Pub Date : 2025-10-05DOI: 10.5194/egusphere-2025-4266
Xuqing Li, Han Chen, Weiyu Wang, Xing Du, Tiefeng Zhou, Munazza Ijaz, Temoor Ahmed, Muhammad Shafiq Shahid, Gabrijel Ondrasek, Branko Petrinec, Zhenhao Zheng, Bin Li, Jianli Yan
Abstract. To find one suitable vegetation restoration type as a good means of restoring newly reclaimed croplands in subtropical of China. This study investigated the effect of vegetables, corn, and peach in soil properties, bacterial communities, and metabolites of newly reclaimed lands after three years restoration. Results from this study indicated that soil physicochemical properties were differentially affected by vegetation restoration of three different plants, while the effect depends on both the vegetation types and the kind of soil parameters. Indeed, the pH, soil bulk density (SBD), soil organic matter (SOM) and total nitrogen (TN) were generally unaffected except a significant reduction in SBD (13.97 %) and SOM (35.41 %) by vegetable and peach, respectively. However, three different plants significantly increased the available phosphorus (AP) (75.03–143.02 %), available potassium (AK) (154.90 % and 103.93 %) and microbial biomass carbon (MBC) (37.71–144.93 %), with the greatest increase by vegetable relative to the control except a significant reduction in the AK (41.73 %) by peach. Furthermore, the analysis of 16S rRNA gene high-throughput sequencing revealed that the vegetation of three plants increased the relative abundances (RAs) of soil bacterial phyla and genera with 6.21–10.54 % increase in operational taxonomic units (OTUs), 6.22–10.53 % increase in Chao1 and 2.30–3.11 % increase in Shannon indices, while redundancy discriminant analysis (RDA) revealed that the change of soil properties were highly related to the variation in bacterial community composition. In addition, 130 significantly differential metabolites (SDMs) that belong to organic acid, amino acid, heterocyclic compounds between vegetable and the control were identified based on liquid chromatography-mass spectrometry (LC-MS) analysis, while the top 20 SDMs were highly correlated with the 7 enriched bacterial genera. Overall, the results showed that the vegetation of three plants, in particular vegetable can ameliorate soil quality of newly reclaimed croplands by improving soil chemical properties, and increasing the richness and complexity of bacterial community structure, as well as specific bacterial genus and metabolites.
{"title":"Impacts of vegetation restoration on soil physicochemical properties, bacterial communities, and metabolites in newly reclaimed croplands","authors":"Xuqing Li, Han Chen, Weiyu Wang, Xing Du, Tiefeng Zhou, Munazza Ijaz, Temoor Ahmed, Muhammad Shafiq Shahid, Gabrijel Ondrasek, Branko Petrinec, Zhenhao Zheng, Bin Li, Jianli Yan","doi":"10.5194/egusphere-2025-4266","DOIUrl":"https://doi.org/10.5194/egusphere-2025-4266","url":null,"abstract":"<strong>Abstract.</strong> To find one suitable vegetation restoration type as a good means of restoring newly reclaimed croplands in subtropical of China. This study investigated the effect of vegetables, corn, and peach in soil properties, bacterial communities, and metabolites of newly reclaimed lands after three years restoration. Results from this study indicated that soil physicochemical properties were differentially affected by vegetation restoration of three different plants, while the effect depends on both the vegetation types and the kind of soil parameters. Indeed, the pH, soil bulk density (SBD), soil organic matter (SOM) and total nitrogen (TN) were generally unaffected except a significant reduction in SBD (13.97 %) and SOM (35.41 %) by vegetable and peach, respectively. However, three different plants significantly increased the available phosphorus (AP) (75.03–143.02 %), available potassium (AK) (154.90 % and 103.93 %) and microbial biomass carbon (MBC) (37.71–144.93 %), with the greatest increase by vegetable relative to the control except a significant reduction in the AK (41.73 %) by peach. Furthermore, the analysis of 16S rRNA gene high-throughput sequencing revealed that the vegetation of three plants increased the relative abundances (RAs) of soil bacterial phyla and genera with 6.21–10.54 % increase in operational taxonomic units (OTUs), 6.22–10.53 % increase in Chao1 and 2.30–3.11 % increase in Shannon indices, while redundancy discriminant analysis (RDA) revealed that the change of soil properties were highly related to the variation in bacterial community composition. In addition, 130 significantly differential metabolites (SDMs) that belong to organic acid, amino acid, heterocyclic compounds between vegetable and the control were identified based on liquid chromatography-mass spectrometry (LC-MS) analysis, while the top 20 SDMs were highly correlated with the 7 enriched bacterial genera. Overall, the results showed that the vegetation of three plants, in particular vegetable can ameliorate soil quality of newly reclaimed croplands by improving soil chemical properties, and increasing the richness and complexity of bacterial community structure, as well as specific bacterial genus and metabolites.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228939","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 : 2025-10-02DOI: 10.5194/soil-11-755-2025
Jennifer Michel, Yves Brostaux, Bernard Longdoz, Hervé Vanderschuren, Pierre Delaplace
