Geoderma最新文献

{"title":"Soil organic carbon stocks and stabilization mechanisms in tidal marshes along estuarine gradients","authors":"Friederike Neiske ,&nbsp;Maria Seedtke ,&nbsp;Annette Eschenbach ,&nbsp;Monica Wilson ,&nbsp;Kai Jensen ,&nbsp;Joscha N. Becker","doi":"10.1016/j.geoderma.2025.117274","DOIUrl":"10.1016/j.geoderma.2025.117274","url":null,"abstract":"<div><div>Tidal marshes in estuaries store large amounts of soil organic carbon (SOC) and are dominated by the interaction of tidal inundation and salinity with biotic ecosystem components, leading to strong spatio-temporal heterogeneity. Little is known how these estuarine conditions affect SOC stabilization. Our aim was to assess (i) SOC stocks, (ii) SOC stabilization mechanisms (aggregation and mineral-association), and (iii) their environmental drivers along estuarine gradients. We analyzed SOC stocks and SOC density fractions in topsoil (0–10 cm) and subsoil (10–30 cm) of three marsh zones representing three flooding regimes (daily, monthly, yearly) in three marsh types along the salinity gradient (salt, brackish, freshwater) of the Elbe Estuary, Germany. The SOC stocks in 0–30 cm ranged between 9.3–74.6 t ha⁻¹, and decreased with increasing salinity and flooding. This was linked to decreasing plant biomass and soil fine texture. Organic matter (OM) associated with minerals (C_MAOM) constituted the largest SOC fraction (59 % of SOC), followed by aggregate-occluded OM (C_oPOM) (24 %) and free particulate OM (C_fPOM) (16 %). The C_MAOM amount in topsoils decreased with increasing salinity, reflecting decreasing soil fine texture. Amounts of C_oPOM were higher in topsoils and high marshes, indicating negative effects of flooding on aggregation. The contribution of C_fPOM to total SOC increased with increasing flooding, likely related to its preservation under reducing soil conditions. Our results emphasize that increasing marine influence (rising salinity and flooding frequency) leads to a decrease in SOC content and stabilization. Therefore, sea–level rise has the potential to negatively impact SOC storage in estuarine marshes.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117274"},"PeriodicalIF":5.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images","authors":"Jie Xue ,&nbsp;Xianglin Zhang ,&nbsp;Songchao Chen ,&nbsp;Zhongxing Chen ,&nbsp;Rui Lu ,&nbsp;Feng Liu ,&nbsp;Bas van Wesemael ,&nbsp;Zhou Shi","doi":"10.1016/j.geoderma.2025.117272","DOIUrl":"10.1016/j.geoderma.2025.117272","url":null,"abstract":"<div><div>Precise monitoring of soil organic carbon (SOC) is urgently needed in agricultural regions to tackle global challenges like food security, water regulation, land degradation, and climate change. Remote sensing technology has emerged as a powerful method for detecting variations in SOC at localized scales. However, its application on a broader, national scale faces limitations, especially in countries like China, where soil landscapes exhibit significant diversity. This study aimed to couple bare soil reflectance and conventional environmental covariates to map Chinese cropland SOC content at a 10-m spatial resolution. First, a new time-series bare soil extraction method, the Two-Dimensional Bare Soil Separation Algorithm, was applied, utilizing Sentinel-2 images from 2018 to 2022 to generate a continuous spectral reflectance composite. Then, nine indices with the strongest correlation to SOC were selected. Additionally, a list of environmental covariates was prepared based on SCORPAN model. Finally, bootstrapping random forest models were fitted using the covariates selected through forward recursive feature selection (FRFS), and the spatial prediction SOC map was created. The results indicated that the framework was suitable for mapping SOC in croplands of China, with the best model using remote sensing indices and environmental covariates selected through FRFS achieving an R² of 0.62, an RMSE of 4.84 g kg⁻¹, and an uncertainty depicted by a 90 % prediction interval range of 17.88 g kg⁻¹. The final map showed that the Northeast China had the highest SOC content in cropland. Climatic conditions, position, and remote sensing indices are key covariates in national-scale SOC estimation. This study can be easily implemented across broad areas for the prediction of SOC with computational efficiency. The 10-m spatial resolution SOC map of China contributes to land management and the development of policies for precision agriculture.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117272"},"PeriodicalIF":5.