Pub Date : 2025-06-25DOI: 10.5194/egusphere-2025-2584
Kyungmin Kim, Maik Geers-Lucas, G. Phillip Robertson, Alexandra N. Kravchenko
Abstract. Plant diversity promotes soil organic carbon (SOC) gains through intricate changes in root-soil interactions and their subsequent influence on soil physical and biological processes. We assessed SOC and pore characteristics of soils under a range of switchgrass-based plant systems, representing a gradient of plant diversity with species richness ranging from 1 to 30 species 12 years after their establishment. We focused on soil biopores as indicators of root activity legacy, measured using X-ray computed micro-tomography scanning, and explored biopore relationships with SOC accumulation. Plant functional richness explained 29 % of bioporosity and 36 % of SOC variation, while bioporosity itself explained 36 % of the variation in SOC. The most diverse plant system (30 species) had the highest SOC, while long-term bare soil fallow and monoculture switchgrass had the lowest. Of particular note was a two-species mixture of switchgrass (Panicum virgatum L.) and ryegrass (Elymus canadensis), which exhibited the highest bioporosity and achieved SOC levels comparable to those of the systems with 6 and 10 plant species, and were inferior only to the system with 30 species. We conclude that plant diversity may enhance SOC through biopore-mediated mechanisms and suggest a potential for identifying specific plant combinations that may be particularly efficient for fostering biopore formation and subsequently SOC sequestration.
{"title":"Soil carbon accrual and biopore formation across a plant diversity gradient","authors":"Kyungmin Kim, Maik Geers-Lucas, G. Phillip Robertson, Alexandra N. Kravchenko","doi":"10.5194/egusphere-2025-2584","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2584","url":null,"abstract":"<strong>Abstract.</strong> Plant diversity promotes soil organic carbon (SOC) gains through intricate changes in root-soil interactions and their subsequent influence on soil physical and biological processes. We assessed SOC and pore characteristics of soils under a range of switchgrass-based plant systems, representing a gradient of plant diversity with species richness ranging from 1 to 30 species 12 years after their establishment. We focused on soil biopores as indicators of root activity legacy, measured using X-ray computed micro-tomography scanning, and explored biopore relationships with SOC accumulation. Plant functional richness explained 29 % of bioporosity and 36 % of SOC variation, while bioporosity itself explained 36 % of the variation in SOC. The most diverse plant system (30 species) had the highest SOC, while long-term bare soil fallow and monoculture switchgrass had the lowest. Of particular note was a two-species mixture of switchgrass (Panicum virgatum L.) and ryegrass (Elymus canadensis), which exhibited the highest bioporosity and achieved SOC levels comparable to those of the systems with 6 and 10 plant species, and were inferior only to the system with 30 species. We conclude that plant diversity may enhance SOC through biopore-mediated mechanisms and suggest a potential for identifying specific plant combinations that may be particularly efficient for fostering biopore formation and subsequently SOC sequestration.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"31 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479065","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 : 2025-06-23DOI: 10.5194/egusphere-2025-2434
Joshua Howard Thompson, Dimitrios Ntarlagiannis, Lee Slater
Abstract. Ground-based electromagnetic induction (EMI) surveys can be used to infer soil properties and (by extension) support nutrient loss risk assessments of agricultural fields. The transport of nutrients from an agricultural field to surrounding surface waters depends on the hydrologic connectivity between the two systems, largely controlled by soil texture. Preexisting soil texture maps and associated soil drainage classifications are often used as proxy information to assess the potential for lateral migration of nutrients in the groundwater; however, the resolution of these maps is inadequate at the scale of individual fields. In this study, we evaluated whether the relationship between EMI data and soil texture was improved by calibrating the apparent electrical conductivity measured by an EMI sensor with a 2D electrical resistivity imaging (ERI) survey. The joint geophysical survey was performed across a ~1-ha field in Princess Anne, Maryland, United States. A calibration-inversion-comparison framework is presented that calibrates the EMI measurements using ERI conductivity models and subsequently inverts the EMI data. A robust validation scheme compared the calibrated and not calibrated EMI conductivity models against grain size, core-scale conductivity measurements and an ERI survey performed roughly 80 m from the first. This study shows that the calibration of EMI data with an ERI profile is significantly improves the quantitative relationship between EMI-derived electrical conductivity and representative soil properties, ensuring a finer-resolution proxy soil map for evaluating subsurface nutrient transport from agricultural fields.
