[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-08-31":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-08-31","水稻早熟高产新突破，APEC粮食安全会议召开",[7,8,9,10,11,12],"中国水稻所张健团队在《科学》发表成果，克隆到稀有等位基因TGW1aJZ，可同时调控水稻熟期和产量，改良品种生育期缩短3-8天，产量提升4.2%-11%。","农业农村部印发《全国农产品产地市场体系发展“十五五”规划》，提出到2030年基本建成现代农产品产地市场体系。","第十一届APEC粮食安全部长级会议在杭州召开，通过《创新与数字化增强亚太区域粮食体系韧性原则文件》。","财政部宣布2026年中央财政安排农业补贴资金2630亿元、常态化帮扶资金1770亿元，支持粮食安全和防止返贫。","中国农科院植保所研发仿生水凝胶Bt制剂，对番茄潜叶蛾防效提升约27%，破解微生物农药田间短板。","中国农科院和江苏农科院国家自然科学基金立项创历史新高，分别获445项和79项。","本期日报涵盖水稻育种前沿突破、农产品市场体系规划、APEC粮食安全会议、中央财政补贴政策、微生物农药创新、基金立项动态及AI农业应用等，聚焦政策、科技与产业融合，展现农业现代化新进展。\n\n---\n*本日报内容整理自公开来源，外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[15,74,116,167,214,270,320,357,396,437,463,517,554,587,623],{"id":16,"title":17,"url":18,"summary":19,"summary_zh":20,"content":21,"source_name":22,"source_url":20,"published_at":23,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":27,"score_detail":28,"sources":36,"tags":38,"primary_topic":43,"secondary_topics":48,"classification":49,"search_phrases":68,"slug":71,"view_count":72,"doi":20,"paper":20,"created_at":73},1133,"中国水稻所张健团队发现水稻“早熟高产”稀有等位基因TGW1a 8月21日发表于《科学》","https:\u002F\u002Fnews.sciencenet.cn\u002Fsbhtmlnews\u002F2026\u002F8\u002F391009.shtm","8月21日,中国水稻研究所水稻生物育种全国重点实验室研究员张健团队在《科学》(Science)发表突破性研究成果,克隆到同时调控水稻熟期和产量的稀有等位基因TGW1aJZ,揭示其编码的类成花素蛋白可通过耦合“开花时钟”与氮素利用效率打破作物“早熟-高产”遗传枷锁的分子机制。改良新品种收获期缩短3-8天,产量增加4.2%-11.0%。研究明确作物“早熟高产”的实质是熟期与氮素利用效率的平衡,为培育早熟高产品种提供理论依据和基因资源。",null,"从7岁到14岁，张健的每一个暑假几乎都在湖南永州度过。外婆家种了几亩水稻，孩子们暑假里的固定“节目”就是跟着舅舅“双抢”。\n\n夏天的“双抢”就是抢收早稻的同时，还要抢时间种下一季的农作物。每天早上，张健和表兄弟们跟着大人，拿着镰刀去田里割稻子，再将稻子用板车推回家。“那时候最怕下雨。”张健说，有时候正在吃午饭，外婆突然喊“下雨了”，所有人丢下碗筷冲上房顶，把稻谷拢在一起盖上薄膜，防止雨淋后发芽。“双抢”的辛苦，就这样刻进了他的记忆里。\n\n我国南方一直采取多熟制粮食生产，在实际生产中，早稻抢收、晚稻抢种、再接油菜的“双抢”环节前后茬口衔接时间极短。数据显示，水稻每缩短1天生育期，通常会导致产量下降0.8%~1.2%。论文通讯作者张健告诉《中国科学报》，理想的办法是缩短作物的生长周期，让它们早熟的同时不减产。这也是破解多熟制茬口矛盾的关键。然而，历经近百年，“早熟与高产”如“鱼和熊掌”不可兼得。这一科学难题的特殊之处在于必须同时寻找控制两个表型性状（熟期与产量）的调控位点。\n\n从华中农业大学张启发院士门下博士毕业到在美国密西西比州立大学做了近6年博士后和助理研究教授，再到回国加入水稻所水稻生物育种全国重点实验室，张健研究的主角一直是水稻。\n\n在水稻所，张健最初的研究策略是做反向遗传学，简单说就是抓住一个基因，搞明白它可能的各种功能和生理机制。2018年前后，团队经历了一次关键的科研方向转型——从反向遗传学策略转为正向遗传学策略，专攻自然变异，也就是通过构建遗传群体寻找调控表型的基因和机制。\n\n要定位调控早熟和高产的基因，首先得寻找合适的亲本材料，才有希望在它们的后代中筛选出目标基因。经过几番选择，他们选定了华南及长江流域主栽籼稻黄华占和东北早熟、特大粒粳稻资源吉资1560为亲本，并利用它们重组自交系群体。\n\n张健将吉资1560比喻为水稻里的巨人——普通水稻千粒重只有二十几克，它却有60克，相当于普通人身高1.7米，他3米多。而且吉资1560的生育期比全国大面积推广的黄华占还短十几天。一个提供资源型稀有基因，一个提供生产应用背景，加上籼粳稻之间遗传距离远、多态性高，使它们成为理想的定位群体。","《科学》(Science)2026年8月21日(科学网2026年8月29日报道)","2026-08-29T01:00:00Z","报道",10,true,93,{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":35},28,24,19,15,7,1,"水稻早熟高产重大突破，发表于Science，兼具科研价值与农业应用前景。",[37],{"name":22,"url":18},[39,40,41,42],"水稻","早熟高产","Science论文","TGW1a",{"code":44,"slug":45,"name":39,"path":46,"parent_code":47},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":54},"classified",false,"2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":26},[56,61],{"code":44,"score":57,"supported":26,"title":58,"abstract":59,"tags":60},12,[39],[39],[39],{"code":62,"score":63,"supported":26,"title":64,"abstract":65,"tags":67},"F2",3,[],[66],"生物育种",[],[69,70],"早熟高产 