steep colluvial deposits and during the prevalent high-intensity precipitation events. The effects of Melinis minutifora and Chrysopogon zizanioides on slope erosion, and the contributions of canopy, biocrusts, and roots were investigated in this study via rainfall simulation experiments. M. minutifora and C. zizanioides effectively reduced soil sediment yield rate (SR) on steep colluvial deposits by 98.1% and 86.6%, respectively. Overall, lower and denser canopies and greater fine root volumes indicated better performance of M. minutifora compared to C. zizanioides. Canopies of both plants exerted the strongest effects and contribution rates for reducing the SR and apparent interrill erodibility, while the effects and contribution rates of biocrusts were slightly higher than those of roots on average. These results highlight the importance of low plant canopies, fine roots, and the protection of biocrusts toward the reduction of rainfall erosion. The results add to the optimization of soil conservation and management practices for steep colluvial deposits.

4 show abstract
0016-7061 * 0016-7061 * 33245864
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Lucas Benedet, Wilson Missina Faria, Sérgio Henrique Godinho Silva, Marcelo Mancini, Luiz Roberto Guimarães Guilherme, José Alexandre Melo Demattê, Nilton Curi

Abstract

Recently, portable X-ray fluorescence (pXRF) spectrometer and visible near-infrared (Vis-NIR) spectroscopy are increasingly being applied for soil types and attributes prediction, but a few works have used them combined in tropical regions. Thus, this work aimed at analyzing models’ performance when predicting soil types at subgroup taxonomic level via pXRF and Vis-NIR separately and together. 315 soil samples were collected in both A and B horizons in three important Brazilian states. Samples undergone laboratorial analyses for soil classification and were submitted to pXRF and Vis-NIR (350–2500 nm) analyses. Vis-NIR spectral data preprocessing was evaluated utilizing Savitzky-Golay (WT) and Savitzky-Golay with Binning (WB) methods. Four classification algorithms were employed in modeling: Support Vector Machine with Linear (SVM-L) and Radial (SVM-R) kernel, C5.0, and Random Forest (RF). Predictions were made using only B horizon and using A + B horizon data. Overall accuracy and Cohen’s Kappa index evaluated model quality. Both sensors displayed efficacy in soil types prediction. A + B horizons data combined using pXRF + Vis-NIR via SVM-R (WT and WB) delivered accurate predictions (89.32% overall accuracy and 0.75 Kappa index), but the best predictions were achieved using only B horizon data via pXRF with RF, pXRF + Vis-NIR (WT) with RF, pXRF + Vis-NIR (WB) with C5.0, and pXRF + Vis-NIR (WB) with RF (89.23% overall accuracy and 0.80 Kappa index). For tropical soils, soil subgroup prediction using only B horizon data obtained by pXRF in tandem with RF algorithm may be a viable alternative to assist in soil classification, especially when the acquisition of Vis-NIR is not possible.

5 show abstract
0016-7061 * 0016-7061 * 33281097
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Deyvid Diego Carvalho Maranhão, Marcos Gervasio Pereira, Leonardo Santos Collier, Lúcia Helena Cunha dos Anjos, Antonio Carlos Azevedo, Rafael de Souza Cavassani

Abstract

Karst terrains in tropical regions are object of few studies on soil properties and pedogenesis. In this study, soils pedogenesis was evaluated in a karst environment in the Cerrado biome, northern region of Brazil. The assumption is that even in the Cerrado conditions the characteristics of the soils are similar to other limestone soils in arid and semi-arid climates. Six soil profiles along a toposequence were sampled in trenches located at the summit (P1), shoulder (P2), backslope (P3), footslope (P4) and toeslope (P5 and P6) positions. The profiles were described, and their morphological (macromorphology and micromorphology), physical, chemical, mineralogical and organic matter attributes analyzed. The soils were classified as Calcic Luvisol (P1), Calcaric Eutric Cambisol (P2), Haplic Kastanozem (P3), Calcic Pellic Vertisol (P4), Orthofluvic Gleyic Calcaric Eutric Fluvisol (P5), Orthofluvic Calcaric Eutric Fluvisol (P6). Melanization and calcification are the main pedogenic processes, and they are controlled by the nature of parent material and the landscape position of the soil profile. The profiles P1, P2 and P3, at footslope and toeslope, show higher values of weathering ratios, which indicates the presence of clay minerals with a higher SiO2/Al2O3 ratio. They also have the highest amounts of CaCO3 which are associated with precipitation and neoformation of calcite. Although soils in the Cerrado biome are mostly highly weathered and with low natural fertility, the soils in the study area show accumulation of carbonates, high levels of exchangeable calcium and magnesium, pH values and base saturation that are associated with specific pedo-environments. The combination of the soil forming factors parent material and karst terrain controlled the dynamics of carbonates, leading to their accumulation in soils, regardless of the regional climatic conditions of the Cerrado biome.



Graphical abstract







6 show abstract
0016-7061 * 0016-7061 * 33281098
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Yongsheng Hong, Long Guo, Songchao Chen, Marc Linderman, Abdul M. Mouazen, Lei Yu, Yiyun Chen, Yaolin Liu, Yanfang Liu, Hang Cheng, Yi Liu

Abstract

Estimating soil organic carbon (SOC) in topsoil can help improve soil quality and food production. This study aimed to explore the potential of airborne hyperspectral image to estimate the SOC of bare topsoil at an agricultural site located in the southeast part of Iowa State, United States. To magnify the subtle spectral signals concerning SOC, and accelerate calibration and improve predictive ability, we developed a framework to combine two advanced spectral algorithms, namely, fractional-order derivative (FOD) and optimal band combination algorithm for SOC predicting. Our case was based on 49 soil samples and a scattered airborne hyperspectral image. Random forest (RF) was utilized to establish SOC estimation models by incorporating the optimal spectral indices processed by different FOD transformations on the basis of the optimal band combination algorithm. Results indicated that when the fractional order increased, overlapping peaks and baseline drifts were gradually removed. However, the magnitude of spectral strength decreased concurrently. More detailed and abundant spectral variability was captured by FOD as compared with those by original reflectance and first and second derivatives. The estimation accuracies developed from the optimal band combination algorithm (cross-validation R
2, 0.36–0.66) were generally better than those from full-spectrum data (cross-validation R
2, 0.32–0.54). The RF model based on the combination of 0.75-order reflectance and optimal band combination algorithm obtained the highest estimation accuracy for SOC with cross-validation R
2 of 0.66. This research provides guidance for future studies in selecting the most appropriate FOD transformation to preprocess spectral data and in using the optimal band combination algorithm to determine the spectral index. Airborne hyperspectral image-based modeling can be further used to map agricultural topsoil SOC to support local-scale agricultural planning.

