[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3118":3,"related-3118":36},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":8,"created_at":35},3118,"广州市农业农村科学院赴花都区开展晚造粮食生产技术指导——专家团队深入水稻\u002F鲜食玉米连片种植区","https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt\u002Fcontent\u002Fpost_11014185.html","广州市农业农村科学院联合花都区农业技术管理中心组建农技专家服务团队，深入花都区粮食生产核心基地开展常态化、精准化田间技术帮扶和实地指导服务。在花东镇水稻种植片区晚造水稻已进入穗分化关键阶段，专家现场指导农户科学追施促穗肥；针对采用自留种的田块开展手把手实操教学、现场示范田间除杂技术；在花山镇鲜食玉米生产基地引导种植户实行错期分批播种；叮嘱种植主体密切监测草地贪夜蛾、茎腐病、南方锈病等重大病虫害发生动态。广州市农业农村科学院长期扎根花都农业生产一线、多方联动共建多处百亩连片水稻示范田。",null,"[](javascript:void(0))\n\n[![Image 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\"种植管理\")[农环植保](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Fnhzb\u002Findex.html \"农环植保\")[农机管理](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Fnjgl\u002Findex.html \"农机管理\")[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Findex.html)\n\n#### [热点专题](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Findex.html)\n\n*   [《广州市加快建设都市现代农业强市规划（2024—...](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Fjsdsxdnyqsgh\u002F \"《广州市加快建设都市现代农业强市规划（2024—2035年）》内容解读（视频）\")\n*   [广州市畜牧兽医屠管行业普法直通车](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002F2025xmsytghypfztc\u002F \"广州市畜牧兽医屠管行业普法直通车\")\n*   [广州市打造美丽中国城市样板美丽乡村优秀案例](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002F2025mlxcyxal\u002F \"广州市打造美丽中国城市样板美丽乡村优秀案例\")\n[更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Frdzt\u002Findex.html)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=ftyg) 接访预告\n\n*   [信访工作条例](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_8373419.html \"信访工作条例\") 2022-06-27 \n*   [广东省信访条例](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_8090013.html \"广东省信访条例\") 2022-02-22 \n*   [广州市农业农村局来信、来访指南](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10033656.html \"广州市农业农村局来信、来访指南\") 2026-06-05 \n*   [广州市农业农村局领导9月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_11012694.html \"广州市农业农村局领导9月接访预告\") 2026-09-20 \n*   [广州市农业农村局领导8月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10967166.html \"广州市农业农村局领导8月接访预告\") 2026-08-17 \n*   [广州市农业农村局领导7月接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10905195.html \"广州市农业农村局领导7月接访预告\") 2026-07-16 \n*   [广州市农业农村局6月份局领导接访安排](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10862146.html \"广州市农业农村局6月份局领导接访安排\") 2026-06-18 \n*   [广州市农业农村局5月份局领导接访安排](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10827005.html \"广州市农业农村局5月份局领导接访安排\") 2026-05-25 \n*   [4月23日广州市农业农村局领导接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10773365.html \"4月23日广州市农业农村局领导接访预告\") 2026-04-16 \n*   [3月23日广州市农业农村局领导接访预告](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhd\u002Fjfyg\u002Fcontent\u002Fpost_10728537.html \"3月23日广州市农业农村局领导接访预告\") 2026-03-16 \n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=wsdc) 网上调查\n\n![Image 7](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_01.jpg)\n\n*   [广州市农业农村局关于2026年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F50869 \"广州市农业农村局关于2026年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于2025年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F44211 \"广州市农业农村局关于2025年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于2024年中央一号文件的调查...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F36886 \"广州市农业农村局关于2024年中央一号文件的调查问卷\")\n*   [广州市农业农村局关于广州市落实中央工作会议精神的...