[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-85":3,"related-85":56},{"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":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":50,"slug":53,"view_count":54,"doi":8,"paper":8,"created_at":55},85,"国新办：2026年上半年农业农村经济稳中向好 农业农村部多措并举","https:\u002F\u002Fjhs.moa.gov.cn\u002Fjjyx\u002F202607\u002Ft20260724_6486169.htm","7月24日,国新办举行新闻发布会,介绍2026年上半年农业农村经济运行情况。农业农村部副部长张兴旺介绍,上半年我国农业受灾情况面上偏轻、局部偏重。农业农村部全力下拨2.19亿元农业生产救灾资金,发布预警信息24期。预计早稻面积保持稳定,小麦丰收,为夺取全年粮食丰收奠定基础。",null,"我接下来再介绍一下农业防灾减灾的情况。今年上半年，我国农业受灾情况是，面上偏轻，局部偏重。从灾种看，涝重于旱，7月份登陆的两个台风，造成局部地区洪涝、农田渍涝、畜禽死亡、设施受损。据气象部门预测，今年主汛期我国极端气候事件偏多，南北有两个多雨带，旱涝并重，局部有可能旱涝急转，登陆台风偏多偏强，也可能北上。同时，强或超强厄尔尼诺事件正在形成，一些地区可能更热、更涝、更旱，灾害风险的不确定性大，农业防灾减灾形势不容乐观。党中央、国务院高度重视农业防灾减灾救灾工作，习近平总书记多次作出重要指示批示。农业农村部坚决贯彻落实党中央、国务院决策部署，提前印发汛期农业防灾减灾相关预案，发布预警信息24期，推动及时下拨2.19亿元的农业生产救灾资金，组织专家制定防灾减灾技术意见，调剂调运消毒药品等防疫防汛物资，派出多个工作组到重灾区一线指导救灾、因灾死亡畜禽无害化处理和动物防疫，支持地方恢复农业生产。\n\n现在正处在“七下八上”防汛关键期，也是秋粮产量形成的关键期，农业农村部将树牢底线思维、极限思维，立足防大汛、抗大旱、防旱涝急转、防强台风，盯紧盯牢暴雨洪涝、夏伏旱、高温热害、低温早霜等灾害，以主动防灾减灾救灾的确定性应对灾害发生的不确定性。在监测上再加密，持续强化与气象、应急管理、水利等部门联合会商，科学分析研判雨情、水情、苗情，及时发布预警信息，做到早发现、早预警。在防范上再加强，建立健全区域间互助协作机制，备足备齐种子种苗、化肥农药、消杀防疫等物资，动员区域农机应急救灾中心和应急救援服务队做好救灾准备，确保有物资备灾、有机具救灾、有人员抗灾。在指导上更精准，指导落实农田排涝、作物恢复、改种补种、设施修复等关键措施，适时开展统防统治、应急防治，尽可能减轻灾害损失。同时，我们将面向“十五五”及更长时期，加快补齐农业防灾减灾救灾的短板弱项，提升农业气候韧性，硬件上，夯实基础设施。持续建设高标准农田，统筹开展农田沟渠修复整治，储备好履带式收割机、移动式烘干机等应急救灾机具，切实提高农业防灾减灾救灾能力。在软件上，健全工作机制。优化农业防灾减灾救灾工作体系，加快构建“天空地”一体化农情灾情监测网络。指导地方因地制宜细化完善预案，完善农业应急救灾服务队伍，常态化抓实组织动员、监测预警、响应联动、应急处置等工作。当然还有非常重要的一条，就是在这些年的工作实践中，我们充分认识到，必须对发生过的灾害进行复盘分析，进一步加强农业防灾减灾救灾理论研究，把握规律性、趋势性，提高防范应对的精准性、有效性。","农业农村部 2026-07-24","2026-07-24T01:00:00Z","报道",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},28,22,16,14,2,1,"农业农村部发布上半年农业防灾减灾情况，含具体数据与措施，但时效性已过一周。",[25],{"name":10,"url":6},[27,28,29,30],"农业农村部","国新办","上半年经济","防汛",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},3,true,[],[34],[],[51,52],"上半年经济 农业农村部 国新办 防汛","上半年经济 农业农村部","上半年经济农业农村部国新办防汛-85",8,"2026-07-31T00:00:53.477155Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,164,225,272,315,355],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":67,"cover_url":8,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":74,"tags":76,"primary_topic":82,"secondary_topics":83,"classification":96,"search_phrases":137,"slug":140,"view_count":141,"doi":142,"paper":143,"created_at":163},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","2026-10-21T00:00:00Z","论文",77,{"impact":19,"substance":70,"depth":71,"authority":72,"freshness":13,"relevant":22,"comment":73},21,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[75],{"name":65,"url":62},[77,78,79,80,81],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[84,90],{"code":85,"slug":86,"name":87,"path":88,"parent_code":89},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,105,111,116,124,131],{"code":32,"score":100,"supported":46,"title":101,"abstract":103,"tags":104},26,[102,33],"winter