Soil Particle Size Fractions Research Articles

Microhabitat influences on phage-bacteria dynamics in an abandoned mine for ecorestoration

Understanding the complex interactions between bacteriophages (phages) and bacteria within varied environmental niches is critical yet underexplored for improving microbe-assisted ecological restoration. This study investigates the influence of microhabitat heterogeneity within an abandoned mine on phage-bacteria interaction patterns, focusing on Pseudomonas-enriched bacterial communities. By isolating viral communities and purifying bacteria from soils of three distinct microhabitats, we assessed the regulatory role of environmental factors on these interactions, crucial for bacterial success in environmental applications. We characterized microhabitat variability by analyzing soil particle size fractions, minerals composition, and elemental content using X-ray diffraction and energy-dispersive X-ray analyses. 16S rRNA sequencing and cross-infection assays revealed that although bacterial communities across different microhabitats are taxonomically similar, their interaction patterns with phages are distinct. Phage communities showed nonselective infectivity across soil types, while bacterial communities exhibited selective adaptation, facilitating colonization across diverse microhabitats. Minerals such as mica, kaolinite, and hematite were found to increase phage infectivity, whereas mixed-layer clay correlated with early lysis. Additionally, higher levels of iron (Fe) and potassium (K) were linked to bacterial resistance strategies. Our findings highlight the importance of understanding asymmetric adaptive strategies between bacteria and phages, driven by microhabitat heterogeneity, for enhancing microbial-mediated nature-based restoration of degraded ecosystems.

Prediction of soil texture using remote sensing data. A systematic review

Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water infiltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be time-consuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artificial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction.

Enlarging effects of microplastics on adsorption, desorption and bioaccessibility of chlorinated organophosphorus flame retardants in landfill soil particle-size fractions

Chlorinated organophosphorus flame retardants (Cl-OPFRs) and microplastics (MPs) are emerging pollutants in landfills, but their synergistic behaviors and triggering risks were rarely focused on, impeding the resource utilization of landfill soils. This study systematically investigated the adsorption/desorption behaviors, bioaccessibility and human health risks of Cl-OPFRs in landfill soil particle-size fractions coexisted with MPs under simulated gastrointestinal conditions. The results showed that the adsorption capacity and bioaccessibility of Cl-OPFRs in humus soil were higher than that in subsoil. MPs promoted the adsorption of tris(1-chloro-2-methylethyl) phosphate (TCPP) and tris(1,3-dichloro-2-propyl) phosphate (TDCPP) in landfill soils by up to 34.6 % and 34.1 % respectively, but inhibited the adsorption of tris(2-chloroethyl) phosphate (TCEP) by up to 43.6 %. The bioaccessibility of Cl-OPFRs in landfill soils was positively correlated with MPs addition ratio but negatively correlated with the KOW of Cl-OPFRs, soil organic matter and particle size. MPs addition increased the residual concentration of Cl-OPFRs and significantly increased the bioaccessibility of TCEP and TDCPP by up to 33.1 % in landfill soils, resulting in higher carcinogenic and noncarcinogenic risks. The study presents the first series of the combined behavior and effects of MPs and Cl-OPFRs in landfill soils, and provides a theoretical reference for landfill risk management.

Proximal sensors for modeling clay mineralogy and characterization of soil textural fractions developed from contrasting parent materials