7 show abstract
0016-7061 * 0016-7061 * 33281099
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Zong Wang, Wenjiao Shi, Wei Zhou, Xiaoyan Li, Tianxiang Yue

Abstract

Digital soil mapping approaches relating to the soil particle size fractions (psf) face the challenge around how to establish the statistical or geostatistical models from large sets of environmental variables, especially in a situation with sparse soil profile data. Recently, many machine learning (ML) models have sprung up with advantages over statistical models. However, few studies focused on the comprehensive comparative analyses between ML and geostatistical models in the soil psf mapping. And the exploration of optimal combination of data transformation and model simulation was even less. Therefore, two transformed methods such as additive log-ratio (ALR) and isometric log-ratio (ILR) transformations combine with two ML models such as boosted regression tree (BRT), random forest (RF) and a classic geostatistical model of regression kriging (RK) were implemented to map soil psf in the Heihe River basin, China. A total of 640 samples and thirteen scorpan factors were collected and used for the comprehensive comparative analysis. Results showed that the scorpan factors such as temperature, precipitation, elevation, soil type, soil organic carbon, vegetation types and normalized difference vegetation index had important impacts on the soil psf mapping. ILR transformation was better than ALR transformation with advantage of improving stability of data distributions and ML models could also improve the mapping performance in comparison with RK models for better handling candidate factors. For these ML models, the RF models had better accuracy performance than the BRT models. In contrast, ILR transformation combined with RF model (ILR_RF) had the best performance, with the lowest root mean square error values (sand, 15.35%; silt, 14.20%; and clay, 6.66%), Aitchison distance value (0.86), standardized residual sum of squares value (0.60), and the highest concordance correlation coefficient value (0.73) and coefficient of determination value (56.69%) for clay content. In addition, ILR_RF had a relatively higher right ratio of soil texture type (68.44%) and better predict performance for most soil texture types. The predicted maps generated from ILR_RF presented more reasonable and smoother transitions. In the future, more ML models should be explored and more variables related to soil psf should be introduced into the models to improve the predictive performance.

8 show abstract
0016-7061 * 0016-7061 * 33281100
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Fei Wang, Zhou Shi, Asim Biswas, Shengtian Yang, Jianli Ding

Abstract

Soil salinization is one of the most predominant processes responsible for land degradation globally. However, monitoring large areas presents significant challenges due to strong spatial and temporal variability. Environmental covariates show promise in predicting salinity over large areas provided a reasonable relationship is developed with field measured salinity at few points. While simple regression-based approaches to complex data mining methods have been used in the prediction, a comprehensive comparison of their performances has not been explored, leading to uncertainty in which algorithms to select. This study compares thirteen popularly and non-popularly used algorithms and their performances following four criteria in predicting soil salinity from environmental covariates from Kuqa Oasis from Xinjiang, China. The environmental covariates used for the prediction include principal components of Landsat satellite images at multiple spectral bands, climate factors (referring to land surface temperature), vegetation indices, salinity and soil-related indices, soil moisture indices, DEM derived indices, land use, landform and soil type and categorized them under parameter categories of the SCORPAN (S, soils; C, climate; O, organisms, biotic factor; R, relief; P, parent material; A, age; and N, space) model. The predictive relationships were developed using the algorithms including some previously used ones such as Multiple Linear regression (MLR), Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN), Stochastic Gradient Treeboost (SGT), M5 Model Tree (M5), Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Regression (SVR) and some that have not previously been used in predicting salinity such as Alternating Model Tree (ATM), Gaussian Processes Regression (GPR), Gaussian Radial Basis Functions (GRBF), Least Median Squared Linear Regression (LMSLR), and Reduced Error Pruning Tree (REPTree). Here, 5-fold cross-validation and an independent dataset (30% overall samples) at three depths, 0–10 cm, 10–30 cm, 30–50 cm, were used for parameter optimization and evaluating the performance of algorithms. The performances of these algorithms were compared against multiple criteria, including the parameterization, error level/fitting accuracy (determination coefficient, R2; root mean squared error, RMSE), stability (based on the Pearson correlation coefficient, R; mean absolute percent error, MAPE; root mean squared error, RMSE; Lin’s concordance correlation coefficient, LCCC) and computational efficiency of the algorithms. Finally, the result showed that CSRI is most important parameter for the prediction of soil salinity at the 0–10 cm and 10–30 cm depths, whereas for the 30–50 cm depth interval, VD was the most important predictor. For depths of 0–10 cm, 10–30 cm and 30–50 cm across all models, the model R2 values ranged from 0.60 to 0.74, 0.15 to 0.31, and 0.30 to 0.47, and the RMSE values ranged from 18.87 to 23.49 dS m−1, 9.94 to 13.48 dS m−1 and 3.79 to 7.11 dS m−1. The optimal algorithms at three depths of 0–10 cm, 10–30 cm and 30–50 cm are RF, M5 and GRBF with considering accuracy and stability. After a comprehensive assessment of algorithm performance, we recommend RF for mapping salinity in an arid environment such as that of Xinjiang and elsewhere globally. However, there is no algorithm that can perform ideally for all datasets. Therefore, we suggest that the algorithm should be carefully chosen according to the purposes of the study.

9 show abstract
0016-7061 * 0016-7061 * 33318101
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Richard J. Haynes, Ya-Feng Zhou

Abstract

The effects of pH (5.0, 6.0 and 7.0) and reaction time (2 h–60 d) on sorption/desorption of Si by a Si-deficient soil were investigated. Sorption isotherms for Si were linear indicating that with increasing surface coverage precipitation reactions were occurring on the sorption surfaces. Sorption of Si was increased as pH was increased from 5.0 to 7.0. Increasing the pH also resulted in a decrease in the percentage of sorbed Si that was subsequently desorbed over 10 desorption cycles. Similarly, increasing the pH during desorption (from 5.0 to 7.0) decreased the percentage Si desorbed as well as the percentage of Si that was released during the first desorption cycle. Thus, increasing pH not only increased the magnitude of sorption but the Si that was sorbed was held more strongly than at lower pH values. Increasing the period of sorption (up to 60 d) resulted in increased Si sorption thus demonstrating the existence of slow reactions between added Si and soil surfaces. Increasing contact time also decreased the percentage sorbed Si that could be desorbed over 12 desorption cycles and the percentage of total Si that was desorbed during the first desorption cycle. As the period of contact during sorption is increased subsequent desorption involves reversal of slow reactions and it is apparent that these are not easily or rapidly reversible. After a 60 d sorption period, only 49% of sorbed Si was desorbed over 12 desorption cycles. It was concluded that over a period of several months sorption can become a process by which Si is sequestered into a non-mobile form so that it cannot be regarded as simply a retention mechanism.