Science论文 水稻 TGW1a","早熟高产 Science论文","早熟高产Science论文水稻TGW1a-1133",4,"2026-08-31T00:08:54.784833Z",{"id":75,"title":76,"url":77,"summary":78,"summary_zh":20,"content":79,"source_name":80,"source_url":20,"published_at":81,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":82,"score_detail":83,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":110,"slug":113,"view_count":114,"doi":20,"paper":20,"created_at":115},1142,"中国农科院水稻所揭示水稻“早熟高产”分子机制 改良品种生育期缩短3-8天、产量提升4.2%-11%","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7677955056439706112\u002F","中国水稻研究所水稻生物育种全国重点实验室张健团队历时8年攻关,从近10万份水稻材料中克隆到同时调控水稻熟期和产量的稀有等位基因TGW1aJZ,8月21日发表于《科学》。TGW1aJZ编码的类成花素蛋白FT-L1不仅促进开花还能直接感知氮素信号,与氮素利用率核心因子相互作用维持其稳定性,进而促进下游氮素利用基因表达。研究团队通过分子育种手段将TGW1aJZ导入南方主栽早籼品种中嘉早17、中早39,以及籼型恢复系华占、R173中,2023—2025连续3年在浙江杭州、广西南宁大田区域试验,改良新品种收获期缩短3-8天,产量增加4.2%-11.0%。","## 我研究团队发现水稻早熟高产基因\n\n2026-08-25 21:07·[九派观天下](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAU_WcNIT9KsWUY6Xv7DGRWV6jRi7wflAqE01eQU1X3dfG7F535YONRYXKuwSw8IqI\u002F?source=tuwen_detail)\n\n【来源：廊坊市科技局_国内动态】\n\n8月21日，中国水稻研究所水稻生物育种全国重点实验室在《科学》杂志发表突破性研究成果：研究团队克隆到同时调控水稻熟期和产量的稀有等位基因TGW1aJZ，并揭示其编码的类成花素蛋白，可通过耦合“开花时钟”与氮素利用效率打破作物“早熟—高产”遗传枷锁的分子机制。研究明确了作物“早熟高产”的实质是熟期与氮素利用效率的平衡，为培育早熟高产品种提供了理论依据和基因资源。\n\n早花会缩短营养生长期，进而降低生物量的积累和产量。“早熟与高产”如“鱼与熊掌”，长期以来难以兼得。\n\n论文通讯作者、中国水稻研究所研究员张健介绍，研究团队对收集的300余份全球水稻种质资源的生育期和产量性状进行综合评价，创制了以华南及长江流域主栽籼稻品种黄华占和东北早熟、特大粒粳稻资源品种吉资1560为亲本的重组自交系群体。他们以千粒重与生育期作为评价关键指标，在1号染色体上定位到同时赋予水稻早熟和高产特性的主效位点qTGW1a，表明该位点是打破水稻“早熟—高产”遗传枷锁的关键钥匙。\n\n研究发现，基于启动子上的大片段序列差异，克隆基因TGW1a可分为3种单倍型。其中，TGW1aJZ为祖先型，主要存在于野生稻中；TGW1aHHZ主要存在于籼稻品种中；TGW1aNIP主要存在于粳稻品种中。\n\n研究团队通过分子育种手段将TGW1aJZ导入南方主栽早籼品种中嘉早17和中早39，以及籼型恢复系华占和R173中，并进一步筛选含TGW1aJZ-Gn1aHHZ的改良品种，于2023—2025年连续3年在浙江杭州、广西南宁进行大田区域试验。结果显示，改良新品种收获期缩短3—8天，产量增加4.2%—11.0%。\n\n“该研究针对当前我国多熟制地区茬口矛盾的农业生产痛点，挖掘到早熟高产关键基因TGW1aJZ，培育了一系列熟期提前3—8天、产量提升4%—11%的改良新品种，为水稻早熟高产改良提供了优异基因资源与应用示范。”张健表示。（记者马爱平）\n\n声明：此文版权归原作者所有，若有来源错误或者侵犯您的合法权益，您可通过邮箱与我们取得联系，我们将及时进行处理。邮箱地址：jpbl@jp.jiupainews.com","中国水稻研究所2026年8月29日","2026-08-29T00:00:00Z",91,{"impact":29,"substance":30,"depth":84,"authority":85,"freshness":33,"relevant":34,"comment":86},18,14,"重大科研突破，揭示早熟高产分子机制，数据详实，权威性高，时效性稍差。",[88],{"name":80,"url":77},[90,91,92,93],"中国农科院","水稻所","TGW1aJZ","类成花素蛋白",{"code":44,"slug":45,"name":39,"path":46,"parent_code":47},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":97},{"candidates":98,"agricultural_evidence":26},[99,104],{"code":44,"score":100,"supported":26,"title":101,"abstract":102,"tags":103},11,[39],[39],[],{"code":62,"score":105,"supported":26,"title":106,"abstract":107,"tags":109},6,[],[66,108],"分子育种",[],[111,112],"类成花素蛋白 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(记者郁静娴)记者从农业农村部获悉：《全国农产品产地市场体系发展“十五五”规划》印发，规划提出，到2030年，契合产业、产需衔接、高效顺畅、内联外通的现代农产品产地市场体系基本建成，农产品供给保障和服务带动小农户能力稳步提升。\n\n规划显示：到2030年，农产品产地批发市场布局更加合理，交易、冷链、物流等设施持续完善；农产品信息监测预警体系更加健全，服务决策和引导预期能力显著提升；农产品产地市场体系标准化、数智化、绿色化水平进一步提高。