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil gas and solute diffusivity as predictors of N2O emissions after spring fertilization: An incubation study with intact soil","authors":"Winnie Ntinyari,&nbsp;Loraine ten Damme,&nbsp;Jianbo Cheng,&nbsp;Lars J. Munkholm,&nbsp;Søren O. Petersen","doi":"10.1016/j.geoderma.2025.117267","DOIUrl":"10.1016/j.geoderma.2025.117267","url":null,"abstract":"<div><div>Agricultural soils are the main source of anthropogenic N₂O emissions to the atmosphere. To effectively mitigate these emissions there is a need to understand the interactions between potential drivers under field conditions. We conducted an incubation experiment with intact soil cores adjusted to one of four matric potentials (−15, −30, −50 or −100 hPa). We measured the relative gas diffusivity <em>(Dp/Do</em>) and effective air-filled porosity (<em>ε_eff</em>) before incubation with four surface-applied fertiliser treatments: NS26-13 (NS) at 50 kg N ha⁻¹ (NS50; starter); NS at 50 + 100 kg N ha⁻¹ (NS150); NS at 50 + 100 kg N ha⁻¹, the main dose of 100 kg N ha⁻¹ containing the nitrification inhibitor 3,4-dimethylpyrazol phosphate (NS150 + DMPP); and an unfertilized Control. We monitored N₂O fluxes for 35 days and found a positive relationship with matric potential and a weak inverse relationship with <em>Dp/Do</em> and <em>ε_eff..</em> After 35 days, the cores were sectioned for determination of mineral N pools and potential ammonia oxidation (PAO). A strong NH₄⁺ gradient, declining with depth, was observed which indicated that nitrification took place at some distance from the surface-applied fertiliser, and this was consistent with PAO and pH distributions. The flux of NH₄⁺ in the soil solution between 0–2 and 2–4 cm depth after 35 days showed a consistent relationship with N₂O emissions at −30 to −100 hPa indicating nitrification was the main source of N₂O in this range. The results of this study suggest that both gas and solute diffusivity have to be considered for predicting N₂O emissions from surface-applied fertilisers.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117267"},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biogeographic patterns and adaptive strategies of microbial carbon metabolic profiles in paddy soils in the Chinese Mollisol region","authors":"Xiaojing Hu ,&nbsp;Haidong Gu ,&nbsp;Mikhail Semenov ,&nbsp;Yongbin Wang ,&nbsp;Jinyuan Zhang ,&nbsp;Zhenhua Yu ,&nbsp;Yansheng Li ,&nbsp;Junjie Liu ,&nbsp;Jian Jin ,&nbsp;Xiaobing Liu ,&nbsp;Guanghua Wang","doi":"10.1016/j.geoderma.2025.117265","DOIUrl":"10.1016/j.geoderma.2025.117265","url":null,"abstract":"<div><div>Microbial adaptive evolution and regulation strategies response to carbon (C) heterogeneity in paddy fields at large spatial scales remain poorly understood. Here, we employed gene- and genomic-centric metagenomic approaches to explore soil microbial biogeographic patterns and functional traits regulating C metabolisms across 120 soils derived from 30 paddy fields in Chinese Mollisols. Our findings revealed that significant distance-decay relationships (DDRs) were observed at both microbial C functional and genomic taxonomic levels. Microbial C cycling profiles were clustered into two groups. HCS (including sites R1-R10) represented soils with high total carbon (TC) at relatively high latitudes, whereas LCS (including sites R11-R30) had low soil C content distributed at low latitudes. Compared with HCS, LCS presented higher abundances of C cycling pathways involving aerobic respiration, C fixation, and methanogenesis pathways, as well as higher levels of carbohydrate esterase (CE) and glycosyl transferase (GT) classes. 211 metagenome-assembled genomes (MAGs) with diverse C metabolic functions were constructed. Among these, high-quality MAG292, assigned to the Nanopelagicales order, had a significantly positive correlation with TC and was more abundant in HCS. Contrarily, MAG153, assigned to the Chitinophagales order, exhibited an opposite trend. Additionally, 133 novel vMAGs were retrieved, and the abundances of phage11, phage16, phage26, and phage120 were higher in LCS than in HCS, containing <em>chiA</em> and GH19 that involved in chitin degradation. HCS had a relatively high abundance of phage89, containing <em>slt</em> and GH23 genes that regulate peptidoglycan lysis. These results indicated that soil viruses potentially lyse bacteria by encoding peptidoglycan lyase, releasing nutrients, and increasing the amount of dead microbial debris that facilitates soil C accumulation at relatively high latitudes. In contrast, at low latitudes, the phages together with microbes may indirectly decrease the soil TC by potentially expressing auxiliary metabolic genes (AMGs) involved in chitin degradation. Our findings indicate the divergent microbial adaptive evolution and soil C regulation strategies response to soil C heterogeneity in paddy soils of Chinese Mollisols.