10 show abstract
0016-7061 * 0016-7061 * 33318102
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Leonardo Deiss, Andrew J. Margenot, Steve W. Culman, M. Scott Demyan

Abstract

Estimating soil properties in diffuse reflectance infrared Fourier transform spectroscopy in the mid-infrared region (mid-DRIFTS) uses statistical modeling (chemometrics) to predict soil properties from spectra. Modeling approaches can have major impacts on prediction accuracy. However, the impact of selecting best parameters for an algorithm (tuning), to optimize non-linear models for predicting soil properties, is relatively unexplored in the domain of soil sciences. This study aimed to evaluate the predictive performance of linear (partial least squares, PLS) and non-linear (support vector machines, SVM) multivariate regression models in estimating soil physical, chemical, and biological properties with mid-DRIFTS. We evaluated the impact of optimizing two hyperparameters (epsilon and cost) based on the noise tolerance in the ε-insensitive loss function of SVM models using two contrasting and diverse sets of soils, one from northern Tanzania (n = 533) and another one from USA Midwest (n = 400). Regression models were trained on calibration sets (75%) and tested on independent validation sets (25%) separately for each dataset. Support vector machines outperformed PLS models for all tested soil properties (clay, sand, pH, total organic carbon, and permanganate oxidizable carbon) in both datasets. Tuning hyperparameters epsilon and cost maintained or improved prediction accuracy of SVM models based on root mean squared errors of independent validation sets. Support vector machines tuned hyperparameters differed among soil properties and also for the same soil property in distinct datasets, suggesting the need for parameterizing non-linear models for specific soil properties and datasets. Optimizing SVM regression models in mid-DRIFTS improves prediction accuracy of soil properties and therefore will likely enable obtaining more robust predictive outcomes even in datasets with diverse land uses, parent materials, and/or soil orders. We recommend that tuning should be included as a routine step when using SVM for estimating soil properties.

11 show abstract
0016-7061 * 0016-7061 * 33318103
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Xiang Wang, Erik L.H. Cammeraat, Karsten Kalbitz

Abstract

Soil erosion strongly influences the transport and fate of carbon (C) and nitrogen (N) in hillslope soils. However, in dynamic landscapes, erosional effects on soil N cycling and primary controls on N bioavailability are not well understood: particularly with respect to differences between topsoil and subsoil. Here we aim to explore the influence of erosion on (i) spatial distributions of soil N fractions and (ii) controls on N bioavailability in eroding vs. depositional sites within the Belgian Loess Belt. Soil samples were fractionated by aggregate size and density. In addition, intact soil samples were incubated to determine the influence of oxygen status (0, 5, and 20%) and labile organic matter on mineralization and nitrification of N in the context of erosion. The results showed that the deposition of eroded upslope soil materials led to N enrichment throughout entire soil profiles. Across both eroding and depositional sites, more than 93% of the total N was associated with minerals. Increased macro-aggregate- and mineral-associated N at the depositional site indicated that aggregation and N stabilized by minerals contribute to N enrichment in the depositional soils. Inorganic N, mostly NO3
−-N, was also larger at the depositional site. Oxygen concentrations were positively related to net N nitrification and mineralization rates regardless of geomorphic position. Glucose addition significantly reduced net N mineralization and nitrification rates. In conclusion, our results indicate that soil erosion might not only lead to spatial variations of N pools but also potentially affect the transformation and bioavailability of N along eroding hillslopes. Future research should consider the fate of different N species in eroding landscapes and consequences for both carbon sequestration and N leaching.

12 show abstract
0016-7061 * 0016-7061 * 33356308
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Yakun Zhang, Wenjun Ji, Daniel D. Saurette, Tahmid Huq Easher, Hongyi Li, Zhou Shi, Viacheslav I. Adamchuk, Asim Biswas

Abstract

Three-dimensional digital soil mapping (3D-DSM) quantifies both the horizontal and the vertical variability of soil properties. Most current studies in 3D-DSM were based on either one-dimensional profile depth functions or two-dimensional horizontal interpolation techniques, which did not allow true 3D visualization of spatial soil heterogeneity. Only a few studies have utilized the 3D variograms for mapping. Recent advances in proximal soil sensing technologies allow measurement and prediction of soil properties rapidly at multiple depths which could serve as input data for DSM. Various soil physical and chemical properties have already shown either direct or indirect relationships with the proximal soil sensing data. This study aims to test the methodology of 3D-DSM by incorporating a 3D regression kriging (RK) with multiple proximal soil sensing techniques. In this study, vis-NIR spectra were collected in-situ at 148 locations to about 1-m depth using the Veris® P4000 soil profiler at Field 26 of Macdonald Farm, McGill University. Additionally, 32 soil cores were collected out of the 148 locations to 1-m maximum depth and sectioned at 10-cm depth intervals for laboratory analysis of volumetric water content (VWC), soil organic matter (SOM), and clay content. Cubist spectral models were developed for each soil property at the 32 locations and then predicted to the 148 locations, which were then randomly split into calibration (70%, 103 locations) and validation (30%, 45 locations) datasets for mapping. The 3D-RK method included a trend prediction between calibration dataset and environmental covariates (including apparent soil electrical conductivity, gamma-ray radiation, and elevation) and a residual kriging. The generalized linear model (GLM), regression tree (RT), and random forest (RF) models were compared for trend prediction. The covariates were also simulated 100 times using sequential Gaussian simulations to fit into 3D-RK and calculate model uncertainty. As a result, complete 3D digital soil maps with uncertainty were developed. We found that the RF model outperformed GLM and RT in regard to interpreting non-linear soil-landscape relationships and resulting in marginally higher validation accuracy and smaller prediction uncertainty for VWC and clay. The GLM model resulted in slightly better validation results and smaller model uncertainty for SOM only. SOM and clay showed large horizontal and vertical variability and affected the spatial distribution of VWC. The validation accuracy was higher in the soil surface for most soil properties due to the uniform environment in the plow layer and sufficient environmental covariates collected at the soil surface. The mapping uncertainty increased with depth for VWC and clay content but decreased with depth for SOM because SOM content decreases with depth.

13 show abstract
0016-7061 * 0016-7061 * 33356309
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Pingzong Zhu, Guanghui Zhang, Hongxiao Wang, Baojun Zhang, Xue Wang

Abstract

Land surface roughness (LSR) plays a critical role in hydrological and erosion processes. The changes in near soil surface characteristics induced by vegetation restoration likely affect LSR greatly. However, few studies have been performed to quantify these potential effects. This study was conducted to investigate the changes in LSR under different restoration age and types, and identify the influencing factors contributing to those changes on the Loess Plateau, China. The photogrammetric method was applied to measure random roughness (RR) in one sloped farmland (as control), six abandoned farmlands with different restoration age, and five abandoned farmlands with different restoration types. The results showed that RR increased significantly after vegetation restoration. Random roughness increased exponentially with vegetation restoration age (R2 = 0.93), and reached a steady state when the restoration age was approximately 25 years. Random roughness was greatly influenced by vegetation restoration types. The mixed forest of Amorpha fruticosa and Pinus tabuliformis had the maximum RR, followed by Caragana korshinskii, Pinus tabuliformis, Robinia pseudoacacia, and Artemisia sacrorum. Compared to control, RR increased by 1.66 to 4.68 mm and 3.68 to 6.67 mm for different restoration age and types. The variations in RR were closely related to the changes in near soil surface characteristics induced by vegetation restoration. Random roughness increased linearly with plant litter coverage, thickness, density, stem diameter, soil organic matter content, soil median grain size, sand content, and water stable aggregates. While, it decreased linearly with soil bulk density, silt and clay contents. The measured RR could be well estimated by plant litter density, soil organic matter content, silt content, and water stable aggregates (NSE = 0.77). These results are helpful to understand the influencing mechanism of vegetation restoration on hydrological and erosion processes on hillslope.

