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An integrated soil health and machine learning framework for quantifying soil degradation in semi-arid agricultural lands 半干旱农用地土壤退化量化的综合土壤健康和机器学习框架

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-02-02 DOI: 10.1016/j.still.2026.107099

Kamal Khosravi Aqdam , Farrokh Asadzadeh , Salar Rezapour , Amin Nouri , Farzin Shabani

Soil degradation (SD), primarily driven by erosion, poses a significant threat to agricultural productivity, ecosystem resilience, and long-term food security in semi-arid regions. This study develops an integrated framework to assess the intensity, spatial distribution, and erosion susceptibility of SD across northwestern Iran. A total of 592 soil samples were collected from 393 farmlands and 199 grasslands. The Soil Health Index (SHI) was calculated using a Minimum Data Set (MDS) approach to select the most representative physicochemical indicators of soil functionality. The 90th percentile of SHI values from grasslands was used as a reference for near-natural soil conditions, enabling quantification of degradation severity in agricultural lands. Machine learning models, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM), were employed to predict spatial patterns of SD. Results showed that Inceptisols exhibited the highest degradation (mean SD = 0.353 ± 0.040), whereas Mollisols were the least degraded (mean SD = 0.199 ± 0.018), reflecting variations in soil organic carbon (SOC), bulk density (BD), and erosion susceptibility. RF outperformed other models (R² = 0.81, RMSE = 0.064), and feature importance analysis identified vegetation indices (NDVI, SAVI) and topographic factors (slope, LS-factor, TWI) as the key determinants of SD. Cold spots with relatively stable soil conditions were observed in northern and northeastern regions. This integrated approach provides a robust basis for mapping erosion-sensitive soils and designing evidence-based conservation strategies, supporting sustainable management of semi-arid agricultural lands.

在半干旱地区，主要由侵蚀引起的土壤退化对农业生产力、生态系统恢复力和长期粮食安全构成重大威胁。本研究开发了一个综合框架来评估伊朗西北部SD的强度、空间分布和侵蚀敏感性。在393个农田和199个草原共采集土壤样品592份。采用最小数据集（MDS）方法计算土壤健康指数（SHI），选取最具代表性的土壤功能理化指标。草地的第90百分位SHI值作为近自然土壤条件的参考，可以量化农用地的退化严重程度。采用随机森林（Random Forest， RF）、人工神经网络（Artificial Neural Network， ANN）和支持向量机（Support Vector Machine， SVM）等机器学习模型预测SD的空间格局。结果表明：在不同土壤条件下，土壤有机质（SOC）、土壤容重（BD）和土壤侵蚀敏感性的变化反映出土壤有机质（SOC）、土壤容重（BD）和土壤侵蚀敏感性的差异，土壤有机质（ineptisols）降解程度最高（平均SD = 0.353 ± 0.040），土壤有机质（mollisol）降解程度最低（平均SD = 0.199 ± 0.018）。RF优于其他模型（R²= 0.81,RMSE = 0.064），特征重要性分析发现植被指数（NDVI、SAVI）和地形因子（坡度、ls因子、TWI）是SD的关键决定因素。土壤条件相对稳定的寒区分布在北部和东北部。这种综合方法为绘制侵蚀敏感土壤图谱和设计基于证据的保护战略提供了坚实的基础，支持半干旱农用地的可持续管理。

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引用次数: 0

Design and evaluation of bio-inspired track shoes for improved traction and mobility in complex topography 仿生跑鞋的设计和评估，以改善复杂地形的牵引力和机动性

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-19 DOI: 10.1016/j.still.2026.107071

