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Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",false,64,{"impact":173,"substance":508,"depth":584,"authority":585,"freshness":385,"relevant":455,"comment":586},17,9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[588],{"name":578,"url":575},[590,591,592,593,594],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[597],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":604},[603,609,614],{"code":144,"score":345,"supported":604,"title":605,"abstract":606,"tags":608},true,[145],[607,145],"土壤",[],{"code":24,"score":585,"supported":604,"title":610,"abstract":612,"tags":613},[611],"artificial intelligence",[],[591],{"code":16,"score":615,"supported":604,"title":616,"abstract":617,"tags":620},7,[],[618,619],"精准农业","precision agriculture",[590],[622,623],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":625,"openalex_id":627,"authors":628,"venue":578,"cited_by_count":424,"oa_url":575,"card":635,"direction":639,"ingested_from":641},"W7223082195",[629,631,633],{"name":630,"orcid":11},"Amine Ben Salah",{"name":632,"orcid":11},"Ines Trabelsi",{"name":634,"orcid":11},"Mohamed Aziz Khelifi",{"tldr":636,"method":637,"finding":638,"direction":639,"opportunity":640},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","openalex","2026-10-22T11:20:58.901747Z",{"id":644,"title":645,"url":646,"summary":647,"summary_zh":648,"content":11,"source_name":649,"source_url":646,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":651,"score_detail":652,"sources":655,"tags":657,"primary_topic":663,"secondary_topics":664,"classification":665,"search_phrases":689,"slug":692,"view_count":424,"doi":693,"paper":694,"created_at":727},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":653,"substance":321,"depth":361,"authority":345,"freshness":585,"relevant":455,"comment":654},16,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[656],{"name":649,"url":646},[658,659,660,661,662],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":666},{"candidates":667,"agricultural_evidence":604},[668,672,678,685],{"code":144,"score":345,"supported":604,"title":669,"abstract":670,"tags":671},[145],[607,145],[],{"code":32,"score":615,"supported":604,"title":673,"abstract":674,"tags":677},[],[675,676],"遥感","remote sensing",[658],{"code":184,"score":679,"supported":604,"title":680,"abstract":681,"tags":684},6,[],[682,683],"气候适应","干旱",[],{"code":176,"score":455,"supported":581,"title":686,"abstract":687,"tags":688},[],[],[661],[690,691],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":693,"openalex_id":695,"authors":696,"venue":649,"cited_by_count":424,"oa_url":646,"card":721,"direction":725,"ingested_from":641},"W7223069171",[697,699,701,703,706,708,711,714,716,718],{"name":698,"orcid":11},"Yue Li",{"name":700,"orcid":11},"Mengfei Shi",{"name":702,"orcid":11},"Fei Zhang",{"name":704,"orcid":705},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":707,"orcid":11},"Gowhar Meraj",{"name":709,"orcid":710},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":712,"orcid":713},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":715,"orcid":11},"Yuanfang Chai",{"name":717,"orcid":11},"Hongtao Xu",{"name":719,"orcid":720},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":722,"method":723,"finding":724,"direction":725,"opportunity":726},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","农业遥感与作物表型","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":729,"title":730,"url":731,"summary":732,"summary_zh":733,"content":11,"source_name":734,"source_url":731,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":735,"score_detail":736,"sources":740,"tags":742,"primary_topic":746,"secondary_topics":747,"classification":750,"search_phrases":783,"slug":786,"view_count":424,"doi":787,"paper":788,"created_at":806},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":653,"substance":737,"depth":584,"authority":738,"freshness":385,"relevant":455,"comment":739},21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[741],{"name":734,"url":731},[590,743,658,744,745],"机器学习","土壤墒情","冬小麦",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[748,749],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":751},{"candidates":752,"agricultural_evidence":604},[753,759,764,769,774,779],{"code":216,"score":754,"supported":604,"title":755,"abstract":757,"tags":758},26,[756,217],"winter wheat",[218,756,217],[745],{"code":144,"score":760,"supported":604,"title":761,"abstract":762,"tags":763},15,[145],[607,145],[744],{"code":24,"score":760,"supported":604,"title":765,"abstract":767,"tags":768},[766],"machine learning",[743,766],[743],{"code":32,"score":385,"supported":604,"title":770,"abstract":771,"tags":773},[],[675,676,772],"multispectral",[658],{"code":152,"score":679,"supported":604,"title":775,"abstract":776,"tags":778},[],[777,153],"灌溉",[],{"code":16,"score":400,"supported":604,"title":780,"abstract":781,"tags":782},[],[590],[590],[784,785],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":787,"openalex_id":789,"authors":790,"venue":734,"cited_by_count":424,"oa_url":731,"card":800,"direction":805,"ingested_from":641},"W7223156204",[791,794,796,798],{"name":792,"orcid":793},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":795,"orcid":11},"Yi Zhang",{"name":797,"orcid":11},"WEI Lei",{"name":799,"orcid":11},"Chengcai Zhang",{"tldr":801,"method":802,"finding":803,"direction":725,"opportunity":804},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":808,"title":809,"url":810,"summary":811,"summary_zh":812,"content":11,"source_name":813,"source_url":810,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":814,"score_detail":815,"sources":817,"tags":819,"primary_topic":825,"secondary_topics":826,"classification":827,"search_phrases":846,"slug":849,"view_count":424,"doi":850,"paper":851,"created_at":875},5549,"Characterising climate mitigation and adaptation performance in oil palm plantation: evidence from Indonesia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1871877","Despite growing demand for ‘climate-friendly’ palm oil, little is known about how existing soil and management conditions align with climate mitigation and adaptation goals. This study characterised the climate mitigation and adaptation performance of oil palm (OP) production across different soil and management conditions based on simulated carbon balance and productivity. The Agricultural Production Systems SIMulator (APSIM)-OilPalm model was used to simulate productivity and carbon balance in 25 industrial OP sites on mineral soils in North Sumatra, Indonesia. K-means clustering was used to group similarity patterns between simulated carbon balance and productivity with observed soil and management conditions data. The model has good skill in simulating OP productivity at sites with standard plant density, achieving coefficient of determination (R 2 ) yield up to 0.79, though performance is poor at plantations with plant density outside the standard range or very low observed yields. Simulated OP productivity exhibits moderate variability from 15.38 to 28.67 t fresh fruit bunch (FFB) ha −1 yr. −1 . Simulated carbon balance, which is comparable to measured values in the region, indicates that all observed OP sites serve as a carbon sink with low variability from −2.09 to −3.86 t C eq. ha −1 yr. −1 , driven by uniformity in climate factors and plant density. Eleven of 25 sites have good mitigation and adaptation performance, indicated by high carbon sinks, yield and soil organic carbon (SOC) increment. These sites are characterised by greater plant density and moderate levels of N fertilisation, sand content and Ganoderma boninense (G. boninense) infection, with low carbon nitrogen (CN) ratio. Over half of sites face climate adaptation challenges, indicated by either low productivity or SOC increment and characterised by low plant density and nitrogen fertiliser with high G. boninense infection and sandy soils. This study contributes information about mitigation and adaptation in determining the management of OP and supports the tracking of climate-friendly palm oil within broader supply chains.","尽管对“气候友好型”棕榈油的需求日益增长，但人们对现有土壤和管理条件如何与气候减缓及适应目标相协调知之甚少。