Proximal sensors combined with X-ray diffraction (XRD) have optimized soil characterization, but scarce studies have focused on predicting the contents of minerals under this scope. The objectives herein were to: a) use the portable X-ray fluorescence (pXRF) spectrometry, diffuse reflectance spectroscopy in the range of visible and near-infrared (Vis-NIR), magnetic susceptibility (χ) and XRD to characterize the mineralogy of soils derived from representative Brazilian soil parent materials, and b) create models to quantify the minerals obtained via XRD. Twenty-two soil profiles developed from gabbro, gneiss, quartzite, mineral and organic sediments were described with 53 soil horizons sampled. Each sample had the sand, silt and clay fractions separated and analyzed with XRD, pXRF, χ, and Vis-NIR. Models were created using the Random Forest algorithm permuting the following predictor variables (separately and combined): pXRF, parent material (PM), χ, soil texture (sand, silt, and clay content), and Vis-NIR. Models’ accuracy was calculated using the leave-one-out cross-validation method. Si, Al, Fe, Ca, K, and Ti contents obtained by pXRF and the χ discriminated the soil particle size fractions according to the parent material. XRD analysis allowed the evaluation of the pedogenetic development of soils and their relation to the respective parent material. The best models for mineral contents were found for hematite (Hm) (104)+(Gt) (130) (R2 = 0.85), Hm (110) + Mh (131) (R2 = 0.76), kaolinite (Kt) (001) (R2 = 0.73), Kt (002) (R2 = 0.80), mica (Mc) (001) (R2 = 0.77), and Mc (020) + Kt (020) (R2 = 0.81). Clay mineralogy content was accurately modeled using only pXRF and parent material data. This approach can facilitate and speed up detailed soil mineralogy characterization. Further studies are encouraged to model the content of minerals found in the sand and silt fractions of soils with diverse mineralogy via proximal sensors and using larger data sets.

National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables and sample size, as their choice greatly impacts model performance. Within the framework of Global Soil Nutrient and Nutrient Budget maps (GSNmap), the FAO Global Soil Partnership (GSP) launched a country-driven digital soil mapping (DSM) approach. The GSP asked the countries if they could implement the DSM prediction of ten soil properties, using their national point data and a set of widely available covariates (GSP_Cov). In this study, we examined the effect of including additional national-based covariates and soil observations on the performance of the prediction models using mainland France as a pilot. The learning soil dataset was based on a systematic 16-to-16 km grid. For a subset of soil properties, we also assessed using repeated k-fold cross-validation the effect of adding to this dataset many other irregularly spread measurements. The GSP_Cov included common widely available covariates that represented information about terrain, climate, and organisms. The second set of covariates consisted of the GSP_Cov, extended to extra covariates available at a national level, such as previously existing soil maps, geological maps, remote sensing products and others. Random Forest approach in combination with the Boruta selection method was employed for mapping ten soil properties: soil organic carbon (SOC), pH (water), total nitrogen (N), available phosphorus (P), available potassium (K), cation exchange capacity (CEC), bulk density (BD), and texture (clay, silt, and sand). The results revealed noteworthy enhancements in prediction performance for more than half of the properties, although, for some of them, the improvements were negligible. The most significant improvements were obtained for pH, CEC and texture, where geological variables and a previous pH map significantly contributed to the increase in accuracy. Adding numerous points (around 25,000) to the learning dataset improved the performance of soil particle-size fractions predictions. By broadening the spectrum of covariates and better covering the feature and geographical spaces considered in soil prediction models, this research underscores the importance of implementing a more diverse range of covariates at a national scale and of densifying soil information to enlarge the feature and geographical spaces of multidimensional soil/covariates combinations. This information should be taken into account in national and continental digital soil mapping endeavours.

Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R2 values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation.

Spatial Analysis Using Geographically Weighted Ordinary Logistic Regression (GWOLR) Method for Prediction of Particle-Size Fraction in Soil Surface

