Pub Date : 2024-12-28DOI: 10.1016/j.geoderma.2024.117156
Yan Zhang, Ajuan Zhang, Zheng Zhou, Ting-Wen Chen, Xueyong Pang, Stefan Scheu
Plant succession dramatically alters both aboveground vegetation and belowground conditions, impacting the organisms residing in the soil. However, the extent to which the taxonomic and functional community composition of soil animals is shaped by the same biotic and environmental factors and their relative importance remains unclear. Here, we considered plant community characteristics, abiotic soil factors, and food-web factors as potential drivers for the taxonomic and functional community composition (based on life forms) of Collembola during plant succession in the subalpine region of southwest China. Our results show that Collembola abundance and richness were lower in grassland, shrubland, and primary forest compared to secondary forest (birch forest). Temperature and moisture were identified as pivotal factors influencing Collembola fitness in grassland, while soil pH was a key factor in primary forest. Overall, abiotic soil factors (i.e., pH, C/N, and temperature), played predominant roles in shaping both the taxonomic and functional community composition of Collembola. Plant community characteristics (i.e., plant richness and litter biomass) were subdominant drivers in structuring functional community composition. By contrast, food-web factors (i.e., fungal biomass and fungi-to-bacteria ratio as bottom-up factors, and predatory mites as top-down factor) exerted a minor impact. Further, functional community composition was generally more closely related to variations in soil abiotic factors and plant community traits than taxonomic community composition. These findings highlight the priority importance of soil abiotic factors over plant community characteristics and food web factors in structuring soil mesofauna communities and emphasize the importance of trait-based approaches for understanding the mechanisms underlying soil animal communities.
{"title":"Driving mechanisms of taxonomic and functional community composition of Collembola during subalpine succession","authors":"Yan Zhang, Ajuan Zhang, Zheng Zhou, Ting-Wen Chen, Xueyong Pang, Stefan Scheu","doi":"10.1016/j.geoderma.2024.117156","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117156","url":null,"abstract":"Plant succession dramatically alters both aboveground vegetation and belowground conditions, impacting the organisms residing in the soil. However, the extent to which the taxonomic and functional community composition of soil animals is shaped by the same biotic and environmental factors and their relative importance remains unclear. Here, we considered plant community characteristics, abiotic soil factors, and food-web factors as potential drivers for the taxonomic and functional community composition (based on life forms) of Collembola during plant succession in the subalpine region of southwest China. Our results show that Collembola abundance and richness were lower in grassland, shrubland, and primary forest compared to secondary forest (birch forest). Temperature and moisture were identified as pivotal factors influencing Collembola fitness in grassland, while soil pH was a key factor in primary forest. Overall, abiotic soil factors (i.e., pH, C/N, and temperature), played predominant roles in shaping both the taxonomic and functional community composition of Collembola. Plant community characteristics (i.e., plant richness and litter biomass) were subdominant drivers in structuring functional community composition. By contrast, food-web factors (i.e., fungal biomass and fungi-to-bacteria ratio as bottom-up factors, and predatory mites as top-down factor) exerted a minor impact. Further, functional community composition was generally more closely related to variations in soil abiotic factors and plant community traits than taxonomic community composition. These findings highlight the priority importance of soil abiotic factors over plant community characteristics and food web factors in structuring soil mesofauna communities and emphasize the importance of trait-based approaches for understanding the mechanisms underlying soil animal communities.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"32 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nitrous oxide (N2O) emissions from soil are partly controlled by aeration and gas transfer in soil, and thus by soil structure. The intensity of N2O emissions is usually expressed according to the water filled pore space (WFPS), calculated using the soil bulk density. These factors, even if they describe the soil structure and the water proportion in the porous network, do not inform about porous network characteristics among scales and their connectivity. The aim of this work was therefore to determine (1) to what extent the soil structure of an agricultural soil controlled N2O emissions during a snap-shot campaign and (2) which metric of gas transfer or soil structure was the most appropriate to describe the N2O emission variability at field scale. N2O emissions were measured with a mobile chamber on a maize crop after fertilization with several soil management practices resulting in four soil states (strip-till versus tillage, compacted soil versus uncompacted) with contrasting soil structure. Soil cylinders and bulk soil were sampled from 24 plots exhibiting a strong gradient in N2O emissions. Classical soil physical and chemical properties were measured, including soil bulk density and water filled pore space. Soil structure also was characterized quantitatively by X-ray tomography at meso and macro scales, and indirectly by gas transfer parameters. Clear differences were observed between low and high emission plots in terms of soil structure, soil temperature and nitrate concentration. However, soil structure appeared more strongly connected to N2O emissions, and some thresholds on soil structural indicators were relevant to disentangle high and low N2O fluxes. Some structural indicators at both scales (e.g. porosity, surface density) and gas transfer parameters (relative gas diffusivity, air permeability) were good descriptors of the observed N2O fluxes. Nevertheless, the gas transfer parameters can be easily measured over a short period of time, whereas the soil structure indicators determined from 3D images require an acquisition and a processing phase that can be time consuming. A good compromise to evaluate the field N2O flux potential from an easy measure would be to evaluate the relative gas diffusivity, which directly controls the diffusion of oxygen in soil and thereby the microbial processes of N2O production.