Abstract. Priming effects in soil science describe the influence of fresh carbon (C) inputs on rates of microbial mineralisation of native soil organic matter, which can either increase (positive priming) or decrease (negative priming). While both positive and negative priming effects occur in natural ecosystems, the latter is less documented in the peer-reviewed literature and the overall impact of priming effects on the C balance of vegetated ecosystems remains elusive. Here, we highlight three aspects which need to be discussed to ensure (rhizosphere) priming effects are correctly perceived in their ecological context and measured at appropriate scales: (i) We emphasize the importance of evaluating net C balances because usually experimental C inputs exceed C-losses meaning even positive priming doesn't cause net C-loss; (ii) We caution against publication bias, which forces overrepresentation of positive priming effects, neglects negative or no priming, and potentially misguides conclusions about C-loss; and (iii) We highlight the need to distinguish between general priming effects and rhizosphere-specific priming, which differ in their scale and driving factors, and hence require different methodological approaches. Future research should focus on scalable experiments linking priming to plant nutrition via C, nutrient and water cycling to understand priming in context of ecosystem functioning.
{"title":"What if publication bias is the rule and net carbon loss from priming the exception?","authors":"Jennifer Michel, Yves Brostaux, Bernard Longdoz, Hervé Vanderschuren, Pierre Delaplace","doi":"10.5194/soil-11-755-2025","DOIUrl":"https://doi.org/10.5194/soil-11-755-2025","url":null,"abstract":"Abstract. Priming effects in soil science describe the influence of fresh carbon (C) inputs on rates of microbial mineralisation of native soil organic matter, which can either increase (positive priming) or decrease (negative priming). While both positive and negative priming effects occur in natural ecosystems, the latter is less documented in the peer-reviewed literature and the overall impact of priming effects on the C balance of vegetated ecosystems remains elusive. Here, we highlight three aspects which need to be discussed to ensure (rhizosphere) priming effects are correctly perceived in their ecological context and measured at appropriate scales: (i) We emphasize the importance of evaluating net C balances because usually experimental C inputs exceed C-losses meaning even positive priming doesn't cause net C-loss; (ii) We caution against publication bias, which forces overrepresentation of positive priming effects, neglects negative or no priming, and potentially misguides conclusions about C-loss; and (iii) We highlight the need to distinguish between general priming effects and rhizosphere-specific priming, which differ in their scale and driving factors, and hence require different methodological approaches. Future research should focus on scalable experiments linking priming to plant nutrition via C, nutrient and water cycling to understand priming in context of ecosystem functioning.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"124 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203432","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 : 2025-10-01DOI: 10.5194/soil-11-715-2025
Elsa Coucheney, Anke Marianne Herrmann, Nicholas Jarvis
Abstract. Simulation models are potentially useful tools to test our understanding of the processes involved in the turnover of soil organic carbon (SOC) and to evaluate the role of management practices in maintaining stocks of SOC. We describe here a simple model of SOC turnover at the soil profile scale that accounts for two key processes determining SOC persistence (i.e. microbial energy limitation and physical protection due to soil aggregation). We tested the model and evaluated the identifiability of key parameters using topsoil SOC contents measured in three treatments with contrasting organic matter inputs (i.e. fallow, mineral fertilized and cropped, with and without straw addition) in a long-term field trial. The estimated total input of organic matter (OM) in the treatment with straw added was roughly three times that of the treatment without straw addition, but only 12 % of the additional OM input remained in the soil after 54 years. By taking microbial energy limitation and enhanced physical protection of root residues into account, the model could explain the differences in C persistence among the three treatments, whilst also accurately matching the time-courses of SOC contents using the same set of model parameters. Models that do not explicitly consider microbial energy limitation and physical protection would need to adjust their parameter values (either decomposition rate constants or the retention coefficient) to match this data. We also performed a sensitivity analysis to identify the most influential parameters in the model determining soil profile stocks of OM at steady-state. Input distributions for soil and crop parameters in the model were defined for the agricultural production region in east-central Sweden that includes Uppsala. This analysis showed that model parameters affecting SOC decomposition rates, including the rate constant for microbial-processed SOC and the parameters regulating physical protection and microbial energy limitation, are more sensitive than parameters determining OM inputs. The development of pedotransfer approaches to estimate SOC decomposition rates from soil properties would therefore support predictive applications of the model at larger spatial scales.