\n\n围绕推动农产品产地市场转型升级、强化农产品市场营销服务、增强农产品市场监测预警能力，规划提出一系列具体部署，包括优化提升产地批发市场、加快完善产地冷链物流设施、建立健全乡村产销服务站点；丰富消费场景，推动农产品消费融入文化旅游、体育赛事等领域，扩大绿色优质农产品消费；加强重点农产品产地、销地、集散地批发市场和大型种植养殖企业信息统计，监测农产品来源去向、价格销量、成本收益、库存数量等情况。","《人民日报》2026年08月31日第8版(腾讯网转载)","2026-08-31T00:00:00Z","政策",90,{"impact":29,"substance":127,"depth":128,"authority":85,"freshness":25,"relevant":34,"comment":129},22,16,"全国性政策规划，目标明确，部署具体，权威性高，时效性强。",[131],{"name":122,"url":119},[133,134,135,136],"十五五规划","产销对接","冷链物流","产地市场",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"E3","cold-chain","冷链仓储物流","\u002Ftopic\u002Ffood-supply\u002Fcold-chain","E",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":145},{"candidates":146,"agricultural_evidence":26},[147,152,157],{"code":138,"score":72,"supported":26,"title":148,"abstract":149,"tags":151},[],[150],"冷链",[135],{"code":153,"score":34,"supported":51,"title":154,"abstract":155,"tags":156},"E5",[],[],[134],{"code":158,"score":34,"supported":51,"title":159,"abstract":160,"tags":161},"J1",[],[],[133],[163,164],"十五五规划 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通过《创新与数字化增强亚太区域粮食体系韧性原则文件》","https:\u002F\u002Fwww.workercn.cn\u002Fpapers\u002Fgrrb\u002F2026\u002F08\u002F27\u002F8\u002Fnews-5.html","8月25日,第十一届APEC粮食安全部长级会议在浙江杭州召开,会议由农业农村部部长张柱主持。会议审议通过《第十一届APEC粮食安全部长级会议联合声明》《创新与数字化增强亚太区域粮食体系韧性原则文件》,后者围绕数据驱动与科学决策、技术创新与全链应用、广泛参与与普惠共享、多元协作与政企携手、系统思维与转型发展5方面原则,规划17项关键行动。FAO总干事屈冬玉强调亚太地区要把科技和AI放在农业粮食系统转型核心,SMART农业:S科学驱动、M机器学习、A人工智能、R韧性、T转型。","本报讯（记者姜雨晴）8月25日，第十一届亚太经合组织（APEC）粮食安全部长级会议在浙江杭州召开。会议通过并发布《创新与数字化增强亚太区域粮食体系韧性原则文件》（以下简称原则文件），呼吁各经济体应用创新成果和人工智能等数字技术，提高粮食体系全链条的决策能力、互联互通水平和响应速度，推动粮食体系更好地预见风险、承受冲击、适应变化和恢复产能，提升运行效率和整体效能。\n\n2026年是APEC“中国年”，我国肩负东道主责任，与各经济体多轮磋商、深入交流，牵头起草该原则文件。8月21日，原则文件经APEC粮食安全政策伙伴关系机制2026年第二次全体会议审议通过后，提交第十一届APEC粮食安全部长级会议审议。\n\n原则文件围绕数据驱动与科学决策、技术创新与全链应用、广泛参与与普惠共享、多元协作与政企携手、系统思维与转型发展等5方面原则，规划了17项关键行动，凝聚各方共识，为今后一个时期深化亚太粮食安全合作提供行动方向。\n\n国家粮食和物资储备局局长刘焕鑫表示，中方愿与APEC各经济体携手，以落实原则文件为契机，秉持开放合作、互利共赢精神，持续开展政策对话，持续推进交往交流，持续深化务实合作，携手建设更具韧性、更有效率、更高质量的亚太粮食体系。","工人日报2026年8月27日","2026-08-25T12:00:00Z",88,{"impact":29,"substance":127,"depth":84,"authority":85,"freshness":105,"relevant":34,"comment":177},"APEC粮食安全部长级会议通过数字化韧性文件，国家级政策事件，信息具体，权威性高，但时效性稍弱。",[179],{"name":173,"url":170},[181,182,183,184],"数字农业","粮食安全","人工智能","APEC",{"code":186,"slug":187,"name":188,"path":189,"parent_code":47},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":192},{"candidates":193,"agricultural_evidence":26},[194,198,204],{"code":186,"score":57,"supported":26,"title":195,"abstract":196,"tags":197},[182],[182],[182],{"code":199,"score":72,"supported":26,"title":200,"abstract":201,"tags":203},"I2",[],[202],"机器学习",[183],{"code":205,"score":34,"supported":51,"title":206,"abstract":207,"tags":208},"I1",[],[],[181],[210,211],"人工智能 数字农业 粮食安全 APEC","人工智能 