Geoderma最新文献

{"title":"A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data","authors":"Yilin Bao ,&nbsp;Xiangtian Meng ,&nbsp;Huanjun Liu ,&nbsp;Mengyuan Xu ,&nbsp;Mingchang Wang","doi":"10.1016/j.geoderma.2025.117453","DOIUrl":"10.1016/j.geoderma.2025.117453","url":null,"abstract":"<div><div>High-accuracy maps of soil organic carbon (SOC) content are essential for agricultural management and ecosystem services. However, traditional remote sensing data can hardly balance spatial and spectral resolution, resulting in the inability to simultaneously obtain high-resolution spatial distribution of SOC and detailed spectral features, thus restricting the comprehensive resolution of soil detail information. To address this challenge, we propose an innovative multimodal remote sensing data integration framework, ResoCroS-Net, which integrates ground (soil samples), air (unmanned aerial vehicle (UAV) images), and space (satellite images) data, realized effective integration of multimodal data at different resolutions, with particular innovations in hierarchical design and data-processing logic. Specifically, ground samples and air images are combined to generate high accuracy SOC maps, which serve as the ’spatial’ baseline for ResoCroS-Net used as a reference for subsequent high-resolution image generation. Next, low spatial resolution images (ZY1-02D, Sentinel-2A) are downscaled to high spatial resolution images using Enhanced Super-Resolution Generative Adversarial Network models within adversarial networks. Meanwhile, typical spectral features in ZY1-02D satellite data were extracted, and a spectral dictionary was constructed for accurate reduction of details in low spectral resolution images. Using spectral unmixing networks and sparse representation networks, low spectral resolution images are downscaled to high spectral resolution. Then, joint spatial-spectral features were extracted by 3D convolutional neural network. Based on this framework, we developed Model (i), which integrates SOC data with spatial-spectral resolution downscaling (SSD) image; Model (ii), which integrates SOC data, UAV image with spatial resolution downscaling (SD) image; and Model (iii), which integrates SOC data, UAV image with SSD image. We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. The SOC prediction experiments conducted in Youyi, the largest state farm in China, demonstrate that Model (iii) achieves the highest accuracy with the GNN model. This model improves coefficient of determination (R²) and ratio of performance to interquartile distance (RPIQ) by 0.09 and 0.28, respectively, and reduces root mean square error (RMSE) by 0.52 g kg⁻¹ compared to Model (ii). In addition, using UAV data as the baseline layer significantly improves prediction accuracy, with R² and RPIQ increasing by 0.17 and 0.58, respectively, and RMSE decreasing by 1.09g kg⁻¹. Regarding model performance in SOC content prediction, GNN is more suitable for Model (i) and Model (iii), while CNN is more appropriate for Model (ii). In conclusion, ResoCroS-Net achieved collaborative opti","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117453"},"PeriodicalIF":5.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695219","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":"The impact of direct and indirect digital soil mapping approaches on spatial uncertainty","authors":"Gábor Szatmári,&nbsp;László Pásztor","doi":"10.1016/j.geoderma.2025.117448","DOIUrl":"10.1016/j.geoderma.2025.117448","url":null,"abstract":"<div><div>While numerous studies have compared different mapping approaches (or inference trajectories) in digital soil mapping (DSM), their impact on uncertainty quantification and propagation has received less attention. The objective of this study was to investigate key questions related to uncertainty quantification and propagation, which may influence the applicability of DSM products. Including, how do different mapping approaches and assumptions made in DSM affect uncertainty quantification and propagation? What are the pros and cons of the mapping approaches from the perspective of uncertainty? Such questions were examined on the example of mapping soil organic carbon (SOC) in the Great Hungarian Plain, Hungary, by combining machine learning with univariate and multivariate geostatistics. Two cases were investigated: in Case 1, the goal was to map SOC stock for the year 2016 using both direct and indirect mapping approaches and to quantify and compare prediction uncertainty at various spatial aggregation levels. In Case 2, the objective was similar, but focused on mapping SOC stock change between 1992 and 2016. A wide range of inference