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.

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 (CO), methane (CH), and nitrous oxide (NO).

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.