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Spatial variation of soil erosion resistance impacted by ephemeral gully on long gentle sloping cropland in the Mollisol region of China Mollisol地区长缓坡耕地短暂沟壑区土壤侵蚀抗力空间分异

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-07 DOI: 10.1016/j.still.2025.107046

Jun Jing , Guanghui Zhang , Yi Zhang , Shukun Xing

Ephemeral gully erosion (EGE) is a major driver of cropland soil degradation, exerting substantial impacts on soil properties and crop growth. These changes, in turn, profoundly alter soil erosion resistance (SER). Although SER is critical for soil conservation, its spatial patterns and driving mechanisms in long, gently sloping (LGS) cropland under the influence of EGE remain insufficiently understood. The aims of this study were to quantify the spatial variation in SER and to identify the influencing factors on a representative LGS cropland in the Mollisol region of Northeast China. Based on in-situ field sampling and controlled flume tests, the spatial heterogeneity of SER under EGE conditions was quantified. The dominant influencing factors and their interactive mechanisms were identified, and a stepwise regression model was developed to estimate rill erodibility (K_r). Results showed that SER varied significantly across slope positions but did not differ markedly between gully positions. K_r linearly increased with decreasing gully position, while critical shear stress (τ_c) first decreased and then increased, with variation ranges of −2.36 % to 50.00 %. Both K_r and τ_c showed quadratic relationships with slope position, with K_r peaking at the middle slope and τ_c at the upper slope. Dominant factors affecting K_r included clay content, sand content, soil cohesion (Coh), mean weight diameter (MWD), root mass density (RMD), and straw mass density (SMD), which collectively explained 79 % of the spatial variability. Notably, SMD had a significant regulatory effect on Coh, RMD, and MWD, and indirectly reduced K_r via these pathways (standardized path coefficient = −0.263). The developed K_r estimation model (K_r = 0.212MWD^−2.405RMD^−0.502) exhibited good predictive performance (R² = 0.860; NSE = 0.863) but requires further validation under field conditions. The findings provide important theoretical support for site-specific erosion control strategies and contribute to the improvement of process-based soil erosion models at the hillslope scale in Mollisol regions of Northeast China.

短暂沟蚀（EGE）是农田土壤退化的主要驱动因素，对土壤性质和作物生长产生重大影响。这些变化反过来又深刻地改变了土壤抗侵蚀性（SER）。虽然SER对土壤保持至关重要，但其空间格局和驱动机制在长缓坡地（LGS）受EGE影响的研究尚不充分。本研究旨在量化东北Mollisol地区具有代表性的LGS农田SER的空间变化，并确定其影响因素。基于现场采样和控制水槽试验，定量分析了EGE条件下SER的空间异质性。确定了影响细沟可蚀性的主要因素及其相互作用机制，建立了细沟可蚀性的逐步回归模型。结果表明，不同坡位间SER差异显著，不同沟位间SER差异不显著。随着沟壑位置的减小，Kr呈线性增加，临界剪应力τc先减小后增大，变化范围为- 2.36 % ~ 50.00 %。Kr和τc均与坡位呈二次关系，其中Kr峰值出现在中坡，τc峰值出现在上坡。影响土壤Kr的主要因子为粘土含量、砂粒含量、土壤黏聚力（Coh）、平均重径（MWD）、根系质量密度（RMD）和秸秆质量密度（SMD），它们共同解释了79 %的空间变异。值得注意的是，SMD对Coh、RMD和MWD具有显著的调节作用，并通过这些途径间接降低了Kr（标准化路径系数= - 0.263）。所建立的Kr估计模型（Kr = 0.212MWD−2.405RMD−0.502）具有较好的预测效果（R²= 0.860,NSE = 0.863），但需要在现场条件下进一步验证。研究结果为土壤侵蚀控制策略提供了重要的理论支持，并有助于改进基于过程的坡面侵蚀模型。

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引用次数: 0

Enhancing predictive modeling of interrill and rill erosion susceptibility in the Eastern Mediterranean using stacking ensemble machine learning algorithms 利用叠加集成机器学习算法增强东地中海细沟间和细沟侵蚀敏感性的预测建模

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-06 DOI: 10.1016/j.still.2025.107053

Hazem Ghassan Abdo , Sahar Mohammed Richi , Hoang Thi Hang , Jasem A. Albanai , Javed Mallick