{"title":"A simple model of the turnover of organic carbon in a soil profile: model test, parameter identification and sensitivity","authors":"Elsa Coucheney, Anke Marianne Herrmann, Nicholas Jarvis","doi":"10.5194/soil-11-715-2025","DOIUrl":"https://doi.org/10.5194/soil-11-715-2025","url":null,"abstract":"Abstract. Simulation models are potentially useful tools to test our understanding of the processes involved in the turnover of soil organic carbon (SOC) and to evaluate the role of management practices in maintaining stocks of SOC. We describe here a simple model of SOC turnover at the soil profile scale that accounts for two key processes determining SOC persistence (i.e. microbial energy limitation and physical protection due to soil aggregation). We tested the model and evaluated the identifiability of key parameters using topsoil SOC contents measured in three treatments with contrasting organic matter inputs (i.e. fallow, mineral fertilized and cropped, with and without straw addition) in a long-term field trial. The estimated total input of organic matter (OM) in the treatment with straw added was roughly three times that of the treatment without straw addition, but only 12 % of the additional OM input remained in the soil after 54 years. By taking microbial energy limitation and enhanced physical protection of root residues into account, the model could explain the differences in C persistence among the three treatments, whilst also accurately matching the time-courses of SOC contents using the same set of model parameters. Models that do not explicitly consider microbial energy limitation and physical protection would need to adjust their parameter values (either decomposition rate constants or the retention coefficient) to match this data. We also performed a sensitivity analysis to identify the most influential parameters in the model determining soil profile stocks of OM at steady-state. Input distributions for soil and crop parameters in the model were defined for the agricultural production region in east-central Sweden that includes Uppsala. This analysis showed that model parameters affecting SOC decomposition rates, including the rate constant for microbial-processed SOC and the parameters regulating physical protection and microbial energy limitation, are more sensitive than parameters determining OM inputs. The development of pedotransfer approaches to estimate SOC decomposition rates from soil properties would therefore support predictive applications of the model at larger spatial scales.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195524","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 : 2025-10-01DOI: 10.5194/soil-11-735-2025
Jolanta Niedźwiecka, Roey Angel, Petr Čapek, Ana Catalina Lara, Stanislav Jabinski, Travis B. Meador, Hana Šantrůčková
Abstract. In ecosystem studies, microbial carbon use efficiency (CUE) is often used to estimate the proportion of organic substrate (glucose) consumed by microbial biomass that is not released from soil as CO2. While most studies assume aerobic conditions, with CO2 and microbial biomass as the predominant products of organic substrate processing, anoxic microniches are common inside soil aggregates. Microorganisms in these microniches perform fermentation and anaerobic respiration using alternative electron acceptors: processes connected with the release of extracellular intermediates. These extracellular intermediates and other compounds are not traditionally accounted for but may represent a significant C flux when compared to microbial biomass formation. Climate change may modulate soil microbial activity by altering soil aeration status on a local level. Therefore, CUE as an intrinsic parameter that is used in ecosystem studies and modelling should be defined for more realistic assumptions regarding soil aeration. This study focused on the effect of oxygen and Fe availability on C mineralisation in forest soils and quantified C distribution between biomass and different extracellular metabolites. Forest soils from two Bohemian Forest (Czechia) sites, with low and high Fe content, were incubated under oxic and anoxic conditions. A solution of 13C-labelled glucose was used to track C incorporation into the biomass, respired CO2, and extracellular metabolites. We estimated CUE based on measured cumulative microbial respiration, residual glucose, biomass, and extracellular metabolites concentration. RNA-SIP was used to identify the active bacteria under each treatment. Under oxic conditions, glucose was rapidly consumed and largely converted to CO2, with greater microbial biomass and CUE observed in the low-Fe soil compared to the high-Fe soil. In contrast, under anoxic conditions, glucose consumption was slower, leading to the accumulation of fermentation products, especially in the high-Fe soil, and higher carbon storage efficiency. Microbial growth and turnover were generally lower under anoxic conditions. A large and diverse portion of the microbial community rapidly incorporated 13C-labelled glucose under oxic conditions, with over 300 active amplicon sequence variants (ASVs) identified – primarily from dominant phyla like Proteobacteria, Actinomycetota, and Bacteroidota. In contrast, anoxic conditions led to much slower and more limited labelling, with only a few ASVs (mainly Firmicutes) incorporating 13C. Our findings confirm that anoxia in soils enhances short-term C preservation and suggest that excluding exudates in mass flux calculations would underestimate C retention in the soil, especially under anoxic conditions.
