Soil最新文献

{"title":"Soil health-based business models: perspectives and policy implications","authors":"Erik Mathijs, Kato Van Ruymbeke","doi":"10.5194/soil-12-835-2026","DOIUrl":"https://doi.org/10.5194/soil-12-835-2026","url":null,"abstract":"Abstract not available","PeriodicalId":48610,"journal":{"name":"Soil","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148870242","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":"Spatially explicit seasonal modelling reveals the impact of vegetation–soil coupling on soil organic carbon prediction in bimodal-rainfall agroecosystems","authors":"Annette Achieng', Ana Cristina Costa, Pedro Cabral","doi":"10.5194/egusphere-2026-4622","DOIUrl":"https://doi.org/10.5194/egusphere-2026-4622","url":null,"abstract":"<strong>Abstract.</strong> Soil organic carbon (SOC) underpins soil fertility, climate regulation, and ecosystem resilience. Yet high-resolution SOC data remain scarce in many African agroecosystems. In bimodal rainfall systems, phenological phases may imprint distinct spectral signals associated with SOC processes. However, these temporal patterns are rarely exploited in digital soil mapping. We hypothesise that SOC spatial variability is most detectable when vegetation–soil coupling is strongest and that geographically weighted machine learning enhances the ecological interpretability of SOC models in data-scarce settings. To test these hypotheses, we develop a spatially explicit, seasonally-informed framework for SOC prediction in bimodal-rainfall agroecosystems. Using environmental covariates and 11-year seasonal Landsat 7 and 8 composites to capture the full phenological cycle in Tanzania (March–May 'Long Rains', June–August 'dry season', October–December 'Short Rains'), we compare global Random Forest (RF) with Geographical Random Forest (GRF) across each seasonal window, using an annual composite as our climatological baseline. Feature selection using Boruta reduced the covariate set by 34–50 % per temporal window without compromising accuracy. Season-specific GRF models achieved R² values of 0.292–0.405 based on spatial cross-validation, whereas random cross-validation inflated performance to 0.444–0.471 (average overestimation = 19 %). October–December covariates provided the highest predictive power using only 13 predictors. Across the temporal windows, near-infrared reflectance, green band (October–December), red band (March–May), and topsoil clay emerged as the most informative predictors. SHapley Additive exPlanations (SHAP) values extracted from the global RF were represented spatially to map the directional influence of these predictors on global predictions and subsequently assessed alongside local feature importance maps to evaluate the extent of spatial non-stationarity. This two-stage interpretability approach confirmed that the GRF models adapted dynamically to localised eco-pedological realities, such as soil texture-driven storage capacity and canopy-driven phenological masking. By demonstrating when, where and how SOC is most observable, the framework provides an operational blueprint for local organisations to design leaner, seasonally targeted soil surveys and remote sensing protocols that strengthen climate smart land management policies and national carbon accounting systems.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"74 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148870246","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":"Leaching Behavior of Steelmaking Slag Fertilizer under Repeated Wetting and Drying Conditions Simulating Upland Soil","authors":"Takayuki Iwama, Shohei Koizumi, Megumi Obara, Shigeru Ueda","doi":"10.5194/egusphere-2025-6356","DOIUrl":"https://doi.org/10.5194/egusphere-2025-6356","url":null,"abstract":"<strong>Abstract.</strong> To determine how steelmaking slag dissolves and modulates soil acidity and exchangeable cations under upland-like repeated wetting–drying conditions, we conducted a soil-column experiment.