[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2386":3,"related-2386":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":62,"slug":65,"view_count":66,"doi":8,"paper":8,"created_at":67},2386,"安徽省农科院茶叶所专家团队赴休宁县开展茶园土壤质量安全专项调查","https:\u002F\u002Fcys.aaas.org.cn\u002F4304505\u002F71046161.html","9月7日国家茶叶产业技术体系岗位专家、安徽省农科院茶园环境与地力提升团队负责人苏有健率团队深入休宁县重点产茶乡镇，开展茶园土壤质量安全调查取样工作。调查发现部分茶园存在土壤酸化趋势加剧、有机质下降、氮磷钾比例失衡等潜在风险，专家组当场提出'对症开方'。",null,"安徽省农业科学院茶叶所 | 2026-09-09","2026-09-09T00:00:00Z","报道",10,false,60,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,16,14,13,5,1,"省级科研机构专家实地调查，揭示茶园土壤酸化、有机质下降等实质风险并提出对策，专业性与信源权威度较好，但属区域性技术调查，影响层级有限，时效性一般。",[24],{"name":9,"url":6},[26,27,28,29,30],"土壤酸化","农业科技服务","茶园土壤","地力提升","茶叶产业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":45,"locked":13,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},11,true,[54],"茶叶",[54],[],{"code":39,"score":51,"supported":52,"title":58,"abstract":60,"tags":61},[59],"土壤",[59],[],[63,64],"农业科技服务 土壤酸化 地力提升 茶叶产业","农业科技服务 土壤酸化","农业科技服务土壤酸化地力提升茶叶产业-2386",2,"2026-09-14T00:06:30.257175Z",{"total":69,"page":21,"page_size":69,"items":70},6,[71,173,235,273,337,421],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":80,"score_detail":81,"sources":87,"tags":89,"primary_topic":95,"secondary_topics":96,"classification":98,"search_phrases":149,"slug":152,"view_count":66,"doi":153,"paper":154,"created_at":172},2308,"Coffee phytobiome dynamics: integrating multitrophic interactions and plant physiology for climate-resilient production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmicb.2026.1951767","Coffee ( Coffea arabica L. and Coffea canephora Pierre ex A. Froehner) is a globally important perennial plantation crop that sustains the livelihoods of millions of smallholder farmers while making substantial contributions to agricultural economies worldwide global agricultural economies. Nevertheless, sustainable coffee production is increasingly constrained by climate change, declining soil fertility, emerging pests and diseases and the environmental costs associated with intensive use of synthetic agrochemicals. Recent advances in plant microbiome research have transformed the understanding of coffee from an individual organism to a holobiont, where the host plant and its associated microorganisms operate as an integrated biological system. The coffee phytobiome encompasses diverse microbial communities inhabiting the rhizosphere, rhizoplane, endosphere, phyllosphere, anthosphere, carposphere and spermosphere, together with complex multitrophic interactions involving shade trees, soil fauna, insects and the surrounding environment. These interactions collectively regulate nutrient acquisition, carbon assimilation, water-use efficiency, hormonal balance, stress tolerance, immune responses and overall plant productivity. This review critically examines current advances in multitrophic plant–microbe interactions that influence physiological adaptation in coffee, with particular emphasis on sustainable production under Indian agroecological conditions. It highlights the functional functions of plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF), endophytic microorganisms and other beneficial microbes in enhancing root development, nutrient cycling, and resilience to biotic and abiotic stresses. Furthermore, the review evaluates recent progress in metagenomics, metatranscriptomics, metabolomics and microbiome engineering for harnessing indigenous microbial resources. Finally, it outlines future research priorities integrating plant physiology, microbial ecology, systems biology, and precision agriculture to develop climate-resilient, resource-efficient, and environmentally sustainable coffee production systems.","咖啡（Coffea arabica L. 