本研究基于模拟碳平衡和生产力，表征了油棕（OP）生产在不同土壤和管理条件下的气候减缓与适应表现。研究采用农业生产系统模拟器（APSIM）-OilPalm模型，模拟了印度尼西亚北苏门答腊25个矿质土壤工业油棕样点的生产力和碳平衡。运用K均值聚类方法，将模拟碳平衡与生产力之间的相似性模式与观测到的土壤和管理条件数据进行分组。该模型在标准种植密度样点的油棕生产力模拟中具有良好能力，决定系数（R²）最高达0.79，但在种植密度超出标准范围或观测产量极低的种植园中表现较差。模拟油棕生产力呈现中等变异性，范围为15.38至28.67 t鲜果串（FFB）ha⁻¹ yr.⁻¹。模拟碳平衡与该地区实测值相当，表明所有观测油棕样点均为碳汇，变异性较低，范围为−2.09至−3.86 t C eq. ha⁻¹ yr.⁻¹，这归因于气候因子和种植密度的一致性。25个样点中有11个具有良好的减缓和适应表现，表现为高碳汇、产量和土壤有机碳（SOC）增量。这些样点的特征是种植密度较大，氮肥施用量、砂含量和灵芝（Ganoderma boninense，G. boninense）感染程度适中，碳氮（CN）比低。超过半数样点面临气候适应挑战，表现为生产力或SOC增量较低，其特征是种植密度和氮肥用量低、灵芝感染率高及砂质土壤。本研究为确定油棕管理中的减缓和适应提供了信息，并支持在更广泛供应链中追踪气候友好型棕榈油。","Frontiers in Sustainable Food Systems",80,{"impact":361,"substance":321,"depth":361,"authority":738,"freshness":585,"relevant":455,"comment":816},"基于APSIM模型对印尼25个油棕园碳平衡与生产力的模拟研究，方法清晰、数据具体，对气候友好型油棕管理与供应链追踪有参考价值，但属区域性论文，影响范围有限。",[818],{"name":813,"url":810},[820,821,822,823,824],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":828},{"candidates":829,"agricultural_evidence":604},[830,834,838,842],{"code":144,"score":679,"supported":604,"title":831,"abstract":832,"tags":833},[],[607,145],[],{"code":160,"score":400,"supported":604,"title":835,"abstract":836,"tags":837},[],[823],[823],{"code":184,"score":400,"supported":604,"title":839,"abstract":840,"tags":841},[],[682],[821],{"code":24,"score":455,"supported":581,"title":843,"abstract":844,"tags":845},[],[],[820],[847,848],"APSIM-OilPalm 油棕 碳平衡","印尼 油棕 气候适应","APSIM-OilPalm油棕碳平衡-5549","10.3389\u002Ffsufs.2026.1871877",{"doi":850,"openalex_id":852,"authors":853,"venue":813,"cited_by_count":424,"oa_url":810,"card":869,"direction":873,"ingested_from":641},"W7223112249",[854,857,860,863,866],{"name":855,"orcid":856},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":858,"orcid":859},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":861,"orcid":862},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":864,"orcid":865},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":867,"orcid":868},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":870,"method":871,"finding":872,"direction":873,"opportunity":874},"用APSIM模型模拟印尼油棕园碳平衡与产量，评估其气候减缓与适应表现。","APSIM-OilPalm模型模拟25个站点，结合K-means聚类分析土壤与管","所有站点均为碳汇，11个站点减缓与适应表现良好，过半站点面临适应挑战。","农业绿色发展与碳","可扩展至不同气候带与泥炭地油棕园，结合遥感数据构建区域尺度气候友好型油棕认证体系。","2026-10-22T11:20:15.883770Z",{"id":877,"title":878,"url":879,"summary":880,"summary_zh":881,"content":11,"source_name":813,"source_url":879,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":882,"score_detail":883,"sources":885,"tags":887,"primary_topic":893,"secondary_topics":894,"classification":896,"search_phrases":916,"slug":919,"view_count":424,"doi":920,"paper":921,"created_at":943},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。",66,{"impact":173,"substance":508,"depth":653,"authority":738,"freshness":585,"relevant":455,"comment":884},"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[886],{"name":813,"url":879},[888,889,890,891,892],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[895],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":897},{"candidates":898,"agricultural_evidence":604},[899,906,910],{"code":286,"score":584,"supported":604,"title":900,"abstract":902,"tags":905},[901],"apple",[903,904,901],"苹果","orchard",[],{"code":144,"score":345,"supported":604,"title":907,"abstract":908,"tags":909},[145],[607,145],[],{"code":176,"score":911,"supported":604,"title":912,"abstract":913,"tags":915},3,[],[914],"生物多样性",[],[917,918],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":920,"openalex_id":922,"authors":923,"venue":813,"cited_by_count":424,"oa_url":879,"card":938,"direction":873,"ingested_from":641},"W7223104118",[924,927,930,932,935],{"name":925,"orcid":926},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":928,"orcid":929},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":931,"orcid":11},"K. Kahu",{"name":933,"orcid":934},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":936,"orcid":937},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":939,"method":940,"finding":941,"direction":873,"opportunity":942},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","2026-10-22T11:20:15.557635Z",{"id":945,"title":946,"url":947,"summary":948,"summary_zh":949,"content":11,"source_name":950,"source_url":947,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":651,"score_detail":951,"sources":953,"tags":955,"primary_topic":961,"secondary_topics":962,"classification":964,"search_phrases":986,"slug":989,"view_count":424,"doi":990,"paper":991,"created_at":1011},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","Agricultural Systems",{"impact":653,"substance":321,"depth":361,"authority":345,"freshness":585,"relevant":455,"comment":952},"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[954],{"name":950,"url":947},[956,957,958,959,960],"棉花","盐碱地治理","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[963],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":965},{"candidates":966,"agricultural_evidence":604},[967,972,976,981],{"code":152,"score":361,"supported":604,"title":968,"abstract":969,"tags":971},[153],[777,153,970],"evapotranspiration",[958],{"code":144,"score":760,"supported":604,"title":973,"abstract":974,"tags":975},[145],[607,145],[957],{"code":248,"score":400,"supported":604,"title":977,"abstract":978,"tags":980},[],[979],"cotton",[956],{"code":192,"score":911,"supported":604,"title":982,"abstract":983,"tags":985},[],[984],"sustainable agriculture",[],[987,988],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":990,"openalex_id":992,"authors":993,"venue":950,"cited_by_count":424,"oa_url":947,"card":1006,"direction":725,"ingested_from":641},"W7223120642",[994,997,999,1002,1004],{"name":995,"orcid":996},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":998,"orcid":11},"Tongkai Guo",{"name":1000,"orcid":1001},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":1003,"orcid":11},"Wenping Bi",{"name":1005,"orcid":11},"Xiaomin Mao",{"tldr":1007,"method":1008,"finding":1009,"direction":725,"opportunity":1010},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z",{"id":1013,"title":1014,"url":1015,"summary":1016,"summary_zh":1017,"content":11,"source_name":1018,"source_url":1015,"published_at":1019,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":735,"score_detail":1020,"sources":1022,"tags":1024,"primary_topic":1029,"secondary_topics":1030,"classification":1031,"search_phrases":1052,"slug":1055,"view_count":424,"doi":1056,"paper":1057,"created_at":1077},5490,"Biochar for post-war land remediation in Ukraine: A critical review supported by bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finteam\u002Fvjag167","This critical review analyzes the current state of scientific research on biochar in Europe and examines its potential use for post-war restoration and improvement of soil fertility in Ukraine. A bibliometric analysis of scientific publications in the Scopus and Web of Science databases for the period 2014-2023 was conducted using VOSviewer visualization and data processing methods. The analysis identified the main areas of research and gaps in knowledge that are relevant to the future application of this technology in Ukraine. Moreover, remote sensing methodologies were employed to identify potential remediation requirements in the Ukrainian territory. This involved the analysis of changes in the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) of a part of Ukraine's agricultural territory situated along the frontline, prior to and following a period of three years during a full-scale war. The results revealed changes in vegetation and moisture levels, indicating land degradation in the border area under study. However, these observations do not constitute direct evidence of soil contamination or the effectiveness of biochar; rather, they indicate areas where further field studies and an assessment of restoration measures are needed. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as a strategic discussion tool to identify the opportunities and challenges associated with the implementation of biochar in Ukraine. This review also identifies the main scientific uncertainties, barriers, economic aspects, and research priorities that should be considered before the large-scale implementation of biochar-based technologies in Ukraine.","这篇批判性综述分析了欧洲生物炭（biochar）科学研究的现状，并探讨其在乌克兰战后恢复与土壤肥力提升中的潜在应用。研究采用VOSviewer可视化与数据处理方法，对Scopus和Web of Science数据库中2014—2023年的科学出版物进行了文献计量分析。分析识别了与该项技术在乌克兰未来应用相关的主要研究领域和知识空白。此外，研究采用遥感方法识别乌克兰境内潜在的修复需求，具体包括对乌克兰沿前线部分农业区域在全面战争前后三年期间的归一化植被指数（NDVI）和归一化水分指数（NDMI）变化进行分析。结果显示植被与水分水平发生变化，表明所研究边境地区存在土地退化。然而，这些观测结果并不构成土壤污染或生物炭有效性的直接证据，而是指示了需要进一步开展田间研究和评估修复措施的区域。研究采用优势、劣势、机会与威胁（SWOT）分析作为战略讨论工具，识别在乌克兰实施生物炭相关的机遇与挑战。本综述还明确了在乌克兰大规模实施基于生物炭的技术之前应考虑的主要科学不确定性、障碍、经济因素及研究优先事项。","Integrated Environmental Assessment and Management","2026-10-19T00:00:00Z",{"impact":653,"substance":737,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1021},"该综述结合文献计量与遥感NDVI\u002FNDMI分析，评估生物炭用于乌克兰战后农田修复的潜力与不确定性，方法新颖、结论审慎，对农业遥感与土壤治理具参考价值，但属区域性研究，未达重大突破层级。",[1023],{"name":1018,"url":1015},[1025,1026,658,1027,1028],"生物炭","土地退化","土壤修复","乌克兰农业",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1032},{"candidates":1033,"agricultural_evidence":604},[1034,1040,1044,1048],{"code":64,"score":345,"supported":604,"title":1035,"abstract":1037,"tags":1039},[1036],"bibliometric",[1038,1036],"文献计量",[],{"code":144,"score":173,"supported":604,"title":1041,"abstract":1042,"tags":1043},[],[607,145],[1027,1025],{"code":32,"score":615,"supported":604,"title":1045,"abstract":1046,"tags":1047},[],[675,676],[658],{"code":136,"score":455,"supported":581,"title":1049,"abstract":1050,"tags":1051},[],[],[1026],[1053,1054],"生物炭 战后土壤修复 乌克兰","NDVI NDMI 遥感 土地退化","生物炭战后土壤修复乌克兰-5490","10.1093\u002Finteam\u002Fvjag167",{"doi":1056,"openalex_id":1058,"authors":1059,"venue":1018,"cited_by_count":424,"oa_url":11,"card":1072,"direction":725,"ingested_from":641},"W7223053668",[1060,1063,1066,1069],{"name":1061,"orcid":1062},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":1064,"orcid":1065},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":1067,"orcid":1068},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":1070,"orcid":1071},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":1073,"method":1074,"finding":1075,"direction":873,"opportunity":1076},"综述生物炭用于乌克兰战后土地修复，结合文献计量与遥感分析。","Scopus\u002FWoS文献计量、VOSviewer、NDVI\u002FNDMI遥感与SWO","前线农区植被与湿度变化显示土地退化，但需田间验证生物炭效果。","可研究战区土地退化的遥感快速诊断与生物炭修复效果的田间验证体系。","2026-10-21T23:30:25.223030Z",{"id":1079,"title":1080,"url":1081,"summary":1082,"summary_zh":1083,"content":11,"source_name":1084,"source_url":1081,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1085,"sources":1087,"tags":1089,"primary_topic":1093,"secondary_topics":1094,"classification":1095,"search_phrases":1120,"slug":1123,"view_count":424,"doi":1124,"paper":1125,"created_at":1155},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.",{"impact":416,"substance":508,"depth":653,"authority":738,"freshness":173,"relevant":455,"comment":1086},"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[1088],{"name":1084,"url":1081},[675,1090,1091,1092],"土地利用","土壤侵蚀","水土保持",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1096},{"candidates":1097,"agricultural_evidence":604},[1098,1102,1106,1111,1116],{"code":144,"score":345,"supported":604,"title":1099,"abstract":1100,"tags":1101},[145],[607,145],[],{"code":176,"score":408,"supported":604,"title":1103,"abstract":1104,"tags":1105},[],[1092],[1090,1092],{"code":152,"score":911,"supported":604,"title":1107,"abstract":1108,"tags":1110},[],[1109],"水资源",[],{"code":40,"score":911,"supported":604,"title":1112,"abstract":1113,"tags":1115},[],[1114],"GIS",[],{"code":32,"score":455,"supported":581,"title":1117,"abstract":1118,"tags":1119},[],[],[675],[1121,1122],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":1124,"openalex_id":1126,"authors":1127,"venue":1084,"cited_by_count":424,"oa_url":1149,"card":1150,"direction":725,"ingested_from":641},"W7223058039",[1128,1131,1134,1137,1140,1143,1146],{"name":1129,"orcid":1130},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":1132,"orcid":1133},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":1135,"orcid":1136},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":1138,"orcid":1139},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":1141,"orcid":1142},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":1144,"orcid":1145},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":1147,"orcid":1148},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":1151,"method":1152,"finding":1153,"direction":873,"opportunity":1154},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z",{"id":1157,"title":1158,"url":1159,"summary":1160,"summary_zh":1161,"content":11,"source_name":1162,"source_url":1159,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":882,"score_detail":1163,"sources":1165,"tags":1167,"primary_topic":1170,"secondary_topics":1171,"classification":1174,"search_phrases":1220,"slug":1223,"view_count":424,"doi":1224,"paper":1225,"created_at":1248},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development",{"impact":738,"substance":361,"depth":760,"authority":416,"freshness":173,"relevant":455,"comment":1164},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[1166],{"name":1162,"url":1159},[1168,957,658,1169,660],"气候变化","智慧灌溉",{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},[1172,1173],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1175},{"candidates":1176,"agricultural_evidence":604},[1177,1182,1186,1190,1194,1200,1204,1210,1215],{"code":184,"score":361,"supported":604,"title":1178,"abstract":1180,"tags":1181},[1179],"climate change",[1168,682,683,1179],[1168],{"code":144,"score":760,"supported":604,"title":1183,"abstract":1184,"tags":1185},[145],[607,145],[957],{"code":152,"score":385,"supported":604,"title":1187,"abstract":1188,"tags":1189},[],[777,1109,153,970],[1169],{"code":32,"score":615,"supported":604,"title":1191,"abstract":1192,"tags":1193},[],[675,676],[658],{"code":263,"score":679,"supported":604,"title":1195,"abstract":1196,"tags":1199},[],[1197,1198],"粮食安全","food security",[],{"code":16,"score":679,"supported":604,"title":1201,"abstract":1202,"tags":1203},[],[618,619],[],{"code":40,"score":679,"supported":604,"title":1205,"abstract":1206,"tags":1209},[],[1207,1208],"地理信息","geographic information",[],{"code":96,"score":911,"supported":604,"title":1211,"abstract":1212,"tags":1214},[],[1213],"养分管理",[],{"code":542,"score":911,"supported":604,"title":1216,"abstract":1217,"tags":1219},[],[1218],"农业政策",[],[1221,1222],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":1224,"openalex_id":1226,"authors":1227,"venue":1162,"cited_by_count":424,"oa_url":1159,"card":1243,"direction":725,"ingested_from":641},"W7222999158",[1228,1230,1232,1234,1236,1238,1241],{"name":1229,"orcid":11},"Asifa Naz",{"name":1231,"orcid":11},"Muhammad Abu Baker Siddiq",{"name":1233,"orcid":11},"Syed Asghar Hussain Shah",{"name":1235,"orcid":11},"Shahrish Nawaz",{"name":1237,"orcid":11},"Muhmmad Irfan",{"name":1239,"orcid":1240},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":1242,"orcid":11},"Muhammad Nouman