<span> </span>Specifically, we aimed to identify the Ca-supplying phases responsible for pH correction, evaluate their persistence during extended leaching, and define the layer-scale reach of the effect to inform application planning (rate, placement, and maintenance). Soil columns incorporating discrete slag-amended layers were prepared together with unamended controls. A repeated wetting–drying leaching test was run up to 24 weeks; after termination, each column was sampled by layer, and soil pH and exchangeable CaO were measured. Additionally, surfaces and cross-sections of slag particles embedded in the columns were observed to identify dissolving phases and secondary precipitates. In the control columns, soil pH remained in the acidic range (4.8–5.5), whereas slag-amended layers maintained pH 6.0–6.5 for 24 weeks in the test columns. Adjacent unamended layers in the test columns showed no detectable change, indicating that the effect was confined to the amended layers. Exchangeable CaO increased in soils mixed with slag. Microstructural observations revealed alteration and dissolution of free lime (f-CaO) and dicalcium silicate (2CaO·SiO₂), with CaCO₃ precipitates on particle surfaces. These Ca-supplying phases persisted after 24 weeks of leaching. Sustained Ca release from f-CaO and 2CaO·SiO₂, together with CaCO₃ precipitation, produced localized, durable pH correction in slag-amended layers while leaving adjacent layers unchanged. The defined reach and persistence provide a mechanistic basis for application planning in acidic upland soils – informing rate, placement within the profile, and maintenance intervals.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"22 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903628","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":"Contribution of soil microbial necromass carbon to soil organic carbon fractions and its influencing factors in different grassland types","authors":"Shenggang Chen, Yaqi Zhang, Jun Ma, Mingyue Bai, Jinxiao Long, Ming Liu, Yinglong Chen, Jianbin Guo, Lin Chen","doi":"10.5194/soil-11-883-2025","DOIUrl":"https://doi.org/10.5194/soil-11-883-2025","url":null,"abstract":"Abstract. Microbial necromass carbon (MNC) is a significant source of soil organic carbon (SOC). However, the contribution of microbial necromass to different organic carbon fractions and their influencing factors in various soil layers under different grassland types remains unclear. This study was conducted through a comprehensive investigation of soil profiles (0–20, 20–40, and 40–100 cm) across four grassland types in Ningxia, China, encompassing meadow steppe, typical steppe, desert steppe, and steppe desert. We quantified mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their respective microbial necromass components, including total microbial necromass carbon (TNC), fungal necromass carbon (FNC), and bacterial necromass carbon (BNC), and analyzed the contributions to SOC fractions and influencing factors. Our findings reveal three key insights. First, the contents of MAOC and POC in the 0–100 cm soil layer were in the following order of magnitude: Meadow steppe > Typical steppe > Desert steppe > Steppe desert, with the average content of POC being 9.3 g kg−1, which was higher than the average content of MAOC (8.73 g kg−1). Second, the content of microbial TNC in MAOC and POC decreased with soil depth, the average content of FNC was 3.02 and 3.85 g kg−1, which were higher than the average content of BNC (1.64 and 2.08 g kg−1). FNC dominated both MAOC and POC, and its contribution was higher than the contribution of BNC. Third, through regression analysis and random forest modeling, we identified key environmental drivers of MNC dynamics: mean annual rainfall, electrical conductance, and soil total nitrogen emerged as primary regulators in surface soils (0–20 cm), while available potassium, SOC, and mean annual temperature dominated deeper soil layers (20–100 cm). This research contributes by: (1) establishing the vertical distribution patterns of MNC and SOC fractions in soil profiles; (2) quantifying the relative contributions of MNC to SOC fractions across different grassland ecosystems soil profiles and elucidating their environmental controls, offers a deeper understanding of the mechanisms driving MNC accumulation in SOC fractions in diverse grassland ecosystems, and providing data support for further research on the microbiological mechanisms of soil organic carbon formation and accumulation in arid and semi-arid regions.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"16 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397942","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":"Mapping the fertosphere’s phosphorus availability distribution in a field trial using a novel diffusive gradients in thin-films (fDGT) technique","authors":"Casey Louise Doolette, Euan Smith, Ehsan Tavakkoli, Lukas van Zwieten, Peter Martin Kopittke, Enzo Lombi","doi":"10.5194/egusphere-2025-3044","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3044","url":null,"abstract":"<strong>Abstract.