和 Coffea canephora Pierre ex A. Froehner）是全球重要的多年生种植作物，维系着数百万小农户的生计，同时为全球农业经济作出重大贡献。然而，可持续咖啡生产日益受到气候变化、土壤肥力下降、新发病虫害以及集约化使用合成农用化学品所带来的环境成本的制约。近年来植物微生物组研究的进展已将人们对咖啡的认知从单一生物体转变为全息生物（holobiont），即宿主植物与其相关微生物作为一个整合的生物系统协同运作。咖啡植物微生物组（phytobiome）涵盖栖息于根际、根表、内生圈、叶际、花际、果际和种际的多样化微生物群落，以及涉及遮荫树、土壤动物、昆虫和周围环境的复杂多营养级相互作用。这些相互作用共同调控养分获取、碳同化、水分利用效率、激素平衡、胁迫耐受性、免疫应答和整体植物生产力。本文综述批判性地审视了影响咖啡生理适应的多营养级植物–微生物相互作用的最新进展，特别关注印度农业生态条件下的可持续生产。文章重点阐述了植物促生根际细菌（PGPR）和丛枝菌根真菌（AMF）、内生微生物及其他有益微生物在促进根系发育、养分循环以及增强生物和非生物胁迫韧性方面的功能。此外，本文评估了宏基因组学、宏转录组学、代谢组学和微生物组工程在利用本土微生物资源方面的最新进展。最后，文章展望了未来研究重点，即整合植物生理学、微生物生态学、系统生物学和精准农业，以开发气候韧性、资源高效和环境可持续的咖啡生产系统。","Frontiers in Microbiology","2026-09-10T00:00:00Z","论文",77,{"impact":82,"substance":83,"depth":84,"authority":18,"freshness":85,"relevant":21,"comment":86},18,20,17,8,"系统综述咖啡植物微生物组与多营养级互作，为气候韧性生产提供微生物组工程与组学新思路，专业性强但属细分作物领域，产业影响中等。",[88],{"name":77,"url":74},[90,91,92,93,94],"智慧农业","气候韧性","土壤健康","咖啡产业","植物微生物组",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[97],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":99},{"candidates":100,"agricultural_evidence":52},[101,108,114,122,129,136,143],{"code":32,"score":102,"supported":52,"title":103,"abstract":105,"tags":107},15,[104],"coffee",[106,104],"咖啡",[93],{"code":39,"score":12,"supported":52,"title":109,"abstract":110,"tags":113},[],[59,111,40,112],"根际","rhizosphere",[92],{"code":115,"score":116,"supported":52,"title":117,"abstract":118,"tags":121},"H7",7,[],[119,120],"气候变化","climate change",[91],{"code":123,"score":116,"supported":52,"title":124,"abstract":125,"tags":128},"I1",[],[126,127],"精准农业","precision agriculture",[90],{"code":130,"score":69,"supported":52,"title":131,"abstract":132,"tags":135},"G2",[],[133,134],"小农户","smallholder",[],{"code":137,"score":138,"supported":52,"title":139,"abstract":140,"tags":142},"H6",3,[],[141],"农业生态",[],{"code":144,"score":138,"supported":52,"title":145,"abstract":146,"tags":148},"J3",[],[147],"全球农业",[],[150,151],"植物微生物组 咖啡产业 土壤健康 智慧农业","植物微生物组 咖啡产业","植物微生物组咖啡产业土壤健康智慧农业-2308","10.3389\u002Ffmicb.2026.1951767",{"doi":153,"openalex_id":155,"authors":156,"venue":77,"cited_by_count":162,"oa_url":163,"card":164,"direction":170,"ingested_from":171},"W7212145113",[157,160],{"name":158,"orcid":159},"Somashekhargouda Patil","https:\u002F\u002Forcid.org\u002F0000-0002-0362-0745",{"name":161,"orcid":8},"C. S. Rudragouda",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmicrobiology\u002Farticles\u002F10.3389\u002Ffmicb.2026.1951767\u002Fpdf",{"tldr":165,"method":166,"finding":167,"direction":168,"opportunity":169},"综述咖啡植物微生物组与多营养级互作，提出整合植物生理与微生物组提升气候韧性生产。","综述微生物组学、多组学与微生物组工程，聚焦印度咖啡农生态。","咖啡作为全生物体，其根际、内生等微生物组通过多营养级互作调控养分、抗逆与生产力。","农业绿色发展与碳","可探索咖啡全生物体多组学数据整合与微生物组工程，构建气候韧性精准调控模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-13T23:30:11.231069Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":8,"content":178,"source_name":179,"source_url":8,"published_at":180,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":181,"score_detail":182,"sources":186,"tags":188,"primary_topic":193,"secondary_topics":194,"classification":201,"search_phrases":230,"slug":233,"view_count":21,"doi":8,"paper":8,"created_at":234},2887,"国家茶叶产业技术体系十五五建设启动会在长沙召开:三大方向锁定种业\u002F绿色生产\u002F数智加工","https:\u002F\u002Fm.voc.com.cn\u002Fxhn\u002Fnews\u002F202609\u002F33792135.html","9月18日,国家茶叶产业技术体系十五五建设启动会在长沙召开。