14 show abstract
0016-7061 * 0016-7061 * 33356310
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Xinmu Zhang, Jingheng Guo, Rolf David Vogt, Jan Mulder, Yajing Wang, Cheng Qian, Jingguo Wang, Xiaoshan Zhang

Abstract

Significant increase in soil organic carbon (SOC) has been found in Chinese croplands. Current literature largely attributes this to the increased organic C inputs from manure, crop straw and root. However, using a meta-analysis of 185 long-term trials and 6669 spatial data pairs across China, we show here that soil acidification is an additional significant cause for the SOC accumulation. Results from long-term experiments showed that soil acidification due to excessive N fertilization coincided with, and significantly (p < 0.01) contributed to, the observed SOC accrual. Spatially, the amount of SOC increase caused by soil acidification decreased with increasing initial content. In addition, the soil’s basal respiration rate (SBRR), microbial metabolic quotient (MMQ) and the percentage of dissolved organic carbon (DOC) relative to total SOC decreased significantly (p < 0.01) with soil pH decline. This indicates that soil acidification depresses the decomposition of organic matter, both by decreasing microbial activity and by increasing protection of SOC by mineral phases. Thus, N-induced soil acidification promotes the SOC accumulation in Chinese croplands, by increasing its stability. In contrast to the current view emphasizing the importance of organic C inputs, our meta-analysis reveals an alternative mechanism connecting N-fertilization and the resulting SOC accumulation in agricultural ecosystems. More research is needed to further clarify its operating processes, relative importance, and agro-environmental consequences.

15 show abstract
0016-7061 * 0016-7061 * 33356311
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Songchao Chen, Vera Leatitia Mulder, Gerard B.M. Heuvelink, Laura Poggio, Manon Caubet, Mercedes Román Dobarco, Christian Walter, Dominique Arrouays

Abstract

The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monitoring of SOC dynamics due to land use change and climate change. However, current estimates of SOC stock and its spatial distribution have large uncertainties. In this study, we test whether we can improve the accuracy of the three existing SOC maps of France obtained at national (IGCS), continental (LUCAS), and global (SoilGrids) scales using statistical model averaging approaches. Soil data from the French Soil Monitoring Network (RMQS) were used to calibrate and evaluate five model averaging approaches, i.e., Granger-Ramanathan, Bias-corrected Variance Weighted (BC-VW), Bayesian Modelling Averaging, Cubist and Residual-based Cubist. Cross-validation showed that with a calibration size larger than 100 observations, the five model averaging approaches performed better than individual SOC maps. The BC-VW approach performed best and is recommended for model averaging. Our results show that 200 calibration observations were an acceptable calibration strategy for model averaging in France, showing that a fairly small number of spatially stratified observations (sampling density of 1 sample per 2500 km2) provides sufficient calibration data. We also tested the use of model averaging in data-poor situations by reproducing national SOC maps using various sized subsets of the IGCS dataset for model calibration. The results show that model averaging always performs better than the national SOC map. However, the Modelling Efficiency dropped substantially when the national SOC map was excluded in model averaging. This indicates the necessity of including a national SOC map for model averaging, even if produced with a small dataset (i.e., 200 samples). This study provides a reference for data-poor countries to improve national SOC maps using existing continental and global SOC maps.

16 show abstract
0016-7061 * 0016-7061 * 33356312
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Farzad Parsadoust, Mehran Shirvani, Hossein Shariatmadari, Mohammad Dinari

Abstract

In this study, the effects of the new biodegradable chelating ligands, i.e., methylglycinediacetic acid (MGDA) and L-glutamic acid-N,N-diacetic acid (GLDA), were compared with that of ethylenediaminetetraacetic acid (EDTA) on lead (Pb) sorption by montmorillonite (MMT) at equimolar Pb:ligand ratios. The results of PHREEQC simulations indicated that Pb was predominantly present in the form of negatively-charged chelates with significantly different sorption behaviors compared to that of the Pb2+ cations. The time-dependent sorption of the Pb species on MMT was biphasic, with an initial fast phase occurring within 3 h followed by a slower one reaching equilibrium after approximately 12 h. The analysis of equilibrium data revealed that both capacity and affinity of MMT for Pb sorption were decreased in the presence of the chelating ligands. The moderating potential of ligands on Pb sorption decreased in the order of EDTA ≫ MGDA > GLDA in accordance with complex stability constants. The FE-SEM/EDX, ATR-FTIR, and XRD evidence indicated that Pb and Pb complexes were adsorbed, most likely through physical forces, on both external planar and edge surfaces of the MMT. In conclusion, GLDA and MGDA represented lower ability than EDTA to depress Pb2+ adsorption on MMT, and hence, lower potential to mobilize Pb in soils and sediments where MMT is an important mineral sorbent.

17 show abstract
0016-7061 * 0016-7061 * 33356313
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): K.K. Benke, S. Norng, N.J. Robinson, K. Chia, D.B. Rees, J. Hopley

Abstract

The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases with measurement sets that may have limited transferability. The pedotransfer function generation was based on a machine learning algorithm incorporating the Generalized Linear Mixed Model with interactions and nested terms, with Residual Maximum Likelihood estimation, and a predictor-frequency ranking system with step-wise reduction of predictors to evaluate the predictive errors in reduced models. The source of the data was the Victorian Soil Information System (VSIS), which is a database administered for soil information and mapping purposes. The database contains soil measurements and information from locations across Victoria and is a repository of historical data, including monitoring studies. In total, data from 93 projects were available for inputs to modelling and analysis, with 5158 samples used to derive predictors for EC and 1954 samples used to derive predictors for OC. Over 500 models were tested by systematically reducing the number of predictors from the full model. Five-fold cross-validation was used for estimation of model mean-squared prediction error (MSPE) and mean-absolute percentage error (MAPE). The results were statistically significant with only a gradual reduction in error for the top-ranked 50 models. The prediction errors (MSPE and MAPE) of the top ranked model for EC are 0.686 and 0.635, and 0.413 and 0.474 for OC respectively. The four most frequently occurring predictors both for EC and OC prediction across the full set of models were found to be soil depth, pH, particle size distribution and geomorphological mapping unit. The possible advantages and disadvantages of this approach were discussed with respect to other machine learning approaches.

