Soil Particle Size Research Articles

Factors affecting soil treatment with the microbially induced carbonate precipitation technique and its optimization

The microbially induced carbonate precipitation (MICP) technique has been used to increase mechanical strength, reduce permeability, and fix radionuclides of soils, etc. To achieve effective soil cementation by MICP, 3 aspects should be considered: MICP efficiency, bacterium retention (in soils after injections), and precipitation uniformity. Here, experiments and statistical analyses were conducted to understand the parameters affecting the 3 aspects. Moreover, the parameters leading to better performance in these aspects were designed and used to conduct MICP soil cementation with varying the number of injections. The results present that temperature and OD600nm of bacterial suspension are the most important parameters affecting MICP efficiency, followed by reaction time, pH, and concentration of cementation solution, and they are all statistically significant. As these parameters increased, MICP efficiency (ratio of CaCO3 formed to Ca2+ added) first increased quickly and then slowly or decreased. The soil particle size distribution and injection rate affected bacterium retention greatly. Smaller particle sizes, wider particle-size-distribution spans, and slower injection rates are beneficial to bacterium retention. However, higher injection rates favour precipitation uniformity. Finally, the unconfined compressive strength (UCS) of the bio-treated soil can be increased further by increasing the number of injections.

Evaluation of Construction and Demolition Waste and Other Alternative Fills for Strip-Reinforced Soil Walls

This article assesses the pullout performance of ribbed metallic strips embedded in fill soils that do not conform to conventional design criteria for mechanically stabilized earth (MSE) walls. These alternative fill soils include gravelly and sandy recycled aggregates from construction and demolition waste, artificial and natural sands, and fine-grained lateritic soil. The research included soil characterization tests and large-scale pullout tests, conducted as part of this study. The results showed that the reinforcement pullout behavior was similar for recycled, artificial, and natural sands, indicating that soil particle size played a crucial role in mobilizing the interface pullout resistance. However, in the case of recycled sand, stress concentration at the reinforcement level led to particle crushing during pullout conditions, causing this material to exhibit less efficient performance compared to other sands. The fine-grained lateritic soil demonstrated inferior behavior compared to sandy soils, despite the interparticle bonding provided by the sesquioxide coating characteristic of intensely weathered tropical soils. Finally, an analytical prediction tool based on experimental results was developed, providing an alternative method to make conjectures about the performance of different soils during the pre-design stages, particularly based on particle size attributes.

Development of land-use-specific pedotransfer functions for predicting bulk density of acidic topsoil in eastern Himalayas (India)

Soil bulk density (BD), although a key input parameter in many soil functions is often overlooked in soil studies, including surveys. Direct measurements over a large area are slow and expensive, especially in resource-poor mountain ecosystems. For soils of eastern Himalayan ecosystems, cost-effective indirect methods such as pedotransfer functions (PTFs) are lacking. We developed seven landuse-specific PTFs for BD estimation using a soil particle size distribution (sand, and silt) and soil organic carbon database of 1206 sampling sites. Land uses were 3 uncultivated (dense forests-DF, open forests-OF, and grasslands-GL), 3 cultivated (shifting cultivation-SC, upland agriculture -UA, lowland paddy-LP), and a perennial plantation (PL). PTFs were developed using five machine learning algorithms (MLAs) and a multi-linear regression (MLR). The best PTFs were chosen based on cross-validation performance with the values of the greatest coefficient of determination (R2) and the smallest root mean square error (RMSE). In addition, the relative performance of thirteen (13) documented PTFs was evaluated on soils in the region. Among the MLAs, the artificial neural network (ANN) showed the best BD prediction for the whole dataset (R2: 0.58, RMSE: 0.09 Mg m−3) and four (e.g. GL, UA, SC, and LP: R2 of 0.65 to 0.69, RMSE of 0.06 to 0. 09 Mg m−3) of the seven land use classes. For the remaining three land uses (e.g., DF, OF and PL), based on the greatest R2 values (0.55 to 0.75), PTFs based on random forests (RF) made the best predictions. However, considering the smallest RMSE values (0.06–0.08 Mg m−3), ANN-PTFs were another option for these three land uses. Of the land uses, the best prediction for BD was obtained for PL (R2: 0.75), while the DF with variable soil properties had the lowest predictive performance (R2: 0.55). The performance of MLA-based PTFs such as the support vector machine (SVM), ridge regression (RR), and extreme gradient boosting (XGB) and MLR-PTFs were lower than the ANN-PTFs and RF-PTFs. The precision of BD prediction for 13 published PTFs was inconsistent and inferior (R2: 0.13 to 0.39) to the ANN-PTFs and RF-PTFs (R2: 0.55 to 0.75) developed in this study. Since PTFs are very region-specific, we suggest ANN-PTFs to estimate the BD of grasslands and cultivated soils while the RF-PTFs (based on the greatest R2) for dense and open forests and plantation soils in the eastern Himalayas and similar mountain ecosystems.

