Geoderma最新文献

{"title":"Intensive smooth cordgrass removal strengthens tidal and temperature impacts on methane emission","authors":"Yueting Deng ,&nbsp;Ruichen Lin ,&nbsp;Han Yang ,&nbsp;Hui Luo ,&nbsp;Lulu Song ,&nbsp;Xudong Zhu","doi":"10.1016/j.geoderma.2026.117719","DOIUrl":"10.1016/j.geoderma.2026.117719","url":null,"abstract":"<div><div>The world’s largest ecosystem restoration via intensive removals of invasive smooth cordgrass (<em>Spartina alterniflora</em>) is being implemented in coastal China, potentially exerting a large impact on soil biogeochemical cycles of greenhouse gases including methane (CH₄). However, the degree to which CH₄ emission and its environmental controls change with such anthropogenic disturbances has been rarely assessed with direct empirical evidence. To quantify these disturbance effects, we utilized the eddy covariance (EC) approach to continuously measure net CH₄ exchange from Jul. 2022 to Oct. 2023, covering both pre- and post-removal periods, in a disturbed coastal wetland of Southeast China experiencing an intensive cordgrass removal in late Oct. 2022. Our analyses, based on this unique EC dataset of high-frequency (30-min) time series CH₄ fluxes, revealed that (a) the removal caused a pulse of CH₄ emission peaking one month later up to 0.76 g CH₄ m⁻² d^-1, with the mean post-removal emission over ten times that of the pre-removal level (0.03 g CH₄ m⁻² d^-1); (b) the removal intensified the controls of tidal inundation and pumping on CH₄ fluxes, showing much stronger pumping effects within two months following the disturbances; (c) the removal also enlarged the temperature sensitivity of CH₄ emission, leading to larger daytime emission especially at afternoon hours; (d) the combination of enhanced tidal impacts and temperature dependence thus promoted the diel variability of CH₄ fluxes during the post-removal period. These results suggest that coastal restoration via intensive cordgrass removals boosts both the magnitude and the diel variability of CH₄ emission, highlighting the necessity of better understanding the climate impact of restoration activities. Future longer flux data with extended years are needed to further assess potential regime shift in soil CH₄ biogeochemistry and long-term evolution of such unintended environmental costs of the restoration.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117719"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146752","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":"Moisture-threshold and structure controls on soil thermal conductivity on the northern Qinghai–Tibet Plateau","authors":"Ren Li ,&nbsp;Yao Xiao ,&nbsp;Tonghua Wu ,&nbsp;Shenning Wang ,&nbsp;Wenhao Liu ,&nbsp;Junjie Ma ,&nbsp;Xiaodong Wu ,&nbsp;Guojie Hu ,&nbsp;Yongliang Jiao ,&nbsp;Shengfeng Tang ,&nbsp;Xiaofan Zhu ,&nbsp;Jianzong Shi ,&nbsp;Yongping Qiao","doi":"10.1016/j.geoderma.2026.117729","DOIUrl":"10.1016/j.geoderma.2026.117729","url":null,"abstract":"<div><div>Soil thermal conductivity (STC) governs near-surface heat exchange and constrains simulations of active-layer evolution and permafrost change. Using a 10-year record from four Qinghai–Tibet Plateau sites (0–10 cm), laboratory Kersten number (<em>K_e</em>)–saturation (<em>S_r</em>) calibrations, and a structure-aware Johansen implementation, we identify a moisture-threshold reversal: under low antecedent moisture the frozen state conducts less heat than the unfrozen state, while at higher moisture the conventional ordering returns. The crossover saturation <em>S_r*</em> is traceable in calibrated <em>K_e</em>–<em>S_r</em> relations and observable from pre-freeze moisture, linking field diagnosis to model parameters. A compact, deployable correction follows: taper the frozen branch for <em>S_r</em> &lt; <em>S_r*</em>, compute endmembers from measured bulk density, porosity, and quartz fraction (BD–n–q), and select the unfrozen <em>K_e</em>(<em>S_r</em>) form by soil class and dryness tendency. The scheme reduces unfrozen-season errors across the core sites and generalizes at an independent hold-out station (TGL) without site-specific tuning. The approach is transparent—inputs are observable and decisions are tied to <em>S_r*</em>—and is most impactful in dry, coarse, and sparsely monitored regions.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117729"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209551","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":"Pedological classification systems as carriers of functional information in terroir interpretation and the formalization of the SCORE-V factorial framework","authors":"Paola Bambina","doi":"10.1016/j.geoderma.2026.117741","DOIUrl":"10.1016/j.geoderma.2026.117741","url":null,"abstract":"<div><div>Understanding how soil variability contributes to wine composition remains a central challenge in terroir science. Although soil classification is widely applied in land evaluation and international data harmonization, its potential to encode functionally relevant edaphic conditions has been only marginally explored in viticultural contexts. This study investigates whether taxonomic descriptors from two major soil classification systems, WRB and Soil Taxonomy, capture pedological information that relates to wine metabolomic profiles. Eight vineyard soils from a Mediterranean wine district were characterized, classified, and linked to the chemical composition of the corresponding wines using multivariate statistical approaches. