Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.geoderma.2026.117721
Han Lyu , Arisa Nishiki , Ruohan Zhong , Ryosuke Kusumi , Mayuko Seki , Soh Sugihara , Randy A. Dahlgren , Shinya Funakawa , Tetsuhiro Watanabe
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 (>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.
提高土壤有机碳（SOC）水平对于可持续农业生产力和减缓气候变化至关重要，特别是在固有低SOC的碱性土壤中。无定形氢氧化铝（Am-Al）对火山和湿润地区土壤有机碳稳定有显著影响，生物炭对温带和热带地区土壤有机碳稳定有显著影响，但其在碱性土壤中的有效性和稳定机制有待进一步探索。我们对低有机碳碱性土壤（5.2 g kg - 1）进行了为期1年的培养，用13c标记的玉米渣（1 g kg - 1）进行了改性，添加或不添加Am-Al或稻壳生物炭（各10 g kg - 1）；通过固态13C NMR和Py-GC/MS对残渣的矿化/保留进行了定量分析，并对其分子组成进行了分析。植物残渣的快速分解在12周左右停止，而植物残渣衍生的碳和天然有机碳分解在整个孵化期内继续进行。在最初的两周内，Am-Al显著降低了玉米矿化，并且在一年后，与添加玉米的对照土壤相比，Am-Al保留了更高比例的残留物来源C （Am-Al: 36%，对照：28%）。13C NMR和热解- gc /MS显示碳水化合物c和糖类的减少幅度较小，碳偏好指数和烷烃的奇偶优势较高，表明Am-Al较好地保存了碳水化合物和角质蜡衍生成分，代表了较少降解的残留物。呼吸动力学和分子指纹图谱表明，Am-Al可能通过非静电吸附和配体交换快速稳定不稳定的植物化合物。生物炭也比对照保留了更多的残渣衍生的碳（33%），但其对矿化的影响在孵育后期（6个月）才显现出来。我们将这种滞后归因于生物炭的表面降解/活化，这可能更有效地稳定残渣衍生的C。总的来说，在植物残基中添加Am-Al或生物炭分别通过即时和延迟机制显著增加残基碳保留。Am-Al或生物炭与植物残渣混合处理在孵育期间产生净正碳平衡，而残渣单独处理产生负碳平衡。因此，在退化的碱性土壤中施用Am-Al和生物炭是一种很有前景的持续碳稳定策略，从而改善有机碳。
{"title":"Amorphous aluminum hydroxide and rice-husk biochar enhance new organic carbon stabilization via different mechanisms","authors":"Han Lyu , Arisa Nishiki , Ruohan Zhong , Ryosuke Kusumi , Mayuko Seki , Soh Sugihara , Randy A. Dahlgren , Shinya Funakawa , 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<sup>−1</sup>) amended with <sup>13</sup>C-labeled maize residue (1 g kg<sup>−1</sup>), with or without Am-Al or rice-husk biochar (each 10 g kg<sup>−1</sup>); residue mineralization/retention was quantified and molecular composition profiled by solid-state <sup>13</sup>C 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%). <sup>13</sup>C 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 (>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}
Pub Date : 2026-03-01Epub Date: 2026-02-20DOI: 10.1016/j.geoderma.2026.117739
Yixiang Jiang, Xiaolong Hu, Liangsheng Shi, Lin Lin, Yuanyuan Zha, Jiateng Ma
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 (Cinputs). Conventional process-based models, such as RothC, typically rely on empirical estimates of Cinputs, 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.
