European Journal of Soil Science最新文献

{"title":"A Quantitative Property-Based Layer and Profile Numerical Soil Classification System for Australia","authors":"Wartini Ng,&nbsp;Alex B. McBratney","doi":"10.1111/ejss.70111","DOIUrl":"https://doi.org/10.1111/ejss.70111","url":null,"abstract":"<p>Most soil classification systems rely on the identification of genetic horizons, delineated through visual observations guided by theories of soil development. However, these systems often differ across countries, creating challenges for information transfer and comparison. In this study, we explore the application of numerical soil classification as a means of establishing a more universally applicable soil classification system. Using a comprehensive set of relevant soil properties—such as available water capacity, bulk density, cation exchange capacity (CEC), effective CEC, pH (in both water and calcium chloride), organic carbon content and soil texture (sand, silt and clay percentages)—clustering analysis was performed using the k-means algorithm. This method generated 40 layer classes and 100 profile classes, offering an innovative perspective on soil variation. The spatial distribution of layer classes exhibited depth-dependent variation, although it was less pronounced than the east-to-west variation across Australia. Notably, the spatial distribution of numerical profile classes aligned well with existing Australian soil classification maps. This approach marks a significant step toward developing a fully quantitative system for soil classification, not only within Australia but also for global applications, enhancing consistency and comparability in soil science.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849295","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":"Swedish Hypersulfidic Soil Material Incubations Suggest Temperature Mainly Drives Regional Microbial Community Variation","authors":"Anders Johnson,&nbsp;Alexandra Nyman,&nbsp;Mats Åström,&nbsp;Mark Dopson","doi":"10.1111/ejss.70106","DOIUrl":"https://doi.org/10.1111/ejss.70106","url":null,"abstract":"<p>Acid sulfate soils impact surrounding ecosystems with pronounced environmental damage via leaching of strong acidity along with the concurrent mobilization of toxic metals present in the soils and, in consequence, they are often described as the nastiest soils on Earth. Within Sweden, acid sulfate soils are distributed mainly under the maximum Holocene marine limit that stretches the length of the country, some 2000 km north to south. Despite only minor geographical differences in the geochemical composition of the Swedish acid sulfate soils, their field oxidation zone microbial community compositions differ along a north–south regional divide. This study compared the 16S rRNA gene amplicon-based microbial community compositions of field oxidation zones (field tested pH &lt; 4.0) with reduced zone samples (field tested pH &gt; 6.5) collected from the same field sites throughout Sweden that had acidified (final pH &lt; 4.0) after laboratory incubation at approximately 20°C. The previously identified regional differences observed in field oxidation zone microbial compositions were notably absent in the laboratory incubation samples. Instead, a commonly shared community was selected for with few statistically significant differences regardless of regional origin. For instance, the potential eurypsychrophilic Baltobacteraceae family was found in higher relative abundances in the northerly region of the field oxidation zone samples than the southern regions and was notably absent from the laboratory incubation samples. Furthermore, the microbial communities of the laboratory incubation samples were dominated by acidophilic autotrophic Acidithiobacillaceae and chemoheterotrophic Rhodanobacteraceae and Burkholderiaceae that have optimal growth temperatures (≥ 20°C) greater than what was experienced by the field oxidation zone samples when sampled (~2°C–9°C). These data suggested that in the absence of significant geochemical differences, temperature was the predominant driver of microbial community composition in Swedish acid sulfate soil materials.</p>","PeriodicalId":12043,"journal":{"name":"European Journal of Soil Science","volume":"76 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejss.70106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836242","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":"Effects of Ammonium Addition on Methane Oxidation in a Paddy Soil: Insights Into Microbial Interactions","authors":"Huabin Li,&nbsp;Xian Wu,&nbsp;Jinli Hu,&nbsp;Ronglin Su,&nbsp;Niklas J. Lehto,&nbsp;Muhammad Shaaban,&nbsp;Yan Wang,&nbsp;Shan Lin,&nbsp;Ronggui Hu","doi":"10.1111/ejss.70110","DOIUrl":"https://doi.org/10.1111/ejss.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>Rice paddies are a major anthropogenic source of methane and a key target for reducing emissions of the greenhouse gas to the atmosphere. The delicate equilibrium between the production and oxidation of methane in paddy soils is shaped largely by the abundances and compositions of different microbial communities within the soil ecosystem and the interactions between them. Ammonium addition can alleviate nitrogen deficiency for methanotrophs, but ammonium can also inhibit their growth when present in excess. However, the threshold concentration for this switch is not currently known. Here we report the results of a nine-day laboratory incubation experiment that sought to examine the effects of increasing ammonium concentrations on methane oxidation in rice paddy soil at refined concentration intervals. We measured methane oxidation rates and analysed the gene abundances and community compositions of methanotrophs and ammonia oxidizers in the incubated soils to decode interactions between these communities. Our results showed that an ammonium concentration of 10 mg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msubsup>\u0000 <mi>NH</mi>\u0000 <mn>4</mn>\u0000 <mo>+</mo>\u0000 </msubsup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{NH}}_4^{+} $$</annotation>\u0000 </semantics></math>-N d.w.s stimulated methane oxidation, but concentrations above 30 mg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msubsup>\u0000 <mi>NH</mi>\u0000 <mn>4</mn>\u0000 <mo>+</mo>\u0000 </msubsup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{NH}}_4^{+} $$</annotation>\u0000 </semantics></math>-N kg⁻¹ d.w.s inhibited the oxidation rate. At the lower ammonium concentration, type Ia methanotrophs appeared to outcompete ammonia oxidizers for nitrogen; however, this was reversed at higher concentration where the proliferation of ammonia oxidizers was stimulated. We show how ammonium stimulated the ammonia-oxidizing bacteria (AOB) to a greater extent than ammonia-oxidizing archaea (AOA), but with much smaller changes in the specific AOB community composition, when compared to the AOA communities. Our findings highlight ammonium concentration as a key factor regulating the interaction between methanotrophs and ammonia oxidizers in paddy soils and identify the threshold where ammonium shifts from promoting to inhibiting methane oxidation.</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":"143835922","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":"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}