{"title":"Aeration and mineral composition of soil mediate microbial CUE","authors":"Jolanta Niedźwiecka, Roey Angel, Petr Čapek, Ana Catalina Lara, Stanislav Jabinski, Travis B. Meador, Hana Šantrůčková","doi":"10.5194/soil-11-735-2025","DOIUrl":"https://doi.org/10.5194/soil-11-735-2025","url":null,"abstract":"Abstract. In ecosystem studies, microbial carbon use efficiency (CUE) is often used to estimate the proportion of organic substrate (glucose) consumed by microbial biomass that is not released from soil as CO2. While most studies assume aerobic conditions, with CO2 and microbial biomass as the predominant products of organic substrate processing, anoxic microniches are common inside soil aggregates. Microorganisms in these microniches perform fermentation and anaerobic respiration using alternative electron acceptors: processes connected with the release of extracellular intermediates. These extracellular intermediates and other compounds are not traditionally accounted for but may represent a significant C flux when compared to microbial biomass formation. Climate change may modulate soil microbial activity by altering soil aeration status on a local level. Therefore, CUE as an intrinsic parameter that is used in ecosystem studies and modelling should be defined for more realistic assumptions regarding soil aeration. This study focused on the effect of oxygen and Fe availability on C mineralisation in forest soils and quantified C distribution between biomass and different extracellular metabolites. Forest soils from two Bohemian Forest (Czechia) sites, with low and high Fe content, were incubated under oxic and anoxic conditions. A solution of 13C-labelled glucose was used to track C incorporation into the biomass, respired CO2, and extracellular metabolites. We estimated CUE based on measured cumulative microbial respiration, residual glucose, biomass, and extracellular metabolites concentration. RNA-SIP was used to identify the active bacteria under each treatment. Under oxic conditions, glucose was rapidly consumed and largely converted to CO2, with greater microbial biomass and CUE observed in the low-Fe soil compared to the high-Fe soil. In contrast, under anoxic conditions, glucose consumption was slower, leading to the accumulation of fermentation products, especially in the high-Fe soil, and higher carbon storage efficiency. Microbial growth and turnover were generally lower under anoxic conditions. A large and diverse portion of the microbial community rapidly incorporated 13C-labelled glucose under oxic conditions, with over 300 active amplicon sequence variants (ASVs) identified – primarily from dominant phyla like Proteobacteria, Actinomycetota, and Bacteroidota. In contrast, anoxic conditions led to much slower and more limited labelling, with only a few ASVs (mainly Firmicutes) incorporating 13C. Our findings confirm that anoxia in soils enhances short-term C preservation and suggest that excluding exudates in mass flux calculations would underestimate C retention in the soil, especially under anoxic conditions.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"35 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195514","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 : 2025-09-29DOI: 10.5194/egusphere-2025-4072
Erik Mathijs, Kato Van Ruymbeke
Abstract. Soil health is foundational to ecological sustainability, economic productivity, and societal wellbeing. However, fragmented perspectives on what constitutes "healthy soil" hinder coherent policies and business models. This article addresses that gap by offering a value-based framework to guide soil-health initiatives. Building on the Total Economic Value (TEV) framework, six complementary perspectives are identified: (1) productivist, (2) ecosystem services, (3) resilience, (4) non-use value, (5) intrinsic value, and (6) social innovation. These represent different motivations and beneficiaries – from private returns through public goods, to moral duties and collective empowerment. Each perspective implies specific opportunities and challenges for policy design. For instance, direct subsidies may be justified in cases where economic returns are delayed or insufficient, while ecosystem service payments require credible measurement and market mechanisms. Resilience investments often suffer from coordination failures, and intrinsic or social values lack clear economic incentives, requiring legal, educational, or institutional support instead. The article argues that no single policy instrument can serve all these perspectives effectively; rather, a differentiated, multi-perspective strategy is needed to align incentives, avoid over-subsidization, and ensure equitable access and accountability. This framework provides a foundation for designing inclusive and adaptive policies that foster sustainable soil stewardship across diverse stakeholders.