土壤有机碳（SOC）是全球碳循环的关键组成部分，但可靠的SOC储量预测仍然受到碳输入（Cinputs）不确定性的制约。传统的基于过程的模型，例如RothC，通常依赖于对投入的经验估计，限制了它们在异质环境和管理条件下的适用性。在这里，我们开发了一个混合建模框架，它将RothC模型与数据驱动的参数估计器集成在一起。碳输入调节器（α）首先通过使用马尔可夫链蒙特卡罗（MCMC）的概率反演来推断，然后通过训练环境协变量的机器学习模型推广到区域尺度。该框架是使用欧洲LUCAS数据集进行评估的，该数据集包括2009年至2018年间收集的7000多个地点土壤表层20厘米土壤的有机碳储量测量值。混合框架显著优于RothC模型，将SOC储量预测的RMSE从25.22 t C ha - 1降低到16.31 t C ha - 1，相应的相对RMSE从约46%降低到30%，并将欧洲不同生态系统的R2从0.43提高到0.70。SHapley加性解释（SHAP）分析发现，初始有机碳、容重、降水和土地利用类型是α的主要调节因子。重要的是，α表现出令人信服的生态合理性，与碳饱和理论一致的基线有机碳负相关，以及反映人为管理和植被对碳分配影响的不同土地利用类型的系统变化。该研究展示了混合方法在协调机制可解释性和数据驱动适应性方面的潜力，为土壤碳监测和可持续土地管理政策制定提供了可扩展的工具。
{"title":"Enhanced soil organic carbon estimation via hybrid modeling: A data-driven solution to carbon inputs","authors":"Yixiang Jiang, Xiaolong Hu, Liangsheng Shi, Lin Lin, Yuanyuan Zha, 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<sub>inputs</sub>). Conventional process-based models, such as RothC, typically rely on empirical estimates of C<sub>inputs</sub>, 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<sup>−1</sup>, with a corresponding relative RMSE from about 46% to 30%, and increasing the R<sup>2</sup> 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}
Accurate estimation of soil unsaturated hydraulic conductivity () 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 dynamics in different soils. Resulting 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 to relative permittivity () and to bulk electrical conductivity () 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 with lab reference gives an overall RMSE = 0.32 mm/min for both and 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 ≤ ≤ 0.40 cm3/cm3), and declines near saturation (> 0.40 cm3/cm3) where thin-film and surface conduction effects emerge. The proposed approach provides a practical pathway to spatially explicit estimation of from time-lapse geophysical data, yet field validation and joint inversion strategies are recommended to improve model transferability.
{"title":"Characterization and determination of soil unsaturated hydraulic conductivity by integrating time-lapse geophysical data with hydrogeological measurements","authors":"Chenyang Zou , Tengfei Wu , Shuangxi Zhang , 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 cm<sup>3</sup>/cm<sup>3</sup>), and declines near saturation (<span><math><msub><mi>θ</mi><mi>v</mi></msub></math></span>> 0.40 cm<sup>3</sup>/cm<sup>3</sup>) 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}
Pub Date : 2026-03-01Epub Date: 2026-02-07DOI: 10.1016/j.geoderma.2026.117710
Liangyi Li , Zipeng Zhang , Minglu Sun , Jianli Ding , Jingzhe Wang , Dong Xu , Yuanyuan Huang
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.
{"title":"Selecting the right samples rather than more samples: A new spectral–environmental similarity strategy for local soil spectral modeling","authors":"Liangyi Li , Zipeng Zhang , Minglu Sun , Jianli Ding , Jingzhe Wang , Dong Xu , 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 R<sup>2</sup>) 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}
Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.geoderma.2026.117712
Christian Vogel , Julian Helfenstein , Michael Massey , Ruben Kretzschmar , Ulrich Schade , René Verel , Oliver Chadwick , Emmanuel Frossard
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 K-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 K-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.