数字农业","人工智能数字农业粮食安全APEC-1128","2026-08-31T00:08:54.562095Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":20,"content":219,"source_name":220,"source_url":20,"published_at":221,"category":124,"cover_url":20,"hotness":25,"is_selected":26,"score":222,"score_detail":223,"sources":226,"tags":228,"primary_topic":233,"secondary_topics":238,"classification":239,"search_phrases":265,"slug":268,"view_count":72,"doi":20,"paper":20,"created_at":269},1129,"财政部:2026年中央财政安排农业补贴资金2630亿元、常态化帮扶资金1770亿元","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7678523631132672550\u002F","财政部副部长廖岷在国新办新闻发布会上通报:2026年中央财政安排农业补贴资金2630亿元、常态化帮扶资金1770亿元,重点支持耕地地力保护、种粮农民收益保障、农机购置与应用补贴、粮油规模种植主体单产提升,以及防止规模性返贫;二轮延包30年已在贵州、西藏之外的29个省(区、市)全面开展整省试点。","我国农村正在推进一项重大土地政策调整——第二轮土地承包到期后，承包期限将再延长三十年。这项被称为“二轮延包”的政策已在贵州、西藏之外的29个省（区、市）全面开展整省试点，其核心在于稳定农村土地承包关系，给亿万农民吃下长效“定心丸”。\n\n一、核心原则：稳定压倒一切\n\n此次延包工作最核心的指导思想是 “大稳定、小调整” 。\n\n“大稳定”是绝对前提：绝大多数农户现有的承包地将保持稳定，顺利延长30年。任何组织和个人都不能借机推倒重来、打乱重分，或违法收回、调整农户的承包地。这意味着农民朋友不必担心现有土地会被收回或大规模打乱重分，可以放心规划长期的农业投入和生产安排。\n\n“小调整”是例外情况：仅在极少数村组，若因自然灾害等特殊情形导致承包地严重毁损，且经本集体经济组织成员大会或代表大会三分之二以上成员同意后，才允许在个别农户之间进行小范围适当调整。且此类调整必须依法依规从严掌握，报乡镇和县级政府部门批准。\n\n‍‍‍ 二、以户为单位，成员权是关键\n\n延包工作明确以户为单位进行。这意味着只要在二轮承包时拥有承包权的家庭，其农户身份就是延包的基础。\n\n“生不增、死不减”的延续：新的承包期将继续以农户为单位。家庭成员全部消亡的农户（即“消亡户”），其承包地应依法由发包方收回，但原承包方应得的承包收益可依照《民法典》规定继承。这维护了土地集体所有的性质，防止私有化。\n\n集体经济组织成员权是根本依据：享有土地承包权益的前提是具备本集体经济组织成员的身份。非本集体组织的成员（例如外来投资者）不参与延包。政策特别关注妇女权益，确保出嫁、离婚、丧偶妇女及入赘婿等群体的知情权和参与权，避免他们在娘家婆家“两头空”，也防止“两头占”。\n\n进城落户农民工地权益有保障：国家明确依法保障进城落户农民的土地承包权益。是否退出承包地，将由农民自愿选择，各地正探索建立农户自愿有偿退出的具体办法。不得以退出承包地作为农民进城落户的条件。\n\n⚖️ 三、尊重历史与现实，化解突出矛盾\n\n针对历史遗留问题和现实新情况，政策提出了应对思路：\n\n无地少地农户的诉求：对于第二轮土地承包期内放弃承包或因各种原因未获得土地的农户（“无地农户”），以及现有人均耕地过少导致生活困难的农户，政策要求依法维护其合法权益，并鼓励通过集体收益分配、提供就业服务、公益性岗位安置等非地方式解决其困难。但原则上不再通过大规模调地来解决。\n\n“确权确股不确地”的从严掌握：此前的确权工作中，部分地方实行了“确权确股不确地”（农户只确认股权份额，不对应具体地块）。此次延包强调确权到户到地。除非群众普遍要求且符合特定条件（如人均承包地不足0.3亩），否则不得违背农民意愿强行推动确股不确地，也不能简单以少数服从多数强迫不愿确股的农民确股。已按此方式确权的，可按原方式延包。\n\n机动地和新增耕地的严格管理：机动地面积不得超过本集体经济组织耕地总面积的5%，且不得新增。依法开垦、复垦等方式新增加的耕地，需县级以上政府明确所有权。确定为集体所有的耕地，纳入集体财产管理，可通过多种方式发展生产经营，收益主要用于分配给成员，确保集体惠农。\n\n四、规范操作：流程公开透明，合同网签备案\n\n为确保公平公正，延包工作设定了规范的程序：\n\nflowchart LR\n\nA[成立工作小组\u003Cbr>民主选举产生] --> B[摸底调查\u003Cbr>逐户逐块核查]\n\nB --> C[制定方案\u003Cbr>民主协商公示≥15日]\n\nC --> D[审核公示\u003Cbr>核查调查成果]\n\nD --> E[签订合同\u003Cbr>应用网签系统示范文本]\n\nE --> F[完善证书\u003Cbr>不动产登记变更]\n\nF --> G[资料归档\u003Cbr>全过程规范整理移交]\n\n整个过程中，公示是关键环节，涉及摸底结果、延包方案、审核成果等都要在村内显著位置进行公示，接受群众监督，一般公示期不少于15日。签订合同后，农业农村部门会将合同信息共享给同级自然资源部门，用于办理不动产登记变更手续。\n\n五、全国推进与地方实践：目标与时间表\n\n根据中央部署，此项工作时间表清晰：\n\n六、深远影响：稳定预期，赋能乡村振兴\n\n这项政策的意义远不止于让农民继续种地：\n\n夯实粮食安全根基：长期稳定的土地关系，能鼓励农民和新型经营主体增加对土壤改良、水利设施、农业机械等长期投入，提升农业生产效率和粮食产能。\n\n促进农业现代化：清晰的产权关系是土地经营权流转和发展适度规模经营的基础。政策强调依法保障流转双方合法权益，引导通过合同约定明确续租事宜，稳定经营主体预期。同时，大力支持农业社会化服务，解决“谁来种地”难题，促进小农户与现代农业发展有机衔接。\n\n激活乡村内生动力：“三权分置”政策体系在延包中进一步完善，旨在激活土地这一核心资源要素。稳定的承包权让农民更安心地流转经营权或发展合作经济，助力乡村振兴。\n\n为社会资本下乡提供稳定预期：明确的规则和长期的产权界定，也降低了社会资本投资农业的不确定性，有利于吸引资金、技术、人才等要素流向乡村。\n\n❓ 常见疑问解答\n\n\u003Cdetails>\n\n\u003Csummary>\u003Cb> 我在外打工，没参与村里的延包会议，会影响我的承包地吗？\u003C\u002Fb>\u003C\u002Fsummary>\n\n不影响。延包是以农户为单位进行的，只要您家的户口没有迁出本集体经济组织（即仍是村集体成员），您的土地承包经营权就会得到延续。村集体有义务通知到户，建议您及时关注并与家人沟通确认。进城落户本身也不会导致失去承包地。\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\n\u003Csummary>\u003Cb> 听说这次有些村“确权确股不确地”了，到底是怎么回事？