We are far from understanding the spatial patterns of dryland soil carbon and nitrogen stocks and how they vary among different land cover types. We used data from 12,000 sites from 129 countries in global drylands to estimate soil organic carbon (SOC) and total nitrogen (STN) stocks in different land cover types, explore the factors driving their spatial distribution, and predict the trends under different climate scenarios in global drylands. SOC and STN stocks in the upper 100 cm reached 419.5 and 38.2 Pg, respectively, with the upper 0–30 cm accounting for half of them. The largest SOC stocks were found in forests, shrublands and grasslands, while STN stocks peaked in forests, bare areas and croplands. The factors driving the spatial patterns of SOC and STN varied among soil depths, with mean annual temperature, pH and aridity being the main factors driving the spatial patterns in SOC and STN density for 0–30 cm, and soil texture the strongest factor for 60–100 cm. Under the Representative Concentration Pathways (RCP) 4.5 scenario, SOC and STN stocks were predicted to decrease by 3.6 % and 4.0 %, respectively, from 2020 to 2100, whereas under the RCP 8.5 scenario, the projected decreases were 5.9 % and 6.4 % respectively. Our results indicate that if we want to accurately predict C and N accumulation, and design effective mitigation measures in terrestrial ecosystems under future climatic scenarios, we need to better explore the drivers that operate at the deeper soil depths, which also accumulate a significant amount of SOC and STN.
{"title":"Environmental drivers of soil carbon and nitrogen accumulation in global drylands","authors":"Xiaobing Zhou , Shihang Zhang , Yusen Chen , Jorge Durán , Yongxing Lu , Hao Guo , Yuanming Zhang","doi":"10.1016/j.geoderma.2024.117075","DOIUrl":"10.1016/j.geoderma.2024.117075","url":null,"abstract":"<div><div>We are far from understanding the spatial patterns of dryland soil carbon and nitrogen stocks and how they vary among different land cover types. We used data from 12,000 sites from 129 countries in global drylands to estimate soil organic carbon (SOC) and total nitrogen (STN) stocks in different land cover types, explore the factors driving their spatial distribution, and predict the trends under different climate scenarios in global drylands. SOC and STN stocks in the upper 100 cm reached 419.5 and 38.2 Pg, respectively, with the upper 0–30 cm accounting for half of them. The largest SOC stocks were found in forests, shrublands and grasslands, while STN stocks peaked in forests, bare areas and croplands. The factors driving the spatial patterns of SOC and STN varied among soil depths, with mean annual temperature, pH and aridity being the main factors driving the spatial patterns in SOC and STN density for 0–30 cm, and soil texture the strongest factor for 60–100 cm. Under the Representative Concentration Pathways (RCP) 4.5 scenario, SOC and STN stocks were predicted to decrease by 3.6 % and 4.0 %, respectively, from 2020 to 2100, whereas under the RCP 8.5 scenario, the projected decreases were 5.9 % and 6.4 % respectively. Our results indicate that if we want to accurately predict C and N accumulation, and design effective mitigation measures in terrestrial ecosystems under future climatic scenarios, we need to better explore the drivers that operate at the deeper soil depths, which also accumulate a significant amount of SOC and STN.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117075"},"PeriodicalIF":5.6,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1016/j.geoderma.2024.117072
Zhongxing Chen , Zheng Wang , Xi Wang , Zhou Shi , Songchao Chen
Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.
