European Journal of Soil Science最新文献

{"title":"Beyond Total C: Integrative Analysis of Carbon Forms in Urban Soils","authors":"Junge Hyun,&nbsp;Alain F. Plante,&nbsp;Jeehwan Bae,&nbsp;Gayoung Yoo","doi":"10.1111/ejss.70107","DOIUrl":"https://doi.org/10.1111/ejss.70107","url":null,"abstract":"<div>\u0000 \u0000 <p>The precise differentiation and quantification of ecosystem-driven organic carbon (OC_eco), black carbon (BC), and inorganic carbon (IC) in soil is essential for understanding the global carbon cycle. However, the absence of a standardised method for differentiating among these carbon types is a notable challenge in soil carbon research. We addressed this gap by establishing CO₂-evolved gas analysis (EGA) with peak deconvolution, a robust approach to parse OC_eco, BC, and IC in soils through CO₂ thermograms derived from ramped combustion. The soils in urban greenery were used for developing this methodology due to their exposure to various carbon sources. Our method's precision was confirmed using model mixtures, exhibiting high accuracy (R² &gt; 0.90) with regression lines approximating the ideal 1:1 line of known versus measured values. Applying this technique, we identified distinct spatial distributions of OC_eco and BC. Their distributions were strongly influenced by the balance between green spaces and impervious surfaces in surrounding land uses. Conversely, IC appears unaffected by such land use dynamics. Our results provide compelling evidence that without a distinct recognition of BC and IC from OC_eco, assessments of urban carbon storage are prone to significant overestimation. Because urban soil C types differ in source and dynamics, our findings call for a recalibration of urban soil carbon accounting frameworks to prevent overestimations of vulnerability or sequestration potential, which is critical for effective climate mitigation strategies and policy planning. In essence, our findings underscore the necessity of advanced soil analysis techniques in urban soil management and provide a valuable tool for future research in urban ecosystem dynamics.</p>\u0000 </div>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning and Artificial Intelligence Applications in Soil Science","authors":"Budiman Minasny,&nbsp;Alex B. McBratney","doi":"10.1111/ejss.70093","DOIUrl":"https://doi.org/10.1111/ejss.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>The awarding of the Nobel Prize in Physics to pioneers in neural networks highlights their substantial influence across diverse disciplines, including soil science. This article explores the evolution and transformative impact of machine learning and artificial intelligence (AI) in soil science. These technologies have revolutionised the modelling of complex soil processes, enhancing our ability to predict and map soil properties, simulate water movement and assess global soil carbon dynamics. The article discusses future directions for AI in soil science, such as developing new mathematical soil matrices and integrating AI with soil science knowledge to improve the precision and efficiency of soil assessments. As AI evolves, its potential in soil science includes generating new hypotheses, optimising soil carbon–mineral associations for better sequestration and enhancing soil phenotyping with high-throughput data analysis. Integrating AI with physical models could lead to more precise, data-driven soil management practices that support net-zero, nature-positive stewardship for improved soil security.</p>\u0000 </div>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing the Russell Review ‘Artificial Intelligence in Soil Science’ by Alexandre M.J.-C. Wadoux","authors":"Gerard B. M. Heuvelink","doi":"10.1111/ejss.70099","DOIUrl":"https://doi.org/10.1111/ejss.70099","url":null,"abstract":"&lt;p&gt;Scientific progress is often driven by technological advancements, and soil science is no exception. Over recent decades, innovations in laboratory techniques—such as DNA sequencing, stable isotope analysis and spectroscopy—have greatly advanced soil chemistry and soil biology. Similarly, remote sensing, GIS and geocomputation have revolutionized soil mapping and enabled novel spatial analyses in soil geography more broadly. Sensor technologies, including electromagnetic induction and ground-penetrating radar, have significantly contributed to soil physics and soil hydrology and, when combined with robotics, have facilitated precision agriculture.