[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-260":3,"related-260":65},{"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":32,"secondary_topics":37,"classification":38,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":8,"created_at":64},260,"立功者风采丨泥腿子蹚出粮食安全突围路——记湖南省农科院镉低积累水稻研发与推广团队","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7670370379470324265\u002F","2025年度湖南省事业单位记大功奖励名单近日揭晓,湖南省农科院镉低积累水稻研发与推广团队、湖南省生态环境监测中心、湖南省矿产资源调查所3个集体获此殊荣。由中国工程院院士、湖南省农科院党委书记柏连阳担任首席专家的湖南省农科院镉低积累水稻研发与推广团队,十余年持续攻关,围绕低镉种质发现、低镉材料创制、低镉品种选育和推广应用,蹚出一条保障粮食安全的新路。团队从不育系出发筛出西子3号——全国首个通过国家审定的低镉水稻品种;赵炳然团队用重离子诱变技术育成首个大面积推广的臻两优8612和首个高档优质香型镉低积累水稻韶香100;自主研发M1TDS技术,在种质幼苗期就提前锁定目标突变体,大幅提升筛选效率。镉低积累水稻在湖南的推广面积从2023年的107万亩跃至2025年的316万亩,全国累计达688.6万亩。",null,"**红网时刻新闻通讯员 黄浔 长沙报道**\n\n**编者按：**2025年度湖南省事业单位记大功奖励名单近日揭晓，湖南省农科院镉低积累水稻研发与推广团队、湖南省生态环境监测中心、湖南省矿产资源调查所3个集体，彭世中（长沙市无线电监测站）、石家文（湘西州永顺县第一中学）、黄楚儒（永州市教育科学研究院）、廖凤初（湖南省地质院）4名个人获此殊荣。即日起，湖南人社推出“立功者风采”系列报道，走近这些获奖单位和个人，记录他们背后的奋斗故事。\n\n水稻天生易富集镉，镉含量超标会对人体健康造成潜在危害。由中国工程院院士、湖南省农科院党委书记柏连阳担任首席专家的湖南省农业科学院镉低积累水稻研发与推广团队，十余年持续攻关，围绕低镉种质发现、低镉材料创制、低镉品种选育和推广应用，蹚出一条保障粮食安全的新路。\n\n**“笨办法”里找突破**\n\n在湖南省农科院副院长李莉的办公桌上，水稻稻穗标本“西子3号”格外显眼——这是全国首个通过国家审定的低镉水稻品种。\n\n杂交水稻育种如同人孕育生命，需要父本和母本结合。恢复系是负责传花粉的父本，不育系是被授粉的母本。\n\n要种出低镉水稻，关键是找到稳定低镉的种质。2013年起，李莉带领团队从杂交稻组合和恢复系中筛选，结果一无所获。\n\n她灵光一现：既然父本找不到，那母本呢？团队小组转向不育系。困难随之而来——不育系在正常情况下不结实，如何获得种子？\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F4400b3aa4326418faf66a793020568a2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786666762&x-signature=UoTrwC88tstEjRgfQ%2FBLGCm2t4E%3D)\n李莉（中）与团队成员查看纯合低镉水稻幼苗。\n\n湖南的七八月，酷暑难耐，水稻恰在正午开花。李莉与队员们顶着烈日，逐株观察花期，手工完成异交授粉。“要从父本中一朵朵采集花粉，一次次涂到母本柱头上帮助结种，只能用这种笨办法。”她笑着解释。\n\n功夫不负有心人。2018年秋，团队发现一份低镉不育系材料。为验证其性状，李莉当即奔赴海南进行试验。四个月后，试验结果证实该材料低镉性状稳定，该材料最终培育出“西子3号”。\n\n**“辟蹊径”攻难题**\n\n在另一条技术路线上，赵炳然带领的团队小组同样艰难攻关。2017年，他们尝试用基因编辑技术敲除水稻的镉吸收主效基因。“就像一把剪刀，精准剪断吸镉开关。”遗憾的是，培养成果因政策限制未能产业化。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Faea7b22550c0f1a67e151d3afb837d41~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786666762&x-signature=X8AD%2F7dctw76FFRgJOs7K%2FKsd7E%3D)\n赵炳然（中）与团队成员探讨实验数据。\n\n是停在功劳簿上，还是另寻出路？团队选择后者——转向重离子诱变，用高能粒子“撞”水稻种子，从成千上万颗里找出“碰巧被撞坏吸镉基因”的幸运儿。\n\n2019年，攻关进入最吃劲的阶段，实验需要大量重离子束流资源。赵炳然带着青年科研人员韶也，向袁隆平院士当面汇报。经袁老亲笔写信协调，当年全国用于植物诱变的重离子束流，几乎全部支持了这支团队。\n\n然而第一批10份材料处理后，只发现1份疑似突变体。国庆假期，韶也昼夜蹲守在实验室，不分白天晚上连轴不间断检测，终于又筛出1份关键材料。这两份材料分别育成首个大面积推广的“臻两优8612”和首个高档优质香型镉低积累水稻“韶香100”。\n\n重离子诱变虽然能“撞”出突变体，可要从成千上万棵突变植株里找出那棵幸运低镉植株，依然是大海捞针。\n\n为了改变这一现状，团队自主研发了“M1TDS”技术，在种质幼苗期就提前锁定目标突变体，大幅提升筛选效率，突破了传统理化诱变长期以来“靠运气、周期长、难定向”的瓶颈。\n\n**“泥腿子”蹚出路**\n\n柏连阳院士跟团队青年科技者说得最多的一句话是：让良种从实验室走进千家万户，是世界上最难走的路，需要“泥腿子”一步步蹚出来。但蹚出来，也是世界上最幸福的一条路。\n\n2025年惊蛰刚过，柏连阳便带队辗转岳阳、长沙、常德等地进行推介，每到一处他都会问：种子采购到没到户？田块准备就没就位？技术培训落没落地？\n\n为了让更多农户种得放心、种得省心，省农科院推出了“三个精准”服务模式：“一品一策”制定栽培手册，“一户一专家”到户到田指导，“一事一议”即时化解生产风险。100余名专家组建14个市州推广指导小组，把技术送到示范田。\n\n数据最有说服力：镉低积累水稻在湖南的推广面积从2023年的107万亩跃至2025年的316万亩，全国累计达688.6万亩——数字背后，是农户碗里更安全的米饭。\n\n“没有路，就蹚出一条来。”湖湘农科人学以致用、敢为人先的精神从未改变。这支脚踩泥巴的队伍，晴天一身汗，雨天两脚泥，把论文写在大地上，把成果装进粮袋里，蹚出一条让中国人端稳饭碗、吃上放心粮的守护之路。","红网 2026-08-05","2026-08-05T00:00:00Z","报道",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},25,22,18,7,1,"报道湖南省农科院镉低积累水稻研发团队突破，涉及粮食安全与农业科技，影响全国，内容详实，信源为省级官媒，时效性稍差。