18 show abstract
0016-7061 * 0016-7061 * 33356314
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): J. Koestel, M. Larsbo, N. Jarvis

Abstract

Soil samples with a volume of approximately 100 mL are commonly used for measuring soil properties needed to parameterize continuum models of transport processes in soils. The necessary assumption that the sampled soil volume corresponds to a representative elementary volume (REV) has only been occasionally tested. Furthermore, the few studies so far have focused on bulk properties such as porosity and bulk density and have not investigated the scale-dependence of pore-space connectivity, which is fundamental for transport properties such as the permeability of soil. In this study, we investigated the scale-dependence of morphologic properties of the soil pore-space in 25 undisturbed soil columns sampled from five different depths (8, 23, 33, 53 and 73 cm) from a field site in southern Norway (Skuterud). We conducted the analyses of scale-dependence on regions of interests of 40 × 40 × 40 mm3 from binarized X-ray images with a resolution of 40 µm. We focused our evaluation on imaged porosity and three measures of pore-space connectivity (the connection probability, the Euler-Poincaré number and the critical pore diameter). As pore network connectivity is scale-dependent and because the connectivity of large pores has a very strong impact on the soil permeability, we conducted our analyses considering three contrasting minimum pore diameters, namely 80, 250 and 500 µm.
We found that the pore connectivity improved with scale, predominantly due to the presence of pores with diameters of less than 0.25 mm. This stresses the importance of image resolution in scale analyses. We moreover observed that both the mean and the standard deviation of the critical pore diameter increased with scale, which may explain why the mean and standard deviation of the saturated hydraulic conductivity are often found to increase with scale. We detected an REV range for the macroporosity between approximately 15 and 65 mm. This range decreased with an increase in the minimum pore diameter considered. However, we also found evidence contradicting the existence of the detected REV range for the macroporosity due to a lack of statistical homogeneity. No REV range could be found for the three investigated connectivity measures, probably because the evaluated scales were too small. Based on our results we conclude that larger soil samples should be used to measure soil properties and investigate processes that depend on the pore network connectivity, such as permeability or water flow and long-range solute transport. We recommend that future studies should investigate REVs for connectivity measures and investigate which REV criteria are most meaningful in a continuum modelling context. Such studies are needed to evaluate whether REVs for transport properties are common in soils. If not, flow and transport models that explicitly account for heterogeneity are necessary.

19 show abstract
0016-7061 * 0016-7061 * 33356315
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Ziqiang Liu, Dengfeng Li, Jiaen Zhang, Muhammad Saleem, Yan Zhang, Rui Ma, Yanan He, Jiayue Yang, Huimin Xiang, Hui Wei

Abstract

Since the advent of industrialization and urbanization, acid rain has emerged as one of the quintessential global environmental issues. However, the effects of acid rain on carbon (C) and nitrogen (N) cycles of terrestrial ecosystems are still far from fully understood, though some studies have reported the sensitivity of living organisms and soil physicochemical properties to acidic conditions. Herein, we conducted intact soil core experiments to understand the effects of artificial acid rains of pH 5.0, 4.0 and 3.0 on soil CO2, CH4, and N2O fluxes and microbial communities in an agricultural soil of southern China. We did not detect any effect of acid rain on CO2 and N2O fluxes as compared to the control; however, acid rain of pH 3.0 significantly reduced the cumulative CH4 flux from the soil. Most noticeably, both acid rains of pH 4.0 and pH 3.0 significantly increased the total amount of soil microbial phospholipid fatty acids (PLFAs) by increasing the PLFA contents of gram-positive bacteria, actinomycetes, fungi, and arbuscular mycorrhizal fungi, though all the acid rain treatments did not change the relative abundance of microbial groups. In addition, both CO2 and CH4 fluxes negatively correlated with the total amount of soil microbial PLFAs; however, the N2O flux positively correlated to soil NO3
−-N contents (p < 0.05). These results confirm the recent theoretical predictions that N-addition (e.g., by acid rain) may alter microbial C utilization pattern by allocating more C to the microbial biomass than to respiration. Overall, our results demonstrated that acid rain substantially altered the soil microbial biomass, and reduced the cumulative CH4 flux from the agricultural soil during the experimental period. Given these findings, we suggest further research to investigate the responses of soil greenhouse gas emissions and microbial communities to long-term acid rain exposures in the context of climate change.



Graphical abstract







20 show abstract
0016-7061 * 0016-7061 * 33356316
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Minjie Hu, Josep Peñuelas, Jordi Sardans, Chuan Tong, Chang Tang Chang, Wenzhi Cao

Abstract

Estuarine tidal marshes play a key role in phosphorus (P) retention and cycling; however, they are suffering from small but significant increases in tidal saltwater intrusion. The likely impacts of these low-level saltwater intrusions on P availability and microbial activity are unclear. Here, we investigated soil P speciation, alkaline phosphatase (ALP) activity, and the phoD phosphatase gene community along a freshwater-oligohaline gradient in the Min River estuary, southeast China. The results indicated that with the transition from freshwater to oligohaline water, the levels of soil-water salinity, pH and sulfate content were greater, and ALP activity was lower, which were associated with higher concentrations of organic P, available P, aluminum-bound P, calcium-bound P, and occluded P and lower levels of iron-bound P. There was a strong shift in the phoD phosphatase community composition in response to the freshwater-oligohaline gradient. Our findings showed that with the transition from freshwater to an oligohaline environment, in addition to the associated increases in salinity and soil pH and decreases in general microbial and biological activity and soil organic carbon, there is a shift in soil P toward more recalcitrant and immediately available fractions with less labile forms.

21 show abstract
0016-7061 * 0016-7061 * 33388690
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Marie Spohn, Isabell Zeißig, Emanuel Brucker, Meike Widdig, Ulrike Lacher, Felipe Aburto

Abstract

Little is known about the contribution of plants and microorganisms to the release of phosphorus (P) from minerals, despite the importance of P solubilization for plant nutrition and soil formation. Therefore, the aim of this study was to assess how plants affect P solubilization and the abundance of phosphorus-solubilizing bacteria (PSB) in the rhizosphere. We conducted an experiment in which we determined the release of P, Si, Fe, and Al from two contrasting saprolites (weathered rocks) that were kept in rhizoboxes with and without the grass Nassella trichotoma. In addition, we determined the pH and the concentration of organic acids in the solution over six weeks as well as the abundance and phylogeny of cultured PSB. In the strongly-weathered (SW) saprolite, a larger proportion of P was present in the NaOH-extractable P fraction, and thus was likely associated with Al and Fe oxides and hydroxides, than in the only moderately-weathered (MW) saprolite. The plants released more P, Si, and Fe from the MW saprolite than from the SW saprolite. In contrast, more Al was released from the SW saprolite than from the MW saprolite. The total P release was increased due to plants by a factor of 1591 in the MW saprolite and by a factor of 711 in the SW saprolite. P was preferentially released from the saprolites compared to Si. The release of elements went along with high consumption of protons, particularly in the MW saprolite, indicating high rates of mineral weathering. Citric acid, which can contribute to P solubilization, was present in the MW saprolite but not in the SW saprolite. The plants maintained a high abundance of PSB in the rhizoplane (surface of the roots) in the SW saprolite, but not in the MW saprolite, where concentrations of dissolved P were higher. In conclusion, the study shows that N. trichotoma strongly increased the release of P from saprolite and enriched PSB in the rhizosphere when growing in saprolite with low P availability. Taken together, our results suggest that plants can increase P solubilization by a factor of>1000 by preferentially releasing P from saprolite and preventing its (re-)adsorption and (re-)precipitation.