{"title":"Soil health-based business models: perspectives and policy implications","authors":"Erik Mathijs, Kato Van Ruymbeke","doi":"10.5194/egusphere-2025-4072","DOIUrl":"https://doi.org/10.5194/egusphere-2025-4072","url":null,"abstract":"<strong>Abstract.</strong> Soil health is foundational to ecological sustainability, economic productivity, and societal wellbeing. However, fragmented perspectives on what constitutes \"healthy soil\" hinder coherent policies and business models. This article addresses that gap by offering a value-based framework to guide soil-health initiatives. Building on the Total Economic Value (TEV) framework, six complementary perspectives are identified: (1) productivist, (2) ecosystem services, (3) resilience, (4) non-use value, (5) intrinsic value, and (6) social innovation. These represent different motivations and beneficiaries – from private returns through public goods, to moral duties and collective empowerment. Each perspective implies specific opportunities and challenges for policy design. For instance, direct subsidies may be justified in cases where economic returns are delayed or insufficient, while ecosystem service payments require credible measurement and market mechanisms. Resilience investments often suffer from coordination failures, and intrinsic or social values lack clear economic incentives, requiring legal, educational, or institutional support instead. The article argues that no single policy instrument can serve all these perspectives effectively; rather, a differentiated, multi-perspective strategy is needed to align incentives, avoid over-subsidization, and ensure equitable access and accountability. This framework provides a foundation for designing inclusive and adaptive policies that foster sustainable soil stewardship across diverse stakeholders.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183247","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 : 2025-09-29DOI: 10.5194/soil-11-681-2025
Jayson Gabriel Pinza, Ona-Abeni Devos Stoffels, Robrecht Debbaut, Jan Staes, Jan Vanderborght, Patrick Willems, Sarah Garré
Abstract. Numerical models can quantify subsoil compaction's hydrological impacts, useful to evaluate water management measures for climate change adaptations on compacted subsoils (e.g., augmenting groundwater recharge). Compaction also affects vegetation growth, which, however, is often parameterized using only limited field measurements or relations with other variables. This study shows that uncertainties in vegetation parameters linked to transpiration (leaf area index [LAI]) and water uptake (root depth distribution) can significantly affect hydrological modeling outcomes. The HYDRUS-1D soil water flow model was used to simulate the soil water balance of experimental grass plots on Belgian Campine Region's sandy soil. The compacted plot has the compact subsoil at 40–55 cm depths while the non-compacted plot underwent de-compaction. Using two year soil moisture sensor data at two depths, these models of these compacted and non-compacted plots were calibrated and validated under three different vegetation parameterizations, reflecting various canopy and root growth reactions to compaction. Water balances were then simulated under future climate scenarios. The experiments reveal that the compacted plots exhibited lower LAI while the non-compacted plots had deeper roots. Considering these vegetations' reactions in models, model simulations show that compaction will not always reduce deep percolation, compensated by the deep rooted non-compacted case model's higher evapotranspiration. Therefore, this affected vegetation growth can also further influence the water balance. Hence, hydrological modeling studies on (de-)compaction should dynamically incorporate vegetation growth above- and belowground, of which field evidence is vital.