wheat",[34,102,33],[81],{"code":85,"score":106,"supported":46,"title":107,"abstract":108,"tags":110},15,[86],[109,86],"土壤",[80],{"code":91,"score":106,"supported":46,"title":112,"abstract":114,"tags":115},[113],"machine learning",[78,113],[78],{"code":117,"score":13,"supported":46,"title":118,"abstract":119,"tags":123},"I3",[],[120,121,122],"遥感","remote sensing","multispectral",[79],{"code":125,"score":57,"supported":46,"title":126,"abstract":127,"tags":130},"H3",[],[128,129],"灌溉","irrigation",[],{"code":132,"score":133,"supported":46,"title":134,"abstract":135,"tags":136},"I1",4,[],[77],[77],[138,139],"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",0,"10.3390\u002Fagronomy16202071",{"doi":142,"openalex_id":144,"authors":145,"venue":65,"cited_by_count":141,"oa_url":62,"card":155,"direction":161,"ingested_from":162},"W7223156204",[146,149,151,153],{"name":147,"orcid":148},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":150,"orcid":8},"Yi Zhang",{"name":152,"orcid":8},"WEI Lei",{"name":154,"orcid":8},"Chengcai Zhang",{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"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。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.887301Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":8,"content":8,"source_name":169,"source_url":8,"published_at":66,"category":67,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":170,"sources":173,"tags":175,"primary_topic":180,"secondary_topics":181,"classification":191,"search_phrases":212,"slug":215,"view_count":22,"doi":8,"paper":216,"created_at":224},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",{"impact":171,"substance":18,"depth":171,"authority":20,"freshness":13,"relevant":22,"comment":172},18,"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[174],{"name":169,"url":167},[77,176,177,178,179],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[182,186],{"code":117,"slug":183,"name":184,"path":185,"parent_code":95},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":187,"slug":188,"name":189,"path":190,"parent_code":89},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":192},{"candidates":193,"agricultural_evidence":46},[194,199,203,208],{"code":32,"score":195,"supported":46,"title":196,"abstract":197,"tags":198},11,[34],[34],[],{"code":117,"score":195,"supported":46,"title":200,"abstract":201,"tags":202},[178],[],[177,176,178],{"code":187,"score":54,"supported":46,"title":204,"abstract":206,"tags":207},[205],"渍害",[],[],{"code":132,"score":22,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[77],[213,214],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":8,"openalex_id":8,"authors":217,"venue":8,"cited_by_count":141,"oa_url":8,"card":218