Fu Zhang , Le Yang , Xinyue Wang , Yakun Zhang , Yumei Liu , Sanling Fu

Agricultural operations in hilly and mountainous terrain rely heavily on the mobility of tracked vehicles, yet conventional track designs frequently suffer from limited adhesion and excessive slippage on clay-rich slopes. Although nature provides efficient locomotion models for such environments, specifically the Saanen goat (Capra hircus), effectively translating the morphology and motion mechanics of goat hooves into engineered structures to solve these traction deficits remains an open challenge. To solve the challenge, a bio-inspired track shoe mimicking the goat hoof’s force–structure coupling was developed. Interfacial shear tests on clay soils were combined with a discrete element method (DEM) simulation framework to optimize unit spacing, height, and arrangement. Subsequently, a composite structure incorporating a 40° hoof flap opening angle was designed and validated through soil trench tests using additively manufactured thermoplastic polyurethane tracks. The optimized configuration (4 mm spacing, 3 mm height, and dual-row arrangement) achieved an adhesion force of 6.89 N, representing a 14.07 % improvement over straight-plate tracks. Notably, the composite structure with the hoof flap provided further gains, increasing adhesion by 6.24 % compared with the optimized single bionic design and by 21.19 % relative to the conventional spur type. These results demonstrate that goat-hoof-inspired structures offer a viable approach to enhancing the traction performance of agricultural vehicles, contributing to improved mobility and operational safety in complex topography.

丘陵和山区的农业作业严重依赖履带车辆的机动性，然而传统的履带设计在富含粘土的斜坡上经常受到有限的附着力和过度滑移的影响。尽管自然界为这种环境提供了有效的运动模型，特别是萨南山羊（Capra hircus），但有效地将山羊蹄的形态和运动力学转化为工程结构来解决这些牵引力缺陷仍然是一个悬而未决的挑战。为了解决这一挑战，仿生跑鞋模仿了羊蹄的力-结构耦合。将黏性土界面剪切试验与离散元法（DEM）模拟框架相结合，优化单元间距、高度和布置。随后，采用增材制造的热塑性聚氨酯履带，设计了一种包含40°蹄瓣张开角的复合结构，并通过土沟试验进行了验证。优化后的结构（4 mm间距，3 mm高度，双排排列）获得了6.89 N的附着力，比直板轨道提高了14.07 %。值得注意的是，带有蹄瓣的复合材料结构提供了进一步的增益，与优化的单一仿生设计相比，附着力提高了6.24 %，与传统的刺型相比，附着力提高了21.19 %。这些结果表明，以羊蹄为灵感的结构为提高农用车辆的牵引性能提供了一种可行的方法，有助于改善复杂地形下的机动性和操作安全性。

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引用次数: 0

Microplastics in terraced topsoil under diverse land uses on the Chinese Loess Plateau 黄土高原不同土地利用方式下梯田表土中的微塑料

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.still.2026.107097

Nannan Yue , Zhongbao Xin

Microplastics (MPs) have emerged as widespread and persistent pollutants in terrestrial ecosystems, yet their distribution and influencing factors in terraced landscapes remain underexplored. Large-scale soil and water conservation projects have been established in the hilly regions of the Loess Plateau, China. However, limited research has addressed how different terraced land use types influence the occurrence and characteristics of soil MPs in this erosion-prone area. The objective of this study was to investigate differences in MPs abundance, color, shape, polymer composition, and vertical distribution among terraced farmland, terraced forestland, terraced orchard, and abandoned cropland in the Qiaozigou watershed. The spatial distribution of MPs within the watershed was examined to assess the effects of land use and soil and water conservation measures on MPs. The results indicated that MPs abundance was high in the terraced watersheds. The MPs abundance in the surface soil (0–10 cm) of terraced farmland was 1300 ± 108.78 items/kg, whereas the MPs content in abandoned cropland was significantly lower, i.e., one-third lower. However, in the reforested areas, no significant difference in MPs content was found compared with that in the terraced farmlands. With increasing soil depth, the MPs abundance in terraced farmland decreased by approximately 50 % relative to the surface layer (0–10 cm), while that in abandoned cropland decreased by about 33 %. Transparent MPs accounted for 77.81 % of all samples, and polyethylene terephthalate (PET) was found to be a prevalent MPs polymer. Densely populated areas with higher levels of human activity typically produce greater amounts of transparent fiber MPs. This study demonstrated the impact of different land use types on MPs in the Qiaozigou watershed, providing new insights into the likely sources of soil MPs under various terrace management strategies.