{"title":"Spectroscopic analysis shows crandallite can be a major component of soil phosphorus","authors":"Christian Vogel , Julian Helfenstein , Michael Massey , Ruben Kretzschmar , Ulrich Schade , René Verel , Oliver Chadwick , 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, CaAl<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>(OH)<sub>5</sub>·H<sub>2</sub>O, 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 <sup>31</sup>P 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}
Pub Date : 2026-03-01Epub Date: 2026-02-16DOI: 10.1016/j.geoderma.2026.117728
Mingsen Wang , Yu Jin , Lin Zhang , Yanfeng Liu
<div><div>The bare soil evaporation process is the link between the atmosphere and soil surface in the hydrologic cycle and, therefore, is a key issue in many fields of hydrological sciences. The selection of different soil–water characteristic curve (SWCC) models is essential to simulate bare soil evaporation processes. Remarkably, there is a notable lack of understanding regarding the quantitative characterization of the impact of some SWCC models on the simulation accuracy of bare soil evaporation, including the van Genuchten model (VG model), Brooks-Corey model (BC model), Fredlund and Xing model (FX model), Log-Normal Distribution model (LN model), modified van Genuchten model (mVG model), modified Brooks-Corey model (mBC model) and modified Log-Normal Distribution model (mLN model). In our study, to evaluate different SWCC methods for the estimate of evaporation frombare soils, we collected three distinct sets of evaporation data from column tests representing different lithologies. Utilizing the numerical simulation, we integrated VG, BC, LN, FX, mVG, mBC and mLN models to Richards equation to construct the simulation models (abbreviated as VG-integrated, BC-integrated, LN-integrated and FX-integrated models, respectively) of unsaturated water flow, comparing the evaporation rates and cumulative evaporation obtained from those integrated models. The FX-integrated model exhibited superior accuracy in predicting evaporation dynamics for Beaver Creek sand (BCS) and Natural silt (NS), with slightly diminished performance for Coarse sand (CS). The FX-integrated model predicts cumulative evaporation pretty well for BCS, NS, and CS, with variances of −6.34%, 10.01%, and 11.25%, respectively. The VG-integrated and LN-integrated models captured the experimentally measured evaporation rates of NS well, with the values of <em>R<sup>2</sup></em> equal to 0.9390 and 0.9467, respectively. The BC-integrated model excelled in simulating CS with the values of <em>R<sup>2</sup></em> equal to 0.9409. The modified integrated model group (mVG-integrated, mBC-integrated, and mLN-integrated model) exhibits systematic improvements—particularly the mBC-integrated model achieves enhanced CS evaporation rate predictions (<em>R<sup>2</sup></em> = 0.9897 vs. BC-integrated’s 0.9409) with 69% lower root mean square error (<em>RMSE</em>; 0.25 vs. 0.83 mm/d), but their performance in simulating BCS and NS evaporation remains inferior to the FX-integrated model. Further analysis underscores the FX-integrated model’s superiority in simulating bare soil evaporation due to the FX model’s ability to estimate air-entry values and fitting SWCC dry-end data more accurately than the VG, BC, and LN models. Consequently, our findings suggest that the FX-integrated model is the preferred choice for simulating bare soil evaporation. The research findings provide practical guidance, especially in accurately assessing evaporation under sustained evaporation conditions in arid areas.</di
{"title":"Quantitative evaluation of different soil–water characteristic curve models on bare soil evaporation simulation","authors":"Mingsen Wang , Yu Jin , Lin Zhang , Yanfeng Liu","doi":"10.1016/j.geoderma.2026.117728","DOIUrl":"10.1016/j.geoderma.2026.117728","url":null,"abstract":"<div><div>The bare soil evaporation process is the link between the atmosphere and soil surface in the hydrologic cycle and, therefore, is a key issue in many fields of hydrological sciences. The selection of different soil–water characteristic curve (SWCC) models is essential to simulate bare soil evaporation processes. Remarkably, there is a notable lack of understanding regarding the quantitative characterization of the impact of some SWCC models on the simulation accuracy of bare soil evaporation, including the van Genuchten model (VG model), Brooks-Corey model (BC model), Fredlund and Xing model (FX model), Log-Normal Distribution model (LN model), modified van Genuchten model (mVG model), modified Brooks-Corey model (mBC model) and modified Log-Normal Distribution model (mLN model). In our study, to evaluate different SWCC methods for the estimate of evaporation frombare soils, we collected three distinct sets of evaporation data from column tests representing different lithologies. Utilizing the numerical simulation, we integrated