[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5000":3,"related-5000":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":99},5000,"Soil phenotyping: Integrating phenosoils and proximal soil sensing for spatiotemporal characterisation of managed soil variability","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.still.2026.107530","The characterisation of soil variability has challenged soil science since the early recognition that soils exhibit complex, structured spatial patterns across landscapes. As agriculture intensifies and soils are expected to deliver more—storing carbon, supporting biodiversity, and sustaining other key functions—there is an urgent need for scalable, quantitative methods to monitor and manage soil variability across space and time. Here we synthesise and advance a framework for soil phenotyping, defined as the quantitative assessment of soil structural, chemical, physical, and biological properties and their spatiotemporal patterns using proximal sensing—explicitly grounded in the distinction between genosoils (baseline soils under minimal human influence) and phenosoils (persistent management-modified soil expressions). Drawing parallels with field plant phenotyping, we argue that soils exhibit phenotypic responses arising from interactions among inherent soil capacity, environmental, and management drivers, and that these responses can be measured efficiently using high-throughput sensing. We review proximal soil sensing technologies, including electromagnetic induction and vis–NIR spectroscopy, and discuss their integration within multi-sensor platforms, digital soil mapping, and machine-learning workflows for data fusion, prediction, and uncertainty quantification. We position soil phenotyping as a practical pipeline from in situ sensing to spatially explicit indicators of soil condition and function, supporting precision agriculture, soil health assessment, and soil security planning. Finally, we identify key challenges for operational deployment, including calibration transferability, sensor harmonisation, spatiotemporal sampling design, and open, interoperable data frameworks, and outline research priorities to accelerate adoption in managed landscapes","土壤变异性的表征一直是土壤科学面临的挑战，早在人们认识到土壤在景观尺度上呈现复杂、结构化的空间格局之时便已如此。随着农业集约化程度提高，以及人们对土壤寄予更多期望——固碳、支撑生物多样性并维持其他关键功能——迫切需要可扩展的定量方法来监测和管理土壤在时空上的变异性。本文综合并推进了一个土壤表型分析框架，将其定义为利用近端传感对土壤结构、化学、物理和生物特性及其时空格局进行定量评估，并明确以基因土壤（genosoils，即在人类影响最小条件下的基线土壤）与表型土壤（phenosoils，即持续受到管理改造的土壤表现）之间的区分为基础。通过与田间植物表型分析的类比，我们认为土壤表现出由固有土壤能力、环境和管理驱动因素之间相互作用所产生的表型响应，并且这些响应可以通过高通量传感高效测量。我们综述了近端土壤传感技术，包括电磁感应和可见光—近红外光谱（vis–NIR spectroscopy），并讨论了它们在多传感器平台、数字土壤制图以及用于数据融合、预测和不确定性量化的机器学习工作流程中的整合。我们将土壤表型分析定位为一条从原位传感走向土壤状况与功能的时空显式指标的实用路径，支撑精准农业、土壤健康评估和土壤安全规划。最后，我们指出了业务化部署面临的关键挑战，包括校准可迁移性、传感器协调统一、时空采样设计以及开放可互操作的数据框架，并概述了加速其在管理景观中应用的研究优先方向。",null,"Soil and Tillage Research","2026-10-13T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出土壤表型学框架并整合近地传感与机器学习，方法新颖、对精准农业与土壤健康管理有实质参考价值，但属学术综述、产业落地尚远。",[24],{"name":10,"url":6},[26,27,28,29,30],"精准农业","土壤健康","数字土壤制图","土壤表型","近地传感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,66],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},15,true,[33],[49,33],"土壤",[27],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I1",7,[],[26,56],"precision agriculture",[26],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":65},"H6",6,[],[63,64],"生物多样性","biodiversity",[],{"code":67,"score":68,"supported":46,"title":69,"abstract":70,"tags":72},"I2",3,[],[71],"机器学习",[],[74,75],"土壤表型 近地传感 土壤健康","phenosoils 数字土壤制图","土壤表型近地传感土壤健康-5000",0,"10.1016\u002Fj.still.2026.107530",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":77,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7221718473",[82,85,87,89],{"name":83,"orcid":84},"Damien