微塑料（MPs）已成为陆地生态系统中广泛存在的持久性污染物，但其在梯田景观中的分布和影响因素仍未得到充分研究。黄土高原丘陵区已经建立了大规模的水土保持工程。然而，关于不同梯田利用方式如何影响该侵蚀易发区土壤MPs的发生和特征的研究有限。摘要本研究旨在探讨桥子沟流域梯田、梯田林地、梯田果园和撂撂地中MPs丰度、颜色、形态、聚合物组成和垂直分布的差异。研究了流域内MPs的空间分布，评价了土地利用和水土保持措施对MPs的影响。结果表明，梯田流域MPs丰度较高。梯田耕层（0 ~ 10 cm）土壤MPs丰度为1300 ± 108.78项/kg，而撂撂地MPs含量极低，约为1 / 3。而复林区与梯田区相比，MPs含量无显著差异。随着土壤深度的增加，梯田的MPs丰度相对于表层（0-10 cm）下降了约50% %，而撂荒地的MPs丰度相对于表层（0-10 cm）下降了约33% %。透明MPs占所有样品的77.81 %，聚对苯二甲酸乙二醇酯（PET）是常见的MPs聚合物。人口密集、人类活动水平较高的地区通常会产生更多的透明纤维MPs。本研究展示了不同土地利用类型对桥子沟流域土壤MPs的影响，为不同梯田管理策略下土壤MPs的可能来源提供了新的见解。

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引用次数: 0

Soil carbon quality determined the responses of respiration components to nitrogen fertilization and straw return 土壤碳质量决定了呼吸组分对施氮和秸秆还田的响应

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-16 DOI: 10.1016/j.still.2026.107072

Shiqi Xu , Zengming Chen , Nan Zhang , Ye Li , Yehong Xu , Weixin Ding

The responses of soil respiration (Rs) and its heterotrophic (Rh) and autotrophic (Ra) components to straw return remain equivocal, with microbial-enzymatic mechanisms under co-applied nitrogen (N) fertilization poorly characterized, limiting rational straw and N managements for soil carbon (C) sequestration in agroecosystems. Here, we quantified annual fluxes of Rs, Rh, and Ra in paired northeast China croplands with equivalent soil organic C quantity but distinct C quality (high vs. low: HCQ vs. LCQ), under four treatments: no N fertilization/straw return (Control), N fertilization-only (NF), straw return-only (SR), and co-applications (SRF). Annual Rs emissions were consistently higher in HCQ than in LCQ soils, primarily driven by Rh elevation from greater substrate C bioavailability. Crucially, N fertilization induced C quality-dependent divergence: suppressing Rs in HCQ soils through stoichiometric constraints that diverted C flux from Rh to microbial assimilation, while maintaining negligible impacts in LCQ soils due to persistent microbial N mining rather than metabolic suppression. Straw return universally stimulated Rs (26–31 %) via hydrolytic enzyme-mediated Rh amplification following C amendments. Notably, HCQ soils exhibited significantly faster straw-induced mineralization than LCQ soils (75 % vs. 43 % efficiency), attributed to HCQ’s superior fungal-oxidase capacity targeting recalcitrant C. Regarding interactive effects, N fertilization dampened straw-induced Rh in HCQ by diverting residue-C from CO₂ release to stabilization, reducing mineralization efficiency to 16 %, whereas in LCQ, SRF maintained Rh at SR levels under persistent substrate constraints. Conversely, Ra consistently depended on N supplementation, mechanistically evidenced by increased plant biomass and chlorophyll content. Under straw return and N fertilization interactions, annual Ra increased only in LCQ soils, consistent with improved mineral N availability and plant N status, enhancing belowground C allocation, whereas HCQ showed little Ra response. Collectively, our findings establish soil C quality as the pivotal regulator dictating microbe-plant resource partitioning. Precision management must implement C quality-stratified straw and N coordination to synchronize climate mitigation with sustainable productivity.