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F28959 \"广州市农业农村局关于广州市落实中央工作会议精神的调查问卷\")\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fc_cat?name=yjzj)民意征集\n\n*   [广州市农业农村局关于公开征求《广州市加快农业农村...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F52347 \"广州市农业农村局关于公开征求《广州市加快农业农村现代化“十五五”规划（征求意见稿）》意见的通知\")2026-08-21\n*   [广州市农业农村局关于公开征求《广州市本地农产品稳...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F51945 \"广州市农业农村局关于公开征求《广州市本地农产品稳产保供重点生产主体培育与管理办法》意见的公告\")2026-07-31\n*   [广州市农业农村局关于公开征求《广州市畜禽养殖管理...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F51346 \"广州市农业农村局关于公开征求《广州市畜禽养殖管理办法（征求意见稿）》 及公平竞争意见和建议的公告\")2026-07-03\n*   [广州市农业农村局关于公开征求《广州市本地农产品稳...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F50320 \"广州市农业农村局关于公开征求《广州市本地农产品稳产保供重点生产主体培育与管理办法》意见的公告\")2026-05-14\n*   [广州市农业农村局关于公开征求《广州市农业农村专家...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F49164 \"广州市农业农村局关于公开征求《广州市农业农村专家库管理办法（修订稿·征求意见稿）》意见的通知\")2026-03-05\n*   [广州市农业农村局关于公开征求《广州市养殖水域滩涂...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fyjzj\u002Fanswer\u002F48522 \"广州市农业农村局关于公开征求《广州市养殖水域滩涂规划（2019～2030年）》部分内容修改意见的公告\")2026-01-15\n\n[![Image 8: 局长信箱](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn1.jpg)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fldxx)[![Image 9: 政府12345](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn2.jpg)](https:\u002F\u002Fwww.gz.gov.cn\u002Fgz12345\u002F)[![Image 10](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002F2016sy_wz_btn04.jpg)](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fhdjlpt\u002Fdwzsk?via=pc)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Ffwyypt\u002Findex.html) 服务应用平台\n\n*   [市农业农村专家库](https:\u002F\u002Fsnyj.nyncj.gz.gov.cn\u002Fportal\u002F \"市农业农村专家库\")\n*   [财政专项资金申请](https:\u002F\u002F112.94.68.237\u002Fportal\u002F \"财政专项资金申请\")\n*   [农产品价格采集系统](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fjsp\u002Flogin\u002Flogin.jsp \"农产品价格采集系统\")\n*   [农机购置补贴平台](http:\u002F\u002F210.76.75.39:2018\u002FGouZBT2021To23_Fromal\u002F \"农机购置补贴平台\")\n*   [集体产权流转平台](https:\u002F\u002F112.94.70.20:8088\u002F \"集体产权流转平台\")\n*   [智慧畜牧兽医平台](http:\u002F\u002Fwww.gzxm.org.cn\u002Famaq-sso-server\u002Flogin;jsessionid=2D3E225ED88BC1346655DD3033C2FD61 \"智慧畜牧兽医平台\")\n*   [农博士综合服务平台](https:\u002F\u002Fwww.gznbs.com:8081\u002Fnyjnewnbs\u002Fjsp\u002Flogin\u002Flogin.jsp \"农博士综合服务平台\")\n\n##### [更多>>](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fweb\u002Findex.html) 数据发布\n\n*   [黄沙水产交易市场行情分析（9月14日—9月18日)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fsjfb\u002Fgzscxq\u002Fcontent\u002Fpost_11010229.html \"黄沙水产交易市场行情分析（9月14日—9月18日)\")\n*   [广州花卉研究中心有限公司市场行情分析（9月14日—9月18日)](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fsjfb\u002Fgzscxq\u002Fcontent\u002Fpost_11009991.html \"广州花卉研究中心有限公司市场行情分析（9月14日—9月18日)\")\n\n*   [三农微博](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fwfw\u002Fsnwb\u002Findex.html)\n*   [三农微信](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fwfw\u002Fsnwx\u002Findex.html)\n*   [农药使用名录](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fnysyml\u002Findex.html)\n*   [主推技术](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fztjs\u002Findex.html)\n*   [主导品种](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fzdpz\u002Findex.html)\n*   [农事管理](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fnsgl\u002Findex.html)\n*   [下载区](http:\u002F\u002Fnyncj.gz.gov.cn\u002Ffw\u002Fxzq\u002Findex.html)\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Findex.html) 常用数据\n\n*   [广州农产品价格数据发布](https:\u002F\u002Fwww.abuya.com.cn:6888\u002Fweb\u002Findex.html \"广州农产品价格数据发布\")\n*   [广州市畜禽屠宰企业名单（2026年9月）](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_10708260.html \"广州市畜禽屠宰企业名单（2026年9月）\")\n*   [广州市2025年12月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_10602819.html \"广州市2025年12月水产养殖病害预测预报\")\n*   [广州市2024年10月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9924348.html \"广州市2024年10月水产养殖病害预测预报\")\n*   [广州市2023年12月水产养殖病害预测预报](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9376420.html \"广州市2023年12月水产养殖病害预测预报\")\n*   [【一图读懂】《2023年广州市农业主导品种和主推...](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fcysj\u002Fcontent\u002Fpost_9131131.html \"【一图读懂】《2023年广州市农业主导品种和主推技术》\")\n\n##### [更多>>](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzxyw\u002Fsn\u002Findex.html)三农\n\n*   [广州农情](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fgznq\u002Findex.html \"广州农情\")\n*   [三品一标](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fspyb\u002Findex.html \"三品一标\")\n*   [省名特优新](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fsmtyx\u002Findex.html \"省名特优新\")\n*   [“粤字号”农业品牌](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fsmpcp\u002Findex.html \"“粤字号”农业品牌\")\n*   [畜禽屠宰企业](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fsj\u002Fsn\u002Fxqtzqy\u002Findex.html \"畜禽屠宰企业\")\n\n 您现在的位置： [首页](http:\u002F\u002Fnyncj.gz.gov.cn\u002F)>[政务](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Ftzgg\u002F)>[政务要闻](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002F)>[工作动态](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt\u002F)\n\n**政务要闻**[工作动态](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fgzdt)[区镇连线](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fqzdt)[政声传递](http:\u002F\u002Fwww.gd.gov.cn\u002Fxxts\u002F)[图片新闻](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Ftpxw)[三农要闻](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fsnyw)[他山之石](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Ftszs)[媒体报道](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fzw\u002Fzwyw\u002Fmtbd)**政务要闻**\n\n## 农技赋能助丰收 精准服务保粮安 —— 广州市农业农村科学院赴花都区开展晚造粮食生产技术指导\n\n###### 来源： **广州市农业农村科学院** 时间： 2026-09-21 14:56:53  浏览次数：_35_\n\n为深入贯彻落实国家粮食安全战略，进一步压紧压实粮食生产责任，持续巩固晚造粮食生产良好态势，近日，广州市农业农村科学院联合花都区农业技术管理中心，组建农技专家服务团队，深入花都区粮食生产核心基地，开展常态化、精准化田间技术帮扶和实地指导服务，全力护航秋粮丰产丰收。\n\n当前正值晚造粮食作物生长发育的关键时期，田间管理的质量直接关系秋粮收成。专家团队先后深入水稻、鲜食玉米连片种植区，实地察看作物长势，系统掌握田间管护情况，围绕水肥精准调控、绿色病虫害防控、自留种提纯复壮、粮食产销对接、惠农补贴政策申报等现实问题，现场为种植户答疑解惑，切实提升农户科学种粮技术水平，充分激发各类种粮主体的生产积极性。\n\n在花东镇水稻种植片区，晚造水稻已进入穗分化关键阶段。农技专家现场指导农户科学追施促穗肥，为实现穗大粒多、稳产丰产筑牢生长根基。针对采用自留种的田块，专家开展手把手实操教学，现场示范田间除杂技术，严守种子纯度关口，从源头上保障稻谷品质。在花山镇鲜食玉米生产基地，专家引导种植户实行错期分批播种，有效规避大批量集中上市带来的收购价格下行风险。同时，叮嘱种植主体密切监测草地贪夜蛾、茎腐病、南方锈病等重大病虫害发生动态，秉持“预防为主、综合防治”理念，扎实做好病虫害监测与防控工作。\n\n广州市农业农村科学院长期扎根花都农业生产一线，持续开展粮食作物新优品种选育与示范推广，多方联动共建多处百亩连片水稻示范田，推进规模化示范种植。针对华南地区台风多发、降雨集中，水稻成熟收割期易发生倒伏、穗上发芽等突出生产难题，下一步，将以品种改良创新、机插秧技术集成应用为双抓手，协同攻关破解生产痛点，充分挖掘粮食单产提升潜力，全力保障广州市粮食生产安全。\n\n![Image 11: 图片1.png](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fimg\u002F1\u002F1692\u002F1692565\u002F11014185.png)![Image 12: 图片2.png](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fimg\u002F1\u002F1692\u002F1692566\u002F11014185.png)\n\n[![Image 13: 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\"隐私安全\")|[使用帮助](http:\u002F\u002Fnyncj.gz.gov.cn\u002Fwzxg\u002Fsybz\u002Findex.html \"使用帮助\")\n\n广州市农业农村科学院版权所有，未经授权禁止复制或建立镜像\n\n主办单位：广州市农业农村局  运营维护及技术支持：广州市农业农村科学院  访问人数统计：_9684890_\n\n[![Image 