European Journal of Soil Science最新文献

Sulfide-bearing sediments (i.e., soils with hypersulfidic material) occur worldwide in many coastal and inland freshwater settings, that if exposed to oxidation, will transform to acid sulfate soils (ASS; a soil with sulfuric material) mobilizing acidity and metals into watercourses with serious environmental consequences and damage to infrastructures. Thus, management techniques to minimize these hazards are important but rely on correct identification of hypersulfidic material. The incubation pH method is one of the most reliable, easy-to-use, and low-cost methods for this purpose. The method simulates the natural oxidation behavior of possible existing hypersulfidic material by exposing the soil to the atmosphere at room temperature while maintaining it moist over a period of time (often between 8 and 19 weeks) for acid-generating oxidation reactions to take place. The soil material is considered hypersulfidic if the amount of acidity produced during oxidation exceeds its acid-neutralizing capacity and thereby lowers the mineral soil pH to < 4 (ΔpH > 0.5) and the organic-rich soil (LOI > 20%) to < 3 (ΔpH > 0.5). For practical reasons, an incubation time of up to 19 weeks is, however, in many cases too long and the main goal in this study was to reduce the required incubation time by optimizing oxidation/acidification conditions with the intention to recommend an accelerated incubation method. To ensure the method's usability, several trial experiments were conducted on various types of soil materials containing sulfidic materials, including peat, sandy/loamy soils, and heavy clays. The trial included testing different temperature ranges, optimizing the sample thickness, determining the effects of stirring versus non-stirring the samples during incubation, and using “quick start” doping agents. The incubation time needed was reduced up to 50% by optimizing the sample thickness from 5 to 2 mm according to the most recent recommendations and, most importantly, by applying heat to 30°C. In mineral soil samples, the incubation time was additionally reduced by stirring the samples. Mineral soils with hypersulfidic material could generally be identified in less than 2 weeks and organic soils with hypersulfidic material in around 4 weeks. Heavy clay soils needed, however, 5 weeks to be identified as hypersulfidic material. Consequently, the best set of recommended incubation methods, based on numerous trial experiments and conditions conducted, for faster identification of different textured soils with hypersulfidic material is as follows: (I) Mineral soils: use a 2 mm thick sample in chip trays; applying heat to 30°C; stirring the samples three times per week. (II) Mineral soils with heavy clay textures: use a 2 mm thick sample (or thinner) in chip trays; applying heat to 30°C; stirring the samples three times per week. (III) Peat/organic soils: use a 2 mm thick sample in chip trays; applying heat to 30°C.

Vanadium (V) is increasingly recognised as a critical mineral due to its potential for decarbonisation technologies. Australia holds an estimated quarter of global V resources, yet there is limited knowledge of how these resources are distributed across the country. Here, we employed digital soil mapping techniques to map the distribution of soil V content at a 90 m resolution. Using remotely sensed data as environmental covariates (i.e., barest Earth Landsat imagery, gamma-ray spectrometry maps) and layers representing soil, climate, and topography, we calibrated Cubist machine learning models on 1315 nationally sampled data points of aqua-regia-extracted V content. Our models performed reasonably well in predicting V content in top outlet sediment (0–10 cm) and bottom outlet sediment (on average ~60–80 cm), with an average concordance correlation coefficient of 0.49 and 0.52 on the validation data. Independent validation using top outlet sediment data from northern Australia (n = 780) demonstrated that our models maintained consistent accuracy. Feature importance ranking revealed that spectral images at barest soil condition, climate and soil texture were the most influential predictors. High V contents predominantly occurred on Kandosols and iron-rich Tenosols, particularly in Western Australia. This first explicit prediction of soil V content provides essential baseline information for sustainable resource planning and management across Australia.