土壤呼吸（Rs）及其异养（Rh）和自养（Ra）组分对秸秆还田的响应尚不明确，氮肥共施下的微生物-酶机制尚不清楚，限制了合理的秸秆和氮肥管理对农业生态系统土壤碳(C)固存的影响。本研究量化了东北土壤有机碳量相当但碳质量不同（高与低：HCQ与LCQ）的配对农田，在不施氮/秸秆还田（对照）、只施氮（NF）、只施秸秆还田（SR）和共施（SRF） 4种处理下，Rs、Rh和Ra的年通量。HCQ土壤的年Rs排放量始终高于LCQ土壤，这主要是由于更高的底物C生物利用度导致Rh升高所致。至关重要的是，氮肥诱导了C质量依赖的差异：通过化学计量限制，将C通量从Rh转移到微生物同化，从而抑制高碳土壤中的Rs，而在低碳土壤中，由于微生物持续的N挖掘而不是代谢抑制，其影响可以忽略不计。秸秆返回通过水解酶介导的Rh扩增在C修改后普遍刺激Rs（26-31 %）。值得注意的是，HCQ土壤的秸秆诱导矿化速度明显快于LCQ土壤（75% % vs. 43% %），这是由于HCQ具有更强的针对顽固性c的真菌氧化酶能力。在相互作用方面，氮肥通过将残余c从CO2释放转移到稳定状态来抑制HCQ中秸秆诱导的Rh，将矿化效率降低至16% %，而在LCQ中，SRF在持续的底物约束下将Rh维持在SR水平。相反，Ra持续依赖于N的补充，其机理表现为植物生物量和叶绿素含量的增加。在秸秆还田和氮肥交互作用下，年Ra仅在低智商土壤中增加，这与改善矿质氮有效性和植物氮状态，促进地下碳分配一致，而高智商土壤对Ra的响应较小。总的来说，我们的研究结果确定土壤C质量是决定微生物-植物资源分配的关键调节因子。精准管理必须实施碳质量分层秸秆和氮协调，使气候减缓与可持续生产力同步。

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引用次数: 0

The economics of conservation agriculture within a conceptual and methodological assessment framework 在概念和方法评估框架内的保护性农业经济学

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-17 DOI: 10.1016/j.still.2026.107084

Abdoulaye Tapsoba, Sébastien Loubier

Conservation agriculture (CA) involves the simultaneous adoption of three agroecological practices: no-tillage or reduced tillage, maintenance of soil organic cover, and crop diversification. This paper develops a conceptual and methodological framework for assessing the economic benefits of CA. More than 150 studies were reviewed to create a conceptual diagram that identifies and links the key economic and environmental effects of adopting CA. The review reveals contradictory impacts of CA on production costs. Labor and machinery costs are significantly reduced, but these savings may be offset by increased pesticide costs due to greater weed pressure. Evidence is also mixed regarding whether CA adoption increases or decreases crop yields and water pollution. However, implementing CA is likely to promote biodiversity, reduce soil erosion, mitigate global warming, and improve soil quality in the long term. These effects vary depending on the CA practices adopted, experiment duration, climatic conditions, soil textures, and crop types. Adopting no-tillage alone may be ineffective at controlling soil erosion and is likely to result in yield losses or insignificant yield gains. CA impacts extend from individual farms to national and global levels and involve various risks and uncertainties. In light of these findings, a methodological approach is proposed to assess the probability distributions of the private and public benefits that CA generates. Assessing these benefits will help farmers and policymakers make informed decisions, thereby ensuring successful transitions to CA practices.

保护性农业（CA）涉及同时采用三种农业生态实践：免耕或少耕、维持土壤有机覆盖和作物多样化。本文开发了一个概念和方法框架来评估CA的经济效益。我们回顾了150多项研究，以创建一个概念图，确定并联系采用CA的关键经济和环境影响。回顾揭示了CA对生产成本的相互矛盾的影响。人工和机械成本显著降低，但这些节省可能会被杂草压力增加导致的农药成本增加所抵消。关于采用CA是增加还是减少作物产量和水污染，证据也很复杂。然而，从长远来看，实施CA可能会促进生物多样性，减少土壤侵蚀，减缓全球变暖，并改善土壤质量。这些影响取决于所采用的CA做法、试验持续时间、气候条件、土壤质地和作物类型。单独采用免耕在控制土壤侵蚀方面可能无效，而且可能导致产量损失或微不足道的产量增加。CA的影响从个体农场延伸到国家和全球层面，并涉及各种风险和不确定性。根据这些发现，提出了一种方法学方法来评估CA产生的私人和公共利益的概率分布。评估这些效益将有助于农民和政策制定者做出明智的决策，从而确保向CA实践的成功过渡。

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引用次数: 0

A novel sample selection strategy for instance-based transfer learning in local soil organic carbon estimation 局部土壤有机碳估算中基于实例迁移学习的样本选择策略

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-09 DOI: 10.1016/j.still.2026.107065

