Pub Date : 2025-04-03DOI: 10.5194/egusphere-2025-1055
Inderjot Chahal, Adam W. Gillespie, Daniel D. Saurette, Laura L. Van Eerd
Abstract. Although soil C is a critical component of soil health, studies robustly exploring the agronomic and pedoclimatic effects on soil C are limited, especially at the landscape scale. Therefore, a dataset of 1511 samples from agricultural fields across Ontario was used to evaluate the impacts of agronomic and pedoclimatic factors on eight soil C indicators including chemistry and thermal stability of soil C using the programmed pyrolysis approach. Soil C quality and stability were largely controlled by the inherent soil characteristics such as soil texture. Significant interactive effects of cropping system and tillage intensity on soil C indicators were observed; however, the number of significant effects varied among the three soil textural classes. All soil C indicators were significantly different among the cropping systems for the coarse textured soils, but the cropping system differences decreased under medium and fine textured soils. From the pyrolysis analysis, the hydrogen index (HI) and oxygen index (OI) also confirmed that the soil C chemistry was influenced by the cropping system. For instance, orchard systems had stable pools of soil C whereas vegetable systems were associated with less advanced degree of soil C decomposition. Remaining soil management variables (cover crop use, tillage intensity, and organic amendments) had less influence on soil C indicators in all soil textural classes. Principal component analysis revealed a close association of soil C indicators with the mean annual precipitation (MAP) and cropping system; suggesting that the quantity and quality of soil C inputs associated with different cropping systems and increase in precipitation had a large influence on soil C. Our results confirm the significant effects of agronomic and pedoclimatic variables on chemistry, thermal stability, and composition of soil C pools, which have long-term implications on soil C storage, mitigating global climate change, and improving soil health.
{"title":"Drivers of soil C quality and stability: Insights from a topsoil dataset at landscape scale in Ontario, Canada","authors":"Inderjot Chahal, Adam W. Gillespie, Daniel D. Saurette, Laura L. Van Eerd","doi":"10.5194/egusphere-2025-1055","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1055","url":null,"abstract":"<strong>Abstract.</strong> Although soil C is a critical component of soil health, studies robustly exploring the agronomic and pedoclimatic effects on soil C are limited, especially at the landscape scale. Therefore, a dataset of 1511 samples from agricultural fields across Ontario was used to evaluate the impacts of agronomic and pedoclimatic factors on eight soil C indicators including chemistry and thermal stability of soil C using the programmed pyrolysis approach. Soil C quality and stability were largely controlled by the inherent soil characteristics such as soil texture. Significant interactive effects of cropping system and tillage intensity on soil C indicators were observed; however, the number of significant effects varied among the three soil textural classes. All soil C indicators were significantly different among the cropping systems for the coarse textured soils, but the cropping system differences decreased under medium and fine textured soils. From the pyrolysis analysis, the hydrogen index (HI) and oxygen index (OI) also confirmed that the soil C chemistry was influenced by the cropping system. For instance, orchard systems had stable pools of soil C whereas vegetable systems were associated with less advanced degree of soil C decomposition. Remaining soil management variables (cover crop use, tillage intensity, and organic amendments) had less influence on soil C indicators in all soil textural classes. Principal component analysis revealed a close association of soil C indicators with the mean annual precipitation (MAP) and cropping system; suggesting that the quantity and quality of soil C inputs associated with different cropping systems and increase in precipitation had a large influence on soil C. Our results confirm the significant effects of agronomic and pedoclimatic variables on chemistry, thermal stability, and composition of soil C pools, which have long-term implications on soil C storage, mitigating global climate change, and improving soil health.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"38 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.5194/egusphere-2025-1067
Jennifer Michel, Yves Brostaux, Bernard Longdoz, Hervé Vanderschuren, Pierre Delaplace
Abstract. Priming effects in soil science describe the influence of labile carbon inputs on rates of microbial soil organic matter mineralisation, which can either increase (positive priming) or decrease (negative priming). While both positive and negative priming effects occur in natural ecosystems, the latter is less documented in the peer-reviewed literature and the overall impact of priming effects on the carbon balance of vegetated ecosystems remains elusive. Here, we highlight three aspects which need to be discussed to ensure (rhizosphere) priming effects are correctly perceived in their ecological context and measured at appropriate scales: (i) We emphasize the importance of evaluating net C balances because usually experimental C inputs exceed C losses meaning even positive priming doesn’t cause net C-loss; (ii) We caution against publication bias, which forces overrepresentation of positive priming effects, neglects negative or no priming, and potentially misguides conclusions about C loss; and (iii) We highlight the need to distinguish between general priming effects and rhizosphere- specific priming, which differ in their scale and driving factors, and hence require different methodological approaches. Future research should explore potential discrepancies between laboratory and field studies and examine the role of rhizosphere priming in nutrient cycling and plant nutrition.