{"title":"Including soil spatial neighbor information for digital soil mapping","authors":"Zhongxing Chen , Zheng Wang , Xi Wang , Zhou Shi , Songchao Chen","doi":"10.1016/j.geoderma.2024.117072","DOIUrl":"10.1016/j.geoderma.2024.117072","url":null,"abstract":"<div><div>Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117072"},"PeriodicalIF":5.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.geoderma.2024.117066
Tiffany L. Carter , Crystal Schaecher , Steve Monteith , Richard Ferguson
Soil organic carbon (SOC) and soil inorganic carbon (SIC) are of longstanding interest due to their relationship with other key soil properties and indications for soil health and carbon storage. At the USDA-NRCS Kellogg Soil Survey Laboratory (KSSL), total carbon (SOC + SIC) is determined via dry combustion analysis, while calcium carbonate (CaCO3) equivalent is determined via manocalcimetry. For calcareous (carbonate bearing) samples, SIC is estimated as 12 % of CaCO3 equivalent, while SOC is estimated as the difference between measured total carbon and estimated SIC. An alternative dry combustion method for the measurement of SOC and SIC pools was evaluated with the goal of directly measuring – not estimating – inorganic and organic carbon on calcareous samples. The alternative temperature ramp dry combustion (TRDC) method comprises two variants that differ in ramp cycle and carrier gases used. One variant operates under continuous oxygen and has temperature ramp plateaus of 400, 600 and 900 °C; thus, it is referred to as the non-gas switching variant or TRDCNGS. The other variant operates under oxygen until 400 °C, then switches to nitrogen gas for a ramp to 900 °C, then reintroduces oxygen at 900 °C; thus, it is referred to as the gas switching variant or TRDCGS. Both variants were applied in duplicate to 110 diverse samples, including 32 calcareous samples, from across the USA that had been previously characterized by the KSSL. Samples were selected to capture wide variability in carbon contents. Comparing carbon data outcomes with data from the legacy KSSL methods revealed the TRDCGS variant as best for calcareous samples, whereas the TRDCNGS variant was preferred for non-calcareous samples. A combination of the two method variants offers an accurate and direct measurement of SOC and SIC. For calcareous samples, mid-infrared (MIR) spectral analysis demonstrated TRDC method as slightly more accurate than legacy KSSL methods for estimating SOC and SIC.
{"title":"Using combustion analysis to simultaneously measure soil organic and inorganic carbon","authors":"Tiffany L. Carter , Crystal Schaecher , Steve Monteith , Richard Ferguson","doi":"10.1016/j.geoderma.2024.117066","DOIUrl":"10.1016/j.geoderma.2024.117066","url":null,"abstract":"<div><div>Soil organic carbon (SOC) and soil inorganic carbon (SIC) are of longstanding interest due to their relationship with other key soil properties and indications for soil health and carbon storage. At the USDA-NRCS Kellogg Soil Survey Laboratory (KSSL), total carbon (SOC + SIC) is determined via dry combustion analysis, while calcium carbonate (CaCO<sub>3</sub>) equivalent is determined via manocalcimetry. For calcareous (carbonate bearing) samples, SIC is estimated as 12 % of CaCO<sub>3</sub> equivalent, while SOC is estimated as the difference between measured total carbon and estimated SIC. An alternative dry combustion method for the measurement of SOC and SIC pools was evaluated with the goal of directly measuring – not estimating – inorganic and organic carbon on calcareous samples. The alternative temperature ramp dry combustion (TRDC) method comprises two variants that differ in ramp cycle and carrier gases used. One variant operates under continuous oxygen and has temperature ramp plateaus of 400, 600 and 900 °C; thus, it is referred to as the non-gas switching variant or TRDC<sub>NGS</sub>. The other variant operates under oxygen until 400 °C, then switches to nitrogen gas for a ramp to 900 °C, then reintroduces oxygen at 900 °C; thus, it is referred to as the gas switching variant or TRDC<sub>GS</sub>. Both variants were applied in duplicate to 110 diverse samples, including 32 calcareous samples, from across the USA that had been previously characterized by the KSSL. Samples were selected to capture wide variability in carbon contents. Comparing carbon data outcomes with data from the legacy KSSL methods revealed the TRDC<sub>GS</sub> variant as best for calcareous samples, whereas the TRDC<sub>NGS</sub> variant was preferred for non-calcareous samples. A combination of the two method variants offers an accurate and direct measurement of SOC and SIC. For calcareous samples, mid-infrared (MIR) spectral analysis demonstrated TRDC method as slightly more accurate than legacy KSSL methods for estimating SOC and SIC.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117066"},"PeriodicalIF":5.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1016/j.geoderma.2024.117077