Kexin Liu , Jiayi Zhang , Zhengzheng Hao , Xiangyu Min , Songchao Chen , Bifeng Hu , Yin Zhou , Zhou Shi , Dongyun Xu

Soil organic carbon (SOC) is a critical indicator of soil health and carbon cycling, essential for sustainable agricultural development and climate change mitigation. Visible near-infrared reflectance spectroscopy (vis-NIR) technology combined with machine learning models has shown potential for rapid and accurate SOC prediction. However, due to significant spatial heterogeneity in SOC distribution across different regions, global models based on soil spectral libraries (SSLs) often lack sufficient generalization capability for local applications. To address this challenge, we propose an innovative sample selection framework for instance-based transfer learning, KNN-FRSS, a forward recursive sample selection method combined with K-nearest neighbor algorithm. This framework identifies the most representative samples from the SSL for the target region using a two-step process: step 1: preliminary selection by local similarity metric, and step 2: precise selection by forward recursive sample selection mechanism, thereby enhancing the adaptability of cross-regional SOC modeling. We employed three predictive models (1D-CNN, Cubist, and PLSR) to evaluate the transferability of the KNN-FRSS strategy in cross-region modeling. In addition, we compared the performance of KNN-FRSS with another sample selection method (RS-LOCAL-v2.0) and feature-based transfer learning approach. These transfer learning methods were evaluated across four distinct regions. The results indicate that all transfer learning methods improved model predictive accuracy in four study regions. Notably, the combination of KNN-FRSS with the 1D-CNN model consistently outperformed the others. Compared to the best-performing models built using local data, this combined approach achieved an improvement in R²ranging from 3 % to 26 %, and a reduction in RMSE by 12.82–43.4 %. Finally, this study provides a feasible path to enhance the effectiveness of transfer learning in soil spectral modeling, and provides methodological support for rapid and high-accuracy SOC prediction across diverse geographic regions.

土壤有机碳（SOC）是土壤健康和碳循环的重要指标，对农业可持续发展和减缓气候变化至关重要。可见近红外反射光谱（vis-NIR）技术与机器学习模型相结合，显示出快速准确预测SOC的潜力。然而，由于土壤有机碳在不同区域的分布具有明显的空间异质性，基于土壤光谱库（SSLs）的全球模型往往缺乏足够的局部应用泛化能力。为了解决这一挑战，我们提出了一种创新的基于实例迁移学习的样本选择框架，KNN-FRSS，一种结合k -最近邻算法的前向递归样本选择方法。该框架通过两步流程确定目标区域SSL中最具代表性的样本：第一步：通过局部相似性度量进行初步选择，第二步：通过前向递归样本选择机制进行精确选择，从而增强了跨区域SOC建模的适应性。我们采用三种预测模型（1D-CNN、Cubist和PLSR）来评估KNN-FRSS策略在跨区域建模中的可移植性。此外，我们还将KNN-FRSS与另一种样本选择方法（RS-LOCAL-v2.0）和基于特征的迁移学习方法的性能进行了比较。这些迁移学习方法在四个不同的地区进行了评估。结果表明，所有迁移学习方法都提高了四个研究区域的模型预测精度。值得注意的是，KNN-FRSS与1D-CNN模型的组合始终优于其他模型。与使用本地数据构建的最佳性能模型相比，这种组合方法实现了R²的改进，范围从3 %到26 %，RMSE降低了12.82-43.4 %。最后，本研究为提高迁移学习在土壤光谱建模中的有效性提供了可行途径，并为快速、高精度地预测不同地理区域土壤有机碳提供了方法支持。

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引用次数: 0

Greenhouse gas emissions in response to tillage and crop phase in a four-year crop rotation 温室气体排放对四年轮作中耕作和作物阶段的响应

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-24 DOI: 10.1016/j.still.2026.107089

Upendra M. Sainju , William B. Stevens , Jalal D. Jabro , William M. Iversen , Brett L. Allen , Sikiru Y. Alasinrin

