Geoderma最新文献

{"title":"Developing diversity indicators from organic matter and microbe to depict their changes across different soil-landscapes in a subtropical hilly area","authors":"Zheng Zhao ,&nbsp;Chun Liu ,&nbsp;Yue Han ,&nbsp;Qingmei Lin ,&nbsp;Ruiling Ma ,&nbsp;Shuotong Chen ,&nbsp;Lianqing Li ,&nbsp;Genxing Pan","doi":"10.1016/j.geoderma.2025.117472","DOIUrl":"10.1016/j.geoderma.2025.117472","url":null,"abstract":"<div><div>Soil organic matter (SOM) molecular diversity and microbial functional diversity are critical for regulating soil organic carbon (SOC) persistence and maintaining soil ecological functions. Heterogeneous soil-landscapes critically regulate SOC sequestration processes and microbial community dynamics through distinct topographic conditions and anthropogenic influence in the area of Earth surface. Understanding how SOM diversity and microbial diversity interact and influence soil ecological functions across soil-landscapes within a heterogeneous area is key to developing targeted land management strategies, thereby achieving sustainable development goals for the region. In this study, topsoil samples (0–15 cm) were collected from four distinct soil-landscapes within a small watershed in a hilly area, including natural forestland (FL) on hillslopes, agricultural orchards (OR) and upland (UL) on downlands, and paddy fields (PF) within the basin. We used organic carbon (OC) molecular assays and microbial assays to investigate SOM molecular composition, and microbial activity and community structure in bulk soil and aggregate fractions, correlating SOM molecular composition with microbes. Our results indicate that the contribution of lignin to SOC decreased (except in PF), amino sugars increased, while glomalin-related soil proteins (GRSPs) remained unchanged, with the SOC loss in agricultural landscapes. Compared to FL, agricultural landscapes of PF, UL and OR showed significantly reduced mass proportions of larger-sized aggregates and decreased lignin contributions in macroaggregates and microaggregates, indicating that plant-derived lignin phenols play a major role in SOC accumulation. Based on SOM composition, microbial community and enzyme activities, we developed synthesis diversity indicators that effectively depict SOM-microbial links and soil ecological function across soil-landscapes, and these indicators showed significant positive correlations with SOC and microbial abundance. Collectively, our findings demonstrated the importance of lignin phenols in SOC accumulation and highlighted the differential roles of microbial-derived C (GRSPs and amino sugars) in SOC dynamics. By integrating investigations of SOM composition with microbial activity and community structure, along with using diversity indicators to describe the changes of SOM and microbes in different soil-landscapes, we can better understand SOM-microbial interactions and soil ecological function maintenance in diverse terrestrial soil-landscapes.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117472"},"PeriodicalIF":6.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil reinforcement potential of multifunctional crop plants on arable and non-arable tropical soils","authors":"Michael O. Adu ,&nbsp;Peter B. Obour ,&nbsp;Francis Kumi ,&nbsp;Emmanuel Arthur ,&nbsp;Paul A. Asare ,&nbsp;Eric Oppong Danso ,&nbsp;Samuel A. Banafo ,&nbsp;Kwabena A. Sanleri ,&nbsp;Hygienus Godswill ,&nbsp;Kofi Atiah ,&nbsp;Kwadwo K. Amoah ,&nbsp;Joel B.K. Asiedu ,&nbsp;Stephen E. Moore ,&nbsp;Donatus B. Angnuureng","doi":"10.1016/j.geoderma.2025.117470","DOIUrl":"10.1016/j.geoderma.2025.117470","url":null,"abstract":"<div><div>Root systems of grasses provide cost-effective, sustainable solution for soil stabilization, yet this potential in the roots of common arable crops remains largely unexplored. The objectives were to (i) evaluate the root tensile strength (<em>Tr</em>) of common arable crops (maize and sorghum) and a fodder plant (Napier grass) to assess their potential contribution to soil reinforcement, compared to vetiver grass, and (ii) assess the contribution of these crop plants to soil shear strength across different soil types and depths. A total of 48 soil columns measuring 70 cm height × 25 cm diameter (3 soil types × 4 crops × 4 replicates) were used for the experiment. The soils included two arable sandy loams and a sea sand. Root traits, including total root length (TRL), root length density (RLD), <em>Tr</em>, root