Abstract. 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.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.
{"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·SiO<sub>2</sub>), with CaCO<sub>3</sub> precipitates on particle surfaces. These Ca-supplying phases persisted after 24 weeks of leaching. Sustained Ca release from f-CaO and 2CaO·SiO<sub>2</sub>, together with CaCO<sub>3</sub> 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}
Pub Date : 2025-10-22DOI: 10.5194/soil-11-883-2025
Shenggang Chen, Yaqi Zhang, Jun Ma, Mingyue Bai, Jinxiao Long, Ming Liu, Yinglong Chen, Jianbin Guo, Lin Chen
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.
摘要。微生物坏死块碳（MNC）是土壤有机碳（SOC）的重要来源。然而，不同草地类型下不同土层微生物坏死块对不同有机碳组分的贡献及其影响因素尚不清楚。本研究通过对宁夏草甸草原、典型草原、荒漠草原和草原荒漠4种草地类型（0 - 20cm、20 - 40cm和40 - 100cm）的土壤剖面进行综合调查。定量分析了矿物伴生有机碳（MAOC）、颗粒有机碳（POC）及其微生物坏死物碳（TNC）、真菌坏死物碳（FNC）和细菌坏死物碳（BNC），并分析了它们对土壤有机碳组分的贡献及其影响因素。我们的发现揭示了三个关键的见解。1 . 0 ~ 100 cm土层中MAOC和POC的含量大小顺序为：草甸草原>典型草原>荒漠草原>草原荒漠，POC的平均含量为9.3 g kg−1，高于MAOC的平均含量（8.73 g kg−1）。MAOC和POC中微生物TNC含量随土层深度的增加而降低，FNC的平均含量分别为3.02和3.85 g kg - 1，高于BNC的平均含量（1.64和2.08 g kg - 1）。FNC在MAOC和POC中均占主导地位，其贡献高于BNC。第三，通过回归分析和随机森林模型，我们确定了跨国公司动态的关键环境驱动因素：年平均降雨量、电导率和土壤全氮是表层土壤（0-20 cm）的主要调节因素，而速效钾、有机碳和年平均温度是深层土壤（20-100 cm）的主要调节因素。本文的研究成果主要体现在：(1)建立了土壤有机质和有机碳组分在土壤剖面中的垂直分布格局；(2)量化不同草地生态系统土壤剖面中MNC对土壤有机碳组分的相对贡献并阐明其环境控制作用，有助于深入了解不同草地生态系统土壤有机碳组分中MNC积累的机制，为进一步研究干旱半干旱区土壤有机碳形成和积累的微生物机制提供数据支持。
{"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}
Pub Date : 2025-10-15DOI: 10.5194/egusphere-2025-3044
Casey Louise Doolette, Euan Smith, Ehsan Tavakkoli, Lukas van Zwieten, Peter Martin Kopittke, Enzo Lombi
Abstract. 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.
{"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 cm<sup>2</sup>) 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}
Pub Date : 2025-10-15DOI: 10.5194/soil-11-811-2025
Luca Peruzzo, Ulrike Werban, Marco Pohle, Mirko Pavoni, Benjamin Mary, Giorgio Cassiani, Simona Consoli, Daniela Vanella
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.
{"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}
Pub Date : 2025-10-15DOI: 10.5194/soil-11-833-2025
Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten
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.
{"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}
Pub Date : 2025-10-14DOI: 10.5194/egusphere-2025-4717
Maximilian Rötzer, Henri Braunmiller, Eva Lehndorff, Nadja Ray, Andrea Scheibe, Alexander Prechtel
Abstract. 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.
{"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}
Pub Date : 2025-10-14DOI: 10.5194/egusphere-2025-4828
Yang Hu, Raphael A. Viscarra Rossel
Abstract. 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 ≤ 20 μm), 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 (Cdef) 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 Cdef. Thus, we aim to (i) develop spectroscopic models to estimate the MAOC and Cdef of 482 Australian topsoil samples, (ii) model MAOC and Cdef using mid-IR spectra and an interpretable machine learning, and (ii) interpret the MAOC and Cdef 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 Cdef. We recorded mid-IR spectra of the samples and used the regression trees method CUBIST to model MAOC content and Cdef. 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 Cdef with R2 of 0.89 and RMSE of 3.72 (g/kg soil). Model interpretation revealed Cdef 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 Cdef, offering a rapid and cost-effective method for assessing and monitoring this critical soil function.