Cropping systems can affect greenhouse gas (GHG) emissions due to variations in farm operations, root respiration, and soil organic matter mineralization that need further exploration. We examined the effect of tillage (conventional till [CT] and no-till [NT]) and crop phases (sugarbeet [Beta vulgaris L.] and corn [Zea mays L.]) on CO₂, N₂O, and CH₄ fluxes and GHG balance (GHGB or sum of CO₂ equivalents of all GHGs) in an irrigated barley (Hordeum vulgare L.)-sugarbeet-corn-soybean (Glycine max L.) rotation from 2016 to 2020 in the US northern Great Plains. A static chamber method was used to measure GHG fluxes at 3–30 d intervals, depending on crop performance and soil environment, throughout the year. While CO₂ peak fluxes occurred mostly during the crop growing season, N₂O peak fluxes occurred throughout the year. The CH₄ flux was minimal, except for some peaks in October 2017 and January and April 2019. Cumulative CO₂ flux from May to April and GHGB were 26–44 % greater for CT with sugarbeet than NT with sugarbeet or corn in 2016–2017 and 24–41 % greater for NT with sugarbeet than CT with corn in 2018–2019 and 2019–2020. Cumulative N₂O flux was 70–244 % greater for CT with sugarbeet than NT with sugarbeet in 2016–2017 and 2019–2020 and 38–73 % greater for NT with sugarbeet than other treatments in 2018–2019. Cumulative CH₄ flux did not vary among treatments in any year. The GHG emissions can be reduced by using CT with corn and NT with corn and sugarbeet compared with CT with sugarbeet during the dry year and using CT with corn compared with other treatments during the wet year in the barley-sugarbeet-corn-soybean rotation under sandy loam soils of the US northern Great Plains, indicating that treatments effect on reducing GHG emissions varied with climatic conditions.

由于农场经营、根系呼吸和土壤有机质矿化的变化，种植制度会影响温室气体（GHG）排放，这些需要进一步探索。研究了2016 - 2020年美国北部大平原灌溉大麦(Hordeum vulgare L.)-甜菜-玉米-大豆（Glycine max L.）轮作中耕作方式（常规耕作[CT]和免耕[NT]）和作物阶段（甜菜[Beta vulgaris L.]和玉米[Zea mays L.]）对CO2、N2O和CH4通量和温室气体平衡（GHGB或所有温室气体CO2当量总和）的影响。根据作物性能和土壤环境的不同，采用静态室法在全年每隔3-30 d测量温室气体通量。CO2峰值通量主要出现在作物生长季节，而N2O峰值通量出现在全年。除2017年10月和2019年1月和4月出现峰值外，CH4通量最小。2016-2017年5 - 4月累积CO2通量和温室气体排放总量，种植甜菜的稻田比种植甜菜或玉米的稻田高26-44 %，2018-2019年和2019-2020年种植甜菜的稻田比种植玉米的稻田高24-41 %。在2016-2017年和2019-2020年期间，含糖甜菜的CT处理的累积N2O通量比含糖甜菜的NT处理高70-244 %，在2018-2019年期间，含糖甜菜的NT处理的累积N2O通量比其他处理高38-73 %。各处理间的累积CH4通量无显著差异。在美国北部大平原砂质壤土下大麦-甜菜-玉米-大豆轮作中，旱年与玉米联合使用CT、玉米与甜菜联合使用NT比旱年与玉米与甜菜联合使用NT更能减少温室气体排放，旱年与玉米联合使用CT比其他处理更能减少温室气体排放，说明不同气候条件下不同处理的温室气体减排效果不同。

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引用次数: 0

Extreme rainfall redistributes and leaches nitrate accumulated in the soil profiles of an intensive agricultural region 极端降雨对集约化农业区土壤剖面中积累的硝酸盐进行了重新分配和淋滤

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-13 DOI: 10.1016/j.still.2026.107063

Shimao Wang , Xiaowei Yu , Jianping Fei , Tianyi Zhao , Yucheng Xia , Jingbo Gao , Zhujun Chen , Gurpal S. Toor , Jianbin Zhou