area ratio (RAR), and root cohesion (<em>Cr</em>), were evaluated in addition to soil shear strength. Power law equations were fitted for <em>Tr</em>-root diameter relationships, and correlation analyses examined the relationship between soil shear strength and root variables. Multiple regression models were used to quantify the contribution of various root traits to the variation in soil shear strength. There were significant differences in <em>Tr</em> among crop plants, with Napier and vetiver grasses displaying the most robust roots. Soil columns planted with crops exhibited 45-80% higher mean soil shear strength than uncultivated soils, depending on the crop species, with Napier grass showing the highest values (63.99 ± 36.86 kPa). The plants grown in arable soils showed higher RAR and <em>Cr</em> in the uppermost soil layer and higher <em>Tr</em> values. Root traits explained 20–65 % of the variability in soil shear strength, with a positive linear relationship, and the contribution of these traits was more pronounced in arable soils (43–50 %) than in sea sand (21 %). The RLD was the main driver of soil shear strength, with the order of contribution to soil strength being RLD &gt; RAR &gt; TRL &gt; root volume &gt; <em>Cr</em>. Therefore, roots of common crops, mainly Napier grass and maize, can enhance soil shear strength, emphasising the potential of these plants for bioengineering applications, but tailored approaches based on specific soil types, crop species, and soil depths are vital.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117470"},"PeriodicalIF":6.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships","authors":"Lei Zhang ,&nbsp;Lin Yang ,&nbsp;Yuxin Ma ,&nbsp;A-Xing Zhu ,&nbsp;Ren Wei ,&nbsp;Jie Liu ,&nbsp;Mogens H. Greve ,&nbsp;Chenghu Zhou","doi":"10.1016/j.geoderma.2025.117466","DOIUrl":"10.1016/j.geoderma.2025.117466","url":null,"abstract":"<div><div>Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional- and global-scale soil–environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg⁻¹ averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117466"},"PeriodicalIF":6.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting spatial–temporal soil organic matter dynamics in a Mollisols region of the northern Songnen Plain, China, during 2009–2018 using a spectral-temporal feature set","authors":"Mei-Wei Zhang ,&nbsp;Xiao-Qing Wang ,&nbsp;Ya-Nan Zhou ,&nbsp;Mei-Nan Zhang ,&nbsp;Huan-Jun Liu ,&nbsp;Hao-Xuan Yang ,&nbsp;Ling-Tao Zeng ,&nbsp;Xiao-Lin Sun","doi":"10.1016/j.geoderma.2025.117461","DOIUrl":"10.1016/j.geoderma.2025.117461","url":null,"abstract":"<div><div>Similar to many other parts of the world, it is a necessity to reveal the spatial–temporal soil organic matter (SOM) dynamics of Mollisols in the northern Songnen Plain, a state key agricultural region in China. Although digital soil mapping (DSM) with temporal environmental covariates could fulfill this purpose, its accuracy still needs to be improved. The present study aimed to evaluate whether a spectral-temporal feature set derived from percentile transformations of time-series remote sensing images could be helpful for improving the accuracy, because the feature set is advantageous in providing wall-to-wall and stable information and having large dimensionality. The evaluation was conducted in the case of the Mollisols region, where a total of 334 soil samples were collected during 2009–2011 and 2014–2018 and measured for SOM contents. As environmental covariates, a spectral-temporal feature set consisting of a series of percentiles (i.e., 10 %, 25 %, 50 %, 75 %, and 90 %) of spectral bands and indices and corresponding means were derived from the MODIS/Terra images for every five years between 2009 and 2018, while the terrain and climate factors were also obtained. With these data, classification and regression tree (CART) and random forest (RF) were both employed to establish spatiotemporal models for predicting SOM content at five-year intervals from 2009 to 2018. Results showed that replacing the commonly used means and medians of spectral bands and indices in the two machine learning models with the spectral-temporal feature set improved the prediction accuracy, with an increase of the mean concordance correlation coefficient (CCC) by 1.94 %∼8.09 %. Further, the optimal RF model with the spectral-temporal feature set was used to generate SOM content maps for every five years between 2009 and 2018, which were validated based on the samples of 2009–2011, showing a CCC of 0.66. The resulted maps showed that the mean SOM content decreased from 2009 to 2018 by 0.04 %. An importance analysis showed that lots of the spectral-temporal features were the most important variables in the RF model, following the first important one (i.e., mean annual precipitation). As there were many spectral bands and indices, their sum importance was far larger than all the other kinds of environmental covariates. It is concluded that the spectral-temporal feature set is promising for deriving the spatial–temporal dynamics of soil in the future.