{"title":"What if publication bias is the rule and net carbon loss from priming the exception?","authors":"Jennifer Michel, Yves Brostaux, Bernard Longdoz, Hervé Vanderschuren, Pierre Delaplace","doi":"10.5194/egusphere-2025-1067","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1067","url":null,"abstract":"<strong>Abstract.</strong> Priming effects in soil science describe the influence of labile carbon inputs on rates of microbial soil organic matter mineralisation, which can either increase (positive priming) or decrease (negative priming). While both positive and negative priming effects occur in natural ecosystems, the latter is less documented in the peer-reviewed literature and the overall impact of priming effects on the carbon balance of vegetated ecosystems remains elusive. Here, we highlight three aspects which need to be discussed to ensure (rhizosphere) priming effects are correctly perceived in their ecological context and measured at appropriate scales: (i) We emphasize the importance of evaluating net C balances because usually experimental C inputs exceed C losses meaning even positive priming doesn’t cause net C-loss; (ii) We caution against publication bias, which forces overrepresentation of positive priming effects, neglects negative or no priming, and potentially misguides conclusions about C loss; and (iii) We highlight the need to distinguish between general priming effects and rhizosphere- specific priming, which differ in their scale and driving factors, and hence require different methodological approaches. Future research should explore potential discrepancies between laboratory and field studies and examine the role of rhizosphere priming in nutrient cycling and plant nutrition.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"52 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.5194/soil-11-267-2025
Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, Lutz Weihermüller
Abstract. Adding mineral fertilizers and nutrients is a common practice in conventional farming and is fundamental to maintain optimal yield and crop quality; nitrogen is the most applied fertilizer and is often used excessively, leading to adverse environmental impacts. To assist farmers in optimal fertilization and crop management, non-invasive geophysical methods can provide knowledge about the spatial and temporal distribution of nutrients in the soil. In recent years, electromagnetic induction (EMI) has been widely used for field characterization, to delineate soil units and management zones, or to estimate soil properties and states. Additionally, ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) have been used in local studies to measure changes in soil properties. Unfortunately, the measured geophysical signals are confounded by horizontal and vertical changes in soil conditions and parameters, and the individual contributions of these conditions and parameters are not easy to disentangle. Within fields, and also between fields, fertilization management might vary in space and time, and, therefore, the differences in pore fluid conductivity caused directly by fertilization or indirectly by different crop performance make the interpretation of large-scale geophysical surveys over field borders complicated. To study the direct effect of mineral fertilization on the soil electrical conductivity, a field experiment was performed on 21 bare-soil plots with seven different fertilization treatments. As fertilizers, calcium ammonium nitrate (CAN) and potassium chloride (KCl) were chosen and applied in three dosages. Soil water content, soil temperature, and bulk electrical conductivity were recorded continuously over 450 d. Additionally, 20 EMI, 7 GPR, and 9 ERT surveys were performed, and on days of ERT measurements, soil samples for nitrate and reference soil electrical conductivity measurements were taken. The results showed that (1) the commonly used CAN application dosage did not impact the geophysical signals significantly. (2) EMI and ERT were able to trace back the temporal changes in nitrate concentrations in the soil profile over more than 1 year. (3) Both techniques were not able to trace the nitrate concentrations in the very shallow soil layer of 0–10 cm, irrespective of the low impact of fertilization on the geophysical signal. (4) The results indicated that past fertilization practices cannot be neglected in EMI studies, especially if surveys are performed over large areas with different fertilization practices or on crops grown with different fertilizer demands or uptakes.