Jie Tian , Weiming Kang , Baoqing Zhang , Xuejin Wang , Zhuoya Shang , Chansheng He
Drainage is a crucial soil hydrological process that governs the partitioning of rainfall into runoff, groundwater recharge, soil water storage and evapotranspiration. Despite its significance, the drainage process is poorly understood due to the difficulty in direct measurements and insufficient understanding of its underlying physical mechanisms. To address these challenges, we present an innovative, physically-based, data-driven approach, SM2D (Soil Moisture to Drainage), to estimate drainage. SM2D was applied and examined using soil moisture data from a large-scale observation network over mountainous areas during 2014–2020. The soil moisture threshold governing drainage initiation proves to be significantly lower than the commonly employed field capacity metric in hydrological models. This threshold is influenced by factors such as mean soil moisture, bulk density, residual soil moisture, soil organic carbon, and parameters n and α of soil retention curve. Notably, field capacity has minimal impact on this threshold. Additionally, our analysis reveals that the drainage process is more influenced by the Soil Water Storage Increment (SWSI) than by mean soil moisture (MSM) that has traditionally been recognized as a key factor in drainage control. In comparison to commonly used exponential equations and those in models such as the Soil & Water Assessment Tool (SWAT), SM2D demonstrates superior performance in estimating drainage. The exponential equation derived from the SWSI outperforms those derived from other soil moisture metrics, including the commonly utilized MSM, challenging prevailing norms in drainage equations. SM2D holds the potential to generate extensive drainage datasets from satellite or large-scale soil moisture observations, advancing large-scale hydrological studies.
{"title":"Drainage estimation across mountainous regions from large-scale soil moisture observations","authors":"Jie Tian , Weiming Kang , Baoqing Zhang , Xuejin Wang , Zhuoya Shang , Chansheng He","doi":"10.1016/j.geoderma.2024.117077","DOIUrl":"10.1016/j.geoderma.2024.117077","url":null,"abstract":"<div><div>Drainage is a crucial soil hydrological process that governs the partitioning of rainfall into runoff, groundwater recharge, soil water storage and evapotranspiration. Despite its significance, the drainage process is poorly understood due to the difficulty in direct measurements and insufficient understanding of its underlying physical mechanisms. To address these challenges, we present an innovative, physically-based, data-driven approach, SM2D (Soil Moisture to Drainage), to estimate drainage. SM2D was applied and examined using soil moisture data from a large-scale observation network over mountainous areas during 2014–2020. The soil moisture threshold governing drainage initiation proves to be significantly lower than the commonly employed field capacity metric in hydrological models. This threshold is influenced by factors such as mean soil moisture, bulk density, residual soil moisture, soil organic carbon, and parameters <em>n</em> and <em>α</em> of soil retention curve. Notably, field capacity has minimal impact on this threshold. Additionally, our analysis reveals that the drainage process is more influenced by the Soil Water Storage Increment (SWSI) than by mean soil moisture (MSM) that has traditionally been recognized as a key factor in drainage control. In comparison to commonly used exponential equations and those in models such as the Soil & Water Assessment Tool (SWAT), SM2D demonstrates superior performance in estimating drainage. The exponential equation derived from the SWSI outperforms those derived from other soil moisture metrics, including the commonly utilized MSM, challenging prevailing norms in drainage equations. SM2D holds the potential to generate extensive drainage datasets from satellite or large-scale soil moisture observations, advancing large-scale hydrological studies.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117077"},"PeriodicalIF":5.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.geoderma.2024.117067
Gábor Szatmári , László Pásztor , Katalin Takács , János Mészáros , András Benő , Annamária Laborczi
The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels.