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) revealed that specific soil descriptors, particularly those associated with horizon architecture, physical behaviour, and secondary carbonate accumulation, account for structured variation in phenolic and aromatic composition. These results indicate that soil classification systems act as carriers of functional information, reflecting pedogenetic attributes that influence grapevine metabolism. In addition, the study introduces SCORE-V, a conceptual factorial model that formalizes the combined influence of Soil, Climate, Organisms, Relief, Ecosystem history, and Viti-vinicultural factors on wine composition. Inspired by Jenny’s state-factor model of soil formation, SCORE-V provides a theoretical scaffold for integrating pedological and viticultural knowledge into a unified interpretation of terroir. By bridging soil classification, metabolomics, and multivariate modelling, this work contributes to a process-based understanding of terroir and offers a foundation for future predictive frameworks supporting site-specific viticultural strategies.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117741"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777806","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 soil total nitrogen and organic matter with hybrid models on small laser-induced breakdown spectroscopy datasets","authors":"Wenqi Guo ,&nbsp;Peng Lin ,&nbsp;Shixiang Ma ,&nbsp;Yangrui Li ,&nbsp;Hongwu Tian ,&nbsp;Shichen Gao ,&nbsp;Zhen Xing ,&nbsp;Daming Dong","doi":"10.1016/j.geoderma.2026.117686","DOIUrl":"10.1016/j.geoderma.2026.117686","url":null,"abstract":"<div><div>The total nitrogen (TN) and organic matter (OM) content of the soil is crucial to improve crop yields and reduce environmental impacts in precision agriculture. Recently, Laser-Induced Breakdown Spectroscopy (LIBS) has become a popular method for predicting soil nutrients because of its rapid, nondestructive, and multielement analytical capabilities. However, the high-dimensionality and complex peak features of LIBS spectra, combined with often limited sample sizes, pose challenges for previous deep learning methods, such as over fitting, feature redundancy, and poor generalization. To address these challenges, we propose a hybrid model tailored for small LIBS datasets to predict soil TN and OM content. This model integrates Partial Least Squares (PLS) for dimensionality reduction and key feature extraction, Convolutional Neural Networks (CNN) for capturing local spectral patterns, and Self-Attention mechanisms for modeling global dependencies. By combining these components with weighted integration, the hybrid model significantly improves prediction accuracy and robustness. Experiments show that the hybrid model outperforms other machine learning and standalone deep learning methods in small LIBS datasets, achieving superior performance with RMSE of 0.39 g/kg and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.75 for the prediction of TN (compared to the second-best method with 0.42 g/kg and 0.71), and RMSE of 8.26 g/kg and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.77 for the prediction of OM (compared to the second-best method with 8.77 g/kg and 0.74). This study presents an effective solution for analyzing high-dimensional spectral data with small datasets, supporting soil health management and sustainable precision agriculture.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117686"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777798","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":"Subsoiling reduces N2O emissions by altering the relative gas diffusivity, O2 status and microbial communities in grazed pasture soil","authors":"Juan Liu ,&nbsp;Timothy Clough ,&nbsp;Sam Carrick ,&nbsp;Jiafa Luo ,&nbsp;Andriy Podolyan ,&nbsp;Naomi Wells ,&nbsp;Chris Chisholm ,&nbsp;Jupei Shen ,&nbsp;Peng Li ,&nbsp;Lianfeng Du ,&nbsp;Hong Pan ,&nbsp;Limei Zhang ,&nbsp;Hong J. Di","doi":"10.1016/j.geoderma.2026.117716","DOIUrl":"10.1016/j.geoderma.2026.117716","url":null,"abstract":"<div><div>Nitrous oxide (N₂O) is a potent greenhouse gas predominantly emitted from grazed pasture through denitrification, driven by soil oxygen (O₂) availability and urine-derived nitrogen (N). Pasture soils are vulnerable to compaction from animal treading, restricting gas diffusion and enhancing N₂O emissions. Although subsoiling