Assessment of Soil Quality under Different Soil Management Strategies: Combined Use of Statistical Approaches to Select the Most Informative Soil Physico-Chemical Indicators

Compost Amendment Impact on Soil Physical Quality Estimated from Hysteretic Water Retention Curve

Capacity-based indicators of soil physical quality (SPQ) and pore distribution parameters were proposed to assess the effects of compost amendment but their determination was limited to desorption water retention experiments. This study also considered the pore size distribution obtained from adsorption experiments to establish the effectiveness of compost amendment in modifying the physical and hydrological attributes of a sandy loam soil. Repacked soil samples with different compost to soil ratios, r, were subjected to a wetting–drying cycle, and the water retention data were fit to the van Genuchten model to obtain the pore volume distribution functions. The soil bulk density was minimally affected by the wetting–drying cycle but a significant negative correlation with r was obtained. The sorption process involved larger and more heterogeneous pores than the desorption one thus resulting in an estimation of the air capacity SPQ indicators (Pmac and AC) that were higher for the wetting–water retention curve (WWRC) than the drying one (DWRC). The opposite result was found for the water storage SPQ indicators (PAWC and RFC). In general, SPQ indicators and pore distribution parameters were generally outside the optimal range but estimates from the DWRC were closer to the reference values. The water entry potential increased and the air entry potential decreased with an increase in the compost rate. Significant correlations were found between the SPQ indicators estimated from the DWRC and r but the same result was not obtained for the WWRC. It was concluded that compost addition could trigger positive effects on soil hydrological processes and agronomic service as both water infiltration during wetting and water storage during drying are favored. However, the effectiveness of the sorption process for evaluating the physical quality of soils needs further investigation.

Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates

Knowledge of the spatial distribution of soil organic carbon (SOC) is of crucial importance for improving crop productivity and assessing the effect of agronomic management strategies on crop response and soil quality. Incorporating secondary variables correlated to SOC allows using information often available at finer spatial resolution, such as proximal and remote sensing data, and improving prediction accuracy. In this study, two nonstationary interpolation methods were used to predict SOC, namely, regression kriging (RK) and multivariate adaptive regression splines (MARS), using as secondary variables electromagnetic induction (EMI) and ground-penetrating radar (GPR) data. Two GPR covariates, representing two soil layers at different depths, and X geographical coordinates were selected by both methods with similar variable importance. Unlike the linear model of RK, the MARS model also selected one EMI covariate. This result can be attributed to the intrinsic capability of MARS to intercept the interactions among variables and highlight nonlinear features underlying the data. The results indicated a larger contribution of GPR than of EMI data due to the different resolution of EMI from that of GPR. Thus, MARS coupled with geophysical data is recommended for prediction of SOC, pointing out the need to improve soil management to guarantee agricultural land sustainability.

Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.