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"湖南省农科院","镉低积累水稻","西子3号","臻两优8612","韶香100","柏连阳",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"partial","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47,"reason":58},[44,52],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"A1",11,true,[49],"水稻",[49],[],{"code":53,"score":46,"supported":47,"title":54,"abstract":56,"tags":57},"A8",[55],"粮食安全",[55],[],"一级领域明确，二级方向需要细分",[60,61],"湖南省农科院 镉低积累水稻 臻两优8612 西子3号","湖南省农科院 镉低积累水稻","湖南省农科院镉低积累水稻臻两优8612西子3号-260",9,"2026-08-07T00:18:30.104649Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,107,221,269,334,392],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":8,"content":73,"source_name":74,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":75,"score_detail":76,"sources":80,"tags":82,"primary_topic":85,"secondary_topics":88,"classification":89,"search_phrases":102,"slug":104,"view_count":105,"doi":8,"paper":8,"created_at":106},286,"湖南省农科院镉低积累水稻研发与推广团队获省事业单位记大功——柏连阳院士领衔,全国累计推广688.6万亩,从「笨办法」到M1TDS技术突破","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20260805A04HNC00","2025年度湖南省事业单位记大功奖励名单近日揭晓,湖南省农科院镉低积累水稻研发与推广团队等3个集体获此殊荣。由中国工程院院士、湖南省农科院党委书记柏连阳担任首席专家的湖南省农科院镉低积累水稻研发与推广团队,十余年持续攻关,围绕低镉种质发现、低镉材料创制、低镉品种选育和推广应用,蹚出一条保障粮食安全的新路。团队从不育系出发筛出「西子3号」——全国首个通过国家审定的低镉水稻品种;赵炳然团队用重离子诱变技术育成首个大面积推广的「臻两优8612」和首个高档优质香型镉低积累水稻「韶香100」;自主研发M1TDS技术,在种质幼苗期就提前锁定目标突变体,大幅提升筛选效率,突破了传统理化诱变「靠运气、周期长、难定向」的瓶颈。湖南省农科院推出「三个精准」服务模式:「一品一策」制定栽培手册,「一户一专家」到户到田指导,「一事一议」即时化解生产风险,100余名专家组建14个市州推广指导小组,镉低积累水稻在湖南的推广面积从2023年的107万亩跃至2025年的316万亩,全国累计达688.6万亩。","编者按：2025年度湖南省事业单位记大功奖励名单近日揭晓，湖南省农科院镉低积累水稻研发与推广团队、湖南省生态环境监测中心、湖南省矿产资源调查所3个集体，彭世中（长沙市无线电监测站）、石家文（湘西州永顺县第一中学）、黄楚儒（永州市教育科学研究院）、廖凤初（湖南省地质院）4名个人获此殊荣。即日起，湖南人社推出“立功者风采”系列报道，走近这些获奖单位和个人，记录他们背后的奋斗故事。\n\n水稻天生易富集镉，镉含量超标会对人体健康造成潜在危害。由中国工程院院士、湖南省农科院党委书记柏连阳担任首席专家的湖南省农业科学院镉低积累水稻研发与推广团队，十余年持续攻关，围绕低镉种质发现、低镉材料创制、低镉品种选育和推广应用，蹚出一条保障粮食安全的新路。","红网\u002F湖南人社 2026-08-05",77,{"impact":17,"substance":77,"depth":78,"authority":13,"freshness":20,"relevant":21,"comment":79},20,15,"省级科研团队获记大功，镉低积累水稻技术突破，全国推广面积大，具有较高产业影响力和信息增量。",[81],{"name":74,"url":71},[26,27,28,29,31,83,84],"M1TDS","省记大功",{"code":45,"slug":86,"name":49,"path":87,"parent_code":33},"rice","\u002Ftopic\u002Ffield-crops\u002Frice",[],{"status":90,"locked":14,"version":40,"method":41,"evidence":91},"classified",{"candidates":92,"agricultural_evidence":47},[93,97],{"code":45,"score":46,"supported":47,"title":94,"abstract":95,"tags":96},[49],[49],[],{"code":53,"score":98,"supported":47,"title":99,"abstract":100,"tags":101},3,[],[55],[],[103,61],"湖南省农科院 镉低积累水稻 省记大功 臻两优8612","湖南省农科院镉低积累水稻省记大功臻两优8612-286",5,"2026-08-07T23:57:50.015704Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":114,"category":115,"cover_url":8,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":129,"secondary_topics":130,"classification":131,"search_phrases":171,"slug":174,"view_count":175,"doi":176,"paper":177,"created_at":220},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":118,"substance":19,"depth":78,"authority":119,"freshness":63,"relevant":21,"comment":120},16,13,"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。",[122],{"name":113,"url":110},[124,125,126,127,128],"有机肥","土壤肥力","养分管理","印度农业","水稻小麦轮作",