Soil erosion assessment is essential for effective conservation planning, particularly through the development of accurate susceptibility maps using advanced modeling techniques. Despite this importance, the Eastern Mediterranean remains underexplored in terms of hybrid modeling approaches for predicting interrill and rill erosion in environmentally sensitive areas. This study aims to develop a robust spatial prediction model for soil erosion susceptibility in the Eastern Mediterranean using a stacking ensemble machine learning framework. The performance of Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Multilayer Perceptron (MLP) models was evaluated individually and in stacked combinations based on 751 erosion and non-erosion events and 15 erosion-related conditioning factors. The results identified slope and land use/land cover (LULC) as the most influential drivers of soil erosion. Among the standalone models, XGBoost showed the highest predictive performance, while the hybrid XGB–RF ensemble achieved the best overall accuracy and reliability. These findings demonstrate the effectiveness of hybrid modeling in enhancing soil erosion prediction and provide a reliable decision-support tool for sustainable land management. The proposed approach offers valuable insights for preventive planning and natural resource protection in the Eastern Mediterranean, particularly in Syria, and represents an important step toward improved erosion modeling in complex topographic and climatic environments.

土壤侵蚀评估对于有效的保护规划至关重要，特别是通过使用先进的建模技术开发准确的敏感性图。尽管这一点很重要，但东地中海在预测环境敏感地区细沟和细沟侵蚀的混合建模方法方面仍未得到充分探索。本研究旨在利用叠加集成机器学习框架建立东地中海地区土壤侵蚀易感性的鲁棒空间预测模型。基于751个侵蚀和非侵蚀事件以及15个与侵蚀相关的条件因素，分别评估了极端梯度增强（XGBoost）、随机森林（RF）和多层感知器（MLP）模型的性能。结果表明，坡度和土地利用/土地覆盖（LULC）是影响土壤侵蚀的最主要驱动因素。在独立模型中，XGBoost显示出最高的预测性能，而混合XGB-RF集成实现了最佳的整体精度和可靠性。这些结果证明了混合模型在提高土壤侵蚀预测方面的有效性，为土地可持续管理提供了可靠的决策支持工具。提出的方法为地中海东部特别是叙利亚的预防性规划和自然资源保护提供了宝贵的见解，并代表了在复杂地形和气候环境中改进侵蚀模型的重要一步。

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引用次数: 0

Soil pH adjustment and the neutralizing effect reshape the rhizobial community in the legume rhizosphere 土壤pH值的调整和中和作用重塑了豆科植物根际根瘤菌群落

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-06 DOI: 10.1016/j.still.2025.107050

Kaili Xia , Shengyi Ouyang , Xi Mo , Yaxuan Gao , Jinlong Liu , Yingxiang Wang , Changfu Tian , Xiaolin Wang

Understanding microbial community assembly is pivotal in microbial ecology. Rhizobia, functioning as legume endosymbionts or free-living soil bacteria, sustain nitrogen fixation in crucial food and forage crops. However, in contrast to the well-studied rhizobia within root nodules, the ecological drivers governing rhizosphere rhizobial community assembly under environmental perturbations, particularly those assessed using the rpoB gene - an essential housekeeping gene valued for its ability to provide species- and strain-level phylogenetic insights remain unresolved. This study first integrated a meta-analysis of rpoB gene high-throughput sequencing data from legume rhizospheres across China, revealing soil pH and longitude as dominant biogeographical drivers. We then investigated the assembly patterns of rhizospheric rhizobial community in response to directed pH adjustment (HCl/NaOH/H₂O treatments) using soils of contrasting pH origins (Jiangxi acidic soil, Shandong neutral soil, and Xizang alkaline soil) and host plants (alfalfa, faba bean, and soybean) via controlled experiments. Phenotypic result demonstrated that pH neutralization increased nodule occupancy. High-resolution rpoB sequencing revealed that pH neutralization increased the alpha diversity of the Pan-Rhizobium community, while pH shifts in general led to simplified co-occurrence networks. Mechanistically, community assembly analysis demonstrated that pH shift promoted deterministic processes by selectively enriching pH-specialized taxa: Brarhizobium under acidity and Rhizobium/Mesorhizobium under alkalinity. These findings provide a mechanistic basis for predicting rhizobial community responses to environmental changes in legume-rhizobia symbiosis, enabling pH-targeted soil management strategies to enhance agricultural sustainability.