Spatial analysis is a method used to understand the spatial variation of geospatial data. In this study, the Geographically Weighted Ordinary Logistic Regression (GWOLR) method was used in spatial analysis to predict the particle size fraction of the surface soil. The particle size fraction of the surface soil is an important parameter in determining soil productivity and environmental quality. However, the particle size fraction in surface soils can vary spatially and is influenced by geographical factors such as elevation, rainfall, and soil texture. This study will be carried out by collecting particle size fraction data and geospatial data at randomly selected locations. Accurate modelling of soil texture is necessary because it‘s a crucial factor in determining how soil management will go. However, because soil texture is a compositional data set, it is one of the soil attributes that is more challenging to model. The challenge presented by this compositional data set is the imposition of constant quantities, specifically the requirement that the total of the fractions of clay, silt, and sand be 100%. Topographical variability can be derived from DEM data, making it an independent variable or predictor for soil texture prediction. The data will then be analyzed using the GWOLR method to predict the particle size fraction at locations that have not been observed before. The resulting prediction model will then be evaluated using cross-validation to check the accuracy of the model. This study will provide benefits for land management and natural resource management and can improve understanding of the spatial variation of particle size fractions in surface soils and the spatial and geographical factors that influence them. The GWOLR model for predicting particle size fractions in surface soils was carried out with a fixed bi-square weight and a bandwidth of 0.28895. The GWOLR model classification accuracy value is 94 percent, this shows that the GWOLR model for predicting soil particle size is more suitable than the ordinal logistic regression model with a classification accuracy of 90 percent. The aims of this study are to: (1) Establish a soil texture prediction model using the GWOLR method; and (2) Test the reliability of the model in predicting surface soil texture.

Single Sample Molecular Chronology.

ConspectusThis Account presents a new discipline, single sample molecular chronology (SSMC), which studies the relative age of an individual compound occurring in several temporal pools of a single sample in complex media. Geochemists have analytically observed for a long time that several pools of the same compound, e.g., a hydrocarbon or a pesticide, can be isolated from the same sample, e.g., a sediment or a soil, to yield a free compound pool obtained by solvent extraction and then a bound compound pool after treatment of the solid residue and further extraction. Yet the study on the significance of these pools has been limited due to the inherent lack of criteria to clearly distinguish the same compound present in various pools, and, as a consequence, the existence of these pools has been criticized as resulting from a default of extraction during analytical fractionation. Our breakthrough was to distinguish isotopically several temporal pools of a plant-derived C31 n-alkane in a soil sample containing naturally 13C-labeled carbon and then to set up a method, 13C-relative dating, to calculate the relative age of these temporal pools. We observed wide differences in the relative age of the C31 n-alkane in temporal pools of a single soil sample, ranging from -6.7 years for a soil humin-bound homologue to +25.1 years for the free homologue in the coarser soil particle-size fraction. Individual compounds can thus be used as molecular clocks to determine the relative age of temporal pools from the same sample. Moreover, our findings represented the first unambiguous proof that bound compounds are cycling slower and are somehow protected in a complex organo-mineral matrix, key information for the mechanism of carbon sequestration. SSMC could be developed in all disciplines of physical, biological, and environmental sciences manipulating complex media, to study the history of individual compounds. This chronochemistry should provide new information about the origin and transformation of individual compounds in biogeochemical systems. For example, historical information on drugs or pollutants encapsulated in temporal pools of a living organism would bring about critical new knowledge about the mechanisms of disease development. Investigations require isotope tracing using any isotope in natural or artificial abundance. Methods to separate temporal pools are suggested.

Predictive map of soil texture classes using decision tree model and neural network with features of geomorphology level

This study aims to compare decision tree (DT) and artificial neural network (ANN) models, in addition, the efficiency of geomorphic surface attributes in predicting soil texture classes. The study area is located in the north of Chaharmahal and Bakhtiari province, central west Iran, and covers 6875 ha. Ninety-six pedons were excavated on separated geoforms. Soil samples of top soil (A horizon) were analyzed for clay, sand, and silt contents. Totally 57 auxiliary variables, including the derivatives of digital elevation model (DEM), Landsat 8 images, geomorphic surface map, geology map, and land-use map, were used to predict both soil texture classes and soil particle size fractions. Root-mean-square error (RMSE), R² or the coefficient of determination ( R_square), overall accuracy, and Kappa coefficient were selected as criteria for evaluating model performance. The R-square coefficients of clay, silt, and sand fractions for both models, respectively, were 0.41, 0.25, and 0.63 for ANN and 0.52, 0.62, and 0.75 for DT. According to RMSE, R-square, overall accuracy, and Kapa coefficient of validation data, the DT model produced better prediction fits to the both soil particle-size fraction and soil texture classes and was the most accurate classifier model. The parameters were 0.59, 0.09, 0.66, and 0.24 for ANN and 0.41, 0.75, 0.76, and 0.60 for DT models, respectively. The accuracy of each individual soil texture class was generally dependent upon the number of soil texture observations in each texture class. According to this fact, both models had better prediction for silty clay loam and clay loam texture classes.

