[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3023":3,"related-3023":36},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":8,"created_at":35},3023,"农业农村部和中国气象局发布秋收连阴雨灾害风险预警：9-21至28日黄淮西部及江汉等地风险较高","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260920A0B4BZ00","农业农村部和中国气象局2026年9月20日联合发布秋收连阴雨灾害风险预警：预计9月21日至28日，西北地区东南部、西南地区东部、江汉、黄淮西部及安徽北部等地降雨天气频繁，秋收连阴雨灾害风险较高；其中23~27日四川东北部、重庆中北部、陕西东南部、河南南部、湖北西北部、安徽西北部等地持续中到大雨，部分地区有暴雨、局地大暴雨，秋收连阴雨灾害风险高。建议上述地区抓住降水间隙及时抢收成熟作物，并注意通风储存、烘干入仓；降水量较大地区雨后及时开沟散墒、排湿降渍。",null,"中国气象局·农业农村部","2026-09-20T10:00:00Z","政策",10,true,91,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},26,22,18,15,1,"两部委联合发布的全国性秋收灾害风险预警，时效性强、区域与时段数据具体，对秋粮抢收有直接指导价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"农业防灾减灾","气象服务","灾害预警","秋收连阴雨","秋粮抢收",[31,32],"农业农村部 中国气象局 秋收连阴雨","黄淮 江汉 秋收连阴雨 预警","农业农村部中国气象局秋收连阴雨-3023",0,"2026-09-21T00:04:31.662997Z",{"total":37,"page":20,"page_size":37,"items":38},6,[39,94,133,157,182,208],{"id":40,"title":41,"url":42,"summary":43,"summary_zh":44,"content":8,"source_name":45,"source_url":42,"published_at":46,"category":47,"cover_url":8,"hotness":12,"is_selected":48,"score":34,"score_detail":49,"sources":51,"tags":53,"search_phrases":57,"slug":60,"view_count":34,"doi":61,"paper":62,"created_at":93},2788,"Integrated satellite monitoring and field validation of the periodically outbursting glacier-dammed lake Nedre Demmevatnet, Norway","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-5025","Abstract. Glacial Lake Outburst Floods (GLOFs) present a significant hazard in warming alpine environments, but detailed, sub-seasonal studies of ice-dammed glacier lakes remain rare due to data scarcity. To overcome this challenge, we reconstruct nearly a decade (2016–2025) of drainage timings, outburst volumes, lake levels, and automated, machine learning-based sub-seasonal lake refilling cycles at the ice-dammed lake Nedre Demmevatnet (southwestern Norway), dammed by the glacier Rembesdalskåka, an outlet glacier of the Hardangerjøkulen ice cap. By integrating publicly available satellite and meteorological datasets, we evaluate remote sensing capabilities and establish an error budget for tracking a small and highly dynamic water body. We validate our spaceborne findings using field measurements, including water-level loggers, time-lapse cameras, a local automatic weather station, and high-resolution UAV photogrammetry, complemented by PlanetScope imagery. Combining Sentinel-1 and Sentinel-2 imagery, we constrain GLOF drainage