了解微生物群落的组装是微生物生态学的关键。根瘤菌作为豆科植物的内共生菌或自由生活的土壤细菌，在重要的粮食和饲料作物中维持固氮作用。然而，与根瘤内根瘤菌的充分研究相比，环境扰动下控制根际根瘤菌群落组装的生态驱动因素，特别是那些使用rpoB基因评估的生态驱动因素，rpoB基因是一种重要的管理基因，因其能够提供物种和品系水平的系统发育见解而受到重视，但仍未得到解决。本研究首先整合了中国豆科植物根际rpoB基因高通量测序数据的meta分析，揭示了土壤pH和经度是主要的生物地理驱动因素。采用对照试验研究了不同pH源土壤（江西酸性土壤、山东中性土壤和西藏碱性土壤）和寄主植物（苜蓿、蚕豆和大豆）根际根瘤菌群落在pH定向调节（HCl/NaOH/ h2o处理）下的聚集模式。表型结果表明，pH中和增加了结节的占用。高分辨率rpoB测序显示，pH中和增加了泛根瘤菌群落的α多样性，而pH的变化通常导致共发生网络的简化。从机制上看，群落组装分析表明，pH变化通过选择性地富集pH特异性分类群，促进了确定性过程：酸性条件下的Brarhizobium和碱性条件下的根瘤菌/中根瘤菌。这些发现为预测根瘤菌群落对豆科植物-根瘤菌共生环境变化的响应提供了机制基础，使以ph为目标的土壤管理策略能够提高农业的可持续性。

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引用次数: 0

The conversion of natural grassland to cropland drives thermodynamic destabilization of subsoil DOM over decades 几十年来，天然草地向农田的转变驱动了地下DOM的热力学失稳

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-05 DOI: 10.1016/j.still.2025.107051

Yuxin Yan, Yumei Peng, Jia Shi, Chunpeng Huo, Zhongmin Fan, Xiang Wang

The conversion of natural grassland to cropland is a widespread practice that threatens soil carbon stocks. However, its impact on the dynamics of dissolved organic matter (DOM), particularly the thermodynamic stability of subsoil DOM, remains poorly quantified. This study investigated the distribution and biochemical characterization of DOM and its transformation processes following 11 and 40 years of conversion from natural grassland to agricultural land on a Haplic Chernozems soil in northern China. The post-conversion agricultural management was no-tillage, focused on rain-fed cultivation of wheat and oats. The chemical characterization of DOM was conducted using UV–visible and fluorescence spectroscopy and Fourier-transform ion cyclotron resonance mass spectrometry combined with PARAFAC analysis and substrate-explicit modeling. The results showed a critical depth-dependent shift of DOM content and stability: while topsoil (0–20 cm) dissolved organic carbon (DOC) decreased by 88 %, its thermodynamic stability increased. Conversely, subsoil (80–100 cm) DOC increased by 1.8-fold, yet this accumulated pool was characterized by lower molecular weight, enrichment of a blue-shifted humic-like component (C2), and significantly higher thermodynamic degradability. Molecular-level evidence revealed that this destabilization was primarily driven by the transformation of complex subsoil organic matter into labile DOM, a process strongly linked to nutrient enrichment. Although derived from a single representative soil type, our findings provide a mechanistic framework for assessing carbon vulnerability in agroecosystems following land-use change.

将天然草地转变为耕地是一种普遍的做法，威胁到土壤碳储量。然而，其对溶解有机质（DOM）动力学的影响，特别是对底土DOM的热力学稳定性的影响，仍然很少量化。研究了中国北方黑钙土天然草地向农田转化11年和40年土壤DOM的分布、生化特征及其转化过程。改造后的农业管理为免耕，以雨养小麦和燕麦为主。利用紫外可见光谱和荧光光谱、傅里叶变换离子回旋共振质谱结合PARAFAC分析和底物显式建模对DOM进行化学表征。结果表明，DOM含量和稳定性随深度发生了临界变化：当表层土壤（0-20 cm）溶解有机碳（DOC）减少88 %时，其热力学稳定性增加。相反，底土（80-100 cm） DOC增加了1.8倍，但该累积池的特点是分子量较低，富集蓝移腐殖质样组分（C2），热力学可降解性显著提高。分子水平的证据表明，这种不稳定主要是由复杂的底土有机质转化为不稳定的DOM所驱动的，这一过程与养分富集密切相关。虽然我们的研究结果来自单一的代表性土壤类型，但我们的研究结果为评估土地利用变化后农业生态系统的碳脆弱性提供了一个机制框架。