Khalid",{"tldr":1244,"method":1245,"finding":1246,"direction":725,"opportunity":1247},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":1250,"title":1251,"url":1252,"summary":1253,"summary_zh":1254,"content":11,"source_name":813,"source_url":1252,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1255,"score_detail":1256,"sources":1258,"tags":1260,"primary_topic":1265,"secondary_topics":1266,"classification":1269,"search_phrases":1316,"slug":1319,"view_count":424,"doi":1320,"paper":1321,"created_at":1341},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。",70,{"impact":416,"substance":508,"depth":653,"authority":738,"freshness":585,"relevant":455,"comment":1257},"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[1259],{"name":813,"url":1252},[821,1261,1262,1263,1264],"碳交易","干旱农业","土壤碳汇","作物选择",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[1267,1268],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":160,"slug":161,"name":162,"path":163,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1270},{"candidates":1271,"agricultural_evidence":604},[1272,1276,1280,1286,1290,1296,1301,1306,1311],{"code":144,"score":345,"supported":604,"title":1273,"abstract":1274,"tags":1275},[145],[607,145],[],{"code":184,"score":385,"supported":604,"title":1277,"abstract":1278,"tags":1279},[],[1168,683,1179],[821],{"code":160,"score":173,"supported":604,"title":1281,"abstract":1282,"tags":1285},[],[1283,1284],"农业碳","carbon sequestration",[1263,1261],{"code":216,"score":679,"supported":604,"title":1287,"abstract":1288,"tags":1289},[],[218,217],[],{"code":279,"score":679,"supported":604,"title":1291,"abstract":1292,"tags":1295},[],[1293,1294],"番茄","黄瓜",[],{"code":240,"score":911,"supported":604,"title":1297,"abstract":1298,"tags":1300},[],[1299],"马铃薯",[],{"code":286,"score":911,"supported":604,"title":1302,"abstract":1303,"tags":1305},[],[1304],"葡萄",[],{"code":176,"score":911,"supported":604,"title":1307,"abstract":1308,"tags":1310},[],[1309],"农业生态",[],{"code":16,"score":911,"supported":604,"title":1312,"abstract":1313,"tags":1315},[],[1314],"smart agriculture",[],[1317,1318],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":1320,"openalex_id":1322,"authors":1323,"venue":813,"cited_by_count":424,"oa_url":1252,"card":1336,"direction":805,"ingested_from":641},"W7223064172",[1324,1326,1328,1331,1333],{"name":1325,"orcid":11},"Tasnim Althalaj",{"name":1327,"orcid":11},"Ross. M. Thompson",{"name":1329,"orcid":1330},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":1332,"orcid":11},"Dan Bumblauskas",{"name":1334,"orcid":1335},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":1337,"method":1338,"finding":1339,"direction":873,"opportunity":1340},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","2026-10-21T23:30:07.383006Z",{"id":1343,"title":1344,"url":1345,"summary":1346,"summary_zh":1347,"content":11,"source_name":813,"source_url":1345,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1348,"score_detail":1349,"sources":1351,"tags":1353,"primary_topic":1358,"secondary_topics":1359,"classification":1361,"search_phrases":1376,"slug":1379,"view_count":424,"doi":1380,"paper":1381,"created_at":1424},5467,"Temperature-directed valorisation of coconut frond through pyrolysis for carbon sequestration and sustainable agricultural residue management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966170","Coconut frond, an abundantly generated yet underutilized agricultural residue, was pyrolysed at 350 °C, 450 °C, and 550 °C alongside traditionally produced biochar (BC Trad) to evaluate the effects of pyrolysis temperature on physicochemical characteristics, fuel properties, carbon stability, and residue-valorisation potential. Comprehensive characterization using proximate and ultimate analyses, BET\u002FBJH, FTIR, XRD, Raman spectroscopy, SEM-EDS, total pyrogenic carbon (TPC), estimated CO 2 -equivalent stable-carbon retention potential, fuel and energy indices, and 90-day carbon mineralization incubation revealed pronounced temperature-dependent transformations. Biochar carbon recovery was highest at 350 °C (78.91%), demonstrating greater retention of feedstock carbon at lower pyrolysis temperature, whereas increasing temperature progressively enhanced carbon concentration, fixed carbon, thermostable carbon fraction, porosity, and surface area. BET surface area increased from 4.45 m 2 g −1 in BC Trad to 16.67 m 2 g −1 in BC550, accompanied by progressive pore refinement and depletion of oxygen-containing functional groups. Raman spectroscopy showed a decline in ID\u002FIG from 1.28 to 0.80, consistent with increasing structural ordering and aromatic condensation at higher temperatures. Fuel quality also improved with increasing temperature, with higher heating value increasing from 20.72 to 25.56 MJ kg −1 and fuel ratio from 1.49 to 4.26. TPC and estimated CO 2 -equivalent stable-carbon retention potential were highest at BC350 (0.29 kg C kg −1 feedstock and 0.86 kg CO 2 kg −1 feedstock, respectively), whereas BC550 exhibited the lowest apparent net soil-system carbon mineralization among the biochars during the 90-day incubation. Separately, its lower H\u002FC and O\u002FC ratios corresponded to a modelled BC +100 stability index of 0.720 and an estimated mean residence time of 347.61 years, providing model-derived indications of longer-term carbon persistence. Overall, the findings demonstrate a clear temperature-dependent trade-off between carbon recovery and carbon stability, with BC350 providing greater structural recalcitrance and apparent short-term mineralization resistance; the optimal pyrolysis temperature therefore depends on the intended end use rather than a single treatment being universally superior. Together, the short-term mineralization results and model-derived stability estimates indicate distinct dimensions of carbon persistence that should be considered when optimizing coconut-frond pyrolysis for sustainable agricultural-residue management, durable soil-carbon sequestration, and circular bioeconomy applications.","椰子叶是一种大量产生但未充分利用的农业废弃物，其在350 °C、450 °C和550 °C下热解，并与传统生产的生物炭（BC Trad）进行对比，以评估热解温度对理化特性、燃料性质、碳稳定性及残余物增值潜力的影响。采用工业分析和元素分析、BET\u002FBJH、FTIR、XRD、拉曼光谱、SEM-EDS、总热解碳（TPC）、估算CO₂当量稳定碳保留潜力、燃料与能量指标以及90天碳矿化培养进行综合表征，揭示了显著的温度依赖性变化。生物炭碳回收率在350 °C时最高（78.91%），表明较低热解温度下原料碳的保留更多，而升高温度则逐步提高碳浓度、固定碳、热稳定碳组分、孔隙率和比表面积。BET比表面积从BC Trad的4.45 m² g⁻¹增至BC550的16.67 m² g⁻¹，同时伴随孔隙细化和含氧官能团的消耗。拉曼光谱显示ID\u002FIG从1.28降至0.80，与较高温度下结构有序性和芳香缩合度增加相一致。燃料品质亦随温度升高而改善，高位热值从20.72增至25.56 MJ kg⁻¹，燃料比从1.49增至4.26。TPC和估算CO₂当量稳定碳保留潜力在BC350时最高（分别为0.29 kg C kg⁻¹原料和0.86 kg CO₂ kg⁻¹原料），而BC550在90天培养期间表现出生物炭中最低的表观净土壤系统碳矿化。此外，其较低的H\u002FC和O\u002FC比对应模拟BC +100稳定性指数为0.720，估算平均停留时间为347.61年，提供了模型推导的较长时期碳持久性指示。总体而言，研究结果揭示了碳回收与碳稳定性之间明显的温度依赖性权衡，BC350表现出更高的结构惰性和表观短期抗矿化性；因此，最佳热解温度取决于预期最终用途，而非单一处理普遍优越。短期矿化结果与模型推导的稳定性估算共同表明，碳持久性具有不同维度，应予考虑。",74,{"impact":416,"substance":321,"depth":361,"authority":738,"freshness":585,"relevant":455,"comment":1350},"该研究系统考察了热解温度对椰叶生物炭碳回收与碳稳定性的权衡关系，数据翔实、结论可靠，对农业废弃物资源化与土壤固碳具有参考价值，但属于细分领域基础研究，公共影响有限。",[1352],{"name":813,"url":1345},[1025,1354,1355,1356,1357],"农业废弃物","秸秆利用","土壤固碳","热解技术",{"code":160,"slug":161,"name":162,"path":163,"parent_code":128},[1360],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1362},{"candidates":1363,"agricultural_evidence":604},[1364,1368,1372],{"code":160,"score":524,"supported":604,"title":1365,"abstract":1366,"tags":1367},[1284],[1284],[],{"code":144,"score":173,"supported":604,"title":1369,"abstract":1370,"tags":1371},[],[607,145],[1356,1025],{"code":168,"score":408,"supported":604,"title":1373,"abstract":1374,"tags":1375},[],[1354],[1354,1355],[1377,1378],"椰叶 热解 生物炭","生物炭 碳封存 农业废弃物","椰叶热解生物炭-5467","10.3389\u002Ffsufs.2026.1966170",{"doi":1380,"openalex_id":1382,"authors":1383,"venue":813,"cited_by_count":424,"oa_url":1418,"card":1419,"direction":873,"ingested_from":641},"W7223016223",[1384,1387,1389,1392,1395,1397,1400,1403,1405,1408,1410,1413,1416],{"name":1385,"orcid":1386},"Morasanahalli Lokesha Sachin","https:\u002F\u002Forcid.org\u002F0009-0008-8615-0822",{"name":1388,"orcid":11},"Yogananda Shivalli Boregowda",{"name":1390,"orcid":1391},"Matadadoddi Nanjundegowda Thimmegowda","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1036",{"name":1393,"orcid":1394},"M. A. Ananthakumar","https:\u002F\u002Forcid.org\u002F0000-0003-4376-9541",{"name":1396,"orcid":11},"Pudiyanda