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117265"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the thickness of alpine meadow soil on headwater hillslopes of the Qinghai-Tibet Plateau","authors":"Xiaole Han ,&nbsp;Jintao Liu ,&nbsp;Pengfei Wu ,&nbsp;Zhenghong Yu ,&nbsp;Xiao Qiao ,&nbsp;Hai Yang","doi":"10.1016/j.geoderma.2025.117271","DOIUrl":"10.1016/j.geoderma.2025.117271","url":null,"abstract":"<div><div>Alpine meadow soils in tectonically active High Mountain Asia (HMA) are highly vulnerable, playing a critical role in the fragile ecosystems of Earth’s “Third Pole”. Understanding the formation and distribution of these soils is essential, yet the mechanisms governing their thickness remain unclear. To address this, we applied a multi-resolution (0.25–30.00 m) stochastic approach to predict soil thickness in a nested headwater catchment in the Lhasa River Basin, namely D5K (415 sites) and its headwater sub-catchment D5KH1 (330 sites). Using a linear mixed-effects model, we quantified uncertainty through within-site and between-site variance analysis, and compared the performance of deep learning (DL) with three traditional machine learning methods—random forest (RF), support vector machine (SVM), and artificial neural networks (ANN)—under three variable selection strategies: (1) all variables, (2) stepwise selection, and (3) a novel Boruta-based recursive method. Field investigations revealed that organic matter accumulation and freeze–thaw cycles are dominant pedogenic factors, producing a root-dense turf layer beneath a dark humus horizon. Freeze-thaw dynamics also contribute to geomorphological features such as landslides and stone stripes. Soil thickness varied significantly across topographic positions—valleys, sideslopes, and ridges (p &lt; 0.001)—with within-site variance (SD = 11.06 cm) slightly exceeding between-site variance (SD = 9.58 cm). Fine-resolution digital elevation models (DEMs) effectively captured these variations, largely because they incorporate critical topographic factors such as the Topographic Position Index (TPI), which Boruta analysis identified as more influential on soil thickness than flow-related indices like the Topographic Wetness Index. Among the tested models, RF combined with the Boruta method and a 0.25 m resolution DEM provided the most accurate predictions, outperforming DL. These findings emphasize that the complexity of DL may not translate into superior performance for all applications, particularly in small dataset terrain-driven ecological studies.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117271"},"PeriodicalIF":5.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning applications in soil spectroscopy","authors":"Giannis Gallios ,&nbsp;Nikolaos Tsakiridis ,&nbsp;Nikolaos Tziolas","doi":"10.1016/j.geoderma.2025.117259","DOIUrl":"10.1016/j.geoderma.2025.117259","url":null,"abstract":"<div><div>Soil spectroscopy has emerged as a key technique for rapid, non-destructive soil property prediction, yet the centralized nature of the training of machine learning models raises concerns around data privacy, accessibility, and transferability. This study proposes the application of Federated Learning (FL) as a decentralized approach to soil spectroscopy, enabling collaboration between multiple data contributors (or clients) without requiring the exchange of raw data. Convolutional neural networks were used to estimate key soil attributes such as soil organic carbon, texture, pH, cation exchange, and total nitrogen using VNIR or MIR spectral data from the Kellogg Soil Survey Laboratory. Three data partitioning approaches were explored involving geopolitical, bioclimatic, independent, and identically distributed (IID) splitting methods to simulate various real-world scenarios of particular interest to the soil community. Each scenario was investigated under two different averaging aggregation strategies: Federated Averaging (FedAvg) and Weighted Averaging (WgtAvg), which are used to develop a consensus model by aggregating the weights of the different contributors. The results show that the FL framework can match, and in some cases exceed, the performance of centralized models, particularly when using IID data. The WgtAvg strategy was particularly effective in boosting prediction accuracy even by 50 % for soil properties where contributors had unequal data sizes. This study highlights the potential of FL as a privacy-preserving framework across networks of soil labs, facilitating enhanced soil health monitoring with decentralized global models.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117259"},"PeriodicalIF":5.