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引用次数: 0

High-resolution mapping of soil mechanical properties by integrating geophysical sensors and terrain attributes 结合地球物理传感器和地形属性的土壤力学特性高分辨率制图

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-05 DOI: 10.1016/j.still.2025.107019

Ameesh Khatkar, Triven Koganti, Amélie Beucher, Alvaro Calleja-Huerta, Lars Juhl Munkholm, Mathieu Lamandé

Soil mechanical properties are essential for evaluating its strength and stability when subjected to stress from farm machinery. Conventional methods for measuring these properties are often slow, labour-intensive, and destructive. This study presents a novel approach that utilizes on-the-go proximal soil sensors (PSS), specifically electromagnetic induction (EMI) and gamma-ray spectroscopy (GRS), alongside terrain attributes, to evaluate their effectiveness in estimating three key soil mechanical properties: (1) precompression stress (σ_pc), (2) compression index (C_C), and (3) strain at 100 kPa (Ɛ_100kPa). For this, a geophysical survey and soil sampling were conducted across three arable fields, yielding 129 bulk soil samples and 516 intact soil cores (100 cm³) collected from 69 sampling points at two depths (0.15 and 0.40 m). Uniaxial confined compression tests (UCCT) were conducted to measure these mechanical properties, with multiple linear regression (MLR) applied for their estimation and cross-validation with the leave-one-out method (LOOCV). Both site-specific and unified datasets were analyzed, revealing higher prediction accuracy at 0.15 m compared to 0.40 m depth. Among the examined soil mechanical properties, C_C was estimated most accurately, followed by σ_pc and Ɛ_100kPa. Estimates of σ_pc derived from on-the-go PSS combined with terrain attributes substantially outperformed those obtained from the existing pedotransfer function. Furthermore, digital maps of these properties were successfully generated to visualize their spatial variability at the field scale. This study shows that on-the-go PSS provide a rapid, field-scale and non-destructive framework for estimating soil mechanical properties, supporting improved soil compaction assessment and monitoring.

土壤的力学特性是评估其强度和稳定性的关键，当受到农业机械的应力。测量这些特性的传统方法通常是缓慢的、劳动密集型的和破坏性的。本研究提出了一种新颖的方法，利用即时近端土壤传感器（PSS），特别是电磁感应（EMI）和伽马射线能谱（GRS），以及地形属性，来评估它们在估计三个关键土壤力学特性方面的有效性：(1)预压缩应力（σpc），(2)压缩指数（CC）和(3)100kPa应变（Ɛ100kPa）。为此，在三个耕地上进行了地球物理调查和土壤取样，从两个深度（0.15和0.40 m）的69个采样点收集了129个散装土壤样品和516个完整土壤岩心（100 cm3）。采用单轴密闭压缩试验（UCCT）来测量这些力学性能，并采用多元线性回归（MLR）进行估计，并用留一法（LOOCV）进行交叉验证。对特定站点和统一数据集进行了分析，发现0.15 m深度的预测精度高于0.40 m深度。所测土壤力学性质中，CC最准确，σpc次之，Ɛ100kPa次之。结合地形属性的动态PSS估算的σpc值明显优于现有土壤传递函数估算的σpc值。此外，成功地生成了这些属性的数字地图，以可视化它们在野外尺度上的空间变异性。该研究表明，移动PSS为估算土壤力学特性提供了快速、现场规模和非破坏性的框架，支持改进的土壤压实评估和监测。

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引用次数: 0

Enhancing farmland soil carbon mapping: Integrating radar backscatter model and machine learning 加强农田土壤碳制图：结合雷达后向散射模型和机器学习

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-03 DOI: 10.1016/j.still.2025.107055

Yujiao Wei , Yiyun Chen , Jiaxue Wang , Zheng Sun , Bo Wang , Chi Zhang , Chen Wu , Tianjie Zhao , Huanfeng Shen