Nitrate accumulation in the soil profile is a major fate of surplus nitrogen (N). However, variations in nitrate distribution in deep soil profiles caused by extreme rainfall are not quantified, particularly in intensive agricultural areas with high N surplus. Our objective was to investigate how extreme rainfall events affect nitrate distribution and leaching in intensively managed kiwifruit orchard regions. Soil samples were collected from deep soil profiles (down to 10 m) in three landforms (loess tableland, alluvial plain, and pluvial fan) located in the northern slope region of the Qinling Mountains. Sampling was conducted in two normal rainfall years and following an extreme rainfall year that broke a 60-year rainfall record for the region. In normal rainfall years, the nitrate accumulation within the 0–10 m soil profile at sampling sites was highest in the loess tableland (10,769 kg N ha⁻¹), followed by alluvial plain (8776 kg N ha⁻¹) and pluvial fan (6682 kg N ha⁻¹). After the extreme rainfall event, nitrate accumulation in 0–10 m depth decreased by 46–62 % across all sites (with reductions exceeding 80 % in the 0–2 m depth). The magnitude of reduction among landforms followed the order: pluvial fan > alluvial plain > loess tableland. Extreme rainfall caused the accumulated nitrate peak in the soil profiles to move downwards, 3.6 m in the loess tableland, 3.8 m in the alluvial plain and 4.0 m in the pluvial fan at all sampling sites. This suggests that extreme rainfall promoted the leaching of nitrate into the deeper soil layers. We observed that the sand content was negatively correlated with nitrate accumulation but positively correlated with nitrate leaching in different landforms. These findings highlight that extreme rainfall events can significantly intensify nitrate leaching through the soil profile. Thus, consideration of extreme rainfall is critical when assessing environmental pollution risks and developing management practices to mitigate N losses.

硝态氮在土壤剖面中的积累是氮素过剩的主要原因。然而，极端降雨引起的深层土壤剖面中硝酸盐分布的变化并没有被量化，特别是在高氮剩余的集约农业地区。我们的目的是调查极端降雨事件如何影响集中管理猕猴桃果园地区硝酸盐分布和淋失。在秦岭北坡区黄土塬地、冲积平原和洪积扇3种地形中采集深层土壤剖面（深度≤10 m）土壤样品。采样是在两个正常降雨年份进行的，以及在一个打破该地区60年降雨记录的极端降雨年份之后进行的。在正常降雨年，各样点0 ~ 10 m土壤剖面的硝态氮累积量以黄土高原最高（10,769 kg N ha−1），其次是冲积平原（8776 kg N ha−1）和洪积扇（6682 kg N ha−1）。极端降雨事件发生后，各站点0-10 m深度的硝酸盐累积量减少了46-62 %（0-2 m深度的减少量超过80 %）。各地貌减少幅度依次为：洪积扇>； 冲积平原>； 黄土塬地。极端降雨导致土壤剖面累积硝酸盐峰值下移，各样点黄土塬区为3.6 m，冲积平原为3.8 m，洪积扇为4.0 m。这表明极端降雨促进了硝酸盐渗入更深的土层。研究发现，在不同的地形中，含砂量与硝态氮积累呈负相关，与硝态氮淋溶呈正相关。这些研究结果表明，极端降雨事件可以显著加剧土壤剖面的硝酸盐淋滤。因此，在评估环境污染风险和制定管理措施以减轻氮损失时，考虑极端降雨是至关重要的。

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引用次数: 0

Bio-matrix film mulching enhances Salvia miltiorrhiza quality by reprogramming the rhizosphere microbiota and activating the secondary metabolic pathway 生物基质地膜覆盖通过重新编程根际微生物群和激活次生代谢途径提高丹参品质

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.still.2026.107094

Jin Xu , Yan Wang , Bing Zhao , Yi-Hao Liu , Mao-Qiang He , Yun-Fu Gu , Xiu-Mei Yu , Qiang Chen

Plastic-film mulching (PM) has been extensively employed in medicinal plant cultivation. However, plastic film residues result in severe soil pollution and further affect plant yields and quality. Bio-matrix film, made by mushroom residues and straw powders, has good potential as a sustainable alternative. Through the integration of multi-omics analyses, encompassing the rhizosphere soil microbiome, root transcriptome, and quantification of medicinal components, this study elucidated the mechanism by which bio-matrix film mulching (BM) enhances the quality of Salvia miltiorrhiza (S. miltiorrhiza), primarily by increasing the content of salvianolic acid B. The results showed that, during the root swelling period, compared with PM, BM significantly enriched Bradyrhizobium (normalized abundances +4.81 %) and Nitrospira C (+12.26 %), which drove soil nitrogen fixation (nifH/K) and nitrification (hao, nxrB), and increased NO₃^--N content (+50.09 %). Transcriptomic analysis revealed that BM induced the activation of the nitrogen metabolism pathway (NRT2, GDH, and GS), and the phenylpropanoid and tyrosine-derived pathway (TAT, RAS, and 4CL) of S. miltiorrhiza, thereby increasing the content of salvianolic acid B by 40.67 % (P < 0.001). During the harvest period, BM further enriched beneficial microbiota, including Lentzea (linked to enhanced C/N/P cycling) and arbuscular mycorrhizal fungi, which collectively improved root nutrient assimilation and reduced the loss rate of salvianolic acid B by 16.51 %. Hence, BM enhanced the quality of S. miltiorrhiza by “rhizosphere microbiota restructuring–soil nutrient activation–plant secondary metabolic network cascade response”. It offers an innovative solution with both ecological and economic benefits for cultivating medicinal plants.

