European Journal of Soil Science最新文献

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.

The use of ecoacoustics to monitor soil ecology was identified as a priority in the 2024 horizon scan of global biological conservation issues. Proponents suggest it will have societal impacts by improving soil health assessments, enhance soil biodiversity monitoring and facilitate the conservation, remediation and management of soil ecosystems. Here we review soil ecoacoustics in terms of its definition, theoretical basis, acoustic indices and statistical inferences. To do this we explain mechanical wave behaviour, mechanoreception by fauna, and tactical signal design with reference to earthworms as ecosystem engineers. Ecoacoustics emerged from research on animal long-distance communication systems, and its direct application to soils has been identified as a problem area. A new field within ecoacoustics has been created for soils, sonoscape investigations, to capture spatio-temporal complexity of ecological features (rather than soil ecology). There is a good case for reclassifying soil ecoacoustic ‘soundscape’ studies as sonoscapes. We identify that further refinement of ecoacoustics is required for applications to soil habitats. The performance of sonoscape investigations is dependent on acoustic indices and statistical inferences, and we question why stationary signal processing is used as the base transform for soils data, and highlight the issue of unbalanced data sets, particularly pertinent to soils as there is limited understanding of what exactly is being detected. We list the key research needs and highlight that integrating soil science and mechanistic modelling of soil processes and wave propagation as an essential component of developing reliable monitoring solutions. Embracing these interdisciplinary avenues will help develop sensing capabilities for soils in robust scientific principles and mitigate the risks of speculative overreach.

Over the past decade, research into nature-based solutions (NBS) for protecting and restoring soil health and ecosystem functionality has notably increased. The close interrelationship between the effectiveness of NBS and soil health is increasingly emphasized in the relevant scientific literature. Nevertheless, soil quality monitoring remains a much-neglected aspect of NBS approaches, both in practical implementation and in the scientific literature. To address this issue, we argue that the selection, validation, and measurement of harmonized soil indicators are essential for the effective planning and long-term management of NBS. Drawing on a series of case studies, literature reviews, and findings from European Union (EU)-funded projects, we highlight the critical role of soil indicators in assessing the performance of NBS for soil and ecosystem restoration. The EU has recently established NBS and soil health as crucial pillars on its political and executive agenda. In particular, soil is prominently featured in key initiatives such as the EU Biodiversity Strategy, the EU Soil Strategy for 2030, the Nature Restoration Law, and the Proposal on Soil Monitoring and Resilience. We conclude that the scientific community, engaging with social and political stakeholders, must spearhead efforts to identify existing gaps and develop standardized protocols for scientifically sound and practical NBS implementation. Recognizing soil health as a key factor in NBS is essential for ensuring their effectiveness, especially in the face of climate change and extreme weather events.