&lt;/p&gt;&lt;p&gt;Computers, perhaps the most prominent technological advancement of all, have long since become indispensable in soil science, with many researchers spending more time in front of screens than in the field or laboratory—much to the regret of many. Until recently, computers primarily served as tools for storing data and maps, processing and analysing measurements, running mathematical and statistical models, and supporting the production of graphical and written reports. However, with the rise of Artificial Intelligence (AI), this is set to change dramatically. Computers are becoming ‘intelligent’, taking on more roles in soil science—whether we welcome it or not.&lt;/p&gt;&lt;p&gt;This &lt;i&gt;Russell Review&lt;/i&gt; by Alexandre Wadoux examines the role of AI in soil science over past decades and explores how AI will transform soil science in the future. Through a systematic literature review, Alexandre identifies the soil science domains where AI has made the most significant contributions. The review begins by defining AI and categorizing its applications in soil science. While AI is used in soil science in diverse ways, its most notable sub-field is machine learning, which involves the development and application of complex, non-linear statistical regression and classification models. These models are trained on paired observations of the dependent and independent variables and subsequently used to predict outcomes for new, unseen cases. Today, machine learning is the dominant technique in digital soil mapping and is increasingly used in pedometrics for other purposes, such as developing pedotransfer functions and soil spectroscopy models. There is also the recent rise of physics-informed machine learning, which embeds physical laws and constraints into machine learning models and holds much promise for process-based, mechanistic modelling in soil science.&lt;/p&gt;&lt;p&gt;The senior editorial team was delighted when Alexandre accepted our invitation to publish this &lt;i&gt;Russell Review&lt;/i&gt; as part of the EJSS 75th anniversary celebrations. A talented young pedometrician, he has already made a substantial impact on the field. He earned his doctorate from Wageningen University in 2019, focusing on spatial sampling design optimization, and then spent 4 years as a postdoctoral researcher at the University of Sydney. He is now bas","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in soil science","authors":"Alexandre M. J.-C. Wadoux","doi":"10.1111/ejss.70080","DOIUrl":"https://doi.org/10.1111/ejss.70080","url":null,"abstract":"<p>Few would disagree that artificial intelligence (AI) holds potential for advancing knowledge and innovation. Over the past decades, substantial research has been devoted to the development and application of AI in soil science. While most of today's AI applications in soil science are related to machine learning (ML), AI also encompasses other fields such as digital image analysis, natural language processing (NLP), expert systems, and knowledge representation. This review aims to provide a comprehensive overview of AI in soil science. A definition of AI that equates intelligence with rationality is provided, followed by a typical classification of AI into the three main domains of sensing and interacting, reasoning and decision-making, and learning and predicting. From this framework, a taxonomy of AI in soil research is derived and serves as a basis for a literature review. The major findings are as follows: i) AI in soil science is diverse, with applications in decision support systems, image classification, prediction with ML and expert systems; ii) AI in soil science is currently almost exclusively characterized by ML; iii) applications of ML are predominantly found in the field of digital soil mapping and for the development of pedotransfer functions; and iv) most AI applications are used for prediction purposes. A few notable exceptions stand apart from mainstream applications, particularly in the realms of NLP, the development of soil cognitive models, and interpretable ML. Based on these findings, I discuss attention points, such as using AI almost exclusively for prediction at the expense of explanation and the lack of integration of soil knowledge in algorithmic AI solutions. I envision that future developments could include the use of AI for text recognition of legacy soil profile data, providing a new source of soil information. Another promising line of research is the language processing of soil texts to build meta-analyses that summarize the growing body of soil science literature. These new applications could foster substantial new contributions to soil science research.