{"title":"Improving the relationship between soil texture and large-scale electromagnetic induction surveys using a direct current electrical resistivity calibration","authors":"Joshua Howard Thompson, Dimitrios Ntarlagiannis, Lee Slater","doi":"10.5194/egusphere-2025-2434","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2434","url":null,"abstract":"<strong>Abstract.</strong> Ground-based electromagnetic induction (EMI) surveys can be used to infer soil properties and (by extension) support nutrient loss risk assessments of agricultural fields. The transport of nutrients from an agricultural field to surrounding surface waters depends on the hydrologic connectivity between the two systems, largely controlled by soil texture. Preexisting soil texture maps and associated soil drainage classifications are often used as proxy information to assess the potential for lateral migration of nutrients in the groundwater; however, the resolution of these maps is inadequate at the scale of individual fields. In this study, we evaluated whether the relationship between EMI data and soil texture was improved by calibrating the apparent electrical conductivity measured by an EMI sensor with a 2D electrical resistivity imaging (ERI) survey. The joint geophysical survey was performed across a ~1-ha field in Princess Anne, Maryland, United States. A calibration-inversion-comparison framework is presented that calibrates the EMI measurements using ERI conductivity models and subsequently inverts the EMI data. A robust validation scheme compared the calibrated and not calibrated EMI conductivity models against grain size, core-scale conductivity measurements and an ERI survey performed roughly 80 m from the first. This study shows that the calibration of EMI data with an ERI profile is significantly improves the quantitative relationship between EMI-derived electrical conductivity and representative soil properties, ensuring a finer-resolution proxy soil map for evaluating subsurface nutrient transport from agricultural fields.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"45 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144371177","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 : 2025-06-19DOI: 10.5194/egusphere-2025-2360
Sunantha Ousaha, Zhenfeng Shao, Zeeshan Afzal
Abstract. Soil bulk density (BD), a key physical property affecting soil compaction, porosity, and carbon stock estimation, exhibits considerable spatial and temporal variability. However, current BD estimation methods especially traditional pedotransfer functions (PTFs) are inherently static and not designed for temporal analysis. This presents a significant limitation for soil monitoring across large and heterogeneous regions. In this study, we developed a machine learning (ML) approach integrated with remote sensing data to map and monitor BD across Thailand from 2004 to 2009 at national scale. We used multispectral indices, topographic variables, climate data, and organic carbon content to train six ML models: Artificial Neural Networks (ANN), Deep Neural Networks, Random Forest, Support Vector Regression, XGBoost, and LightGBM. Model performance was evaluated using in-situ BD measurements from 236 soil samples collected in 2004. For benchmarking purposes, 76 published PTFs were also assessed on the same dataset. Results showed that the ANN model achieved the highest prediction accuracy (R2 = 0.986; RMSE = 0.017 g cm-3), outperforming both other ML models and all PTFs. Temporal analysis using the ANN model revealed a 7.27 % increase in mean BD and a 41.23 % reduction in standard deviation between 2004 and 2009, indicating increased soil compaction and reduced variability. Feature importance analysis identified organic carbon, vegetation indices, slope, and temperature as the most influential variables. The resulting high-resolution BD maps captured national-scale spatial and temporal trends and provide a robust foundation for soil quality monitoring, carbon accounting, and sustainable land use planning in tropical agroecosystems.
摘要。土壤容重（BD）是影响土壤压实度、孔隙度和碳储量估算的关键物理性质，具有显著的时空变异性。然而，目前的BD估计方法，特别是传统的土壤传递函数（ptf），本质上是静态的，不适合时间分析。这对跨大型异质区域的土壤监测提出了重大限制。在这项研究中，我们开发了一种结合遥感数据的机器学习（ML）方法，在2004年至2009年期间在泰国全国范围内绘制和监测BD。我们使用多光谱指数、地形变量、气候数据和有机碳含量来训练6个ML模型：人工神经网络（ANN）、深度神经网络、随机森林、支持向量回归、XGBoost和LightGBM。利用2004年收集的236个土壤样品的原位BD测量来评估模型的性能。为了进行基准测试，还在同一数据集上评估了76个已发表的ptf。结果表明，人工神经网络模型预测准确率最高(R2 = 0.986；RMSE = 0.017 g cm-3)，优于其他ML模型和所有ptf。利用人工神经网络模型进行的时间分析显示，2004年至2009年间，平均BD增加了7.27%，标准偏差减少了41.23%，表明土壤压实度增加，变异性减少。特征重要性分析发现有机碳、植被指数、坡度和温度是影响最大的变量。由此产生的高分辨率BD地图捕捉了国家尺度的时空趋势，为热带农业生态系统的土壤质量监测、碳核算和可持续土地利用规划提供了坚实的基础。
{"title":"Reducing Temporal Uncertainty in Soil Bulk Density Estimation Using Remote Sensing and Machine Learning Approaches","authors":"Sunantha Ousaha, Zhenfeng Shao, Zeeshan Afzal","doi":"10.5194/egusphere-2025-2360","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2360","url":null,"abstract":"<strong>Abstract.</strong> Soil bulk density (BD), a key physical property affecting soil compaction, porosity, and carbon stock estimation, exhibits considerable spatial and temporal variability. However, current BD estimation methods especially traditional pedotransfer functions (PTFs) are inherently static and not designed for temporal analysis. This presents a significant limitation for soil monitoring across large and heterogeneous regions. In this study, we developed a machine learning (ML) approach integrated with remote sensing data to map and monitor BD across Thailand from 2004 to 2009 at national scale. We used multispectral indices, topographic variables, climate data, and organic carbon content to train six ML models: Artificial Neural Networks (ANN), Deep Neural Networks, Random Forest, Support Vector Regression, XGBoost, and LightGBM. Model performance was evaluated using in-situ BD measurements from 236 soil samples collected in 2004. For benchmarking purposes, 76 published PTFs were also assessed on the same dataset. Results showed that the ANN model achieved the highest prediction accuracy (R<sup>2</sup> = 0.986; RMSE = 0.017 g cm<sup>-3</sup>), outperforming both other ML models and all PTFs. Temporal analysis using the ANN model revealed a 7.27 % increase in mean BD and a 41.23 % reduction in standard deviation between 2004 and 2009, indicating increased soil compaction and reduced variability. Feature importance analysis identified organic carbon, vegetation indices, slope, and temperature as the most influential variables. The resulting high-resolution BD maps captured national-scale spatial and temporal trends and provide a robust foundation for soil quality monitoring, carbon accounting, and sustainable land use planning in tropical agroecosystems.