{"title":"Space-time modelling of soil organic carbon stock change at multiple scales: Case study from Hungary","authors":"Gábor Szatmári , László Pásztor , Katalin Takács , János Mészáros , András Benő , Annamária Laborczi","doi":"10.1016/j.geoderma.2024.117067","DOIUrl":"10.1016/j.geoderma.2024.117067","url":null,"abstract":"<div><div>The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117067"},"PeriodicalIF":5.6,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.geoderma.2024.117074
Wei Wei , Ya Liu , Ping Li , Changfeng Ding
Understanding and predicting the aging process of exogenous selenium (Se) in soil is crucial for Se biofortification. However, the long-term aging of selenite in various soils has rarely been reported, and the key factors influencing this aging process remain unclear. Our study involved nineteen typical Chinese soils with varying physiochemical properties, all spiked with potassium selenite (1.0 mg kg−1 Se) and incubated for 180 days. Soil available Se extracted using a 0.1 M K2HPO4-KH2PO4 solution was measured through the whole aging process. The average available Se% (the percentage of available Se in aged soils to total added Se) of all soils decreased from 55.4 % on the day 1 to 32.6 % on day 60, remaining stable thereafter. Pseudo-second-order equation provided the optimal fit (R2 > 0.989, P < 0.01) for characterizing the dynamic process of selenite aging in soil, indicating that chemisorption, rather than internal diffusion, controlled the main rate-limiting step in the selenite aging process. Both machine learning and traditional correlation analysis indicated aging time was the most critical feature and the key soil property that contributed to available Se was pH. Empirical models incorporating soil properties and aging time were developed to predict changes of available Se in soil during aging under aerobic conditions. The reliability of the prediction model was further validated using data collected from previous studies. The developed aging model could potentially be used to scale biofortification data of Se generated from different soils under different aging times.
{"title":"Characterization and modeling of exogenous selenite aging in soils using machine learning and traditional data analysis","authors":"Wei Wei , Ya Liu , Ping Li , Changfeng Ding","doi":"10.1016/j.geoderma.2024.117074","DOIUrl":"10.1016/j.geoderma.2024.117074","url":null,"abstract":"<div><div>Understanding and predicting the aging process of exogenous selenium (Se) in soil is crucial for Se biofortification. However, the long-term aging of selenite in various soils has rarely been reported, and the key factors influencing this aging process remain unclear. Our study involved nineteen typical Chinese soils with varying physiochemical properties, all spiked with potassium selenite (1.0 mg kg<sup>−1</sup> Se) and incubated for 180 days. Soil available Se extracted using a 0.1 M K<sub>2</sub>HPO<sub>4</sub>-KH<sub>2</sub>PO<sub>4</sub> solution was measured through the whole aging process. The average available Se% (the percentage of available Se in aged soils to total added Se) of all soils decreased from 55.4 % on the day 1 to 32.6 % on day 60, remaining stable thereafter. Pseudo-second-order equation provided the optimal fit (R<sup>2</sup> > 0.989, P < 0.01) for characterizing the dynamic process of selenite aging in soil, indicating that chemisorption, rather than internal diffusion, controlled the main rate-limiting step in the selenite aging process. Both machine learning and traditional correlation analysis indicated aging time was the most critical feature and the key soil property that contributed to available Se was pH. Empirical models incorporating soil properties and aging time were developed to predict changes of available Se in soil during aging under aerobic conditions. The reliability of the prediction model was further validated using data collected from previous studies. The developed aging model could potentially be used to scale biofortification data of Se generated from different soils under different aging times.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117074"},"PeriodicalIF":5.6,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.geoderma.2024.117073
Yi-Wen Cao , Xiao-Bo Wang , Chao Wang , Edith Bai , Nanping Wu
As an essential nutrient element for biological growth and metabolism, sulfur is closely interlinked with the carbon and nitrogen cycles, and it is one of the limiting elements for grassland productivity. Here we investigated the spatial distribution of sulfur contents and 34S stable isotope along the North China Transect (NCT), with the aim to explore the shaping role of the aridity index (AI) gradient on sulfur cycling dynamic in arid and semi-arid grasslands. In the area with AI < 0.12, soil sulfur contents and sulfur isotopic compositions (δ34S) showed no correlation with AI, indicating that abiotic processes predominantly govern the sulfur cycle in this area. In the area where 0.12 ≤ AI < 0.32, both sulfur contents and δ34S values increased with rising AI, with microbial-mediated reduction being the primary sulfur cycling process. In the area with 0.32 ≤ AI < 0.60, soil sulfur contents continued to increase with higher AI, but δ34S significantly decreased as AI increased, suggesting plant uptake as the dominant sulfur cycling process in this area. This study demonstrated the significant impact of AI on sulfur dynamics, providing insights into the different drivers of sulfur cycling along the aridity gradient, and offering guidance for developing targeted strategies under global climate change.