VG, BC, LN, FX, mVG, mBC and mLN models to Richards equation to construct the simulation models (abbreviated as VG-integrated, BC-integrated, LN-integrated and FX-integrated models, respectively) of unsaturated water flow, comparing the evaporation rates and cumulative evaporation obtained from those integrated models. The FX-integrated model exhibited superior accuracy in predicting evaporation dynamics for Beaver Creek sand (BCS) and Natural silt (NS), with slightly diminished performance for Coarse sand (CS). The FX-integrated model predicts cumulative evaporation pretty well for BCS, NS, and CS, with variances of −6.34%, 10.01%, and 11.25%, respectively. The VG-integrated and LN-integrated models captured the experimentally measured evaporation rates of NS well, with the values of <em>R<sup>2</sup></em> equal to 0.9390 and 0.9467, respectively. The BC-integrated model excelled in simulating CS with the values of <em>R<sup>2</sup></em> equal to 0.9409. The modified integrated model group (mVG-integrated, mBC-integrated, and mLN-integrated model) exhibits systematic improvements—particularly the mBC-integrated model achieves enhanced CS evaporation rate predictions (<em>R<sup>2</sup></em> = 0.9897 vs. BC-integrated’s 0.9409) with 69% lower root mean square error (<em>RMSE</em>; 0.25 vs. 0.83 mm/d), but their performance in simulating BCS and NS evaporation remains inferior to the FX-integrated model. Further analysis underscores the FX-integrated model’s superiority in simulating bare soil evaporation due to the FX model’s ability to estimate air-entry values and fitting SWCC dry-end data more accurately than the VG, BC, and LN models. Consequently, our findings suggest that the FX-integrated model is the preferred choice for simulating bare soil evaporation. The research findings provide practical guidance, especially in accurately assessing evaporation under sustained evaporation conditions in arid areas.</di","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117728"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209593","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}
Pub Date : 2026-03-01Epub Date: 2026-03-04DOI: 10.1016/j.geoderma.2026.117754
Stephan Costabel, Claus Florian Stange
{"title":"Corrigendum to “Nuclear magnetic resonance relaxometry to characterise the decomposition degree of peat soils” [Geoderma 456 (2025) 117244]","authors":"Stephan Costabel, Claus Florian Stange","doi":"10.1016/j.geoderma.2026.117754","DOIUrl":"10.1016/j.geoderma.2026.117754","url":null,"abstract":"","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117754"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360695","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}
Soil organic carbon (SOC) storage and persistence are strongly controlled by reactive metal phases, particularly organically complexed aluminum (Al) and iron (Fe) and short-range-order (SRO) minerals. However, their global relevance and the specific metal phases involved remain uncertain due to substantial variability in parent material, soil age, and rock-climate-SOC interactions. Andisols, derived from volcaniclastic materials and enriched in SOC and reactive metals, provide an ideal system to assess metal-SOC associations across broad pedogenic gradients. We compiled a global Andisol database of over 2850 soil samples across 34 countries, covering wide ranges of mean annual temperature (−2 °C to 30 °C), precipitation (60–6000 mm y−1), and soil pH in water (3.1–9.3). Most samples clustered within pH 4.5–6.5, corresponding to an Al-buffered domain where soil pH is predominantly regulated by Al hydrolysis reactions and equilibria among reactive Al pools. Generalized additive mixed model analyses identified organically complexed Al (pyrophosphate-extractable Al, Alp) as the strongest global predictor of SOC (relative importance = 40%) after accounting for soil depth. SRO Al minerals (acid oxalate-extractable Al minus Alp) showed moderate importance (relative importance = 10%), whereas reactive Fe and clay content had minor effects. Exchangeable calcium contributed significantly only at pH > ∼6.3, consistent with a transition toward base-cation buffering. The persistence of strong SOC-Alp relationships within the Al-buffered domain, together with consistent pH-dependent shifts in reactive Al and Fe pools, suggests that complexation with pedogenic Al released through weathering may exert a first-order control on mineral-protected SOC beyond Andisols and provides a mechanistic basis for incorporation into global-scale models. Identifying dominant stabilization mechanisms remains critical for determining whether SOC persistence is primarily regulated by carbon inputs, metal supply, or their combined effects. Given its integration of organically complexed and SRO Al phases and its broad data availability, acid oxalate-extractable Al emerges as the most practical proxy for mineral-protected SOC at the global scale.