Shadali Fathima",{"name":1398,"orcid":1399},"C. M. Sunil","https:\u002F\u002Forcid.org\u002F0000-0002-9625-5686",{"name":1401,"orcid":1402},"K. M. Pravalika","https:\u002F\u002Forcid.org\u002F0009-0004-5258-392X",{"name":1404,"orcid":11},"M.C. Anjali",{"name":1406,"orcid":1407},"Sathisha Govinakere Sambegowda","https:\u002F\u002Forcid.org\u002F0009-0000-8620-6461",{"name":1409,"orcid":11},"B. Divya",{"name":1411,"orcid":1412},"M. K. Shruthi","https:\u002F\u002Forcid.org\u002F0000-0003-3703-9817",{"name":1414,"orcid":1415},"Santosh Onte","https:\u002F\u002Forcid.org\u002F0009-0002-8367-1303",{"name":1417,"orcid":11},"Sanjeev Kumar","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966170\u002Fpdf",{"tldr":1420,"method":1421,"finding":1422,"direction":873,"opportunity":1423},"研究不同热解温度对椰叶生物炭碳固存与稳定性的影响。","350\u002F450\u002F550°C热解椰叶，多手段表征并做90天矿化培养。","碳回收率与碳稳定性随温度呈权衡，最佳温度取决于用途。","可探索热解温度与还田情景耦合的长期碳固存模型及经济性评估。","2026-10-21T23:30:07.083785Z",{"id":1426,"title":1427,"url":1428,"summary":1429,"summary_zh":1430,"content":11,"source_name":813,"source_url":1428,"published_at":650,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1431,"sources":1433,"tags":1435,"primary_topic":1440,"secondary_topics":1441,"classification":1442,"search_phrases":1478,"slug":1481,"view_count":424,"doi":1482,"paper":1483,"created_at":1506},5466,"Microbial and enzymatic response of soil to organic and natural management practices in chickpea-based cropping systems at the Satpura foothills","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1814274","Inroduction Natural and organic farming systems are increasingly recognized as sustainable agroecological approaches that promote regenerative practices, ecological balance, and reduced reliance on external inputs. This study aimed to assess the effects of different nutrient management strategies on the soil enzymatic and biological properties of chickpea-based cropping systems. Methods A field experiment was conducted during the rabi seasons of 2022–23 and 2023–24 at the Centre for Organic Agriculture Research and Training, Department of Agronomy, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra, India. The experiment followed a strip-plot design with four replications. Four cropping systems were evaluated in a 2:2 row proportion: sole chickpea, chickpea intercropped with coriander, chickpea intercropped with linseed, and chickpea intercropped with sorghum. Three nutrient management strategies were assessed: organic farming, natural farming, and an integrated approach combining organic and natural farming inputs. Results Across both years, sole chickpea consistently supported greater soil biological activity than the intercropping systems. Compared with chickpea–sorghum intercropping under natural farming, sole chickpea managed using integrated organic and natural farming practices significantly increased soil nitrogen, phosphorus, and potassium by 60%, 100%, and 192%, respectively. Fungal, bacterial, and actinomycete populations increased by 78.0%, 85.4%, and 109.8%, respectively. Soil microbial biomass carbon and nitrogen increased by 42.4% and 64.6%, respectively. Urease, dehydrogenase, acid phosphatase, and alkaline phosphatase activities increased by 75.6%, 100%, 116.8%, and 42.4%, respectively. Among the nutrient management strategies, the integrated application of organic and natural farming inputs showed superior performance in improving soil biological properties compared with either approach alone. Discussion The findings indicate that sole chickpea combined with integrated organic and natural nutrient management can improve soil microbial populations, enzymatic activities, nutrient availability, and overall soil biological health. This management strategy may contribute to long-term soil fertility and sustainable agricultural production in Inceptisols of the Satpura foothill region of India under semi-arid conditions.","引言 自然农业与有机农业体系日益被视为可持续的农业生态方法，能够促进再生性实践、生态平衡并减少对外部投入的依赖。本研究旨在评估不同养分管理策略对鹰嘴豆（chickpea）种植体系土壤酶学及生物学特性的影响。方法 于2022—23年度和2023—24年度rabi季在印度马哈拉施特拉邦阿科拉市Dr. Panjabrao Deshmukh Krishi Vidyapeeth农学系有机农业研究与培训中心开展田间试验。试验采用条区设计（strip-plot design），设4次重复。以2：2行比评估4种种植体系：鹰嘴豆单作、鹰嘴豆与芫荽间作、鹰嘴豆与亚麻间作、鹰嘴豆与高粱间作。评估3种养分管理策略：有机农业、自然农业以及有机与自然农业投入相结合的集成方式。结果 两年间，鹰嘴豆单作始终较间作体系支持更高的土壤生物学活性。与自然农业下的鹰嘴豆—高粱间作相比，采用有机与自然农业集成实践的鹰嘴豆单作使土壤氮、磷、钾分别显著提高60%、100%和192%；真菌、细菌和放线菌数量分别增加78.0%、85.4%和109.8%；土壤微生物生物量碳和氮分别增加42.4%和64.6%；脲酶、脱氢酶、酸性磷酸酶和碱性磷酸酶活性分别提高75.6%、100%、116.8%和42.4%。在养分管理策略中，有机与自然农业投入的集成施用较任一单一方式在改善土壤生物学特性方面表现更优。讨论 研究结果表明，鹰嘴豆单作结合有机与自然养分集成管理可改善土壤微生物种群、酶活性、养分有效性及整体土壤生物学健康。该管理策略可能有助于印度萨特普拉山麓地区半干旱条件下始成土（Inceptisols）的长期土壤肥力和可持续农业生产。",{"impact":385,"substance":508,"depth":584,"authority":738,"freshness":585,"relevant":455,"comment":1432},"田间试验数据扎实、结论明确，但属印度区域尺度的农学论文，对国内智慧农业主题页聚合价值有限，可作国际有机农业参考。",[1434],{"name":813,"url":1428},[1436,1437,1213,1438,1439],"间作系统","有机农业","土壤微生物","鹰嘴豆",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1443},{"candidates":1444,"agricultural_evidence":604},[1445,1449,1455,1461,1465,1470,1474],{"code":144,"score":760,"supported":604,"title":1446,"abstract":1447,"tags":1448},[145],[607,145],[1438],{"code":240,"score":679,"supported":604,"title":1450,"abstract":1451,"tags":1454},[],[1452,1453],"高粱","sorghum",[],{"code":256,"score":679,"supported":604,"title":1456,"abstract":1457,"tags":1460},[],[1458,1459],"间作","intercropping",[],{"code":96,"score":400,"supported":604,"title":1462,"abstract":1463,"tags":1464},[],[1213],[1213],{"code":192,"score":400,"supported":604,"title":1466,"abstract":1467,"tags":1469},[],[1468],"可持续农业",[1437],{"code":176,"score":911,"supported":604,"title":1471,"abstract":1472,"tags":1473},[],[1309],[],{"code":184,"score":911,"supported":604,"title":1475,"abstract":1476,"tags":1477},[],[683],[],[1479,1480],"鹰嘴豆 有机农业 土壤微生物","Satpura 鹰嘴豆 间作","鹰嘴豆有机农业土壤微生物-5466","10.3389\u002Ffsufs.2026.1814274",{"doi":1482,"openalex_id":1484,"authors":1485,"venue":813,"cited_by_count":424,"oa_url":1500,"card":1501,"direction":873,"ingested_from":641},"W7223012759",[1486,1488,1490,1492,1494,1496,1498],{"name":1487,"orcid":11},"Nalla Charitha",{"name":1489,"orcid":11},"Adinath N. Paslawar",{"name":1491,"orcid":11},"Vilas Kashinath Kharche",{"name":1493,"orcid":11},"Madastu Saikrishna",{"name":1495,"orcid":11},"Yogesh V. Ingle",{"name":1497,"orcid":11},"Akanksha Singh",{"name":1499,"orcid":11},"Anita B. Chorey","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1814274\u002Fpdf",{"tldr":1502,"method":1503,"finding":1504,"direction":873,"opportunity":1505},"比较有机与自然农法下鹰嘴豆种植体系对土壤微生物和酶活性的影响。","两年条区试验，4种种植体系×3种养分管理，测土壤微生物数量、生物量碳氮及酶活性。","单作鹰嘴豆配合有机+自然综合管理显著提升土壤养分、微生物数量与酶活性。","可探究综合有机-自然管理下微生物群落结构与碳固持的长期耦合机制。","2026-10-21T23:30:06.876518Z",{"id":1508,"title":1509,"url":1510,"summary":1511,"summary_zh":11,"content":11,"source_name":1512,"source_url":11,"published_at":1513,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1255,"score_detail":1514,"sources":1516,"tags":1518,"primary_topic":1521,"secondary_topics":1522,"classification":1523,"search_phrases":1542,"slug":1545,"view_count":424,"doi":11,"paper":1546,"created_at":1554},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",{"impact":416,"substance":508,"depth":584,"authority":738,"freshness":173,"relevant":455,"comment":1515},"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[1517],{"name":1512,"url":1510},[591,743,618,1519,1520],"土壤数据","作物分类",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1524},{"candidates":1525,"agricultural_evidence":604},[1526,1530,1534,1538],{"code":144,"score":508,"supported":604,"title":1527,"abstract":1528,"tags":1529},[607,145],[607],[1519],{"code":24,"score":69,"supported":581,"title":1531,"abstract":1532,"tags":1533},[],[],[591,743],{"code":16,"score":455,"supported":581,"title":1535,"abstract":1536,"tags":1537},[],[],[618],{"code":32,"score":455,"supported":581,"title":1539,"abstract":1540,"tags":1541},[],[],[1520],[1543,1544],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":11,"openalex_id":11,"authors":1547,"venue":11,"cited_by_count":424,"oa_url":11,"card":1548,"direction":639,"ingested_from":1553},[],{"tldr":1549,"method":1550,"finding":1551,"direction":639,"opportunity":1552},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"id":1556,"title":1557,"url":1558,"summary":1559,"summary_zh":11,"content":11,"source_name":1560,"source_url":11,"published_at":579,"category":1561,"cover_url":11,"hotness":385,"is_selected":581,"score":1562,"score_detail":1563,"sources":1566,"tags":1568,"primary_topic":1573,"secondary_topics":1574,"classification":1575,"search_phrases":1600,"slug":1603,"view_count":424,"doi":11,"paper":11,"created_at":1604},5425,"中国农科院资划所构建节水控盐综合治理模式 已在鄂尔多斯推广255万亩","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20261021\u002F20261021_3\u002Fnmrb_20261021_13431_3_2112642787740585998.html","农民日报报道，中国农业科学院农业资源与农业区划研究所牵头构建的节水控盐综合治理模式，已在内蒙古鄂尔多斯沿黄灌溉区累计推广 255 万亩。