Search

• Lithology, pedogenesis and relief affect soil magnetic susceptibility. • Pedogenesis strongly affect magnetic susceptibility on more evolved soil classes. • Lithology contribute more to magnetic susceptibility in less evolved soil classes. • Magnetic susceptibility had good correlation with soil texture, CEC and iron content. • Field sensor can be used in delimitation of soil classes and digital soil mapping. Soil magnetic susceptibility (κ) has potential to be used as a pedoenvironmental indicator from which mineralogy, pedogeochemical, pedogeomorphological and pedogenic processes can be inferred. It can be used in pedosphere studies, as an auxiliary information for appropriate and sustainable soil use and management. This research aimed to analyze how pedogenesis and geochemical processes affect the κ and some of its attributes, as well as its potential use in discriminating soil great groups, following the digital soil mapping approach. The study area is located in São Paulo State - Brazil. Soil samples were collected for physical–chemical analysis from 79 locations (0–20 cm depth). At these sites, magnetic susceptibility was measured with a portable field instrument and analyzed in terms of geology, relief and soil class. The results showed that geology strongly affects κ, mainly in diabase derived soils, followed by metamorphosed siltstone and siltstone. In fluvial sediments, the κ exhibits different behaviors due to different sediments deposited by the Capivari River. In less evolved soils, such as Cambisols, lithology is a more important contributor to κ than pedogenesis. In more evolved soils, pedogenesis increases κ, whereas argilluviation/ferralitization reduces it. The κ values did not decrease significantly or even increase downslope, due to the presence of diabase on the lower parts. Differences in κ where observed between diabase bedrock located in different parts of the study area, indicating more of an influence by geomorphic processes rather than lithology. With respect to soil attributes, positive correlations between κ and base saturation, cation exchange capacity, organic matter, and iron and clay content were found, whereas a negative correlation was found between κ and sand content. The κ correlates with changes in lithology and soil class demonstrating its application as a potential tool for the discrimination of soil great groups and digital soil mapping. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon sequestration potential of the clay fraction. • Determination of the carbon saturation deficit of each mineral of the clay fraction. • Identification of the equation for the prediction of carbon sequestration potential. • Identification of land uses with the greatest carbon sequestration potential. The capacity of soil to sequester carbon (C) is a key process that promotes the reduction of CO 2 in the atmosphere. Soils can absorb as much as 20% of anthropogenic carbon emissions, which can contribute to mitigate climate change. This capacity relies on the organo-mineral association, which includes different minerals, Fe and Al oxides, which have a critical soil organic carbon (SOC) sorption surface. Based on an equation of the potential C saturation deficit of fine soil particles (<20 μm/silt and clay fractions) for tropical regions, this study investigated the SOC sequestration potential of the clay fraction for soils in Piracicaba region, São Paulo State, Brazil as influenced by the clay minerals. This potential was fitted to a spatial regression model for soil depths 0–20 cm and 80 to 100 cm. In the surface layer, the sequestration potential was mostly explained by the relative abundance of soil minerals (Kaolinite, Hematite, Goethite and Gibbsite) determined using vis-NIR-SWIR spectroscopy. A direct relationship was observed with Goethite and Gibbsite, indicating that low concentrations would reduce the sequestration potential. At 80 to 100 cm depth, Kaolinite and Hematite explained most variation in SOC sequestration potential. Additionally, the C associated with the mineral fraction and the C saturation potential as a function of minerals were modeled and a strong importance of hematite in the C sequestration and stabilization cycle was identified at both depths. The individual mineral contribution to SOC sequestration potential was also mapped, which identified high contributions of goethite and gibbsite for deep soil layers. The influence of land use on the carbon sequestration potential of minerals was observed, with the greatest potential being found in areas with pasture and cropping mosaics and grassland and forest mosaics, with a high presence of kaolinite and hematite. These minerals have a greater potential for carbon sequestration at greater depths and, therefore, could be critical in climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

The free iron content is a vital indicator of pedogenic processes in tropical soils and can be used to understand the soil's weathering history and aid in classification. Despite its importance in agriculture and pedology, laboratory analyses of soil iron content are not widely used because they are costly and time-consuming. Remote sensing data combined with digital soil mapping are effective tools to regionalise soil iron content. They can reduce the number of soil samples needed to characterise soil variability and, consequently, laboratory analysis costs. This study aimed to create a strategy for mapping free iron content using a 35-year time series of Landsat images combined with topographic parameters at two spatial resolutions (5 and 30 m) in a region with high variability in soils and geology in the state of São Paulo, Brazil. The dataset comprised 344 observations of free iron content at 0–20 cm depth over a 2574 km2 area. The dataset was split into calibration and a validation set (85:15%), and the environmental variables were chosen based on the scorpan factors. Spatial prediction functions for free iron were developed using several machine learning algorithms linking soil observations with the environmental variables. We found that the temporal bare soil image improved model performance. Although 5 and 30 m resolution terrain data differed slightly, the best-fit model was obtained at 5 m resolution (root mean square error, 25.09 g kg−1; adjusted R2, 0.84). Among the evaluated machine learning algorithms, Random Forest was the most accurate method for predicting free iron distribution in the study area. The free iron content map can identify soil types in more detail and should be prioritised in future pedological studies. [Display omitted] • Four out of eight machine learning fit well total free iron predictions. • Hybrid method has a high performance as well as the Random Forest. • Predictions using 5 m or 30 m resolution terrain data have a slight difference. • Integrating approaches improve free iron content predictions. [ABSTRACT FROM AUTHOR]