[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1100":3,"related-1100":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":57,"slug":60,"view_count":61,"doi":8,"paper":8,"created_at":62},1100,"\"站院联建、校地协同\"服务新型农业经营主体带农户提质增效交流会在汝州召开 同步启动全省\"小麦玉米周年绿色吨半粮\"创建工作","https:\u002F\u002Fk.sina.com.cn\u002Farticle_7517400647_1c0126e470590948ug.html","8月24日,河南省\"站院联建、校地协同\"服务新型农业经营主体带农户提质增效交流会在汝州召开,同步启动全省\"小麦玉米周年绿色吨半粮\"创建工作。淇县项目示范田实现小麦玉米周年亩产1820.8公斤,较普通农户种植模式增产超两成,同时实现节肥20%—30%、节水18%—33%。",null,"*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   [体育](http:\u002F\u002Fsports.sina.com.cn\u002F)\n*   [财经](http:\u002F\u002Ffinance.sina.com.cn\u002F)\n*   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张蓝今\n\n8月24日，河南省“站院联建、校地协同”服务新型农业经营主体带农户提质增效交流会在汝州召开，同步启动全省“小麦玉米周年绿色吨半粮”创建工作。此次交流会系统梳理了省农业农村厅牵头部署、创新实施的基层农技推广试点工作阶段性成果，汇聚农业农村部、全国农技推广中心、河南农业大学及全省各地农业农村部门、新型农业经营主体代表，共探农技赋能粮食稳产增产、农业提质增效的高质量发展路径。\n\n基层农技力量薄弱、科研成果落地不畅、产学研衔接脱节，是制约农业现代化发展、影响粮食产能提升的关键堵点。为破解科研与生产“两张皮”难题，打通农业科技进村入户“最后一公里”，省农业农村厅立足全省农业发展大局、主动改革创新，牵头启动基层农技推广体系改革试点，创新推行“站院联建、校地协同”工作模式。推动各地基层农技推广站与农业高校科技小院联手，整合政府、高校、新型农业经营主体多方力量，打通科技进村入户“最后一公里”，破解科研生产“两张皮”难题。\n\n经过两年多的试点实践探索，我省成功构建起“一站、一院、一团队、一基地、一产业、一机制”的“六个一”基层农技推广服务创新体系，打造出“需求对接—科研研发—技术推广—田间应用—效果反馈”的全链条闭环服务模式，形成了可复制、可推广、可落地的农技服务“河南样板”。目前，我省已在濮阳、鹤壁、新乡、许昌、平顶山等地布局建设近20个创新试点，常态化开展田间技术指导、高产示范观摩、农技专题培训等活动，年均开展各类服务300余场，覆盖基层农技人员、种植大户、普通农户超万人次，全方位激活基层农业科技服务动能。\n\n“站院联建、校地协同”农技推广模式创新试点项目，真正让农业科技从“纸上”走向“田间”，为粮食生产带来实打实的提质增效。数据显示，淇县项目示范田实现小麦玉米周年亩产1820.8公斤，较普通农户种植模式增产超两成，同时实现节肥20%—30%、节水18%—33%，走出了绿色高效、节本增收的现代农业发展路子。在今年汛期台风“白海豚”引发的涝情灾害中，该协同服务模式的优势充分彰显：依托校、站、地联动机制，基层农技站快速摸排全域灾情、摸清生产短板，高校专家第一时间下沉田间地头，精准制订减灾救灾、灾后复产技术方案，高效处置各类农业险情，全力守护秋粮生产安全。\n\n聚焦新型农业经营主体培育和农户增收赋能，全省试点工作持续细化服务举措、完善服务体系。通过组建种植大户专项服务团队，推动传统“经验种田”向数据化、标准化、科学化种田转型；依托粮王大赛、科技小院论坛等特色平台，搭建技术展示、经验交流、成果共享的专业载体；织密院地、校地协同服务网络，畅通科研单位、农技部门、经营主体沟通渠道，整合全省优质专家资源、示范基地资源，让广大农业经营主体和农户实实在在享受科技红利。同时，各地常态化开设田间课堂，分层分类培育本土“田秀才”“土专家”，将高校科研课题、青年实践阵地扎根田间地头，实现科研创新、人才培育、助农兴农一体推进。\n\n本次交流会上，业内专家围绕水肥一体化应用、站院联建实践创新等核心内容开展专题分享，农业农村部、全国农技推广中心相关负责人作专项业务培训，各地合作社代表结合亲身经历，分享了“站院联建、校地协同”试点改革带来的种植效益提升、生产方式转变等一线体验。会后，参会代表前往汝州、郏县科技小院示范基地开展实地观摩，直观感受我省农技推广模式创新成效。\n\n据了解，在省农业农村厅统筹指导下，我省将持续深化长效协作机制，织密校地协同网络，打通科技小院、科研单位、农业农村部门沟通渠道，重点推广水肥一体化、绿色防控、抗逆减灾等集成技术。锚定农业强省“四强两高”建设目标，持续释放农业科技创新动能，以科教力量赋能乡村全面振兴，为扛稳扛牢粮食安全重任贡献更多河南力量。\n\n特别声明：以上文章内容仅代表作者本人观点，不代表新浪网观点或立场。如有关于作品内容、版权或其它问题请于作品发表后的30日内与新浪网联系。\n\n[![Image 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性犯罪者再犯几率达12.8%](https:\u002F\u002Fn.sinaimg.cn\u002Fnews\u002Ftransform\u002F299\u002Fw180h119\u002F20201019\u002F1d08-kaqzmiw7450166.png)性犯罪者再犯几率达12.8%](https:\u002F\u002Fnews.sina.com.cn\u002F2020-09-03\u002Fdoc-iivhuipp2286397.shtml)\n\n*   [](javascript:;)\n*   [](javascript:;)\n\n*   [联系我们](http:\u002F\u002Fwww.sina.com.cn\u002Fcontactus.html)|[广告营销](https:\u002F\u002Fmarketing.hd.weibo.com\u002F?fr=C003004_P001)|[通行证注册](http:\u002F\u002Flogin.sina.com.cn\u002Fsignup\u002Fsignup)\n*   [产品答疑](http:\u002F\u002Fhelp.sina.com.cn\u002F)|[招聘信息](http:\u002F\u002Fcareer.sina.com.cn\u002F)|[网站律师](http:\u002F\u002Fcorp.sina.com.cn\u002Flawfirm\u002Fsina.htm)\n*   [SINA English](http:\u002F\u002Fenglish.sina.com\u002F)\n\n举报邮箱：jubao@vip.sina.com\n\nCopyright © 1996-2026 SINA Corporation\n\nAll Rights Reserved 新浪公司 [版权所有](http:\u002F\u002Fcorp.sina.com.cn\u002Fchn\u002Fcopyright.html)\n\n[新浪首页](https:\u002F\u002Fwww.sina.com.cn\u002F)[相关新闻](javascript:;)[改版调查](https:\u002F\u002Fvote.sina.com.cn\u002F106)[](https:\u002F\u002Fk.sina.com.cn\u002Farticle_7517400647_1c0126e470590948ug.html)[返回顶部](javascript:;)","河南日报农村版(新浪网转载)","2026-08-27T01:00:00Z","报道",10,false,50,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},15,12,8,7,1,"省级农业服务模式交流会，有实质内容但深度一般，信源为省级媒体转载，时效性尚可。",[24],{"name":10,"url":6},[26,27,28,29],"河南省农科院","站院联建","校地协同","吨半粮",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"partial","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45,"reason":56},[42,50],{"code":43,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},"A2",11,true,[47],"小麦",[47],[],{"code":51,"score":44,"supported":45,"title":52,"abstract":54,"tags":55},"A3",[53],"玉米",[53],[],"一级领域明确，二级方向需要细分",[58,59],"河南省农科院 校地协同 站院联建 吨半粮","河南省农科院 校地协同","河南省农科院校地协同站院联建吨半粮-1100",4,"2026-08-30T00:11:10.001764Z",{"total":64,"page":21,"page_size":64,"items":65},6,[66,126,238,286,349,406],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":8,"content":71,"source_name":72,"source_url":8,"published_at":73,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":89,"classification":90,"search_phrases":120,"slug":123,"view_count":124,"doi":8,"paper":8,"created_at":125},409,"揭榜挂帅!河南省攻坚主粮镰刀菌病害——殷贵鸿团队联合4单位组建创新联合体","https:\u002F\u002F5g.dahe.cn\u002Fnews\u002F202608102118624","8月8日,2026年度第一批河南省重大产业关键技术攻关'揭榜挂帅'项目——'小麦-玉米周年两熟重大镰刀菌病害成灾机理及全程绿色防控关键技术研发与集成示范'项目启动会在河南农业大学龙子湖校区举行。由镰刀菌侵染引起的小麦赤霉病、茎基腐病及玉米穗腐病每年全国发生面积约2.9亿亩,约占病虫害发生总面积的1\u002F4。项目由河南农业大学殷贵鸿研究员领衔揭榜,集聚河南农业大学、河南省农业科学院、河南科技学院、河南省植物保护植物检疫站等4家单位科研力量,联动5家种业、新型肥料、绿色防控领域龙头企业,组建跨学科跨单位创新联合体。","本报讯（全媒体记者 谭勇）8月8日，2026年度第一批河南省重大产业关键技术攻关“揭榜挂帅”项目——“小麦-玉米周年两熟重大镰刀菌病害成灾机理及全程绿色防控关键技术研发与集成示范”项目启动会在河南农业大学龙子湖校区举行。\n\n小麦和玉米是我国重要的粮食作物，而由镰刀菌侵染引起的小麦赤霉病、茎基腐病及玉米穗腐病是主要病害，此类病害每年全国发生面积约2.9亿亩，约占病虫害发生总面积的1\u002F4。由于全球气候变化、连续多年秸秆还田等，由镰刀菌引起的上述病害逐年加重，造成显著产量损失，严重影响了农户的收益。更为严重的是，其病原菌产生的呕吐毒素（DON）、伏马菌素（FB）等真菌毒素对籽粒品质影响巨大，甚至导致无法食用和饲用，对人畜安全造成严重威胁。\n\n河南作为全国粮食生产核心区，是全球最大的小麦-玉米周年两熟种植区，常年小麦种植面积8500多万亩、产量约占全国的1\u002F4，玉米种植面积5800万亩、产量约占全国的7%。近年来受全球气候变化影响，小麦赤霉病、茎基腐病及玉米穗腐病等镰刀菌病害在我省呈逐年加重态势，严重威胁粮食安全。\n\n本次“揭榜挂帅”项目正是针对我省小麦-玉米主粮产业核心痛点，将攻克镰刀菌病害防控难题作为守住粮食安全底线、保障农产品质量安全的重要政治任务和迫切民生工程。该项目由河南农业大学殷贵鸿研究员领衔揭榜，集聚河南农业大学、河南省农业科学院、河南科技学院、河南省植物保护植物检疫站等4家单位的科研力量，联动5家种业、新型肥料、绿色防控领域的行业龙头企业，组建跨学科、跨单位创新联合体。\n\n“项目将围绕镰刀菌病害成灾机理、智能监测预警、抗病新品种培育、绿色防控技术研发及集成示范推广等核心领域开展全链条、系统化技术攻关，着力构建适配我省小麦-玉米周年两熟种植模式的绿色高效防控技术体系。”殷贵鸿表示，通过育种、植保、栽培学科融合，政府、高校科研院所、企业平台结合，科研人员、推广人员、种粮大户密切配合，把论文写在大地上、成果送进千万家。\n\n作为我省农业领域本年度重点推进的“揭榜挂帅”专项，该项目对提升我省粮食生产核心竞争力、推动农业绿色转型、筑牢国家粮食安全屏障，具有十分重大的现实意义和战略意义。","大河网-河南日报 2026-08-10","2026-08-10T10:00:00Z",80,{"impact":76,"substance":77,"depth":78,"authority":44,"freshness":79,"relevant":21,"comment":80},24,20,16,9,"省级重大农业科技攻关项目启动，聚焦镰刀菌病害防控，涉及粮食安全，信息具体，来源权威，时效性好。",[82],{"name":72,"url":69},[84,85,26,86,87],"揭榜挂帅","镰刀菌","河南农大","殷贵鸿",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":91},{"candidates":92,"agricultural_evidence":45,"reason":56},[93,98,102,108,114],{"code":43,"score":94,"supported":45,"title":95,"abstract":96,"tags":97},3,[],[47],[],{"code":51,"score":94,"supported":45,"title":99,"abstract":100,"tags":101},[],[53],[],{"code":103,"score":94,"supported":45,"title":104,"abstract":105,"tags":107},"F1",[],[106],"种业",[],{"code":109,"score":94,"supported":45,"title":110,"abstract":111,"tags":113},"F3",[],[112],"肥料",[],{"code":115,"score":94,"supported":45,"title":116,"abstract":117,"tags":119},"F4",[],[118],"绿色防控",[],[121,122],"河南省农科院 揭榜挂帅 河南农大 殷贵鸿","河南省农科院 揭榜挂帅","河南省农科院揭榜挂帅河南农大殷贵鸿-409",5,"2026-08-11T23:57:01.847666Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":8,"source_name":132,"source_url":129,"published_at":133,"category":134,"cover_url":8,"hotness":13,"is_selected":14,"score":135,"score_detail":136,"sources":140,"tags":142,"primary_topic":148,"secondary_topics":149,"classification":150,"search_phrases":188,"slug":191,"view_count":192,"doi":193,"paper":194,"created_at":237},5550,"Integrated effects of fermented organic manure on soil fertility, productivity, and economic