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"6 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320102","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 : 2025-06-18DOI: 10.5194/egusphere-2025-2592
Sana Boubehziz, Emily C. Cooledge, David Robert Chadwick, Vidal Barrón, Antonio Rafael Sánchez-Rodríguez, Davey Leonard Jones
Abstract. Mediterranean agroecosystems are vulnerable to extreme heat-stress, especially because of their low organic matter content. Bioamendments may enhance soil nutrient content and microbial resilience to heatwaves. However, their effectiveness under these conditions is still unclear. We investigated the effect of bioamendments (composted olive mill pomace, biosolids and solid urban residue) and a conventional fertiliser (diammonium phosphate) on microbial carbon use efficiency (CUE), and soil biogeochemistry in two different soils, a calcareous Vertisol and a non-calcareous Inceptisol, with low P availability, subjected to extreme heat-stress. We conducted warming experiments (20, 30, 40, or 50 °C), to monitor 14C-glucose mineralization and to evaluate modifications in soil biochemical properties. As result of warming, both soils microorganisms exhibited thermotolerance until 40 °C, with a critical shift in microbial respiration observed at 50 °C. Consequently, microbial CUE, which was a function of the bioamendments and soil, significantly declined from 0.47–0.65 at 20 °C to 0.27–0.45 at 50 °C (p < 0.05), with the control decreasing by 0.010 ± 0.001 °C-1 (Vertisol) and 0.007 ± 0.001 °C-1 (Inceptisol). Moreover, composted olive mill pomace-treated soils enhanced the resistance of soils to heat stress as they produced the highest microbial CUE at 40 °C in the Inceptisol and 50 °C in both soils (0.43 ± 0.02 Inceptisol vs. 0.45 ± 0.02 Vertisol). Soil biogeochemistry varied with temperature and treatment, while available P in soils treated with diammonium phosphate was reduced with temperature in both soils, and available P added with bioamendments was not affected by temperature but was increased with biosolids for all temperatures in the Inceptisol. In conclusion, organic matter rich bioamendments (composted olive mill pomace) may enhance the resistance of Mediterranean agricultural soils subjected to extreme heat-stress events (50 °C).
{"title":"Do composted bioamendments enhance the resistance of Mediterranean agricultural soils and their microbial carbon use efficiency to extreme heat-stress events?","authors":"Sana Boubehziz, Emily C. Cooledge, David Robert Chadwick, Vidal Barrón, Antonio Rafael Sánchez-Rodríguez, Davey Leonard Jones","doi":"10.5194/egusphere-2025-2592","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2592","url":null,"abstract":"<strong>Abstract.</strong> Mediterranean agroecosystems are vulnerable to extreme heat-stress, especially because of their low organic matter content. Bioamendments may enhance soil nutrient content and microbial resilience to heatwaves. However, their effectiveness under these conditions is still unclear. We investigated the effect of bioamendments (composted olive mill pomace, biosolids and solid urban residue) and a conventional fertiliser (diammonium phosphate) on microbial carbon use efficiency (CUE), and soil biogeochemistry in two different soils, a calcareous Vertisol and a non-calcareous Inceptisol, with low P availability, subjected to extreme heat-stress. We conducted warming experiments (20, 30, 40, or 50 °C), to monitor <sup>14</sup>C-glucose mineralization and to evaluate modifications in soil biochemical properties. As result of warming, both soils microorganisms exhibited thermotolerance until 40 °C, with a critical shift in microbial respiration observed at 50 °C. Consequently, microbial CUE, which was a function of the bioamendments and soil, significantly declined from 0.47–0.65 at 20 °C to 0.27–0.45 at 50 °C (<em>p</em> < 0.05), with the control decreasing by 0.010 ± 0.001 °C<sup>-1</sup> (Vertisol) and 0.007 ± 0.001 °C<sup>-1</sup> (Inceptisol). Moreover, composted olive mill pomace-treated soils enhanced the resistance of soils to heat stress as they produced the highest microbial CUE at 40 °C in the Inceptisol and 50 °C in both soils (0.43 ± 0.02 Inceptisol vs. 0.45 ± 0.02 Vertisol). Soil biogeochemistry varied with temperature and treatment, while available P in soils treated with diammonium phosphate was reduced with temperature in both soils, and available P added with bioamendments was not affected by temperature but was increased with biosolids for all temperatures in the Inceptisol. In conclusion, organic matter rich bioamendments (composted olive mill pomace) may enhance the resistance of Mediterranean agricultural soils subjected to extreme heat-stress events (50 °C).","PeriodicalId":48610,"journal":{"name":"Soil","volume":"11 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311396","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 : 2025-06-16DOI: 10.5194/soil-11-457-2025