,"direction":159,"ingested_from":223},[],{"tldr":219,"method":220,"finding":221,"direction":159,"opportunity":222},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":8,"content":230,"source_name":231,"source_url":8,"published_at":232,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":4,"score_detail":233,"sources":236,"tags":238,"primary_topic":243,"secondary_topics":244,"classification":245,"search_phrases":267,"slug":270,"view_count":141,"doi":8,"paper":8,"created_at":271},5502,"全国秋粮收获超七成 中晚稻已收购1800万吨","https:\u002F\u002Fwww.peopleweekly.cn\u002Fhtml\u002F2026\u002Fsannong_1019\u002F279469.html","农业农村部最新农情调度显示，全国秋粮收获过七成，其中东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。冬小麦播种过一成，冬油菜播种过三成。国家粮食和物资储备局数据显示，各类经营主体已收购中晚稻1800万吨；秋粮产量占全年粮食产量约四分之三。","本报北京10月18日电  （记者郁静娴）据农业农村部最新农情调度，全国秋粮收获过七成。其中，东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。同时，各地秋冬种有序推进，冬小麦播种过一成，冬油菜播种过三成。\n\n本报北京10月18日电  （记者王东辉）记者从国家粮食和物资储备局获悉：各地秋粮旺季收购正有序展开。初步统计，各类经营主体已收购中晚稻1800万吨。\n\n当前秋粮已开始收获上市，秋粮产量占全年粮食产量的3\u002F4左右，涉及品种多、数量规模大、覆盖区域广。相关部门将加强秋粮收购工作统筹组织，抓好市场化收购和政策性收储，落实落细各项政策举措，全力保障农民售粮渠道顺畅、市场平稳运行。","人民日报 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日签约揭牌，作物所聘请科技成果转化推广专员刘洪彬担任技术指导员。基地以展示济麦系列优质高产及特色营养小麦新品种为核心，同步开展新品种新技术推广与人才培训，并从良种繁育前端与特色营养小麦加工后端两端拉长产业链。","10月20日，在位于阳信县农村技术协会的小麦播种田里，大型播种机在100多亩小麦品种展示基地回穿梭，不时传出技术人员下达的技术指令声，“播种机进地播种时一定检查好播种定量指针”“底肥和种子同期播种必须保持好安全距离”等技术措施不时从现场技术指导老师刘洪彬的嘴里发出。\n\n原来这是山东省农科院作物所山东省农业科学院作物研究所阳信小麦试验示范基地2026年的小麦品种播种现场，阳信县农技协会会长王保友在忙着记录数据、搬运种子，确保严格程序，完成高质量播种工作。\n\n今年9月15日，山东省农业科学院作物研究所阳信县小麦试验示范基地在阳信举行签约揭牌仪式。在随后召开的座谈会上，省农科院作物研究所小麦专家围绕济麦系列小麦新品种的特征特性、栽培要点等进行详细介绍。\n\n近年来，阳信县持续深化产学研合作，积极引进推广小麦新品种、新技术，粮食综合生产能力稳步提升。此次小麦试验示范基地的共建，将进一步推动济麦系列优良品种在该县规模化应用，打造农业科技创新与示范推广的县域样板，为全县小麦等粮食作物单产提升注入科技动能。\n\n基地由山东省农业科学院作物研究所与阳信农村专业技术协会共同打造建设，聘请山东省农科院作物所科技成果转化推广专员刘洪彬为小麦高质量栽培技术指导员。坚持“技术成果带动、试验示范结合、责任权利清晰、合作互利共赢”的原则，围绕山东省农业科学院作物研究所育成的小麦新品种（系）进行试验示范，开展新品种、新技术推广及人才培训，打造科技引领型小麦产业提质增效的阳信样板。旨在建设小麦新品种（系）展示平台，展示济麦系列优质高产及特色营养小麦新品种（系）。建立优质原粮生产基地，促进优质特色营养小麦产业化开发。以基地为核心，带动周边发展高产优质特色营养小麦生产，为当地面粉加工企业提供优质原料.延长小麦产业链，打造小麦产业提质增效的样板。\n\n在新品种展示示范的基础上，从两端拉长小麦产业链，前端引入种业企业，进一步发展小麦良种繁育，打造良种繁育基地；后端开发特色营养小麦加工，形成良种繁育—示范推广—产品加工完整产业链，不断提升小麦产业附加值。","山东省农业科学院 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分钟，适合制作馒头。","10月19日，济麦1806生产经营权独占实施许可签约仪式在济南举办。作物所理事长兼党委书记赵海军、山东东润种业有限公司总经理刘延福，小麦遗传育种团队、成果转化科以及东润种业相关人员参加活动。\n\n小麦遗传育种团队执行专家李豪圣介绍了济麦1806品种特征特性以及栽培技术要点。双方针对济麦1806的推广进行座谈交流，下一步将以该品种的转让为合作启点，加快新品种推广步伐，同时在项目申报、人才培养、成果培育上开展深入合作。\n\n济麦1806是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026年通过国家审定。两年区试全生育期234.2天，株高68.3厘米，倒伏达标点率100%；亩穗数35.8万穗，穗粒数35.7粒，千粒重40.7克，亩产435公斤，较对照增产4.6%；生产试验平均亩产346.6公斤，增产8.96%。品质较稳定，适合制作馒头。多年品质分析结果表明，容重792 g\u002FL-814 g\u002FL，蛋白质（干基）12.3%-13.4%，湿面筋（14%湿基）26.5% -29.6%，吸水率60.0%-62.0%，稳定时间5.3-6.4 min。（撰写：郭军 核稿：李峰）","山东省农业科学院 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