windows to ± 2 days. Lake volume sensitivity tests revealed that while satellite outline errors are minor (2.6–4.2 %), using the static regional digital elevation model ArcticDEM (2014) causes a 25.5 % volume underestimation compared to our 2022 UAV bathymetry due to rapid ice-dam retreat and lakebed erosion. Crucially, the time gap between the last cloud-free satellite image and the GLOF introduces a relative daily volume underestimation of 1.7 %, which scales up significantly during cloudy periods. Our validated multi-sensor remote sensing approach enables valuable glaciological insights, revealing that GLOF timings shifted earlier by an average of 10 days, alongside shortened refilling periods over the past 9 years. Between 2016 and 2022, pre-GLOF lake levels reached 1236–1239 m a.s.l., closely matching the theoretical hydrostatic flotation threshold. In contrast, the 2023 event drained at approximately 1224 m a.s.l. – more than 10 m below this threshold – following a shortened melt period indicated by fewer positive degree days, possibly due to opening of subglacial channels through melt. Finally, we synthesize the workflow developed and tested on our case study into an operational framework that facilitates transferability to other rapidly changing ice-dammed lakes.","摘要：冰川湖溃决洪水（GLOFs）在变暖的高山环境中构成重大危害，但由于数据稀缺，针对冰坝冰川湖的详细次季节研究仍然很少。为克服这一挑战，我们重建了冰坝湖Nedre Demmevatnet（挪威西南部）近十年（2016—2025年）的排水时间、溃决水量、湖泊水位以及基于机器学习的自动化次季节湖泊再蓄水周期。该湖由Hardangerjøkulen冰帽的溢出冰川Rembesdalskåka所阻塞。通过整合公开可用的卫星和气象数据集，我们评估了遥感能力，并建立了追踪一个小型且高度动态水体的误差预算。我们利用实地测量验证了星载观测结果，包括水位记录仪、延时相机、当地自动气象站和高分辨率无人机摄影测量，并辅以PlanetScope影像。结合Sentinel-1和Sentinel-2影像，我们将GLOF排水窗口限定在±2天以内。湖泊体积敏感性测试表明，虽然卫星轮廓误差较小（2.6—4.2%），但由于冰坝快速退缩和湖床侵蚀，使用静态区域数字高程模型ArcticDEM（2014年）会导致体积较我们的2022年无人机测深结果低估25.5%。至关重要的是，最后一幅无云卫星影像与GLOF之间的时间间隔会导致相对日体积低估1.7%，在多云时段这一误差会显著放大。我们经过验证的多传感器遥感方法带来了有价值的冰川学认识，揭示了过去9年中GLOF发生时间平均提前了10天，同时再蓄水期缩短。2016年至2022年间，GLOF前湖泊水位达到海拔1236—1239 m，与理论静水浮力阈值密切吻合。相比之下，2023年事件在海拔约1224 m处排水——低于该阈值超过10 m——此前融化期缩短，表现为正积温日数减少，可能是由于融化导致冰下通道打开。最后，我们将本案例研究中开发和测试的工作流程综合为一个操作框架，以促进其向其他快速变化的冰坝湖推广应用。","OpenAlex","2026-09-16T00:00:00Z","论文",false,{"impact":34,"substance":34,"depth":34,"authority":34,"freshness":34,"relevant":34,"comment":50},"冰川湖溃决遥感监测研究，属冰川水文与灾害领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[52],{"name":45,"url":42},[54,55,27,56],"遥感监测","卫星遥感","冰川湖",[58,59],"卫星遥感 灾害预警 遥感监测 冰川湖","卫星遥感 灾害预警","卫星遥感灾害预警遥感监测冰川湖-2788","10.5194\u002Fegusphere-2026-5025",{"doi":61,"openalex_id":63,"authors":64,"venue":8,"cited_by_count":34,"oa_url":85,"card":86,"direction":90,"ingested_from":92},"W7213228080",[65,67,70,73,76,79,82],{"name":66,"orcid":8},"Ronja