{"title":"Assessing soil fertilization effects using time-lapse electromagnetic induction","authors":"Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, Lutz Weihermüller","doi":"10.5194/soil-11-267-2025","DOIUrl":"https://doi.org/10.5194/soil-11-267-2025","url":null,"abstract":"Abstract. Adding mineral fertilizers and nutrients is a common practice in conventional farming and is fundamental to maintain optimal yield and crop quality; nitrogen is the most applied fertilizer and is often used excessively, leading to adverse environmental impacts. To assist farmers in optimal fertilization and crop management, non-invasive geophysical methods can provide knowledge about the spatial and temporal distribution of nutrients in the soil. In recent years, electromagnetic induction (EMI) has been widely used for field characterization, to delineate soil units and management zones, or to estimate soil properties and states. Additionally, ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) have been used in local studies to measure changes in soil properties. Unfortunately, the measured geophysical signals are confounded by horizontal and vertical changes in soil conditions and parameters, and the individual contributions of these conditions and parameters are not easy to disentangle. Within fields, and also between fields, fertilization management might vary in space and time, and, therefore, the differences in pore fluid conductivity caused directly by fertilization or indirectly by different crop performance make the interpretation of large-scale geophysical surveys over field borders complicated. To study the direct effect of mineral fertilization on the soil electrical conductivity, a field experiment was performed on 21 bare-soil plots with seven different fertilization treatments. As fertilizers, calcium ammonium nitrate (CAN) and potassium chloride (KCl) were chosen and applied in three dosages. Soil water content, soil temperature, and bulk electrical conductivity were recorded continuously over 450 d. Additionally, 20 EMI, 7 GPR, and 9 ERT surveys were performed, and on days of ERT measurements, soil samples for nitrate and reference soil electrical conductivity measurements were taken. The results showed that (1) the commonly used CAN application dosage did not impact the geophysical signals significantly. (2) EMI and ERT were able to trace back the temporal changes in nitrate concentrations in the soil profile over more than 1 year. (3) Both techniques were not able to trace the nitrate concentrations in the very shallow soil layer of 0–10 cm, irrespective of the low impact of fertilization on the geophysical signal. (4) The results indicated that past fertilization practices cannot be neglected in EMI studies, especially if surveys are performed over large areas with different fertilization practices or on crops grown with different fertilizer demands or uptakes.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"183 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-31DOI: 10.5194/egusphere-2025-1046
Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, Mikko Sparf
Abstract. Peatlands are Earth's most carbon-dense terrestrial ecosystems and their carbon density varies with the depth of the peat layer. Accurate mapping of peat depth is crucial for carbon accounting and land management, yet existing maps lack the resolution and accuracy needed for these applications. This study evaluates whether digital soil mapping using remotely sensed data can improve existing maps of peat depth in western and southeastern Norway. Specifically, we assessed the predictive value of LiDAR-derived terrain variables and airborne radiometric data across two, >10 km2 sites. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution. The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Terrain variables were better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. Radiometric variables showed inconsistent predictive value – improving performance at one site while degrading it at the other. The accuracy of the national map of peat depth did not measure up to any of our remote sensing models, even though it was calibrated to the same data. Still, weak relationships with remotely sensed variables made peat depth hard to predict overall. Based on these findings, we conclude that digital soil mapping can improve existing, broad-scale maps of peat depth in Norway, but highly localized carbon stock assessments are best made from field measurements. Furthermore, the inability of models to identify peat presence outside known peatlands highlights the need for integrated mapping of peat lateral extent and depth. Together, these pathways promise more accurate landscape-scale carbon stock assessments and better-informed land management policies.