A new process-based and scale-aware desert dust emission scheme for global climate models – Part I: Description and evaluation against inverse modeling emissions

Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass and produces numerous important impacts on the Earth system. However, current global climate models (GCMs) and land-surface models (LSMs) struggle to accurately represent key dust emission processes, in part because of inadequate representations of soil particle sizes that affect the dust emission threshold, surface roughness elements that absorb wind momentum, and boundary-layer characteristics that control wind fluctuations. Furthermore, because dust emission is driven by small-scale (∼ 1 km or smaller) processes, simulating the global cycle of desert dust in GCMs with coarse horizontal resolutions (∼ 100 km) presents a fundamental challenge. This representation problem is exacerbated by dust emission fluxes scaling nonlinearly with wind speed above a threshold wind speed that is sensitive to land-surface characteristics. Here, we address these fundamental problems underlying the simulation of dust emissions in GCMs and LSMs by developing improved descriptions of (1) the effect of soil texture on the dust emission threshold, (2) the effects of nonerodible roughness elements (both rocks and green vegetation) on the surface wind stress, and (3) the effects of boundary-layer turbulence on driving intermittent dust emissions. We then use the resulting revised dust emission parameterization to simulate global dust emissions in a standalone model forced by reanalysis meteorology and land-surface fields. We further propose (4) a simple methodology to rescale lower-resolution dust emission simulations to match the spatial variability of higher-resolution emission simulations in GCMs. The resulting dust emission simulation shows substantially improved agreement against regional dust emissions observationally constrained by inverse modeling. We thus find that our revised dust emission parameterization can substantially improve dust emission simulations in GCMs and LSMs.

Efficiency of Different Soil Sterilization Approaches and Their Effects on Soil Particle Size Distribution

Efficiency of Different Soil Sterilization Approaches and Their Effects on Soil Particle Size Distribution

What C:N ratios in soil particle-size fractions really say: N is preferentially sorbed by clays over organic C

Factors affecting soil organic carbon (SOC) retention in the tropics are relatively well known, but this is not the case for N retention and thus C:N ratios, a common proxy for organic matter stability. Recent data suggest that SOC and N concentrations vary with climate, whereas soil C:N ratios depend on particle-size fractions contents. However, such knowledge is still incipient for tropical soils, native vegetations and deeper soil layers, which are critical to understand how land use and climate change affect C and N fluxes. We assessed SOC, N and C:N ratios in bulk soils and sand, silt and clay fractions, and their correlations with other soil properties under native tropical forests in the highlands near Lavras, Brazil. Soil samples were collected to 1-m depth in soils of contrasting texture and mineralogy, formed on quartzite, sericite-schist, gabbro, itabirite, ironstone, meta-limestone, gneiss, and phyllite. Mean SOC and N concentrations were generally high, whereas mean soil C:N ratios varied from 9.5 to 18.7 among parent materials, and only in three soils C:N ratios varied considerably with depth. Enrichment factors (EFs) were generally < 1 in the sand and silt fractions for SOC and especially N. Conversely, EFs for clay were mostly > 1 for SOC and even higher (up to 6.58) for N. Such trend indicates that SOC and N are concentrated in clays, and more importantly, that N sorption is favored compared with SOC, and likely dependent on clay mineralogy. Soil C:N ratios decreased with increasing values of exchangeable bases, due to higher N in the most fertile soils, suggesting that a mechanism of R-NH3+ cationic sorption is likely involved. Our results suggest that soil N is a critical component of the overall stabilization of soil organic matter onto clays of tropical soils, deserving further investigation also under temperate climate and other zones.