Dynamics of soil organic carbon pools following conversion of savannah to cocoa agroforestry systems in the Centre region of Cameroon

Afforestation of gramineous-woody savannah with cocoa agroforestry systems (cAFS) is a common farmer practice in Cameroon considered as sustainable. Nevertheless, the effects of afforestation of savannah with cAFS on soil organic carbon (SOC) turnover and content, and the factors controlling SOC accumulation and stabilization are unknown. SOC content at 0–10 cm soil layer, and SOC distribution in soil particle size fractions (0–20 μm fraction considered as mineral-associated organic carbon, MAOC; 50–2000 μm considered as particulate organic carbon, POC; and 20–50 μm), were compared in different systems settled on degraded savannah (orthic ferralsols). These systems included annual cropland (≈ 5 years old), cocoa monoculture (≈10 years old), and cAFS (from 20 to 60 years old) including different shade tree species such as Albizia adianthifolia, Canarium schweinfurthii, Dacryodes edulis, Milicia excelsa and Ceiba pentandra. Savannah and nearby secondary forest patches were also included in the design as controls. Soil 13C was analysed to investigate the soil carbon turnover after afforestation (C3 plants) of gramineous savannah (C4 plants).SOC significantly increased in the 0–10 cm depth from 10.6 ± 3.1 g C kg−1 in degraded savannah to 17.9 ± 5.6 g C kg−1 in cAFS reaching similar levels as in nearby secondary forests (16.3 ± 5.8 g C kg−1), while annual cropland and cocoa monoculture presented a non-significant decrease in SOC content. These changes were due to rapid loss of SOC derived from savannah plants (C4) – about 76% within the first 15 years after conversion, and higher gain of SOC derived from C3 plants in cAFS than in the other land uses (e.g. from 3.4 ± 1.5 g C kg−1 in savannah to 17.8 ± 5.7 g C kg−1 in cAFS). This SOC enrichment in cAFS was distributed in POC (64%), MAOC (30%) and the intermediate 20–50 μm soil fraction (6%). The higher annual litter input accumulated on a longer period in cAFS (20 to 60 years) than in cocoa monoculture (10 years) concomitant with the lower litter recalcitrance of associated trees compared to cocoa could explain the higher enrichment of SOC in all fractions in cAFS. The soil pH and exch. Ca2+ differed under the different shade tree species, and were positively correlated to SOC content. The highest contents of soil exch. Ca2+ induced by Ceiba and Milicia in the top 10 cm soil layer could contribute to increase SOC enrichment under those species through soil aggregation and related C stabilization. We found no strong evidence of the effect of soil texture on additional soil carbon accumulation in cAFS, especially for the more stable C pool (MAOC). Our results evidenced that savannah afforestation with cAFS appears as a valuable option for top soil carbon enrichment and should consider tree species associated to cocoa to enhance soil C sequestration, soil quality and cocoa production sustainability.

Impact of particle size separation on the stabilisation efficiency of heavy-metal-contaminated soil: a meta-analysis.