Jun Murase, Kannika Sajjaphan, Chatprawee Dechjiraratthanasiri, Ornuma Duangngam, Rawiwan Chotiphan, Wutthida Rattanapichai, Wakana Azuma, Makoto Shibata, Poonpipope Kasemsap, Daniel Epron
Abstract. Forest soils, as crucial sinks for atmospheric methane in terrestrial ecosystems, are significantly impacted by changes in ecosystem dynamics due to deforestation and agricultural practices. This study investigated the methane oxidation potential of rubber plantation soils in Thailand, focusing on the effect of fertilization. The methane oxidation activity of the topsoils (0–10 cm) in the dry season was extremely low and increased slightly in the wet season, with lower activity for higher fertilization levels. The methane oxidation potential of the topsoil was too low to explain the in situ methane uptake. Soils below 10 cm depth in unfertilized rubber plantations showed higher activity than the surface soils, and methane oxidation was detected down to, at least, 60 cm depth. In contrast, soils under the high-fertilization treatment exhibited similarly low activity of methane oxidation up to 60 cm depth compared to surface soils during both dry and wet seasons, indicating that fertilization of para rubber plantations negatively impacts the methane oxidation potential of the soils over the deep profile without recovery in the dry (off-harvesting) season with no fertilization. Methane uptake per area, estimated by integrating the methane oxidation potentials of soil layers, was comparable to the field flux data, suggesting that methane oxidation in the soil predominantly occurs at depths below the surface layer. These findings have significant implications for understanding the environmental impacts of tropical forest land uses on methane dynamics and underscore the importance of understanding methane oxidation processes in soils.
{"title":"Methane oxidation potential of soils in a rubber plantation in Thailand affected by fertilization","authors":"Jun Murase, Kannika Sajjaphan, Chatprawee Dechjiraratthanasiri, Ornuma Duangngam, Rawiwan Chotiphan, Wutthida Rattanapichai, Wakana Azuma, Makoto Shibata, Poonpipope Kasemsap, Daniel Epron","doi":"10.5194/soil-11-457-2025","DOIUrl":"https://doi.org/10.5194/soil-11-457-2025","url":null,"abstract":"Abstract. Forest soils, as crucial sinks for atmospheric methane in terrestrial ecosystems, are significantly impacted by changes in ecosystem dynamics due to deforestation and agricultural practices. This study investigated the methane oxidation potential of rubber plantation soils in Thailand, focusing on the effect of fertilization. The methane oxidation activity of the topsoils (0–10 cm) in the dry season was extremely low and increased slightly in the wet season, with lower activity for higher fertilization levels. The methane oxidation potential of the topsoil was too low to explain the in situ methane uptake. Soils below 10 cm depth in unfertilized rubber plantations showed higher activity than the surface soils, and methane oxidation was detected down to, at least, 60 cm depth. In contrast, soils under the high-fertilization treatment exhibited similarly low activity of methane oxidation up to 60 cm depth compared to surface soils during both dry and wet seasons, indicating that fertilization of para rubber plantations negatively impacts the methane oxidation potential of the soils over the deep profile without recovery in the dry (off-harvesting) season with no fertilization. Methane uptake per area, estimated by integrating the methane oxidation potentials of soil layers, was comparable to the field flux data, suggesting that methane oxidation in the soil predominantly occurs at depths below the surface layer. These findings have significant implications for understanding the environmental impacts of tropical forest land uses on methane dynamics and underscore the importance of understanding methane oxidation processes in soils.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"3 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296153","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. Soils play a highly dynamic role in the carbon cycle, by acting as either a carbon source or sink. Despite their importance in the global carbon cycle, uncertainties surrounding soil-atmosphere interactions remain, due to the many mechanisms that underlie soil carbon dynamics. One of the main mechanisms determining the decomposition of organic C in soil is the access microbial decomposers have to substrates. While not yet formally tested, there is evidence to support the idea that microbial decomposer access to substrates is diffusion-limited. This is underlined by soil respiration rates being strongly dependent on water availability. In recent years, non-destructive geophysical tools, including electrical conductivity measurements, have been used to determine the water content of soils and connectedness of the water phase in the soil pore network. As both respiration and electrical conductivity may depend on water availability and connectivity, our study aimed to determine whether electrical conductivity measurements could be used as a proxy of diffusion-limited microbial activity in soils. This was done by measuring electrical conductivity and respiration rates at different matric potentials. Sieved and undisturbed top and subsoil samples taken from conventional tillage and conservation agriculture management plots were used. Our results revealed an initial increase and consecutive drop in soil respiration associated with a decrease in the matric potential. The electrical conductivity followed a similar decrease throughout the experimental range and these showed a significant non-linear relationship. These results thus suggest that both measured variables depend on the connectedness of the aqueous phase and suggest that they could be used as groundwork for further investigations into soil respiration and electrical conductivity dynamics.