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117461"},"PeriodicalIF":6.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drivers of soil organic carbon stocks and stability along elevation gradients","authors":"Nicolas Bonfanti ,&nbsp;Philippe Choler ,&nbsp;Norine Khedim ,&nbsp;Jean-Christophe Clément ,&nbsp;Pierre Barré ,&nbsp;Romain Goury ,&nbsp;François Baudin ,&nbsp;Lauric Cécillon ,&nbsp;Amélie Saillard ,&nbsp;Wilfried Thuiller ,&nbsp;Jerome Poulenard","doi":"10.1016/j.geoderma.2025.117452","DOIUrl":"10.1016/j.geoderma.2025.117452","url":null,"abstract":"<div><div>Estimating SOC stocks and stability, as well as modeling their response to rising temperatures, is crucial for predicting climate change impacts. This is particularly true in mountainous regions, where low temperatures slow down SOC decomposition, resulting in higher SOC stocks compared to soils at lower elevations. However, these stocks are also more vulnerable to warming, increasing the risk of SOC depletion. Such conditions create the potential for a positive feedback loop in which warming accelerates SOC losses, further amplifying climate change impacts on these sensitive ecosystems.</div><div>To better understand the factors controlling SOC stocks and stability in mountain soils, we sampled 170 soil profiles along 29 elevation gradients in the western Alps from 280 to 3160 m a.s.l. We assessed SOC stocks and chemical composition using mid-infrared spectroscopy method and SOC stability with Rock-Eval® thermal analysis. Our findings, based on an unprecedented dataset, reveal a clear elevational pattern in SOC properties. SOC stocks increase with elevation up to the montane belt (1200–1500  m a.s.l.), remain relatively stable through the subalpine zone, and then decline beyond the subalpine/alpine boundary (2200–2400  m a.s.l.). Notably, this transition is also marked by a significant drop in SOC stability, suggesting a shift in the dominant stabilization processes at higher elevations. Our results also indicate that SOC stocks and stability are influenced by a complex interplay of factors.</div><div>At higher elevations, climate emerges to be the dominant factor, whereas lithology and weathering play a more significant role at lower elevations. These results suggest that at high-elevations, harsh climatic conditions favor stabilization of SOC, while less developed soils limit organo-mineral interactions. In contrast, at warmer, lower elevations with higher carbon fluxes, more developed soils facilitate organo-mineral interactions, thereby enhancing SOC stability in the long term. Consequently, alpine grasslands, which contain substantial stocks of labile carbon stabilized by climatic conditions, appear to be particularly vulnerable to the effects of climate warming.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117452"},"PeriodicalIF":6.6,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cropping diversity is a main driver of soil health under intensive organic cropping systems","authors":"Stéphanie Lavergne ,&nbsp;Caroline Halde ,&nbsp;Derek H. Lynch","doi":"10.1016/j.geoderma.2025.117441","DOIUrl":"10.1016/j.geoderma.2025.117441","url":null,"abstract":"<div><div>The ability of organic cropping systems to sustain soil health may vary with management intensity. Little research has examined the impact of varying management practices on soil health within intensive organic field crop production systems. A field survey was conducted in the fall of 2019, 2020, and 2021 on 10 certified organic farms in Québec, Canada. Their cropping systems comprised an intensive three-year maize (<em>Zea mays</em> L.)