{"title":"What is the most relevant soil structure parameter to describe field-measured N2O emissions?","authors":"Emile Maillet, Agnès Grossel, Isabelle Cousin, Laurent Arbaret, Lionel Cottenot, Marine Lacoste","doi":"10.1016/j.geoderma.2024.117155","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117155","url":null,"abstract":"Nitrous oxide (N<ce:inf loc=\"post\">2</ce:inf>O) emissions from soil are partly controlled by aeration and gas transfer in soil, and thus by soil structure. The intensity of N<ce:inf loc=\"post\">2</ce:inf>O emissions is usually expressed according to the water filled pore space (WFPS), calculated using the soil bulk density. These factors, even if they describe the soil structure and the water proportion in the porous network, do not inform about porous network characteristics among scales and their connectivity. The aim of this work was therefore to determine (1) to what extent the soil structure of an agricultural soil controlled N<ce:inf loc=\"post\">2</ce:inf>O emissions during a snap-shot campaign and (2) which metric of gas transfer or soil structure was the most appropriate to describe the N<ce:inf loc=\"post\">2</ce:inf>O emission variability at field scale. N<ce:inf loc=\"post\">2</ce:inf>O emissions were measured with a mobile chamber on a maize crop after fertilization with several soil management practices resulting in four soil states (strip-till versus tillage, compacted soil versus uncompacted) with contrasting soil structure. Soil cylinders and bulk soil were sampled from 24 plots exhibiting a strong gradient in N<ce:inf loc=\"post\">2</ce:inf>O emissions. Classical soil physical and chemical properties were measured, including soil bulk density and water filled pore space. Soil structure also was characterized quantitatively by X-ray tomography at meso and macro scales, and indirectly by gas transfer parameters. Clear differences were observed between low and high emission plots in terms of soil structure, soil temperature and nitrate concentration. However, soil structure appeared more strongly connected to N<ce:inf loc=\"post\">2</ce:inf>O emissions, and some thresholds on soil structural indicators were relevant to disentangle high and low N<ce:inf loc=\"post\">2</ce:inf>O fluxes. Some structural indicators at both scales (e.g. porosity, surface density) and gas transfer parameters (relative gas diffusivity, air permeability) were good descriptors of the observed N<ce:inf loc=\"post\">2</ce:inf>O fluxes. Nevertheless, the gas transfer parameters can be easily measured over a short period of time, whereas the soil structure indicators determined from 3D images require an acquisition and a processing phase that can be time consuming. A good compromise to evaluate the field N<ce:inf loc=\"post\">2</ce:inf>O flux potential from an easy measure would be to evaluate the relative gas diffusivity, which directly controls the diffusion of oxygen in soil and thereby the microbial processes of N<ce:inf loc=\"post\">2</ce:inf>O production.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"47 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-28DOI: 10.1016/j.geoderma.2024.117148
R.M. Lark, L. Mlambo, H. Pswarayi, D. Zardetto, S. Gourlay
Large sample surveys with households, or individuals within households, as the basic sampled units, are important sources of information on variables related to household income, economic activity, food security and nutritional status. In many circumstances the advantages of supplementing these surveys with sampling of the soil from fields or other land units which the households cultivate may seem obvious, as a source of information on the quality of the soil on which households depend, and potential limitations on their food security such as soil pH or nutrient status. However, it is not certain that household surveys, designed to examine social and economic variables, will be efficient for collecting soil information, or will provide adequate estimates of soil property means at scales of interest. Additional sampling might be necessary, so an attendant question is whether this is feasible. In this paper we use data on soil pH and soil carbon inferred by spectral measurements on soil specimens collected from land cultivated by households in Uganda and Ethiopia to estimate variance components for these properties, and from these the standard errors for mean values at District (Uganda) or Zone (Ethiopia) level by household surveys with different designs. Similar calculations were done for direct measurement of soil carbon and soil pH from a spatial sample in Malawi from which variograms were used to infer the variance components corresponding to the levels of a household survey. The results allow the calculation of sample sizes at different levels of the design, required to allow estimates of particular quantities to be obtained with specified precision. The numbers of sampled enumeration areas required to obtain estimates of district or zone-level means with the arbitrary specified precision were large, but the feasibility of such sampling must be judged for a particular application, and the precision appropriate for that. The presented method makes that possible.