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":132},{"candidates":133,"agricultural_evidence":47,"reason":58},[134,141,146,153,160,166],{"code":135,"score":78,"supported":47,"title":136,"abstract":138,"tags":140},"H2",[137],"soil",[139,137],"土壤",[125],{"code":45,"score":142,"supported":47,"title":143,"abstract":144,"tags":145},14,[86],[49,86],[],{"code":147,"score":142,"supported":47,"title":148,"abstract":150,"tags":152},"A2",[149],"wheat",[151,149],"小麦",[],{"code":154,"score":155,"supported":47,"title":156,"abstract":157,"tags":159},"F3",8,[],[158,126],"肥料",[126,124],{"code":161,"score":98,"supported":47,"title":162,"abstract":163,"tags":165},"A7",[],[164],"轮作",[],{"code":167,"score":21,"supported":14,"title":168,"abstract":169,"tags":170},"J3",[],[],[127],[172,173],"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":176,"openalex_id":178,"authors":179,"venue":113,"cited_by_count":175,"oa_url":212,"card":213,"direction":217,"ingested_from":219},"W7223144837",[180,182,185,186,189,192,194,195,197,200,202,205,207,209],{"name":181,"orcid":8},"Raghwendra Singh",{"name":183,"orcid":184},"Ajay Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8743-6477",{"name":183,"orcid":184},{"name":187,"orcid":188},"Preeti P. Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7843-9783",{"name":190,"orcid":191},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0002-9773-6641",{"name":193,"orcid":8},"Abhishek Singh",{"name":193,"orcid":8},{"name":196,"orcid":8},"Saurabh Raj Pandey",{"name":198,"orcid":199},"Vinod Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0004-2583-6565",{"name":201,"orcid":8},"Narendra Raghubanshi",{"name":203,"orcid":204},"Yash Pal Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8345-9084",{"name":206,"orcid":8},"Mahesh Kumar",{"name":208,"orcid":8},"Shivangi Singh",{"name":210,"orcid":211},"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":214,"method":215,"finding":216,"direction":217,"opportunity":218},"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":222,"title":223,"url":224,"summary":225,"summary_zh":8,"content":226,"source_name":227,"source_url":8,"published_at":228,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"primary_topic":240,"secondary_topics":241,"classification":242,"search_phrases":264,"slug":267,"view_count":175,"doi":8,"paper":8,"created_at":268},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":231,"substance":155,"depth":66,"authority":66,"freshness":63,"relevant":21,"comment":232},12,"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。",[234],{"name":227,"url":224},[49,236,237,238,239],"院地合作","秸秆利用","粮食单产提升","天长市",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":243},{"candidates":244,"agricultural_evidence":47,"reason":58},[245,249,254,259],{"code":45,"score":231,"supported":47,"title":246,"abstract":247,"tags":248},[49],[49],[49],{"code":53,"score":231,"supported":47,"title":250,"abstract":252,"tags":253},[251],"单产提升",[251],[238],{"code":255,"score":21,"supported":14,"title":256,"abstract":257,"tags":258},"G1",[],[],[236],{"code":260,"score":21,"supported":14,"title":261,"abstract":262,"tags":263},"H5",[],[],[237],[265,266],"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