24](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fghs.png)公安机关备案号：44011102001333](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=44011102001333)[粤ICP备20004752号-1](http:\u002F\u002Fbeian.miit.gov.cn\u002F)网站标识码：4401000027\n\n[![Image 25](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fnred.png)](http:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=080C6254AE2B3C44E053022819AC84D6)\n\n![Image 26](https:\u002F\u002Fzfwzgl.www.gov.cn\u002Fexposure\u002Fimages\u002Fjiucuo.png?v=4401000027)\n\n![Image 27](https:\u002F\u002Fnyncj.gz.gov.cn\u002Fimages\u002Fwzafw.png)","广州市农业农村科学院","2026-09-18T00:00:00Z","报道",10,false,39,{"impact":17,"substance":17,"depth":18,"authority":13,"freshness":19,"relevant":20,"comment":21},8,6,7,1,"地市级农科院常规技术指导通稿，正文缺失、信息增量有限，仅具地方服务动态价值，不建议进入每日精选。",[23],{"name":10,"url":6},[25,26,27,28,29],"水稻","鲜食玉米","晚造粮食生产","农技指导","广州花都",[31,32],"广州农科院 花都 晚造","水稻 鲜食玉米 技术指导","广州农科院花都晚造-3118",0,"2026-09-22T00:05:36.766978Z",{"total":18,"page":20,"page_size":18,"items":37},[38,64,89,131,171,206],{"id":39,"title":40,"url":41,"summary":42,"summary_zh":8,"content":43,"source_name":44,"source_url":8,"published_at":45,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":46,"score_detail":47,"sources":52,"tags":54,"search_phrases":59,"slug":62,"view_count":34,"doi":8,"paper":8,"created_at":63},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z",76,{"impact":48,"substance":48,"depth":49,"authority":50,"freshness":18,"relevant":20,"comment":51},22,17,9,"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[53],{"name":44,"url":41},[55,56,25,57,58],"智慧农业","农业人工智能","精准农业","无人機巡田",[60,61],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z",{"id":65,"title":66,"url":67,"summary":68,"summary_zh":8,"content":8,"source_name":69,"source_url":8,"published_at":70,"category":12,"cover_url":8,"hotness":13,"is_selected":71,"score":72,"score_detail":73,"sources":77,"tags":79,"search_phrases":84,"slug":87,"view_count":34,"doi":8,"paper":8,"created_at":88},3032,"中国农科院基因组所超级稻种质创新团队揭示水稻器官边界建成的分子机制登The Plant Cell","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F94a1ad7de45342d0b77e6c154d55e0ab.htm","中国农业科学院农业基因组研究所超级稻种质创新团队近日揭示水稻器官边界建成的分子机制，相关研究成果发表在《植物细胞》（The Plant Cell）上。研究发现水稻中存在一类转录因子，在器官边界特异表达，像\"分子开关\"调控下游功能基因，可同时调控分蘖起始、叶枕发育等多类边界发育过程，最终决定水稻分蘖数、叶片夹角等多项核心产量性状。研究完善了单子叶植物器官边界发育的基础理论，为禾本科作物的株型改良提供了基因靶标，得到国家自然科学基金、中国农业科学院科技创新工程、广东省重点领域研发计划等项目资助。","中国农业科学院","2026-09-20T00:00:00Z",true,87,{"impact":48,"substance":48,"depth":74,"authority":75,"freshness":13,"relevant":20,"comment":76},18,15,"国家级科研机构在核心期刊发表的水稻器官边界分子机制突破，为禾本科作物株型改良提供基因靶标，专业增量与权威性俱佳，值得进入每日精选。",[78],{"name":69,"url":67},[25,80,81,82,83],"种业振兴","分子育种","农业科技","株型改良",[85,86],"中国农科院基因组所 水稻 器官边界","The Plant Cell 水稻 分蘖 分子机制","中国农科院基因组所水稻器官边界-3032","2026-09-21T00:04:33.543357Z",{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":8,"source_name":95,"source_url":92,"published_at":96,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":104,"tags":106,"search_phrases":109,"slug":112,"view_count":34,"doi":113,"paper":114,"created_at":130},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z","论文",72,{"impact":100,"substance":101,"depth":49,"authority":102,"freshness":50,"relevant":20,"comment":103},12,21,13,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[105],{"name":95,"url":92},[55,56,25,107,108],"多模态融合","遥感",[110,111],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":113,"openalex_id":115,"authors":116,"venue":95,"cited_by_count":34,"oa_url":92,"card":123,"direction":127,"ingested_from":129},"W7213619014",[117,119,121],{"name":118,"orcid":8},"Nurfadhilah