Lappe",{"name":68,"orcid":69},"Ursula Enzenhofer","https:\u002F\u002Forcid.org\u002F0009-0005-4802-1243",{"name":71,"orcid":72},"Pascal E. Egli","https:\u002F\u002Forcid.org\u002F0000-0003-2549-8362",{"name":74,"orcid":75},"Yongmei Gong","https:\u002F\u002Forcid.org\u002F0000-0002-3839-5824",{"name":77,"orcid":78},"Liss M. Andreassen","https:\u002F\u002Forcid.org\u002F0000-0001-6494-4252",{"name":80,"orcid":81},"Andreas Kääb","https:\u002F\u002Forcid.org\u002F0000-0002-6017-6564",{"name":83,"orcid":84},"Martina Calovi","https:\u002F\u002Forcid.org\u002F0000-0002-2317-1190","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-5025\u002Fegusphere-2026-5025.pdf",{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"结合卫星与实地观测重建挪威冰坝湖近十年溃决周期与水量，并建立可迁移监测框架。","Sentinel-1\u002F2、PlanetScope、UAV摄影测量、水位记录仪与气","溃决时间提前约10天，2023年溃决水位低于浮力阈值10米以上。","农业遥感与作物表型","可将该多源遥感与误差预算框架迁移至其他冰坝湖，并探索云覆盖下水量估算的改进方法。","openalex","2026-09-17T23:30:34.612397Z",{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":8,"source_name":100,"source_url":97,"published_at":101,"category":47,"cover_url":8,"hotness":102,"is_selected":48,"score":103,"score_detail":104,"sources":107,"tags":111,"search_phrases":116,"slug":119,"view_count":34,"doi":120,"paper":121,"created_at":132},2297,"Uses of Artificial Intelligence In Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22720772","Abstract Artificial Intelligence (AI) is becoming an important technology in the field of Geography. It helps geographers collect, process, analyze, and interpret large amounts of geographical data quickly and accurately. AI is widely used with Geographic Information Systems (GIS), Remote Sensing, satellite images, GPS, and spatial databases. It can help in land-use and land-cover mapping, urban planning, population analysis, disaster management, climate studies, agriculture, and environmental monitoring. AI-based techniques such as machine learning and deep learning can identify patterns and changes in geographical areas that may be difficult to detect through traditional methods. AI also helps in predicting natural hazards such as floods, droughts, landslides, and forest fires. In urban areas, it supports transportation planning, infrastructure development, and smart-city management. In agriculture, AI can assist in crop monitoring, soil analysis, and yield prediction. However, the effective use of AI requires reliable data, skilled users, proper technology, and attention to data privacy and accuracy. Thus, Artificial Intelligence has great potential to improve geographical research, spatial analysis, planning, and sustainable development.","摘要 人工智能（AI）正成为地理学领域的一项重要技术。