Predicting the interrill erosion rate on hillslopes incorporating soil aggregate stability on the Loess Plateau of China

The Water Erosion Prediction Project (WEPP) model is a physical process-based model that can predict both interrill and rill erosion on hillslopes. The interrill erodibility (Ki) is quantified by the soil particle size distribution, which is a key parameter in WEPP model. However, the important effects of soil structure on soil erodibility are ignored, which seriously affects the prediction accuracy of the model. In this study, simulated rainfall experiments (60, 90, 120 mm h−1) were performed to improve the prediction equation of interrill erosion incorporating the aggregate instability index (As). Rare earth elements (REEs) were employed as tracers to distinguish interrill and rill erosion on slopes in a box (2 m long, 0.5 m wide, 0.4 m deep) with gradients of 10°, 20° and 30°. The Le Bissonnais method was used to determine the soil aggregate stability. The results showed that aggregate stability played a crucial role in interrill erosion under similar runoff rates. The improved equation contains four parameters, As, runoff rate (q), rainfall intensity (I) and slope gradient (Sf), with a coefficient of determination (R2) of 0.81 and model efficiency (ME) of 0.79. This equation exhibits greatly improved efficiency in estimating the interrill erosion rate compared to the WEPP model. Furthermore, a new equation incorporating the As, flow velocity (v), flow depth (h) and hydraulic gradient (J) was developed, with R2 of 0.90 and ME of 0.89. This study provides a deeper understanding of the mechanism of interrill erosion and improves upon the physical process-based soil erosion model.

The relationship between the granulometric composition of grassland soils and their content of mineral nitrogen and organic carbon

The study analysed the relationship between the granulometric composition of grassland soils as determined by laser diffraction and their content of mineral forms of nitrogen and organic carbon. The content of mineral forms of nitrogen (NO 3-N and NH 4-N) in soil samples – after their extraction with 1% solution K 2SO 4, was determined by flow colourimetry. Soil organic carbon content was determined using the Tyurin method. The study examined soil samples collected from 169 control and measurement sites located in different regions of Poland in terms of conditions for agricultural production. Statistical analyses of the research results showed that the grain size of grassland soils had a significant effect on their ammonium nitrogen content but not on their nitrate nitrogen and organic carbon content. In this respect, it was found that there was a positive correlation between the share of the sand fraction and the content of ammonium nitrogen in soils and an opposite relation between the share of coarse silt, fine silt and clay and the content of the aforementioned component. Results of the analyses differ considerably from the results of studies by other authors on the influence of soil grain size distribution on the content of mineral nitrogen and organic carbon in soils based on classical methods of measurements of soil particle size distribution. There is a need to develop solutions to convert and compare results obtained by laser diffraction and standard methods.

Spatial variability of arsenic fractionation in an abandoned arsenic-containing mine: Insights into soil particle sizes and quantitative mineralogical analysis

Soil particle sizes and mineral phases play a major role in the migration of arsenic (As) in mine. In this study, soil As fractionation and mineralogical composition in different particle sizes soil at naturally mineralized and anthropogenically disturbed zones from an abandoned mine were comprehensively studied. The results showed that soil As contents in anthropogenically disturbed mining zone (MZ), processing zone (PZ), and smelting zone (SZ) were increased with decreasing of soil particle sizes. The contents of As in the fine soil particles (0.45–2 μm) reached to 850–4800 mg·kg−1, which mainly existed at readily soluble, specifically sorbed, and Al-oxide fractions, and contributed to 25.9–62.6 % of the total As contents in soil. Conversely, soil As contents in naturally mineralized zone (NZ) were decreased with decreasing of soil particle sizes and As was mainly accumulated in the coarse fraction of soil (0.075–2 mm). Despite the speciation of As in 0.075–2 mm soil mainly existed as residual fraction, the content of non-residual As fraction reached up to 1636 mg·kg−1, indicating a high potential risk of As in naturally mineralized soil. The utilization of scanning electron microscopy, fourier transform infrared spectroscopy combined with mineral liberation analyzer revealed that soil As in NZ and PZ was mainly retained by iron (hydrogen)oxide, while whereas the dominant host minerals for soil As in MZ and SZ were the surrounding rocks of calcite and the iron-rich silicate mineral biotite. Notably, both of the calcite and biotite exhibited high mineral liberation, which was partly contributed to a significant portion of mobile As fraction in MZ and SZ soil. The results suggested that the potential risks of soil As from SZ and MZ at abandoned mine, particularly in the fine soil particles, should be a prior concern.