土壤有机碳（SOC）的储存和维持受活性金属相，特别是有机络合铝（Al）和铁（Fe）以及短程序（SRO）矿物的强烈控制。然而，由于母质、土壤年龄和岩石-气候-有机碳相互作用的实质性变化，它们的全球相关性和所涉及的特定金属相仍然不确定。来源于火山碎屑物质的苯二酚富含有机碳和活性金属，是评估金属-有机碳关系的理想系统。我们编制了一个来自34个国家的2850多个土壤样本的全球Andisol数据库，涵盖了广泛的年平均温度（- 2°C至30°C）、降水（60-6000 mm y - 1）和土壤pH值（3.1-9.3）。大多数样品聚集在pH 4.5-6.5之间，对应于一个Al缓冲域，其中土壤pH主要由Al水解反应和活性Al池之间的平衡调节。广义加性混合模型分析发现，在考虑土壤深度后，有机络合Al（焦磷酸盐-可提取Al， Alp）是有机碳的最强全球预测因子（相对重要性= 40%）。SRO Al矿物（酸草酸-可提取Al - Alp）的重要性中等（相对重要性= 10%），而活性铁和粘土含量的影响较小。交换性钙仅在pH >； ~ 6.3时起显著作用，与向碱-阳离子缓冲的过渡一致。在Al缓冲区内持续存在的强SOC- alp关系，以及反应性Al和Fe池中一致的ph依赖变化，表明通过风化释放的成土Al络合作用可能对Andisols以外的矿物保护SOC施加一级控制，并为纳入全球尺度模型提供了机制基础。确定主要的稳定机制对于确定有机碳持久性是否主要受碳输入、金属供应或它们的综合影响至关重要。鉴于其有机络合和SRO Al相的集成及其广泛的数据可用性，草酸可提取Al成为全球范围内矿物保护SOC最实用的替代品。
{"title":"Organo-aluminum complexation as a dominant metal control on soil carbon storage in Andisols: Global evidence across pedogenic and pH gradients","authors":"Morimaru Kida , Hirohiko Nagano , Hiroaki Shimada , Jumpei Fukumasu , Rota Wagai","doi":"10.1016/j.geoderma.2026.117740","DOIUrl":"10.1016/j.geoderma.2026.117740","url":null,"abstract":"<div><div>Soil organic carbon (SOC) storage and persistence are strongly controlled by reactive metal phases, particularly organically complexed aluminum (Al) and iron (Fe) and short-range-order (SRO) minerals. However, their global relevance and the specific metal phases involved remain uncertain due to substantial variability in parent material, soil age, and rock-climate-SOC interactions. Andisols, derived from volcaniclastic materials and enriched in SOC and reactive metals, provide an ideal system to assess metal-SOC associations across broad pedogenic gradients. We compiled a global Andisol database of over 2850 soil samples across 34 countries, covering wide ranges of mean annual temperature (−2 °C to 30 °C), precipitation (60–6000 mm y<sup>−1</sup>), and soil pH in water (3.1–9.3). Most samples clustered within pH 4.5–6.5, corresponding to an Al-buffered domain where soil pH is predominantly regulated by Al hydrolysis reactions and equilibria among reactive Al pools. Generalized additive mixed model analyses identified organically complexed Al (pyrophosphate-extractable Al, Al<sub>p</sub>) as the strongest global predictor of SOC (relative importance = 40%) after accounting for soil depth. SRO Al minerals (acid oxalate-extractable Al minus Al<sub>p</sub>) showed moderate importance (relative importance = 10%), whereas reactive Fe and clay content had minor effects. Exchangeable calcium contributed significantly only at pH > ∼6.3, consistent with a transition toward base-cation buffering. The persistence of strong SOC-Al<sub>p</sub> relationships within the Al-buffered domain, together with consistent pH-dependent shifts in reactive Al and Fe pools, suggests that complexation with pedogenic Al released through weathering may exert a first-order control on mineral-protected SOC beyond Andisols and provides a mechanistic basis for incorporation into global-scale models. Identifying dominant stabilization mechanisms remains critical for determining whether SOC persistence is primarily regulated by carbon inputs, metal supply, or their combined effects. Given its integration of organically complexed and SRO Al phases and its broad data availability, acid oxalate-extractable Al emerges as the most practical proxy for mineral-protected SOC at the global scale.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117740"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777804","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}
Pub Date : 2026-03-01Epub Date: 2026-02-21DOI: 10.1016/j.geoderma.2026.117746
Michaela Bachmann , Ye Tian , Jakob Heinzle , Werner Borken , Erich Inselsbacher , Wolfgang Wanek , Andreas Schindlbacher