它帮助地理学者快速、准确地采集、处理、分析和解释大量地理数据。人工智能与地理信息系统（GIS）、遥感、卫星影像、全球定位系统（GPS）及空间数据库广泛结合使用。它可辅助土地利用与土地覆盖制图、城市规划、人口分析、灾害管理、气候研究、农业及环境监测。基于人工智能的技术，如机器学习和深度学习，能够识别地理区域中传统方法难以探测的模式与变化。人工智能还有助于预测洪水、干旱、滑坡和森林火灾等自然灾害。在城市地区，它支持交通规划、基础设施建设及智慧城市管理。在农业领域，人工智能可协助作物监测、土壤分析及产量预测。然而，人工智能的有效应用需要可靠的数据、熟练的使用者、适当的技术，并需关注数据隐私与准确性。因此，人工智能在改进地理研究、空间分析、规划及可持续发展方面具有巨大潜力。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,46,{"impact":105,"substance":12,"depth":105,"authority":105,"freshness":34,"relevant":20,"comment":106},12,"综述性论文，泛谈AI在地理与农业中的应用，缺乏新数据与新方法，且发布日期在未来，时效性不足，不宜进入每日精选。",[108,109],{"name":100,"url":97},{"name":100,"url":110},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22720771",[112,113,114,115,27],"智慧农业","农业人工智能","遥感","地理信息",[117,118],"农业人工智能 地理信息 智慧农业 灾害预警","农业人工智能 地理信息","农业人工智能地理信息智慧农业灾害预警-2297","10.5281\u002Fzenodo.22720772",{"doi":120,"openalex_id":122,"authors":123,"venue":100,"cited_by_count":34,"oa_url":97,"card":126,"direction":131,"ingested_from":92},"W7212350488",[124],{"name":125,"orcid":8},"Omprakash Wamanrao Jadhav",{"tldr":127,"method":128,"finding":129,"direction":90,"opportunity":130},"综述人工智能在地理学中的应用，涵盖GIS、遥感、城市规划、农业与环境监测等。","综述性分析，涉及机器学习、深度学习与GIS、遥感、GPS等地理数据技术。","AI能高效识别地理模式与变化，预测自然灾害，并助力农业监测与智慧城市管理。","可聚焦AI在农业遥感中的可解释性与小样本迁移学习，提升作物监测精度与泛化能力。","智慧农业 \u002F 农业物联网","2026-09-13T23:30:09.354631Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":8,"content":8,"source_name":138,"source_url":8,"published_at":139,"category":140,"cover_url":8,"hotness":12,"is_selected":48,"score":141,"score_detail":142,"sources":147,"tags":149,"search_phrases":152,"slug":155,"view_count":34,"doi":8,"paper":8,"created_at":156},1552,"数智赋能黔山田：贵州山地气象大模型、AI 基地经理、\"1 小时烘干圈\"点亮 2026 数博会","https:\u002F\u002Fnews.china.com.cn\u002F2026-09\u002F02\u002Fcontent_118676280.shtml","2026 中国国际大数据产业博览会（数博会）以\"词元—数据要素价值释放新路径\"为主题在贵阳开幕。专为贵州山地量身定制的\"山地气象大模型\"走进贵州大棚、茶山和果园；安顺经开区中药材基地通过平板电脑查看土壤 pH 值和空气湿度，AI\"基地经理\"结合几十年种植数据集与实时传感器数据自动弹出农事指令；湄潭数字茶园通过 AI 模型提前预判虫害风险；岑巩县 5 万余亩水稻制种构建\"1 小时烘干圈\"，每亩烘干、转运、错峰销售协同发力。","中国网","2026-09-02T00:00:00Z","报道",70,{"impact":143,"substance":18,"depth":144,"authority":12,"freshness":145,"relevant":20,"comment":146},20,14,8,"报道数博会上的农业AI应用，涵盖气象、种植管理、烘干等环节，具行业代表性，但信源为央媒，深度一般。",[148],{"name":138,"url":136},[112,113,150,151,26],"山地农业","数博会",[153,154],"农业人工智能 