十四五期间茶叶体系累计选育茶树新品种119个,新建示范基地12.17万亩,绿色生产技术应用达1001.7万亩次,核心示范区化学农药施用量减少54.4%。十五五将重点围绕茶树种业创新提质与新品种高效利用、茶园绿色智能化生产技术研发与示范、茶叶数智化加工利用技术与装备研究三方面展开攻关。","![Image 2](https:\u002F\u002Fm.voc.com.cn\u002Fassets\u002Fportal\u002Fimages\u002Ffd-img.png)\n\n![Image 3: 新湖南](https:\u002F\u002Fm.voc.com.cn\u002Fassets\u002Fportal\u002Fimages\u002FfxyLOGO.png)湖南日报新媒体\n\n[![Image 4](https:\u002F\u002Fm.voc.com.cn\u002Fassets\u002Fportal\u002Fimages\u002Fhome.png)](https:\u002F\u002Fm.voc.com.cn\u002F)[打开](https:\u002F\u002Fh5-xhncloud.voc.com.cn\u002Fxinhunan?pkgname=com.dingtai.wxhn.activity&scheme=xhn%3A%2F%2Fnews%2FopenDetail%3Fid%3D33792135%26classid%3D6727%26isAtlas%3D0%26from%3D0%26zt%3D0%26news_id%3D33792135%26news_type%3D0)\n\n茶叶体系“十五五”怎么干？长沙这场会议明确三大方向\n\n新湖南 • 要闻\n\n浏览量：33202\n\n2026-09-18 22:23:09\n\n新湖南客户端9月18日讯（全媒体记者 黄梅 通讯员 钟妮 张帆）今日，国家茶叶产业技术体系“十五五”建设启动会在长沙召开。中国工程院院士、湖南省农业科学院党委书记柏连阳，中国工程院院士、湖南师范大学校长刘仲华，国家茶叶产业技术体系首席科学家王新超出席会议。\n\n![Image 5](https:\u002F\u002Fvod-hszx-xhncloud.voc.com.cn\u002F214\u002F2026\u002F09\u002F18\u002F3a65aac96fdc6eba1d80081517333344a67dcc4f1789740525979.png?pid=45359280)（活动现场）​\n\n会议期间，农业农村部科技发展中心项目专员朱旭君对《现代农业产业技术体系建设专项管理办法》进行解读。中国农业科学院茶叶研究所财务处高级会计师陈伟东就体系经费管理与使用注意事项作专题报告，为体系规范化、制度化运行提供明确指引。湖南省茶叶产业技术体系首席专家、湖南省农业科学院茶叶研究所所长李赛君作湖南体系建设经验交流。\n\n王新超介绍，“十四五”期间，茶叶体系累计选育茶树新品种119个，新建新品种示范基地12.17万亩，绿色生产技术累计应用面积达1001.7万亩次，核心示范区化学农药施用量减少54.4%。\n\n“十五五”时期，体系将重点围绕茶树种业创新提质与新品种高效利用、茶园绿色智能化生产技术研发与示范、茶叶数智化加工利用技术与装备研究及示范三方面展开攻关。种业方面，重点选育抗逆品种、宜机化品种和适配新消费场景的专用特色品种。绿色生产方面，构建“灾前预防—灾中预警—灾后修复”全链条防灾减灾技术体系，攻坚茶园机械化智能化装备技术。加工方面，开发多元化新产品，推动加工环节标准化、规模化、智能化升级。\n\n作者：黄梅\n\n责编：伍镆\n\n一审：黄梅\n\n二审：刘思佳\n\n三审：蒋玉青\n\n来源：新湖南客户端\n\n[![Image 6](https:\u002F\u002Fimg2.voc.com.cn\u002F9\u002F2026\u002F09\u002F02\u002F07b9a444f8521e926f7f10a6d9cd559f93c328051788316089164.gif?pid=44805140)](https:\u002F\u002Fm.voc.com.cn\u002Fportal\u002Fnews\u002Fxyb?tid=33792135)\n\n![Image 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2](https:\u002F\u002Fm.voc.com.cn\u002Fxhn\u002Fnews\u002F202609\u002F33792135.html)","新湖南\u002F湖南日报","2026-09-19T00:00:00Z",86,{"impact":183,"substance":184,"depth":84,"authority":51,"freshness":12,"relevant":21,"comment":185},26,22,"国家级茶叶产业技术体系十五五启动会，明确种业、绿色生产、数智加工三大攻关方向并披露十四五具体数据，产业级信息增量充足，值得进入每日精选。",[187],{"name":179,"url":176},[189,190,191,30,192],"十五五","种业振兴","绿色生产","数智加工",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[195],{"code":196,"slug":197,"name":198,"path":199,"parent_code":200},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",{"status":45,"locked":13,"version":46,"method":47,"evidence":202},{"candidates":203,"agricultural_evidence":52},[204,209,214,220,225],{"code":32,"score":18,"supported":52,"title":205,"abstract":206,"tags":208},[54],[54,207],"茶树",[],{"code":196,"score":16,"supported":52,"title":210,"abstract":212,"tags":213},[211],"种业",[211],[190],{"code":215,"score":138,"supported":52,"title":216,"abstract":217,"tags":219},"F4",[],[218],"农药",[],{"code":221,"score":21,"supported":13,"title":222,"abstract":223,"tags":224},"H8",[],[],[191],{"code":226,"score":21,"supported":13,"title":227,"abstract":228,"tags":229},"J1",[],[],[189],[231,232],"国家茶叶产业技术体系 十五五 长沙","茶树 种业创新 宜机化品种","国家茶叶产业技术体系十五五长沙-2887","2026-09-19T00:06:06.500842Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":8,"content":240,"source_name":241,"source_url":8,"published_at":242,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":162,"score_detail":243,"sources":245,"tags":247,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":268,"slug":271,"view_count":66,"doi":8,"paper":8,"created_at":272},968,"中国农科院资划所：揭示南方灌区土壤酸化多维驱动机制","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F","近日中国农业科学院农业资源与农业区划研究所联合多家协同单位，针对我国南方灌区土壤酸化加剧、制约水稻产能提升的产业痛点，系统揭示了氮沉降、作物吸收、酸雨输入、有机酸代谢「四维」驱动机制，提出分区分类的改良对策。