":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":8,"source_name":275,"source_url":272,"published_at":276,"category":115,"cover_url":8,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":315,"slug":318,"view_count":175,"doi":319,"paper":320,"created_at":333},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":231,"substance":279,"depth":19,"authority":66,"freshness":155,"relevant":21,"comment":280},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。",[282],{"name":275,"url":272},[284,285,286,287,288],"智慧农业","遥感","作物监测","产量估测","叶面积指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":292},{"candidates":293,"agricultural_evidence":47,"reason":58},[294,298,305,310],{"code":147,"score":66,"supported":47,"title":295,"abstract":296,"tags":297},[],[151,149],[],{"code":299,"score":66,"supported":47,"title":300,"abstract":301,"tags":304},"A3",[],[302,303],"玉米","corn",[],{"code":306,"score":98,"supported":14,"title":307,"abstract":308,"tags":309},"I3",[],[],[287,286,288,285],{"code":311,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},"I1",[],[],[284],[316,317],"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":319,"openalex_id":321,"authors":322,"venue":275,"cited_by_count":175,"oa_url":272,"card":326,"direction":332,"ingested_from":219},"W7222967882",[323],{"name":324,"orcid":325},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"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":335,"title":336,"url":337,"summary":338,"summary_zh":8,"content":339,"source_name":340,"source_url":8,"published_at":341,"category":12,"cover_url":8,"hotness":13,"is_selected":47,"score":342,"score_detail":343,"sources":346,"tags":348,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":387,"slug":390,"view_count":175,"doi":8,"paper":8,"created_at":391},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":344,"substance":18,"depth":19,"authority":78,"freshness":66,"relevant":21,"comment":345},24,"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。",[347],{"name":340,"url":337},[349,350,351,352,353],"农业人工智能","种业振兴","智能育种","知识图谱","科研平台",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":357},{"candidates":358,"agricultural_evidence":47,"reason":58},[359,363,367,371,377,382],{"code":45,"score":98,"supported":47,"title":360,"abstract":361,"tags":362},[],[49],[],{"code":147,"score":98,"supported":47,"title":364,"abstract":365,"tags":366},[],[151],[],{"code":299,"score":98,"supported":47,"title":368,"abstract":369,"tags":370},[],[302],[],{"code":372,"score":373,"supported":14,"title":374,"abstract":375,"tags":376},"I2",2,[],[],[349,352],{"code":378,"score":21,"supported":14,"title":379,"abstract":380,"tags":381},"F1",[],[],[350],{"code":383,"score":21,"supported":14,"title":384,"abstract":385,"tags":386},"F2",[],[],[351],[388,389],"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":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":8,"source_name":398,"source_url":395,"published_at":399,"category":115,"cover_url":8,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":405,"primary_topic":409,"secondary_topics":410,"classification":411,"search_phrases":459,"slug":462,"view_count":175,"doi":463,"paper":464,"created_at