山地农业 智慧农业 气象服务","农业人工智能 山地农业","农业人工智能山地农业智慧农业气象服务-1552","2026-09-04T00:05:30.346254Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":8,"content":8,"source_name":162,"source_url":8,"published_at":163,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":164,"score_detail":165,"sources":169,"tags":171,"search_phrases":177,"slug":180,"view_count":144,"doi":8,"paper":8,"created_at":181},322,"三部门下达农业防灾减灾和水利救灾资金21.54亿元 支持多省灾后恢复与秋粮防灾","https:\u002F\u002Fsociety.people.com.cn\u002Fn1\u002F2026\u002F0804\u002Fc1008-40773255.html","人民日报8月4日报道,财政部会同农业农村部、水利部近日下达农业防灾减灾和水利救灾资金21.54亿元:支持广西、湖北、湖南、广东、重庆、贵州6省份开展水利工程设施水毁修复等洪涝、地震灾后救灾;支持辽宁、吉林、浙江、广西、湖北5省开展在田作物肥水管理、绝收地块改种补种、损毁设施修复;支持山西、新疆、甘肃3省区做好洪涝、风雹灾后救灾;同时支持河北、内蒙古、吉林、黑龙江、江苏、安徽、江西、山东、河南、湖北、湖南、新疆、四川13省及新疆兵团、北大荒农垦集团实施\"一喷多促\"等秋粮防灾举措。","人民日报 2026-08-04 04版","2026-08-04T00:00:00Z",88,{"impact":166,"substance":17,"depth":18,"authority":19,"freshness":167,"relevant":20,"comment":168},28,5,"全国性救灾资金政策，涉及多省秋粮防灾，信息具体，央媒权威，但时效略过一周。",[170],{"name":162,"url":160},[25,172,173,174,175,176],"人民日报","水利救灾","秋粮防灾","一喷多促","21.54亿元",[178,179],"农业防灾减灾 一喷多促 人民日报 水利救灾","农业防灾减灾 一喷多促","农业防灾减灾一喷多促人民日报水利救灾-322","2026-08-09T23:54:47.120479Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":8,"content":187,"source_name":188,"source_url":8,"published_at":189,"category":11,"cover_url":8,"hotness":102,"is_selected":13,"score":190,"score_detail":191,"sources":194,"tags":199,"search_phrases":203,"slug":206,"view_count":12,"doi":8,"paper":8,"created_at":207},316,"农业农村部部署台风\"白海豚\"防范应对工作","https:\u002F\u002Fwww.moa.gov.cn\u002Fxw\u002Fzwdt\u002F202608\u002Ft20260808_6486529.htm","农业农村部针对2026年第13号台风\"白海豚\"发布防范应对工作部署,要求各地农业农村部门紧盯台风移动路径与风雨影响,加密会商研判,提前组织危险区域人员转移,抓紧抢收早稻、再生稻与当季果蔬,做好晚稻病虫害防治和设施大棚加固,全面落实渔业、海上养殖、畜牧业、农村沼气、农家乐等安全防范措施,做好灾后恢复生产与动植物疫病防控准备,确保人民生命财产安全和农业生产稳定。","本网讯　为贯彻落实习近平总书记关于防汛救灾重要指示精神和党中央、国务院决策部署，农业农村部日前就做好台风“白海豚”防范应对工作作出部署，要求各地强化责任落实、监测预警、防范应对、灾后恢复和指导服务，努力减轻灾害影响，保障人民群众生命财产安全。同时，会同中国气象局滚动会商台风趋势及对农业生产影响，联合发布农田渍涝和风灾预警信息；根据《农业重大自然灾害应急预案》规定，对上海、江苏、浙江、安徽、福建、江西、山东7省（市）启动农业防汛防台应急响应，指导提前储备种子720万公斤，农药、消毒药1000多吨，农用水泵、烘干机等救灾机具2.5万台，抢收成熟作物、起捞水产品，加固种养设施，组织台风影响区1059艘渔船回港、2.2万人上岸。下一步，农业农村部将密切关注台风动向，加密监测预警，加强风险隐患排查，及时调度雨情灾情，落实农业安全生产措施，指导做好灾后恢复生产，最大限度减轻灾害损失。","农业农村部新闻 2026-08-08","2026-08-08T00:00:00Z",92,{"impact":166,"substance":17,"depth":18,"authority":19,"freshness":192,"relevant":20,"comment":193},9,"农业农村部针对台风部署应急响应，涉及多省、具体措施和数据，权威且时效性强。",[195,196],{"name":188,"url":185},{"name":197,"url":198},"农业农村部新闻办公室 