Accurate farmland soil carbon mapping is essential for assessing soil health and monitoring carbon dynamics. Currently, most studies primarily rely on vegetation indicators (e.g., optical vegetation indices) or other surface features that are indirectly related to soil carbon. In contrast, surface soil properties beneath vegetation cover play a more direct and critical role in governing soil carbon dynamics and spatial distribution. Here, we developed an innovative soil carbon mapping framework based on machine learning, which integrates multi-source remote sensing data to leverage complementary vegetation and soil surface characteristics. Furthermore, the radar backscatter model was incorporated to enhance prediction accuracy by capturing additional soil structural properties. Specifically, Sentinel-2 (S2) optical data were used to derive comprehensive surface indicators including vegetation indices, brightness indices, and moisture indices. Sentinel-1 (S1) radar data provided complementary ground surface information beneath vegetation cover through its microwave penetration capability. To ensure that S1 backscatter accurately reflects soil physical properties, we applied the Water-Cloud Model (WCM) for vegetation correction, and compared correction results using NDVI and NDWI. A total of 154 topsoil samples were systematically collected from the central Songliao Plain, China, to validate the framework’s performance. By employing the XGBoost algorithm, we developed soil carbon prediction models under four distinct modeling strategies: (Ι) climatic conditions, topographical features, and S2-derived variables; (Ⅱ) adding uncorrected S1-derived variables; (Ⅲ) adding NDVI-corrected S1-derived variables; and (Ⅳ) adding NDWI-corrected S1-derived variables. The results indicated that compared with strategy Ι, strategy Ⅱ achieved an RMSE of 1.70 g/kg (9.09 % lower) and an R² of 0.47 (30.56 % higher), demonstrating the added value of radar information. The NDWI-corrected model (strategy Ⅳ) performed best, with an RMSE of 1.66 g/kg (11.23 % lower than strategy Ι) and an R² of 0.50 (38.89 % higher), highlighting the effectiveness of vegetation correction using NDWI. These findings emphasize how integrating optical and radar remote sensing can enrich the data dimensions used in soil carbon mapping. Proper vegetation correction is also crucial for improving mapping accuracy. Together, these approaches provide a scalable framework for precise farmland soil carbon prediction.

准确的农田土壤碳制图对于评估土壤健康状况和监测土壤碳动态至关重要。目前，大多数研究主要依靠植被指标（如光学植被指数）或其他与土壤碳间接相关的地物。相比之下，植被覆盖下的表层土壤性质对土壤碳动态和空间分布起着更为直接和关键的控制作用。在这里，我们开发了一个基于机器学习的创新土壤碳制图框架，该框架集成了多源遥感数据，以利用互补的植被和土壤表面特征。此外，还结合雷达后向散射模型，通过捕获额外的土壤结构特性来提高预测精度。具体而言，利用Sentinel-2 （S2）光学数据，得到了包括植被指数、亮度指数和湿度指数在内的综合地表指标。Sentinel-1 （S1）雷达数据通过其微波穿透能力提供了植被覆盖下的补充地面信息。为了保证S1后向散射能准确反映土壤物理性质，我们采用水云模型（Water-Cloud Model， WCM）进行植被校正，并比较了NDVI和NDWI的校正结果。系统采集了松辽平原中部154个表层土壤样本，验证了框架的性能。通过使用XGBoost算法，我们建立了四种不同建模策略下的土壤碳预测模型：（Ι）气候条件、地形特征和s2衍生变量；（Ⅱ）添加未校正的s1衍生变量；（Ⅲ）添加ndvi修正后的s1衍生变量；和（Ⅳ）添加ndwi校正的s1衍生变量。结果表明，与Ι策略相比，Ⅱ策略的RMSE为1.70 g/kg（降低9.09 %），R2为0.47（提高30.56 %），体现了雷达信息的附加价值。NDWI校正模型（策略Ⅳ）表现最好，RMSE为1.66 g/kg（比策略Ι低11.23 %），R2为0.50（高38.89 %），突出了NDWI植被校正的有效性。这些发现强调了将光学和雷达遥感相结合可以丰富土壤碳制图中使用的数据维度。适当的植被校正对提高制图精度也至关重要。总之，这些方法为精确的农田土壤碳预测提供了一个可扩展的框架。

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引用次数: 0

Biochar particle size shapes soil water–oxygen conditions and delays senescence in sweet corn under mulched drip irrigation 膜下滴灌条件下生物炭颗粒大小影响土壤水氧条件，延缓甜玉米衰老