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating Effective Reuse of Soil Research Data: The BonaRes Repository","authors":"Susanne Lachmuth,&nbsp;Cenk Dönmez,&nbsp;Carsten Hoffmann,&nbsp;Xenia Specka,&nbsp;Nikolai Svoboda,&nbsp;Katharina Helming","doi":"10.1111/ejss.70103","DOIUrl":"https://doi.org/10.1111/ejss.70103","url":null,"abstract":"<p>Soil plays a paramount role in addressing complex challenges related to climate change, the agri-food system, and ecosystem services. This importance makes soil research data highly relevant for meta-analysis, research synthesis, modelling, and assessment. As data-intensive techniques proliferate in studying global change impacts on agricultural systems, effective data management and reuse are essential. Repositories that adhere to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are crucial for maximizing the value and efficiency of research data. While publishing in an Open Access repository is necessary for data reusability, it alone is not sufficient. Specialized repositories enhance data reuse potential by addressing discipline-specific needs through targeted metadata and technical frameworks. The BonaRes Repository was developed for agricultural soil research data and is guided by the FAIR principles, with a focus on data reusability. Here, we introduce the repository's infrastructures and services, including specialized tools for data quality assurance and the management of soil profile as well as long-term field experiment data. We emphasize the ability of these infrastructures and services to promote data publication and reuse specifically in soil and agricultural sciences. We review examples of data reuse, highlighting their scientific contributions to the understanding of soil and agricultural systems. Finally, we discuss the remaining challenges in achieving FAIR and open soil data publication and reusability. From 2018 to date, the BonaRes Repository has facilitated 815 data publications; 62 papers have reused the published data. Reuse applications range widely—from extracting study site metadata or environmental covariates to reanalysing (meta)data in light of new research questions, to developing scenarios and conducting model calibration and evaluation. A key insight from our review of data reuse is that researchers frequently apply reused data to advance method development. Initiatives such as reciprocal metadata harvesting and integration into larger national and international research data infrastructure will further expand the scope and reuse of the repository's data, including in broader agrosystems science.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Pixels: Soil Erosion Risk Mapping and Its Impact on the Implementation of Regulatory Measures by Farms","authors":"Elmar M. Schmaltz,&nbsp;Seth Callewaert,&nbsp;Petra Deproost,&nbsp;Lisbeth L. Johannsen","doi":"10.1111/ejss.70105","DOIUrl":"https://doi.org/10.1111/ejss.70105","url":null,"abstract":"<p>A series of modelling scenarios were employed to determine the influence of raster resolution on soil erosion risk maps using both the Water and Tillage Erosion Model (WaTEM) and the Revised Universal Soil Loss Equation (RUSLE) in the regions of Flanders (Belgium) and Lower Austria (Austria) using field-specific data from the Integrated Administration and Control System (IACS). The impact of these maps on farms when used as areas for regulatory measures was also investigated. Three different resampling techniques were employed to assess the impact of varying data resolution on the accuracy of soil erosion risk maps. These techniques included (i) resampling input data, (ii) resampling RUSLE factors and (iii) resampling the output erosion risk map. The resampling of input data resulted in the most pronounced discrepancies in erosion values in both regions. The impact analysis, assessing the effect of data resolution and resampling techniques, was conducted with the objective of identifying fields and farms that were most affected by erosion. This was achieved by applying erosion thresholds of 11 and 2 t ha⁻¹ year⁻¹. The results indicate that raster resolution has a significant influence on model accuracy, with lower resolutions resulting in substantial deviations in erosion estimates. The analysis reveals that lower resolution data and certain resampling methods have a disproportionate impact on smaller farms, resulting in high erosion values in regions with a generally high erosion potential. The study highlights the necessity of utilising the best available data and robust modelling techniques to generate reliable soil erosion risk maps. These findings have significant policy implications, suggesting that erosion control measures and agricultural regulations should be informed by accurate, high-resolution data to ensure fair and effective soil conservation practices.