{"title":"Adding soil sampling to household surveys: Information for sample design from pilot data","authors":"R.M. Lark, L. Mlambo, H. Pswarayi, D. Zardetto, S. Gourlay","doi":"10.1016/j.geoderma.2024.117148","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117148","url":null,"abstract":"Large sample surveys with households, or individuals within households, as the basic sampled units, are important sources of information on variables related to household income, economic activity, food security and nutritional status. In many circumstances the advantages of supplementing these surveys with sampling of the soil from fields or other land units which the households cultivate may seem obvious, as a source of information on the quality of the soil on which households depend, and potential limitations on their food security such as soil pH or nutrient status. However, it is not certain that household surveys, designed to examine social and economic variables, will be efficient for collecting soil information, or will provide adequate estimates of soil property means at scales of interest. Additional sampling might be necessary, so an attendant question is whether this is feasible. In this paper we use data on soil pH and soil carbon inferred by spectral measurements on soil specimens collected from land cultivated by households in Uganda and Ethiopia to estimate variance components for these properties, and from these the standard errors for mean values at District (Uganda) or Zone (Ethiopia) level by household surveys with different designs. Similar calculations were done for direct measurement of soil carbon and soil pH from a spatial sample in Malawi from which variograms were used to infer the variance components corresponding to the levels of a household survey. The results allow the calculation of sample sizes at different levels of the design, required to allow estimates of particular quantities to be obtained with specified precision. The numbers of sampled enumeration areas required to obtain estimates of district or zone-level means with the arbitrary specified precision were large, but the feasibility of such sampling must be judged for a particular application, and the precision appropriate for that. The presented method makes that possible.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"337 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-27DOI: 10.1016/j.geoderma.2024.117146
Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová
The acceleration of soil erosion in undulating landscapes due to human activities has led to a larger area of land being affected by intensive soil redistribution. Colluvisols, sedimentary soils formed on concave slope positions, are considered to be important indicators of soil-landscape processes and soil organic carbon pools. In this study, we investigated the effectiveness of hyperspectral imaging in visible and near-infrared range to assess the detailed variability (both vertical and within each colluvial layer and in-situ soil horizon) of soil organic carbon (SOC) and CaCO3 concentrations in three deep Colluvisols developed on loess and located at different slope positions in southeast Czechia, and evaluate whether this in-detail mapped microvariability can be used as a proxy to assess the dynamics and history of colluvial sedimentation. A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. The results showed that RF provided the best performance for both SOC (R2 = 0.75) and CaCO3 (R2 = 0.76) contents. The maps depict significant differences in the vertical variability of the predicted properties in the profiles depending on the different intensity, form and period of sedimentation resulting from the slope position. The within-horizon/layer variability of SOC proves to be a suitable indicator of the character of deposition. High variability has been shown mainly in the medieval layers, where it reflects high-energy material redeposition, while low variability in the oldest and youngest parts of the profiles is probably associated with the type of deposited material and frequent pedoturbation, respectively. The within-horizon/layer variability of CaCO3, on the other hand, is independent of the dynamics of deposition. The study showed that imaging spectroscopy is a suitable tool to capture the detailed pattern of the colluvial matrix and, with appropriate sampling and processing, is applicable even in very deep soil profiles.
{"title":"Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging","authors":"Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová","doi":"10.1016/j.geoderma.2024.117146","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117146","url":null,"abstract":"The acceleration of soil erosion in undulating landscapes due to human activities has led to a larger area of land being affected by intensive soil redistribution. Colluvisols, sedimentary soils formed on concave slope positions, are considered to be important indicators of soil-landscape processes and soil organic carbon pools. In this study, we investigated the effectiveness of hyperspectral imaging in visible and near-infrared range to assess the detailed variability (both vertical and within each colluvial layer and in-situ soil horizon) of soil organic carbon (SOC) and CaCO<ce:inf loc=\"post\">3</ce:inf> concentrations in three deep Colluvisols developed on loess and located at different slope positions in southeast Czechia, and evaluate whether this in-detail mapped microvariability can be used as a proxy to assess the dynamics and history of colluvial sedimentation. A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO<ce:inf loc=\"post\">3</ce:inf> content in each profile. The results showed that RF provided the best performance for both SOC (R<ce:sup loc=\"post\">2</ce:sup> = 0.75) and CaCO<ce:inf loc=\"post\">3</ce:inf> (R<ce:sup loc=\"post\">2</ce:sup> = 0.76) contents. The maps depict significant differences in the vertical variability of the predicted properties in the profiles depending on the different intensity, form and period of sedimentation resulting from the slope position. The within-horizon/layer variability of SOC proves to be a suitable indicator of the character of deposition. High variability has been shown mainly in the medieval layers, where it reflects high-energy material redeposition, while low variability in the oldest and youngest parts of the profiles is probably associated with the type of deposited material and frequent pedoturbation, respectively. The within-horizon/layer variability of CaCO<ce:inf loc=\"post\">3</ce:inf>, on the other hand, is independent of the dynamics of deposition. The study showed that imaging spectroscopy is a suitable tool to capture the detailed pattern of the colluvial matrix and, with appropriate sampling and processing, is applicable even in very deep soil profiles.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"27 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-26DOI: 10.1016/j.geoderma.2024.117151
Abdel Rahman S. Alsaleh, Mariam Alcibahy, Fahim Abdul Gafoor, Hamed Al Hashemi, Bayan Athamneh, Ali A. Al Hammadi, Lakmal Seneviratne, Maryam R. Al Shehhi
This study introduces a remote sensing approach to estimate soil organic carbon in arid agricultural fields, emphasizing sustainable land management. The United Arab Emirates (UAE) serves as the case study, representing a region where soil organic carbon dynamics have not been previously assessed. A total of 186 topsoil samples were collected and analyzed for soil organic carbon. Spectral data from field measurements, the DLR Earth Sensing Imaging Spectrometer (DESIS), and Sentinel-2 were integrated, marking the first application of this combination for soil organic carbon prediction. To address the challenges of arid environments, the study introduced specialized preprocessing techniques, including a novel vegetation index (UAEVI) for masking vegetation, principal component analysis for filling missing attributes, area normalization, and Savitzky-Golay smoothing to reduce noise and enhance spectral data. Soil organic carbon exhibited significant spectral correlations, with negative relationships observed in the wavelength ranges 401–416, 670–698, and 926–957 nm, and strong positive relationships in the ranges 519–560, 744–785, 937, and 1610 nm. A ridge regression model was developed and validated, achieving an Coefficient of Determination (R2) of 0.671, Root Mean Squared Error (RMSE) of 0.120 %, and Ratio of Performance to InterQuartile distance (RPIQ) of 2.271. The model demonstrated reliable performance in mapping soil organic carbon, achieving results comparable to studies in non-arid climates. Seasonal analysis highlighted the influence of meteorological parameters on soil organic carbon trends, and the model was successfully applied to monitor temporal changes in soil organic carbon within a sub-region from June 2022 to December 2023, revealing a slight increase in soil organic carbon over this period. This research emphasizes the effectiveness of integrating hyperspectral (DESIS) and multispectral (Sentinel-2) data with advanced preprocessing techniques for soil organic carbon estimation in arid environments. This study offers a scalable framework for more accurate and timely soil assessments, promising significant improvements in the management of arid soil ecosystems.