该研究为南方红壤区酸化耕地治理与产能提升提供原创理论与靶向技术支撑。依托中国农科院集中突破机制，研究成果已在江西、湖南、广东三省红壤区开展田间试验示范，为保障国家口粮绝对安全和南方灌区绿色发展贡献农科方案。","*   [![Image 1: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F01470ea4e2424d42a168333cf811f005.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd9537ce4a367482e8f8ff43692d28822.htm) 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2026-08-25","2026-08-25T00:00:00Z",{"impact":162,"substance":162,"depth":162,"authority":162,"freshness":162,"relevant":162,"comment":244},"标题与正文不符，正文为多条科研进展列表，无土壤酸化相关具体内容，不相关。",[246],{"name":241,"url":238},[248,249,250,26,251,252],"中国农科院","资划所","南方灌区","红壤","水稻产能",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":256},{"candidates":257,"agricultural_evidence":52},[258,262],{"code":39,"score":51,"supported":52,"title":259,"abstract":260,"tags":261},[59],[59],[],{"code":263,"score":138,"supported":52,"title":264,"abstract":265,"tags":267},"A1",[],[266],"水稻",[],[269,270],"中国农科院 南方灌区 土壤酸化 水稻产能","中国农科院 南方灌区","中国农科院南方灌区土壤酸化水稻产能-968","2026-08-26T00:09:31.393413Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":279,"source_url":276,"published_at":280,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":285,"tags":287,"primary_topic":292,"secondary_topics":293,"classification":300,"search_phrases":316,"slug":319,"view_count":162,"doi":320,"paper":321,"created_at":336},5583,"Active-Labeling 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",64,{"impact":85,"substance":83,"depth":84,"authority":283,"freshness":12,"relevant":21,"comment":284},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。",[286],{"name":279,"url":276},[90,288,289,290,291],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[294],{"code":295,"slug":296,"name":297,"path":298,"parent_code":299},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":13,"version":46,"method":47,"evidence":301},{"candidates":302,"agricultural_evidence":52},[303,307,312],{"code":39,"score":18,"supported":52,"title":304,"abstract":305,"tags":306},[40],[59,40],[],{"code":295,"score":283,"supported":52,"title":308,"abstract":310,"tags":311},[309],"artificial intelligence",[],[288],{"code":123,"score":116,"supported":52,"title":313,"abstract":314,"tags":315},[],[126,127],[90],[317,318],"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":320,"openalex_id":322,"authors":323,"venue":279,"cited_by_count":162,"oa_url":276,"card":330,"direction":334,"ingested_from":171},"W7223082195",[324,326,328],{"name":325,"orcid":8},"Amine Ben Salah",{"name":327,"orcid":8},"Ines Trabelsi",{"name":329,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":331,"method":332,"finding":333,"direction":334,"opportunity":335},"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。","2026-10-22T11:20:58.901747Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":8,"source_name":343,"source_url":340,"published_at":344,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":356,"secondary_topics":357,"classification":358,"search_phrases":382,"slug":385,"view_count":162,"doi":386,"paper":387,"created_at":420},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":17,"substance":184,"depth":82,"authority":18,"freshness":283,"relevant":21,"comment":347},"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。",[349],{"name":343,"url":340},[351,352,353,354,355],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":359},{"candidates":360,"agricultural_evidence":52},[361,365,372,378],{"code":39,"score":18,"supported":52,"title":362,"abstract":363,"tags":364},[40],[59,40],[],{"code":366,"score":116,"supported":52,"title":367,"abstract":368,"tags":371},"I3",[],[369,370],"遥感","remote