performance of the rice–wheat cropping system in Uttar Pradesh","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968370","The rice–wheat cropping system (RWCS) is a major cereal-based production system in Uttar Pradesh, India, where sustaining crop productivity and soil fertility under intensive cultivation is a major challenge. This study evaluated the field-level performance of fermented organic manure (FOM) integrated with inorganic fertilizers in terms of crop productivity, selected soil fertility indicators, and economic returns during 2025–2026. A total of 224 frontline demonstrations (FLDs) were conducted through 25 Krishi Vigyan Kendras (KVKs) across Uttar Pradesh. The analysis included demonstrations with sufficiently complete and consistent field-level records, comprising 40 rice and 110 wheat demonstrations under farmers’ field conditions and 6 rice and 11 wheat demonstrations at KVK instructional farms. Seven nutrient management treatments were evaluated at KVK farms, compared with three treatments under farmers’ field conditions. Crop performance, post-harvest soil fertility indicators, and economic returns were assessed. The treatment combining 100% recommended inorganic fertilizers with 125% FOM (T₃) recorded the highest numerical means for most growth and yield attributes and grain yield in both crops. Compared with 100% recommended inorganic fertilizers alone, T₃ increased grain yield by 6.4–7.3% in rice and 10.8–12.7% in wheat. FOM-integrated treatments also showed higher numerical values of soil organic carbon (SOC) and available nitrogen, phosphorus, and potassium after the rice–wheat cropping cycle. Under T₃, SOC increased from 0.271% to 0.304% at KVK instructional farms and from 0.447% to 0.515% under farmers’ field conditions. Although T₃ generally recorded the highest numerical gross returns, the additional costs of FOM reduced net returns and benefit–cost (B) ratios in several cases. Under farmers’ field conditions, inorganic fertilizers alone recorded higher numerical net returns and B ratios for both crops. The findings indicate that integrating FOM with recommended inorganic fertilizers was associated with improved crop productivity and selected soil fertility indicators under the demonstrated rice–wheat systems. However, the economic advantage depended on the costs of FOM production, transportation, and application. As the demonstrations were participatory and lacked formal plot-level randomization and inferential statistical testing, the observed differences represent descriptive numerical responses rather than statistically established treatment effects. Further controlled trials and cost optimization are needed to establish the agronomic and economic viability of FOM-integrated nutrient management.","稻麦种植制度（RWCS）是印度北方邦以谷类作物为主的重要生产体系，在集约化种植下维持作物生产力与土壤肥力是该体系面临的主要挑战。本研究评估了发酵有机肥（FOM）与无机肥料配合施用在2025—2026年度对作物生产力、选定土壤肥力指标及经济效益的田间表现。研究通过北方邦25个农业科技推广中心（KVKs）开展了224项前线示范（FLDs）。分析纳入了田间记录足够完整且一致的示范，包括农民田块条件下的40项水稻和110项小麦示范，以及KVK教学农场的6项水稻和11项小麦示范。在KVK农场评估了7个养分管理处理，并与农民田块条件下的3个处理进行比较。评估了作物表现、收获后土壤肥力指标及经济效益。100%推荐无机肥料配合125% FOM的处理（T₃）在两种作物的多数生长和产量性状及籽粒产量上记录了最高的数值均值。与单施100%推荐无机肥料相比，T₃使水稻籽粒产量提高6.4%—7.3%，小麦提高10.8%—12.7%。整合FOM的处理在稻麦轮作周期后也表现出较高的土壤有机碳（SOC）及有效氮、磷、钾数值。在T₃处理下，KVK教学农场的SOC从0.271%提高到0.304%，农民田块条件下从0.447%提高到0.515%。尽管T₃总体上记录了最高的数值总收益，但FOM的额外成本在多种情况下降低了净收益和效益成本比（B）。在农民田块条件下，单施无机肥料在两种作物上均记录了较高的数值净收益和B比。研究结果表明，在示范的稻麦系统中，FOM与推荐无机肥料配合施用与作物生产力及选定土壤肥力指标的改善相关。然而，经济优势取决于FOM的生产、运输和施用成本。