Dispersive soil is a common problematic soil that segregates in water, resulting in instability of slopes and posing significant threats to the safety of earth structures. However, there is a limited amount of research on the hydraulic erosion of dispersive soil. Splash erosion is the initial stage of hydraulic erosion. A clear understanding of splash erosion is crucial for investigating dispersive soil hydraulic erosion. This study explored the influence of soil dispersity and rainfall intensity on the characteristics of splash erosion by conducting consecutive single-drop rainfall tests on artificially prepared non-dispersive and dispersive soil samples with varying sodium carbonate content. High-speed cameras and single-lens reflex (SLR) cameras were used to capture the variations of the splash crown, splash pan, and splash crater. Results showed that the content of sodium carbonate significantly affected splash erosion. High soil dispersity resulted in smaller masses of splashed soil material and infiltration water, but a larger mass of splashed water. It also led to a smaller volume and depth of the splash crater. The critical value of the content of sodium carbonate for soil dispersity discrimination was 0.15 %, the same as the threshold for the splash process to stop significant changes. The shape of the splash crown also helped analyze the process of splash erosion and was relevant to the splash crater shape. Increasing soil dispersity diminished the impact of rainfall intensity on splash erosion. These findings provide new comprehension of the mechanism of splash erosion of dispersive soil and lay a foundation for the systematic study of hydraulic erosion of dispersive soil in engineering slopes. 

Interactive effect of soil dispersity and rainfall intensity on splash erosion: Insights from laboratory tests

Machine learning-enabled soil classification for precision agriculture: a study on spectral analysis and soil property determination

Improving forest age prediction performance using ensemble learning algorithms base on satellite remote sensing data

Advice for phosphorus (P) fertilisation is typically based on soil testing using extractive methods, but does not consider P sorption processes. Traditional soil P sorption capacity is examined from a Langmuir isotherm batch experimental design, which is time-consuming, labour intensive and expensive. The objective of this work was overcome these limitations, and successfully predict the isothermal parameter of P sorption maximum capacity (Smax, mg·kg-1) from MIR spectroscopy. This study created spectral libraries from bench-top (Bruker) and handheld (Agilent) MIR spectrometers by scanning samples in two particle size ranges, < 0.100 mm (ball-milled) and <2 mm. The four spectral libraries created used an archive of samples with a database of sorption parameters where soils were classified into low and high sorption capacities using a threshold value of Smax = 450.03 mg·kg-1. Chemometric regression models of partial least squares (PLS), Cubist, support vector machine (SVM) regression and random forest (RF) were evaluated for Smax prediction. Bruker spectral libraries with both soil particle sizes yielded 'excellent models', with SVM predicting Smax values with high accuracy (RPIQV = 4.50 and 4.25 for the spectral libraries of the ball-milled and <2mm samples, respectively). In comparison, the Agilent handheld spectral libraries contained more noise and less resolution. For Agilent MIR spectroscopy, more homogeneous samples after ball-milling resulted in a higher accurate Smax prediction. For Agilent libraries of ball-milled samples, an 'approximate quantitative model' (RPIQV = 2.74) was obtained from the raw spectra using the Cubist algorithm. However, for Agilent spectroscopy of <2 mm samples, the best performing Cubist algorithm can only achieve a 'fair model' (RPIQV = 2.23) with the potential to discriminate between 'low' and 'high' Smax values. The results suggest that the Bruker bench-top spectrometer can predict the Langmuir Smax value with high accuracy without the need to ball-mill samples, highlighting the availability of the MIR spectrometer as a rapid alternative method for understanding soil P sorption capacity. However, for handheld spectrometers, the Agilent instruments can only make approximate quantitative predictions of Smax for ball milled samples. For <2mm samples, Agilent can only be used to classify low and high sorption capacity soils. 

Comparing the Potential of Bench-Top and Handheld Mid-Infrared Spectrometers for Predicting Soil Phosphorus (P) Sorption Capacity and Evaluating the Influence of Sample Preparation

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.

Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China

In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs. However, remote sensing data and its combination with soil properties were reliably used to predict PSPs (R2 =0.41 for MBC by RF model, R2=0.49 for PBC by Cu model, R2=0.37 for SPR by Cu model, and R2=0.38 for SBC by RF model). The lowest RMSE values were obtained for MBC by RF model, PBC by SVM model, SPR by Cubist model and SBC by RF model. The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area. The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity (CEC) and clay content, and among the remote sensing indices B5/B7, Midindex, Coloration index, Saturation index, and OSAVI were the most imperative factors for predicting PSPs. Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape. 

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2210314?needAccess=true&role=button

Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

Soil characteristics, especially clay content and clay type, have significant impacts on splash erosion. This investigation was conducted to determine the effects of clay content and clay type (zeolite, phlogopite, bentonite, and kaolinite) on the shear strength and splash erosion of clay–sand mixtures compared with a clay soil under controlled conditions. Clay–sand mixtures were prepared by mixing 15, 30, and 45 kg 100 kg−1 of the selected clays with pure sand and a clay soil; these mixtures were pre-treated with three levels of wetting and drying (W&D) cycles, and then shear strength was measured. The splash erosion rate was measured at three levels of water status: air-dry, plastic limit (PL), and liquid limit (LL). The highest values of splash erosion were observed in the samples without the W&D cycle, and after applying the W&D cycles, splash erosion decreased. Moreover, splash erosion was higher in the air-dry and LL groups. Splash erosion decreased with increased clay content because of the structure development in the mixtures. In general, as clay content increased, splash erosion was reduced and shear strength was significantly increased. From the highest to lowest mean of cohesion, the results showed the following order: bentonite > kaolinite > phlogopite > clay soil > zeolite. However, splash erosion showed the following order for the studied clays: zeolite > clay soil > phlogopite > kaolinite > bentonite. Nonlinear power models, best-fitted relations between splash erosion and shear strength, and the shear strength could explain about 30–33% of the splash erosion variability in this study.

/pdf/impacts-of-clay-content-and-type-on-shear-strength-and-528xvf5v.pdf

Impacts of Clay Content and Type on Shear Strength and Splash Erosion of Clay–Sand Mixtures

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Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

Impacts of Clay Content and Type on Shear Strength and Splash Erosion of Clay–Sand Mixtures