{"title":"Estimation of soil organic carbon in arid agricultural fields based on hyperspectral satellite images","authors":"Abdel Rahman S. Alsaleh, Mariam Alcibahy, Fahim Abdul Gafoor, Hamed Al Hashemi, Bayan Athamneh, Ali A. Al Hammadi, Lakmal Seneviratne, Maryam R. Al Shehhi","doi":"10.1016/j.geoderma.2024.117151","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117151","url":null,"abstract":"This study introduces a remote sensing approach to estimate soil organic carbon in arid agricultural fields, emphasizing sustainable land management. The United Arab Emirates (UAE) serves as the case study, representing a region where soil organic carbon dynamics have not been previously assessed. A total of 186 topsoil samples were collected and analyzed for soil organic carbon. Spectral data from field measurements, the DLR Earth Sensing Imaging Spectrometer (DESIS), and Sentinel-2 were integrated, marking the first application of this combination for soil organic carbon prediction. To address the challenges of arid environments, the study introduced specialized preprocessing techniques, including a novel vegetation index (UAEVI) for masking vegetation, principal component analysis for filling missing attributes, area normalization, and Savitzky-Golay smoothing to reduce noise and enhance spectral data. Soil organic carbon exhibited significant spectral correlations, with negative relationships observed in the wavelength ranges 401–416, 670–698, and 926–957 nm, and strong positive relationships in the ranges 519–560, 744–785, 937, and 1610 nm. A ridge regression model was developed and validated, achieving an Coefficient of Determination (R<ce:sup loc=\"post\">2</ce:sup>) of 0.671, Root Mean Squared Error (RMSE) of 0.120 %, and Ratio of Performance to InterQuartile distance (RPIQ) of 2.271. The model demonstrated reliable performance in mapping soil organic carbon, achieving results comparable to studies in non-arid climates. Seasonal analysis highlighted the influence of meteorological parameters on soil organic carbon trends, and the model was successfully applied to monitor temporal changes in soil organic carbon within a sub-region from June 2022 to December 2023, revealing a slight increase in soil organic carbon over this period. This research emphasizes the effectiveness of integrating hyperspectral (DESIS) and multispectral (Sentinel-2) data with advanced preprocessing techniques for soil organic carbon estimation in arid environments. This study offers a scalable framework for more accurate and timely soil assessments, promising significant improvements in the management of arid soil ecosystems.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"34 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-26DOI: 10.1016/j.geoderma.2024.117154
Marcus Schiedung, Pierre Barré, Christopher Peoplau
Soil organic carbon (SOC) is significantly affected by land use change (LUC). Consequently, LUC is a major controlling factor of total SOC contents and SOC pool dynamics. Several methods have been developed to assess distinct SOC pools, which includes particle size separation, thermal analysis and soil reflectance mid-infrared spectroscopy. All of which are considered to have a potential as high through put methods to generate large datasets. Here, we used 23 sites covering six different types of LUC to assess differences in fast and slow cycling SOC derived from three approaches. We used i) particle size fractionation to obtain coarse (>50 µm) and fine (<50 µm) SOC fractions; ii) thermal Rock-Eval® 6 analysis in compilation with the PARTYSOCv2.0EU model to estimate active and stable SOC pools and iii) mid-infrared spectroscopy to determine the relative SOC composition and derive fast (aliphatic compounds) and slow (aromatic/carboxylic compounds) cycling SOC pools. The particle size SOC fractions and thermal SOC pools showed similar dynamics but differed substantially in the magnitude with LUC. The fine SOC fraction contained around two-thirds of the total SOC across all land uses and was strongly responsive by nearly matching the relative changes of total SOC (slope of 0.76 and R2 = 0.91). Therefore, the fine fraction SOC might be more dynamic than considered until now. In comparison, the stable SOC pool calculated using PARTYSOCv2.0EU was less responsive to the relative changes (slope of 0.43 and R2 = 0.72) and contained around 40 % of the total SOC. This underlines that both physical and thermal approaches separate biogeochemically distinct pools. The qualitative assessment by mid-infrared spectroscopy related well to the thermal SOC pools but not to the particle size fractions. The initial land-use SOC composition, as a ratio of the corresponding fast and slow cycling SOC pool, can be a suitable predictor for SOC evolution. This was particularly true for thermal and mid-infrared spectroscopy derived SOC pools. We show that three conceptually different methods (physical, thermal and mid-infrared spectroscopic) are suitable to determine SOC pool changes for a large diversity of LUC, but the sensitivity of the individual pools can differ strongly, depending on the method.