地膜覆盖在药用植物栽培中得到了广泛的应用。然而，地膜残留造成了严重的土壤污染，进一步影响了植物的产量和品质。以蘑菇渣和秸秆粉为原料制备的生物基质膜具有良好的可持续发展潜力。本研究通过多组学分析，包括根际土壤微生物组、根转录组和药用成分定量分析，阐明了生物基质地膜（BM）提高丹参品质的机制，主要是通过提高丹参酚酸b的含量。结果表明，在根肿胀期，与PM相比，BM显著富集缓生根瘤菌（标准化丰度+4.81 %）和硝化螺菌C(+12.26 %)，促进土壤固氮（nifH/K）和硝化（hao, nxrB），提高NO3—N含量（+50.09 %）。转录组学分析显示，BM激活了丹参的氮代谢途径（NRT2、GDH和GS）以及苯丙氨酸和酪氨酸衍生途径（TAT、RAS和4CL），从而使丹参酚酸B的含量增加了40.67% % （P <； 0.001）。在收获期间，BM进一步丰富了有益菌群，包括Lentzea（与C/N/P循环增强有关）和丛枝菌根真菌，这些菌群共同促进了根系养分同化，使丹酚酸B的损失率降低了16.51% %。因此，BM通过“根际微生物群重构-土壤养分活化-植物次生代谢网络级联响应”提高了丹参品质。为药用植物的培育提供了一种兼具生态效益和经济效益的创新解决方案。

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引用次数: 0

Prediction model for soil depth profile and tillage resistance in plow tillage 犁耕土壤深度剖面与耕作阻力预测模型

IF 6.8 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research

Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.still.2026.107082

Yuying Song , Xuan Zheng , Jinbao Liu , Huaijun Yang , Fan Li , Heyan Hu

Plowing is the most fundamental operation in agricultural cultivation. Numerical simulation and optimization of post-tillage soil distribution and draft force are crucial for understanding the soil–plow interaction and improving moldboard design. In this study, discrete element method (DEM) simulations were conducted based on dimensional analysis and Buckingham's π theorem. Using the initial soil coordinates, moldboard bulk angle, lateral soil throw, soil turning angle, and soil furrow profile as independent variables, and soil depth displacement and tillage resistance as dependent variables, predictive models for both soil depth distribution after plowing and draft force were established. The optimization parameters for the moldboard geometry were determined using response surface methodology (RSM) and an orthogonal experimental design. The coefficient of determination (R²) for all predictive models exceeded 0.8. The average error between the measured and predicted values was 7.45 % for the soil depth distribution and 11.01 % for the tillage resistance. The proposed model demonstrates high accuracy and can provide theoretical support for the design of high-performance plowed surfaces.

耕地是农业耕作中最基本的操作。耕后土壤分布和牵引力的数值模拟与优化对于理解土犁相互作用和改进犁板设计具有重要意义。在本研究中，离散元法（DEM）基于量纲分析和白金汉π定理进行了模拟。以初始土壤坐标、挡泥板体积角、侧抛土量、土壤转弯角、土壤沟廓线为自变量，以土壤深度位移和耕作阻力为因变量，建立了犁后土壤深度分布和牵引力的预测模型。采用响应面法（RSM）和正交试验设计确定了模板几何形状的优化参数。所有预测模型的决定系数（R²）均超过0.8。实测值与预测值的平均误差分别为7.45 %和11.01 %。该模型具有较高的精度，可为高性能犁面设计提供理论支持。

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引用次数: 0