The separation of heavy-metal-contaminated soil by particle size is crucial for minimising the volume of contaminated soil because of the pronounced variability in the heavy-metal distribution among different soil particle sizes. However, relevant analyses on the effect of soil particle size sorting on stabilisation are limited. Therefore, we screened 2766 peer-reviewed papers published from January 2010 to April 2022 in the Web of Science database, of which 117 met the screening requirements, and conducted a meta-analysis to explore how soil particle size sorting and the interaction between sorting particle size and soil properties affect the stabilisation of heavy metals. The results showed that: (1) For fractionations ≤0.15 mm and from 0.15-2 mm, the materials demonstrating the highest average unit stabilisation efficiency were phosphate (45.0%/%) and organic matter (59.5%/%), respectively. (2) The smaller the size of soil particles, the greater the effect of the initial pH on stabilisation efficiency. (3) Similarly, for soil organic matter, smaller particle sizes (≤0.15 mm) combined with a lower initial content (≤1%) significantly increased the heavy metal stabilisation efficiency. (4) The impact of soil particle size fractionation on unit stabilisation efficiency was observed to be similar for typical heavy metals, specifically Cd and Pb. The relationship between particle size and unit stabilisation efficiency shows an inverted U shape. Particle size sorting can affect the distribution of heavy metals, but the type of stabilisation agent should also be considered in combination with the soil properties and heavy metal types.

Mapping soil particle-size fractions based on compositional balances

Mapping of soil particle-size fractions (PSFs) combined with log-ratio transformation has been widely employed, particularly for isometric log-ratio (ILR). Regression kriging (RK), as a hybrid interpolator, represents a method to enhance prediction accuracy. However, different choices of ILR balance yield distinct transformed data. It remains unclear and lacks a comparison as to whether these results exhibit robustness when employing RK modeling. In this study, we compared the performance of four modelling approaches–generalized linear model (GLM), random forest (RF), and their hybrid models (i.e., GLMRK and RFRK). These models were applied to three ILR transformed datasets based on different balances, resulting in a total of 12 models, in the upper reaches of the Heihe River Basin, China. The results indicated that RF tended to provide more accurate predictions of soil PSFs, while GLM was better at producing predictions with less bias. The study recommends the use of RK, as it was found to broaden the value ranges of predictions, adjust bias, and enhance accuracy, especially when applied in conjunction with RF models. Furthermore, prediction maps generated from RK unveiled finer details of soil sampling points. The choices of ILR balance resulted in varying data distributions for the components of sand, silt, and clay. These components tended to cluster at approximately 120° in three groups, thereby indicating that even components with small content also exert significant influence on soil compositions. Rather than solely focusing on the relative abundance of components, this study suggests that the alignment of ILR components with a normal distribution is crucial for better model performance, especially for the first ILR component. Opting for the most abundant component as the first permutation may not always lead to optimal results for soil PSF mapping. This study provides insights into the role of data distribution and ILR balance selection in soil PSF mapping with transformed data.

Sensitivity analysis of magnetic susceptibility under different climatic conditions during the late Pleistocene-Holocene period

Understanding the climate change experienced by the Loess Plateau in China from the end of the late Pleistocene to the Holocene is helpful for exploring global palaeoclimate change and predicting future climate change. However, sensitivity studies on palaeoclimate proxies under different geological periods and different climatic conditions are still lacking, which affects the accurate identification of palaeoclimate changes and the precision with which future climate change predictions can be made. Three soil profiles in Luochuan County were sampled, and the magnetic susceptibility (χlf), frequency-dependent susceptibility (χfd%), clay content (<0.005 mm), clay/coarse silt ratio, calcium carbonate content (CaCO3), and total content of organic carbon (TOC) of the profiles were analysed. The results showed that the climate changed from dry and cold to warm and wet to dry and cool since the end of the late Pleistocene. The precipitation (MAP) and temperature (MAT) since the end of the late Pleistocene were reconstructed by using the magnetic susceptibility of different soil particle size fractions (>2 mm, 2–1 mm, 1–0.5 mm, 0.5–0.25 mm, <0.25 mm), and the results were compared with those from other researchers. The results showed that the magnetic susceptibility of the fine fraction (<0.25 mm) was more sensitive at 0–3200 cal. yr. BP, 8500–13,000 cal. yr. BP, and 15,000–25,000 cal. yr. BP, while that of the coarse fraction (>0.25 mm) was more sensitive at 3200–8500 cal. yr. BP. Based on this finding, the precipitation and temperature of this period were reconstructed. This study provides a research method for selecting suitable climate proxies to describe climate changes in different geological periods to accurately conduct quantitative or semiquantitative assessments of climate change.