硫作为生物生长和代谢所必需的营养元素，与碳、氮循环密切相关，是草原生产力的限制性元素之一。本文研究了华北断裂带硫含量和34S稳定同位素的空间分布，旨在探讨干旱和半干旱草原干旱指数（AI）梯度对硫循环动态的塑造作用。在干旱指数为0.12的地区，土壤硫含量和硫同位素组成（δ34S）与干旱指数无相关性，表明该地区的硫循环主要受非生物过程的影响。在 0.12 ≤ AI < 0.32 的区域，硫含量和 δ34S 值都随着 AI 的上升而增加，微生物介导的还原是主要的硫循环过程。在 0.32 ≤ AI < 0.60 的地区，土壤中的硫含量随着 AI 的升高而继续增加，但随着 AI 的升高，δ34S 显著下降，这表明植物吸收是该地区最主要的硫循环过程。这项研究证明了人工合成指数对硫动力学的重大影响，为深入了解干旱梯度硫循环的不同驱动因素提供了见解，并为在全球气候变化下制定有针对性的策略提供了指导。
{"title":"Sulfur biogeochemical dynamics of grassland soils in northern China transect along an aridity gradient","authors":"Yi-Wen Cao , Xiao-Bo Wang , Chao Wang , Edith Bai , Nanping Wu","doi":"10.1016/j.geoderma.2024.117073","DOIUrl":"10.1016/j.geoderma.2024.117073","url":null,"abstract":"<div><div>As an essential nutrient element for biological growth and metabolism, sulfur is closely interlinked with the carbon and nitrogen cycles, and it is one of the limiting elements for grassland productivity. Here we investigated the spatial distribution of sulfur contents and <sup>34</sup>S stable isotope along the North China Transect (NCT), with the aim to explore the shaping role of the aridity index (AI) gradient on sulfur cycling dynamic in arid and semi-arid grasslands. In the area with AI < 0.12, soil sulfur contents and sulfur isotopic compositions (δ<sup>34</sup>S) showed no correlation with AI, indicating that abiotic processes predominantly govern the sulfur cycle in this area. In the area where 0.12 ≤ AI < 0.32, both sulfur contents and δ<sup>34</sup>S values increased with rising AI, with microbial-mediated reduction being the primary sulfur cycling process. In the area with 0.32 ≤ AI < 0.60, soil sulfur contents continued to increase with higher AI, but δ<sup>34</sup>S significantly decreased as AI increased, suggesting plant uptake as the dominant sulfur cycling process in this area. This study demonstrated the significant impact of AI on sulfur dynamics, providing insights into the different drivers of sulfur cycling along the aridity gradient, and offering guidance for developing targeted strategies under global climate change.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117073"},"PeriodicalIF":5.6,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.geoderma.2024.117069
Lulu Zhang , Cornelis A.M. Van Gestel , Yingshi Liu , Zhian Li
The sub-lethal ecotoxicity of field-contaminated soils toward small soil fauna, such as enchytraeids, remains understudied but holds paramount importance in soil pollution assessment. This study employed Enchytraeus crypticus to evaluate metal-contaminated soils from a mining area across various levels of biological organization, including individual level responses (survival, growth, reproduction, Cd/Pb/Zn accumulation), cellular level effects (peroxidase (POD), superoxide dismutase (SOD), glutathione (GSH), catalase (CAT), acetylcholinesterase (AChE), lipid peroxidation malondialdehyde (MDA)) and genetic alterations (olive tail moment (OTM) and tail DNA%). The study revealed considerable Cd and Pb accumulation, exerting adverse impacts on the reproduction and growth of the enchytraeids after a 21-day exposure. Changes in cellular and genetic parameters occurred with increasing exposure concentration and duration, indicating heightened lipid peroxidation and DNA damage in enchytraeids. A noteworthy metal detoxification process, evident at a physical level, was identified in E. crypticus, characterized by an initial escalation in toxicity followed by a subsequent decline. A distinctive complementary mechanism governing oxidative damage was detected in the enchytraeids, with an initial suppression of CAT activity, followed by inductions in SOD, POD, and GSH activity. Over the exposure duration, MDA content and DNA damage in the enchytraeids exhibited concentration-dependent shifts indicating their potential as efficient early-warning indicators for assessing the impact of Pb-Zn mining soils. This study contributes to a comprehensive understanding of the toxicological implications of metal-contaminated soils within the soil-enchytraeid framework.