</strong> Phosphorus (P) is limiting to crop growth worldwide and optimising P fertiliser use is essential for maintaining crop productivity and avoiding negative environmental impacts. To achieve this, a thorough understanding of the chemistry and potential plant availability of P fertilisers in soil is required, particularly the chemistry in the region of soil surrounding the fertiliser granules i.e. the fertosphere. The diffusive gradients in thin-films (DGT) technique is commonly used to estimate potentially bioavailable nutrient concentrations and the distribution of nutrients in the environment, including for P. This method correlates strongly to plant available nutrients because it mimics plant nutrient uptake by acting as an infinite sink. The technique has been used to obtain two-dimensional (2D) images of labile P concentrations or P fluxes in soil using X-ray fluorescence microscopy (XFM) and laser ablation (LA) ICP-MS in laboratory settings. Conventional DGTs are tedious to prepare and difficult to deploy at a scale (10s of cm²) relevant to field scale observations. We recently developed a DGT with a gel-free binding layer that addresses these limitations. This innovative design is robust and simplifies preparation and analysis, making it ideal for field deployment. Here, we describe the details of the design of this novel field DGT (fDGT) device and evaluate its effectiveness in assessing the spatial availability of P from different fertilizer sources in a barley field trial in calcareous soil. Using X-ray fluorescence microscopy (XFM) analysis of DGT binding layers, we demonstrate that there are distinct reaction zones along the P fertiliser band in the field, and that differences between P treatments can be visualised and quantified using this novel fDGT. This approach provides a foundation for expanded use of field-deployable DGTs in studying macronutrient dynamics and supports the development of more efficient, site-specific fertiliser strategies to improve P use efficiency in agricultural systems. As the next step, we propose to further develop and refine this fDGT device and to make it applicable for other macro and/or nutrients. This will ultimately support research that aims to assist farmers by enhancing fertiliser use efficiency.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"9 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295633","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":"High-resolution frequency-domain electromagnetic mapping for the hydrological modeling of an orange orchard","authors":"Luca Peruzzo, Ulrike Werban, Marco Pohle, Mirko Pavoni, Benjamin Mary, Giorgio Cassiani, Simona Consoli, Daniela Vanella","doi":"10.5194/soil-11-811-2025","DOIUrl":"https://doi.org/10.5194/soil-11-811-2025","url":null,"abstract":"Abstract. While aboveground precision agriculture technologies provide spatial and temporal datasets that are ever increasing in terms of density and precision, belowground information lags behind and has been typically limited to time series. As recognized in agrogeophysics, geophysical methods can address the lack of subsurface spatial information. This study focuses on high-resolution frequency-domain electromagnetic induction (FDEM) mapping as an ideal complement to aboveground and belowground time series that are commonly available in precision agriculture. Focused on a Sicilian orange orchard, this study first investigates some methodological challenges behind seemingly simple FDEM survey choices and processing steps, as well as their interplay with the spatial heterogeneity of agricultural sites. Second, this study shows how the detailed FDEM-based spatial information can underpin a surface/subsurface hydrological model that integrates time series from soil moisture sensors and micro-meteorological sensors. While FDEM has long been recognized as a promising solution in agrogeophysics, this study demonstrates how the approach can be successfully applied in an orchard, whose 3D subsurface variability is a complex combination of root water uptake, irrigation, evapotranspiration, and row–interrow dynamics. The resulting hydrological model reproduces the observed spatiotemporal water dynamics with parameters that agree with the results from soil laboratory analysis, supporting gamma-ray and electrical resistivity tomography datasets. The implementation of a hydrological model positively aligns with the increasing number and variety of methods in precision agriculture, as well as with the need for better predictive capability.