由于示范为参与式，缺乏正式的田块水平随机化和推断性统计检验，观察到的差异代表描述性数值响应，而非统计学上确立的处理","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z","论文",71,{"impact":78,"substance":137,"depth":17,"authority":138,"freshness":79,"relevant":21,"comment":139},18,13,"基于224个前线示范的实地数据，结论有参考价值，但缺乏随机化与统计检验，且为印度区域研究，属细分领域进展。",[141],{"name":132,"url":129},[143,144,145,146,147],"有机肥","土壤肥力","养分管理","印度农业","水稻小麦轮作",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":151},{"candidates":152,"agricultural_evidence":45,"reason":56},[153,160,168,173,177,183],{"code":154,"score":17,"supported":45,"title":155,"abstract":157,"tags":159},"H2",[156],"soil",[158,156],"土壤",[144],{"code":161,"score":162,"supported":45,"title":163,"abstract":165,"tags":167},"A1",14,[164],"rice",[166,164],"水稻",[],{"code":43,"score":162,"supported":45,"title":169,"abstract":171,"tags":172},[170],"wheat",[47,170],[],{"code":109,"score":19,"supported":45,"title":174,"abstract":175,"tags":176},[],[112,145],[145,143],{"code":178,"score":94,"supported":45,"title":179,"abstract":180,"tags":182},"A7",[],[181],"轮作",[],{"code":184,"score":21,"supported":14,"title":185,"abstract":186,"tags":187},"J3",[],[],[146],[189,190],"发酵有机肥 稻麦轮作","Uttar Pradesh 稻麦系统","发酵有机肥稻麦轮作-5550",0,"10.3389\u002Ffsufs.2026.1968370",{"doi":193,"openalex_id":195,"authors":196,"venue":132,"cited_by_count":192,"oa_url":229,"card":230,"direction":234,"ingested_from":236},"W7223144837",[197,199,202,203,206,209,211,212,214,217,219,222,224,226],{"name":198,"orcid":8},"Raghwendra Singh",{"name":200,"orcid":201},"Ajay Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8743-6477",{"name":200,"orcid":201},{"name":204,"orcid":205},"Preeti P. Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7843-9783",{"name":207,"orcid":208},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0002-9773-6641",{"name":210,"orcid":8},"Abhishek Singh",{"name":210,"orcid":8},{"name":213,"orcid":8},"Saurabh Raj Pandey",{"name":215,"orcid":216},"Vinod Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0004-2583-6565",{"name":218,"orcid":8},"Narendra Raghubanshi",{"name":220,"orcid":221},"Yash Pal Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8345-9084",{"name":223,"orcid":8},"Mahesh Kumar",{"name":225,"orcid":8},"Shivangi Singh",{"name":227,"orcid":228},"Reena Kumari","https:\u002F\u002Forcid.org\u002F0000-0002-1101-5473","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1968370\u002Fpdf",{"tldr":231,"method":232,"finding":233,"direction":234,"opportunity":235},"评估发酵有机肥配施化肥对稻麦轮作产量、土壤肥力与经济收益的综合影响。","印度北方邦224个农户与KVK示范基地田间示范，比较7种养分管理处理。","全量化肥配125%发酵有机肥增产稻6.4-7.3%、麦10.8-12.7%并提升有机碳，但净收益常因","农业绿色发展与碳","可研究有机肥成本分摊与碳交易补偿机制，量化其经济可行性与土壤固碳协同效益。","openalex","2026-10-22T11:20:16.098808Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":8,"content":243,"source_name":244,"source_url":8,"published_at":245,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":249,"tags":251,"primary_topic":256,"secondary_topics":257,"classification":258,"search_phrases":281,"slug":284,"view_count":192,"doi":8,"paper":8,"created_at":285},5437,"上海农科院揭榜天长市粮食单产提升项目 攻关水稻秸秆田边发酵技术","https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html","天长市农业农村局公示 2026 年粮食单产提升「揭榜挂帅」项目实施单位，上海市农业科院与南京农业大学工学院分别揭榜水稻秸秆田边发酵技术研究与示范、秸秆田间就地焚燎消杀还田技术模式示范推广两个项目，公示期为 2026 年 10 月 20 日至 26 日。该举措旨在破解秸秆直接还田弊端、缓解秸秆禁烧压力，同时提高粮食单产，是科研院所参与县域粮食单产提升「揭榜挂帅」的具体落地案例。","403\n\nForbidden\n\nError Times: Wed, 21 Oct 2026 00:10:36 GMT   \nIP: 2600:1900:0:2105::1c01 Node information: PS-ATL-017OX49   \nURL: https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html   \nRequest-Id: 6ad802fc_PS-ATL-017OX49_7380-31487   \nCheck: Details\n\nERROR\n\nThe Requested URL could not be retrieved\n\nWhile trying to retrieve the URL:\n\nhttps:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html\n\nThe following error was encountered:\n\n*   Invalid Request\n\nThe access control configuration prevents your request at this time.