J. Field","https:\u002F\u002Forcid.org\u002F0000-0002-6877-8332",{"name":86,"orcid":9},"Alex McBratney",{"name":88,"orcid":9},"Budiman Minasny",{"name":90,"orcid":91},"John Triantafilis","https:\u002F\u002Forcid.org\u002F0000-0003-1561-0242",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出土壤表型组框架，用近端传感量化管理导致的土壤时空变异。","整合电磁感应与可见-近红外光谱，结合多传感器平台、数字土壤制图和机器学习。","区分本原土与表型土，可高效监测土壤功能并支撑精准农业与土壤健康评估。","农业遥感与作物表型","开发跨传感器校准迁移与时空采样优化方法，构建开放互操作的土壤表型数据框架。","openalex","2026-10-15T23:30:26.546545Z",{"total":60,"page":21,"page_size":60,"items":101},[102,190,319,370,403,489],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":130,"search_phrases":172,"slug":175,"view_count":21,"doi":176,"paper":177,"created_at":189},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",72,{"impact":111,"substance":112,"depth":113,"authority":114,"freshness":20,"relevant":21,"comment":115},12,21,17,13,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[117],{"name":108,"url":105},[119,26,120,27,121],"智慧农业","遥感","覆盖作物",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[124],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":131},{"candidates":132,"agricultural_evidence":46},[133,137,142,147,154,161,167],{"code":32,"score":45,"supported":46,"title":134,"abstract":135,"tags":136},[33],[49,33],[27],{"code":125,"score":111,"supported":46,"title":138,"abstract":140,"tags":141},[139],"remote sensing",[120],[120],{"code":52,"score":143,"supported":46,"title":144,"abstract":145,"tags":146},8,[],[26,56],[119,26],{"code":148,"score":60,"supported":46,"title":149,"abstract":150,"tags":153},"A4",[],[151,152],"油菜","rapeseed",[],{"code":155,"score":60,"supported":46,"title":156,"abstract":157,"tags":160},"H8",[],[158,159],"可持续农业","sustainable agriculture",[],{"code":162,"score":68,"supported":46,"title":163,"abstract":164,"tags":166},"B1",[],[165],"萝卜",[],{"code":168,"score":21,"supported":14,"title":169,"abstract":170,"tags":171},"A7",[],[],[121],[173,174],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":176,"openalex_id":178,"authors":179,"venue":108,"cited_by_count":77,"oa_url":9,"card":184,"direction":96,"ingested_from":98},"W7221669942",[180,182],{"name":181,"orcid":9},"Ali Mirzakhani Nafchi",{"name":183,"orcid":9},"Salman Mirzaee",{"tldr":185,"method":186,"finding":187,"direction":96,"opportunity":188},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":198,"score_detail":199,"sources":201,"tags":203,"primary_topic":207,"secondary_topics":208,"classification":209,"search_phrases":283,"slug":286,"view_count":77,"doi":287,"paper":288,"created_at":318},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":18,"substance":112,"depth":17,"authority":19,"freshness":143,"relevant":21,"comment":200},"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[202],{"name":196,"url":193},[119,204,205,26,27,206],"农业人工智能","种业振兴","多组学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":210},{"candidates":211,"agricultural_evidence":46},[212,218,222,229,236,243,250,257,261,267,273,278],{"code":52,"score":213,"supported":46,"title":214,"abstract":215,"tags":217},19,[56],[26,216,56],"数字孪生",[119,26],{"code":32,"score":53,"supported":46,"title":219,"abstract":220,"tags":221},[],[49,33],[27],{"code":223,"score":60,"supported":46,"title":224,"abstract":225,"tags":228},"A1",[],[226,227],"水稻","rice",[],{"code":230,"score":60,"supported":46,"title":231,"abstract":232,"tags":235},"A2",[],[233,234],"小麦","wheat",[],{"code":237,"score":60,"supported":46,"title":238,"abstract":239,"tags":242},"A3",[],[240,241],"玉米","maize",[],{"code":244,"score":60,"supported":46,"title":245,"abstract":246,"tags":249},"A8",[],[247,248],"粮食安全","food security",[],{"code":251,"score":60,"supported":46,"title":252,"abstract":253,"tags":256},"H7",[],[254,255],"气候变化","climate