Effect of Biochar on Soil-Water Characteristics of Soils: A Pore-Scale Study

The mechanical and hydraulic properties of soils are largely governed by their soil-water characteristics, which are influenced by factors such as pore size distribution (PSD). While biochar is commonly used as an additive to alter soil properties, its effect on PSD and the soil water retention curve (SWRC) at the pore scale remains unclear. This study aims to investigate the impact of biochar on PSD and SWRC in soils of different types. The study utilized nuclear magnetic resonance (NMR) and the filter paper method to obtain the particle size distribution (PSD) curve and soil water retention curve (SWRC) of various types of soils, ranging from fine-grained to coarse-grained, with different amounts of biochar. The results and discussion revealed that the impact of biochar on soil PSD and SWRC varied depending on the soil type and biochar content. In fine-grained soils, the addition of 8% biochar resulted in the greatest decrease in mesopore size and percentage, while in medium-grained soils, 6% biochar had the most significant effect, and in coarse-grained soils, the optimum biochar content was 2%. At low suction states, the addition of 8% and 2% biochar improved the water retention of fine-grained and medium-grained soils, respectively, but had little effect on coarse-grained soils. Overall, the effect of biochar on soil-water characteristics was non-monotonic and depended on the relative sizes of biochar and soil particles. Therefore, selecting the optimum biochar-to-soil ratio requires careful consideration of these factors.

Rare earth elements in soil around coal mining and utilization: Contamination, characteristics, and effect of soil physicochemical properties

REEs are emerging contaminants, and soils nearby coal and coal ash with high REEs composition are vulnerable to REEs contamination. Besides, coal industry can alter surrounding soil characteristics. However, there is information paucity about REEs contamination and geochemical behaviors along with soil characteristics around coal industrial areas, which are essential for understanding their toxicity and mobilization. The study was conducted in soils surrounding Kriel coal-fired power plant (KCM) and Greenside coal mining in Witbank (GSCM), South Africa. Multivariate statistical analysis, pollution and fractionation indices, and BCR sequential extraction were applied. The ∑REEs in the soils were compared to ∑REEs abundance in the upper earth's crust (UEC), and slightly higher ∑REEs was found in KCM but slightly lower in GSCM. Generally, LREEs are abundant. The soil REEs were normalized using the Post-Archean Australian Shale (PAAS) and Eu and Gd in KCM and Gd in GSCM were >1. Contamination assessment revealed that the soils are slightly to moderately contaminated by REEs. ∑REEs in KCM was significantly correlated with soil particle sizes of 2.00–50.00 μm, Al2O3, Fe2O3, and MnO, while with 2.00–3.00 μm and Al2O3 in GSCM. Fractionation characteristics showed a positive Ce anomaly, with positive linear regressions with Fe2O3 and MnO. In contrast, a negative Eu anomaly was found with positive linear regressions with Al, Ca, and Mg-oxides. Oxidizable fractioned REEs accounted for 32.33% of the ∑REEs in GSCM and 35.85% in KCM, and their high EF suggests enrichment that could be due to coal mining and utilization. Most soil physicochemical properties appear to be negatively correlated with the exchangeable REEs. Overall, the soils are contaminated by REEs and the REEs characteristics are considerably influenced by major elements oxide, U, and Th contents. Therefore, more attention should be paid to REEs contamination and impacts around coal mining and utilization.