Mardianti Andini",{"name":120,"orcid":8},"Mike Yuliana",{"name":122,"orcid":8},"Moch. Zen Samsono Hadi",{"tldr":124,"method":125,"finding":126,"direction":127,"opportunity":128},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","智慧农业 \u002F 农业物联网","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","openalex","2026-09-20T23:30:08.419613Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":8,"source_name":137,"source_url":134,"published_at":138,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":142,"tags":144,"search_phrases":148,"slug":151,"view_count":34,"doi":152,"paper":153,"created_at":170},2951,"Comparative Analysis of Geographical Factors Affecting Paddy (Oryza sativa L.) Yields in Türkiye Using Random Forest and ANOVA: The Case of Kırıkkale, Balıkesir, Diyarbakır and Şanlıurfa","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i9.2678-2694.8977","Rice (Oryza sativa L.) is a staple food for nearly half of the global population and a strategic crop for Turkey, where inter-provincial yield disparities remain pronounced. This study aims to classify provincial rice yield levels in Turkey for the 2004–2024 period using TurkStat data and to quantify the relative contribution of 14 environmental, edaphic and agronomic parameters driving these differences. Preliminary analyses identified Kırıkkale (21-year mean 908.3 kg\u002Fda) as the high-yield province, Balıkesir (747.7 kg\u002Fda) as the medium-yield province, and Diyarbakır (448.1 kg\u002Fda) and Şanlıurfa (440.5 kg\u002Fda) as the low- and lowest-yield provinces, respectively. A 14-parameter dataset compiled from field measurements and published province-level studies was analysed using Principal Component Analysis (PCA), Random Forest (RF) classification and one-way Analysis of Variance (ANOVA).With a 70\u002F30 train\u002Ftest split, the RF model achieved 93.47% accuracy, 0.9764 ROC-AUC and a mean variance of 0.0145. Gini-based variable importance ranked soil moisture, organic matter, soil pH, rainfall and temperature as the most influential drivers of yield, and ANOVA confirmed statistically significant differences across yield classes for these variables (all p \u003C 0.001). Findings indicate that low yields in south-eastern Anatolia are largely driven by inadequate soil moisture management, low organic matter content, elevated soil pH and summer heat stress, whereas Kırıkkale’s high yields are associated with more balanced soil–water relations. Results provide evidence-based guidance for region-specific rice production policies and data-driven decision support in Türkiye.","水稻（Oryza sativa L.）是全球近半数人口的主粮，也是土耳其的战略性作物，但该国各省之间的产量差异依然显著。本研究旨在利用土耳其统计局（TurkStat）数据，对2004—2024年期间土耳其各省水稻产量水平进行分类，并量化14项环境、土壤和农艺参数对上述差异的相对贡献。初步分析确定，Kırıkkale省（21年均值908.3 kg\u002Fda）为高产区，Balıkesir省（747.7 kg\u002Fda）为中产区，Diyarbakır省（448.1 kg\u002Fda）和Şanlıurfa省（440.5 kg\u002Fda）分别为低产区和最低产区。基于田间实测数据和已发表的省级研究，构建了包含14项参数的数据集，并采用主成分分析（PCA）、随机森林（RF）分类和单因素方差分析（ANOVA）进行分析。在70\u002F30的训练\u002F测试集划分下，RF模型达到了93.47%的准确率、0.9764的ROC-AUC值以及0.0145的平均方差。基于基尼系数的变量重要性排序显示，土壤水分、有机质、土壤pH、降雨量和温度是影响产量最重要的驱动因素，ANOVA证实这些变量在不同产量类别间均存在统计学显著差异（均p \u003C 0.001）。研究结果表明，安纳托利亚东南部地区的低产主要归因于土壤水分管理不足、有机质含量低、土壤pH偏高以及夏季高温胁迫，而Kırıkkale省的高产则与更为均衡的土壤—水分关系有关。