Climate warming alters biogeochemical cycles, especially in high-altitude forests where warming accelerates soil organic matter decomposition and CO2 efflux. Faster nitrogen (N) mineralization can enhance N availability to plants but may also increase N losses if soil microbial N use efficiency declines. However, long-term data on soil N loss mechanisms remain scarce. Key N cycling processes affect the natural 15N:14N isotope ratio (δ15N) differentially, with (de)nitrification yielding 15N-depleted products and leaving residual pools 15N-enriched. We investigated belowground N cycling after 14 years of soil warming (+4 °C) in a temperate old-growth forest in Achenkirch, Austria, by measuring δ15N values in belowground N pools (root N, bulk soil N, microbial biomass N, ammonium, nitrate) through isotope ratio mass spectrometry. Warming had no effect on δ15N of bulk soil N, microbial biomass N, and nitrate, but significantly increased δ15N in root N (−5.0 to −4.1‰) and in soil ammonium (−2.9 to 1.1‰). Root δ15N, reflecting inorganic soil N, indicates that warming-induced N losses caused 15N enrichment of inorganic soil N. Elevated ammonium δ15N points to increased rates of nitrification, while nitrate δ15N patterns imply denitrification (60–65% of nitrate sink) exceeding leaching as the main loss pathway, which aligns with available field observations. Coupled plant–soil δ15N analysis thus revealed decadal warming-driven changes in N cycling and identified coupled nitrification–denitrification as a key pathway of soil N loss, which is otherwise difficult to measure directly.
{"title":"Changes in natural 15N abundance highlight warming-induced stimulation of soil nitrate losses by coupled nitrification–denitrification in an old-growth montane forest","authors":"Michaela Bachmann , Ye Tian , Jakob Heinzle , Werner Borken , Erich Inselsbacher , Wolfgang Wanek , Andreas Schindlbacher","doi":"10.1016/j.geoderma.2026.117746","DOIUrl":"10.1016/j.geoderma.2026.117746","url":null,"abstract":"<div><div>Climate warming alters biogeochemical cycles, especially in high-altitude forests where warming accelerates soil organic matter decomposition and CO<sub>2</sub> efflux. Faster nitrogen (N) mineralization can enhance N availability to plants but may also increase N losses if soil microbial N use efficiency declines. However, long-term data on soil N loss mechanisms remain scarce. Key N cycling processes affect the natural <sup>15</sup>N:<sup>14</sup>N isotope ratio (δ<sup>15</sup>N) differentially, with (de)nitrification yielding <sup>15</sup>N-depleted products and leaving residual pools <sup>15</sup>N-enriched. We investigated belowground N cycling after 14 years of soil warming (+4 °C) in a temperate old-growth forest in Achenkirch, Austria, by measuring δ<sup>15</sup>N values in belowground N pools (root N, bulk soil N, microbial biomass N, ammonium, nitrate) through isotope ratio mass spectrometry. Warming had no effect on δ<sup>15</sup>N of bulk soil N, microbial biomass N, and nitrate, but significantly increased δ<sup>15</sup>N in root N (−5.0 to −4.1‰) and in soil ammonium (−2.9 to 1.1‰). Root δ<sup>15</sup>N, reflecting inorganic soil N, indicates that warming-induced N losses caused <sup>15</sup>N enrichment of inorganic soil N. Elevated ammonium δ<sup>15</sup>N points to increased rates of nitrification, while nitrate δ<sup>15</sup>N patterns imply denitrification (60–65% of nitrate sink) exceeding leaching as the main loss pathway, which aligns with available field observations. Coupled plant–soil δ<sup>15</sup>N analysis thus revealed decadal warming-driven changes in N cycling and identified coupled nitrification–denitrification as a key pathway of soil N loss, which is otherwise difficult to measure directly.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117746"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777800","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}
Pub Date : 2026-03-01Epub Date: 2026-02-13DOI: 10.1016/j.geoderma.2026.117724
Xuejia Huang , Yuanqun Wu , Xinxin He , Yuanying Peng , Tianyi Yan , Wende Yan , Xiaoyong Chen