{"title":"Electrical conductivity measurements as a proxy for diffusion-limited microbial activity in soils","authors":"Orsolya Fülöp, Naoise Nunan, Mamadou Gueye, Damien Jougnot","doi":"10.5194/egusphere-2025-1730","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1730","url":null,"abstract":"<strong>Abstract.</strong> Soils play a highly dynamic role in the carbon cycle, by acting as either a carbon source or sink. Despite their importance in the global carbon cycle, uncertainties surrounding soil-atmosphere interactions remain, due to the many mechanisms that underlie soil carbon dynamics. One of the main mechanisms determining the decomposition of organic C in soil is the access microbial decomposers have to substrates. While not yet formally tested, there is evidence to support the idea that microbial decomposer access to substrates is diffusion-limited. This is underlined by soil respiration rates being strongly dependent on water availability. In recent years, non-destructive geophysical tools, including electrical conductivity measurements, have been used to determine the water content of soils and connectedness of the water phase in the soil pore network. As both respiration and electrical conductivity may depend on water availability and connectivity, our study aimed to determine whether electrical conductivity measurements could be used as a proxy of diffusion-limited microbial activity in soils. This was done by measuring electrical conductivity and respiration rates at different matric potentials. Sieved and undisturbed top and subsoil samples taken from conventional tillage and conservation agriculture management plots were used. Our results revealed an initial increase and consecutive drop in soil respiration associated with a decrease in the matric potential. The electrical conductivity followed a similar decrease throughout the experimental range and these showed a significant non-linear relationship. These results thus suggest that both measured variables depend on the connectedness of the aqueous phase and suggest that they could be used as groundwork for further investigations into soil respiration and electrical conductivity dynamics.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"195 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296154","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 : 2025-06-13DOI: 10.5194/soil-11-435-2025
Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Sander Bombeke, Evi Matthyssen, Anne Waverijn, Jan Diels
Abstract. Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this setup across different fields and soil types. In this study, a pooled error covariance matrix was derived using soil moisture sensor measurements from three TEROS 10 (Meter Group, Inc., USA) sensors per MZ and soil moisture sampling campaigns conducted over three growing seasons, covering 93 cropping cycles in agricultural fields with diverse soil textures in Belgium. The MZ soil moisture estimated from a composite of nine soil samples with a small standard error (0.0038 m3 m−3) was considered the “true” MZ soil moisture. Based on these measurement data, we established a pooled linear recalibration of the TEROS 10 manufacturer's sensor calibration function. Then, for each individual sensor as well as for each MZ, we identified systematic offsets and temporally varying residual deviations between the calibrated sensor data and sampling data. Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. Since a systematic offset persists over time, it contributes to the temporal covariance of sensor observational errors. Therefore, we estimated the temporal covariance of observational errors of the individual and the MZ-averaged sensor measurements from the variance of the systematic offsets across all sensors and MZ averages, while the random error variance was derived from the variance of the pooled residual deviations. The total error variance was then obtained as the sum of these two components. Due to spatial soil moisture correlation, the variance and temporal covariance of MZ-averaged sensor observational errors could not be derived accurately from the individual sensor error variances and temporal covariances, assuming that the individual observational errors of the three sensors in a MZ were not correlated with each other. The pooled error covariance matrix of the MZ-averaged soil moisture measurements indicated a significant autocorrelation of sensor observational errors of 0.518, as the systematic error standard deviation (σα‾= 0.033 m3 m−3) was similar to the random error standard deviation (σϵ‾= 0.032 m3 m−3). To illustrate the impact of error covariance in probabilistic soil hydrological modeling, a case study was presented incorporating the pooled error covariance matrix in a Bayesian inverse modeling framework. These results demonstrate that the common assump