2026-08-10","http:\u002F\u002Fw.icama.org.cn\u002Fzwb\u002Fdetail\u002F32972",[200,201,25,202],"农业农村部","秋粮田管","台风白海豚",[204,205],"农业防灾减灾 农业农村部 台风白海豚 秋粮田管","农业防灾减灾 农业农村部","农业防灾减灾农业农村部台风白海豚秋粮田管-316","2026-08-09T23:54:46.952871Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":8,"content":213,"source_name":214,"source_url":8,"published_at":163,"category":11,"cover_url":8,"hotness":12,"is_selected":48,"score":215,"score_detail":216,"sources":219,"tags":221,"search_phrases":225,"slug":228,"view_count":167,"doi":8,"paper":8,"created_at":229},275,"财政部会同农业农村部下达17.63亿元农业防灾减灾和水利救灾资金——支持辽宁吉林等5省灾后恢复+13省区秋粮防灾","https:\u002F\u002Fcj.sina.com.cn\u002Farticle\u002Fnorm_detail?url=https%3A%2F%2Ffinance.sina.com.cn%2Froll%2F2026-08-04%2Fdoc-inimeefc5642119.shtml","财政部会同农业农村部近日下达农业防灾减灾和水利救灾资金17.63亿元,支持秋粮生产和农业灾后恢复工作。三大使用方向:一是支持辽宁、吉林、浙江、广西、湖北等5省(自治区)开展抢收抢烘、在田作物肥水管理、绝收地块改种补种、畜牧渔业补栏补苗、损毁设施修复等灾后生产恢复工作;二是支持山西、新疆、甘肃等3省(自治区)及时做好农作物改种补种、畜牧补栏等洪涝、风雹灾后救灾工作,加快恢复农业生产;三是聚焦防救衔接,支持河北、内蒙古、吉林、黑龙江、江苏、安徽、江西、山东、河南、湖北、湖南、新疆、四川等13省(自治区)及新疆生产建设兵团、北大荒农垦集团实施喷施作业等秋粮防灾举措,助力夺取秋粮和全年粮食丰收。当前我国正处在「七下八上」防汛关键期,也是秋粮产量形成关键期,秋粮占全年粮食产量四分之三,农业农村部最新农情调度显示,今年秋粮基本种在了丰产期,预计面积稳中略增,苗情长势整体正常。","夏粮落袋，秋粮接棒。秋粮产量占到全年粮食产量的四分之三。农业农村部最新农情调度显示，今年秋粮基本种在了丰产期，预计面积稳中略增。目前苗情长势整体正常，各地各部门多措并举确保全年粮食丰收。\n\n在河南商丘柘城县，玉米茁壮挺拔，大豆长势喜人。农技专家顶着太阳穿梭田间，细致查看作物长势，排查病虫害隐患，重点针对大豆玉米带状复合种植模式开展现场指导。\n\n“夏玉米已进入吐丝散粉期，大豆处于结荚鼓粒期，是决定产量的核心阶段。高温高湿环境容易滋生病虫害，复合种植地块易并发玉米螟、甜菜夜蛾、叶部病害等，需要重点防范。”柘城县农业农村局农技站站长、推广研究员、科技特派员黄雅领对农户嘱咐着秋管的要点。\n\n“老师”的倾囊相授与“学生”的虚心求教，让夯实粮食安全的目标变得具体可感。“大豆玉米带状复合种植管理要求高，农技人员上门精准支招，科学防控病虫害，大家对今年秋粮丰收有信心。”忙碌之余，种粮大户刘标言语中透着从容与淡定。\n\n夯实秋粮产量的努力不只在河南。山东德州夏津县，77万亩玉米已进入小喇叭口期，智能无人机集成了厘米级定位、地形自适应跟随等技术，能够精准开展田管作业。\n\n黑龙江北大荒军川农场，4万多亩大豆用上了专属“营养配方”，时速160公里的航化飞机掠过豆田，将防病、防虫、补肥一次完成。\n\n秋粮主要包括水稻、玉米、大豆，分布在东北、黄淮海、长江中下游、西南地区等地的13个粮食主产区。当前，我国正处在“七下八上”防汛关键期，也是秋粮产量形成的关键期，农业农村部副部长张兴旺在上半年农业农村经济运行情况新闻发布会上表示，要立足防大汛、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。\n\n在近日召开的全国秋粮生产现场推进会上，农业农村部相关负责人表示，要扎实推进奋战100天强田管抗灾害夺秋粮丰收行动，分作物分区域落实肥水管理、“一喷多促”等措施，促进苗情转化升级。\n\n要落实汛期农业防灾减灾预案，盯牢暴雨洪涝、干旱、高温热害、台风等灾害，加强监测预警，及早防范应对，用好农业生产救灾资金，最大程度减轻灾害损失。\n\n要盯紧水稻二化螟和“两迁”害虫、玉米螟和南方锈病等重大病虫害，推进统防统治、联防联控、应急防控，全力打好重大病虫害防控攻坚战。\n\n在财政支持方面，近日，财政部会同农业农村部下达农业防灾减灾和水利救灾资金17.63亿元。\n\n一是支持辽宁、吉林、浙江、广西、湖北等5省（自治区）开展抢收抢烘、在田作物肥水管理、绝收地块改种补种、畜牧渔业补栏补苗、损毁设施修复等灾后生产恢复工作。\n\n二是支持山西、新疆、甘肃等3省（自治区）及时做好农作物改种补种、畜牧补栏等洪涝、风雹灾后救灾工作，加快恢复农业生产。\n\n三是聚焦防救衔接，支持河北、内蒙古、吉林、黑龙江、江苏、安徽、江西、山东、河南、湖北、湖南、新疆、四川等13省（自治区）及新疆生产建设兵团、北大荒农垦集团实施喷施作业等秋粮防灾举措，助力夺取秋粮和全年粮食丰收。\n\n“要深刻认识抓好秋粮田管和防灾减灾的重要性紧迫性，切实增强责任感使命感，盯紧盯牢关键环节，落实落细关键措施，全面夯实秋粮生产基础。”上述负责人说。","新华社\u002F农民日报 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