\u003C\u002Fb>\u003C\u002Fsummary>\n\n这是一种特殊的承包方式。指农户承包的是本集体的土地份额（股权），而非具体某一块地（地块）。通常适用于土地细碎化严重、难以划分到户到地的地区。政策对此类方式从严掌握，必须经过民主程序，充分尊重农民意愿。已经这样确权的村组，延包时可以继续沿用这种方式。\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\n\u003Csummary>\u003Cb> 我想把我的承包地流转给别人种，这次延包后还能签长期租赁合同吗？\u003C\u002Fb>\u003C\u002Fsummary>\n\n当然可以，而且权益会更有保障。延包后，您的承包期更长了，因此更有底气与受让方（承租方）协商签订较长期限的流转合同。政策明确要求“依法保障流转双方合法权益”，并通过合同约定等方式明确承包延期后的续租事宜，从而稳定双方的预期。建议在合同中约定清楚流转期限、价格及支付方式、违约责任等。\n\n\u003C\u002Fdetails>\n\n小结一下：二轮延包绝非简单的“时间延期”，它是一次对农村土地产权关系的系统性巩固和升级。它坚守了“公有”性质和“家庭经营”基础这两条底线，又通过细化规则回应了时代挑战，旨在构建一个更加稳定、更有效率、更具包容性的新型农村土地制度，为中国农业农村的现代化建设和乡村的全面振兴筑牢坚实的制度根基。","财政部国新办新闻发布会(今日头条转载)","2026-08-21T07:00:00Z",84,{"impact":29,"substance":127,"depth":84,"authority":85,"freshness":224,"relevant":34,"comment":225},2,"全国性重大土地政策解读，信息详实，但时效性较低。",[227],{"name":220,"url":217},[229,230,231,232],"常态化帮扶","中央财政","农业补贴","二轮延包",{"code":234,"slug":235,"name":236,"path":237,"parent_code":20},"G","rural-services","农业服务与乡村发展","\u002Ftopic\u002Frural-services",[],{"status":240,"locked":51,"version":52,"method":53,"evidence":241},"partial",{"candidates":242,"agricultural_evidence":26,"reason":264},[243,248,253,258],{"code":244,"score":57,"supported":26,"title":245,"abstract":246,"tags":247},"G3",[231],[231],[231],{"code":249,"score":57,"supported":26,"title":250,"abstract":251,"tags":252},"G6",[229],[229],[229],{"code":186,"score":63,"supported":26,"title":254,"abstract":255,"tags":257},[],[256],"单产提升",[],{"code":259,"score":63,"supported":26,"title":260,"abstract":261,"tags":263},"F5",[],[262],"农机",[],"一级领域明确，二级方向需要细分",[266,267],"常态化帮扶 中央财政 二轮延包 农业补贴","常态化帮扶 中央财政","常态化帮扶中央财政二轮延包农业补贴-1129","2026-08-31T00:08:54.611886Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":20,"content":275,"source_name":276,"source_url":20,"published_at":81,"category":24,"cover_url":20,"hotness":25,"is_selected":51,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":288,"secondary_topics":294,"classification":301,"search_phrases":315,"slug":318,"view_count":34,"doi":20,"paper":20,"created_at":319},1135,"中国农科院植保所研发仿生水凝胶破解微生物农药田间短板 对番茄潜叶蛾防效提升约27%","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F9e49e8e34ca6455792c65ad8b57245a3.htm","近日,中国农业科学院植物保护研究所研发出一种生物膜仿生的苏云金芽胞杆菌(Bt)水凝胶制剂,显著提升了抗逆性和持效期,对番茄潜叶蛾的田间防治效果显著提高。研究成果发表在《化学工程期刊》(Chemical Engineering Journal)上。科研团队使用叶酸和锌超分子水凝胶体系将Bt芽胞和晶体蛋白包封其中,模拟生物膜的三维多孔网络结构。与传统可湿性粉剂相比,Bt水凝胶制剂在施药4天后对番茄潜叶蛾的防治效果接近85%,施药14天后防效仍可达72%,较可湿性粉剂提高约27%。","近日，中国农业科学院植物保护研究所研发出一种生物膜仿生的苏云金芽胞杆菌水凝胶制剂，显著提升了抗逆性和持效期，对番茄潜叶蛾的田间防治效果显著提高。相关研究成果发表在《化学工程期刊（Chemical Engineering Journal）》上。\n\n番茄潜叶蛾是全球性重大入侵害虫，被列为我国一类农作物病虫害。Bt是全球应用最广泛的微生物杀虫剂，对番茄潜叶蛾具有良好活性。然而，在田间应用中Bt产品面临两大瓶颈，一是紫外辐射导致产品失活，二是雨水冲刷造成有效成分大量流失。\n\n科研团队受细菌生物膜启发，使用一种叶酸和锌超分子水凝胶体系，将Bt芽胞和晶体蛋白包封其中，成功模拟了生物膜的三维多孔网络结构和保护功能。与传统可湿性粉剂相比，Bt水凝胶制剂在施药4天后对番茄潜叶蛾的防治效果接近85%，施药14天后防效仍可达72%，较可湿性粉剂提高约27%，展示出该制剂良好的应用前景，破解了微生物农药田间应用的关键难题。\n\n该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。