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-02 DOI: 10.1016/j.still.2025.107049

Lifeng Zhou , Hanzhi Tao , Yang Qiliang , Hao Feng , Kadambot H.M. Siddique , Ting Jin

In clay soil regions, soil hypoxia frequently induces premature senescence of sweet corn under mulched drip irrigation (MDI), particularly in late-season crops within continuous multi-season planting systems. While biochar’s effect on soil moisture is well documented, its influence on soil oxygen dynamics remains unclear. In this study, unsorted biochar particles (UBP), large biochar particles (LBP), and small biochar particles (SBP) were applied, with no biochar as the control (CK). We evaluated soil pore distribution, gas transport indicators, moisture content, and oxygen partial pressure (pO₂), and assessed their impact on root and leaf senescence and grain yield in early- and late-season sweet corn crops. LBP increased total soil porosity and reduced soil bulk density, whereas UBP and SBP had no significant effect. LBP enlarged macropores (30–100 μm) and micropores (3–10 μm), resulting in a bimodal pore distribution, in contrast to the single-peak distribution (10–30 μm) in CK and SBP. LBP also enhanced macropore connectivity and reduced tortuosity, leading to higher air-filled porosity, air permeability, and gas diffusivity. SBP improved soil water-holding capacity but impeded gas transport due to pore “fineness”. Consequently, LBP decreased residual water content and increased plant-available water, balancing the tradeoff between water and oxygen under MDI. Soil hypoxia occurred in SBP and CK, causing roots to float and extend horizontally, whereas LBP prevented these effects. LBP significantly increased soil pO₂ and delayed senescence, ultimately enhancing sweet corn yield in both growing seasons. We recommend applying large biochar particles (2.0–4.0 mm) to improve aeration and pO₂ in clay soils. Additionally, the influence of fine soil particles on biochar’s internal pore structure warrants further study, particularly in irrigated farmland.

在粘土地区，土壤缺氧经常导致膜下滴灌（MDI）甜玉米早衰，特别是在连续多季种植系统中的晚季作物。虽然生物炭对土壤水分的影响已被充分记录，但其对土壤氧动力学的影响仍不清楚。本研究采用未分选生物炭颗粒（UBP）、大生物炭颗粒（LBP）和小生物炭颗粒（SBP），无生物炭作为对照（CK）。研究了土壤孔隙分布、气体输送指标、水分含量和氧分压（pO2）对甜玉米早、晚两季根系和叶片衰老及籽粒产量的影响。LBP增加了土壤总孔隙度，降低了土壤容重，而UBP和SBP没有显著影响。大孔（30 ~ 100 μm）和微孔（3 ~ 10 μm）呈双峰分布，而CK和SBP呈单峰分布（10 ~ 30 μm）。LBP还增强了大孔隙的连通性，减少了弯曲度，从而提高了充气孔隙度、透气性和气体扩散率。SBP提高了土壤的持水能力，但由于孔隙的“细度”，阻碍了气体的输送。因此，LBP降低了剩余水分含量，增加了植物有效水分，在MDI下平衡了水和氧之间的平衡。SBP和CK均发生土壤缺氧，导致根系漂浮和水平伸展，而LBP则阻止了这些影响。LBP显著提高了土壤pO2，延缓了衰老，最终提高了两个生长季节甜玉米的产量。我们建议使用大的生物炭颗粒（2.0-4.0 mm）来改善粘土中的通气性和pO2。此外，土壤细颗粒对生物炭内部孔隙结构的影响值得进一步研究，特别是在灌溉农田。

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引用次数: 0

Biochar application enhances tolerance to boron toxicity in rice (Oryza sativa) seedlings 施用生物炭提高水稻幼苗对硼毒性的耐受性

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-01-02 DOI: 10.1016/j.still.2025.107048