Transfer and transformation characteristics of Zn and Cd in soil-rotation plant (Brassica napus L and Oryza sativa L) system and its influencing factors

Rice–rape rotation is a widely practiced cropping system in China. However, changes in soil properties and management could change the bioavailability of Cd, In order to explore the occurrence state, transportation and transformation characteristics of heavy metals Cd and Zn in rice-rape rotation system in Guizhou karst area with high background value of Cd. In the karst rice–rape rotation area, the physical and chemical properties of soil, chemical specifications and activities of Cd and Zn at different soil depths and during various crop growth stages, and the bioaccumulation of Cd and Zn in different tissues of rice and rape were studied by field experiment and laboratory analysis. The bioaccumulation of Cd and Zn and the effects of physical and chemical soil properties on the activities and bioavailabilities of Cd and Zn during rice–rape rotation were explored. The findings revealed that soil particle size, composition, pH, redox potential, soil organic matter, and Cd and Zn contents varied dramatically, especially in deep soils. The physical and chemical properties of the deep and surface soils were significantly related to the bioaccumulation of Cd and Zn. Cd and Zn are activated during crop rotation. Cd was easier to be enriched in rice, while Zn was easier to be enriched in rape. The correlation between Cd and Zn contents in Brassica campestris L and their enrichment abilities were not significant, but that in Oryza sativa L were significant. During rice-rape rotation, the chemical speciations and activities of Cd and Zn changed with the changes of soil properties and waterlogging environment. This study had important basic guiding significance for the evaluation, prevention and control of heavy metal pollution, and improving soil quality in different rotation systems in karst areas, and was conducive to promoting the safe production of rape and rice.

A systematic approach to predicting and mapping soil particle size distribution from unknown samples using large mid-infrared spectral libraries covering large-scale heterogeneous areas

Soil spectroscopy has the potential to replace wet chemical methods. However, high prediction accuracy of unknown samples is necessary to confidently map predicted values across large spatial scales. This study developed a systematic approach using MIR spectroscopy to predict soil particle size of 9,009 unknown samples, representing an area of approximately 35,716 km2 in Ireland. A systematic approach for MIR spectroscopy combined with chemometrics was augmented by including spectral control charts to identify unrepresentative spectra in predicting of unknown samples. Moreover, ∼2% of the predicted values (external validation) were selected, covering a wide range of clay, sand and silt, and analysing them using the classical reference method. This step assesses the accuracy of predicted values and provided confidence when projecting predicted values onto soil particle size maps at multiple regional scales.The MIR models were based on the support vector regression algorithm, and were built using a calibration dataset of ∼1,000 mineral and organo-mineral (MOM) soils, i.e., non-peat soils, analysed using the pipette method. The model was used to predict soil particle size from 9009 unknown samples described as MOM soils. Spectral control charts correctly identified 97.59% of the peat soils (3078 out of 3154 samples) as ‘out of control’ samples, which the spectral model should not predict, and about 90% of the MOM soils (5254 out of 5855 samples) were classified as ‘under control’ samples, which the spectra models can predict. The accuracy calculated for the ∼2% of samples selected from the MOM unknown samples (n = 5254) was similar to the accuracy in the internal validation set (n = 280), with R2val values of 0.90, 0.84 and 0.71, RMSEP values of 2.21, 4.31 and 3.91%; RPIQval values of 4.97, 3.71 and 2.30, for clay, sand and silt prediction, respectively. This systematic approach can predict soil attributes using large spectral libraries, providing confidence for building regional and national scale soil maps.

Effects of Returning Straw to Field on Soil Particle Size and Corn Yield in a Smart City

Abstract In view of the influence of straw returning interval time on soil particle size and corn yield, in order to determine the interval time of straw returning to the field, a study on the influence of straw returning to the field on soil particle size and corn yield was put forward. The experiment of straw returning to the field every other year, straw returning to the field continuously, and straw returning to the field continuously with nitrogen fertilizer was made. The results showed that although there was a trend of increasing corn yield and 100 grain weight in the year of straw returning, the difference was not significant; in the fifth year of straw returning, the effect of increasing corn yield and 100 grain weight was obvious, the increase range of corn yield was 3.07∼11.69 %, the increase of 100 grain weight was 1.41∼5.20 %, the effect of continuous returning and returning every other year on yield and 100 grain weight was not significant, but increasing nitrogen had a significant increase. The yield and 100 grain weight increased by 5.51 % and 3.65 %, respectively. The continuous return of straw to the field, the return of straw to the field every other year, and the increase of nitrogen had no effect on the seed yield of maize.