（通讯员 郭建英）","中国农业科学院植保所(Chemical Engineering Journal 2026)",82,{"impact":127,"substance":127,"depth":279,"authority":85,"freshness":33,"relevant":34,"comment":280},17,"国家级科研机构在权威期刊发表的新成果，破解微生物农药田间应用难题，数据详实，影响深远。",[282],{"name":276,"url":273},[284,285,286,287],"植保所","番茄潜叶蛾","仿生水凝胶","Bt农药",{"code":289,"slug":290,"name":291,"path":292,"parent_code":293},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[295],{"code":296,"slug":297,"name":298,"path":299,"parent_code":300},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":50,"locked":51,"version":52,"method":53,"evidence":302},{"candidates":303,"agricultural_evidence":26},[304,310],{"code":296,"score":128,"supported":26,"title":305,"abstract":308,"tags":309},[306,307],"农药","植保",[],[],{"code":289,"score":57,"supported":26,"title":311,"abstract":313,"tags":314},[312],"番茄",[312],[285],[316,317],"仿生水凝胶 番茄潜叶蛾 植保所 Bt农药","仿生水凝胶 番茄潜叶蛾","仿生水凝胶番茄潜叶蛾植保所Bt农药-1135","2026-08-31T00:08:54.836422Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":20,"content":325,"source_name":326,"source_url":20,"published_at":327,"category":24,"cover_url":20,"hotness":25,"is_selected":51,"score":277,"score_detail":328,"sources":332,"tags":334,"primary_topic":20,"secondary_topics":338,"classification":339,"search_phrases":352,"slug":355,"view_count":34,"doi":20,"paper":20,"created_at":356},1134,"中国农业科学院2026年国家自然科学基金获批项目创历史新高 共获资助445项","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fnkyw\u002F10ddf337c64c4138b0b5b265addf9972.htm","8月26日,国家自然科学基金2026年集中受理期项目资助结果公布。中国农业科学院共获资助445项,同比增长37.3%,全院资助项数增幅显著超过申请项数增幅,立项规模创历史新高。其中面上项目188项、青C项目242项、青A项目3项、青B项目5项、重点项目4项、重点国合1项、外国学者项目1项。立项数超过20项的研究所达8个:基因组所、作科所、蔬菜所、资划所、兰兽研、加工所、植保所和牧医所。“十四五”期间全院共获批各类基金项目1783项,较“十三五”增长12.4%和33.4%。","8月26日，国家自然科学基金2026年集中受理期项目资助结果公布。中国农业科学院共获资助445项，同比增长37.3%。全院资助项数增幅显著超过申请项数增幅，立项规模创历史新高。其中，面上项目188项、青C项目242项、青A项目3项、青B项目5项、重点项目4项、重点国合1项、外国学者项目1项。立项数超过20项的研究所有8个，分别为基因组所、作科所、蔬菜所、资划所、兰兽研、加工所、植保所和牧医所。\n\n国家自然科学基金是我院基础研究获得资助的主渠道之一，在全院科技创新体系中占据重要位置，获资助情况已成为衡量我院原始创新能力的重要标尺。院所高度重视基金申报立项，“十四五”期间，全院共获批各类基金项目1783项，较“十三五”增长12.4%和33.4%。基金项目实施不断夯实全院基础研究底盘，赋能原创性成果产出，推动人才梯队建设。\n\n目前，国家自然科学基金集中接收期间的联合基金、青A延续等其他类型项目正在评审或审批过程中。（通讯员 张月遥）","中国农业科学院新闻2026年8月26日","2026-08-26T09:00:00Z",{"impact":329,"substance":330,"depth":32,"authority":32,"freshness":33,"relevant":34,"comment":331},25,20,"国家级科研机构基金项目成果，数据详实，对农业科技发展有重要参考价值。",[333],{"name":326,"url":323},[90,335,336,337],"国家自然科学基金","立项","基础研究",[],{"status":340,"locked":51,"version":52,"method":53,"evidence":341},"needs_review",{"candidates":342,"agricultural_evidence":26,"reason":351},[343,347],{"code":289,"score":63,"supported":26,"title":344,"abstract":345,"tags":346},[],[291],[],{"code":296,"score":63,"supported":26,"title":348,"abstract":349,"tags":350},[],[307],[],"多个产业证据相当，需要确认主归属",[353,354],"国家自然科学基金 中国农科院 基础研究 立项","国家自然科学基金 