Muhammad Riaz , Lei Yan , Xia Hao

Boron (B) is an essential micronutrient for plant physiological processes, yet excessive soil concentrations can severely impair plant health, particularly in sensitive crops such as rice. Although biochar is known to improve soil conditions and mitigate various environmental stressors, its capacity to alleviate B toxicity remains insufficiently studied. This research examined the effects of biochar application on rice seedling growth and soil microbial communities under boron toxicity (BT). The treatments were designated as CK (control), BC (biochar with normal boron), BT (B toxicity), and BC+BT (biochar with B toxicity). Boron stress significantly reduced shoot length, fresh and dry biomass, and leaf chlorophyll content. In contrast, BC+BT markedly improved these growth traits relative to BT alone. Biochar also altered the distribution of B fractions in soil by lowering easily soluble and residual B while increasing organically bound B. Changes in soil properties under BC included higher total nitrogen (TN), available potassium (AK), and soil organic matter (SOM). Furthermore, the study revealed clear differences in soil bacterial diversity, with the BC+BT treatment showing higher alpha-diversity metrics than the other treatments, while fungal diversity remained largely unchanged. Community composition analyses indicated that biochar application reshaped both bacterial and fungal community structures. These findings highlight the potential of biochar as an effective soil amendment for mitigating the adverse effects of B contamination on rice seedlings and improving overall soil health.

硼(B)是植物生理过程中必需的微量营养素，但土壤中硼浓度过高会严重损害植物健康，特别是在水稻等敏感作物中。虽然已知生物炭可以改善土壤条件并减轻各种环境压力，但其减轻B毒性的能力仍未得到充分研究。研究了硼中毒条件下施用生物炭对水稻幼苗生长和土壤微生物群落的影响。处理分为CK（对照）、BC（含正常硼的生物炭）、BT （B毒性）和BC+BT（含B毒性的生物炭）。硼胁迫显著降低了茎长、鲜干生物量和叶片叶绿素含量。与单独施用BT相比，BC+BT显著改善了这些生长性状。生物炭还改变了土壤中B组分的分布，降低了易溶性和残余B，增加了有机结合B，改变了土壤性质，包括提高了全氮（TN）、速效钾（AK）和土壤有机质（SOM）。此外，研究还揭示了土壤细菌多样性的明显差异，BC+BT处理的α多样性指标高于其他处理，而真菌多样性基本保持不变。群落组成分析表明，施用生物炭重塑了细菌和真菌的群落结构。这些发现强调了生物炭作为一种有效的土壤改良剂的潜力，可以减轻B污染对水稻幼苗的不利影响，并改善整体土壤健康。

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引用次数: 0

Integrating soil spectral libraries with laboratory hyperspectral imaging for profile organic carbon prediction in paddy soils 结合土壤光谱库和实验室高光谱成像技术预测水稻土剖面有机碳

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2025-12-31 DOI: 10.1016/j.still.2025.107028

Shuo Li , Yuwei Zhou , Abdul Mounem Mouazen , Songchao Chen , Raphael A. Viscarra Rossel , Asim Biswas , Wenjun Ji , Zhou Shi , Shanqin Wang

To promote soil organic carbon (SOC) storage and to meet growing food demand with limited land, it is essential to understand the spatial characteristics of SOC across the entire profile of paddy soils. The establishment of soil spectral libraries (SSLs) at various geographical scales has made near-infrared (NIR: 700–1100 nm) and shortwave-infrared (SWIR: 1100–2500 nm) hyperspectral imaging (HSI) more feasible for the rapid and cost-effective estimation of SOC. This study aimed to integrate SSLs with HSI for fine-scale mapping of elemental concentrations with a high spatial resolution of 1 mm per pixel (image size: 980 × 160 pixels) with a spectral range of 900–1700 nm. We apply SOC-reflectance calibrations from Global Soil Spectral Library (GSSL) to an independent local field site for the entire profile in paddy soils to a depth of 1-m, combined with spectral similarity with continuum removal (SS-CR) analysis, three spectral matching methods (e.g., Euclidean distances [ED], Mahalanobis distances [MD], and Spectral angle mapper [SAM]) and two modeling algorithms (e.g., random forest [RF] and Cubist). Additionally, we compared the performance of different Global and Local models in characterizing the distribution of SOC across the entire profile. Results indicated that although the Cubist-Local model provided good prediction accuracy (R² ≥ 0.77, RMSE ≤ 0.77 %, RPIQ ≥ 1.90), its ability to fine-scale mapping of the profile SOC was limited. In contrast, the RF-Local model based on the ED spectral matching method (ED-RF-Local) not only achieved the best performance (R² = 0.80, RMSE = 0.78 %, RPIQ = 1.86), but also successfully mapped SOC across the entire soil profile. This model used two-fifths of the samples compared to the Global model. The findings of this study provide a valuable reference for SOC prediction and mapping at the field scale and across the entire soil profile using HSI techniques and GSSL, emphasizing the potential for predictions in paddy soils with high vertical resolution.

