Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran.

The soil's physical and mechanical (SPM) properties have significant impacts on soil processes, such as water flow, nutrient movement, aeration, microbial activity, erosion, and root growth. To digitally map some SPM properties at four global standard depths, three machine learning algorithms (MLA), namely, random forest, Cubist, and k-nearest neighbor, were employed. A total of 200-point observation was designed with the aim of a field survey across the Marvdasht Plain in Fars Province, Iran. After sampling from topsoil (0 to 30 cm) and subsoil depths (30 to 60 cm), the samples were transferred to the laboratory to determine the mean weight diameter (MWD) and geometric mean diameter (GMD) of aggregates in the laboratory. In addition, shear strength (SS) and penetration resistance (PR) were measured directly during the field survey. In parallel, 79 environmental factors were prepared from topographic and remote sensing data. Four soil variables were also included in the modeling process, as they were co-located with SPM properties based on expert opinion. For selecting the most influential covariates, the variance inflation factor (VIF) and Boruta methods were employed. Two covariate dataset scenarios were used to assess the impact of soil and environmental factors on the modeling of SPM properties including SPM and environmental covariates (scenario 1) and SPM, environmental covariates, and soil variables (scenario 2). From all covariates, nine soil and environmental factors were selected for modeling the SPM properties, of which four of them were the soil variables, three were related to remote sensing, and two factors had topographic sources. The results indicated that scenario 2 outperformed in all standard depths. The findings suggested that clay and SOM are key factors in predicting SPM, highlighting the importance of considering soil variables in addition to environmental covariates for enhancing the accuracy of machine learning prediction. The k-nearest neighbor algorithm was found to be highly effective in predicting SPM, while the random forest algorithm yielded the highest R2 value (0.92) for penetration resistance properties at 15-30 depth. Overall, the approach used in this research has the potential to be extended beyond the Marvdasht Plain of Fars Province, Iran, as well as to other regions worldwide with comparable soil-forming factors. Moreover, this study provides a valuable framework for the digital mapping of SPM properties, serving as a guide for future studies seeking to predict SPM properties. Globally, the output of this research has important significance for soil management and conservation efforts and can facilitate the development of sustainable agricultural practices.

Characterization of clay and nanoclay extracted from a semi-arid Vertisol and investigation of their carbon sequestration potential.

Mitigation of global climate change by means such as soil carbon (C) sequestration has become an important area of research. Soil organic matter (SOM) that is stabilized with clay minerals is the most persistent in soils. Currently, little is known regarding the C sequestration ability of nanoclay extracted from Vertisols in semi-arid regions. Therefore, the aim of this study was to extract and characterize nanoclay and bulk clay from a Vertisol from Iran, in terms of physicochemical surface properties and resistance of SOM to chemical oxidation. The clay fractions were studied before and after H2O2 treatment by total C analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), pyrolysis gas chromatography mass spectrometry (GC-MS), Fourier transform infrared (FTIR) spectroscopy, specific surface area analysis, and zeta potential. TEM and SEM images showed that the diameter of the extracted nanoclays was 16-46 nm and their morphology was more porous than bulk soil clay. The nanoclay had a much greater specific surface area (111.9 m2 g-1) than the bulk clay (67.9 m2 g-1). According to total C, FTIR, and zeta potential results, the nanoclay was enriched with 1.4 times more C than the bulk clay after peroxide treatment, indicating enhanced soil C stabilization in the nanoclay. About 45% of the peroxide-resistant SOM in the nanoclay was associated with N-containing compounds, indicating that these compounds contribute to SOM stability. The results demonstrate important role of nanoclay in soil C sequestration in Vertisols.

Heavy metals content and distribution in basil (Ocimum basilicum L.) as influenced by cadmium and different potassium sources.

Despite the fact that cadmium (Cd) is a non-essential element for plants, it can influence nutrients and affect human health. Potassium (K) can influence the transportation of heavy metals (HMs) in soil-plant systems. Here, a greenhouse experiment was conducted to evaluate the effect of Cd and K fertilizers on the different partitioning forms of HMs, their concentrations, uptake in the shoots and roots of Ocimum basilicum. Treatments comprised 2 levels of Cd (0 and 40 mg kg-1) and three levels of K (0, 100, and 200 mg kg-1) from three sources, i.e. KCl, K2SO4, and K-nano-chelate. 40 mg Cd kg-1 increased the shoot (above ground parts) Cd concentration. Addition of K as KCl, K2SO4, and K-nano-chelate increased the presence of Cd in shoots by 86, 82 and 76%, respectively, compared to the control. Using the nano-chelate of K can increase the accumulation of Cd in plants grown on contaminated soils to lesser content than that of the other forms of K. Application of 40 mg Cd kg-1 reduced the concentration of Zn, Cu, and Mn in the shoot, but increased shoot Fe concentration. Transfer factor (TF), which is the ratio of metal concentration in shoot to its concentration in root, of the studied HMs, was significantly affected by Cd and K treatments. Therefore, the proper form and dose of chemical fertilizers should be applied in Cd-contaminated soils.

Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils.