{"title":"Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes","authors":"Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, Mikko Sparf","doi":"10.5194/egusphere-2025-1046","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1046","url":null,"abstract":"<strong>Abstract.</strong> Peatlands are Earth's most carbon-dense terrestrial ecosystems and their carbon density varies with the depth of the peat layer. Accurate mapping of peat depth is crucial for carbon accounting and land management, yet existing maps lack the resolution and accuracy needed for these applications. This study evaluates whether digital soil mapping using remotely sensed data can improve existing maps of peat depth in western and southeastern Norway. Specifically, we assessed the predictive value of LiDAR-derived terrain variables and airborne radiometric data across two, >10 km<sup>2</sup> sites. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution. The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Terrain variables were better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. Radiometric variables showed inconsistent predictive value – improving performance at one site while degrading it at the other. The accuracy of the national map of peat depth did not measure up to any of our remote sensing models, even though it was calibrated to the same data. Still, weak relationships with remotely sensed variables made peat depth hard to predict overall. Based on these findings, we conclude that digital soil mapping can improve existing, broad-scale maps of peat depth in Norway, but highly localized carbon stock assessments are best made from field measurements. Furthermore, the inability of models to identify peat presence outside known peatlands highlights the need for integrated mapping of peat lateral extent and depth. Together, these pathways promise more accurate landscape-scale carbon stock assessments and better-informed land management policies.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"15 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-28DOI: 10.5194/egusphere-2025-911
William Trenti, Mauro De Feudis, Sara Marinari, Sergio Murolo, Giulia Tabanelli, Federico Puliga, Rosita Marabottini, Alessandra Zambonelli, Fausto Gardini, Livia Vittori Antisari
Abstract. Ink disease caused by the soil-borne Phytophthora cambivora and Phytophthora cinnamomi is threatening sweet chestnut (Castanea sativa) groves in Europe. This study aims to explore whether soil morphology and its related properties influence the development of chestnut ink disease considering the whole soil depth. In C. sativa stand in Northern Italy, along a small altitudinal transect, soil profiles were dug close to ink diseased plants (INK1 at 978 m a.s.l.) and healthy plants (INK2 988 m a.s.l. and INK3 at 998 m a.s.l.) and each soil horizon evaluated for its properties. Further, INK1, INK2 and INK3 had a slope of 3, 9 and 30 %, respectively. The results showed that the lower slope position of INK1 combined with the lower slope gradient than INK2 and INK3 might have promoted the transport of clay particles and water from the latters to the former. Such process allowed the accumulation of clay within the whole INK1 soil profile increasing the saturated hydraulic conductivity and the wilting point. Such soil features might promote the water accumulation within the deeper soil horizons of INK1 which would explain the presence of Phytophthora spp. DNA. The presence of the root pathogen in INK1 might have affected the microbial functionality as observed by the higher abundance of the contact and medium-distance exploration ectomycorrhizal fungal community than the long-distance types. Finally, such study highlighted the pivotal role of soil processes (i.e., clay and water transport) to shape the soil microbial community and soil-borne pathogens because of the changes of edaphic properties.
{"title":"Do morphological hillslope features affect soil properties and processes promoting chestnut ink disease? The study case of the Northern Apennine mountains","authors":"William Trenti, Mauro De Feudis, Sara Marinari, Sergio Murolo, Giulia Tabanelli, Federico Puliga, Rosita Marabottini, Alessandra Zambonelli, Fausto Gardini, Livia Vittori Antisari","doi":"10.5194/egusphere-2025-911","DOIUrl":"https://doi.org/10.5194/egusphere-2025-911","url":null,"abstract":"<strong>Abstract.