A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray (γ-ray) spectrometry is commonly used for clay content and type mapping. Fusion between ECa and γ-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, γ-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and γ-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.

Cadmium and zinc isotope compositions indicate metal sources and retention mechanisms in different soil particle size fractions

Soil particle size may significantly affect metal distribution and stable isotopic behavior. Here, two soils were separated into four particle size fractions, namely fine sand, silt, fine silt, and colloidal particles and used to determine cadmium (Cd) and zinc (Zn) concentrations and isotope compositions. Concentrations of Cd and Zn were generally enriched in the finer particles and positively correlated with the iron (Fe) and manganese (Mn) oxide contents. However, Cd concentration in the fine sand was higher than in the silt fraction due to the higher soil organic matter contents in the former particle fraction. The maximum δ114/110Cd value was found in the colloidal particles (−0.02 and 0.01‰) of both soils while the minimum was in the silt particles (−0.12 and 0.06‰). Incorporation into the mineral lattice of Fe and Mn oxides is suggested to explain the slight enrichment of heavy Cd isotopes in the colloidal fraction. The similar δ66Zn values of the four particle fractions (0.20–0.29‰ with a mean of 0.25‰) indicate similar Zn sources in different particle sizes. Metal isotopic fingerprint of different soil particle size fractions provides further insight into the underlying metal retention mechanisms within soil micro-zones and helps in tracing metal sources and biogeochemical processes.

A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information

Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land-use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg−1, respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.

Estimating soil water and salt contents from field measurements with time domain reflectometry using machine learning algorithms

Soil water and salt contents are key soil physical parameters that play a crucial role in soil-related hydrological, ecological, environmental, and agricultural processes. Time domain reflectometry (TDR) is commonly used to measure in-situ soil water and salt contents, and provide possible solutions to quickly obtain soil bulk density (BD). However, the measurement accuracy is greatly influenced by the interaction of soil water and salt contents on the measured soil dielectric constant and electrical conductivity, especially for salinized soils. To accurately estimate the soil gravimetric (GWC) and volumetric (VWC) water contents, soil salt content (TS), and BD based on the TDR measurements, we designed different model input schemes to quantify the effect of different soil factors, and applied eight machine learning algorithms to map the non-linear relationship between model inputs and each target soil property. Results of a case study in Hetao Irrigation District in Northwest China indicated that soil particle-size fractions (psfs) are important inputs to predict all the above soil properties. Furthermore, BD mainly contributes to the prediction of soil GWC, and soil surface temperature (T) is effective in improving the GWC and TS estimations. Among eight machine learning algorithms used, extreme gradient boosting (XGB) and gradient boosting regression tree (GBRT) showed good robustness and strong learning capacity. It is recommended to apply XGB to precisely estimate GWC and BD, which resulted in the coefficients of determination (R2) of 0.80 and 0.69, respectively. On the other hand, GBRT precisely estimated the VWC and TS with R2 of 0.71 and 0.84, respectively. The evaluation of spatial distribution characteristic indicated that it is reliable to obtain the spatial distributions of the above soil properties from the TDR measurements based on the recommended model input schemes and machine learning algorithms.

Progress on spatial prediction methods for soil particle-size fractions

Progress on spatial prediction methods for soil particle-size fractions

Spatial Prediction of Soil Particle-Size Fractions Using Digital Soil Mapping in the North Eastern Region of India

Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R2 for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives.

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.