Soil & Tillage Research最新文献

{"title":"Design and evaluation of bio-inspired track shoes for improved traction and mobility in complex topography","authors":"Fu Zhang ,&nbsp;Le Yang ,&nbsp;Xinyue Wang ,&nbsp;Yakun Zhang ,&nbsp;Yumei Liu ,&nbsp;Sanling Fu","doi":"10.1016/j.still.2026.107071","DOIUrl":"10.1016/j.still.2026.107071","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107071"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microplastics in terraced topsoil under diverse land uses on the Chinese Loess Plateau","authors":"Nannan Yue ,&nbsp;Zhongbao Xin","doi":"10.1016/j.still.2026.107097","DOIUrl":"10.1016/j.still.2026.107097","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107097"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated soil health and machine learning framework for quantifying soil degradation in semi-arid agricultural lands","authors":"Kamal Khosravi Aqdam ,&nbsp;Farrokh Asadzadeh ,&nbsp;Salar Rezapour ,&nbsp;Amin Nouri ,&nbsp;Farzin Shabani","doi":"10.1016/j.still.2026.107099","DOIUrl":"10.1016/j.still.2026.107099","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107099"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil carbon quality determined the responses of respiration components to nitrogen fertilization and straw return","authors":"Shiqi Xu ,&nbsp;Zengming Chen ,&nbsp;Nan Zhang ,&nbsp;Ye Li ,&nbsp;Yehong Xu ,&nbsp;Weixin Ding","doi":"10.1016/j.still.2026.107072","DOIUrl":"10.1016/j.still.2026.107072","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107072"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The economics of conservation agriculture within a conceptual and methodological assessment framework","authors":"Abdoulaye Tapsoba,&nbsp;Sébastien Loubier","doi":"10.1016/j.still.2026.107084","DOIUrl":"10.1016/j.still.2026.107084","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107084"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel sample selection strategy for instance-based transfer learning in local soil organic carbon estimation","authors":"Kexin Liu ,&nbsp;Jiayi Zhang ,&nbsp;Zhengzheng Hao ,&nbsp;Xiangyu Min ,&nbsp;Songchao Chen ,&nbsp;Bifeng Hu ,&nbsp;Yin Zhou ,&nbsp;Zhou Shi ,&nbsp;Dongyun Xu","doi":"10.1016/j.still.2026.107065","DOIUrl":"10.1016/j.still.2026.107065","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107065"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greenhouse gas emissions in response to tillage and crop phase in a four-year crop rotation","authors":"Upendra M. Sainju ,&nbsp;William B. Stevens ,&nbsp;Jalal D. Jabro ,&nbsp;William M. Iversen ,&nbsp;Brett L. Allen ,&nbsp;Sikiru Y. Alasinrin","doi":"10.1016/j.still.2026.107089","DOIUrl":"10.1016/j.still.2026.107089","url":null,"abstract":"<div><div>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 [<em>Beta vulgaris</em> L.] and corn [<em>Zea mays</em> L.]) on CO₂, N₂O, and CH₄ fluxes and GHG balance (GHGB or sum of CO₂ equivalents of all GHGs) in an irrigated barley (<em>Hordeum vulgare</em> L.)-sugarbeet-corn-soybean (<em>Glycine max</em> 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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107089"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extreme rainfall redistributes and leaches nitrate accumulated in the soil profiles of an intensive agricultural region","authors":"Shimao Wang ,&nbsp;Xiaowei Yu ,&nbsp;Jianping Fei ,&nbsp;Tianyi Zhao ,&nbsp;Yucheng Xia ,&nbsp;Jingbo Gao ,&nbsp;Zhujun Chen ,&nbsp;Gurpal S. Toor ,&nbsp;Jianbin Zhou","doi":"10.1016/j.still.2026.107063","DOIUrl":"10.1016/j.still.2026.107063","url":null,"abstract":"<div><div>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 &gt; alluvial plain &gt; 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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107063"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-matrix film mulching enhances Salvia miltiorrhiza quality by reprogramming the rhizosphere microbiota and activating the secondary metabolic pathway","authors":"Jin Xu ,&nbsp;Yan Wang ,&nbsp;Bing Zhao ,&nbsp;Yi-Hao Liu ,&nbsp;Mao-Qiang He ,&nbsp;Yun-Fu Gu ,&nbsp;Xiu-Mei Yu ,&nbsp;Qiang Chen","doi":"10.1016/j.still.2026.107094","DOIUrl":"10.1016/j.still.2026.107094","url":null,"abstract":"<div><div>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 <em>Salvia miltiorrhiza</em> (<em>S. miltiorrhiza</em>), primarily by increasing the content of salvianolic acid B. The results showed that, during the root swelling period, compared with PM, BM significantly enriched <em>Bradyrhizobium</em> (normalized abundances +4.81 %) and <em>Nitrospira</em> C (+12.26 %), which drove soil nitrogen fixation (<em>nifH</em>/<em>K</em>) and nitrification (<em>hao</em>, <em>nxrB</em>), and increased NO₃^--N content (+50.09 %). Transcriptomic analysis revealed that BM induced the activation of the nitrogen metabolism pathway (<em>NRT2</em>, <em>GDH</em>, and <em>GS</em>), and the phenylpropanoid and tyrosine-derived pathway (<em>TAT</em>, <em>RAS</em>, and <em>4CL</em>) of <em>S. miltiorrhiza</em>, thereby increasing the content of salvianolic acid B by 40.67 % (<em>P</em> &lt; 0.001). During the harvest period, BM further enriched beneficial microbiota, including <em>Lentzea</em> (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 <em>S. miltiorrhiza</em> 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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107094"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction model for soil depth profile and tillage resistance in plow tillage","authors":"Yuying Song ,&nbsp;Xuan Zheng ,&nbsp;Jinbao Liu ,&nbsp;Huaijun Yang ,&nbsp;Fan Li ,&nbsp;Heyan Hu","doi":"10.1016/j.still.2026.107082","DOIUrl":"10.1016/j.still.2026.107082","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"259 ","pages":"Article 107082"},"PeriodicalIF":6.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033234","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}