trajectories (e.g., “calculate then model”, “model then calculate”), prior data transformations (e.g., square root, standard normalization), and uncertainty propagation techniques (e.g., Taylor method, analytical solution) were applied and compared from the perspective of both prediction accuracy (e.g., mean error, root mean square error) and uncertainty (e.g., prediction interval coverage probability plot, interval scores). The results showed that both the chosen inference trajectories and the assumptions made in DSM significantly impact uncertainty estimates not only at point support but also at larger supports. It also highlighted the importance of accounting for the correlation of interpolation errors when conducting uncertainty propagation. Additionally, this research emphasized the need to identify and quantify the contribution of different error sources in uncertainty propagation, as this can be the key to reduce the overall uncertainty associated with the given soil property or function of interest.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117448"},"PeriodicalIF":5.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686819","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":"Nitrogen deposition reduces biodiversity but strengthens plant–microbe interactions","authors":"Zonghao Hu ,&nbsp;Yi Fan ,&nbsp;Junjie Yang ,&nbsp;Shuang Pang ,&nbsp;Rong Mao ,&nbsp;Yang Zhang ,&nbsp;Xingguo Han ,&nbsp;Ximei Zhang","doi":"10.1016/j.geoderma.2025.117449","DOIUrl":"10.1016/j.geoderma.2025.117449","url":null,"abstract":"<div><div>Nitrogen deposition can reduce plant and soil microbial diversity, and thus, it was traditionally thought to decouple plant–microbe interactions. However, few studies quantified these interactions directly and systematically. Here, we conducted a long-term nitrogen deposition experiment with nine rates (0–50 g nitrogen m⁻² yr⁻¹) in the Eurasian steppe. Although nitrogen addition reduced biodiversity, it strengthened the relationships between plant and microbial community indices (biomass, richness, and composition) in a structural equation model, increased the Pearson’s correlation coefficient (r) between plant biomass and microbial biomass, and also increased the density of the plant–microbe interaction subnetwork. These results challenge the traditional decoupling viewpoint and indicate that plant–microbe interactions strengthen to adapt to increasing nitrogen deposition pressure.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117449"},"PeriodicalIF":5.6,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664737","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":"Dead-fallen shrubs improve soil quality and promote herb seedling settlement in an arid sandy land of northern China","authors":"Bo Wang ,&nbsp;Fei Huang ,&nbsp;Rebecca L. Schneider ,&nbsp;Stephen J. Morreale ,&nbsp;Shaohui Liang ,&nbsp;Hongmei Wang ,&nbsp;Yulei Ma ,&nbsp;Weifeng Liu ,&nbsp;Yingge Xie ,&nbsp;Gao-Lin Wu ,&nbsp;Zhigang Li","doi":"10.1016/j.geoderma.2025.117447","DOIUrl":"10.1016/j.geoderma.2025.117447","url":null,"abstract":"<div><div>Shrubs planted to combat desertification in China’s arid sandy lands often succumb to water deficits, yet the effects of dead-fallen shrubs on soil quality and herb establishment remain unclear. This study investigated soil properties and herb seedling settlement across bare sandy land (BSD), standing living shrubs (SLS), and dead-fallen shrubs at three decay stages (3–5, 7–9, and 11–12 years) in China’s Mu Us Sandy Land. Soil temperature, moisture, enzyme activities, physicochemical properties, and herb parameters (height, density, coverage, biomass) were analyzed and overall soil quality index (SQI) was calculated. Results revealed that dead-fallen shrubs significantly reduced soil temperature, increased soil water storage, improved soil physicochemical properties, eventually leading to a higher SQI under dead-fallen shrubs and a lower SQI under BSD and SLS in 0–5 cm soil. The herb seedling settlement parameters under dead-fallen shrubs were also higher than both under BSD and SLS. Regression analysis indicated that seedlings height, density, coverage and biomass were positively correlated with SQI, both in 0–5 cm and 5–10 cm soil. Structural equation modeling revealed that herb seedling settlement at 0–5 cm was driven by physicochemical properties, while at 5–10 cm soil depth relied on physical properties. Soil temperature exerted a direct negative influence on herb seedling establishment in both soil depths. This study highlighted that dead-fallen shrubs, particularly at advanced decay stages, outperform living shrubs in enhancing soil quality and facilitating herb establishment by modulating microhabitats and nutrient cycling, offering novel insights for sustainable restoration in arid ecosystems.