change",[],{"code":155,"score":60,"supported":46,"title":258,"abstract":259,"tags":260},[],[158,159],[],{"code":67,"score":262,"supported":46,"title":263,"abstract":264,"tags":266},4,[],[265],"artificial intelligence",[204],{"code":268,"score":68,"supported":46,"title":269,"abstract":270,"tags":272},"F3",[],[271],"肥料",[],{"code":274,"score":21,"supported":14,"title":275,"abstract":276,"tags":277},"F1",[],[],[205],{"code":279,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},"F2",[],[],[206],[284,285],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":287,"openalex_id":289,"authors":290,"venue":196,"cited_by_count":77,"oa_url":193,"card":311,"direction":317,"ingested_from":98},"W7221567543",[291,294,296,299,301,303,306,309],{"name":292,"orcid":293},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":295,"orcid":9},"Wang Qingxia",{"name":297,"orcid":298},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":300,"orcid":9},"Shuzhen Ye",{"name":302,"orcid":9},"Zhijuan Ji",{"name":304,"orcid":305},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":307,"orcid":308},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":310,"orcid":9},"Younan Ouyang",{"tldr":312,"method":313,"finding":314,"direction":315,"opportunity":316},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":9,"content":9,"source_name":324,"source_url":9,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":357,"slug":360,"view_count":77,"doi":9,"paper":361,"created_at":369},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",70,{"impact":111,"substance":328,"depth":113,"authority":114,"freshness":143,"relevant":21,"comment":329},20,"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。",[331],{"name":324,"url":322},[204,71,26,333,334],"土壤数据","作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":338},{"candidates":339,"agricultural_evidence":46},[340,344,349,353],{"code":32,"score":328,"supported":46,"title":341,"abstract":342,"tags":343},[49,33],[49],[333],{"code":67,"score":345,"supported":14,"title":346,"abstract":347,"tags":348},2,[],[],[204,71],{"code":52,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[26],{"code":125,"score":21,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[334],[358,359],"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":9,"openalex_id":9,"authors":362,"venue":9,"cited_by_count":77,"oa_url":9,"card":363,"direction":315,"ingested_from":368},[],{"tldr":364,"method":365,"finding":366,"direction":315,"opportunity":367},"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":371,"title":372,"url":373,"summary":374,"summary_zh":9,"content":375,"source_name":376,"source_url":9,"published_at":377,"category":378,"cover_url":9,"hotness":13,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":389,"secondary_topics":390,"classification":391,"search_phrases":398,"slug":401,"view_count":77,"doi":9,"paper":9,"created_at":402},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 class=\\\"rich_media_content\\\"\\u003e\\u003csection data-nest-level=\\\"2\\\" data-pm-slice=\\\"0 0 []\\\" style=\\\"box-sizing: border-box; color: rgb(62, 62, 62); font-size: 16px; font-style: normal; font-weight: 400; text-align: justify\\\"\\u003e\\u003cp data-pm-slice=\\\"0 0 []\\\" 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; padding: 0px; text-align: justify; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 2em; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan leaf=\\\"\\\"\\u003e\\u003cspan style=\\\"font-size: 17px; letter-spacing: 1px\\\" 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; text-decoration-style: initial; text-decoration-thickness: initial; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003c!