Machine Learning-Based Classification of Soil Parent Materials Using Elemental Concentration and Vis-NIR Data.

In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0-30 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma-optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe2O3, Al2O3, MgO, and MnO; XRF: SiO2, CaO, Fe2O3, Al2O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results.

Carbon Footprint Management by Agricultural Practices.

Global attention to climate change issues, especially air temperature changes, has drastically increased over the last half-century. Along with population growth, greater surface temperature, and higher greenhouse gas (GHG) emissions, there are growing concerns for ecosystem sustainability and other human existence on earth. The contribution of agriculture to GHG emissions indicates a level of 18% of total GHGs, mainly from carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Thus, minimizing the effects of climate change by reducing GHG emissions is crucial and can be accomplished by truly understanding the carbon footprint (CF) phenomenon. Therefore, the purposes of this study were to improve understanding of CF alteration due to agricultural management and fertility practices. CF is a popular concept in agro-environmental sciences due to its role in the environmental impact assessments related to alternative solutions and global climate change. Soil moisture content, soil temperature, porosity, and water-filled pore space are some of the soil properties directly related to GHG emissions. These properties raise the role of soil structure and soil health in the CF approach. These properties and GHG emissions are also affected by different land-use changes, soil types, and agricultural management practices. Soil management practices globally have the potential to alter atmospheric GHG emissions. Therefore, the relations between photosynthesis and GHG emissions as impacted by agricultural management practices, especially focusing on soil and related systems, must be considered. We conclude that environmental factors, land use, and agricultural practices should be considered in the management of CF when maximizing crop productivity.

Spatial responses of soil carbon stocks, total nitrogen, and microbial indices to post-wildfire in the Mediterranean red pine forest.

Wildfire is a key ecological event that alters vegetation and soil quality attributes including biochemical attributes at spatial scale. This knowledge can provide insights into the development of better rehabilitation or restoration strategies that depend on the ecological dynamics of vegetation, fungi, and animals. The present study aimed to understand the causes and consequences of spatial variability of soil organic carbon, microbial biomass C concentrations, and soil quality indices as impacted by wildfire in a red pine forest. This study was conducted using kriging and inverse distance neighborhood similarity (IDW) interpolations methods. The carbon stocks were significantly (P = 0.002) higher in burned areas compared to those of unburned areas by 255% whereas microbial biomass carbon and microbial respiration were significantly (P < 0.0001 and P = 0.02) lower in burned areas by 66% and 90%, The Pearson's correlation analysis showed that carbon stocks were positively correlated with pH (0.61), total nitrogen (0.60) and ash quantity (0.41), but negatively correlated with microbial biomass carbon (-0.46) and nitrogen (-0.61), and microbial respiration (-0.48). The IDW interpolation method better-predicted pH, bulk density, and microbial biomass carbon and nitrogen compared to kriging interpolation, whereas the kriging interpolation method was better than IDW interpolation for the other studied soil properties. We concluded that pH, EC, SOC, C/N, MR, MBC/SOC, and MBC/MBN can be reliable indicators to monitor the effect of wildfire on forest soils. The wildfire event increased soil carbon stocks, TN, pH, and qCO2, but decreased MBC and MBN.