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agricultural Decision Support Tools in Europe: What Kind of Tools Are Needed to Foster Soil Health?","authors":"Dylan Warren Raffa,&nbsp;Timo A. Räsänen,&nbsp;Alessandra Trinchera,&nbsp;Meriem Jouini,&nbsp;Sofia Delin,&nbsp;Raimonds Kasparinskis,&nbsp;Baiba Dirnēna,&nbsp;Zeynep Demir,&nbsp;Ülfet Erdal,&nbsp;Marjoleine Hanegraaf","doi":"10.1111/ejss.70097","DOIUrl":"https://doi.org/10.1111/ejss.70097","url":null,"abstract":"<p>Decision support tools (DSTs) are crucial in aiding agricultural decision-making, particularly in improving soil health by enhancing nutrient management, soil organic matter (SOM) and water retention. Despite the availability of numerous DSTs in Europe, their adoption, effectiveness and development needs are not well understood, as most research is based on literature reviews rather than direct feedback from stakeholders. This study aims at filling this gap by conducting an expert survey of the most widely used digital DSTs across Europe on SOM, water retention and nutrient use efficiency in agriculture. We aimed at evaluating the current use, limitations and development needs of DSTs and offering recommendations to improve the effectiveness and adoption of DSTs in the context of soil health. A questionnaire was distributed to experts in 24 countries. Answers were received from 18 countries, including 14 European Union (EU) nations, Norway, the UK, Switzerland and Turkey. A total of 115 DSTs were identified aligning with our definition of DST, with agronomists, consultants and farmers being the primary users. Adoption of DSTs was rated moderate (score: 3.1/5), with tools featuring user-friendly interfaces and alignment with farmer goals achieving higher adoption rates. DSTs were rated better suited to achieve farm-level goals (score: 4.1/5) than regional (score: 3.6/5) or national objectives (score: 3.5/5). Major barriers to adoption included limited end-user involvement in DST development, which may hinder alignment with practical needs. Considering all the received questionnaires, the most frequently cited areas for improvement were nutrient use efficiency (45%), SOM (24%) and water retention (18%). Respondents emphasised the need for better integration of new farming systems (e.g., organic farming, agroforestry), more detailed process descriptions, integration of multiple processes, inclusion of economic modules and improved user interfaces. This study presents the first comprehensive evaluation of DSTs in Europe, revealing a diverse yet moderately adopted landscape. Increasing user engagement, enhancing technical integration and improving accessibility are essential for promoting a wider use of DSTs to improve soil health. By adopting these recommendations, DSTs can play a key role in achieving the EU's sustainability goals, fostering resilient agricultural systems and addressing environmental challenges such as soil degradation and climate change.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SoilManageR—An R Package for Deriving Soil Management Indicators to Harmonise Agricultural Practice Assessments","authors":"Olivier Heller,&nbsp;Andreas Chervet,&nbsp;Fabien Durand-Maniclas,&nbsp;Thomas Guillaume,&nbsp;Franziska Häfner,&nbsp;Michael Müller,&nbsp;Raphaël Wittwer,&nbsp;Thomas Keller","doi":"10.1111/ejss.70102","DOIUrl":"https://doi.org/10.1111/ejss.70102","url":null,"abstract":"<p>Understanding the effects of agricultural soil management on the soil system and its functions is crucial to ensure the sustainable use of soil. Due to the countless ways in which soil can be managed, it is not an easy task to compare soil management practices across different locations and over time. One approach to making soil management comparable is the use of numerical soil management indicators. However, due to the lack of standardisation of soil management data and indicators, the comparability of results across studies remains limited. To address these shortcomings, we developed SoilManageR, an accessible R package. The first version of SoilManageR calculates numerical soil management indicators for carbon (C) input, tillage intensity, soil cover duration, nitrogen (N) fertilisation, equivalent livestock units per area, and plant diversity. In this paper, we present the functionality of SoilManageR and demonstrate its capabilities with three case studies. The cases were selected to compare soil management across space, time and context, as well as to relate soil management to soil quality. For this, we calculated soil management indicators for 16 experimental treatments from six agricultural long-term experiments and for 18 farmers' fields in Switzerland. We found