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.

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.

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems.

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0-30, 30-60, and 60-90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0-30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 x 150 m(2)) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.

Effect of shifting cultivation on distribution of nutrient elements and carbohydrates within water-stable aggregates in northern Iran.

This study attempts to evaluate the nutrient element and carbohydrate distribution within Water-Stable Aggregates (WSA) of two natural ecosystems, native forest and pasturelands, under different land uses. Soil samples were collected from depths of (0-20) cm in Typic Haploxeroll soils. The overall pattern indicated that Mean Weight Diameter (MWD) and WSA were greater in the pasture and forest soils compared with the adjacent cultivated soils and aggregates of > 1.0 mm size were dominant in the uncultivated soils, whereas the cultivated soils comprised aggregates of the size < or = 0.5 mm. Distribution of organic carbon, nitrogen, phosphorus and carbohydrates within the WSA showed preferential enrichment of these parameters in the macroaggregate fraction (4.75-1.0 mm) for the uncultivated soils and microaggregate fraction (> 0.25 mm) for the cultivated soils. Average distribution of total exchangeable bases within WSA showed that cultivation of forest pastureland soils significantly led to reduce in these nutrient in the 4.75-2.0 mm fraction and increase in concentration of these cations in < 0.25 mm fraction. Since smaller aggregates are preferentially removed by erosion, this study emphasizes the need for sustainable soil management practices that they will minimize nutrient loss when forest or pastures lands are converted to cropland.

Assessment of some soil properties by spatial variability in saline and sodic soils in Arsanjan plain, Southern Iran.

Spatial patterns for several soil parameters such soil texture, Exchangeable Sodium Percentage (ESP), Electrical Conductivity (ECe), soil pH, Cation Exchange Capacity (CEC) were examined in saline and sodic soils in Arsanjan plain, Southern Iran, in order to identify their spatial distribution for implementation of a site-specific management. Soil samples were collected from 0-30, 30-60 and 60-90 cm soil depths at 85 sampling sites. Data were analyzed both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied between soil parameters. Soil pH and ESP had the minimum and maximum variability at all depths, respectively. Soil properties indicated moderate to strong spatial dependence. ECe exhibited moderate spatial dependence at three depths; pH and ESP had a moderate spatial dependence at 0-30 cm and strong spatial dependence at 30-60 and 60-90 cm depths. Clay and CEC exhibited strong spatial dependence for the 0-30 cm and weak spatial dependence at 30-60 and 60-90 cm depths. Sand and silt had a non-spatial dependence at 0-30 cm and weak spatial dependency at 30-60 and 60-90 cm depths. The spatial variability in small distances of ECe, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1168 m. The results reported herein indicated that the strong spatial dependency of soil properties would lead to the extrinsic factors such as ground water level and drainage. It is important to know the spatial dependence of soil parameters, as management parameters with strong spatial dependence will be more readily managed and an accurate site-specific scheme for precision farming more easily developed.