22 show abstract
0016-7061 * 0016-7061 * 33388691
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Jinquan Li, Ming Nie, Elise Pendall

Abstract

Soil organic carbon (SOC) and available nitrogen (N) stocks are controlled by the complex interplay of soil physical, chemical, and biological conditions. However, the interrelations of SOC or available N with these drivers as well as their relative importance are rarely evaluated quantitatively. Using investigations of SOC density (SOCD) and available N density (ND) with other detailed soil properties of topsoil (0–10 cm) and subsoil (20–30 cm) from 33 sites under different ecosystems in the vicinity of an eddy flux tower near Sydney, Australia, we investigated the controls of soil physical, chemical, and biological properties (a total of 19 variables) on SOCD and available ND. Structural equation models showed that only physical and chemical properties significantly and directly affected SOCD and available ND. Among these variables, physical and chemical properties were the most influential factors, while the relative influences of microbial biodiversity and enzyme activity were small based on boosted regression tree analysis. In addition, the effects of variables on SOCD and available ND differed between the topsoil and subsoil. In the topsoil, soil physical properties had the highest relative influence followed by chemical properties, enzyme activities, and microbial biodiversity; in the subsoil, however, soil chemical properties had the highest relative importance followed by physical properties, enzyme activities, and microbial biodiversity. This comprehensive soil characterization provides the biogeochemical context for ecosystem carbon cycling being monitored at a nearby eddy flux tower, and demonstrates the importance of including accurate measurements of soil physical and chemical properties to reduce uncertainty in soil C and N predictions in process-based models. However, this is a local-scale study, and large-scale studies are warranted to gain further understanding on this issue.



Graphical abstract

Physical and chemical properties dominantly control soil organic carbon (SOC) and available nitrogen (N) stocks at a local scale. Physical properties were dominant in the topsoil (0–10 cm) while chemical properties were dominant in the subsoil (20–30 cm) in controlling SOC and available N stocks.






23 show abstract
0016-7061 * 0016-7061 * 33388692
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Hong Gao, Xinyue Zhang, Liangjie Wang, Xianglin He, Feixue Shen, Lin Yang

Abstract

Selection of training samples plays an important role in updating conventional soil maps with data mining models. In this paper, we developed a method to determine spatial locations of training samples based on spatial neighborhood analysis of environmental covariates for each soil polygon. Training samples were selected based on a single environmental variable or integrated variables generated using multiple variables. Sensitivity analysis was also conducted to test the effect of different spatial neighborhood sizes and selected sample numbers on soil mapping accuracy. Random selection of training samples from soil polygons and soil types respectively were applied to compare with the proposed method in a study area in Raffelson watershed in La Crosse, Wisconsin of USA. Random forest was adopted as the soil prediction model. Results showed that training samples selected using single variables such as Topographic Wetness Index (TWI), slope, plan curvature, profile curvature or slope length factor with the proposed method improved the overall mapping accuracies compared with the conventional soil map, of which using TWI achieved the highest improvement of 27%. The proposed method using TWI, slope or slope length factor performed better than random selection strategies. Random selection from soil polygons generated higher overall mapping accuracies than from soil types. It was concluded that using composite environmental variables which could represent the soil forming environment of a study area well is recommended when applying the proposed method. The proposed method is not sensitive to the selected sample number, but an appropriate neighborhood size is needed for using the proposed method. In our study area with small spatial coverage, neighborhood size 5 × 5 or 3 × 3 is recommended.

24 show abstract
0016-7061 * 0016-7061 * 33388693
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Xiaobing Zhou, Ye Tao, Benfeng Yin, Colin Tucker, Yuanming Zhang

Abstract

Biological soil crusts (BSCs) are a primary source of nitrogen (N) in deserts through N-fixation. N-fixation rates may be determined by the species present within BSCs, and environmental factors like temperature and moisture, which vary seasonally. These same factors may also govern dynamics of within-soil N transformations. Few studies have explored the dynamics of different N forms in response to seasonal microclimate variations in BSCs, especially across both the growing season and the snow-covered season (winter). In this study, monthly changes in multiple soil N pools across a full year and beneath three soil cover types – bare soil, cyanobacteria BSC, and lichen BSC – were assessed across a full year in the Gurbantunggut Desert, Central Asia. We focused on multiple organic and inorganic N pools including: total N, alkali-hydrolyzable N, DON, inorganic N (NH4
+-N, NO3
−-N), free amino acids, and microbial biomass N. We found that different N forms had divergent trends during the year, peaked in different months, and showed non-synchronous responses related to seasonal temperature and moisture patterns. Most N forms were most abundant in lichen BSC > cyanobacteria BSC > bare soil, although significant differences were only observed in a few months. In the growing season, available N forms such as soil DON and inorganic N were related to both water and temperature. In winter, available N was strongly related to temperature variability prior to the sampling dates. The high NH4
+-N/NO3
−-N in the early growing season suggest that N fixation or N mineralization were highest at this time. Our results indicate non-synchronous availability of different N forms, and elevated N availability beneath lichen BSCs, which may maintain the diverse N uptake requirements for different plant species.

25 show abstract
0016-7061 * 0016-7061 * 33388694
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): I.G. Torre, Juan J. Martín-Sotoca, J.C. Losada, Pilar López, A.M. Tarquis

Abstract

Characterization of the complex soil structure is one the cornerstones of soil science and pore space detection is a crucial step in this process. Synthetic soil image construction has been proved to be an efficient resource for validating different binarization methods given that, unlike in real world, ground truth information is known. In this work, we introduce an improved Truncated Multifractal Method (TMM), to better simulate synthetic computed tomography (CT) soil images and then we generate 150 synthetic images with three different porosities (7%, 12% and 17%), both in greyscale and in binary scale (pore spaces). Synthetic images are then compared with two sets of 260 slides of real CT soil images, in order to validate the goodness of the method. All images are subjected to multifractal analysis where we show a detailed comparative analysis of parameters such as lacunarity, characteristic length and multifractal spectrum, that are calculated both for the whole set of synthetic (greyscale and binary) and for the sets of real CT soil images. With respect to lacunarity, a not previously reported inverse relationship between binary and grey lacunarity is found for this range of porosities. Moreover, we have also reported a new relationship between lacunarity and characteristic length. Similar multifractal results, that we obtain for real CT and synthetic soil images, prove that TMM is a reasonable solution to create simulated CT soil images. Finally, a segmentation test was carried out, using TMM synthetic greyscale soil images and its binary counterpart as ground-truth information, evaluating global (Otsu) and local (Combining Singularity-CA) binarization methods, where we report better performance for the last.

