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.

We explored how a portable tool can quickly and affordably test city soils for harmful metals like lead and arsenic. Traditional lab methods are accurate but slow and costly. By reviewing 84 studies, we found that portable X-ray fluorescence tools work well when properly used and calibrated. This can help identify contaminated areas faster, making urban gardening safer and helping communities grow food in healthier soil.

Addressing soil health and food security challenges requires weaving different ways of knowing. This paper presents a case study from Aotearoa/New Zealand, showing how combining soil science with mātauraka Māori (Māori knowledge) helps reveal past food growing practices, guiding efforts to restore traditional horticulture and enhance Māori soil health. This study also offers a practical example of integrating knowledge systems to tackle complex environmental issues in real-world contexts.

Soil health is of critical importance, and many soils are threatened. Benchmarking sustainable soil management is a challenge as there is no comprehensive indicator set. Here, I introduce a novel conceptual approach to soil health, representing soil functions as complex and hard-to-measure variables. I outline a new methodology to study soil health using latent-variable models to represent soil functions. This is a new starting point for soil health research and ultimately monitoring.

This study explores the link between soil magnetic susceptibility and cation exchange capacity (CEC) to improve prediction models for CEC in European soils. The results show that magnetic susceptibility significantly enhances CEC prediction in sandy soils, achieving high accuracy (R² = 0.94). This offers a rapid, cost-effective way to estimate CEC, emphasizing the value of geophysical data integration in soil assessment.

This study presents a new, low-cost sensor system for measuring soil CO₂ gas continuously over long periods. Built using easy-to-get hardware components, the system costs USD 700. It was tested for six months in desert soil, proving to be reliable, easy to maintain, and capable of capturing important changes in soil CO₂. The CO₂ flux calculations from this system closely matched those from a standard measurement device, making it a practical tool for research requiring multiple sensor systems.

Farmers need precise information about their fields to use water, fertilizers, and other resources efficiently. This study combines underground soil data and satellite images to create detailed field maps using advanced machine learning. By testing different ways of processing data, we ensured a balanced and accurate approach. The results help farmers manage their land more effectively, leading to better harvests and more sustainable farming practices.