研究结果为土耳其制定区域特异性水稻生产政策和数据驱动的决策支持提供了循证依据。","Turkish Journal of Agriculture - Food Science and Technology","2026-09-17T00:00:00Z",74,{"impact":100,"substance":48,"depth":74,"authority":102,"freshness":50,"relevant":20,"comment":141},"基于21年省级数据与随机森林、ANOVA量化水稻产量驱动因子，方法规范、结论可靠，但属土耳其区域研究，对国内三农实践参考价值有限。",[143],{"name":137,"url":134},[25,145,146,57,147],"产量预测","农业大数据","土壤墒情",[149,150],"土耳其 水稻 产量 随机森林","Kırıkkale Balıkesir 水稻 产量","土耳其水稻产量随机森林-2951","10.24925\u002Fturjaf.v14i9.2678-2694.8977",{"doi":152,"openalex_id":154,"authors":155,"venue":137,"cited_by_count":34,"oa_url":162,"card":163,"direction":169,"ingested_from":129},"W7213465585",[156,159],{"name":157,"orcid":158},"Mehmet ÖZCANLI","https:\u002F\u002Forcid.org\u002F0000-0003-2228-8298",{"name":160,"orcid":161},"Kerim Karadağ","https:\u002F\u002Forcid.org\u002F0000-0001-5167-4054","https:\u002F\u002Fwww.agrifoodscience.com\u002Findex.php\u002FTURJAF\u002Farticle\u002Fdownload\u002F8977\u002F4317",{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"用随机森林和方差分析比较土耳其四省水稻产量差异，识别关键地理驱动因子。","基于2004–2024年TurkStat数据，用PCA、随机森林分类和单因素AN","土壤水分、有机质、pH、降雨和温度是产量主因；东南部低产源于土壤水分不足、有机质低、pH高和夏季热胁","农业人工智能与决策模型","可引入时序遥感与土壤传感器数据，构建跨区域可迁移的产量预测与精准水肥管理模型。","农业遥感与作物表型","2026-09-19T23:30:34.632573Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":8,"source_name":177,"source_url":174,"published_at":11,"category":97,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"search_phrases":188,"slug":191,"view_count":34,"doi":192,"paper":193,"created_at":205},2920,"Breeding rice for optimal maturity across diverse sowing windows under future climate change scenarios in Chongqing area","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1899019","Introduction It is of great significance to optimize rice cultivars for different sowing dates under future climate change for rice sustainable production in Chongqing. Methods In this study, using the APSIM-Rice model and Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) scenarios, we investigated the changes of yield, water consumption and water use efficiency (WUE) across six sowing dates and three cultivars (early, normal and late-maturing cultivars) under baseline period (1981 – 2010) and future climate 2scenarios (2031-2100, SSP2-4.5 and SSP5-8.5). In this study, the climate model BCC-CSM2-MR was selected due to its reliable simulation of China’s climate and compatibility with the APSIM-Rice model. Results Results showed that rice yield with normal cultivar in the baseline period peaked on March 30, with the value of 6,310 kg ha− −1 , and reached its minimum on March 1, with the value of 4,580 kg ha− −1 . Water consumption during the rice growing period increased with the delayed sowing dates (212 mm on March 1 to 276 mm on April 20). The response trend of water use efficiency (WUE) to different sowing dates was identical to that of yield, with the maximum WUE of 23.30 kg ha− −1 mm −1 achieved when sown on March 30. Under future SSP2-4.5 and SSP5-8.5 scenarios, early sowing (March 1–20) consistently enhanced yield and WUE for normal cultivars (maximum increments were 25.40% and 29.00% for yield and WUE), while late sowing (March 30–April 20) caused severe losses (up to 26.90% and 34.60% for yield and WUE for April 10 under SSP5-8.5 in the 2060s), with water consumption rising across all sowing dates. Early-maturing cultivars reduced yield (10.60%–44.60%) and WUE (7.70%–43.90%) across all sowing dates under future scenarios, whereas late-maturing cultivars synergized with early sowing to boost yield (up to 25.62%) and