{"title":"Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders","authors":"Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Sander Bombeke, Evi Matthyssen, Anne Waverijn, Jan Diels","doi":"10.5194/soil-11-435-2025","DOIUrl":"https://doi.org/10.5194/soil-11-435-2025","url":null,"abstract":"Abstract. Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this setup across different fields and soil types. In this study, a pooled error covariance matrix was derived using soil moisture sensor measurements from three TEROS 10 (Meter Group, Inc., USA) sensors per MZ and soil moisture sampling campaigns conducted over three growing seasons, covering 93 cropping cycles in agricultural fields with diverse soil textures in Belgium. The MZ soil moisture estimated from a composite of nine soil samples with a small standard error (0.0038 m3 m−3) was considered the “true” MZ soil moisture. Based on these measurement data, we established a pooled linear recalibration of the TEROS 10 manufacturer's sensor calibration function. Then, for each individual sensor as well as for each MZ, we identified systematic offsets and temporally varying residual deviations between the calibrated sensor data and sampling data. Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. Since a systematic offset persists over time, it contributes to the temporal covariance of sensor observational errors. Therefore, we estimated the temporal covariance of observational errors of the individual and the MZ-averaged sensor measurements from the variance of the systematic offsets across all sensors and MZ averages, while the random error variance was derived from the variance of the pooled residual deviations. The total error variance was then obtained as the sum of these two components. Due to spatial soil moisture correlation, the variance and temporal covariance of MZ-averaged sensor observational errors could not be derived accurately from the individual sensor error variances and temporal covariances, assuming that the individual observational errors of the three sensors in a MZ were not correlated with each other. The pooled error covariance matrix of the MZ-averaged soil moisture measurements indicated a significant autocorrelation of sensor observational errors of 0.518, as the systematic error standard deviation (σα‾= 0.033 m3 m−3) was similar to the random error standard deviation (σϵ‾= 0.032 m3 m−3). To illustrate the impact of error covariance in probabilistic soil hydrological modeling, a case study was presented incorporating the pooled error covariance matrix in a Bayesian inverse modeling framework. These results demonstrate that the common assump","PeriodicalId":48610,"journal":{"name":"Soil","volume":"173 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278268","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 : 2025-06-12DOI: 10.5194/soil-11-413-2025
Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomáš Laburda, Petr Kavka
Abstract. Future global change is likely to give rise to novel combinations of the factors which enhance or inhibit soil erosion by water. Thus, there is a need for erosion models, necessarily process-focused ones, which are able to reliably represent the rates and extents of soil erosion under unprecedented circumstances. The process-focused cellular automaton erosion model RillGrow is, given initial soil surface microtopography for a plot-sized area, able to predict the emergent patterns produced by runoff and erosion. This study explores the use of structure-from-motion photogrammetry as a means to calibrate and evaluate this model by capturing detailed, time-lapsed data for soil surface height changes during erosion events. Temporally high-resolution monitoring capabilities (i.e. 3D models of elevation change at 0.1 Hz frequency) permit the evaluation of erosion models in terms of the sequence of the formation of erosional features. Here, multiple objective functions using three different spatio-temporal averaging approaches are assessed for their suitability in calibrating and evaluating the model's output. We used two sets of data from field- and laboratory-based rainfall simulation experiments lasting 90 and 30 min, respectively. By integrating 10 different calibration metrics, the outputs of 2000 and 2400 RillGrow runs for, respectively, the field and laboratory experiments were analysed. No single model run was able to adequately replicate all aspects of either the field or the laboratory experiments. The multiple objective function approaches highlight different aspects of model performance, indicating that no single objective function can capture the full complexity of erosion processes. They also highlight different strengths and weaknesses of the model. Depending on the focus of the evaluation, an ensemble of objective functions may not always be necessary. These results underscore the need for more nuanced evaluation of erosion models, e.g. by incorporating spatial-pattern comparison techniques to provide a deeper understanding of the model's capabilities. Such calibrations are an essential complement to the development of erosion models which are able to forecast the impacts of future global change. For the first time, we use data with a very high spatio-temporal resolution to calibrate a soil erosion model.