{"title":"Separating fast from slow cycling soil organic carbon – A multi-method comparison on land use change sites","authors":"Marcus Schiedung, Pierre Barré, Christopher Peoplau","doi":"10.1016/j.geoderma.2024.117154","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117154","url":null,"abstract":"Soil organic carbon (SOC) is significantly affected by land use change (LUC). Consequently, LUC is a major controlling factor of total SOC contents and SOC pool dynamics. Several methods have been developed to assess distinct SOC pools, which includes particle size separation, thermal analysis and soil reflectance mid-infrared spectroscopy. All of which are considered to have a potential as high through put methods to generate large datasets. Here, we used 23 sites covering six different types of LUC to assess differences in fast and slow cycling SOC derived from three approaches. We used i) particle size fractionation to obtain coarse (>50 <ce:hsp sp=\"0.25\"></ce:hsp>µm) and fine (<50 <ce:hsp sp=\"0.25\"></ce:hsp>µm) SOC fractions; ii) thermal Rock-Eval® 6 analysis in compilation with the PARTY<ce:inf loc=\"post\">SOC</ce:inf>v2.0<ce:inf loc=\"post\">EU</ce:inf> model to estimate active and stable SOC pools and iii) mid-infrared spectroscopy to determine the relative SOC composition and derive fast (aliphatic compounds) and slow (aromatic/carboxylic compounds) cycling SOC pools. The particle size SOC fractions and thermal SOC pools showed similar dynamics but differed substantially in the magnitude with LUC. The fine SOC fraction contained around two-thirds of the total SOC across all land uses and was strongly responsive by nearly matching the relative changes of total SOC (slope of 0.76 and R<ce:sup loc=\"post\">2</ce:sup> = 0.91). Therefore, the fine fraction SOC might be more dynamic than considered until now. In comparison, the stable SOC pool calculated using PARTY<ce:inf loc=\"post\">SOC</ce:inf>v2.0<ce:inf loc=\"post\">EU</ce:inf> was less responsive to the relative changes (slope of 0.43 and R<ce:sup loc=\"post\">2</ce:sup> = 0.72) and contained around 40 % of the total SOC. This underlines that both physical and thermal approaches separate biogeochemically distinct pools. The qualitative assessment by mid-infrared spectroscopy related well to the thermal SOC pools but not to the particle size fractions. The initial land-use SOC composition, as a ratio of the corresponding fast and slow cycling SOC pool, can be a suitable predictor for SOC evolution. This was particularly true for thermal and mid-infrared spectroscopy derived SOC pools. We show that three conceptually different methods (physical, thermal and mid-infrared spectroscopic) are suitable to determine SOC pool changes for a large diversity of LUC, but the sensitivity of the individual pools can differ strongly, depending on the method.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"68 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-25DOI: 10.1016/j.geoderma.2024.117159
Lifei Sun, Yanci Qiao, Wolfgang Wanek, Daryl L. Moorhead, Yongxing Cui, Yujiao Peng, Liquan Song, Baoqing Hu, Tuo Zhang, Shuailin Li
Microbial nitrogen use efficiency (NUE) reflects the allocation of microbially-acquired N between growth (anabolism) and the release of inorganic N to the environment (catabolism), and is central to understanding soil N cycling. However, the effects of N addition on microbial NUE are unclear. We determined microbial NUE in surface (0–10 cm) and subsurface (10–20 cm) soils in a temperate forest by the combined substrate-independent 18O-H2O tracer technique and 15N isotope pool dilution in a multi-level N addition experiment. We found that high N treatment (75 kg N ha−1 yr−1 as urea fertilizer) significantly decreased NUE in surface soil, but not in the subsurface soil. The decrease in NUE in surface soil was related to soil acidification, likely induced by N addition, and to reduced phosphorus availability, suggesting increased phosphorus limitation to microbial metabolism with N addition. Microbial NUE was inversely related to inorganic N flux (as NH4+) in both surface and subsurface soils and positively related to microbial biomass in surface soil. Our empirical evidence confirms that microbial NUE is a sensitive proxy and controlling branchpoint between soil microbial N immobilization and inorganic N cycling, which should be explicitly included in biogeochemical models to better predict soil N dynamics.