WUE (up to 32.85%) but exacerbated losses for late sowing, with water consumption increasing significantly (up to 56.30% under SSP5-8.5 in the 2060s). Discussion These findings provide critical scientific support for optimizing sowing date and cultivar combinations, thereby enhancing the climate resilience and sustainability of rice production in Chongqing and similar subtropical monsoon regions. However, in the future, more climate models, extreme climate impacts, and agronomic factors should be considered","引言 优化不同播期下的水稻品种对重庆未来气候变化背景下的水稻可持续生产具有重要意义。方法 本研究利用APSIM-Rice模型和耦合模式比较计划第六阶段（CMIP6）共享社会经济路径（SSP）情景，研究了基准期（1981—2010年）和未来气候情景（2031—2100年，SSP2-4.5和SSP5-8.5）下6个播期和3个品种（早熟、中熟和晚熟品种）的产量、耗水量和水分利用效率（WUE）变化。本研究选择气候模式BCC-CSM2-MR，因其对中国气候的模拟可靠且与APSIM-Rice模型兼容。结果 结果表明，基准期中熟品种水稻产量在3月30日达到峰值，为6 310 kg ha⁻¹，在3月1日降至最低，为4 580 kg ha⁻¹。水稻生育期耗水量随播期推迟而增加（3月1日的212 mm增至4月20日的276 mm）。水分利用效率（WUE）对不同播期的响应趋势与产量一致，3月30日播种时WUE最高，为23.30 kg ha⁻¹ mm⁻¹。在未来SSP2-4.5和SSP5-8.5情景下，早播（3月1—20日）持续提高中熟品种的产量和WUE（产量和WUE的最大增幅分别为25.40%和29.00%），而晚播（3月30日—4月20日）造成严重损失（在2060年代SSP5-8.5情景下，4月10日播种的产量和WUE损失分别高达26.90%和34.60%），且所有播期的耗水量均增加。在未来情景下，早熟品种在所有播期均降低产量（10.60%—44.60%）和WUE（7.70%—43.90%），而晚熟品种与早播协同提高产量（最高25.62%）和WUE（最高32.85%），但加剧了晚播的损失，耗水量显著增加（在2060年代SSP5-8.5情景下最高达56.30%）。讨论 这些发现为优化播期和品种组合提供了关键科学支撑，从而增强重庆及类似亚热带季风区水稻生产的气候韧性和可持续性。然而，未来应考虑更多气候模式、极端气候影响和农艺因素。","Frontiers in Sustainable Food Systems",78,{"impact":180,"substance":48,"depth":74,"authority":102,"freshness":50,"relevant":20,"comment":181},16,"基于APSIM-Rice与CMIP6情景模拟重庆水稻播期与品种组合，数据扎实、结论对区域气候适应性育种有参考价值，但属细分领域研究，影响力有限。",[183],{"name":177,"url":174},[55,25,185,186,187],"气候变化","品种选育","播期优化",[189,190],"APSIM-Rice 水稻 播期","重庆 水稻 品种 气候","APSIM-Rice水稻播期-2920","10.3389\u002Ffsufs.2026.1899019",{"doi":192,"openalex_id":194,"authors":195,"venue":177,"cited_by_count":34,"oa_url":174,"card":200,"direction":167,"ingested_from":129},"W7213555399",[196,198],{"name":197,"orcid":8},"Jianzhao Tang",{"name":199,"orcid":8},"Jianping Zhang",{"tldr":201,"method":202,"finding":203,"direction":167,"opportunity":204},"用APSIM-Rice与CMIP6情景模拟重庆不同播期和品种水稻产量、耗水与水分利用效率。","APSIM-Rice模型结合CMIP6 SSP2-4.5\u002FSSP5-8.5情景及","未来早播配晚熟品种可增产提效，晚播则大幅减产，各播期耗水均上升。","可引入多模型集合、极端气候与氮肥管理等农艺因素，优化播期-品种组合的适应策略。","2026-09-19T23:30:08.567885Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":8,"source_name":212,"source_url":209,"published_at":11,"category":97,"cover_url":8,"hotness":13,"is_selected":71,"score":213,"score_detail":214,"sources":220,"tags":222,"search_phrases":226,"slug":229,"view_count":34,"doi":230,"paper":231,"created_at":274},2912,"Spatial optimization of water-saving irrigation in Chinese rice paddies: Balancing yield, greenhouse gases, and cost using NSGA-II","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104976","CONTEXT Optimizing the spatial allocation of water-saving irrigation (WSI) promotion is critical for balancing rice production, greenhouse gas (GHG) mitigation, and economic costs, yet remains challenging at a national scale due to computational intractability. OBJECTIVE This study aims to develop a spatially explicit optimization framework to identify WSI promotion pathways across China's rice paddies that simultaneously maximize yield gain, maximize GHG reduction, and minimize implementation cost. METHODS We coupled K-means clustering with a multi-objective evolutionary algorithm (NSGA-II). Based on machine-learning predicted yield and GHG emissions for 157,417 flooded irrigation grids, we first