--IMG_0--\\u003e\\u003c\u002Fsection\\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; padding: 0px; text-align: justify; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 2em; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan leaf=\\\"\\\"\\u003e\\u003cspan style=\\\"font-size: 17px; letter-spacing: 1px\\\" 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; padding: 0px; text-align: justify; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 2em; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px\\\"\\u003e\\u003cspan leaf=\\\"\\\"\\u003e\\u003cspan style=\\\"font-size: 17px; letter-spacing: 1px\\\" textstyle=\\\"\\\"\\u003e该研究得到京津冀国家科技重大专项、国家现代农业产业体系专项基金等项目支持。\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fp\\u003e\\u003csection class=\\\"js_darkmode__9\\\" data-nest-level=\\\"3\\\" data-pm-slice=\\\"1 3 []\\\" data-style=\\\"margin-right: 8px; margin-bottom: 8px; margin-left: 8px; outline: 0px; font-family: system-ui, -apple-system, BlinkMacSystemFont, \\u0026#34;Helvetica Neue\\u0026#34;, \\u0026#34;PingFang SC\\u0026#34;, \\u0026#34;Hiragino Sans GB\\u0026#34;, \\u0026#34;Microsoft YaHei UI\\u0026#34;, \\u0026#34;Microsoft YaHei\\u0026#34;, Arial, sans-serif; letter-spacing: 0.544px; text-wrap: wrap; background-color: rgb(255, 255, 255); text-align: center; visibility: visible;\\\" nodeleaf=\\\"\\\" 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 style=\\\"color: rgb(136, 136, 136); font-size: 15px\\\" textstyle=\\\"\\\"\\u003e\\u003cimg alt=\\\"图片\\\" class=\\\"__bg_gif rich_pages wxw-img js_darkmode__10\\\" data-aistatus=\\\"1\\\" data-fileid=\\\"100014628\\\" data-imgfileid=\\\"100039142\\\" data-ratio=\\\"0.36923076923076925\\\" data-src=\\\"https:\u002F\u002Finews.gtimg.com\u002Fom_bt\u002FGgXXkAjkV5hA2SwBSyHUj5Xi2B3qIU84LhhHRRNeQFHH4AA\u002F0\\\" data-type=\\\"gif\\\" data-w=\\\"65\\\" src=\\\"https:\u002F\u002Finews.gtimg.com\u002Fom_bt\u002FGgXXkAjkV5hA2SwBSyHUj5Xi2B3qIU84LhhHRRNeQFHH4AA\u002F0\\\" style=\\\"-webkit-tap-highlight-color: transparent; background-size: 16px !important; border-radius: 8px; border-style: none; box-sizing: border-box !important; color: rgb(34, 34, 34); height: auto; letter-spacing: 0.544px; line-height: 0px; margin: 0px; max-width: 100%; outline: 0px; overflow-wrap: break-word !important; padding: 0px; vertical-align: middle; visibility: visible !important; width: 65px !important\\\" width=\\\"65px\\\" data-aigc-mark=\\\"0\\\" loading=\\\"lazy\\\"\u002F\\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 class=\\\"js_darkmode__12\\\" data-nest-level=\\\"4\\\" 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来源\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan class=\\\"js_darkmode__13\\\" data-nest-level=\\\"4\\\" 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丨资划所\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan data-nest-level=\\\"5\\\" 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\\u003cbr\u002F\\u003e\\u003c\u002Fspan\\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\\u003cspan data-nest-level=\\\"5\\\" 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作者丨\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003cspan data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan 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张倩茹\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fspan\\u003e\\u003c\u002Fsection\\u003e\\u003csection class=\\\"js_darkmode__11\\\" data-nest-level=\\\"3\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cspan data-nest-level=\\\"4\\\" data-pm-slice=\\\"0 0 []\\\"\\u003e\\u003cp 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-pm-slice=\\\"1 1 [\\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","2026-10-19T00:00:00Z","报道",85,{"impact":18,"substance":18,"depth":17,"authority":45,"freshness":143,"relevant":21,"comment":381},"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。",[383],{"name":376,"url":373},[385,27,386,387,388],"生物炭","农业环保","微塑料","抗生素耐药基因",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":392},{"candidates":393,"agricultural_evidence":46},[394],{"code":32,"score":114,"supported":46,"title":395,"abstract":396,"tags":397},[49],[49],[27,385],[399,400],"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":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":421,"classification":427,"search_phrases":443,"slug":446,"view_count":77,"doi":447,"paper":448,"created_at":488},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","2026-10-15T00:00:00Z",69,{"impact":111,"substance":328,"depth":413,"authority":114,"freshness":143,"relevant":21,"comment":414},16,"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。",[416],{"name":409,"url":406},[71,120,418,419,28],"土壤碳","Landsat",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[422,426],{"code":67,"slug":423,"name":424,"path":425,"parent_code":129},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},{"status":39,"locked":14,"version":40,"method":41,"evidence":428},{"candidates":429,"agricultural_evidence":46},[430,434,439],{"code":32,"score":19,"supported":46,"title":431,"abstract":432,"tags":433},[33],[49,33],[],{"code":67,"score":20,"supported":46,"title":435,"abstract":437,"tags":438},[436],"machine learning",[],[71],{"code":125,"score":143,"supported":46,"title":440,"abstract":441,"tags":442},[],[120,139],[419,120],[444,445],"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":447,"openalex_id":449,"authors":450,"venue":409,"cited_by_count":77,"oa_url":9,"card":483,"direction":96,"ingested_from":98},"W7222107492",[451,454,457,460,463,466,469,472,474,477,480],{"name":452,"orcid":453},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":455,"orcid":456},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":458,"orcid":459},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":461,"orcid":462},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":464,"orcid":465},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":467,"orcid":468},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":470,"orcid":471},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":473,"orcid":9},"A. E. Shatunov",{"name":475,"orcid":476},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":478,"orcid":479},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":481,"orcid":482},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":484,"method":485,"finding":486,"direction":96,"opportunity":487},"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",{"id":490,"title":491,"url":492,"summary":493,"summary_zh":494,"content":9,"source_name":495,"source_url":492,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":506,"secondary_topics":507,"classification":508,"search_phrases":525,"slug":528,"view_count":77,"doi":529,"paper":530,"created_at":550},5072,"Exploring the influence of farmers’ networks and social interactions to sustain soil health management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1823295","Introduction Soil health management practices have increasingly been promoted across US agroecosystems to address many interrelated environmental and economic food system challenges. Sustaining soil health management behavior—through farmer’s adoption and continued use of practices—is key to ensuring that these challenges are addressed and also known to be in part influenced by farmers social networks and interactions. Methods Drawing on the diffusion of innovation theories, this grounded theory study explores the influence of networks and social interactions on farmers sustaining soil health management. In-depth interviews were conducted