微生物氮利用效率（NUE）反映了微生物获得的氮在生长（合成代谢）和向环境释放无机氮（分解代谢）之间的分配，是理解土壤氮循环的核心。然而，氮添加对微生物氮肥利用效率的影响尚不清楚。采用与底物无关的18O-H2O示踪技术和15N同位素池稀释相结合的多层次加氮实验，测定了温带森林表层（0-10 cm）和地下（10-20 cm）土壤的微生物氮肥利用效率。我们发现，高氮处理（75 kg N ha - 1 yr - 1作为尿素肥）显著降低了表层土壤的氮素利用效率，但对地下土壤没有影响。表层土壤氮素利用效率的下降与土壤酸化（可能是由N添加引起的）和磷有效性的降低有关，表明添加N增加了磷对微生物代谢的限制。微生物氮素利用效率与表层和地下土壤无机氮通量（如NH4+）呈负相关，与表层土壤微生物生物量呈正相关。我们的经验证据证实，微生物氮素利用效率是土壤微生物氮固定和无机氮循环之间的敏感代理和控制分支点，应明确将其纳入生物地球化学模型，以更好地预测土壤氮动态。
{"title":"Nitrogen input decreases microbial nitrogen use efficiency in surface soils of a temperate forest in northeast China","authors":"Lifei Sun, Yanci Qiao, Wolfgang Wanek, Daryl L. Moorhead, Yongxing Cui, Yujiao Peng, Liquan Song, Baoqing Hu, Tuo Zhang, Shuailin Li","doi":"10.1016/j.geoderma.2024.117159","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117159","url":null,"abstract":"Microbial nitrogen use efficiency (NUE) reflects the allocation of microbially-acquired N between growth (anabolism) and the release of inorganic N to the environment (catabolism), and is central to understanding soil N cycling. However, the effects of N addition on microbial NUE are unclear. We determined microbial NUE in surface (0–10 cm) and subsurface (10–20 cm) soils in a temperate forest by the combined substrate-independent <ce:sup loc=\"post\">18</ce:sup>O-H<ce:inf loc=\"post\">2</ce:inf>O tracer technique and <ce:sup loc=\"post\">15</ce:sup>N isotope pool dilution in a multi-level N addition experiment. We found that high N treatment (75 kg N ha<ce:sup loc=\"post\">−1</ce:sup> yr<ce:sup loc=\"post\">−1</ce:sup> as urea fertilizer) significantly decreased NUE in surface soil, but not in the subsurface soil. The decrease in NUE in surface soil was related to soil acidification, likely induced by N addition, and to reduced phosphorus availability, suggesting increased phosphorus limitation to microbial metabolism with N addition. Microbial NUE was inversely related to inorganic N flux (as NH<ce:inf loc=\"post\">4</ce:inf><ce:sup loc=\"post\">+</ce:sup>) in both surface and subsurface soils and positively related to microbial biomass in surface soil. Our empirical evidence confirms that microbial NUE is a sensitive proxy and controlling branchpoint between soil microbial N immobilization and inorganic N cycling, which should be explicitly included in biogeochemical models to better predict soil N dynamics.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"87 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-24DOI: 10.1016/j.geoderma.2024.117153
Jing Lyu, Alfred Obia, Gerard Cornelissen, Jan Mulder, Andreas Botnen Smebye, Andrew R. Zimmerman
Understanding the stability and movement of biochar in soil is pivotal for its effective use in soil improvement and carbon sequestration projects. Building on a previous study that evaluated the migration of three size fractions of maize biochar carbon (BC) after 4.5 years in a Zambian loamy sand soil using δ13C isotopes, this study compares the results with those using chemothermal oxidation (CTO) and benzene polycarboxylic acid (BPCA) biomarkers. While the δ13C method registered the most BC in the application layer (0–7 cm), it detected less BC in lower layers (7–30 cm, 3.2–7.9 % downward migration), and with a greater variance, than the other two methods. The BPCA method detected relatively more BC in the lower layers (9.1–20.2 % downward migration), particularly for fine-sized biochar. It also detected the most BC in the control soil plot and outside the experimental block, which suggests either its efficiency in fine biochar detection or an issue with false positive detection. The CTO method, though less sensitive in detecting fine biochar particle BC, was strongly correlated with δ13C isotope results, thus representing a cost-effective and simpler alternative to the other BC quantification methods. These findings underscore the necessity of methodological consideration in biochar C quantification to ensure accurate assessment of its distribution and stability. This is a pressing need for correct assignment of climate mitigation credits. More field studies should be carried out involving multiple biochar types and quantification methods to refine our understanding of biochar C dynamics in soil.