Hydraulic conductivity (Kψ) is one of the most important soil properties that influences water and chemical movement within the soil and is a vital factor in various management practices, like drainage, irrigation, erosion control, and flood protection. Therefore, it is an essential component in soil monitoring and managerial practices. The importance of Kψ in soil-water relationship, difficulties for its measurement in the field, and its high variability led us to evaluate the potential of stepwise multiple linear regression (SMLR), and multilayer perceptron (MLPNNs) and radial-basis function (RBFNNs) neural networks approaches to predict Kψ at tensions of 15, 10, 5, and 0 cm (K15, K10, K5, and K0, respectively) using easily measurable attributes in calcareous soils. A total of 102 intact (by stainless steel rings) and composite (using spade from 0-20 cm depth) soil samples were collected from different land uses of Fars Province, Iran. The common physico-chemical attributes were determined by the common standard laboratory approaches. Additionally, the mentioned hydraulic attributes were measured using a tension-disc infiltrometer (with a 10 cm radius) in situ. Results revealed that the most of studied soil structure-related parameters (soil organic matter, soluble sodium, sodium adsorption ratio, mean weight diameter of aggregates, pH, and bulk density) are more correlated with K5 and K0 than particle-size distribution-related parameters (sand, silt, and standard deviation and geometric mean diameter of particles size). For K15 and K10, the opposite results were obtained. The applied approaches predicted K15, K10, K5, and K0 with determination coefficient of validation data (R2val) of 0.52 to 0.63 for SMLR; 0.71 to 0.82 for MLPNNs; and 0.58 to 0.78 for RBFNNs. In general, the capability of the applied methods for predicting Kψ at all the applied tensions was ranked as MLPNNs > RBFNNs > SMLR. Although the SMLR method provided easy to use pedotransfer functions for predicting Kψ in calcareous soils, the present study suggests using the MLPNNs approach due to its high capability for generating accurate predictions.

Applicability of the sigmoid model to estimate heavy metal uptake in maize and sorghum as affected by organic acids.

Although assisted phytoremediation using chemical treatments is a suitable technique for the removal of heavy metals (HMs), the estimation of this process using simple models is also crucial. For this purpose, a greenhouse trial was designed to evaluate the effectiveness of citric, oxalic, and tartaric acid on Cd, Pb, Ni, and Zn phytoremediation by maize and sorghum and to estimate this process using sigmoid HMs uptake model. Results showed that mean values of root and shoot dry weight and metals uptake, translocation factor (TF) of Pb and Zn, and uptake efficiency (UE) of Cd in maize were higher than sorghum but the TF of Cd and the phytoextraction efficiency (PEE) and UE of Pb in sorghum were higher than maize. Citric, oxalic, and tartaric acid significantly increased the UE of Pb by 17.7%, 22.5%, and 32.5%, respectively. Tartaric acid significantly increased the mean values of shoot dry weight, shoot Cd, Pb, and Ni uptake, and PEE of Pb and Ni, but decreased TF of Zn. The R2, NRMSE, and KM values indicated the ability of sigmoid HM uptake model in estimating HMs uptake in maize and sorghum treated with organic acids. Thus, tartaric acid was more effective than citric and oxalic acids to enhance phytoremediation potential. Sigmoid HM uptake model is suitable to estimate the HMs uptake in plants treated with organic acids at different growth stages.

Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables.

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

Feasibility of Vis-NIR spectroscopy approach to predict soil biological attributes in arid land soils.

Visible and near-infrared (Vis-NIR) reflectance spectroscopy has recently emerged as an efficient and cost-effective tool for monitoring soil parameters and provides an extensive array of measurements swiftly. This study sought to predict fundamental biological attributes of calcareous soils using spectral reflectance data in the Vis-NIR range through the application of partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) techniques. The objective was to derive spectrotransfer functions (STFs) to predict selected soil biological attributes. A total of 97 composite samples were collected from three distinct agricultural land uses, i.e., sugarcane, wheat, and date palm, in the Khuzestan Province, Iran. The samples were analyzed using both standard laboratory analysis and proximal sensing approach within the Vis-NIR range (400-2500 nm). Biological status was evaluated by determining soil enzyme activities linked to nutrient cycling including acid phosphatase (ACP), alkaline phosphatase (ALP), dehydrogenase (DEH), soil microbial respiration (SMR), microbial biomass phosphorus (Pmic), and microbial biomass carbon (Cmic). The results indicated that the developed PLSR models exhibited superior predictive performance in most biological parameters compared to the STFs, although the differences were not significant. Specifically, the STFs acceptably accurately predicted ACP, ALP, DEH, SMR, Pmic, and Cmic with R2val (val = validation dataset) values of 0.68, 0.67, 0.65, 0.65, 0.76, and 0.72, respectively. These findings confirm the potential of Vis-NIR spectroscopy and the effectiveness of the associated STFs as a rapid and reliable technique for assessing biological soil quality. Overall, in the context of predicting soil properties using spectroscopy-based approaches, emphasis must be placed on developing straightforward, easily deployable, and pragmatic STFs.