26 show abstract
0016-7061 * 0016-7061 * 33433423
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Mohamed Emran, Serena Doni, Cristina Macci, Grazia Masciandaro, Mohamed Rashad, Maria Gispert

Abstract

In the recent decades, soil salinity became the main human-induced soil degradation causes in Egypt’s Nile Delta Valley (ENDV) by affecting the stabilization processes of soil organic matter (SOM). However, soil organic carbon (SOC) is highly conserved by aggregating the stabilized organic molecules under sound agricultural management. In particular, labile SOM fractions assumed to be dually influenced by salinity and agricultural management practices other than the stabilized fractions. This work aimed to study various labile and stable SOM fractions that are more susceptible to the current agricultural practices in salt-affected soils of the ENDV area. Three different agro-ecological sites were studied: Eastern (EH, EM soils) and Western (WM, WL soils) Delta regions dominated by Vertic Torrifluvents, and Coastal region (NCH, NCM soils) dominated by Typic Calcitorrerts of high CaCO3 contents. Two different salinity levels were detected in each site; low in WL soils, medium in WM, NCM, and EM soils, and high in EH and NCH soils. The least values in EM, WL, and NCM soils were due to the recurrent legume applications. The carbon content of glomalin-related soil protein (GRSP) (C-GRSP) was positively correlated with SOC and water-extractable organic carbon (WEOC) fraction confirming the contribution of GRSP to the stabilization of SOM. The lower soil β-glucosidase, phosphatase, and protease enzymes activities were in those soils with larger salinity levels in each site as NCH < NCM, WM < WL, and EH < EM reflecting the effect of soil salinity and CaCO3 contents on soil metabolic activities. Extracted organic carbon (EOC) in both humic and fulvic fractions was higher in EH, WM, WL, and EM soils than in NCH and NCM soils. The chemical composition of SOM obtained by the pyrolysis gas chromatography showed that lignocellulosic and condensed aromatic structures in SOM increased significantly with CaCO3 and salinity. In conclusion, the considered SOM fractions such as WEOC, EOC, GRSP, C-GRSP, together with the pyrolytic results can be considered as significant indicators in the dynamic stability of SOM. Intercropping with legumes may increase the stability of SOM fractions in salt-affected soils of degraded lands. In calcareous soils, severe alteration in SOC conservation was observed and negatively influenced the active constituents of SOM.

27 show abstract
0016-7061 * 0016-7061 * 33433424
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Yi Zhang, Shenyan Dai, Xinqi Huang, Ying Zhao, Jun Zhao, Yi Cheng, Zucong Cai, Jinbo Zhang

Abstract

Heterotrophic nitrification occurs extensively and is important for NO3
– production in acidic soils. However, whether low soil pH stimulates heterotrophic nitrification is unknown and the potential microbial driver is unclear. Thus, a pH gradient (3.5, 4.5, 5.5, 6.5, 7.5) was manipulated in forest (SF with initial pH 4.5) and cropland (SC with initial pH 5.5) soils in subtropical China to illustrate the effect of soil pH on heterotrophic nitrification. After 30 days of pH regulation, 1% C2H2 was used to inhibit autotrophic nitrification and reveal heterotrophic nitrification via 15N-labelling experiments. During 30 days of pH regulation, soil microbial properties (e.g. gene abundance and composition of fungi and bacteria) were also determined to study the potential microbial driver of heterotrophic nitrification. The results showed that the gross heterotrophic nitrification rates increased from <0.3 mg N kg−1 day−1 in the pH 7.5 treatments to>1 mg N kg−1 day−1 in the pH 3.5 treatments, and the contribution of heterotrophic nitrification to the total nitrification was enhanced to more than 60% in the low pH treatments in both SF and SC. With soil acidification, more organic than inorganic N substrate was used in heterotrophic nitrification. Fungi showed a positive correlation with the gross heterotrophic nitrification rate (P < 0.01) and with the contribution of heterotrophic 15N-NO3
– production to total 15N-NO3
– production (P < 0.01), suggesting that fungi were the dominant heterotrophic nitrifiers in acidic soils. In addition, Phialocephala, Chloridium, and Tararomyces may have the potential for heterotrophic nitrification in our studied acidic soils. The present study suggested the decreasing soil pH could affect fungal abundance and composition, in turn, stimulate heterotrophic nitrification after a short term of pH regulation.

28 show abstract
0016-7061 * 0016-7061 * 33433425
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Anna Schneider, Florian Hirsch, Alexander Bonhage, Alexandra Raab, Thomas Raab

Abstract

The stratigraphy and properties of soils can be significantly altered by past land use, even in areas that have been continuously used for forestry. An example of such a pedological legacy of past forest use is the soils associated with relict charcoal hearths (RCHs). The physical properties and moisture regime of RCH soils and their ecological implications have hardly been studied, although RCHs are widespread in many forest areas and might be used as model sites to study long-term effects of soil amendment with biochar. The objective of our study was to characterize the soil moisture regime and hydraulic properties of RCH soils through comparison to reference forest soils on sandy substrates in the northeastern German lowlands. RCH and reference soils were studied in woodlands around the historic iron works in Peitz (Brandenburg, Germany), combining laboratory analyses of bulk density, pore size distribution, and saturated hydraulic conductivity with sensor-based monitoring of soil moisture contents and matric potentials. The laboratory analyses reveal a low bulk density and high porosity of the RCH substrates, which is mainly related to larger volumes of coarse and fine pores. Soil moisture monitoring shows higher water contents in RCH soils under relatively wet conditions and lower water contents under dry conditions, as well as strong hysteresis effects, especially after dry periods. The results therefore affirm that the legacies of charcoal production increase spatial and temporal variations in soil moisture, which in turn can cause increased variability in ecological site conditions in charcoal production areas. They furthermore reveal that the high porosity of charcoal-enriched substrates is not necessarily associated with higher water retention and plant-available water contents, and that the pore size distribution and water retention in RCHs differ from those characteristically found for biochar-amended soils.