":520},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":19,"substance":18,"depth":19,"authority":142,"freshness":63,"relevant":21,"comment":402},"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。",[404],{"name":398,"url":395},[284,349,406,407,408],"图像分割","表型组学","多光谱成像",{"code":33,"slug":34,"name":35,"path":36,"parent_code":8},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":412},{"candidates":413,"agricultural_evidence":47,"reason":58},[414,418,423,430,437,444,449,455],{"code":45,"score":66,"supported":47,"title":415,"abstract":416,"tags":417},[],[49,86],[],{"code":299,"score":66,"supported":47,"title":419,"abstract":420,"tags":422},[],[302,421],"maize",[],{"code":424,"score":66,"supported":47,"title":425,"abstract":426,"tags":429},"A5",[],[427,428],"高粱","sorghum",[],{"code":431,"score":66,"supported":47,"title":432,"abstract":433,"tags":436},"A6",[],[434,435],"棉花","cotton",[],{"code":438,"score":66,"supported":47,"title":439,"abstract":440,"tags":443},"B7",[],[441,442],"温室","greenhouse",[],{"code":372,"score":105,"supported":47,"title":445,"abstract":446,"tags":448},[],[447],"artificial intelligence",[349,406],{"code":306,"score":450,"supported":47,"title":451,"abstract":452,"tags":454},4,[],[453],"multispectral",[408],{"code":311,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[284],[460,461],"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":463,"openalex_id":465,"authors":466,"venue":398,"cited_by_count":175,"oa_url":395,"card":515,"direction":330,"ingested_from":219},"W7222893982",[467,469,472,475,478,480,482,485,488,491,494,497,500,503,506,509,511,513],{"name":468,"orcid":8},"Fahimeh Orvati Nia",{"name":470,"orcid":471},"Joshua Peeples","https:\u002F\u002Forcid.org\u002F0000-0001-7861-7789",{"name":473,"orcid":474},"Seth C. Murray","https:\u002F\u002Forcid.org\u002F0000-0002-2960-8226",{"name":476,"orcid":477},"Andrew J. McFarland","https:\u002F\u002Forcid.org\u002F0009-0000-3516-8274",{"name":479,"orcid":8},"Troy Vann",{"name":481,"orcid":8},"Shima Salehi",{"name":483,"orcid":484},"Robert G. Hardin","https:\u002F\u002Forcid.org\u002F0000-0002-0714-8281",{"name":486,"orcid":487},"David D. Baltensperger","https:\u002F\u002Forcid.org\u002F0000-0003-4511-5680",{"name":489,"orcid":490},"Amir M. H. Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0002-0584-753X",{"name":492,"orcid":493},"J. Alex Thomasson","https:\u002F\u002Forcid.org\u002F0000-0002-5557-105X",{"name":495,"orcid":496},"Henry Y. Fadamiro","https:\u002F\u002Forcid.org\u002F0000-0002-4530-9680",{"name":498,"orcid":499},"Nithya K. Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-1659-7396",{"name":501,"orcid":502},"Suresh D. Pillai","https:\u002F\u002Forcid.org\u002F0000-0002-9766-8765",{"name":504,"orcid":505},"Rebecca Roston","https:\u002F\u002Forcid.org\u002F0000-0002-3063-5002",{"name":507,"orcid":508},"Joslin Ishimwe","https:\u002F\u002Forcid.org\u002F0009-0003-6999-7718",{"name":510,"orcid":8},"Diptadeep Basak",{"name":512,"orcid":8},"Nazar Oladepo",{"name":514,"orcid":8},"Uday Santhosh Raju Vysyaraju",{"tldr":516,"method":517,"finding":518,"direction":330,"opportunity":519},"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"]