This study examines soils in the highly industrialized Dunkerque agglomeration in France. Our work reveals the contamination of urban soils by metals from industrial dust, including Cr, Ni, Mo, Mn, Cd and Zn. While Cr, Ni and Mo are relatively stable in soils, Mn, Cd and Zn are more mobile and may pose environmental and health problems. Our findings highlight the need for careful consideration of future land use near industrial emitters, such as allotment gardens, due to these potential hazards.

This study evaluated the impact of contrasting cover crops on topsoil mineral nitrogen (SMN), N₂O emissions, and carbon (C) sequestration. Non-legume cover crops reduced SMN levels, showed potential for mitigating indirect N₂O emissions, and increased C sequestration but did not reduce cumulative N₂O emissions compared to fallow. The results highlight the need for tailored cover crop strategies to balance SMN capture, N₂O emissions, and C sequestration effectively.

This study explores how rubber plant root traits and soil organic matter improve soil stability via cohesive forces. Older plantations (>11 years) showed higher root density, soil organic matter, and optimal cellulose-to-lignin ratios, enhancing soil cohesion and aggregate stability. These findings highlight the role of mature rubber plants in reducing soil degradation and offer insights for sustainable land management and agricultural productivity in tropical regions like the island of Hainan.

Agriculture may play a significant role in climate change mitigation. For this reason, it is necessary to have good estimations of the greenhouse gas (GHG) emissions from agricultural activities. In this work, two different chamber systems to determine GHGs were compared. Our results highlighted that automated chamber systems, compared to manual chamber systems, are a powerful tool for quantifying GHG fluxes, allowing us to capture the large temporal variability that characterizes them.

This study explores the impact of converting forests to agricultural land on soil C stabilization. By analyzing soil samples from a forest and crop Andosols, we found that C-stabilizing mineral–organic associations were in the form of amorphous coprecipitates in both soils. However, their quantity was significantly lower in the crop topsoil, suggesting their vulnerability to agricultural conversion. This highlights the need to develop strategies to preserve these associations in crop soils.

Uncertainty quantification plays a crucial role in reporting machine learning models in soil spectroscopy. This study introduces Monte Carlo conformal prediction (MC-CP), a novel method for uncertainty quantification in deep-learning soil spectral models. MC-CP outperformed two established methods, providing the most reliable results. Its efficiency and robustness make it a practical choice for implementing soil spectral models in decision making.