{"title":"Responses in different levels of biological organization in the soil invertebrate Enchytraeus crypticus exposed to field-contaminated soils from a mining area","authors":"Lulu Zhang , Cornelis A.M. Van Gestel , Yingshi Liu , Zhian Li","doi":"10.1016/j.geoderma.2024.117069","DOIUrl":"10.1016/j.geoderma.2024.117069","url":null,"abstract":"<div><div>The sub-lethal ecotoxicity of field-contaminated soils toward small soil fauna, such as enchytraeids, remains understudied but holds paramount importance in soil pollution assessment. This study employed <em>Enchytraeus crypticus</em> to evaluate metal-contaminated soils from a mining area across various levels of biological organization, including individual level responses (survival, growth, reproduction, Cd/Pb/Zn accumulation), cellular level effects (peroxidase (POD), superoxide dismutase (SOD), glutathione (GSH), catalase (CAT), acetylcholinesterase (AChE), lipid peroxidation malondialdehyde (MDA)) and genetic alterations (olive tail moment (OTM) and tail DNA%). The study revealed considerable Cd and Pb accumulation, exerting adverse impacts on the reproduction and growth of the enchytraeids after a 21-day exposure. Changes in cellular and genetic parameters occurred with increasing exposure concentration and duration, indicating heightened lipid peroxidation and DNA damage in enchytraeids. A noteworthy metal detoxification process, evident at a physical level, was identified in <em>E. crypticus</em>, characterized by an initial escalation in toxicity followed by a subsequent decline. A distinctive complementary mechanism governing oxidative damage was detected in the enchytraeids, with an initial suppression of CAT activity, followed by inductions in SOD, POD, and GSH activity. Over the exposure duration, MDA content and DNA damage in the enchytraeids exhibited concentration-dependent shifts indicating their potential as efficient early-warning indicators for assessing the impact of Pb-Zn mining soils. This study contributes to a comprehensive understanding of the toxicological implications of metal-contaminated soils within the soil-enchytraeid framework.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117069"},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.geoderma.2024.117071
Xingxing Yu , Bo Xiao , Yousong Cao , Stephen R. Hoon , Giora J. Kidron
Biocrusts are an important component of dryland ecosystems as they perform crucial ecological functions like stabilizing soils, mediating the hydrological cycle, and improving nutrient availability. The high mechanical stability of biocrusts is understood to be linked to exopolymeric substances (EPS), which in turn, are responsible for the adsorption of various ions and chemical compounds. This study aimed to investigate the chemical composition of biocrusts and assess potential correlations between their chemical composition and mechanical stability. Samples of three types of biocrusts (cyanobacteria, cyanobacteria and moss mixed, and moss crusts) and bare soil (as control) were collected from the northern Loess Plateau of China. The inorganic ions and organic compounds present in biocrusts were quantified using inductively coupled plasma-optic emission spectrometry, ion chromatography, and gas chromatography-mass spectrometry. Biocrust mechanical stability was assessed by penetration resistance (PR) and the mean weight diameter (MWD) of aggregates. Finally, the contribution of inorganic ions and organic compounds to the stability of the biocrusts was elucidated. The results indicated that all three types of biocrusts were more stable than bare soil, with moss crusts being the most stable. Chemical analyses revealed an enrichment of inorganic ions such as K+, Ca2+, Na+, Mg2+, SO42–, and halogen ions within the biocrusts, while they showed a depletion of Fe2+, Al3+, and NO3–. Ten types of organic compounds were identified in biocrusts and bare soil, with medium-chain alkanes and long-chain acids being the dominant compounds. In some cases, acids accounted for more than 40 % of the total organic compound content of the biocrusts. Redundancy analysis showed that the content of inorganic ions, such as Ca2+ and Mg2+, and organic compounds such as acids, amides, and alkenes, exhibited the closest association with the biocrust stability. Partial least squares path modeling indicated that both inorganic ions and organic compounds indirectly affected biocrust stability by influencing electric conductivity, bulk density, EPS, and fine particle (clay and silt) content. A strong association was found between the inorganic ions and both PR and MWD (0.658 and 0.744, respectively), whilst the total effects of organic compounds on PR and MWD were 0.814 and 0.801, respectively. It is suggested that both the magnitude and types of chemicals associated with EPS indicate their capability to grant mechanical stability of the biocrusts, which in turn is conducive to maintaining the critical functions of biocrusts in global drylands.