该模式针对轻、中、重度盐碱地分类施策，轻度盐碱地培肥沃土、中度节水阻盐、重度发展适生种植，并配套耐盐品种筛选、灌排协同调控与空天地一体化监测体系。在河南内黄县农业科技试验示范基地，作物栽培与机械化所等多单位集成成果，建成沙土地小麦玉米「吨半田」，产量、品质与土壤地力同步改善。","农民日报 2026-10-21","报道",85,{"impact":1564,"substance":321,"depth":653,"authority":738,"freshness":385,"relevant":455,"comment":1565},24,"国家级科研机构牵头、推广面积达255万亩的盐碱地综合治理模式，数据具体、技术体系完整，具备进入每日精选的价值。",[1567],{"name":1560,"url":1558},[957,1569,1570,1571,1572],"耐盐品种","节水控盐","灌排协同","空天地监测",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1576},{"candidates":1577,"agricultural_evidence":604},[1578,1583,1587,1591,1596],{"code":144,"score":615,"supported":604,"title":1579,"abstract":1580,"tags":1582},[],[607,1581],"盐碱地",[957],{"code":216,"score":911,"supported":604,"title":1584,"abstract":1585,"tags":1586},[],[218],[],{"code":224,"score":911,"supported":604,"title":1588,"abstract":1589,"tags":1590},[],[226],[],{"code":112,"score":911,"supported":604,"title":1592,"abstract":1593,"tags":1595},[],[1594],"机械化",[],{"code":152,"score":911,"supported":604,"title":1597,"abstract":1598,"tags":1599},[],[777],[],[1601,1602],"中国农科院资划所 节水控盐 鄂尔多斯","鄂尔多斯 盐碱地 255万亩","中国农科院资划所节水控盐鄂尔多斯-5425","2026-10-21T00:10:00.398184Z",{"id":1606,"title":1607,"url":1608,"summary":1609,"summary_zh":11,"content":1610,"source_name":1611,"source_url":11,"published_at":1019,"category":1561,"cover_url":11,"hotness":385,"is_selected":581,"score":1562,"score_detail":1612,"sources":1614,"tags":1616,"primary_topic":1621,"secondary_topics":1622,"classification":1623,"search_phrases":1630,"slug":1633,"view_count":424,"doi":11,"paper":11,"created_at":1634},5423,"中国农科院资划所揭示生物炭阻控微塑料介导土壤抗生素耐药基因传播机制","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20261019A084M600","中国农业科学院农业资源与农业区划研究所土壤植物互作创新团队揭示了生物炭阻控微塑料介导土壤抗生素耐药基因传播的作用机制，并构建微塑料-生物炭-抗生素耐药基因三元界面作用机理与数据驱动预测框架，成果发表在《Journal of Hazardous Materials》。研究指出微塑料进入土壤会吸附抗生素、重金属和病原微生物，为微生物定殖和生物膜提供专属微环境，而生物炭可从物理、遗传和生态三个层面削弱耐药基因传播。团队还提出面向治理的优先路线图，为新污染物协同治理与农田土壤安全利用提供理论支撑。","window.DATA = {\"url\":\"https:\u002F\u002Fview.inews.qq.com\u002Fa\u002F20261019A084M600\",\"article_id\":\"20261019A084M600\",\"atype\":\"0\",\"article_type\":\"0\",\"title\":\"生物炭为微塑料介导的土壤抗生素耐药基因传播“踩刹车”\",\"desc\":\"近日，中国农业科学院农业资源与农业区划研究所土壤植物互作创新团队揭示了生物炭阻控微塑料介导土壤抗生素耐药基因传播的作用机制，并构建“微塑料—生物炭—抗生素耐药基因”三元界面作用机理与数据驱动预测框架。相关研究成果发表在《危险材料杂志（Journal of Hazardous Materials）》上。抗生素耐药基因是潜伏在土壤中...\",\"iNewsRecommendLevel\":1,\"abstract\":\"近日，中国农业科学院农业资源与农业区划研究所土壤植物互作创新团队揭示了生物炭阻控微塑料介导土壤抗生素耐药基因传播的作用机制，并构建“微塑料—生物炭—抗生素耐药基因”三元界面作用机理与数据驱动预测框架。相关研究成果发表在《危险材料杂志（Journal of Hazardous Materials）》上。抗生素耐药基因是潜伏在土壤中...\",\"catalog1\":\"agriculture\",\"ad_channel_sign\":\"news\",\"introduction\":\"\",\"media\":\"中国农业科学院\",\"media_id\":\"20124819\",\"pubtime\":\"2026-10-19 14:48:21\",\"comment_id\":\"1261019605040990\",\"political\":0,\"cmsId\":\"20261019A084M600\",\"cms_id\":\"20261019A084M600\",\"closeAllAd\":0,\"closeAllFavorite\":false,\"originContent\":{\"directory\":{\"enable\":2},\"text\":\"\\u003cdiv 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textstyle=\\\"\\\"\\u003e近日，中国农业科学院农业资源与农业区划研究所土壤植物互作创新团队揭示了\\u003c!--VERTICAL_CARD_BEGIN_0--\\u003e生物炭\\u003c!--VERTICAL_CARD_END_0--\\u003e阻控微塑料介导土壤抗生素耐药基因传播的作用机制，并构建“微塑料—生物炭—抗生素耐药基因”三元界面作用机理与数据驱动预测框架。相关研究成果发表在\\u003c\u002Fspan\\u003e\\u003cspan style=\\\"font-size: 17px; font-weight: bold; letter-spacing: 1px\\\" textstyle=\\\"\\\"\\u003e《危险材料杂志（Journal of Hazardous Materials）》\\u003c\u002Fspan\\u003e\\u003cspan style=\\\"font-size: 17px; letter-spacing: 1px\\\" textstyle=\\\"\\\"\\u003e上。\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fp\\u003e\\u003csection nodeleaf=\\\"\\\" style=\\\"-webkit-font-smoothing: subpixel-antialiased; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); color: rgb(0, 0, 0); font-size: 16px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 20px; orphans: 2; outline: none; padding: 0px; text-align: center; text-decoration-color: initial; 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textstyle=\\\"\\\"\\u003e抗生素耐药基因是潜伏在土壤中的长期生态隐患。微塑料进入土壤会吸附抗生素、\\u003c!--SECURE_LINK_BEGIN_0--\\u003e重金属\\u003c!--SECURE_LINK_END_0--\\u003e和病原微生物，为微生物定殖和生物膜提供专属的“\\u003c!--VERTICAL_CARD_BEGIN_1--\\u003e塑料际\\u003c!--VERTICAL_CARD_END_1--\\u003e”微环境，共同加速耐药基因的传播风险。生物炭则可在物理、遗传和生态三个层面削弱耐药基因传播。它的效果取决于原料、\\u003c!--VERTICAL_CARD_BEGIN_2--\\u003e热解温度\\u003c!--VERTICAL_CARD_END_2--\\u003e、微塑料类型和老化程度以及土壤本底等因素。科研人员还提出面向治理的优先路线图，并倡导建立标准化数据报告规范、开放数据库及外部验证模型框架，旨在推动从描述性相关分析向可比较、可验证、可预测的风险管控范式转变，为新污染物协同治理与农田土壤安全利用提供坚实的理论支撑。\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c!--EOP_0--\\u003e\\u003c\u002Fp\\u003e\\u003c!--PARAGRAPH_0--\\u003e\\u003cp style=\\\"-webkit-font-smoothing: subpixel-antialiased; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); color: rgb(0, 0, 0); font-size: 16px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; line-height: 1.75em; margin: 8px 0px; orphans: 2; outline: none; 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textstyle=\\\"\\\"\\u003e通讯员丨\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-pm-slice=\\\"1 1 [\\u0026#34;para\\u0026#34;,{\\u0026#34;tagName\\u0026#34;:\\u0026#34;section\\u0026#34;,\\u0026#34;attributes\\u0026#34;:{\\u0026#34;style\\u0026#34;:\\u0026#34;box-sizing: border-box;font-style: normal;font-weight: 400;text-align: justify;font-size: 16px;color: rgb(62, 62, 62);\\u0026#34;,\\u0026#34;data-pm-slice\\u0026#34;:\\u0026#34;0 0 []\\u0026#34;,\\u0026#34;data-nest-level\\u0026#34;:\\u0026#34;2\\u0026#34;},\\u0026#34;namespaceURI\\u0026#34;:\\u0026#34;http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\\u0026#34;,\\u0026#34;data-layout-id\\u0026#34;:null},\\u0026#34;para\\u0026#34;,{\\u0026#34;tagName\\u0026#34;:\\u0026#34;p\\u0026#34;,\\u0026#34;attributes\\u0026#34;:{\\u0026#34;style\\u0026#34;:\\u0026#34;outline: none; margin: 0px 0px 20px; padding: 0px; -webkit-font-smoothing: subpixel-antialiased; text-align: justify; color: rgb(0, 0, 0); font-family: \\\\\\u0026#34;Microsoft YaHei\\\\\\u0026#34;, 微软雅黑, SimSun, 宋体, SimHei, \\\\\\u0026#34;STHeiti Light\\\\\\u0026#34;, STHeiti, \\\\\\u0026#34;Lucida Grande\\\\\\u0026#34;, Tahoma, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; text-indent: 2em;\\u0026#34;},\\u0026#34;namespaceURI\\u0026#34;:\\u0026#34;http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\\u0026#34;,\\u0026#34;data-layout-id\\u0026#34;:null}]\\\" leaf=\\\"\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); flex-direction: column; font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: normal; letter-spacing: 0.034em; line-height: 1.6; margin: 0px; max-width: none !important; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; visibility: visible; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan style=\\\"color: rgb(136, 136, 136); font-size: 15px\\\" textstyle=\\\"\\\"\\u003e姬悦\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fp\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fsection\\u003e\\u003csection