Impacts of Soil Properties, Topography, and Environmental Features on Soil Water Holding Capacities (SWHCs) and Their Interrelationships

Soil water holding capacities (SWHCs) are among the most important factors for understanding the water cycle in forested catchments because they control available plant water that supports evapotranspiration. The direct determination of SWHCs, however, is time consuming and expensive, so many pedotransfer functions (PTFs) and digital soil mapping (DSM) models have been developed for predicting SWHCs. Thus, it is important to select the correct soil properties, topographies, and environmental features when developing a prediction model, as well as to understand the interrelationships among variables. In this study, we collected soil samples at 971 forest sites and developed PTF and DSM models for predicting three kinds of SWHCs: saturated water content (θS) and water content at pF1.8 and pF2.7 (θ1.8 and θ2.7). Important explanatory variables for SWHC prediction were selected from two variable importance analyses. Correlation matrix and sensitivity analysis based on the developed models showed that, as the matric suction changed, the soil physical and chemical properties that influence the SWHCs changed, i.e., soil structure rather than soil particle distribution at θS, coarse soil particles at θ1.8, and finer soil particle at θ2.7. In addition, organic matter had a considerable influence on all SWHCs. Among the topographic features, elevation was the most influential, and it was closely related to the geological variability of bedrock and soil properties. Aspect was highly related to vegetation, confirming that it was an important variable for DSM modeling. Information about important variables and their interrelationship can be used to strengthen PTFs and DSM models for future research.

Certain Soil Surfactants Could Become a Source of Soil Water Repellency after Repeated Application

Repeated application of soil surfactants, or wetting agents, is a common practice for alleviating soil water repellency associated with soil organic coatings. However, wetting agents are organic compounds that may also coat soil particle surfaces and reduce wettability. For this experiment, hydrophobic sands from the field and fresh, wettable sands were collected and treated with either a polyoxyalkylene polymer (PoAP) or alkyl block polymer (ABP) wetting agent, or water only treatments served as a control. Following repeated treatment application and sequential washings, dissolved and particulate organic carbon (OC) were detected in the leachates of both sand systems. The total amount of OC recovered in leachates was 88% or less than the OC introduced by the wetting agents, indicating sorption of wetting agent monomers to soil particle surfaces regardless of soil hydrophobicity status. While ABP treatment did not alter solid phase organic carbon (SOC) in the sands studied, PoAP application increased SOC by 16% and 45% which was visible in scanning electronic microscopy images, for hydrophobic and wettable sands, respectively. PoAP application also increased the hydrophobicity of both sands that were studied. In contrast, ABP treatment increased the wettability of hydrophobic sand. Our results provide strong evidence that certain wetting agents may increase soil hydrophobicity and exacerbate wettability challenges if used repeatedly over time.

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.