</strong> Ink disease caused by the soil-borne Phytophthora cambivora and Phytophthora cinnamomi is threatening sweet chestnut (Castanea sativa) groves in Europe. This study aims to explore whether soil morphology and its related properties influence the development of chestnut ink disease considering the whole soil depth. In C. sativa stand in Northern Italy, along a small altitudinal transect, soil profiles were dug close to ink diseased plants (INK1 at 978 m a.s.l.) and healthy plants (INK2 988 m a.s.l. and INK3 at 998 m a.s.l.) and each soil horizon evaluated for its properties. Further, INK1, INK2 and INK3 had a slope of 3, 9 and 30 %, respectively. The results showed that the lower slope position of INK1 combined with the lower slope gradient than INK2 and INK3 might have promoted the transport of clay particles and water from the latters to the former. Such process allowed the accumulation of clay within the whole INK1 soil profile increasing the saturated hydraulic conductivity and the wilting point. Such soil features might promote the water accumulation within the deeper soil horizons of INK1 which would explain the presence of Phytophthora spp. DNA. The presence of the root pathogen in INK1 might have affected the microbial functionality as observed by the higher abundance of the contact and medium-distance exploration ectomycorrhizal fungal community than the long-distance types. Finally, such study highlighted the pivotal role of soil processes (i.e., clay and water transport) to shape the soil microbial community and soil-borne pathogens because of the changes of edaphic properties.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"61 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-20DOI: 10.5194/egusphere-2025-1122
Shenggang Chen, Yaqi Zhang, Jun Ma, Mingyue Bai, Jinxiao Long, Ming Liu, Yinglong Chen, Jianbin Guo, Lin Chen
Abstract. Microbial necromass carbon(MNC) is a significant source of soil organic carbon (SOC), the quantitative contribution of MNC to distinct SOC fractions and its regulatory mechanisms across various grassland types remain largely unexplored. This study through a comprehensive investigation of soil profiles (0–20 cm, 20–40 cm, and 40–100 cm) across four grassland types in Ningxia, China, encompassing meadow steppe (MS), typical steppe (TS), desert steppe (DS), and steppe desert (SD). We quantified mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their respective microbial necromass components, including total microbial necromass carbon (TNC), fungal necromass carbon (FNC), and bacterial necromass carbon (BNC), and analyzed the contributions to SOC fractions and influencing factors. Our findings reveal three key insights. First, the contents of MAOC and POC in the 0–100 cm soil layer were in the following order of magnitude: Meadow steppe (MS) >Typical steppe (TS) > Desert steppe (DS) > Steppe desert (SD), with the average content of POC was 9.3 g/kg, which was higher than the average content of MAOC (8.73 g/kg). Second, the content of microbial TNC in MAOC and POC decreased with the depth of the soil layer, the average content of FNC was 3.02 g/kg and 3.85 g/kg, which was higher than the average content of BNC (1.64 g/kg and 2.08 g/kg). FNC dominated both MAOC and POC, and its contribution was higher than the contribution of BNC. Thid, through regression analysis and random forest modeling, we identified key environmental drivers of MNC dynamics: mean annual rainfall (MAP), electrical conductance (EC), and soil total nitrogen(TN) emerged as primary regulators in surface soils (0–20cm), while available potassium(AK), SOC, and mean annual temperature (MAT) dominated deeper soil layers (20–100 cm). This research by: 1) establishing the vertical distribution patterns of MNC and SOC fractions in soil profiles; 2) quantifying the relative contributions of MNC to SOC fractions across different grassland ecosystems soil profiles and elucidating their environmental controls, offering a deeper understanding of the mechanisms driving MNC to soc fractions accumulation in diverse grassland ecosystems, and provide data support for further research on the microbiological mechanisms of soil organic carbon formation and accumulation in arid and semi-arid regions.