{"title":"Towards the mechanical stability of biocrusts in drylands: Insights from inorganic ions and organic compounds and their interactions","authors":"Xingxing Yu , Bo Xiao , Yousong Cao , Stephen R. Hoon , Giora J. Kidron","doi":"10.1016/j.geoderma.2024.117071","DOIUrl":"10.1016/j.geoderma.2024.117071","url":null,"abstract":"<div><div>Biocrusts are an important component of dryland ecosystems as they perform crucial ecological functions like stabilizing soils, mediating the hydrological cycle, and improving nutrient availability. The high mechanical stability of biocrusts is understood to be linked to exopolymeric substances (EPS), which in turn, are responsible for the adsorption of various ions and chemical compounds. This study aimed to investigate the chemical composition of biocrusts and assess potential correlations between their chemical composition and mechanical stability. Samples of three types of biocrusts (cyanobacteria, cyanobacteria and moss mixed, and moss crusts) and bare soil (as control) were collected from the northern Loess Plateau of China. The inorganic ions and organic compounds present in biocrusts were quantified using inductively coupled plasma-optic emission spectrometry, ion chromatography, and gas chromatography-mass spectrometry. Biocrust mechanical stability was assessed by penetration resistance (PR) and the mean weight diameter (MWD) of aggregates. Finally, the contribution of inorganic ions and organic compounds to the stability of the biocrusts was elucidated. The results indicated that all three types of biocrusts were more stable than bare soil, with moss crusts being the most stable. Chemical analyses revealed an enrichment of inorganic ions such as K<sup>+</sup>, Ca<sup>2+</sup>, Na<sup>+</sup>, Mg<sup>2+</sup>, SO<sub>4</sub><sup>2–</sup>, and halogen ions within the biocrusts, while they showed a depletion of Fe<sup>2+</sup>, Al<sup>3+</sup>, and NO<sub>3</sub><sup>–</sup>. Ten types of organic compounds were identified in biocrusts and bare soil, with medium-chain alkanes and long-chain acids being the dominant compounds. In some cases, acids accounted for more than 40 % of the total organic compound content of the biocrusts. Redundancy analysis showed that the content of inorganic ions, such as Ca<sup>2+</sup> and Mg<sup>2+</sup>, and organic compounds such as acids, amides, and alkenes, exhibited the closest association with the biocrust stability. Partial least squares path modeling indicated that both inorganic ions and organic compounds indirectly affected biocrust stability by influencing electric conductivity, bulk density, EPS, and fine particle (clay and silt) content. A strong association was found between the inorganic ions and both PR and MWD (0.658 and 0.744, respectively), whilst the total effects of organic compounds on PR and MWD were 0.814 and 0.801, respectively. It is suggested that both the magnitude and types of chemicals associated with EPS indicate their capability to grant mechanical stability of the biocrusts, which in turn is conducive to maintaining the critical functions of biocrusts in global drylands.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117071"},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.geoderma.2024.117070
Yunqi Xiong , Zhenzhen Zheng , Baofa Yin , Guoliang Li , Xinrong Wan , Ruyan Qian , Linfeng Li , Shuntian Guan , Yuan Liu , Yanfen Wang , Xiaoyong Cui , Jianqing Du , Kai Xue , Yanbin Hao