\n\nPlease contact your service provider if you feel this is incorrect.\n\n[return](https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html#)","天长市人民政府 2026-10-20","2026-10-20T00:00:00Z",41,{"impact":18,"substance":19,"depth":64,"authority":64,"freshness":79,"relevant":21,"comment":248},"地市级院地合作动态，主题相关但正文仅403字且原文不可访问，信息增量与专业深度有限，暂不宜进入每日精选。",[250],{"name":244,"url":241},[166,252,253,254,255],"院地合作","秸秆利用","粮食单产提升","天长市",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":259},{"candidates":260,"agricultural_evidence":45,"reason":56},[261,265,271,276],{"code":161,"score":18,"supported":45,"title":262,"abstract":263,"tags":264},[166],[166],[166],{"code":266,"score":18,"supported":45,"title":267,"abstract":269,"tags":270},"A8",[268],"单产提升",[268],[254],{"code":272,"score":21,"supported":14,"title":273,"abstract":274,"tags":275},"G1",[],[],[252],{"code":277,"score":21,"supported":14,"title":278,"abstract":279,"tags":280},"H5",[],[],[253],[282,283],"上海农科院 天长市 水稻秸秆","天长市 粮食单产提升 秸秆发酵","上海农科院天长市水稻秸秆-5437","2026-10-21T00:10:01.423686Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":8,"source_name":292,"source_url":289,"published_at":293,"category":134,"cover_url":8,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":298,"tags":300,"primary_topic":306,"secondary_topics":307,"classification":308,"search_phrases":330,"slug":333,"view_count":192,"doi":334,"paper":335,"created_at":348},5404,"Validation of a Day-of-Year Standardized Canopy Indicator for Regional Crop Monitoring: Suitability and Decoupling Boundaries","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1707.v1","Translating satellite-derived crop canopy anomalies into regional production signals is critical for agricultural monitoring. However, standard anomaly indicators assume a stable, direct coupling between greenness and production, ignoring phenological shifts, sub-seasonal cropping dynamics, and harvested acreage fluctuations. I evaluate the validation boundaries and expected conditions of a pixel-level, day-of-year (DOY)-first Leaf Area Index (LAI) Z-score anomaly indicator with a leave- year-out baseline (2003–2022) across five contrasting global breadbaskets: Australia, Indo-Gangetic Plains (IGP), North China Plain (NCP), US Corn Belt, and Ukraine. The analysis shows that DOY- first standardization reduces temporal composite timing mismatches, though coupling with regional statistics varies systematically. Retrospective validation reveals that direct canopy-yield coupling remains robust exclusively in rainfed Australia (Pearson’s r = 0.90), where yield variability dominates production variance (explaining 90% of variance) and moisture stress limits canopy development. In contrast, three interpretation boundaries are identified: (1) greening trend-sensitivity in the IGP, where detrending reduces the association to r = 0.53; (2) temporal representativeness bias in the double-cropped NCP, requiring crop-calendar windowing to recover the wheat signal (r = 0.51); and (3) area-shock-sensitivity in Ukraine (where harvested area contractions explain 78% of production variance), decoupling canopy vigor from production (r = 0.40). Furthermore, the LAI Z-score provides a more interpretable canopy-response measure than the Vegetation Condition Index (VCI) in dense canopies where NDVI saturates. These validation levels are synthesized into a prototype diagnostic decision framework to support indicator configuration in operational monitoring systems.","将卫星反演的作物冠层异常转化为区域产量信号对农业监测至关重要。然而，标准异常指标假定绿度与产量之间存在稳定、直接的耦合关系，忽略了物候偏移、次季节作物动态以及收获面积波动。我评估了一种像元级、以年积日（DOY）为首要标准化维度的叶面积指数（LAI）Z分数异常指标在五个对比鲜明的全球粮仓——澳大利亚、印度河-恒河平原（IGP）、华北平原（NCP）、美国玉米带和乌克兰——的验证边界与预期适用条件，该指标采用留一年基线（2003—2022年）。