为了促进土壤有机碳（SOC）的储存，在有限的土地条件下满足日益增长的粮食需求，有必要了解水稻土全剖面有机碳的空间特征。不同地理尺度土壤光谱库（SSLs）的建立，使得近红外（NIR: 700-1100 nm）和短波红外（SWIR: 1100-2500 nm）高光谱成像（HSI）技术更有可能实现土壤有机碳的快速、经济估算。本研究旨在将SSLs与HSI相结合，在光谱范围为900-1700 nm的情况下，以1 mm /像素（图像尺寸：980 × 160像素）的高空间分辨率进行元素浓度的精细制图。我们将全球土壤光谱库（GSSL）的soc反射率校准应用于水稻土中1 m深度的独立局部现场，结合光谱相似性与连续体去除（SS-CR）分析，三种光谱匹配方法（例如欧几里得距离[ED]，马氏距离[MD]和光谱角度映射[SAM]）和两种建模算法（例如随机森林[RF]和Cubist）。此外，我们还比较了不同的Global和Local模型在整个剖面中表征SOC分布的性能。结果表明，虽然cuist - local模型具有较好的预测精度（R2≥0.77,RMSE≤0.77 %,RPIQ≥1.90），但其对剖面SOC的精细映射能力有限。相比之下，基于ED光谱匹配方法的RF-Local模型（ED-RF-Local）不仅表现最佳（R2 = 0.80, RMSE = 0.78 %,RPIQ = 1.86），而且成功地绘制了整个土壤剖面的SOC。与全球模型相比，该模型使用了五分之二的样本。本研究结果为利用HSI技术和GSSL在农田尺度和整个土壤剖面上进行有机碳预测和制图提供了有价值的参考，并强调了高垂直分辨率水稻土的预测潜力。

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引用次数: 0

Spectra-based predictive mapping of soil erodibility and analysis of its influence mechanism: A typical case study for Northeast China 基于光谱的土壤可蚀性预测制图及其影响机制分析——以东北地区为例

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2025-12-29 DOI: 10.1016/j.still.2025.107044

Yuling Shi , Zihao Wu , Pu Shi , Yuanli Zhu

Soil erosion in Northeast China’s black soil region poses serious challenges to agricultural productivity and ecosystem sustainability. This study proposes a novel framework for high-resolution (10 m) mapping of soil erodibility by integrating Sentinel-2 spectral data with a gradient boosting decision tree (GBDT) model. A comprehensive soil erodibility index (CSEI) was developed to represent the combined effects of soil texture, structure, and organic stability. The GBDT model was used to identify the dominant environmental drivers and their nonlinear relationships with CSEI. Results indicate that the normalized difference tillage index (NDTI), soil moisture, and mean annual precipitation are the key influencing factors, collectively explaining 69.3 % of the spatial variability in soil erodibility. Threshold effects were observed, including an inverse S-curve for soil moisture and an inverted-U response to precipitation, reflecting shifts in erosion mechanisms under varying surface conditions. These findings provide quantitative evidence for targeted soil conservation and land-use optimization, supporting management strategies such as conservation tillage, slope-specific terracing, and vegetation restoration to mitigate erosion risks in vulnerable landscapes.

东北黑土区土壤侵蚀对农业生产力和生态系统可持续性构成严重挑战。该研究通过将Sentinel-2光谱数据与梯度增强决策树（GBDT）模型相结合，提出了一个高分辨率（10 m）土壤可蚀性制图的新框架。建立了综合土壤可蚀性指数（CSEI），以反映土壤质地、结构和有机稳定性的综合影响。采用GBDT模型识别主导环境驱动因素及其与CSEI的非线性关系。结果表明，归一化差异耕作指数（NDTI）、土壤水分和年平均降水量是影响土壤可蚀性空间变异的关键因子，共同解释了69.3% %的土壤可蚀性空间变异。观察到阈值效应，包括土壤湿度的反s曲线和降水的倒u响应，反映了不同地表条件下侵蚀机制的变化。这些发现为有针对性的土壤保持和土地利用优化提供了定量证据，支持了保护性耕作、特定坡度梯田和植被恢复等管理策略，以减轻脆弱景观的侵蚀风险。

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引用次数: 0