that experimental treatments were representative of the management of the farmers' fields in terms of tillage intensity and soil cover, but that farmers' fields tended to exhibit higher livestock integration, leading to higher C and N inputs through organic amendments. We related soil management indicators to selected soil quality indicators in experimental treatments and showed that tillage intensity is the most important management driver of earthworm biomass, whereas C and N inputs were the best predictors of the organic carbon content of the topsoil. Finally, we applied SoilManageR to three sites of the Swiss Soil Monitoring Network and identified significant reductions of N inputs across time in two sites. We demonstrate that SoilManageR is a versatile tool for quantifying multiple aspects of soil management intensity, which can be useful to analyse how policy changes affect soil management. Additionally, SoilManageR can be used to assess soil management impacts on soil quality and provide guidance based on these insights.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterisation of Analytical Uncertainty in Chamber Soil Flux Measurements","authors":"Nicholas Cowan,&nbsp;Pete Levy,&nbsp;Maddalena Tigli,&nbsp;Galina Toteva,&nbsp;Julia Drewer","doi":"10.1111/ejss.70104","DOIUrl":"https://doi.org/10.1111/ejss.70104","url":null,"abstract":"<p>Flux chamber methodologies are used at the global scale to measure the exchange of trace gases between terrestrial surfaces (soils) and the atmosphere. These methods evolved as a simplistic necessity to measure gas fluxes from a time when gas analysers were limited in capability and costs were prohibitively high, since which thousands of studies have deployed a wide variety of chamber methodologies to build vast datasets of soil fluxes. However, analytical limitations of the methods are often overlooked and are poorly understood by the flux community, leading to confusion and misreporting of observations in some cases. In recent years, the number of commercial suppliers of gas analysers claiming to be capable of measuring trace gas fluxes from chambers has drastically increased, with a myriad of analysers (and low-cost sensors) now on offer with a wide variety of capabilities. While chamber designs and the capabilities of analysers vary by orders of magnitude, the rudimentary analytical uncertainties of individual flux measurements can still be standardised for direct comparison of methods. This study aims to serve as a guide to calculate the analytical uncertainty of chamber flux methodologies in a standardised way for direct comparisons. We provide comparisons of a variety of chamber measurement methodologies (closed static and dynamic chamber methods) to highlight the impact of analytical noise, chamber size, enclosure time and number of gas samples. With the associated tools, researchers, commercial suppliers and other stakeholders in the flux community can easily estimate the limitations of a particular methodology to establish and tailor the suitability of particular chambers and instruments to experimental requirements.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics of Pore Water and Air Distribution in Granite Residual Soil During Drying by X-Ray Computed Tomography","authors":"Tiande Wen,&nbsp;Lifeng Zeng,&nbsp;Yinwei Luo,&nbsp;Lin Gao,&nbsp;Longtan Shao,&nbsp;Xiangsheng Chen","doi":"10.1111/ejss.70101","DOIUrl":"https://doi.org/10.1111/ejss.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates the dynamic distribution of pore water and air in granite residual soil (GRS) under varying drying conditions using advanced X-ray computed tomography (CT). The research focuses on microstructural changes during drying, particularly the interaction between pore water and air phases. Results reveal a transition from interconnected pore water networks to isolated water clusters as matric suction increases. Initially, air exists as isolated bubbles within the pore network, but as suction surpasses the air entry value, air pathways become connected, replacing water in the pores. An inverse relationship between pore water and air phases is observed, with increasing air volume and decreasing water content indicating a progressive displacement of water by air. Quantitative analysis shows reduced pore water porosity and increased pore air porosity across different suction levels. These changes modify the pore network structure, leading to decreased relative water permeability and increased relative air permeability, highlighting the critical role of matric suction in governing soil hydraulic behaviour.</p>\u0000 </div>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}