with eight participants of a partnership integrating on-farm experimentation and outreach in the U.S. Midwestern agroecosystem. Results Four interrelated dimensions emerged as influencing sustained soil health management: (1) peer influences and social interactions, including land tenure arrangements, perceived acceptance within farming communities, and farming traditions; (2) co-learning processes that supported collaborative knowledge development; (3) expansion of farmer networks through on-farm experimentation and demonstration activities; and (4) experiential tradeoffs associated with practice implementation. Farmers described how participation in on-farm research increased opportunities for peer exchange, field-day engagement, and knowledge sharing while strengthening confidence in management decisions through trialability. At the same time, participants highlighted barriers to long-term practice use related to social expectations, management challenges, and competing farm priorities. Discussion Our findings emphasize that soil health management practice adoption is not a one-time decision, but rather a dynamic process in part influenced by farmers networks and social interactions. Applying factors such as landowner–tenant relationships, perception of acceptance within specific network groups, farming practices carried out by previous generations, and collaborative construction of soil health knowledge as well as prioritizing social components when designing on-farm approaches to experimentation can help sustain soil health management.","引言 在美国的农业生态系统中，土壤健康管理实践日益受到推广，以应对粮食系统中许多相互关联的环境和经济挑战。维持土壤健康管理行为——通过农民的采纳和持续使用实践——是确保这些挑战得到解决的关键，并且已知这部分受到农民社会网络和互动的影响。方法 借鉴创新扩散理论，本扎根理论研究探讨了网络和社会互动对农民维持土壤健康管理的影响。对美国中西部农业生态系统中一个整合了农场试验和推广的合作项目的八名参与者进行了深度访谈。结果 出现了四个相互关联的维度影响持续的土壤健康管理：（1）同伴影响和社会互动，包括土地权属安排、在农业社区内感知到的接受度以及农业传统；（2）支持协作知识发展的共同学习过程；（3）通过农场试验和示范活动扩大农民网络；（4）与实践实施相关的经验性权衡。农民描述了参与农场研究如何增加了同伴交流、田间日活动和知识分享的机会，同时通过可试验性增强了管理决策的信心。与此同时，参与者强调了与长期实践使用相关的障碍，涉及社会期望、管理挑战和相互竞争的农场优先事项。讨论 我们的发现强调，土壤健康管理实践的采纳不是一次性决定，而是一个动态过程，部分受到农民网络和社会互动的影响。应用诸如地主与佃农关系、在特定网络群体内感知到的接受度、前几代人开展的农业实践以及土壤健康知识的协作构建等因素，并在设计农场试验方法时优先考虑社会因素，可以帮助维持土壤健康管理。","Frontiers in Sustainable Food Systems",63,{"impact":143,"substance":17,"depth":413,"authority":114,"freshness":143,"relevant":21,"comment":498},"基于扎根理论的质性研究，样本仅8人但揭示了农户网络与社交互动对土壤健康管理持续采纳的作用机制，对农技推广与数字乡村社交化传播有参考价值。",[500],{"name":495,"url":492},[502,27,503,504,505],"农业技术推广","田间试验","农户社交网络","采纳行为",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":509},{"candidates":510,"agricultural_evidence":46},[511,515,520],{"code":32,"score":45,"supported":46,"title":512,"abstract":513,"tags":514},[33],[49,33],[27],{"code":59,"score":68,"supported":46,"title":516,"abstract":517,"tags":519},[],[518],"农业生态",[],{"code":521,"score":21,"supported":14,"title":522,"abstract":523,"tags":524},"G1",[],[],[502],[526,527],"美国中西部 土壤健康 农户网络","农场试验 知识共享 土壤管理","美国中西部土壤健康农户网络-5072","10.3389\u002Ffsufs.2026.1823295",{"doi":529,"openalex_id":531,"authors":532,"venue":495,"cited_by_count":77,"oa_url":492,"card":545,"direction":317,"ingested_from":98},"W7222303587",[533,536,539,542],{"name":534,"orcid":535},"Fernanda Souza Krupek","https:\u002F\u002Forcid.org\u002F0000-0002-6132-2770",{"name":537,"orcid":538},"Taylor K. Ruth","https:\u002F\u002Forcid.org\u002F0000-0002-5269-9154",{"name":540,"orcid":541},"Daren D. Redfearn","https:\u002F\u002Forcid.org\u002F0000-0003-0295-3131",{"name":543,"orcid":544},"Andrea Basche","https:\u002F\u002Forcid.org\u002F0000-0001-6805-8522",{"tldr":546,"method":547,"finding":548,"direction":317,"opportunity":549},"基于扎根理论访谈八位农户，探究社交网络与互动如何影响土壤健康管理行为的持续。","扎根理论，深度访谈美国中西部八位参与农场试验的农户。","持续管理受同伴影响、共同学习、网络扩展和体验权衡四维度共同作用，非一次性决策。","可研究数字社交平台与在线农户网络如何补充线下互动，促进土壤健康管理持续采纳。","2026-10-16T23:30:06.505721Z"]