Phosphorus (P) availability is often limited in subtropical acidic soils due to fixation by iron and aluminum oxides, constraining nutrient uptake and productivity in Camellia oleifera plantations. However, the mechanisms by which the effects of artificial nitrogen (N) application and natural N fixation via legume intercropping on soil P dynamics remain poorly understood. In this study, the independent effects of legume intercropping and N application on soil P fractions, soil biochemical properties and leaf nutrient content were investigated in C. oleifera plantations in subtropical China. Six treatments were applied: monoculture with weeding, monoculture without weeding, intercropping with Cassia tora or peanut, and monoculture with low or high N application (25 or 50 g urea per plant). Soil P fractions, soil organic carbon, total N, pH, ammonium (NH4+-N), nitrate (NO3−-N), acid and alkaline phosphatase activities, and leaf C, N, and P contents were measured at the growth (July) and mature (September) stages. Results showed that both legume intercropping and low N application independently enhanced total and labile soil P, increased soil organic carbon, and improved leaf nutrient contents compared to the control. High N initially reduced labile P but partially recovered by maturity. Phosphatase activities declined at maturity but remained higher in intercropped and fertilized plots, indicating improved P cycling. Nitrate N concentrations increased from the growth stage to the mature stage. These results suggest that legume intercropping and N application, when applied independently, each promote soil P availability and plant nutrient uptake, highlighting practical strategies to enhance soil fertility and sustain C. oleifera production in subtropical acidic soils.
{"title":"Effects of legume intercropping and nitrogen application on soil phosphorus availability and leaf nutrient status in subtropical Camellia oleifera plantations","authors":"Xuejia Huang , Yuanqun Wu , Xinxin He , Yuanying Peng , Tianyi Yan , Wende Yan , Xiaoyong Chen","doi":"10.1016/j.geoderma.2026.117724","DOIUrl":"10.1016/j.geoderma.2026.117724","url":null,"abstract":"<div><div>Phosphorus (P) availability is often limited in subtropical acidic soils due to fixation by iron and aluminum oxides, constraining nutrient uptake and productivity in <em>Camellia oleifera</em> plantations. However, the mechanisms by which the effects of artificial nitrogen (N) application and natural N fixation via legume intercropping on soil P dynamics remain poorly understood. In this study, the independent effects of legume intercropping and N application on soil P fractions, soil biochemical properties and leaf nutrient content were investigated in <em>C. oleifera</em> plantations in subtropical China. Six treatments were applied: monoculture with weeding, monoculture without weeding, intercropping with <em>Cassia tora</em> or peanut, and monoculture with low or high N application (25 or 50 g urea per plant). Soil P fractions, soil organic carbon, total N, pH, ammonium (NH<sub>4</sub><sup>+</sup>-N), nitrate (NO<sub>3</sub><sup>−</sup>-N), acid and alkaline phosphatase activities, and leaf C, N, and P contents were measured at the growth (July) and mature (September) stages. Results showed that both legume intercropping and low N application independently enhanced total and labile soil P, increased soil organic carbon, and improved leaf nutrient contents compared to the control. High N initially reduced labile P but partially recovered by maturity. Phosphatase activities declined at maturity but remained higher in intercropped and fertilized plots, indicating improved P cycling. Nitrate N concentrations increased from the growth stage to the mature stage. These results suggest that legume intercropping and N application, when applied independently, each promote soil P availability and plant nutrient uptake, highlighting practical strategies to enhance soil fertility and sustain <em>C. oleifera</em> production in subtropical acidic soils.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"467 ","pages":"Article 117724"},"PeriodicalIF":6.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172929","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}
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