分析表明，以DOY为首要标准化维度可减少时间合成时相不匹配，但其与区域统计数据的耦合关系存在系统性差异。回顾性验证显示，冠层与产量的直接耦合仅在雨养条件下的澳大利亚保持稳健（Pearson's r = 0.90），该地区产量变异性主导产量方差（解释90%的方差），且水分胁迫限制冠层发育。相比之下，识别出三个解释边界：（1）IGP的绿度趋势敏感性，去趋势后关联性降至r = 0.53；（2）双季种植的NCP的时间代表性偏差，需按作物日历开窗以恢复小麦信号（r = 0.51）；（3）乌克兰的面积冲击敏感性（收获面积缩减解释78%的产量方差），导致冠层活力与产量解耦（r = 0.40）。此外，在NDVI饱和的密集冠层中，LAI Z分数提供了比植被条件指数（VCI）更具可解释性的冠层响应度量。这些验证层级被综合为一个原型诊断决策框架，以支持业务化监测系统中的指标配置。","Preprints.org","2026-10-19T00:00:00Z",65,{"impact":18,"substance":296,"depth":137,"authority":64,"freshness":19,"relevant":21,"comment":297},21,"预印本提出按年积日标准化的LAI异常指标，并在五大农业区验证其适用边界，方法有新意但尚未经同行评审，属细分领域进展。",[299],{"name":292,"url":289},[301,302,303,304,305],"智慧农业","遥感","作物监测","产量估测","叶面积指数",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":309},{"candidates":310,"agricultural_evidence":45,"reason":56},[311,315,320,325],{"code":43,"score":64,"supported":45,"title":312,"abstract":313,"tags":314},[],[47,170],[],{"code":51,"score":64,"supported":45,"title":316,"abstract":317,"tags":319},[],[53,318],"corn",[],{"code":321,"score":94,"supported":14,"title":322,"abstract":323,"tags":324},"I3",[],[],[304,303,305,302],{"code":326,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},"I1",[],[],[301],[331,332],"LAI Z-score 作物监测","遥感 作物产量 估测","LAIZ-score作物监测-5404","10.20944\u002Fpreprints202610.1707.v1",{"doi":334,"openalex_id":336,"authors":337,"venue":292,"cited_by_count":192,"oa_url":289,"card":341,"direction":347,"ingested_from":236},"W7222967882",[338],{"name":339,"orcid":340},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":342,"method":343,"finding":344,"direction":345,"opportunity":346},"验证了按年积日标准化的LAI Z-score冠层指标在五大农业区的适用边界。","用2003–2022年留一年基线，在五个全球粮仓区做LAI DOY标准化与产量相","仅雨养澳大利亚冠层-产量强耦合(r=0.90)，其余区域受趋势、物候和面积冲击影响而解耦。","农业遥感与作物表型","可发展融合物候窗口、去趋势与收获面积因子的分区自适应冠层异常指标诊断框架。","农业人工智能与决策模型","2026-10-20T23:30:40.675672Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":8,"content":354,"source_name":355,"source_url":8,"published_at":356,"category":12,"cover_url":8,"hotness":13,"is_selected":45,"score":357,"score_detail":358,"sources":361,"tags":363,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":401,"slug":404,"view_count":192,"doi":8,"paper":8,"created_at":405},5335,"中国农业科学院「农知育芯 BRIS」智能育种平台在洛阳发布 打造智能育种科研副脑","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fa297dd7421fa4a3ba08c08863fbe3eb7.htm","10 月 14—16 日中国农学会图书情报分会 2026 年学术年会在河南洛阳举办。中国农业科学院农业信息研究所副所长赵瑞雪介绍,农知育芯(BRIS,Breeding Research Intelligent Service)集成 8 项分析工具、3 项自研算法,汇聚 2011—2025 年育种相关核心期刊文献,整合 6800 万条 NCBI 基因索引、57 万条 UniProt 专家校验蛋白信息;自主构建的育种知识图谱包含 7.9 万余个知识实体、12.6 万余条实体关系,覆盖水稻、玉米、小麦和拟南芥四类作物。","10月14日至16日，中国农学会图书情报分会2026年学术年会在河南洛阳举办。会议由中国农学会图书情报分会主办，中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)、河南省农业科学院农业信息技术研究所、洛阳市农林科学院联合承办。中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)副所长赵瑞雪在会上介绍农知育芯（BRIS，Breeding Research Intelligent Service）阶段性研究成果。农知育芯融合育种文献、权威数据库、知识图谱和分析算法，集成8项分析工具、3项自研算法，为研究人员提供知识获取、任务规划、证据推理全链条智能科研服务。\n\n智能育种是AI+农业的重要场景。随着高通量测序技术快速发展，育种科研数据快速增长，基因、性状和调控通路等知识分散于海量文献和数据库，候选基因筛选、功能解析和多组学研究面临知识检索繁琐、数据分析复杂、验证周期长和试错成本高等挑战。而通用大模型也难以充分满足专业知识、结果依据和溯源能力等科研要求。\n\n面向这一需求，农知育芯打通问答与计算、分析与决策、知识与工具三类能力。科研人员可通过自然语言提出问题或发起任务，系统自动调度工具与算法，汇聚文献、数据库、知识图谱及用户数据，形成分析结果与科研建议。在关键候选基因发现等任务中，平台可辅助科研人员将筛选过程由“大海捞针”转向“精准发现”，堪称智能育种的“科研副脑”。\n\n该平台以高质量知识图谱引导推理，结合分析工具计算、模型研判和知识增强开展协同验证，并对文献、数据库、工具和参数进行全链路核查，形成“问题—数据—结论”的科研闭环。\n\n据悉，农知育芯已汇聚2011年至2025年育种相关核心期刊文献，整合6800万条NCBI基因索引、57万条UniProt专家校验蛋白信息；自主构建的育种知识图谱包含7.9万余个知识实体、12.6万余条实体关系，覆盖水稻、玉米、小麦和拟南芥四类作物，形成文献、权威数据库和知识图谱互为校验的证据网络。\n\n该平台自主研发差异表达分析、GWAS、QTL定位、WGCNA共表达网络等8项经典生物信息与统计遗传分析工具，并构建多效基因挖掘、基因功能预测、通路与网络挖掘3项自研育种算法。