29 show abstract
0016-7061 * 0016-7061 * 33433426
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Jordon Wade, Gabriel Maltais-Landry, Dawn E. Lucas, Giulia Bongiorno, Timothy M. Bowles, Francisco J. Calderón, Steve W. Culman, Rachel Daughtridge, Jessica G. Ernakovich, Steven J. Fonte, Dinh Giang, Bethany L. Herman, Lindsey Guan, Julie D. Jastrow, Bryan H.H. Loh, Courtland Kelly, Meredith E. Mann, Roser Matamala, Elizabeth A. Miernicki, Brandon Peterson

Abstract

Soil organic matter is central to the soil health framework. Therefore, reliable indicators of changes in soil organic matter are essential to inform land management decisions. Permanganate oxidizable carbon (POXC), an emerging soil health indicator, has shown promise for being sensitive to soil management. However, strict standardization is required for widespread implementation in research and commercial contexts. Here, we used 36 soils—three from each of the 12 USDA soil orders—to determine the effects of sieve size and soil mass of analysis on POXC results. Using replicated measurements across 12 labs in the US and the EU (n = 7951 samples), we quantified the relative importance of 1) variation between labs, 2) variation within labs, 3) effect soil mass, and 4) effect of soil sieve size on the repeatability of POXC. We found a wide range of overall variability in POXC values across labs (0.03 to 171.8%; mean = 13.4%), and much of this variability was attributable to within-lab variation (median = 6.5%) independently of soil mass or sieve size. Greater soil mass (2.5 g) decreased absolute POXC values by a mean of 177 mg kg−1 soil and decreased analytical variability by 6.5%. For soils with organic carbon (SOC)>10%, greater soil mass (2.5 g) resulted in more frequent POXC values above the limit of detection whereas the lower soil mass (0.75 g) resulted in POXC values below the limit of detection for SOC contents <5%. A finer sieve size increased absolute values of POXC by 124 mg kg−1 while decreasing the analytical variability by 1.8%. In general, soils with greater SOC contents had lower analytical variability. These results point to potential standardizations of the POXC protocol that can decrease the variability of the metric. We recommend that the POXC protocol be standardized to use 2.5 g for soils <10% SOC. Sieve size was a relatively small contributor to analytical variability and therefore we recommend that this decision be tailored to the study purpose. Tradeoffs associated with these standardizations can be mitigated, ultimately providing guidance on how to standardize POXC for routine analysis.

30 show abstract
0016-7061 * 0016-7061 * 33433427
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Alexis M. Adams, Adam W. Gillespie, Gurbir S. Dhillon, Gourango Kar, Colin Minielly, Saidou Koala, Badiori Ouattara, Anthony A. Kimaro, Andre Bationo, Jeff J. Schoenau, Derek Peak

Abstract

Joint application of mineral and organic fertilizers and incorporation of legumes into cropping systems, known as integrated soil fertility management (ISFM), has improved short-term crop productivity in sub-Saharan Africa. Little research exists, however, on the effectiveness of long-term ISFM in improving soil quality and productivity. This study determined the long-term effects of different ISFM treatments on soil chemical properties and OM dynamics up to 20 cm soil depth at a long-term research site at Saria, Burkina Faso. The ISFM treatments applied from 1960 to 2008 included broadcasted fertilizer (100 kg ha−1 14-23-14 (NPK) with 50 kg ha−1 urea; and NPK with an additional 50 kg ha−1 urea and 50 kg ha−1 KCl) supplemented with crop residue retention, and with manure application at 5000 or 40000 kg ha−1. In addition, continuous cropping of Sorghum bicolor (sorghum) was compared to yearly rotation between sorghum and Vigna unguiculata (cowpea). The large manure rate (40,000 kg ha−1) supplement was most effective in buffering fertilizer-application-induced pH decline and increasing grain yield, soil carbon (C), nitrogen (N), and phosphorus (P) concentrations (p < 0.05). Manure application also enhanced the microbial cycling and retention of C and N microbial byproducts compared to other fertilizer treatments, as indicated by C and N X-ray Absorption Near Edge Structure (XANES) spectroscopies. Legume-cereal cropping led to increased abundance of C and N functional groups indicative of reduced OM breakdown compared to the continuous cropping system. Supplemental application of manure with mineral fertilizers under mixed cereal-legume cropping was found to be most effective in improving long-term soil fertility and crop productivity in the Sahel.

31 show abstract
0016-7061 * 0016-7061 * 33433428
Publication date: 15 April 2020

Source: Geoderma, Volume 365
Author(s): Mihai Octavian Cimpoiaşu, Oliver Kuras, Tony Pridmore, Sacha J. Mooney

Abstract

Understanding the processes that control mass and energy exchanges between soil, plants and the atmosphere plays a critical role for understanding the root zone system, but it is also beneficial for practical applications such as sustainable agriculture and geotechnics. Improved process understanding demands fast, minimally invasive and cost-effective methods of monitoring the shallow subsurface. Geoelectrical monitoring methods fulfil these criteria and have therefore become of increasing interest to soil scientists. Such methods are particularly sensitive to variations in soil moisture and the presence of root material, both of which are essential drivers for processes and mechanisms in soil and root zone systems. This review analyses the recent use of geoelectrical methods in the soil sciences, and highlights their main achievements in focal areas such as estimating hydraulic properties and delineating root architecture. We discuss the specific advantages and limitations of geoelectrical monitoring in this context. Standing out amongst the latter are the non-uniqueness of inverse model solution and the appropriate choice of pedotransfer functions between electrical parameters and soil properties. The relationship between geoelectrical monitoring and alternative characterization methodologies is also examined. Finally, we advocate for future interdisciplinary research combining models of root hydrology and geoelectrical measurements. This includes the development of more appropriate analogue root electrical models, careful separation between different root zone contributors to the electrical response and integrating spatial and temporal geophysical measurements into plant hydrological models to improve the prediction of root zone development and hydraulic parameters.

32 show abstract
0016-7061 * 0016-7061 * 33461843
Publication date: 1 May 2020

Source: Geoderma, Volume 366
Author(s): Xiaoyu Jia, Yangquanwei Zhong, Jin Liu, Guangyu Zhu, Zhouping Shangguan, Weiming Yan

Abstract

Soil microbes play an important role in ecosystem processes, including carbon (C) and nutrient cycling. Nitrogen (N) enrichment is known to affect soil microbes, but whether other factors affect the impact of N enrichment on soil microbial biomass and composition and extracellular enzyme activities (EEAs) remains unclear. In this study, to evaluate the responses of soil microbial characteristics, including microbial biomass, microbial community composition and EEAs to N enrichment, we conducted a meta-analysis using 1248 global data series from 120 published papers at 125 sites that cover five types of biomes worldwide. The results showed that N enrichment significantly decreased microbial biomass carbon (MBC) and arbuscular mycorrhizal fungi (AMF) across all studies. In addition, the responses of soil microbes depended on the N enrichment rate, and different thresholds (the N rate at which the microbial response changes) of MBC (64.85 kg N ha−1 year−1), microbial biomass nitrogen (MBN, 57.00 kg N ha−1 year−1), bacterial biomass (106.75 kg N ha−1 year−1), fungal biomass (70.50 kg N ha−1 year−1), β-N-acetyl-glucosaminidase (NAG) (83.27 kg N ha−1 year−1) and peroxidase activity (19.75 kg N ha−1 year−1) were observed under N enrichment. Moreover, the responses of soil microbes to N enrichment were affected by biome type, N enrichment rate and type, experimental duration, precipitation and soil type. Furthermore, the results showed that N enrichment significantly altered soil physical and chemical properties, which may affect soil microbial biomass and composition under N enrichment. Our findings highlight that N enrichment decreased the soil microbial biomass and showed a significant effect on soil EEAs across all terrestrial ecosystems, with more pronounced effects observed with increasing N rate and duration.

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