class=\\\"js_darkmode__11\\\" data-nest-level=\\\"3\\\" data-pm-slice=\\\"0 0 []\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: 0.544px; margin: 0px; max-width: 100%; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; visibility: visible; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fsection\\u003e\\u003csection class=\\\"js_darkmode__11\\\" data-nest-level=\\\"3\\\" data-pm-slice=\\\"0 0 []\\\" style=\\\"-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); box-sizing: border-box !important; color: rgba(0, 0, 0, 0.9); font-size: 17px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: 0.544px; margin: 0px; max-width: 100%; orphans: 2; outline: 0px; overflow-wrap: break-word !important; padding: 0px; text-align: center; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; visibility: visible; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fsection\\u003e\\u003csection data-nest-level=\\\"3\\\" data-pm-slice=\\\"0 \n[…全文已截断…]","中国农业科学院 2026-10-19",{"impact":321,"substance":321,"depth":361,"authority":760,"freshness":173,"relevant":455,"comment":1613},"国家级科研机构在环境领域顶级期刊发表的新机制与新预测框架，专业增量明确，对土壤污染治理与绿色农业有参考价值，值得进入每日精选。",[1615],{"name":1611,"url":1608},[1025,1617,1618,1619,1620],"土壤健康","农业环保","微塑料","抗生素耐药基因",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1624},{"candidates":1625,"agricultural_evidence":604},[1626],{"code":144,"score":738,"supported":604,"title":1627,"abstract":1628,"tags":1629},[607],[607],[1617,1025],[1631,1632],"中国农科院资划所 生物炭 微塑料","土壤 抗生素耐药基因 生物炭","中国农科院资划所生物炭微塑料-5423","2026-10-21T00:10:00.151313Z",{"id":1636,"title":1637,"url":1638,"summary":1639,"summary_zh":1640,"content":11,"source_name":1641,"source_url":1638,"published_at":1019,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1255,"score_detail":1642,"sources":1644,"tags":1646,"primary_topic":1651,"secondary_topics":1652,"classification":1653,"search_phrases":1678,"slug":1681,"view_count":424,"doi":1682,"paper":1683,"created_at":1700},5389,"A Wideband Flexible Waterproof Antenna for Internet of Underground Things: Design, Characterization, and Field Validation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204815","The Internet of Underground Things (IoUT) is seen as a new technology that could revolutionize soil management, improving irrigation efficiency, soil and plant health, and food safety using reliable and resilient wireless connectivity. Rapid deployment of IoUT in precision agriculture requires flexible, waterproof antennas specifically designed for operation in soil. This study introduces a methodology for the design and characterization of waterproof, flexible antennas that maintain robust performance in soil across varying gravimetric water contents (GWC). Applying this methodology, a wideband, waterproof, and flexible antenna was designed and fabricated. Its parameters and characteristics were evaluated numerically and experimentally in soils with different GWCs to determine its suitability for IoUT applications. Within the GWC range of 10% to 20%, which is optimal for supporting plant health and water use, the antenna demonstrates a wide bandwidth of 0.6 to 1.8 GHz (|S11| \u003C −10 dB) and radiation efficiency ranging from 3.56% to 0.15% at 868 MHz. Experimental results obtained in free space, dry sand, and garden soil agree reasonably well with simulation predictions, showing that the proposed antenna is suitable for IoUT applications. Field validation demonstrated that the antenna prototype maintained its impedance response after two months of in-soil operation, indicating stable performance over the evaluated period.","地下物联网（Internet of Underground Things, IoUT）被视为一项可能彻底改变土壤管理的新技术，通过可靠且具有韧性的无线连接提升灌溉效率、土壤与植物健康以及食品安全。在精准农业中快速部署IoUT需要专为土壤环境运行而设计的柔性防水天线。本研究提出了一种防水柔性天线的设计与表征方法，使天线在不同重量含水率（gravimetric water content, GWC）的土壤中保持稳健性能。应用该方法，设计并制作了一款宽带防水柔性天线。在不同GWC的土壤中对其参数与特性进行了数值仿真与实验评估，以确定其用于IoUT应用的适用性。在10%至20%的GWC范围内（该范围最有利于维持植物健康与水分利用），天线在868 MHz处展现出0.6至1.8 GHz的宽带宽（|S11| \u003C −10 dB）以及3.56%至0.15%的辐射效率。在自由空间、干沙和花园土壤中获得的实验结果与仿真预测吻合良好，表明所提出的天线适用于IoUT应用。现场验证表明，天线原型在土壤中运行两个月后仍保持其阻抗响应，表明在所评估时段内性能稳定。","Electronics",{"impact":416,"substance":508,"depth":584,"authority":738,"freshness":173,"relevant":455,"comment":1643},"面向地下物联网的柔性防水天线设计与田间验证，方法新颖、数据扎实，对精准农业无线感知有参考价值，但属细分技术进展，公共影响有限。",[1645],{"name":1641,"url":1638},[590,1647,1648,1649,1650],"物联网","土壤水分","地下物联网","柔性天线",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1654},{"candidates":1655,"agricultural_evidence":604},[1656,1660,1664,1670,1674],{"code":144,"score":615,"supported":604,"title":1657,"abstract":1658,"tags":1659},[],[607,145],[1648],{"code":16,"score":615,"supported":604,"title":1661,"abstract":1662,"tags":1663},[],[618,619],[590],{"code":465,"score":679,"supported":604,"title":1665,"abstract":1666,"tags":1669},[],[1667,1668],"食品安全","food safety",[],{"code":152,"score":679,"supported":604,"title":1671,"abstract":1672,"tags":1673},[],[777,153],[],{"code":48,"score":400,"supported":604,"title":1675,"abstract":1676,"tags":1677},[],[1647],[1647],[1679,1680],"IoUT 柔性防水天线","地下物联网 土壤水分","IoUT柔性防水天线-5389","10.3390\u002Felectronics15204815",{"doi":1682,"openalex_id":1684,"authors":1685,"venue":1641,"cited_by_count":424,"oa_url":1638,"card":1695,"direction":805,"ingested_from":641},"W7222978032",[1686,1689,1692],{"name":1687,"orcid":1688},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":1690,"orcid":1691},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":1693,"orcid":1694},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":1696,"method":1697,"finding":1698,"direction":805,"opportunity":1699},"设计并验证了一款用于地下物联网的宽带柔性防水天线。","采用数值仿真与实验测试，在不同含水率土壤中评估天线性能。","在10%-20%含水率下带宽0.6-1.8GHz，埋土两个月阻抗响应稳定。","可探索天线辐射效率随土壤含水率下降的补偿机制，或结合土壤传感节点做长期田间组网验证。","2026-10-20T23:30:13.823825Z",{"id":1702,"title":1703,"url":1704,"summary":1705,"summary_zh":11,"content":1706,"source_name":1707,"source_url":11,"published_at":1708,"category":1561,"cover_url":11,"hotness":385,"is_selected":581,"score":1709,"score_detail":1710,"sources":1712,"tags":1714,"primary_topic":1720,"secondary_topics":1721,"classification":1722,"search_phrases":1738,"slug":1741,"view_count":455,"doi":11,"paper":11,"created_at":1742},5337,"中国农科院资划所揭示根际荧光类假单胞菌 III 型分泌系统演化机制 New Phytologist","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572940.shtm","中国农业科学院农业资源与农业区划研究所农业微生物资源创新团队从我国 15 个省(自治区、直辖市)采集 114 份根际土壤样品,筛选出 110 株携带 III 型分泌系统基因的菌株,对其中 43 株进行基因组测序,结合公共数据库已有数据共获得 88 株 III 型分泌系统阳性菌株基因组数据。研究发现这些菌株分属荧光假单胞菌 7 个亚群,III 型分泌系统可划分为 6 种演化类型,经历了多次独立的基因横向转移。该研究打破了 III 型分泌系统仅为病原菌致病工具的认知。论文 DOI: 10.1111\u002Fnph.71510。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,姬悦 来源：中国科学报 发布时间：2026\u002F10\u002F15 11:59:53 选择字号：小 中 大 有益假单胞菌也有III型分泌系统，其演化机制获揭示 近日，中国农业科学院农业资源与农业区划研究所农业微生物资源创新团队揭示了Ⅲ型分泌系统在根际荧光类假单胞菌中的演化机制。相关研究成果发表在《新植物学家（New Phytologist）》上。 Ⅲ型分泌系统历来被认为是植物病原菌的侵染“专用武器”。致病菌为在宿主体内生存、繁殖、扩散，会分泌一些蛋白性质的毒性因子。目前认为革兰阴性致病菌在长期进化过程中形成入侵宿主细胞的特异性分泌系统共有5种类型(Ⅰ～Ⅴ型)，其中最显著的是细菌Ⅲ型分泌系统。然而近年来，在根际有益荧光假单胞菌中也发现了该系统，其来源及功能分化机制尚不清楚。 科研团队从我国15个省（自治区、直辖市）采集114份根际土壤样品，筛选出110株携带Ⅲ型分泌系统基因的菌株，对其中43株进行基因组测序，结合公共数据库已有数据共获得88株Ⅲ型分泌系统阳性菌株基因组数据。 研究发现，这些菌株分属荧光假单胞菌7个亚群，其Ⅲ型分泌系统可划分为6种演化类型，经历了多次独立的基因横向转移，传播和功能分化受生态位适应驱动。功能分析显示，携带该系统的菌株普遍具有植物促生防病功能。 该研究打破了Ⅲ型分泌系统仅为病原菌致病工具的认知，为理解微生物—植物互作中“致病”与“共生”功能分化奠定了理论基础。 