{"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><sub>def</sub></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><sub>def</sub></em>. Thus, we aim to (i) develop spectroscopic models to estimate the MAOC and C<em><sub>def</sub></em> of 482 Australian topsoil samples, (ii) model MAOC and C<em><sub>def</sub></em> using mid-IR spectra and an interpretable machine learning, and (ii) interpret the MAOC and C<em><sub>def</sub></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><sub>def</sub></em>. We recorded mid-IR spectra of the samples and used the regression trees method CUBIST to model MAOC content and C<em><sub>def</sub></em>. We interpreted these models by examining the regression trees and using SHAP. The models were unbiased and estimated MAOC content with R<sup>2</sup> of 0.86 and RMSE of 2.77 (g/kg soil), and C<em><sub>def</sub></em> with R<sup>2</sup> of 0.89 and RMSE of 3.72 (g/kg soil). Model interpretation revealed C<em><sub>def</sub></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><sub>def</sub></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}
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.
摘要。水溶性有机碳（WSOC）是土壤有机碳库的重要组成部分。而北方森林深层土壤水有机碳的生物降解性及其组成变化尚不清楚。本文基于光谱技术，对北缘南缘深层土壤进行了28 d的实验室培养，分析了培养过程中WSOC的分子组成、生物降解性和组成变化。结果表明：上层2 m土壤中可生物降解的WSOC平均含量为0.228 g kg - 1，占总WSOC的平均比例为86.41%；在2.0 ~ 7.4 m土层中，可降解WSOC平均含量为0.144 g kg - 1，占总WSOC的80.79%。光谱分析表明，表层土壤水分有机碳主要由分子量较大的高芳香腐植酸类物质组成。芳香性和分子量均随深度的增加而降低，深层土壤中土壤有机碳主要以黄腐酸类物质为主，具有较高的生物降解性。总体而言，我们的研究结果表明，北方森林中水溶性有机碳在浅层和深层土壤中都具有较高的生物降解性。
{"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}
Abstract. In Pacific Rim regions highly exposed to climate variability, accurate projections of soil organic carbon (SOC) are critical for furture effective land management and climate adaptation strategies. This study integrated digital soil mapping with CMIP6-based climate projections to estimate the spatiotemporal distribution of SOC stocks in subtropical (Zhuoshui River) and tropical (Laonong River) watersheds in Taiwan. We collected 1377 soil samples and data on 18 environmental covariates and modeled SOC stocks at a 20-m resolution through the Cubist and random forest algorithms, which were also combined with regression kriging. The Cubist-based kriging model was discovered to achieve the highest performance in SOC stock prediction. Forested areas were found to contain >80 % of SOC stocks, and tropical zones were discovered to store substantially less carbon than subtropical zones. Future emission scenarios revealed spatial heterogeneity in SOC stock dynamics. In scenario SSP1-2.6, a maximum SOC stock decline of approximately 20.9 % was predicted, particularly for uplands, because of erosion induced by extreme rainfall events (R95p and R99p), whereas in scenarios SSP2-4.5 and SSP5-8.5, increases of 7.9 % to 58 % were predicted, respectively; particularly corresponded to forested areas because of enhanced productivity caused by increased TNx and TXx (extremes of minimum and maximum temperature). Partial least squares path modeling revealed a climate–topography interaction in SOC stocks, dominated by topography and followed by prolonged dry spells. Examining the interactions between climatic extremes, landscape types, and SOC stocks is essential for enhancing soil resilience and ensuring stable SOC stocks in the future.