{"title":"Comparison of three quantification methods used to detect biochar carbon migration in a tropical soil: A 4.5-year field experiment in Zambia","authors":"Jing Lyu, Alfred Obia, Gerard Cornelissen, Jan Mulder, Andreas Botnen Smebye, Andrew R. Zimmerman","doi":"10.1016/j.geoderma.2024.117153","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117153","url":null,"abstract":"Understanding the stability and movement of biochar in soil is pivotal for its effective use in soil improvement and carbon sequestration projects. Building on a previous study that evaluated the migration of three size fractions of maize biochar carbon (BC) after 4.5 years in a Zambian loamy sand soil using δ<ce:sup loc=\"post\">13</ce:sup>C isotopes, this study compares the results with those using chemothermal oxidation (CTO) and benzene polycarboxylic acid (BPCA) biomarkers. While the δ<ce:sup loc=\"post\">13</ce:sup>C method registered the most BC in the application layer (0–7 cm), it detected less BC in lower layers (7–30 cm, 3.2–7.9 % downward migration), and with a greater variance, than the other two methods. The BPCA method detected relatively more BC in the lower layers (9.1–20.2 % downward migration), particularly for fine-sized biochar. It also detected the most BC in the control soil plot and outside the experimental block, which suggests either its efficiency in fine biochar detection or an issue with false positive detection. The CTO method, though less sensitive in detecting fine biochar particle BC, was strongly correlated with δ<ce:sup loc=\"post\">13</ce:sup>C isotope results, thus representing a cost-effective and simpler alternative to the other BC quantification methods. These findings underscore the necessity of methodological consideration in biochar C quantification to ensure accurate assessment of its distribution and stability. This is a pressing need for correct assignment of climate mitigation credits. More field studies should be carried out involving multiple biochar types and quantification methods to refine our understanding of biochar C dynamics in soil.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"132 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-24DOI: 10.1016/j.geoderma.2024.117152
Ayush Joshi Gyawali, Marissa Wiseman, Jason P. Ackerson, Sarah Coffman, Kevin Meissner, Cristine L.S. Morgan
To be commercially viable, soil carbon project developers need to be able to measure soil carbon stocks across large scales (e.g., 100,000 to 1,000,000 ha). These measurements need to be accurate, unbiased, inexpensive, and fast. One potential measurement modality for carbon markets is visible and near-infrared diffuse reflectance spectroscopy (VisNIR). VisNIR has been widely used to predict soil properties including soil organic carbon (SOC) concentration and stock under both lab settings and in situ soil conditions. Recent developments in low-cost spectrometers have enabled the creation of easy to operate, rapidly deployed, handheld VisNIR-equipped devices for in situ soil measurement. Our objective for this study is to 1) test one such handheld in situ VisNIR probe (handheld probe) to measure SOC stocks to 30 cm depth in Midwest US Mollisols, 2) to quantify the role of bulk density and SOC concentration in VisNIR probe calibration for probe-based estimation on SOC stock in Midwest US Mollisols, and 3) to quantify the effect of indirect (SOC + BD) vs direct calibration modeling (SOC stock directly) of SOC stocks using VisNIR data. We collected handheld probe measurements and soil core samples from six non-contiguous farms across the state of Illinois, USA. A one-farm hold out PLSR modeling approach was taken for SOC concentration, bulk density, 5-cm incremented SOC stocks down to 45 cm; and 0 to 30 cm SOC stocks using the in situ VisNIR spectra from the handheld probe. Models accurately predicted SOC concentration (R2 = 0.72, RMSE = 0.33 %, RPIQ = 2.39, bias = 0.0005 %), 5-cm increment SOC stocks (R2 = 0.68, RPIQ = 2.41 Mg/ha, bias = 0.05 Mg/ha) and 0 to 30 cm SOC stocks (R2 = 0.88, RMSEP = 7.8, bias = -0.49 Mg/ha, RPIQ = 4.19 Mg/ha). Models were not able to accurately predict bulk density (R2 = 0.28). Direct SOC stock modeling resulted in lower bias compared to indirect computation of SOC stock (bias = 0.05 and 0.15 Mg/ha for direct and indirect methods, respectively) and results demonstrated that, in this loess landscape, SOC stock prediction accuracy was driven by accurate prediction of SOC concentration, rather than accurate prediction of bulk density. The handheld probe shows promise as a rapid, low-cost tool for measuring SOC stocks in the midwestern Mollisols and can provide the data necessary to support large spatial scale soil carbon market development. These results justify continued investment in in situ spectral libraries for the handheld probes and eventually posit a modeling framework for measurement-based soil carbon accounting.
{"title":"Measuring in situ soil carbon stocks: A study using a novel handheld VisNIR probe","authors":"Ayush Joshi Gyawali, Marissa Wiseman, Jason P. Ackerson, Sarah Coffman, Kevin Meissner, Cristine L.S. Morgan","doi":"10.1016/j.geoderma.2024.117152","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117152","url":null,"abstract":"To be commercially viable, soil carbon project developers need to be able to measure soil carbon stocks across large scales (e.g., 100,000 to 1,000,000 ha). These measurements need to be accurate, unbiased, inexpensive, and fast. One potential measurement modality for carbon markets is visible and near-infrared diffuse reflectance spectroscopy (VisNIR). VisNIR has been widely used to predict soil properties including soil organic carbon (SOC) concentration and stock under both lab settings and <ce:italic>in situ</ce:italic> soil conditions. Recent developments in low-cost spectrometers have enabled the creation of easy to operate, rapidly deployed, handheld VisNIR-equipped devices for <ce:italic>in situ</ce:italic> soil measurement. Our objective for this study is to 1) test one such handheld <ce:italic>in situ</ce:italic> VisNIR probe (handheld