Livestock grazing may affect small mammalian herbivore-soil microbe interactions and their association with the structure and functions of the ecosystem. However, the role of factors such as vegetation and soil nutrients in regulating these impacts is not clear. Here we conducted a 9-year experiment in temperate steppe to study how Brandt’s vole (Lasiopodomys brandtii) affects the soil microbial community under different livestock grazing intensities. This experiment contained 12 field enclosures with three livestock grazing intensities: control (CK), light grazing (LG), and moderate grazing (MG). We found that vole activity does not significantly change soil microbial diversity under non-grazing conditions. However, under livestock grazing conditions, vole activity led to a significant reduction in soil bacterial diversity and an increase in fungal diversity, demonstrating the impacts of livestock grazing on rodents-soil microbe interactions. The activity of voles significantly altered soil bacterial community composition, with changes primarily attributed to variations in the relative abundance of the phyla Actinobacteria, Bacteroidetes, Firmicutes, Gemmatimonadetes, and Proteobacteria. The soil fungal community remained relatively stable despite vole activity, which can be attributed to the richness of fungal colonies in mycelium and their low sensitivity to changes in external conditions. Vole activity also influenced soil microbial functional groups, and the variations in these groups were further amplified by livestock grazing. Furthermore, the shift in the microbial community composition and diversity induced by vole activity were mainly associated with the reduction of plant aboveground biomass. Overall, our study suggested that livestock grazing enhanced the changes in the soil microbial community induced by rodents, underscoring the importance of managing livestock grazing regimes for grassland conservation.
{"title":"Livestock grazing strengthens the effect of vole activity on the soil microbial community","authors":"Yunqi Xiong , Zhenzhen Zheng , Baofa Yin , Guoliang Li , Xinrong Wan , Ruyan Qian , Linfeng Li , Shuntian Guan , Yuan Liu , Yanfen Wang , Xiaoyong Cui , Jianqing Du , Kai Xue , Yanbin Hao","doi":"10.1016/j.geoderma.2024.117070","DOIUrl":"10.1016/j.geoderma.2024.117070","url":null,"abstract":"<div><div>Livestock grazing may affect small mammalian herbivore-soil microbe interactions and their association with the structure and functions of the ecosystem. However, the role of factors such as vegetation and soil nutrients in regulating these impacts is not clear. Here we conducted a 9-year experiment in temperate steppe to study how Brandt’s vole (<em>Lasiopodomys brandtii</em>) affects the soil microbial community under different livestock grazing intensities. This experiment contained 12 field enclosures with three livestock grazing intensities: control (CK), light grazing (LG), and moderate grazing (MG). We found that vole activity does not significantly change soil microbial diversity under non-grazing conditions. However, under livestock grazing conditions, vole activity led to a significant reduction in soil bacterial diversity and an increase in fungal diversity, demonstrating the impacts of livestock grazing on rodents-soil microbe interactions. The activity of voles significantly altered soil bacterial community composition, with changes primarily attributed to variations in the relative abundance of the phyla Actinobacteria, Bacteroidetes, Firmicutes, Gemmatimonadetes, and Proteobacteria. The soil fungal community remained relatively stable despite vole activity, which can be attributed to the richness of fungal colonies in mycelium and their low sensitivity to changes in external conditions. Vole activity also influenced soil microbial functional groups, and the variations in these groups were further amplified by livestock grazing. Furthermore, the shift in the microbial community composition and diversity induced by vole activity were mainly associated with the reduction of plant aboveground biomass. Overall, our study suggested that livestock grazing enhanced the changes in the soil microbial community induced by rodents, underscoring the importance of managing livestock grazing regimes for grassland conservation.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"451 ","pages":"Article 117070"},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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