alleviates compaction, its impact on soil O₂ status and N₂O emissions, particularly under high urine N load, remain poorly understood and rarely investigated. This in-situ field study (March-August 2023) evaluated the effect of subsoiling on soil moisture, O₂ content, relative gas diffusivity (D_p/D_o), functional gene abundance, N₂O emissions, and pasture production. Treatments included non-subsoiling or subsoiling, each with or without synthetic ruminant urine (713 kg N ha⁻¹). Subsoiling improved macroporosity, enhanced O₂ availability, increased D_p/D_o at 5, 10 and 20 cm depth (<em>P &lt; 0.001</em>), and reduced moisture at 10 cm depth (<em>P &lt; 0.001</em>). Subsoiling significantly reduced N₂O emissions by 52% and 81% of non-subsoiled plots for non-urine and urine treatments, respectively (<em>P &lt; 0.05</em>). D_p/D_o was strongly correlated with N₂O fluxes during the first 15 days following urine application (<em>R² = 0.59</em>–<em>0.87</em>), suggesting its utility as a predictive indicator under high substrate availability. Molecular analysis showed reduced <em>nirK</em> gene abundance under subsoiling, with limited response for other denitrification genes. Subsoiling had no significant effect on pasture yield or N uptake. Overall, subsoiling mitigates N₂O emissions by improving soil aeration and D_p/D_o while maintaining productivity, offering a promising strategy for sustainable N management in grazed pasture soils.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117716"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134156","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":"Enhanced soil organic carbon estimation via hybrid modeling: A data-driven solution to carbon inputs","authors":"Yixiang Jiang,&nbsp;Xiaolong Hu,&nbsp;Liangsheng Shi,&nbsp;Lin Lin,&nbsp;Yuanyuan Zha,&nbsp;Jiateng Ma","doi":"10.1016/j.geoderma.2026.117739","DOIUrl":"10.1016/j.geoderma.2026.117739","url":null,"abstract":"<div><div>Soil organic carbon (SOC) is a key component of the global carbon cycle, yet reliable SOC stock prediction remains constrained by uncertainties in carbon inputs (C_inputs). Conventional process-based models, such as RothC, typically rely on empirical estimates of C_inputs, limiting their applicability across heterogeneous environmental and management conditions. Here, we develop a hybrid modeling framework that integrates the RothC model with a data-driven parameter estimator. The carbon input modifier (α) is first inferred through a probabilistic inversion using Markov Chain Monte Carlo (MCMC) and subsequently generalized to the regional scale by training machine learning models on environmental covariates. The framework was evaluated using the European LUCAS dataset, which includes SOC stock measurements for the top 20 cm of soil from more than 7000 sites collected between 2009 and 2018. The hybrid framework substantially outperformed the RothC model, reducing the RMSE of SOC stock predictions from 25.22 to 16.31 t C ha⁻¹, with a corresponding relative RMSE from about 46% to 30%, and increasing the R² from 0.43 to 0.70 across diverse European ecosystems. SHapley Additive exPlanations (SHAP) analysis identified initial SOC, bulk density, precipitation, and land-use type as dominant regulators of α. Importantly, α exhibited compelling ecological plausibility, as evidenced by a negative correlation with baseline SOC consistent with carbon saturation theory, as well as systematic variations across land-use types reflecting anthropogenic management and vegetation influences on carbon partitioning. This study demonstrates the potential of hybrid approaches to reconcile mechanistic interpretability with data-driven adaptability, providing a scalable tool for soil carbon monitoring and sustainable land management policy development.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117739"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777803","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":"Characterization and determination of soil unsaturated hydraulic conductivity by integrating time-lapse geophysical data with hydrogeological measurements","authors":"Chenyang Zou ,&nbsp;Tengfei Wu ,&nbsp;Shuangxi Zhang ,&nbsp;Fang Chen","doi":"10.1016/j.geoderma.2026.117742","DOIUrl":"10.1016/j.geoderma.2026.117742","url":null,"abstract":"<div><div>Accurate estimation of soil unsaturated hydraulic conductivity (<span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span>) is critical for predicting vadose zone flow dynamics and characterizing subsurface hydrological processes. Traditional point scale tests are invasive and lack the spatiotemporal resolution required to capture field heterogeneity. This study presents an innovative framework that couples time-lapse ground penetrating radar (GPR), electrical resistivity tomography (ERT) with an improved instantaneous profile (IIP) inversion to non-destructively quantify <span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span> dynamics