{"title":"Quantifying hydrological impacts of compacted sandy subsoils using soil water flow simulations: the importance of vegetation parameterization","authors":"Jayson Gabriel Pinza, Ona-Abeni Devos Stoffels, Robrecht Debbaut, Jan Staes, Jan Vanderborght, Patrick Willems, Sarah Garré","doi":"10.5194/soil-11-681-2025","DOIUrl":"https://doi.org/10.5194/soil-11-681-2025","url":null,"abstract":"Abstract. Numerical models can quantify subsoil compaction's hydrological impacts, useful to evaluate water management measures for climate change adaptations on compacted subsoils (e.g., augmenting groundwater recharge). Compaction also affects vegetation growth, which, however, is often parameterized using only limited field measurements or relations with other variables. This study shows that uncertainties in vegetation parameters linked to transpiration (leaf area index [LAI]) and water uptake (root depth distribution) can significantly affect hydrological modeling outcomes. The HYDRUS-1D soil water flow model was used to simulate the soil water balance of experimental grass plots on Belgian Campine Region's sandy soil. The compacted plot has the compact subsoil at 40–55 cm depths while the non-compacted plot underwent de-compaction. Using two year soil moisture sensor data at two depths, these models of these compacted and non-compacted plots were calibrated and validated under three different vegetation parameterizations, reflecting various canopy and root growth reactions to compaction. Water balances were then simulated under future climate scenarios. The experiments reveal that the compacted plots exhibited lower LAI while the non-compacted plots had deeper roots. Considering these vegetations' reactions in models, model simulations show that compaction will not always reduce deep percolation, compensated by the deep rooted non-compacted case model's higher evapotranspiration. Therefore, this affected vegetation growth can also further influence the water balance. Hence, hydrological modeling studies on (de-)compaction should dynamically incorporate vegetation growth above- and belowground, of which field evidence is vital.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"504 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183029","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. The mollisol region of Northeast China constitutes a critical grain production base. However, prolonged intensive farming has disrupted native soil structures, driving soil degradation and generating excessive crop residues that constrain sustainable agricultural development. To address these challenges, a field experiment evaluated four mechanized tillage-sowing practices: Plow Tillage with Precise Sowing (PTS), Rotary Tillage with Precise Sowing (RTS), No-Tillage Sowing (NTS), and No-Tillage with Stubble Mulching and Sowing (NTMS). This study systematically assessed the impacts of these practices on soil compaction through analysis of soil penetration resistance (SPR), while further examining their effects on soil water content (SWC) and soil bulk density (SBD). Results demonstrated that NTMS significantly increased SWC, whereas NTS resulted in higher SBD and SPR than other practices. Both PTS and RTS improved SWC relative to NTS and reduced SBD more effectively than NTS or NTMS. Across all practices, SPR exhibited consistent trends during the soybean growth cycle, peaking at the podding stage. NTMS outperformed alternative practices by optimizing soil physical properties, thereby enhancing soil quality and slowing degradation processes in the black soil. Collectively, NTMS implemented within a maize-soybean rotation system offers a viable solution to address maize straw surplus and soil degradation in Northeast China's mollisol region.
{"title":"No-tillage with Stubble Mulching Enhances Soil Physical Properties and Reduces Soil Penetration Resistance: A Comparative Study in Mollisol Region of Northeast China","authors":"Dawei Wang, Hao Sun, Linding Wei, Boxiang Wang, Jinyou Qiao, Jian Sun, Haitao Chen","doi":"10.5194/egusphere-2025-3174","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3174","url":null,"abstract":"<strong>Abstract.</strong> The mollisol region of Northeast China constitutes a critical grain production base. However, prolonged intensive farming has disrupted native soil structures, driving soil degradation and generating excessive crop residues that constrain sustainable agricultural development. To address these challenges, a field experiment evaluated four mechanized tillage-sowing practices: Plow Tillage with Precise Sowing (PTS), Rotary Tillage with Precise Sowing (RTS), No-Tillage Sowing (NTS), and No-Tillage with Stubble Mulching and Sowing (NTMS). This study systematically assessed the impacts of these practices on soil compaction through analysis of soil penetration resistance (SPR), while further examining their effects on soil water content (SWC) and soil bulk density (SBD). Results demonstrated that NTMS significantly increased SWC, whereas NTS resulted in higher SBD and SPR than other practices. Both PTS and RTS improved SWC relative to NTS and reduced SBD more effectively than NTS or NTMS. Across all practices, SPR exhibited consistent trends during the soybean growth cycle, peaking at the podding stage. NTMS outperformed alternative practices by optimizing soil physical properties, thereby enhancing soil quality and slowing degradation processes in the black soil. Collectively, NTMS implemented within a maize-soybean rotation system offers a viable solution to address maize straw surplus and soil degradation in Northeast China's mollisol region.