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.

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).

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 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.

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.

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.

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.

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.

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.