{"title":"Using 3D observations with high spatio-temporal resolution to calibrate and evaluate a process-focused cellular automaton model of soil erosion by water","authors":"Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomáš Laburda, Petr Kavka","doi":"10.5194/soil-11-413-2025","DOIUrl":"https://doi.org/10.5194/soil-11-413-2025","url":null,"abstract":"Abstract. Future global change is likely to give rise to novel combinations of the factors which enhance or inhibit soil erosion by water. Thus, there is a need for erosion models, necessarily process-focused ones, which are able to reliably represent the rates and extents of soil erosion under unprecedented circumstances. The process-focused cellular automaton erosion model RillGrow is, given initial soil surface microtopography for a plot-sized area, able to predict the emergent patterns produced by runoff and erosion. This study explores the use of structure-from-motion photogrammetry as a means to calibrate and evaluate this model by capturing detailed, time-lapsed data for soil surface height changes during erosion events. Temporally high-resolution monitoring capabilities (i.e. 3D models of elevation change at 0.1 Hz frequency) permit the evaluation of erosion models in terms of the sequence of the formation of erosional features. Here, multiple objective functions using three different spatio-temporal averaging approaches are assessed for their suitability in calibrating and evaluating the model's output. We used two sets of data from field- and laboratory-based rainfall simulation experiments lasting 90 and 30 min, respectively. By integrating 10 different calibration metrics, the outputs of 2000 and 2400 RillGrow runs for, respectively, the field and laboratory experiments were analysed. No single model run was able to adequately replicate all aspects of either the field or the laboratory experiments. The multiple objective function approaches highlight different aspects of model performance, indicating that no single objective function can capture the full complexity of erosion processes. They also highlight different strengths and weaknesses of the model. Depending on the focus of the evaluation, an ensemble of objective functions may not always be necessary. These results underscore the need for more nuanced evaluation of erosion models, e.g. by incorporating spatial-pattern comparison techniques to provide a deeper understanding of the model's capabilities. Such calibrations are an essential complement to the development of erosion models which are able to forecast the impacts of future global change. For the first time, we use data with a very high spatio-temporal resolution to calibrate a soil erosion model.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"22 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268643","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. This study investigates the intricate relationship between soil properties and water-related processes, with a focus on their collective impact on ecosystem service provision, particularly water regulation. Conducted in three diverse regions Marchfeld (Austria), Bologna (North Italy) and Rmel (Tunisia), the research aims to identify key soil properties that influence water infiltration (INF), groundwater recharge (GWR), and crop water stress indexes (CWSI). Key soil characteristics such as saturated hydraulic conductivity (KS ), available water content (AWC), bulk density (BD), saturated water content (θs ), organic matter (OM), clay content and soil depth were analyzed for their role in regulating water movement and the overall hydrological balance. Pairwise correlation and multiple linear regression analyses were used to assess the interactions among soil water balance processes and soil properties. The results reveal significant variations between regions in terms of the factors that control infiltration, groundwater recharge, and CWSI. For example, in Marchfeld infiltration showed a strong positive correlation with BD (r = 0.74, p < 0.001), while CWSI had the most significant negative correlation with soil depth (r = -0.35, p < 0.001). Futhermore, multiple linear regression models were developed to assess the relevance of the different soil properties and of their interactions on the components of the soil water balance. As an example, in Marchfeld, the model for infiltration (r = 0.79, p < 0.001) was highly predictive, incorporating Clay, OM and soil depth. These results emphasize the critical role of key soil properties KS , AWC, BD, OM, clay content, θs and soil depth in controlling soil water processes. The study highlights the value of using these properties in predictive models to inform water management practices to optimize crop performance and soil conservation in different agricultural settings.
摘要。本研究探讨了土壤性质与水相关过程之间的复杂关系，重点研究了它们对生态系统服务提供，特别是水调节的集体影响。该研究在奥地利的Marchfeld、意大利北部的Bologna和突尼斯的Rmel三个不同的地区进行，旨在确定影响水入渗（INF）、地下水补给（GWR）和作物水分胁迫指数（CWSI）的关键土壤特性。分析了饱和导水率（KS）、有效含水量（AWC）、容重（BD）、饱和含水量（θs）、有机质（OM）、粘土含量和土壤深度等关键土壤特征对水分运动和整体水文平衡的调节作用。采用两两相关分析和多元线性回归分析评价了土壤水分平衡过程与土壤性质之间的相互作用。结果表明，控制入渗、地下水补给和CWSI的因素在区域间存在显著差异。例如，Marchfeld浸润与BD呈强正相关(r = 0.74, p <；0.001)，而CWSI与土壤深度负相关最为显著(r = -0.35, p <；0.001)。此外，还建立了多元线性回归模型，以评估不同土壤性质及其相互作用与土壤水分平衡成分的相关性。例如，在Marchfeld中，入渗模型(r = 0.79, p <；0.001)具有高度预测性，包括粘土、OM和土壤深度。这些结果强调了关键土壤性质KS、AWC、BD、OM、粘粒含量、θs和土壤深度在控制土壤水分过程中的关键作用。该研究强调了在预测模型中利用这些特性为水管理实践提供信息的价值，从而优化不同农业环境下的作物性能和土壤保持。