\n\n依托上述知识资源、分析工具和算法能力，农知育芯面向育种科研人员提供智能问答和六大科研场景分析工具，涵盖关键候选基因发现、候选基因功能解析、分子机制与调控通路、基因型—表型—转录组联合研判、种质群体遗传解析和分析结果综合研判。\n\n该平台能提供快速问答、结构化分析和深度研究三类任务模式，可适配知识查询、多组学数据分析和综合育种前景评估等不同需求。系统可自动完成数据分析，汇聚多源文献证据，一键导出标准化PDF科研报告，并提供候选基因优先级排序和后续实验建议，支持PC端科研分析和移动端田间使用，服务育种科研人员和相关专业学生。","中国农业科学院 2026-10-14~16","2026-10-14T00:00:00Z",85,{"impact":76,"substance":359,"depth":137,"authority":17,"freshness":64,"relevant":21,"comment":360},22,"国家级科研机构发布的智能育种平台，知识资源与分析工具细节扎实，属AI+农业重要进展，值得精选。",[362],{"name":355,"url":352},[364,365,366,367,368],"农业人工智能","种业振兴","智能育种","知识图谱","科研平台",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":372},{"candidates":373,"agricultural_evidence":45,"reason":56},[374,378,382,386,392,396],{"code":161,"score":94,"supported":45,"title":375,"abstract":376,"tags":377},[],[166],[],{"code":43,"score":94,"supported":45,"title":379,"abstract":380,"tags":381},[],[47],[],{"code":51,"score":94,"supported":45,"title":383,"abstract":384,"tags":385},[],[53],[],{"code":387,"score":388,"supported":14,"title":389,"abstract":390,"tags":391},"I2",2,[],[],[364,367],{"code":103,"score":21,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[365],{"code":397,"score":21,"supported":14,"title":398,"abstract":399,"tags":400},"F2",[],[],[366],[402,403],"农知育芯 BRIS 智能育种","中国农科院 农业信息研究所 育种平台","农知育芯BRIS智能育种-5335","2026-10-20T02:47:43.320559Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":8,"source_name":412,"source_url":409,"published_at":413,"category":134,"cover_url":8,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":417,"tags":419,"primary_topic":423,"secondary_topics":424,"classification":425,"search_phrases":472,"slug":475,"view_count":192,"doi":476,"paper":477,"created_at":533},5314,"A data‐driven image extraction and analysis pipeline for plant phenotyping in controlled environments","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70116","Abstract Advances in automation, imaging, and artificial intelligence have enabled large‐scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Automated Precision Phenotyping Greenhouse and expanded plant growth and phenotyping (PGP v2) data across maize ( Zea mays L.), cotton ( Gossypium hirsutum ), rice ( Oryza sativa ), and sorghum ( Sorghum bicolor L.). The pipeline integrates pseudo‐red‐green‐blue composite image generation, plant detection and segmentation, image stitching, vegetation‐index analysis, texture analysis, morphological trait extraction, and temporal comparison of image‐derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, segment anything model (SAM) v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. Segment anything model for long sequences (SAM2Long)maintained plant‐instance associations across vertically stacked frames, while scale‐invariant feature transform–based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863‐dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment‐level temporal analysis of mutagenized sorghum lines and cold‐stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatment‐ or stress‐related responses. The combined dataset and workflow provide structured, automated, and well‐documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlled‐environment plant science and crop improvement.","摘要　自动化、成像和人工智能的进步使得大规模植物表型分析成为可能，但图像分析仍然是作物改良和生物学发现的关键瓶颈。我们利用德克萨斯农工大学农业生命科学自动化精准表型温室（Texas A&M AgriLife Automated Precision Phenotyping Greenhouse）的图像，开发了一个集成多光谱表型分析框架，并扩展了玉米（Zea mays L.）、棉花（Gossypium hirsutum）、水稻（Oryza sativa）和高粱（Sorghum bicolor L.）的植物生长与表型分析（plant growth and phenotyping，PGP v2）数据。该流程整合了伪红绿蓝合成图像生成、植物检测与分割、图像拼接、植被指数分析、纹理分析、形态性状提取以及图像衍生特征的时间比较，以量化植物结构、光谱反射率和纹理随时间的变化。在评估的分割方法中，segment anything model（SAM）v3在不同作物结构下提供了最高且最一致的准确性，尽管其所需计算时间多于经典方法。