clustered these grids into 500 environmentally and agronomically homogeneous groups. We then formulated a continuous optimization problem with cluster-level conversion rates as decision variables, simultaneously maximizing national yield gain, maximizing GHG reduction, and minimizing implementation cost. RESULTS AND CONCLUSIONS The Pareto front comprised 100 non-dominated solutions spanning promotion rates from 61.8% to 80.3%. The optimal solution, selected by normalized scoring, achieved 6.47 Mt. yield gain and 63.5 Mt. CO 2 e GHG reduction at a cost of 10.14 billion CNY, corresponding to a national promotion rate of 80.3%. Cluster-scale conversion rates exhibited significant spatial heterogeneity and positive correlation with cluster size ( r = 0.23), revealing economies of scale in WSI promotion. Compared with a random promotion strategy at 90% adoption, our optimized solution delivered 74% higher yield gain with 9.7 percentage points lower promotion effort while achieving comparable GHG reduction. SIGNIFICANCE Our framework provides a spatially explicit decision-support tool for precision agricultural policy, demonstrating that smart spatial allocation can substantially enhance the efficiency of limited resources in scaling climate-smart agricultural practices.","背景 优化节水灌溉（WSI）推广的空间配置对于平衡水稻生产、温室气体（GHG）减排和经济成本至关重要，但由于计算上的不可处理性，在全国尺度上仍具挑战性。目的 本研究旨在开发一个空间显式优化框架，以识别中国稻田的WSI推广路径，同时最大化产量增益、最大化GHG减排并最小化实施成本。方法 我们将K-means聚类与多目标进化算法（NSGA-II）相结合。基于机器学习预测的157,417个淹水灌溉网格的产量和GHG排放，我们首先将这些网格聚类为500个在环境和农艺上同质的组。然后，我们构建了一个以组级转换率为决策变量的连续优化问题，同时最大化全国产量增益、最大化GHG减排并最小化实施成本。结果与结论 Pareto前沿包含100个非支配解，推广率从61.8%到80.3%不等。通过归一化评分选出的最优解实现了6.47 Mt的产量增益和63.5 Mt CO₂e的GHG减排，成本为101.4亿元人民币，对应全国推广率为80.3%。组尺度转换率表现出显著的空间异质性，并与组规模呈正相关（r = 0.23），揭示了WSI推广中的规模经济。与90%采纳率的随机推广策略相比，我们的优化方案在推广力度低9.7个百分点的情况下实现了高出74%的产量增益，同时实现了相当的GHG减排。意义 我们的框架为精准农业政策提供了一个空间显式的决策支持工具，表明智能空间配置可以显著提高有限资源在推广气候智慧型农业实践中的效率。","Agricultural Systems",89,{"impact":215,"substance":216,"depth":217,"authority":218,"freshness":50,"relevant":20,"comment":219},24,23,19,14,"基于NSGA-II的全国稻田节水灌溉空间优化框架，数据规模大、结论具体，对气候智慧型农业政策有决策参考价值。",[221],{"name":212,"url":209},[55,25,223,224,225],"空间优化","节水灌溉","农业减排",[227,228],"中国稻田 节水灌溉 空间优化","NSGA-II 水稻 温室气体 减排","中国稻田节水灌溉空间优化-2912","10.1016\u002Fj.agsy.2026.104976",{"doi":230,"openalex_id":232,"authors":233,"venue":212,"cited_by_count":34,"oa_url":209,"card":268,"direction":272,"ingested_from":129},"W7213551595",[234,236,239,242,244,246,248,250,253,255,258,261,264,266],{"name":235,"orcid":8},"Qiang Xu",{"name":237,"orcid":238},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":240,"orcid":241},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":243,"orcid":8},"Hui Gao",{"name":245,"orcid":8},"Min Jiang",{"name":247,"orcid":8},"Wenya Chen",{"name":249,"orcid":8},"Yourui Cao",{"name":251,"orcid":252},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":254,"orcid":8},"A. I. Abdo",{"name":256,"orcid":257},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":259,"orcid":260},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":262,"orcid":263},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":265,"orcid":8},"Xia Liang",{"name":267,"orcid":8},"Huiqing Bai",{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"构建空间优化框架，为中国稻田节水灌溉推广寻找兼顾产量、温室气体与成本的路径。","耦合K-means聚类与NSGA-II多目标进化算法，基于15.7万网格的机器学","最优方案增产6.47 Mt、减排63.5 Mt CO2e，成本101.4亿元，推广率80.3%，存在","农业绿色发展与碳","可将该空间优化框架扩展到其他气候智慧型农业技术，并耦合农户采纳行为与政策激励。","2026-09-19T23:30:05.508592Z"]