Soil particle size-dependent constitutive modeling of frozen soil under impact loading

In this study, the soil particle size dependence of the impact dynamic mechanical properties of frozen soil was investigated using a split Hopkinson pressure bar, and the meso-deformation mechanism of frozen soil was analyzed via electron microscopy. Under impact loading, the dynamic stress-strain curves of frozen soil showed strain hardening, strain softening, strain rate-temperature sensitivity, and soil particle size dependence, and the soil particle size dependence of frozen soil was considered to be significantly influenced by non-uniform meso-deformation, which primarily appeared in the form of microcracks surrounding the coarse particles. A constitutive model comprising a multiplicative hardening law was proposed, and soil particle size dependence was introduced through the equivalent strain gradient of the soil cell element. The impact compression process of frozen soil was simulated in ABAQUS/Explicit using the proposed model, and the mechanism of frozen soil deformation was summarized by the finite element analysis: the stress concentration around coarse particles induces the initiation of microcracks, the propagation of microcracks forms a crack network, eventually leading to the softening behavior of frozen soil.

Assessment of nitrogen and phosphorus in the soil of longitudinal direction affected by in-situ oxidation remediation

ABSTRACT The application of in-situ chemical oxidative remediation for contaminated soils has attracted extensive attention, but the effects of remediation processes on soil physical and chemical properties are rarely studied. Herein, a ferrous-activated persulphate oxidation system for remediating dibutyl phthalate (DBP)-polluted soil was simulated in the soil column to explore the effects of in-situ oxidative remediation on soil properties in the longitudinal direction. The DBP content in the soil column was used as an indicator of oxidation strength and the correlation between N, P, soil particle size and oxidation strength was analysed. The experiment results showed that the settling performance of polluted soil after remediation improved and the distribution of the soil particle size at 128 nm disappeared after oxidation, indicating that the suspended solids in the experimental soil were mainly fine clay particles. The oxidation system can promote the conversion of organic nitrogen to inorganic nitrogen and migration characteristics of nitrogen and phosphorus, to aggravate the loss of TN and TP in the soil. The average soil particle size (d 50), TN, NH4-N, available phosphorus (Ava-P), exchangeable phosphorus (Ex-P) and organic phosphorus(Or-P) were significantly correlated with oxidation strength; and stable pH in the soil column (pH = 3), showing that the changes in the longitudinal direction of d 50 (smaller), TN, NH4-N, Ava-P, Ex-P, and Or-P resulted from the weakening of the longitudinal oxidation strength in the direction of the soil column.

Experimental Study on the Effect of Coarse Grain Content on the Dilatancy and Particle Breakage Characteristics of Coarse-Grained Soils

The dilatancy deformation and particle breakage effects of coarse-grained soil have a significant influence on the safety and stability of high earth and rock-fill dams. To study the shear deformation and particle breakage characteristics of coarse-grained soil, four groups of soil sample with different coarse grain contents were to conduct consolidated drained triaxial compression tests by DJSZ-150 large-scale triaxial compression test machine. The results showed that the volume of coarse-grained soil changes significantly under deviation stress. The soil samples underwent positive dilatancy under the confining pressures of 200 kPa and 400 kPa but negative dilatancy under the confining pressures of 600 kPa and 800 kPa. When increasing the coarse grain contents in the soil sample under the same confining pressure, the positive dilatancy was more significant. A three-parameter dilatancy equation of coarse-grained soil considering confining pressure was established, in which the parameter L = 4 could serve as a criterion for whether the coarse-grained soil underwent positive dilatancy. Under the load, the coarse-grained soil showed three particle breakage modes: abrasion, attrition, and fracture. The coarse-grained soil particle size distribution before and after breakage conformed to the fractal characteristics, and the particle breakage rate increased significantly with larger soil particle size fractal dimension differences before and after the test. The coarse grain content and the confining pressure are two important factors affecting the mechanical properties of coarse-grained soil, and they control the shear deformation of soil.