in different soils. Resulting <span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span> estimates were validated against laboratory soil water characteristic curve (SWCC)-based predictions from van Genuchten Mualem (VGM) and Childs–Collis-George (CCG) models. Parsimonious, logarithmic constitutive models were established linking <span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span> to relative permittivity (<span><math><msub><mi>ε</mi><mi>r</mi></msub></math></span>) and to bulk electrical conductivity (<span><math><mi>σ</mi></math></span>) for two soils, with corresponding predictive performance assessed by root mean square error (RMSE) and uncertainty summarized with the coefficient of variation (CV). Comparison between model estimated <span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span> with lab reference gives an overall RMSE = 0.32 mm/min for both <span><math><msub><mi>ε</mi><mi>r</mi></msub></math></span> and <span><math><mi>σ</mi></math></span> based functions, whilst Monte Carlo uncertainty propagation yields CV≈2.5–5.4% in the intermediate moisture range and CV≈7.1–8.6% near saturation, indicating that model confidence is highest in drained to partially saturated regimes (0.20 ≤<span><math><msub><mi>θ</mi><mi>v</mi></msub></math></span> ≤ 0.40 cm³/cm³), and declines near saturation (<span><math><msub><mi>θ</mi><mi>v</mi></msub></math></span>&gt; 0.40 cm³/cm³) where thin-film and surface conduction effects emerge. The proposed approach provides a practical pathway to spatially explicit estimation of <span><math><msub><mi>k</mi><mi>θ</mi></msub></math></span> from time-lapse geophysical data, yet field validation and joint inversion strategies are recommended to improve model transferability.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117742"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778644","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":"Amorphous aluminum hydroxide and rice-husk biochar enhance new organic carbon stabilization via different mechanisms","authors":"Han Lyu ,&nbsp;Arisa Nishiki ,&nbsp;Ruohan Zhong ,&nbsp;Ryosuke Kusumi ,&nbsp;Mayuko Seki ,&nbsp;Soh Sugihara ,&nbsp;Randy A. Dahlgren ,&nbsp;Shinya Funakawa ,&nbsp;Tetsuhiro Watanabe","doi":"10.1016/j.geoderma.2026.117721","DOIUrl":"10.1016/j.geoderma.2026.117721","url":null,"abstract":"<div><div>Increasing soil organic carbon (SOC) levels is essential for sustainable agricultural productivity and climate change mitigation, particularly in alkaline soils with inherently low SOC. While amorphous Al hydroxide (Am-Al) significantly influences SOC stabilization in volcanic and humid-region soils, and biochar enhances SOC in temperate and tropical regions, their effectiveness and stabilization mechanisms in alkaline soils require further exploration. We conducted a 1-year incubation of low SOC alkaline soils (5.2 g kg⁻¹) amended with ¹³C-labeled maize residue (1 g kg⁻¹), with or without Am-Al or rice-husk biochar (each 10 g kg⁻¹); residue mineralization/retention was quantified and molecular composition profiled by solid-state ¹³C NMR and Py-GC/MS. Rapid decomposition of plant residue ceased around 12 weeks, while plant residue-derived C and native SOC decomposition continued throughout the incubation period. Am-Al significantly reduced maize mineralization within the initial two weeks and retained a higher proportion of residue-derived C than the control soil with maize addition after one year (Am-Al: 36% vs. Control: 28%). ¹³C NMR and pyrolysis–GC/MS showed smaller decreases in carbohydrate-C and saccharides and a higher carbon preference index and odd–even predominance of alkanes, indicating that Am-Al better preserved carbohydrate- and cuticular-wax-derived components, proxies for less-degraded residues. Respiration dynamics and molecular fingerprints indicate Am-Al rapidly stabilizes labile plant compounds, possibly through non-electrostatic sorption and ligand-exchange. Biochar also retained more residue-derived C (33%) than the control, but its effects on mineralization emerged later in the incubation (&gt;6 months). We attribute this lag to surface degradation/activation of the biochar, which may stabilize residue-derived C more efficiently. Overall, adding Am-Al or biochar with plant residues significantly increased residue-derived C retention through immediate and delayed mechanisms, respectively. Treatments combining Am-Al or biochar with plant residue yielded a net positive C balance over the incubation, whereas residue alone was negative. Thus, the application of Am-Al and biochar with plant residues represents a promising strategy for sustained C stabilization, thereby improving SOC in degraded alkaline soils.