{"title":"Soil physico-chemical indicators for ecosystem services: a focus on water regulation","authors":"Binyam Alemu Yosef, Angelo Basile, Antonio Coppola, Fabrizio Ungaro, Marialaura Bancheri","doi":"10.5194/egusphere-2025-1927","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1927","url":null,"abstract":"<strong>Abstract.</strong> This study investigates the intricate relationship between soil properties and water-related processes, with a focus on their collective impact on ecosystem service provision, particularly water regulation. Conducted in three diverse regions Marchfeld (Austria), Bologna (North Italy) and Rmel (Tunisia), the research aims to identify key soil properties that influence water infiltration (INF), groundwater recharge (GWR), and crop water stress indexes (CWSI). Key soil characteristics such as saturated hydraulic conductivity (<em>K<sub>S </sub></em>), available water content (AWC), bulk density (BD), saturated water content (<em>θ<sub>s </sub></em>)<em>,</em> organic matter (OM), clay content and soil depth were analyzed for their role in regulating water movement and the overall hydrological balance. Pairwise correlation and multiple linear regression analyses were used to assess the interactions among soil water balance processes and soil properties. The results reveal significant variations between regions in terms of the factors that control infiltration, groundwater recharge, and CWSI. For example, in Marchfeld infiltration showed a strong positive correlation with BD (r = 0.74, p < 0.001), while CWSI had the most significant negative correlation with soil depth (r = -0.35, p < 0.001). Futhermore, multiple linear regression models were developed to assess the relevance of the different soil properties and of their interactions on the components of the soil water balance. As an example, in Marchfeld, the model for infiltration (r = 0.79, p < 0.001) was highly predictive, incorporating Clay, OM and soil depth. These results emphasize the critical role of key soil properties <em>K<sub>S </sub></em>, AWC, BD, OM, clay content, <em>θ<sub>s</sub></em> and soil depth in controlling soil water processes. The study highlights the value of using these properties in predictive models to inform water management practices to optimize crop performance and soil conservation in different agricultural settings.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"22 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268644","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 : 2025-06-05DOI: 10.5194/egusphere-2025-2078
Vasiliki Barou, Jorge Prieto-Rubio, Mario Zabal-Aguirre, Javier Parladé, Ana Rincón
Abstract. Black truffle (Tuber melanosporum Vittad.), a valued edible fungus, has been thoroughly studied for its ability to modify soil conditions and influence microbial communities in its environment as it dominates the space. While direct associations of black truffle with microbial guilds offer insights into its competitiveness, the role of these interactions in ecosystem functions remain unclear. This study aims to assess the patterns of soil fungal community within the black truffle brûlés across different producing systems (managed vs wild) and seasons (autumn vs spring), to determine the role of T. melanosporum in the structure of the fungal networks, and to identify the contribution of main fungal guilds to soil functioning in these systems. To address this, network analysis was employed to construct the fungal co-occurrence networks in the brûlés of black truffle plantations and wild production areas in forests. Black truffle plantations showed greater fungal homogeneity, network complexity and links compared to forests, indicating enhanced stability, possibly due to reduced plant diversity and uniform conditions, while seasonality did not affect the fungal network structure. Despite its dominance in the brûlés, T. melanosporum was not a hub species in neither truffle-producing systems and exhibited few interactions, mainly with saprotrophs and plant pathogens. Saprotrophic fungi, with partial contributions from ectomycorrhizal and plant pathogen guilds, were the key contributors to carbon and nutrient cycling in both systems. These results improve our understanding of the ecology, biodiversity and functioning of black truffle-dominated soils that could enable more effective management strategies in black truffle plantations.
{"title":"Managed black truffle-producing systems have greater soil fungal network complexity and distinct functional roles compared to wild systems","authors":"Vasiliki Barou, Jorge Prieto-Rubio, Mario Zabal-Aguirre, Javier Parladé, Ana Rincón","doi":"10.5194/egusphere-2025-2078","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2078","url":null,"abstract":"<strong>Abstract.</strong> Black truffle (<em>Tuber melanosporum</em> Vittad.), a valued edible fungus, has been thoroughly studied for its ability to modify soil conditions and influence microbial communities in its environment as it dominates the space. While direct associations of black truffle with microbial guilds offer insights into its competitiveness, the role of these interactions in ecosystem functions remain unclear. This study aims to assess the patterns of soil fungal community within the black truffle brûlés across different producing systems (managed <em>vs</em> wild) and seasons (autumn <em>vs</em> spring), to determine the role <em>of T. melanosporum</em> in the structure of the fungal networks, and to identify the contribution of main fungal guilds to soil functioning in these systems. To address this, network analysis was employed to construct the fungal co-occurrence networks in the brûlés of black truffle plantations and wild production areas in forests. Black truffle plantations showed greater fungal homogeneity, network complexity and links compared to forests, indicating enhanced stability, possibly due to reduced plant diversity and uniform conditions, while seasonality did not affect the fungal network structure. Despite its dominance in the brûlés, <em>T. melanosporum</em> was not a hub species in neither truffle-producing systems and exhibited few interactions, mainly with saprotrophs and plant pathogens. Saprotrophic fungi, with partial contributions from ectomycorrhizal and plant pathogen guilds, were the key contributors to carbon and nutrient cycling in both systems. These results improve our understanding of the ecology, biodiversity and functioning of black truffle-dominated soils that could enable more effective management strategies in black truffle plantations.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"45 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218900","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：