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117447"},"PeriodicalIF":5.6,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664738","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":"Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model","authors":"Bifeng Hu ,&nbsp;Yibo Geng ,&nbsp;Hanjie Ni ,&nbsp;Zhou Shi ,&nbsp;Zheng Wang ,&nbsp;Nan Wang ,&nbsp;Jipeng Luo ,&nbsp;Modian Xie ,&nbsp;Qian Zou ,&nbsp;Thomas Optiz ,&nbsp;Hongyi Li","doi":"10.1016/j.geoderma.2025.117446","DOIUrl":"10.1016/j.geoderma.2025.117446","url":null,"abstract":"<div><div>Information on the spatial distribution of soil organic carbon (SOC) in regional farmland is crucial for improving management and production. Mapping SOC in farmlands is challenging due to the strong variation of SOC caused by the influence of natural and anthropogenic activities. Additionally, currently widely used predictive models usually suffer from a lack of model interpretability. To fill these gaps, here we use a Bayesian spatial model termed Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) to produce the fine scale SOC map in the farmland of Jiangxi Province, south China based on an extensive soil survey dataset (n = 16,050). The competitive adaptive reweighted sampling algorithm − partial least square (CARS-PLS) algorithm is adopted to select the most related covariates from the original covariates pool. Then, the performance of Random Forest (RF), Geographically Weighted Regression (GWR), and Ordinary Kriging (OK) was compared with INLA-SPDE. Finally, an interpretable machine learning model, the SHapley Additive exPlanation (SHAP), is used to quantify the environmental covariates’ contribution to mapping SOC, as well as mapping spatial varying primary covariates for predicting SOC in the study area. We find that INLA-SPDE was able to handle a large data and performed much better than OK and GWR with an improvement of 38.89 % and 117.39 % in R², respectively. It also outperforms RF. Overall, amount of straw return, mean annual precipitation, mean annual solar radiation are the most important covariates for mapping SOC. Locally, soil management are the most important covariates for mapping SOC in 50.52 % regions of the study area, followed by climate factors (22.06 %), soil properties (17.09 %), terrain (6.38 %), lithology (2.21 %) and biota factors (1.72 %). Our study demonstrates the advantages of INLA-SPDE on mapping SOC compared with geostatistical and RF for SOC mapping and provides valuable implications for interpreting the results of digital soil mapping.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117446"},"PeriodicalIF":5.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653313","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":"Defining pH-associated ecological clusters and their responses to agricultural intensification","authors":"Yi Ren ,&nbsp;Shunan Xu ,&nbsp;Yuanyuan Yan ,&nbsp;Zhiyun Chen ,&nbsp;Liangliang Liu ,&nbsp;Xing Zhou ,&nbsp;Zucong Cai ,&nbsp;Xinqi Huang","doi":"10.1016/j.geoderma.2025.117445","DOIUrl":"10.1016/j.geoderma.2025.117445","url":null,"abstract":"<div><div>Soil pH is a major factor influencing the participation of soil microorganisms in biogeochemical processes. However, different microbial taxa exhibit varied responses to pH levels, and their ecological functions remain unclear. This gap limits our understanding of the precise pathways through which pH affects the structure and function of soil microbial communities. In this study, we identified seven ecological clusters based on their responses to pH: Acidophile, Conditioned Acidophile, Alkaliphile, Conditioned Alkaliphile, Opportunist, Conservative, and Liberalist. Acidophiles and Conditioned Acidophiles exhibited a positive correlation with nutrient cycling functions, whereas Alkaliphiles and Conditioned Alkaliphiles showed a negative correlation. Additionally, agricultural intensification (farmland vs. greenhouse) with frequent perturbation of soil pH directly or indirectly influences the abundance of pH-associated ecological clusters. Acidophiles and Opportunists were enriched in intensive agriculture soils, while Liberals were predominantly found in traditional agriculture soils. Changes in microbial community composition significantly influence soil ecosystem functions, with Acidophiles, Liberalists, and Opportunists potentially serving as the primary contributors to nutrient cycling functions, animal &amp; zoonotic pathogens, and plant beneficial microbes, respectively. Our results highlight the complex interactions between soil pH, microbial ecological clusters, and soil functionality, emphasizing the need for targeted agricultural management practices to maintain soil health.