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"89 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141408","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. Soil CO2 flux (Fs) is a carbon cycling metric crucial for assessing ecosystem carbon budgets and global warming. However, global Fs datasets often suffer from low temporal-spatial resolution, as well as from spatial bias. Fs observations are severely deficient in tundra and dryland ecosystems due to financial and logistical constraints of current methods for Fs quantification. In this study, we introduce a novel, low-cost sensor system (LC-SS) for long-term, continuous monitoring of soil CO2 concentration and flux. The LC-SS, built from affordable, open-source hardware and software, offers a cost-effective solution (∼ USD 700 and ∼ 50 h for assembling and troubleshooting), accessible to low-budget users, and opens the scope for research with a large number of sensor system replications. The LC-SS was tested over ∼ 6 months in arid soil conditions, where fluxes are small, and accuracy is critical. CO2 concentration and soil temperature were measured at 10 min intervals at depths of 5 and 10 cm. The LC-SS demonstrated high stability during the tested period. Both diurnal and seasonal soil CO2 concentration variabilities were observed, highlighting the system's capability of continuous, long-term, in-situ monitoring of soil CO2 concentration. In addition, Fs was calculated using the measured CO2 concentration via the gradient method and validated with Fs measured by the flux chamber method using the well-accepted LI-COR gas analyzer system. Gradient method Fs was in good agreement with flux chamber Fs (RMSE = 0.15 µmol m−2 s−1), highlighting the potential for alternative or concurrent use of the LC-SS with current methods for Fs estimation – particularly in environments characterized by consistently low soil water content, such as drylands. Leveraging the accuracy and cost-effectiveness of the LC-SS (below 10 % of automated gas analyzer system cost), strategic implementation of LC-SSs could be a promising means to effectively increase the number of measurements, spatially and temporally, ultimately aiding in bridging the gap between global Fs uncertainties and current measurement limitations.
{"title":"Overcoming barriers in long-term, continuous monitoring of soil CO2 flux: a low-cost sensor system","authors":"Thi Thuc Nguyen, Nadav Bekin, Ariel Altman, Martin Maier, Nurit Agam, Elad Levintal","doi":"10.5194/soil-11-639-2025","DOIUrl":"https://doi.org/10.5194/soil-11-639-2025","url":null,"abstract":"Abstract. Soil CO2 flux (Fs) is a carbon cycling metric crucial for assessing ecosystem carbon budgets and global warming. However, global Fs datasets often suffer from low temporal-spatial resolution, as well as from spatial bias. Fs observations are severely deficient in tundra and dryland ecosystems due to financial and logistical constraints of current methods for Fs quantification. In this study, we introduce a novel, low-cost sensor system (LC-SS) for long-term, continuous monitoring of soil CO2 concentration and flux. The LC-SS, built from affordable, open-source hardware and software, offers a cost-effective solution (∼ USD 700 and ∼ 50 h for assembling and troubleshooting), accessible to low-budget users, and opens the scope for research with a large number of sensor system replications. The LC-SS was tested over ∼ 6 months in arid soil conditions, where fluxes are small, and accuracy is critical. CO2 concentration and soil temperature were measured at 10 min intervals at depths of 5 and 10 cm. The LC-SS demonstrated high stability during the tested period. Both diurnal and seasonal soil CO2 concentration variabilities were observed, highlighting the system's capability of continuous, long-term, in-situ monitoring of soil CO2 concentration. In addition, Fs was calculated using the measured CO2 concentration via the gradient method and validated with Fs measured by the flux chamber method using the well-accepted LI-COR gas analyzer system. Gradient method Fs was in good agreement with flux chamber Fs (RMSE = 0.15 µmol m−2 s−1), highlighting the potential for alternative or concurrent use of the LC-SS with current methods for Fs estimation – particularly in environments characterized by consistently low soil water content, such as drylands. Leveraging the accuracy and cost-effectiveness of the LC-SS (below 10 % of automated gas analyzer system cost), strategic implementation of LC-SSs could be a promising means to effectively increase the number of measurements, spatially and temporally, ultimately aiding in bridging the gap between global Fs uncertainties and current measurement limitations.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133609","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 : 2025-09-25DOI: 10.5194/soil-11-655-2025
Salar Saeed Dogar, Cosimo Brogi, Dave O'Leary, Ixchel M. Hernández-Ochoa, Marco Donat, Harry Vereecken, Johan Alexander Huisman
Abstract. Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures aboveground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI–NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and aboveground variability. The final EMI–NDVI-derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modeling and soil surveying.