相关论文信息：https:\u002F\u002Fdoi.org\u002Fabs\u002F10.1111\u002Fnph.71510 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572940)\n\n相关新闻 相关论文\n*   1\n*   [微型机器人靶向心脏感染病灶精准送药](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572918.shtm)\n\n*   2\n*   [这个打破常识的发现因何而来](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572917.shtm)\n\n*   3\n*   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2026-10-15","2026-10-15T00:00:00Z",82,{"impact":321,"substance":321,"depth":361,"authority":345,"freshness":679,"relevant":455,"comment":1711},"国家级科研机构在《新植物学家》发表的基础研究，样本覆盖15省114份根际土壤，结论打破Ⅲ型分泌系统仅为致病工具的认知，对有益菌促生防病应用有理论价值，但属基础研究、产业影响间接，时效已过5天。",[1713],{"name":1707,"url":1704},[1715,1716,1717,1718,1719],"根际微生物","农业微生物","生物防治","假单胞菌","植物促生",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1723},{"candidates":1724,"agricultural_evidence":604},[1725,1730,1734],{"code":144,"score":760,"supported":604,"title":1726,"abstract":1728,"tags":1729},[1727],"根际",[607,1727],[1715],{"code":104,"score":455,"supported":581,"title":1731,"abstract":1732,"tags":1733},[],[],[1717],{"code":168,"score":455,"supported":581,"title":1735,"abstract":1736,"tags":1737},[],[],[1716],[1739,1740],"中国农科院资划所 假单胞菌 Ⅲ型分泌系统","根际荧光假单胞菌 演化机制","中国农科院资划所假单胞菌Ⅲ型分泌系统-5337","2026-10-20T02:47:43.472006Z",{"id":1744,"title":1745,"url":1746,"summary":1747,"summary_zh":11,"content":1748,"source_name":1749,"source_url":11,"published_at":1750,"category":1561,"cover_url":11,"hotness":385,"is_selected":604,"score":1751,"score_detail":1752,"sources":1755,"tags":1757,"primary_topic":1760,"secondary_topics":1761,"classification":1762,"search_phrases":1773,"slug":1775,"view_count":424,"doi":11,"paper":11,"created_at":1776},5261,"中国农科院资划所揭示根际有益假单胞菌III型分泌系统演化机制","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F47830d02a0fd4284840be0dcdaab61aa.htm","中国农业科学院农业资源与农业区划研究所农业微生物资源创新团队从我国15省(自治区、直辖市)114份根际土壤样品中筛选出110株携带III型分泌系统基因的菌株,结合公共数据库共获得88株基因组,揭示其在荧光假单胞菌7个亚群中可划分为6种演化类型,经历多次独立基因横向转移,功能分析显示普遍具有植物促生防病功能,研究成果发表于《New Phytologist》。","近日，中国农业科学院农业资源与农业区划研究所农业微生物资源创新团队揭示了Ⅲ型分泌系统在根际荧光类假单胞菌中的演化机制。相关研究成果发表在《新植物学家（New Phytologist）》上。\n\nⅢ型分泌系统历来被认为是植物病原菌的侵染“专用武器”。致病菌为在宿主体内生存、繁殖、扩散，会分泌一些蛋白性质的毒性因子。目前认为革兰阴性致病菌在长期进化过程中形成入侵宿主细胞的特异性分泌系统共有5种类型(Ⅰ～Ⅴ型)，其中最显著的是细菌Ⅲ型分泌系统。然而近年来，在根际有益荧光假单胞菌中也发现了该系统，其来源及功能分化机制尚不清楚。\n\n科研团队从我国15个省（自治区、直辖市）采集114份根际土壤样品，筛选出110株携带Ⅲ型分泌系统基因的菌株，对其中43株进行基因组测序，结合公共数据库已有数据共获得88株Ⅲ型分泌系统阳性菌株基因组数据。\n\n研究发现，这些菌株分属荧光假单胞菌7个亚群，其Ⅲ型分泌系统可划分为6种演化类型，经历了多次独立的基因横向转移，传播和功能分化受生态位适应驱动。功能分析显示，携带该系统的菌株普遍具有植物促生防病功能。\n\n该研究打破了Ⅲ型分泌系统仅为病原菌致病工具的认知，为理解微生物—植物互作中“致病”与“共生”功能分化奠定了理论基础。","中国农业科学院(2026-10-16)","2026-10-16T00:00:00Z",86,{"impact":321,"substance":1753,"depth":361,"authority":760,"freshness":173,"relevant":455,"comment":1754},23,"国家级科研机构发表于核心期刊的原创研究，样本覆盖15省88株基因组，结论具理论突破性，专业信息增量充足，值得进入每日精选。",[1756],{"name":1749,"url":1746},[1715,1716,1718,1758,1759],"植物促生防病","基因横向转移",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1763},{"candidates":1764,"agricultural_evidence":604},[1765,1769],{"code":144,"score":760,"supported":604,"title":1766,"abstract":1767,"tags":1768},[1727],[607,1727],[1715],{"code":168,"score":455,"supported":581,"title":1770,"abstract":1771,"tags":1772},[],[],[1716],[1774,1740],"中国农科院资划所 假单胞菌 III型分泌系统","中国农科院资划所假单胞菌III型分泌系统-5261","2026-10-19T00:07:39.707658Z",{"id":1778,"title":1779,"url":1780,"summary":1781,"summary_zh":11,"content":11,"source_name":950,"source_url":1780,"published_at":1782,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1709,"score_detail":1783,"sources":1786,"tags":1788,"primary_topic":1790,"secondary_topics":1791,"classification":1793,"search_phrases":1808,"slug":1811,"view_count":424,"doi":1812,"paper":1813,"created_at":1831},5229,"Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105017","Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation。Agricultural Systems","2026-10-17T00:00:00Z",{"impact":361,"substance":321,"depth":1784,"authority":345,"freshness":585,"relevant":455,"comment":1785},19,"耦合HYDRUS-2D与AquaCrop模拟覆膜滴灌下水盐与作物生长，方法有创新、结论可靠，属农业信息化与节水灌溉领域值得精选的论文。",[1787],{"name":950,"url":1780},[820,958,959,1789],"水盐调控",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1792],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1794},{"candidates":1795,"agricultural_evidence":604},[1796,1800,1804],{"code":152,"score":416,"supported":604,"title":1797,"abstract":1798,"tags":1799},[153],[153],[958],{"code":144,"score":524,"supported":604,"title":1801,"abstract":1802,"tags":1803},[145],[145],[],{"code":24,"score":455,"supported":581,"title":1805,"abstract":1806,"tags":1807},[],[],[820],[1809,1810],"HYDRUS-2D AquaCrop 覆膜滴灌","土壤水盐 作物生长 模拟","HYDRUS-2DAquaCrop覆膜滴灌-5229","10.1016\u002Fj.agsy.2026.105017",{"doi":1812,"openalex_id":1814,"authors":1815,"venue":950,"cited_by_count":424,"oa_url":1780,"card":11,"direction":11,"ingested_from":641},"W7222801463",[1816,1818,1820,1822,1824,1827,1829],{"name":1817,"orcid":11},"Yunfeng Li",{"name":1819,"orcid":11},"Jun Wang",{"name":1821,"orcid":11},"Hexiang Zheng",{"name":1823,"orcid":11},"Delong Tian",{"name":1825,"orcid":1826},"Jie Ren","https:\u002F\u002Forcid.org\u002F0000-0001-8795-0372",{"name":1828,"orcid":11},"Hao Liu",{"name":1830,"orcid":11},"Jingsheng Sun","2026-10-18T23:30:04.248249Z",{"id":1833,"title":1834,"url":1835,"summary":1836,"summary_zh":1837,"content":11,"source_name":1838,"source_url":1835,"published_at":1708,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1839,"sources":1841,"tags":1843,"primary_topic":1847,"secondary_topics":1848,"classification":1851,"search_phrases":1866,"slug":1869,"view_count":424,"doi":1870,"paper":1871,"created_at":1911},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science",{"impact":416,"substance":508,"depth":653,"authority":738,"freshness":173,"relevant":455,"comment":1840},"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[1842],{"name":1838,"url":1835},[743,675,1844,1845,1846],"土壤碳","Landsat","数字土壤制图",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[1849,1850],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1852},{"candidates":1853,"agricultural_evidence":604},[1854,1858,1862],{"code":144,"score":345,"supported":604,"title":1855,"abstract":1856,"tags":1857},[145],[607,145],[],{"code":24,"score":585,"supported":604,"title":1859,"abstract":1860,"tags":1861},[766],[],[743],{"code":32,"score":173,"supported":604,"title":1863,"abstract":1864,"tags":1865},[],[675,676],[1845,675],[1867,1868],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":1870,"openalex_id":1872,"authors":1873,"venue":1838,"cited_by_count":424,"oa_url":11,"card":1906,"direction":725,"ingested_from":641},"W7222107492",[1874,1877,1880,1883,1886,1889,1892,1895,1897,1900,1903],{"name":1875,"orcid":1876},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":1878,"orcid":1879},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":1881,"orcid":1882},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":1884,"orcid":1885},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":1887,"orcid":1888},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":1890,"orcid":1891},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":1893,"orcid":1894},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":1896,"orcid":11},"A. E. Shatunov",{"name":1898,"orcid":1899},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":1901,"orcid":1902},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":1904,"orcid":1905},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":1907,"method":1908,"finding":1909,"direction":725,"opportunity":1910},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"technologies":1913,"keywords":1945},[1914,1915,1916,1917,1918,1919,1921,1922,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943],{"name":591,"count":477},{"name":675,"count":141},{"name":618,"count":516},{"name":658,"count":1564},{"name":743,"count":653},{"name":1920,"count":524},"农业遥感",{"name":1647,"count":615},{"name":1114,"count":400},{"name":1924,"count":911},"农业传感器",{"name":1926,"count":69},"深度学习",{"name":1928,"count":455},"人工智能",{"name":1930,"count":455},"传感器",{"name":1932,"count":455},"农业机器人",{"name":1934,"count":455},"卫星遥感",{"name":1936,"count":455},"图像分割",{"name":1938,"count":455},"多模态",{"name":1940,"count":455},"强化学习",{"name":1942,"count":455},"无人机遥感",{"name":1944,"count":455},"高光谱",[1946,1947,1948,1949,1950,1951,1953,1954,1956,1957,1958,1959],{"name":590,"count":1348},{"name":957,"count":361},{"name":1617,"count":653},{"name":744,"count":416},{"name":1025,"count":416},{"name":1952,"count":416},"种业振兴",{"name":1468,"count":173},{"name":1955,"count":173},"土壤改良",{"name":226,"count":173},{"name":1715,"count":615},{"name":1197,"count":615},{"name":1716,"count":679}]