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117445"},"PeriodicalIF":5.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653312","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":"Windthrow modifies soil solution chemistry and nutrient leaching in the Canadian black spruce boreal forest","authors":"Marie Renaudin ,&nbsp;Daniel Houle ,&nbsp;Jean-David Moore ,&nbsp;Louis Duchesne","doi":"10.1016/j.geoderma.2025.117443","DOIUrl":"10.1016/j.geoderma.2025.117443","url":null,"abstract":"<div><div>Soil solution chemistry is directly related to vegetation nutrition and growth in forest ecosystems. However, the impacts of natural disturbances on boreal forest soil solution composition and nutrient fluxes remain unclear. In this study, we explore the effects of a windthrow on soil solution chemistry collected weekly between 2012 and 2018 during the snow-free period at a Canadian black spruce boreal forest site. We show that the windthrow had an important effect on soil solution chemistry within only a few days, inducing much higher NO₃⁻ and NH₄⁺ concentrations and a strong pH drop, persisting up to six years after the disturbance. Following the windthrow, soil solution major ion concentrations (i.e., K⁺, Ca²⁺, Mg²⁺, Mn²⁺, Cl⁻, SO₄²⁻) similarly increased but with various intensities and recovery times. This windthrow also occurred on a site receiving a chronic ammonium nitrate treatment as part of a N deposition simulation experiment, which showed that two decades of N treatment had nearly no impacts on soil solution NO₃⁻ and NH₄⁺ concentrations. Therefore, our results indicate that windthrows could potentially alter the North American boreal forest soil chemistry much more than elevated N deposition corresponding to 200 years of accelerated ambient N deposition. While this finding needs to be supported by larger studies, it clearly highlights the significance of wind disturbances’ impacts on nutrient cycling and calls for more research as windthrow frequency is predicted to increase with global change.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117443"},"PeriodicalIF":5.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633336","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":"Proficiency tests for soil analysis services via proximal sensing","authors":"Tiago R. Tavares ,&nbsp;José A.M. Demattê ,&nbsp;Abdul M. Mouazen ,&nbsp;Eyal Ben-Dor ,&nbsp;Adriano A. Anselmi ,&nbsp;Victor Monseff de A. Campos ,&nbsp;Budiman Minasny ,&nbsp;Sérgio H. Godinho Silva ,&nbsp;Sabine Chabrillat ,&nbsp;Konstantinos Karyotis ,&nbsp;Heitor Cantarella ,&nbsp;Cristiano A. Andrade ,&nbsp;Maurício R. Cherubin ,&nbsp;Hudson W.P. de Carvalho ,&nbsp;José Lavres","doi":"10.1016/j.geoderma.2025.117434","DOIUrl":"10.1016/j.geoderma.2025.117434","url":null,"abstract":"<div><div>Soil Spectral Libraries (SSL) and spectroscopy-based methods using varied regions of the electromagnetic spectrum for direct soil analysis are rapidly gaining traction in agricultural and environmental applications. However, the absence of standardized tests, specifically designed for these new methods, prompts concerns about performance consistency and transparency in the marketplace. This discussion paper underscores the critical factors that influence predictive performance and stresses the need for proficiency tests tailored to sensor-based techniques. It also highlights the significance of error metrics for accurate interpretation. Establishing robust proficiency tests is crucial for ensuring reliable soil analysis services and promoting the adoption of best practices in this technology.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117434"},"PeriodicalIF":5.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653311","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":"Uncovering the importance of soil type, landscape connectivity, and pedodiversity for nature’s contributions to people in a typical agricultural region","authors":"Guangyi Deng ,&nbsp;Haibo Jiang ,&nbsp;Yang Wen ,&nbsp;Chunguang He ,&nbsp;Lianxi Sheng ,&nbsp;Dehai Gu ,&nbsp;Shuai Ma","doi":"10.1016/j.geoderma.2025.117442","DOIUrl":"10.1016/j.geoderma.2025.117442","url":null,"abstract":"<div><div>Soil type, landscape connectivity, and pedodiversity contribute directly and indirectly to nature’s contributions to people (NCP) by affecting soil properties and ecological processes such as soil and water movement. However, the effects of soil type, landscape connectivity, and pedodiversity