面向长序列的segment anything model（SAM2Long）在垂直堆叠帧之间维持了植物实例关联，而基于尺度不变特征变换（scale-invariant feature transform）的拼接在单株植物超出单一视野时重建了植物拼接图。对于每株植物和每个成像日期，该流程生成了一个863维特征向量，涵盖植被指数、光谱统计量、纹理描述符和形态性状。该框架通过两个案例研究进行了评估：诱变高粱品系的处理水平时间分析，以及使用独立成像系统对玉米进行的冷胁迫表型分析。在这两项研究中，提取的特征支持了表型变异的统计分析和多变量分析，并能够根据处理或胁迫相关响应区分植物。合并的数据集和工作流程为受控环境植物科学和作物改良提供了跨多种作物、实验设置和成像系统的结构化、自动化且记录完善的表型分析。","The Plant Phenome Journal","2026-10-18T00:00:00Z",81,{"impact":137,"substance":359,"depth":137,"authority":162,"freshness":79,"relevant":21,"comment":416},"该研究提出一套面向多作物的自动化多光谱表型分析流程，方法新颖、数据规模可观，对智慧育种与受控环境植物科学研究具有较高参考价值。",[418],{"name":412,"url":409},[301,364,420,421,422],"图像分割","表型组学","多光谱成像",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":426},{"candidates":427,"agricultural_evidence":45,"reason":56},[428,432,437,444,451,458,463,468],{"code":161,"score":64,"supported":45,"title":429,"abstract":430,"tags":431},[],[166,164],[],{"code":51,"score":64,"supported":45,"title":433,"abstract":434,"tags":436},[],[53,435],"maize",[],{"code":438,"score":64,"supported":45,"title":439,"abstract":440,"tags":443},"A5",[],[441,442],"高粱","sorghum",[],{"code":445,"score":64,"supported":45,"title":446,"abstract":447,"tags":450},"A6",[],[448,449],"棉花","cotton",[],{"code":452,"score":64,"supported":45,"title":453,"abstract":454,"tags":457},"B7",[],[455,456],"温室","greenhouse",[],{"code":387,"score":124,"supported":45,"title":459,"abstract":460,"tags":462},[],[461],"artificial intelligence",[364,420],{"code":321,"score":61,"supported":45,"title":464,"abstract":465,"tags":467},[],[466],"multispectral",[422],{"code":326,"score":21,"supported":14,"title":469,"abstract":470,"tags":471},[],[],[301],[473,474],"Texas A&M 自动化表型温室","玉米 高粱 多光谱表型","TexasAM自动化表型温室-5314","10.1002\u002Fppj2.70116",{"doi":476,"openalex_id":478,"authors":479,"venue":412,"cited_by_count":192,"oa_url":409,"card":528,"direction":345,"ingested_from":236},"W7222893982",[480,482,485,488,491,493,495,498,501,504,507,510,513,516,519,522,524,526],{"name":481,"orcid":8},"Fahimeh Orvati Nia",{"name":483,"orcid":484},"Joshua Peeples","https:\u002F\u002Forcid.org\u002F0000-0001-7861-7789",{"name":486,"orcid":487},"Seth C. Murray","https:\u002F\u002Forcid.org\u002F0000-0002-2960-8226",{"name":489,"orcid":490},"Andrew J. McFarland","https:\u002F\u002Forcid.org\u002F0009-0000-3516-8274",{"name":492,"orcid":8},"Troy Vann",{"name":494,"orcid":8},"Shima Salehi",{"name":496,"orcid":497},"Robert G. Hardin","https:\u002F\u002Forcid.org\u002F0000-0002-0714-8281",{"name":499,"orcid":500},"David D. Baltensperger","https:\u002F\u002Forcid.org\u002F0000-0003-4511-5680",{"name":502,"orcid":503},"Amir M. H. Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0002-0584-753X",{"name":505,"orcid":506},"J. Alex Thomasson","https:\u002F\u002Forcid.org\u002F0000-0002-5557-105X",{"name":508,"orcid":509},"Henry Y. Fadamiro","https:\u002F\u002Forcid.org\u002F0000-0002-4530-9680",{"name":511,"orcid":512},"Nithya K. Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-1659-7396",{"name":514,"orcid":515},"Suresh D. Pillai","https:\u002F\u002Forcid.org\u002F0000-0002-9766-8765",{"name":517,"orcid":518},"Rebecca Roston","https:\u002F\u002Forcid.org\u002F0000-0002-3063-5002",{"name":520,"orcid":521},"Joslin Ishimwe","https:\u002F\u002Forcid.org\u002F0009-0003-6999-7718",{"name":523,"orcid":8},"Diptadeep Basak",{"name":525,"orcid":8},"Nazar Oladepo",{"name":527,"orcid":8},"Uday Santhosh Raju Vysyaraju",{"tldr":529,"method":530,"finding":531,"direction":345,"opportunity":532},"构建多光谱图像提取分析流程，实现多作物表型自动化量化。","用温室多光谱图像，集成SAM v3分割、SIFT拼接与863维特征提取。","SAM v3分割最准，特征可区分高粱诱变与玉米冷胁迫响应。","可探索轻量化分割模型与跨成像系统迁移，降低计算成本并拓展田间表型。","2026-10-19T23:30:17.276726Z"]