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"11 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295625","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":"Quantifying spatial uncertainty to improve soil predictions in data-sparse regions","authors":"Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten","doi":"10.5194/soil-11-833-2025","DOIUrl":"https://doi.org/10.5194/soil-11-833-2025","url":null,"abstract":"Abstract. Artificial neural networks (ANNs) are valuable tools for predicting soil properties using large datasets. However, a common challenge in soil sciences is the uneven distribution of soil samples, which often results from past sampling projects that heavily sample certain areas while leaving similar yet geographically distant regions under-sampled. One potential solution to this problem is to transfer an already trained model to other similar regions. Robust spatial uncertainty quantification is crucial for this purpose, yet this is often overlooked in current research. We address this issue by using a Bayesian deep learning technique, Laplace approximations, to quantify spatial uncertainty. This produces a probability measure encoding where the model’s prediction is deemed to be reliable and where a lack of data should lead to a high uncertainty. We train such an ANN on a soil landscape dataset from a specific region in southern Germany and then transfer the trained model to another unseen but, to some extent, similar region without any further model training. The model effectively generalized alluvial patterns, demonstrating its ability to recognize repetitive features of river systems. However, the model showed a tendency to favour overrepresented soil units, underscoring the importance of balancing training datasets to reduce overconfidence in dominant classes. Quantifying uncertainty in this way allows stakeholders to better identify regions and settings in need of further data collection, enhancing decision-making and prioritizing efforts in data collection. Our approach is computationally lightweight and can be added post hoc to existing deep learning solutions for soil prediction, thus offering a practical tool to improve soil property predictions in under-sampled areas, as well as optimizing future sampling strategies, ensuring that resources are allocated efficiently for maximum data coverage and accuracy.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"92 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295623","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":"Coupled C and N turnover in a dynamic pore scale model reveal the impact of exudate quality on microbial necromass formation","authors":"Maximilian Rötzer, Henri Braunmiller, Eva Lehndorff, Nadja Ray, Andrea Scheibe, Alexander Prechtel","doi":"10.5194/egusphere-2025-4717","DOIUrl":"https://doi.org/10.5194/egusphere-2025-4717","url":null,"abstract":"<strong>Abstract.</strong> The adequate quantification of soil organic carbon (SOC) turnover is a pressing need for improving soil health and understanding climate dynamics. It is controlled by the complex interplay of microbial activity, availability of carbon (C) and nitrogen (N) sources, and the dynamic restructuring of the soil's architecture. Accurate modeling of SOC dynamics requires the representation of these processes at small spatial scales. We present a mechanistic, spatially explicit model at the pore scale, which couples enzymatic degradation of particulate organic matter (POM), microbial necromass and root exudates with microbial growth and turnover, C respiration and N cycling depending on the C/N ratios of the different organic carbon sources. It is combined with a cellular automaton model for simulating soil structure dynamics including the stabilization of soil particles, POM or microbial necromass via organo‐mineral associations. The virtual soil simulations use µCT data of aggregates and parameters from rhizosphere experiments without parameter fitting to explore the influence of (i) soil structural heterogeneity and connectivity, (ii) N limitation, and (iii) necromass formation on SOC storage. Our results highlight that evolving soil architecture and pore connectivity control substrate accessibility, creating micro‐scale hot and cold spots for microbes. N availability consistently co-limits microbial growth, while a favorable C/N ratio of root exudates substantially reduces respiration and increases CUE over extended periods. Necromass emerges as long‐term SOC pool, as N from short‐term root exudation pulses promotes biomass growth and is converted into slowly degradable necromass, which can be physically protected through occlusion. The findings align with lab experiments and additionally allow us to elucidate the spatial and temporal dynamics of the drivers of carbon turnover.