{"title":"Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production","authors":"Salar Saeed Dogar, Cosimo Brogi, Dave O'Leary, Ixchel M. Hernández-Ochoa, Marco Donat, Harry Vereecken, Johan Alexander Huisman","doi":"10.5194/soil-11-655-2025","DOIUrl":"https://doi.org/10.5194/soil-11-655-2025","url":null,"abstract":"Abstract. Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures aboveground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI–NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and aboveground variability. The final EMI–NDVI-derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modeling and soil surveying.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"40 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133610","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 : 2025-09-24DOI: 10.5194/egusphere-2025-3669
David Moreno-Pérez, María-Belén Turrión, Felipe Bravo, Irene Ruano, Celia Herrero de Aza, Frederico Tupinambá-Simões
Abstract. Accurate estimation of soil organic carbon (SOC) in forest ecosystems is essential for quantifying their contribution as carbon sinks and improving management strategies in the face of climate change. The objective of this study was to model SOC in Pinus halepensis Mill. stands using structural metrics derived from LiDAR data from the National Aerial Orthophotography Plan (PNOA). The study area covered 46.8 hectares located in the municipality of Ampudia, Palencia (Spain). To carry out the work, systematic soil sampling and a forest inventory were conducted. LiDAR technology was also applied and 87 structural metrics were obtained. These metrics were integrated with edaphic variables and above-ground biomass data to build predictive models of carbon stock using multivariate regression techniques. Among the models evaluated, the Random Forest algorithm showed the best performance in cross-validation (R² = 0.81; RMSE = 7.73 Mg/ha), demonstrating adequate predictive capacity compared to other models. The proposed approach made it possible to evaluate the potential of LiDAR data from airborne laser scanning (ALS), acquired within the framework of general mapping programmes, as an effective tool for the spatial estimation of SOC. This procedure, validated on an empirical basis, provides a useful methodological basis for advancing in the estimation of SOC through remote sensing, contributing to improve the quantification of soil-related ecosystem services.
{"title":"Estimating soil organic carbon stocks in Pinus halepensis mill. stands using lidar data and field inventory","authors":"David Moreno-Pérez, María-Belén Turrión, Felipe Bravo, Irene Ruano, Celia Herrero de Aza, Frederico Tupinambá-Simões","doi":"10.5194/egusphere-2025-3669","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3669","url":null,"abstract":"<strong>Abstract.</strong> Accurate estimation of soil organic carbon (SOC) in forest ecosystems is essential for quantifying their contribution as carbon sinks and improving management strategies in the face of climate change. The objective of this study was to model SOC in <em>Pinus halepensis</em> Mill. stands using structural metrics derived from LiDAR data from the National Aerial Orthophotography Plan (PNOA). The study area covered 46.8 hectares located in the municipality of Ampudia, Palencia (Spain). To carry out the work, systematic soil sampling and a forest inventory were conducted. LiDAR technology was also applied and 87 structural metrics were obtained. These metrics were integrated with edaphic variables and above-ground biomass data to build predictive models of carbon stock using multivariate regression techniques. Among the models evaluated, the Random Forest algorithm showed the best performance in cross-validation (R² = 0.81; RMSE = 7.73 Mg/ha), demonstrating adequate predictive capacity compared to other models. The proposed approach made it possible to evaluate the potential of LiDAR data from airborne laser scanning (ALS), acquired within the framework of general mapping programmes, as an effective tool for the spatial estimation of SOC. This procedure, validated on an empirical basis, provides a useful methodological basis for advancing in the estimation of SOC through remote sensing, contributing to improve the quantification of soil-related ecosystem services.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127817","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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