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.

We can use hydrological models to estimate how water is allocated in soils with compaction. However, compaction can also affect how much plants can grow in the field. Here, we show that when we consider this affected plant growth in our sandy soil compaction model, the resulting water allocation can change a lot. Thus, to get more reliable model results, we should know the plant growth (above and below the ground) in the field and include them in the models.

Simulation models can be used to evaluate changes in soil organic carbon (SOC) stocks in agricultural soils that are important to soil health and climate change mitigation. We describe a simple model that considers SOC turnover in a soil profile regulated by two processes: physical protection due to aggregation and microbial energy limitation. It accurately reproduces trends in SOC in plots receiving different OC inputs and matches survey data on SOC in the soil profile in one region of Sweden.

Studies on how microbes use C in soils typically assume oxic conditions but often overlook anaerobic processes and extracellular metabolite release. We examined how O₂ and Fe content affect C mineralisation in forest soils by tracking ¹³C flow into biomass, CO₂, metabolites, and active microbes under oxic and anoxic conditions. Results showed that anoxic conditions preserved C longer, especially in high-Fe soils. We conclude that microbial exudates play a role in anoxic C stabilisation.

We discuss three aspects to ensure (rhizosphere) priming effects are correctly perceived in their ecological context and measured at appropriate scales. (i) The first aspect is that there is little empirical evidence for net C losses from priming. (ii) The second aspect is critical publication bias. (iii) The third aspect is a need to distinguish between priming effects (PE) and rhizosphere priming effects (RPE).

Peat depth is crucial to peatland management but often unknown. We used machine learning to map peat depth in two Norwegian landscapes, based on terrain and remotely sensed radiation. We found that terrain, especially elevation and valley bottom flatness, predicted peat depth better than radiation. Our approach improved existing maps but struggled to identify very deep peat, demonstrating that it can support regional planning but not replace field measurements for local carbon stock assessments.

Water-soluble organic carbon (WSOC) is crucial in boreal forests, but its behavior across soil depths is poorly understood. Our study found that shallow soils contain complex WSOC molecules with high biodegradability, while deeper soils have simpler molecules that are also highly biodegradable. These results reveal the significant role of WSOC in carbon cycling across soil layers, improving our understanding of carbon dynamics in boreal ecosystems impacted by climate change.

Both spatial and temporal information is important in agriculture. Information regarding the aboveground variables is ever increasing in terms of density and precision. On the contrary, belowground information lags behind and has been typically limited to time series. This study uses methods that map the subsurface spatial variability. Numerical simulations of aboveground and belowground water fluxes are then based on such spatial information and additional time-oriented datasets that are common in agriculture.

We developed an uncertainty method to show where machine learning (ML) models predicting soil units are most reliable, especially for transfer tasks. The model was able to correctly predict soil patterns, especially along rivers, in a new but similar region without retraining. It was too confident about common soil types, showing the need for balanced data. This helps improve soil maps and guides better planning for future data collection, saving time and resources while showing uncertainty.