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"62 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295626","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":"Estimating soil carbon sequestration potential with mid-IR spectroscopy and explainable machine learning","authors":"Yang Hu, Raphael A. Viscarra Rossel","doi":"10.5194/egusphere-2025-4828","DOIUrl":"https://doi.org/10.5194/egusphere-2025-4828","url":null,"abstract":"<strong>Abstract.</strong> Soil carbon sequestration refers to the process of capturing atmospheric carbon through plant photosynthesis and storing it in soil as organic carbon. The primary mechanism for carbon sequestration is via organic carbon molecules adsorbing onto mineral surfaces of the soil's fine fraction (clay + silt <em>≤</em> 20 <em>μ</em>m), forming mineral-associated organic carbon (MAOC). Soil has a finite capacity to stabilise and sequester organic carbon, known as carbon saturation capacity, which depends on the proportion of reactive minerals in the soil. The difference between the current MAOC content and the carbon saturation capacity is referred to as the organic carbon saturation deficit (C<em>_def</em>) or sequestration potential. Fourier-transformed (FTIR) mid-infrared (mid-IR) spectroscopy can simultaneously measure soil properties relevant to carbon stabilisation, organic carbon functional groups, clay and iron-oxide mineralogy and particle size. Therefore, we hypothesise that mid-IR spectroscopy can effectively and accurately estimate C<em>_def</em>. Thus, we aim to (i) develop spectroscopic models to estimate the MAOC and C<em>_def</em> of 482 Australian topsoil samples, (ii) model MAOC and C<em>_def</em> using mid-IR spectra and an interpretable machine learning, and (ii) interpret the MAOC and C<em>_def</em> models using the explainable artificial intelligence (AI) algorithm SHapley Additive exPlanations (SHAP). Using frontier line analysis, we fitted a function to the upper envelope of the MAOC vs clay + silt relationship to derive C<em>_def</em>. We recorded mid-IR spectra of the samples and used the regression trees method CUBIST to model MAOC content and C<em>_def</em>. We interpreted these models by examining the regression trees and using SHAP. The models were unbiased and estimated MAOC content with R² of 0.86 and RMSE of 2.77 (g/kg soil), and C<em>_def</em> with R² of 0.89 and RMSE of 3.72 (g/kg soil). Model interpretation revealed C<em>_def</em> estimates relied on negative interactions with absorptions from organic matter functional groups and positive interactions with absorptions from clay minerals. Our results show that mid-IR spectra can effectively estimate MAOC and soil C<em>_def</em>, offering a rapid and cost-effective method for assessing and monitoring this critical soil function.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"4 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295662","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":"High biodegradability of water-soluble organic carbon in soils at the southern margin of the boreal forest","authors":"Yuqi Zhu, Chao Liu, Rui Liu, Hanxi Wang, Xiangwen Wu, Zihao Zhang, Shuying Zang, Xiaodong Wu","doi":"10.5194/soil-11-793-2025","DOIUrl":"https://doi.org/10.5194/soil-11-793-2025","url":null,"abstract":"Abstract. Water-soluble organic carbon (WSOC) is an important component of the soil organic carbon pool. While the biodegradability and its compositional changes of WSOC in deep soils in boreal forests remain unknown. Here, based on spectroscopic techniques, we conducted a 28 d laboratory incubation to analyze the molecular composition, biodegradability, and compositional changes of WSOC during a laboratory incubation for deep soils at the southern boreal margin. The results showed that in the upper 2 m soils, the average content of biodegradable WSOC was 0.228 g kg−1 with an average proportion of 86.41 % in the total WSOC. In the soil layer between 2.0–7.4 m, the average biodegradable WSOC content was 0.144 g kg−1, accounting for 80.79 % of the total WSOC. Spectroscopic analysis indicates that the WSOC in the upper soils is primarily composed of highly aromatic humic acid-like matter with larger molecular weights than those in deep soils. Both the aromaticity and molecular weight decrease with depth, and the WSOC is mainly composed of fulvic acid-like matter in the deep soils, suggesting high biodegradability of WSOC in the deep soils. Overall, our results suggest that the water-soluble organic carbon in the boreal forests exhibits high biodegradability both in the shallow layer and deep soils.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"61 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246603","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}