{"title":"Contribution of soil Microbial Necromass Carbon to Soil Organic Carbon fractions and its influencing factors in different grassland types","authors":"Shenggang Chen, Yaqi Zhang, Jun Ma, Mingyue Bai, Jinxiao Long, Ming Liu, Yinglong Chen, Jianbin Guo, Lin Chen","doi":"10.5194/egusphere-2025-1122","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1122","url":null,"abstract":"<strong>Abstract.</strong> Microbial necromass carbon(MNC) is a significant source of soil organic carbon (SOC), the quantitative contribution of MNC to distinct SOC fractions and its regulatory mechanisms across various grassland types remain largely unexplored. This study through a comprehensive investigation of soil profiles (0–20 cm, 20–40 cm, and 40–100 cm) across four grassland types in Ningxia, China, encompassing meadow steppe (MS), typical steppe (TS), desert steppe (DS), and steppe desert (SD). We quantified mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their respective microbial necromass components, including total microbial necromass carbon (TNC), fungal necromass carbon (FNC), and bacterial necromass carbon (BNC), and analyzed the contributions to SOC fractions and influencing factors. Our findings reveal three key insights. First, the contents of MAOC and POC in the 0–100 cm soil layer were in the following order of magnitude: Meadow steppe (MS) >Typical steppe (TS) > Desert steppe (DS) > Steppe desert (SD), with the average content of POC was 9.3 g/kg, which was higher than the average content of MAOC (8.73 g/kg). Second, the content of microbial TNC in MAOC and POC decreased with the depth of the soil layer, the average content of FNC was 3.02 g/kg and 3.85 g/kg, which was higher than the average content of BNC (1.64 g/kg and 2.08 g/kg). FNC dominated both MAOC and POC, and its contribution was higher than the contribution of BNC. Thid, through regression analysis and random forest modeling, we identified key environmental drivers of MNC dynamics: mean annual rainfall (MAP), electrical conductance (EC), and soil total nitrogen(TN) emerged as primary regulators in surface soils (0–20cm), while available potassium(AK), SOC, and mean annual temperature (MAT) dominated deeper soil layers (20–100 cm). This research by: 1) establishing the vertical distribution patterns of MNC and SOC fractions in soil profiles; 2) quantifying the relative contributions of MNC to SOC fractions across different grassland ecosystems soil profiles and elucidating their environmental controls, offering a deeper understanding of the mechanisms driving MNC to soc fractions accumulation in diverse grassland ecosystems, and provide data support for further research on the microbiological mechanisms of soil organic carbon formation and accumulation in arid and semi-arid regions.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"37 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The Sudano-Sahelian zone of Cameroon, characterized by a low annual rainfall, faces challenges in soil fertility preservation due to agricultural intensification and unsustainable practices. This study aims to evaluate the effect of trachyte and basalt powders inputs on soil and maize yield in Guiring experimental farm. Fieldwork involved collecting and describing samples of trachyte, basalt, and soil and setting up the experimental design. In the laboratory, the ground rock samples underwent geochemical analysis, and the soil samples were analysed for their mineralogical and physicochemical properties. The experiment followed a completely randomized block design with three repetitions and six treatments (T0, T1, T2, T3, T4 and T5). The soil consists of kaolinite, smectite, sepiolite, and quartz. Its texture is dominated by sand fraction, with a neutral pH (6.98). The organic matter (1.30 to 3.17 %) and total nitrogen contents (0.11 to 0.13 %) are relatively low. The concentrations of potassium, magnesium, sodium, and calcium vary from 0.10 to 0.40 cmolc kg-1, 0.72 to 5.44 cmolc kg-1, 0.13 to 0.56 cmolc kg-1, and 2.64 to 6 cmolc kg-1, respectively. The cation exchange capacity is moderate to high, ranging from 18.70 to 25 cmolc kg-1, while the available phosphorus content is high, ranging from 12.60 to 30.30 mg kg-1. The studied soils are moderately suitable for maize cultivation. Fertilization trials showed a significant improvement in maize growth and yield, within plots treated with basalt powder yielding higher (2558.64 kg ha-1 and 2931.16 kg ha-1) than those treated with trachyte powder (2362.87 kg ha-1and 2763.91 kg ha-1) and the control plots (645.83 kg ha-1). Plots treated with NPK fertilizer recorded the highest yield (3164.45 kg ha-1). Although the treatment with conventional fertiliser resulted in a relative higher yield, the advantage of using rock powders lies in their environmental benefits, long-term effectiveness, and more affordable cost.
摘要。喀麦隆的苏丹-萨赫勒地区的特点是年降雨量少，由于农业集约化和不可持续的做法，在土壤肥力保持方面面临挑战。本研究旨在评价粗叶菌粉和玄武岩粉投入量对桂陵试验田土壤和玉米产量的影响。野外工作包括收集和描述粗面岩、玄武岩和土壤样品，并建立实验设计。在实验室中，对地面岩石样品进行了地球化学分析，对土壤样品进行了矿物学和理化性质分析。试验采用完全随机区组设计，3个重复，6个处理（T0、T1、T2、T3、T4和T5）。土壤由高岭石、蒙脱石、海泡石和石英组成。其质地以砂粒为主，pH为中性（6.98）。有机质含量（1.30 ~ 3.17%）和全氮含量（0.11 ~ 0.13%）较低。钾、镁、钠和钙的浓度分别为0.10至0.40、0.72至5.44、0.13至0.56和2.64至6 cmolc kg-1。阳离子交换容量中高，在18.70 ~ 25 cmolc kg-1之间，有效磷含量高，在12.60 ~ 30.30 mg kg-1之间。所研究的土壤适宜种植玉米。施肥试验表明，玄武岩粉处理的玉米产量（2558.64 kg ha-1和2931.16 kg ha-1）显著高于粗叶菌粉处理（2362.87 kg ha-1和2763.91 kg ha-1）和对照（645.83 kg ha-1）。施用氮磷钾的地块产量最高（3164.45 kg hm -1）。虽然用常规肥料处理导致相对较高的产量，但使用岩石粉的优势在于其环境效益，长期有效性和更实惠的成本。