sensing",[351],{"code":115,"score":69,"supported":52,"title":373,"abstract":374,"tags":377},[],[375,376],"气候适应","干旱",[],{"code":137,"score":21,"supported":13,"title":379,"abstract":380,"tags":381},[],[],[354],[383,384],"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":386,"openalex_id":388,"authors":389,"venue":343,"cited_by_count":162,"oa_url":340,"card":414,"direction":418,"ingested_from":171},"W7223069171",[390,392,394,396,399,401,404,407,409,411],{"name":391,"orcid":8},"Yue Li",{"name":393,"orcid":8},"Mengfei Shi",{"name":395,"orcid":8},"Fei Zhang",{"name":397,"orcid":398},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":400,"orcid":8},"Gowhar Meraj",{"name":402,"orcid":403},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":405,"orcid":406},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":408,"orcid":8},"Yuanfang Chai",{"name":410,"orcid":8},"Hongtao Xu",{"name":412,"orcid":413},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":415,"method":416,"finding":417,"direction":418,"opportunity":419},"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":422,"title":423,"url":424,"summary":425,"summary_zh":426,"content":8,"source_name":427,"source_url":424,"published_at":280,"category":79,"cover_url":8,"hotness":12,"is_selected":13,"score":80,"score_detail":428,"sources":431,"tags":433,"primary_topic":437,"secondary_topics":443,"classification":446,"search_phrases":480,"slug":483,"view_count":162,"doi":484,"paper":485,"created_at":502},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",{"impact":17,"substance":429,"depth":84,"authority":19,"freshness":12,"relevant":21,"comment":430},21,"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。",[432],{"name":427,"url":424},[90,434,351,435,436],"机器学习","土壤墒情","冬小麦",{"code":438,"slug":439,"name":440,"path":441,"parent_code":442},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[444,445],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":295,"slug":296,"name":297,"path":298,"parent_code":299},{"status":45,"locked":13,"version":46,"method":47,"evidence":447},{"candidates":448,"agricultural_evidence":52},[449,454,458,463,468,475],{"code":438,"score":183,"supported":52,"title":450,"abstract":452,"tags":453},[451,439],"winter wheat",[440,451,439],[436],{"code":39,"score":102,"supported":52,"title":455,"abstract":456,"tags":457},[40],[59,40],[435],{"code":295,"score":102,"supported":52,"title":459,"abstract":461,"tags":462},[460],"machine learning",[434,460],[434],{"code":366,"score":12,"supported":52,"title":464,"abstract":465,"tags":467},[],[369,370,466],"multispectral",[351],{"code":469,"score":69,"supported":52,"title":470,"abstract":471,"tags":474},"H3",[],[472,473],"灌溉","irrigation",[],{"code":123,"score":476,"supported":52,"title":477,"abstract":478,"tags":479},4,[],[90],[90],[481,482],"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":484,"openalex_id":486,"authors":487,"venue":427,"cited_by_count":162,"oa_url":424,"card":497,"direction":170,"ingested_from":171},"W7223156204",[488,491,493,495],{"name":489,"orcid":490},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":492,"orcid":8},"Yi Zhang",{"name":494,"orcid":8},"WEI Lei",{"name":496,"orcid":8},"Chengcai Zhang",{"tldr":498,"method":499,"finding":500,"direction":418,"opportunity":501},"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。","2026-10-22T11:20:18.887301Z"]