{"title":"Soil Organic Carbon Projections and Climate Adaptation Strategies across Pacific Rim Agro-ecosystems","authors":"Chien-Hui Syu, Chun-Chien Yen, Selly Maisyarah, Bo-Jiun Yang, Yu-Min Tzou, Shih-Hao Jien","doi":"10.5194/egusphere-2025-4258","DOIUrl":"https://doi.org/10.5194/egusphere-2025-4258","url":null,"abstract":"<strong>Abstract.</strong> In Pacific Rim regions highly exposed to climate variability, accurate projections of soil organic carbon (SOC) are critical for furture effective land management and climate adaptation strategies. This study integrated digital soil mapping with CMIP6-based climate projections to estimate the spatiotemporal distribution of SOC stocks in subtropical (Zhuoshui River) and tropical (Laonong River) watersheds in Taiwan. We collected 1377 soil samples and data on 18 environmental covariates and modeled SOC stocks at a 20-m resolution through the Cubist and random forest algorithms, which were also combined with regression kriging. The Cubist-based kriging model was discovered to achieve the highest performance in SOC stock prediction. Forested areas were found to contain >80 % of SOC stocks, and tropical zones were discovered to store substantially less carbon than subtropical zones. Future emission scenarios revealed spatial heterogeneity in SOC stock dynamics. In scenario SSP1-2.6, a maximum SOC stock decline of approximately 20.9 % was predicted, particularly for uplands, because of erosion induced by extreme rainfall events (R95p and R99p), whereas in scenarios SSP2-4.5 and SSP5-8.5, increases of 7.9 % to 58 % were predicted, respectively; particularly corresponded to forested areas because of enhanced productivity caused by increased TNx and TXx (extremes of minimum and maximum temperature). Partial least squares path modeling revealed a climate–topography interaction in SOC stocks, dominated by topography and followed by prolonged dry spells. Examining the interactions between climatic extremes, landscape types, and SOC stocks is essential for enhancing soil resilience and ensuring stable SOC stocks in the future.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"62 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241894","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}
Pub Date : 2025-10-06DOI: 10.5194/soil-11-763-2025
Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, Mikko Sparf
Abstract. Peatlands are Earth's most carbon-dense terrestrial ecosystems and their carbon density varies with the depth of the peat layer. Accurate mapping of peat depth is crucial for carbon accounting and land management, yet existing maps lack the resolution and accuracy needed for these applications. This study evaluates whether digital soil mapping using remotely sensed data can improve existing maps of peat depth in western and southeastern Norway. Specifically, we assessed the predictive value of lidar-derived terrain variables and airborne radiometric data across two, >10 km2 sites. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution. The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Terrain variables were better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. Radiometric variables showed inconsistent and weak predictive value – improving performance at one site while degrading it at the other. Our remote sensing models had better accuracy than the national map of peat depth, even when we calibrated the national map to the same depth data. Still, weak relationships with remotely sensed variables made peat depth hard to predict overall. Based on these findings, we conclude that digital soil mapping can improve the existing, national map of peat depth in Norway, but detailed local maps are best made from tailored field measurements. Together, these pathways promise more accurate landscape-scale carbon stock assessments and better-informed land management policies.
{"title":"Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes","authors":"Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, Mikko Sparf","doi":"10.5194/soil-11-763-2025","DOIUrl":"https://doi.org/10.5194/soil-11-763-2025","url":null,"abstract":"Abstract. Peatlands are Earth's most carbon-dense terrestrial ecosystems and their carbon density varies with the depth of the peat layer. Accurate mapping of peat depth is crucial for carbon accounting and land management, yet existing maps lack the resolution and accuracy needed for these applications. This study evaluates whether digital soil mapping using remotely sensed data can improve existing maps of peat depth in western and southeastern Norway. Specifically, we assessed the predictive value of lidar-derived terrain variables and airborne radiometric data across two, >10 km2 sites. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution. The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Terrain variables were better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. Radiometric variables showed inconsistent and weak predictive value – improving performance at one site while degrading it at the other. Our remote sensing models had better accuracy than the national map of peat depth, even when we calibrated the national map to the same depth data. Still, weak relationships with remotely sensed variables made peat depth hard to predict overall. Based on these findings, we conclude that digital soil mapping can improve the existing, national map of peat depth in Norway, but detailed local maps are best made from tailored field measurements. Together, these pathways promise more accurate landscape-scale carbon stock assessments and better-informed land management policies.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"8 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228936","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}
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