{"title":"Effect of trachyte and basalt powder on the growth and yield of maize (Zea Mays L.) in the Sudano-Sahelian zone of Cameroon (Central Africa)","authors":"Bienvenu Sidsi, Claudine Vounba, Simon Djakba Basga, Aubin Nzeugang Nzeukou, Merlin Dedzo Gountie, Désiré Tsozué","doi":"10.5194/egusphere-2025-930","DOIUrl":"https://doi.org/10.5194/egusphere-2025-930","url":null,"abstract":"<strong>Abstract.</strong> The Sudano-Sahelian zone of Cameroon, characterized by a low annual rainfall, faces challenges in soil fertility preservation due to agricultural intensification and unsustainable practices. This study aims to evaluate the effect of trachyte and basalt powders inputs on soil and maize yield in Guiring experimental farm. Fieldwork involved collecting and describing samples of trachyte, basalt, and soil and setting up the experimental design. In the laboratory, the ground rock samples underwent geochemical analysis, and the soil samples were analysed for their mineralogical and physicochemical properties. The experiment followed a completely randomized block design with three repetitions and six treatments (T0, T1, T2, T3, T4 and T5). The soil consists of kaolinite, smectite, sepiolite, and quartz. Its texture is dominated by sand fraction, with a neutral pH (6.98). The organic matter (1.30 to 3.17 %) and total nitrogen contents (0.11 to 0.13 %) are relatively low. The concentrations of potassium, magnesium, sodium, and calcium vary from 0.10 to 0.40 cmol<sub>c</sub> kg<sup>-1</sup>, 0.72 to 5.44 cmol<sub>c</sub> kg<sup>-1</sup>, 0.13 to 0.56 cmol<sub>c</sub> kg<sup>-1</sup>, and 2.64 to 6 cmol<sub>c</sub> kg<sup>-1</sup>, respectively. The cation exchange capacity is moderate to high, ranging from 18.70 to 25 cmol<sub>c</sub> kg<sup>-1</sup>, while the available phosphorus content is high, ranging from 12.60 to 30.30 mg kg<sup>-1</sup>. The studied soils are moderately suitable for maize cultivation. Fertilization trials showed a significant improvement in maize growth and yield, within plots treated with basalt powder yielding higher (2558.64 kg ha<sup>-1</sup> and 2931.16 kg ha<sup>-1</sup>) than those treated with trachyte powder (2362.87 kg ha<sup>-1</sup>and 2763.91 kg ha<sup>-1</sup>) and the control plots (645.83 kg ha<sup>-1</sup>). Plots treated with NPK fertilizer recorded the highest yield (3164.45 kg ha<sup>-1</sup>). Although the treatment with conventional fertiliser resulted in a relative higher yield, the advantage of using rock powders lies in their environmental benefits, long-term effectiveness, and more affordable cost.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"56 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-20DOI: 10.5194/egusphere-2025-1012
Hannah Van Ryckel, Lynn Van Aelst, Toon van Dael, Erik Smolders
Abstract. Subsurface soil acidity severely limits crop growth and is challenging to adjust by surface liming. There have been several proposals for subsurface liming using the combination of lime and an organic amendment, as organic anions may migrate deeper in acid subsoil than carbonates. This study aimed to identify mechanisms of subsurface liming, postulating that it is hindered by dissolved organic carbon (DOC) adsorption but enhanced in structured compared to sieved soils due to preferential flow in macropores. Column leaching experiments were set up using three sieved acid soils with contrasting properties, of which one was additionally sampled as undisturbed soil cores. The upper layer of each soil was treated with lime, compost, or a combination of both, in addition to an untreated control and columns were leached with artificial rainwater. Deeper subsurface liming in the lime+compost treatment than in the lime treatment was detected in only one of the three soils. The effect of compost on the migration of alkalinity was explained by differences in DOC sorption among soils, the lowest sorption leading to deepest subsurface liming. Imaging of in situ pH using a planar optode showed evidence of preferential alkalinity flow in the structured soil, however destructive sampling of bulk soil layers did not confirm this. We conclude that combining lime with an organic amendment can effectively ameliorate subsoil acidity but this requires weakly DOC adsorbing subsoils. The role of soil structure on this process needs to be corroborated with plant responses to identify benefits of liming the macropores.