-soybean (<em>Glycine</em> max [L.] Merr.) – small grain (i.e., winter or spring cereals) rotation. On each farm, soil health was measured on the three rotated crop fields in the fall of 2019, 2020, and 2021 (n = 90). The relationships between soil health indicators and indices of management practices were assessed. The 3-year Crop Diversity Index (CDI) ranged from 2 to 16 across the sampled fields, with the highest values observed where cover crops were used annually, and winter cereals were included in the rotation. Soil physical health indicators were positively influenced by higher CDI values. In contrast, higher Soil Tillage Intensity Ratings for tillage (STIR_tillage) had a negative effect on soil organic carbon (SOC) concentrations. Soil health indicators did not vary among crop phases, except for water-stable aggregates (WSA) which was greater in small grain fields (43.5 %) than in soybean fields (33.9 %). The results from this study demonstrated that soil health was positively influenced by increased crop diversity and reduced tillage intensity. These findings will help organic growers choose and refine best management practices to maintain soil health when cropping intensively.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117441"},"PeriodicalIF":6.6,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Bouasria (2025) concerning the methodology and scientific aspects of our study (Asrat et al., 2024)","authors":"Tadesse Gashaw Asrat","doi":"10.1016/j.geoderma.2025.117438","DOIUrl":"10.1016/j.geoderma.2025.117438","url":null,"abstract":"","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117438"},"PeriodicalIF":6.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Global patterns and controls of the soil microbial biomass response to elevated CO2”. [Geoderma 428 (2022)116153]","authors":"Shucheng Li ,&nbsp;Shu Xie ,&nbsp;Shijie Zhang ,&nbsp;Shilin Miao ,&nbsp;Shiming Tang ,&nbsp;Hongyang Chen ,&nbsp;Qiuwen Zhan","doi":"10.1016/j.geoderma.2025.117429","DOIUrl":"10.1016/j.geoderma.2025.117429","url":null,"abstract":"","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117429"},"PeriodicalIF":6.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Was the balanced stratified coverage sampling not well spatially distributed, or was it inadequately implemented?","authors":"Abdelkrim Bouasria","doi":"10.1016/j.geoderma.2025.117439","DOIUrl":"10.1016/j.geoderma.2025.117439","url":null,"abstract":"","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117439"},"PeriodicalIF":6.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chinese milk vetch incorporation inhibits nitrification by suppressing comammox Nitrospira in subtropical paddy soils","authors":"Mengmeng Feng ,&nbsp;Yongxin Lin ,&nbsp;Jia Liu ,&nbsp;Xiangyin Ni ,&nbsp;Yuheng Cheng ,&nbsp;Hang-Wei Hu ,&nbsp;Juntao Wang ,&nbsp;Luyuan Sun ,&nbsp;Zi-Yang He ,&nbsp;Ji-Zheng He","doi":"10.1016/j.geoderma.2025.117469","DOIUrl":"10.1016/j.geoderma.2025.117469","url":null,"abstract":"<div><div>Chinese milk vetch (<em>Astragalus sinicus</em> L.) incorporation (CVI), straw return (SR), and nitrogen reduction (NR) are common agricultural practices, but their impacts on soil nitrogen (N) cycling processes and associated microbial communities remain poorly understood. In this study, CVI, SR, and NR effects on soil net N mineralization and potential nitrification rates, the abundance/activity of ammonia oxidizers, and comammox <em>Nitrospira</em> (COMX) community structure were examined. While CVI significantly increased the net N mineralization rate and acid-hydrolysable N fraction, SR and NR did not affect these values. At the same time, CVI decreased the potential nitrification rate and reduced COMX clade A <em>amoA</em> gene and transcript copy number, whereas SR and NR increased <em>amoA</em> gene copy number. DNA stable isotope probing (DNA-SIP) revealed that COMX clade A played a critical role in nitrification. COMX community richness was reduced by CVI and increased by SR. COMX community structure was also shaped by CVI, with soil NH₄⁺-N and pH acting as two key moderators of these effects. Additionally, CVI increased the influence of deterministic processes on COMX community assembly. Together, these findings indicate that CVI enhances N mineralization while simultaneously reducing nitrification, potentially improving N retention. These results enhance our mechanistic understanding of N cycling, allowing for the optimization of fertilization strategies to balance agronomic productivity with environmental sustainability.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117469"},"PeriodicalIF":6.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}