on changes in NCP under long-term agricultural activities are largely unknown. Using the Songnen Plain of China as a representative agricultural region, this study examined changes in three NCP (soil organic carbon, soil retention, and water yield). These changes were assessed across different soil types over the period 1980–2020 using variance analysis. Landscape ecological metrics were applied to evaluate landscape connectivity and pedodiversity. The spatial relationships between these landscape properties and changes in NCP were further quantified using geographically weighted regression. In addition, a random forest model was used to evaluate the relative importance of soil properties and other environmental factors in driving changes in these indicators. The results showed that Haplic Phaeozems exhibited significantly higher levels of NCP compared to other soil types. Specifically, soil organic carbon ranged from 19.50 to 19.74 g/kg, soil retention from 22.83 to 27.03 t/ha, and water yield from 119.81 to 146.83 mm. In contrast, western regions characterized by fragile soils—such as Gleyic Chernozems, Calcic Gleysols, and Haplic Chernozems—experienced declines in one or more NCP indicators. Landscape connectivity and pedodiversity were identified as critical determinants of NCP levels, in some cases exceeding the influence of climate and vegetation. Moreover, pedodiversity showed region-specific effects, enhancing water yield in southern areas while suppressing carbon storage in the southwest. While climate and vegetation are the dominant factors shaping zonal soils such as Phaeozems and Chernozems, this study found that landscape connectivity and pedodiversity also served as critical determinants of NCP levels under long-term agricultural activities. Tillage under straw return and terrace construction on sloping land can effectively improve the three NCP. This study provides new insights into the role of soils in providing NCP.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117442"},"PeriodicalIF":5.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633334","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 microbial diversity: A key factor in pathogen suppression and inoculant performance","authors":"Caroline Sayuri Nishisaka ,&nbsp;Hélio Danilo Quevedo ,&nbsp;João Paulo Ventura ,&nbsp;Fernando Dini Andreote ,&nbsp;Tim H. Mauchline ,&nbsp;Rodrigo Mendes","doi":"10.1016/j.geoderma.2025.117444","DOIUrl":"10.1016/j.geoderma.2025.117444","url":null,"abstract":"<div><div>Soil microbial diversity plays a crucial role in plant health, influencing pathogen suppression and biocontrol efficacy. This study investigated how soil microbial diversity modulates interactions between the pathogen <em>Bipolaris sorokiniana</em> and the biocontrol bacterium <em>Pseudomonas inefficax</em> in the wheat rhizosphere. Using a dilution-to-extinction method, we established five soil microbial diversity levels: natural soil, dilutions at 10^-1, 10^-3, 10^-6, and fully autoclaved soil. This gradient allowed us to evaluate disease severity, plant growth, and rhizosphere microbiome shifts. Inoculation with <em>Pseudomonas inefficax</em> significantly reduced disease severity caused by <em>Bipolaris sorokiniana</em>, particularly in low-diversity soils, emphasizing the effectiveness of <em>P. inefficax</em> in these simplified environments where microbial competition is reduced. Despite higher pathogen abundance in low-diversity soils, <em>P. inefficax</em> effectively mitigated disease severity, likely through direct antagonistic activity. Alpha diversity indices confirmed a reduction in microbial diversity across the gradient, while beta diversity analyses revealed distinct shifts among treatments. Although <em>Chitinophaga</em>, <em>Pseudomonas</em> and <em>Dyadobacter</em> were significantly enriched in natural soils with inoculation of the <em>P. inefficax</em>, statistically significant disease suppression was not observed under these higher-diversity conditions. On the other hand, in low-diverse soils (autoclaved soil), where disease is suppressed with <em>P. inefficax</em> inoculation, <em>Fluviicola</em> showed a significant enrichment when compared with the treatment inoculated only with the pathogen, suggesting that this bacterial taxon can play a role in disease suppression along with the inoculant. These findings underscore the critical role of the soil microbial diversity in shaping the success of biocontrol interventions.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117444"},"PeriodicalIF":5.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633335","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}