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consequences of subtropical land-use intensity for the abundance and diversity of earthworm ecological categories","authors":"Xishuai Liu ,&nbsp;Bingbing Wan ,&nbsp;Dingyi Wang ,&nbsp;Xiaoxu Qi ,&nbsp;Yan Du ,&nbsp;Jun Jiang ,&nbsp;Xiaoyun Chen ,&nbsp;Feng Hu ,&nbsp;Manqiang Liu ,&nbsp;Joann K. Whalen","doi":"10.1016/j.geoderma.2025.117270","DOIUrl":"10.1016/j.geoderma.2025.117270","url":null,"abstract":"<div><div>Understanding how soil biodiversity, especially of macrofauna like earthworms, responds to land-use intensity is crucial for developing sustainable land-use strategies. This work is a two-year field investigation of earthworm community responses to increasing land-use intensity, from undisturbed fallow land to actively cultivated agricultural lands (including fallow land, tea plantation, orange plantation, camphora plantation, synthetic fertilizer-amended cropland, compost-amended cropland, and vermicompost-amended cropland) in a subtropical region. Earthworm abundance and diversity increased with land-use intensity, likely due to the compensatory effects of organic amendments, which improve the habitat and resource availability, thereby alleviating the potential negative impacts of tillage and harvesting. Notably, earthworm abundance was higher in cropland (70 ind. m⁻²) than in other land-use types, such as fallow (4 ind. m⁻²) and plantation (22 ind. m⁻²). Greater earthworm abundance was associated with higher soil pH and more food resources, as indicated by high microbial biomass carbon (C), the humification index, and the particulate organic C fraction. Anecic and endogeic earthworms increased more than epigeic earthworms from fallow lands to plantations and croplands, reflecting their ecological adaptability to the soil conditions in managed lands with higher land-use intensity. This suggests that soil ecological restoration practices may enhance the role of earthworms related to soil structure dynamics and carbon sequestration. Our study provides empirical evidence that soil macrofauna have ecological adaptations to cope with agricultural intensification across landscapes.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117270"},"PeriodicalIF":5.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature sensitivity of bacterial species-level preferences of soil carbon pools","authors":"Jinyang Zheng ,&nbsp;Kees Jan van Groenigen ,&nbsp;Iain P. Hartley ,&nbsp;Ran Xue ,&nbsp;Mingming Wang ,&nbsp;Shuai Zhang ,&nbsp;Ting Sun ,&nbsp;Wu Yu ,&nbsp;Bin Ma ,&nbsp;Yu Luo ,&nbsp;Zhou Shi ,&nbsp;Zhongkui Luo","doi":"10.1016/j.geoderma.2025.117268","DOIUrl":"10.1016/j.geoderma.2025.117268","url":null,"abstract":"<div><div>Soil organic carbon (SOC) mineralization, driven by soil microbial communities, plays a crucial role in the global carbon cycle. However, the temperature sensitivity of microbial preferences for SOC substrates remains poorly understood, limiting our ability to predict SOC dynamics under climate change. Here we combined bacterial community profiling, laboratory incubations, and a pool-based carbon model to investigate the relationships between bacterial species abundances and two SOC pools with fast and slow decay rates, respectively, at different incubation temperatures. Only about half of identified bacterial species is significantly (<em>P</em> &lt; 0.05) associated with the mineralization of the two pools and their temperature sensitivity (<em>Q₁₀</em>). More importantly, we find that the association of the species with the two pools shifts in terms of both magnitude and direction with incubation temperature. The proportion of species associated with the <em>Q₁₀</em> of fast pool decreased, while those associated with the <em>Q₁₀</em> of slow pool increased with warming. Meanwhile, species specifically associated with the fast pool exhibit stronger temperature sensitivity compared to species specifically associated with the slow pool at lower temperatures, and vice versa at higher temperatures. These results suggest that common bacterial species associated with SOC mineralization adjust their substrate preferences in response to temperature variations, potentially impacting SOC composition and dynamics under warming.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117268"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards site-independent calibration of in situ soil pH sensor data: Relevance of spatial and temporal proximity, sample size and data spread for calibration model performance","authors":"Sebastian Vogel ,&nbsp;Mandy Gebbers ,&nbsp;Ingmar Schröter ,&nbsp;Wolfgang Schwanghart ,&nbsp;Eric Bönecke ,&nbsp;Jörg Rühlmann ,&nbsp;Eckart Kramer ,&nbsp;Robin Gebbers","doi":"10.1016/j.geoderma.2025.117261","DOIUrl":"10.1016/j.geoderma.2025.117261","url":null,"abstract":"<div><div>Proximal soil sensing data usually require calibration to extract meaningful information, e.g. for precision agriculture. For optimal application, calibration modeling is typically based on reference data collected from field-wise sampling. However, this approach is neither time- nor cost-effective and hinders the adoption of proximal soil sensing in practical agriculture. Thus, minimizing calibration sampling is essential.