Liming enhances both organic phosphorus (P) mineralization and the precipitation of inorganic phosphates with calcium (Ca) cations. To better understand how P storage and cycling in soil profiles are regulated by the interaction of long-term P fertilization and liming, we collected soil samples from three German arable long-term field experiments in Berlin-Dahlem (Albic Luvisol; sandy topsoil [0–30 cm], and loamy subsoil [> 30 cm]), Dikopshof (Haplic Luvisol; silty-loamy topsoil, and clayey-loamy subsoil), and Thyrow (Albic Luvisol; sandy soil). Treatments within each of these experiments had received mineral fertilization with NKPCa (N: nitrogen; K: potassium; P: phosphorus; Ca: calcium, referring to liming), NKCa, NKP, and NK or no fertilizer application (none) for at least 60 years. Soil P stocks down to 100 cm depth were assessed by Hedley sequential P fractionation and the oxygen isotopic composition of 1 M HCl-extractable phosphate (δ¹⁸O_P) was analyzed as an indicator of the degree of microbial P cycling over the decades of experimental duration. We found that mineral P fertilization increased soil total P stocks in all P fractions regardless of differences in soil clay content among the different experiments. Liming significantly decreased NaHCO₃-Pi (Pi: inorganic P) and NaOH-Pi stocks by up to 50% across the three experiments and soil depths, but tended to increase Po (organic P) stocks in these fractions by up to 40%, reflecting enhanced P uptake into plant and microbial biomass when acidic soil conditions were improved by lime application. Soil HCl-Pi stocks in treatments with long-term P fertilization and liming were larger by a factor of up to 1.8 compared to the unfertilized control plots, while especially the plots without P fertilization showed smaller δ¹⁸O_P values of 11‰ in the subsoil. These results indicate that, on the one hand, biological P cycling was enhanced in fertilized treatments, but on the other hand, soluble Pi was precipitated as secondary Ca–P minerals into stable P fractions. These changes occurred both in the topsoil and upper subsoil (30–50 cm). We conclude that the combined application of long-term P fertilization and liming to the surface soil also increased the utilization of subsoil P.

Machine learning (ML) models for image segmentation typically require a significant amount of accurately annotated data for training, which is rarely readily available in plant and soil science datasets due to the high time and monetary costs of manually labelling the images. Training datasets can be augmented with synthetically generated images that aim to match the visual features and biological properties of the original dataset. Segmentation masks can be created automatically during the synthetic image generation process, removing the need for tedious manual annotation and ensuring high accuracy of the labels. We present an adaptable semi-automatic pipeline for creating annotated synthetic micro-computed tomography (micro-CT) volumes at scale using the 3D modelling tool Blender, and we demonstrate our method using a dataset of micro-CT images of tomato plant roots embedded in sieved soil columns. First, the foreground is generated using a mathematical L-system model to give a 3D model of the target sample. Then, the surrounding material is created and textured to simulate the relative density of the materials in which the object is embedded. The final stage is to render the images by slicing the volume at defined regular intervals, generating both the synthetic micro-CT image and the corresponding labels at each slice. We use our synthetically generated images alongside real data to create augmented datasets to train a U-Net-based segmentation model. Our results demonstrate that when there is a small amount of real annotated data available, using synthetic data in the training dataset can improve the segmentation accuracy, and we show the impact of varying the texturing process.

Human activities have significant impacts on the European terrestrial landscape, contributing to anthropogenic climate change. Soil health, crucial for human life, is at a critical phase, with nearly 70% of European soil considered unhealthy. To address this, the European Commission has launched the Soil Mission, ‘A Soil Deal for Europe,’ to restore soil health by 2050, and adopted the Soil Monitoring Law in 2025 to ensure the target is successfully achieved. In order for such achievements to take place, a systems perspective is essential in understanding how land use and soil management contribute to soil health. The DPSIR (Drivers, Pressures, States, Impacts, and Responses) framework, developed as a policy support tool by the European Environment Agency (EEA), offers a valuable tool for systems thinking and has been widely used to analyse complex human-environment interactions. By breaking down complex problems and establishing causal linkages, DPSIR allows us to frame the diverse issues associated with environmental resources and support its adaptive management. With growing interest in the systems approach for combining soil health and land use, bolstered by the research demands of the EU soil mission, there is a need for a standardised approach of the DPSIR framework to support and ensure an efficient and widespread adaptation of systems thinking for soil resources. However, DPSIR's use for soil and land resources has been limited at present. This study aims to develop a customised DPSIR framework for land use and soil management, providing insights into its better application and adaptability. We built on the user experiences by exploring nine case studies across Europe of DPSIR application within the context of soil and land use, and conducted a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the application of the framework. The developed generic DPSIR framework capitalised on the identified strengths and opportunities to provide an encompassing systems approach for soil resources. Further strategies for adaptation of the framework are provided with an aim to make it a comprehensive tool supporting the EU's soil mission and promoting a systems approach to soil health and land use management.