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117721"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172867","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":"Selecting the right samples rather than more samples: A new spectral–environmental similarity strategy for local soil spectral modeling","authors":"Liangyi Li ,&nbsp;Zipeng Zhang ,&nbsp;Minglu Sun ,&nbsp;Jianli Ding ,&nbsp;Jingzhe Wang ,&nbsp;Dong Xu ,&nbsp;Yuanyuan Huang","doi":"10.1016/j.geoderma.2026.117710","DOIUrl":"10.1016/j.geoderma.2026.117710","url":null,"abstract":"<div><div>Addressing the dual challenges of limited sample size and high environmental heterogeneity in small-scale soil organic carbon (SOC) spectral modeling, this study proposes a fundamental hypothesis: selecting samples that are similar to the target region in both “spectral features and environmental characteristics” is more effective for improving prediction accuracy and stability. Based on this assumption, we developed a synergistic sample transfer strategy that integrates spectral similarity with environmental similarity under the Third Law of Geography, aiming to systematically screen the most comparable samples from the global soil spectral library to enhance the performance and robustness of local SOC modeling. A spectral-environmental similarity framework was established to identify samples that are simultaneously similar to the target region in spectral properties and environmental settings, and instance-based transfer modeling experiments were conducted in five representative small-sample regions (A-E). Results show that the synergistic strategy significantly improved modeling performance in most regions, with maximum increases in predictive power (as indicated by R²) of up to 18% compared with the baseline global transfer model. Remarkably, even when the number of global samples was reduced from 20,961 to around 200, the proposed strategy still outperformed local modeling and conventional global modeling approaches. In relatively stable environments, higher weights on environmental similarity yielded the best models, whereas in highly heterogeneous regions, spectral similarity played a more dominant role. The synergistic strategy also optimized the distribution of important spectral bands, enhanced SOC-responsive features in the visible region (450–750 nm), suppressed redundant information, and improved modeling efficiency. This study demonstrates that the proposed spectral-environmental synergistic sample transfer modeling method not only challenges the conventional assumption that “more samples guarantee better models” but also provides a novel pathway and theoretical support for the efficient use of global soil spectral libraries in regional SOC modeling.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117710"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134155","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":"Spectroscopic analysis shows crandallite can be a major component of soil phosphorus","authors":"Christian Vogel ,&nbsp;Julian Helfenstein ,&nbsp;Michael Massey ,&nbsp;Ruben Kretzschmar ,&nbsp;Ulrich Schade ,&nbsp;René Verel ,&nbsp;Oliver Chadwick ,&nbsp;Emmanuel Frossard","doi":"10.1016/j.geoderma.2026.117712","DOIUrl":"10.1016/j.geoderma.2026.117712","url":null,"abstract":"<div><div>Phosphorus (P) bioavailability is crucial for the productivity of natural and agricultural ecosystems, and soil P speciation plays a major role therein. Better understanding of P forms present in soil is thus essential to predict bioavailability. However, P speciation studies are only as powerful as the reference spectra used to interpret them, and most studies rely on a limited set of reference spectra. Most studies on soil P forms differentiate between Ca-bound P (e.g. apatite), organic P, Fe-bound P, and Al-bound P. In our analysis of a Ca, Al, and P rich soil from the Kohala region of Hawaii, we identified the mineral crandallite, CaAl₃(PO₄)₂(OH)₅·H₂O, a mineral previously not considered to play a significant role in soils. Crandallite was first identified with powder X-ray diffraction. Subsequently reference spectra were collected, and the presence of crandallite was confirmed using micro-focused P <em>K</em>-edge X-ray absorption near edge structure (XANES) spectroscopy, micro-infrared spectroscopy, and solid-state ³¹P nuclear magnetic resonance (NMR) spectroscopy. Crandallite XANES spectra were distinct from other common XANES spectra due to the presence of features in the post-edge region of the spectrum. Linear combination fitting of bulk P <em>K</em>-edge XANES spectra allowed the determination of the proportion of crandallite to the total P content, indicating that crandallite comprises up to half, possibly even more of the soil P in the samples. Crandallite is therefore an important and potentially overlooked component of soil P, which pedogenically forms in soils with high P, Al, and Ca contents, where it could play an important role in P bioavailability.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117712"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152893","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}