Multispectral Remote Sensing Monitoring of Soil Particle-Size Distribution in Arid and Semi-Arid Mining Areas in the Middle and Upper Reaches of the Yellow River Basin: A Case Study of Wuhai City, Inner Mongolia Autonomous Region

Particle size distribution is an important characteristic of reclaimed soil in arid and semi-arid mining areas in western China, which is important in the ecological environment protection and control of the Yellow River Basin. Large-scale coal resource mining disturbances have caused serious damage to the fragile ecological environment. The timely and accurate dynamic monitoring of mining area topsoil information has practical significance for ecological restoration and management evaluation. Investigating Wuhai City in the Inner Mongolia Autonomous Region of China, this study uses Landsat8 OLI multispectral images and measured soil sample particle size data to analyze soil spectral characteristics and establish a particle size content prediction model to retrieve the particle size distribution in the study area. The experimental results and analysis demonstrate that: (1) the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version) atmospheric correction model is more accurate than the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) model in arid and semi-arid areas with undulating terrain; (2) 0–40 cm is the optimum soil thickness for modeling and predicting particle size content in this study; and (3) the multi-band prediction model is more precise than the single-band prediction model. The multi-band model’s sequence of advantages and disadvantages is SVM (Support Vector Machine) &gt; MLR (Multiple Linear Regression) &gt; PLSR (Partial Least Squares Regression). Among them, the 6SV-SVM model has the highest precision, and the prediction precision R2 of the 3 particle sizes’ contents is above 0.95, which can effectively predict the soil particle-size distribution and provide effective data to support topsoil quality change monitoring in the mine land reclamation area.

Improving crop nutrition, soil carbon storage and soil physical fertility using ramial wood chips

Ramial wood chips (RWC) amendment has great potential in sustainable agriculture; however more data is needed to assess its effect on soil fertility and carbon (C) storage. In this study, we investigated the effect of a single application of RWC amendment on a silty clay loam soil. During the 5 year experiment, we measured biomass production, grain yields and crop nutrient uptake. At the end of the experiment, we measured soil micro- and macronutrients, soil organic C (SOC) content and thermal stability, microbial biomass C (Cmic), and organic C and total N contents in soil particle size fractions. Soil physical properties, including structural porosity, air and water capacity, were also measured. Neither crop biomass production nor grain yields were affected by RWC. However, RWC was found to affect nutrient uptake, with improved N, P and Mg uptakes for the 2nd and 3rd crops after RWC amendment, and decreased Mn, Fe and Zn uptakes in the second half of the study period. The initially low SOC content increased by 10%, mainly in the mineral-associated organic matter fraction, resulting in a higher SOC stability. The increase in SOC following RWC amendment decreased the bulk density and increased the easily available water capacity due to a larger structural porosity. The increased porosity in the 15–30μm diameter range was ascribed to a change in SOC quality. In conclusion, RWC amendment improved macronutrient uptake in the short term, but decreased micronutrient uptake in the medium term. RWC increased SOC content and positively affected SOC quality, thus improving soil physical properties including water capacity and aeration.

Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties

In almost every earthwork, it is essential to compact soil so that the densest possible state of the soil can be achieved. The suitability of soil for earthworks relies on the compaction characteristics; Optimum Moisture Content (OMC), and Maximum Dry Density (MDD). The determination of the compaction characteristics in the laboratory, for a vast volume of soil, is time-consuming. Therefore, for the initial assessment of soil, it is crucial to determine the compaction characteristics from physical soil properties. In this work, three different models of the Artificial Neural Network (ANN), M5P-tree, and Multiple Linear Regression (MLR) are used to predict the compaction characteristics of the soil. In the models, particle size and plasticity properties of soil are combined and, seven input parameters of gravel, sand, silt, and clay contents, plastic limit, liquid limit, and plasticity index are comprised. To develop the models, 1038 datasets are compiled and processed. Several statistical analyses, including coefficient of determination (R2), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), and Objective (OBJ) value, are used to assess the effectiveness of the proposed models. The findings demonstrated that overall, the ANN model performed better in predicting the OMC, while the MLR model performed better in predicting MDD. Further, from the sensitivity analyses, it was indicated that the plastic limit has more influence on the value of OMC while, to predict the MDD, both sand content and the plasticity index play a foremost role.