probe) to measure SOC stocks to 30 cm depth in Midwest US Mollisols, 2) to quantify the role of bulk density and SOC concentration in VisNIR probe calibration for probe-based estimation on SOC stock in Midwest US Mollisols, and 3) to quantify the effect of indirect (SOC + BD) vs direct calibration modeling (SOC stock directly) of SOC stocks using VisNIR data. We collected handheld probe measurements and soil core samples from six non-contiguous farms across the state of Illinois, USA. A one-farm hold out PLSR modeling approach was taken for SOC concentration, bulk density, 5-cm incremented SOC stocks down to 45 cm; and 0 to 30 cm SOC stocks using the <ce:italic>in situ</ce:italic> VisNIR spectra from the handheld probe. Models accurately predicted SOC concentration (R<ce:sup loc=\"post\">2</ce:sup> = 0.72, RMSE = 0.33 %, RPIQ = 2.39, bias = 0.0005 %), 5-cm increment SOC stocks (R<ce:sup loc=\"post\">2</ce:sup> = 0.68, RPIQ = 2.41 Mg/ha, bias = 0.05 Mg/ha) and 0 to 30 cm SOC stocks (R<ce:sup loc=\"post\">2</ce:sup> = 0.88, RMSEP = 7.8, bias = -0.49 Mg/ha, RPIQ = 4.19 Mg/ha). Models were not able to accurately predict bulk density (R<ce:sup loc=\"post\">2</ce:sup> = 0.28). Direct SOC stock modeling resulted in lower bias compared to indirect computation of SOC stock (bias = 0.05 and 0.15 Mg/ha for direct and indirect methods, respectively) and results demonstrated that, in this loess landscape, SOC stock prediction accuracy was driven by accurate prediction of SOC concentration, rather than accurate prediction of bulk density. The handheld probe shows promise as a rapid, low-cost tool for measuring SOC stocks in the midwestern Mollisols and can provide the data necessary to support large spatial scale soil carbon market development. These results justify continued investment in <ce:italic>in situ</ce:italic> spectral libraries for the handheld probes and eventually posit a modeling framework for measurement-based soil carbon accounting.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"29 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-21DOI: 10.1016/j.geoderma.2024.117149
G. Galamini, G. Ferretti, C. Rosinger, S. Huber, A. Mentler, E. Diaz–Pines, B. Faccini, K.M. Keiblinger
Recycling nutrients in agroecosystems is becoming increasingly important to promote agricultural sustainability. Struvite and nitrogen (N)-enriched zeolites produced via wastewater treatment offer the potential for nutrient recycling. However, their effects on soil properties, particularly on microbial physiology, remain largely unknown; especially regarding microbial feedback, from which losses or sequestration of essential elements may result. This study investigates the short-term (three days) physiological responses of soil microorganisms, changes in available nutrients, and the immediate effects on soil organic matter (SOM) and carbon dioxide (CO2) emissions following the application of struvite and N-enriched zeolites derived from liquid digestate, alongside natural zeolites amendments in an acidic sandy soil. All treatments increased soil pH, which emerged as a driving factor in the dissolution of labile organic carbon (C) and the microbial production of N-, C-, and phosphorus (P)-acquiring extracellular enzymes. As soil pH increased, the stoichiometric ratio of microbial biomass C (Cmic) to microbial biomass N (Nmic), along with the enzymatic C:N ratio decreased, suggesting a superior effect on microbial N-cycling compared to C-cycling. Carbon dioxide emissions increased, particularly with the application of organic fertilizer (digestate), where the highest microbial metabolic quotient reflected increased catabolic activity due to the immediate availability of organic C. Overall, zeolitized tuffs demonstrated the potential to mitigate CO2 emissions, likely due to CO2 adsorption capacity.
{"title":"Potential for agricultural recycling of struvite and zeolites to improve soil microbial physiology and mitigate CO2 emissions","authors":"G. Galamini, G. Ferretti, C. Rosinger, S. Huber, A. Mentler, E. Diaz–Pines, B. Faccini, K.M. Keiblinger","doi":"10.1016/j.geoderma.2024.117149","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117149","url":null,"abstract":"Recycling nutrients in agroecosystems is becoming increasingly important to promote agricultural sustainability. Struvite and nitrogen (N)-enriched zeolites produced via wastewater treatment offer the potential for nutrient recycling. However, their effects on soil properties, particularly on microbial physiology, remain largely unknown; especially regarding microbial feedback, from which losses or sequestration of essential elements may result. This study investigates the short-term (three days) physiological responses of soil microorganisms, changes in available nutrients, and the immediate effects on soil organic matter (SOM) and carbon dioxide (CO<ce:inf loc=\"post\">2</ce:inf>) emissions following the application of struvite and N-enriched zeolites derived from liquid digestate, alongside natural zeolites amendments in an acidic sandy soil. All treatments increased soil pH, which emerged as a driving factor in the dissolution of labile organic carbon (C) and the microbial production of N-, C-, and phosphorus (P)-acquiring extracellular enzymes. As soil pH increased, the stoichiometric ratio of microbial biomass C (C<ce:inf loc=\"post\">mic</ce:inf>) to microbial biomass N (N<ce:inf loc=\"post\">mic</ce:inf>), along with the enzymatic C:N ratio decreased, suggesting a superior effect on microbial N-cycling compared to C-cycling. Carbon dioxide emissions increased, particularly with the application of organic fertilizer (digestate), where the highest microbial metabolic quotient reflected increased catabolic activity due to the immediate availability of organic C. Overall, zeolitized tuffs demonstrated the potential to mitigate CO<ce:inf loc=\"post\">2</ce:inf> emissions, likely due to CO<ce:inf loc=\"post\">2</ce:inf> adsorption capacity.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"22 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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：