</div><div>For the case of pH mapping in precision agriculture, we have investigated whether calibration efforts can be reduced by reference sampling, which is less dependent on location and timing. This study utilized 612 sensor and standard lab pH measurements from 62 fields of two farms in Northeast Germany. We assess the effects of spatial and temporal proximity, sample size and data spread on the performance of univariate linear regression models for pH sensor data calibration. Moreover, the performance of site-independent calibration was analyzed for five domain levels: (i) regional, (ii) farm, (iii) spatial neighborhood (NH), (iv) temporal NH and (v) field. This multi-level approach aims at assessing the generalizability of calibration models.</div><div>Partial correlation analysis revealed that sample size is the most crucial factor for optimizing model performance. Field-wise calibration yielded a 34 % lower mean RMSE than regional, farm, or neighborhood models but required the most reference samples. In contrast, neighborhood models reduced sample size by approximately 74 %, farm models by 87 % and the regional model by 86 %, while achieving similar RMSEs. Thus, farm or regional models can significantly reduce sampling effort and costs in practical precision agriculture. The spread of the pH value was the next important factor influencing model performance. The pH range in the sensor measurements selected for calibration should be larger than 1 unit to obtain good quality calibration models. Finally, spatial and temporal dispersion had the least effect on calibration performance.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117261"},"PeriodicalIF":5.6,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nitrification inhibitors reduce N2O emissions from Mollisols by potentially targeting Nitrosospira cluster 3a and denitrifiers","authors":"Yanqiang Cao ,&nbsp;Yulian Liu ,&nbsp;Zengming Chen ,&nbsp;Nan Zhang ,&nbsp;Shiqi Xu ,&nbsp;Deyan Liu ,&nbsp;Bin Yin ,&nbsp;Weixin Ding","doi":"10.1016/j.geoderma.2025.117266","DOIUrl":"10.1016/j.geoderma.2025.117266","url":null,"abstract":"<div><div>Nitrification inhibitors (NIs) are widely used to mitigate nitrous oxide (N₂O) emissions from agricultural soils. However, its efficiency is highly uncertain owing to varying environmental conditions and still-debated inhibition mechanisms, especially the responses of the nitrifiers and denitrifiers responsible for N₂O production. Here, we conducted microcosm incubations to investigate the comparative effectiveness of three NIs (DCD, DMPP, and nitrapyrin) on N₂O emissions from cultivated Mollisols under contrasting moisture levels (60 % and 90 % water-filled pore space, WFPS). The soil nitrification rate, N₂O-related gene abundance, and community composition of ammonia-oxidizing bacteria (AOB) were determined. The results showed that all NIs effectively inhibited nitrification, with DCD and DMPP having higher efficacy than nitrapyrin, regardless of soil moisture conditions. Interestingly, a greater decrease in N₂O emissions was observed under 90 % WFPS than 60 % WFPS (39.0–47.6 % vs. 26.8–31.9 %). NIs selectively decreased the <em>amoA</em> gene abundance in AOB rather than in ammonia-oxidizing archaea. The most abundant AOB (&gt; 90 %) belonged to <em>Nitrosospira</em>. RDA analysis revealed that the AOB <em>Nitrosospira</em> cluster 3a had the greatest relationship with N₂O emissions, and its abundance was significantly decreased with NI amendments. Moreover, non-target denitrifying genes (<em>nirS</em> and <em>nirK</em>) were suppressed, particularly at high moisture levels, which further contributed to reduced N₂O emissions. Overall, our findings highlight the significant role of environmental conditions, keystone species of AOB, and non-targeted effects on denitrifiers on the efficacy of NIs. Additionally, these results imply that NIs are a potent option for fertilizer management to mitigate N₂O emissions and that DCD and DMPP have promising prospects for the cultivated Mollisols agro-system under climate change with intensifying extreme rainfall events.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"456 ","pages":"Article 117266"},"PeriodicalIF":5.6,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}