{"title":"Organic matter-mediated leaching of alkalinity in limed acid soils is affected by dissolved organic carbon adsorption and soil structure","authors":"Hannah Van Ryckel, Lynn Van Aelst, Toon van Dael, Erik Smolders","doi":"10.5194/egusphere-2025-1012","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1012","url":null,"abstract":"<strong>Abstract.</strong> Subsurface soil acidity severely limits crop growth and is challenging to adjust by surface liming. There have been several proposals for subsurface liming using the combination of lime and an organic amendment, as organic anions may migrate deeper in acid subsoil than carbonates. This study aimed to identify mechanisms of subsurface liming, postulating that it is hindered by dissolved organic carbon (DOC) adsorption but enhanced in structured compared to sieved soils due to preferential flow in macropores. Column leaching experiments were set up using three sieved acid soils with contrasting properties, of which one was additionally sampled as undisturbed soil cores. The upper layer of each soil was treated with lime, compost, or a combination of both, in addition to an untreated control and columns were leached with artificial rainwater. Deeper subsurface liming in the lime+compost treatment than in the lime treatment was detected in only one of the three soils. The effect of compost on the migration of alkalinity was explained by differences in DOC sorption among soils, the lowest sorption leading to deepest subsurface liming. Imaging of in situ pH using a planar optode showed evidence of preferential alkalinity flow in the structured soil, however destructive sampling of bulk soil layers did not confirm this. We conclude that combining lime with an organic amendment can effectively ameliorate subsoil acidity but this requires weakly DOC adsorbing subsoils. The role of soil structure on this process needs to be corroborated with plant responses to identify benefits of liming the macropores.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"92 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.5194/egusphere-2025-166
Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten
Abstract. Artificial Neural Networks (ANNs) are valuable tools for predicting soil properties using large datasets. However, a common challenge in soil sciences is the uneven distribution of soil samples, which often results from past sampling projects that heavily sample certain areas while leaving similar yet geographically distant regions under-sampled. One potential solution to this problem is to transfer an already trained model to other similar regions. Robust spatial uncertainty quantification is crucial for this purpose, yet often overlooked in current research. We address this issue by using a Bayesian deep learning technique, Laplace Approximations, to quantify spatial uncertainty. This produces a probability measure encoding where the model’s prediction is deemed reliable, and where a lack of data should lead to a high uncertainty. We train such an ANN on a soil landscape dataset from a specific region in southern Germany and then transfer the trained model to another unseen but to some extend similar region, without any further model training. The model effectively generalized alluvial patterns, demonstrating its ability to recognize repetitive features of river systems. However, the model showed a tendency to favor overrepresented soil units, underscoring the importance of balancing training datasets to reduce overconfidence in dominant classes. Quantifying uncertainty in this way allows stakeholders to better identify regions and settings in need of further data collection, enhancing decision-making and prioritizing efforts in data collection. Our approach is computationally lightweight and can be added post-hoc to existing deep learning solutions for soil prediction, thus offering a practical tool to improve soil property predictions in under-sampled areas, as well as optimizing future sampling strategies, ensuring resources are allocated efficiently for maximum data coverage and accuracy.
{"title":"Quantifying spatial uncertainty to improve soil predictions in data-sparse regions","authors":"Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten","doi":"10.5194/egusphere-2025-166","DOIUrl":"https://doi.org/10.5194/egusphere-2025-166","url":null,"abstract":"<strong>Abstract.</strong> Artificial Neural Networks (ANNs) are valuable tools for predicting soil properties using large datasets. However, a common challenge in soil sciences is the uneven distribution of soil samples, which often results from past sampling projects that heavily sample certain areas while leaving similar yet geographically distant regions under-sampled. One potential solution to this problem is to transfer an already trained model to other similar regions. Robust spatial uncertainty quantification is crucial for this purpose, yet often overlooked in current research. We address this issue by using a Bayesian deep learning technique, Laplace Approximations, to quantify spatial uncertainty. This produces a probability measure encoding where the model’s prediction is deemed reliable, and where a lack of data should lead to a high uncertainty. We train such an ANN on a soil landscape dataset from a specific region in southern Germany and then transfer the trained model to another unseen but to some extend similar region, without any further model training. The model effectively generalized alluvial patterns, demonstrating its ability to recognize repetitive features of river systems. However, the model showed a tendency to favor overrepresented soil units, underscoring the importance of balancing training datasets to reduce overconfidence in dominant classes. Quantifying uncertainty in this way allows stakeholders to better identify regions and settings in need of further data collection, enhancing decision-making and prioritizing efforts in data collection. Our approach is computationally lightweight and can be added post-hoc to existing deep learning solutions for soil prediction, thus offering a practical tool to improve soil property predictions in under-sampled areas, as well as optimizing future sampling strategies, ensuring resources are allocated efficiently for maximum data coverage and accuracy.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.5194/soil-11-247-2025
Nathalie Fromin
Abstract not available
摘要不可用
{"title":"Impacts of soil storage on microbial parameters","authors":"Nathalie Fromin","doi":"10.5194/soil-11-247-2025","DOIUrl":"https://doi.org/10.5194/soil-11-247-2025","url":null,"abstract":"Abstract not available","PeriodicalId":48610,"journal":{"name":"Soil","volume":"54 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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