中国农科院","国家自然科学基金中国农科院基础研究立项-1134","2026-08-31T00:08:54.808244Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":20,"content":362,"source_name":363,"source_url":20,"published_at":364,"category":124,"cover_url":20,"hotness":25,"is_selected":51,"score":365,"score_detail":366,"sources":368,"tags":370,"primary_topic":375,"secondary_topics":381,"classification":382,"search_phrases":391,"slug":394,"view_count":105,"doi":20,"paper":20,"created_at":395},1125,"农业农村部公告第1055号:批准氟雷拉纳莫昔克丁滴剂等14种兽药产品为新兽药","https:\u002F\u002Fm.jbzyw.com\u002Fview\u002F516019","农业农村部于8月24日发布第1055号公告,批准氟雷拉纳莫昔克丁滴剂、氟雷拉纳、枸橼酸马罗匹坦、枸橼酸马罗匹坦片、戊巴比妥钠、戊巴比妥钠溶液、禽腺病毒(I群,4型)灭活疫苗(HB-2株)、猪圆环病毒2型\u002F猪肺炎支原体二联灭活疫苗、鸡新城疫\u002F禽流感H9\u002F禽腺病毒三联灭活疫苗、甲硝唑氯化钠注射液、胰岛素注射液、复方非泼罗尼吡喹酮莫昔克丁滴剂、盐酸莫西沙星滴眼液等14种兽药产品为新兽药;批准非洲猪瘟病毒抗体检测试纸条等2种产品注册。本批次覆盖犬猫伴侣动物相关品种4种,均纳入兽用处方药管理。","根据《兽药管理条例》和《兽药注册办法》规定，经审查，批准氟雷拉纳莫昔克丁滴剂等14种兽药产品为新兽药，核发《新兽药注册证书》，发布产品质量标准、工艺规程、说明书和标签，自发布之日起执行。\n\n批准非洲猪瘟病毒抗体检测试纸条等2种兽药产品注册，发布产品质量标准、工艺规程、说明书和标签，自发布之日起执行。\n\n批准鸡新城疫、禽流感（H9亚型）、禽腺病毒病（I群，4型）三联灭活疫苗（La Sota株＋F6株＋LC株）变更注册，发布修订后的质量标准、工艺规程、说明书和标签，自发布之日起执行；批准阿莫西林克拉维酸钾片变更注册，发布修订后的说明书和标签，自发布之日起执行，此前发布的产品说明书和标签同时废止；批准猪口蹄疫O型灭活疫苗（OZK\u002F93株+OR\u002F80株）等3种兽药产品变更注册，有关单位按照农业农村部批准的变更事项，自行修改原批准的说明书和标签对应内容，并印制、使用。","农业农村部公告第1055号(鸡病专业网转载)","2026-08-24T01:00:00Z",80,{"impact":329,"substance":330,"depth":32,"authority":32,"freshness":114,"relevant":34,"comment":367},"农业农村部批准新兽药及变更注册，涉及非洲猪瘟检测等，属全国性政策，信息具体，权威性高，但发布已一周，时效性一般。",[369],{"name":363,"url":360},[371,372,373,374],"兽药注册","新兽药","氟雷拉纳","处方药管理",{"code":376,"slug":377,"name":378,"path":379,"parent_code":380},"C5","animal-health","疫病防控与兽药","\u002Ftopic\u002Flivestock\u002Fanimal-health","C",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":383},{"candidates":384,"agricultural_evidence":26},[385],{"code":376,"score":128,"supported":26,"title":386,"abstract":388,"tags":390},[387],"兽药",[389,387],"非洲猪瘟",[371,372],[392,393],"处方药管理 兽药注册 氟雷拉纳 新兽药","处方药管理 兽药注册","处方药管理兽药注册氟雷拉纳新兽药-1125","2026-08-31T00:08:54.359492Z",{"id":397,"title":398,"url":399,"summary":400,"summary_zh":20,"content":401,"source_name":402,"source_url":20,"published_at":403,"category":404,"cover_url":20,"hotness":25,"is_selected":51,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":415,"secondary_topics":419,"classification":420,"search_phrases":431,"slug":434,"view_count":63,"doi":435,"paper":20,"created_at":436},1147,"数字技术与收入不平等:中国农村微观数据证据——农村电商的双面效应","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1937169\u002Ffull","Frontiers in Sustainable Food Systems 2026年8月28日接收(7月14日投稿)。以中国农村电商(RE)发展为案例,分析数字经济对农村收入不平等(RII)的影响及其机制。研究发现:RE发展提高农民收入但扩大收入不平等差距,高收入农民从RE的收入创造效应中受益更多,在农业和非农业部门都扩大了高低收入农户之间的收入差距。改善农村公路和宽带基础设施、选拔具有外出工作经验的更高教育水平的村管理者、升级家庭网络条件、为低收入农民提供数字技能培训可缓解RE对RII的扩大效应。","[](https:\u002F\u002Fwww.frontiersin.org\u002F)\n\n[![Image 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1937169\u002Ffull) Frontiers in Sustainable Food Systems](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n About us\n\nAbout us\n\n*   Who we are\n*   [Mission and values](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fmission)\n*   [History](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fhistory)\n*   [Leadership](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fleadership)\n*   [Awards](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fawards)\n\n*   Impact and progress\n*   [Frontiers' impact](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fimpact)\n*   [Our annual reports](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fannual-reports)\n*   [Thought leadership](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fthought-leadership)\n*   [Researcher 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Agricultural and Food Economics\n\nVolume 10 - 2026 |  doi: 10.3389\u002Ffsufs.2026.1937169\n\nPublished in\n\n[![Image 6: Frontiers in Sustainable Food Systems](https:\u002F\u002Fd2csxpduxe849s.cloudfront.net\u002Fmedia\u002FE32629C6-9347-4F84-81FEAEF7BFA342B3\u002FF3B1CE16-45F3-4D96-BE72BE0491F0DAF2\u002Fwebimage-08DFEB67-EB21-43D0-995D8A1DFF53DB18.png)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n[Frontiers in Sustainable Food Systems](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n*   #### [Agricultural and Food Economics](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Fsections\u002Fagricultural-and-food-economics)\n\n*   [**4.1** impact factor](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n*   [**6** citescore](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n[Part of a Research Topic Adoption and Economics of ICT-Enabled Agricultural Extension and Information Services Submission open * **10k** views * **9** articles](https:\u002F\u002Fwww.frontiersin.org\u002Fresearch-topics\u002F78643\u002Fadoption-and-economics-of-ict-enabled-agricultural-extension-and-information-services)\n\n### Reviewers\n\nReviewed by\n\n![Image 7: 2 Anonymous reviewers](https:\u002F\u002Fstatic2.frontiersin.org\u002Fstatic-resources\u002Fimages\u002Fdefault_profileimage.webp)\n\n2 Anonymous reviewers\n\nOutline\n\n*    [Abstract](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1937169\u002Ffull#h1)\n\n## ORIGINAL RESEARCH article\n\nFront. Sustain. Food Syst.\n\nSec. Agricultural and Food Economics\n\n# The Digital Technology and Income Inequality: Evidence from Micro Data in Rural China\n\n*   [![Image 8: Hua Lu](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F1727534\u002F74) Hua Lu 1](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F1727534)\n*   Z Z \nZeyun Zhong 2,3 \n\n*   1. Jiangxi University of Finance and Economics, Nanchang, China\n\n*   2. Gandong University, Nanchang, China\n\n*   3. Ecological Civilization Research Center, Fuzhou, China\n\n*   See more\n\nArticle metrics\n\n[View details](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1937169\u002Ffull#metrics)\n\n53\n\nViews\n\nThe final, formatted version of the article will be published soon.\n\nNotify me\n\nSelect one o","《Frontiers in Sustainable Food Systems》2026-08-28接收","2026-08-28T00:00:00Z","论文",75,{"impact":84,"substance":330,"depth":84,"authority":57,"freshness":33,"relevant":34,"comment":407},"论文基于中国农村微观数据，探讨农村电商对收入不平等的双面效应，具有政策参考价值，但发表于国际期刊，国内影响力有限。",[409],{"name":402,"url":399},[411,412,413,414],"农村电商","数字技术","收入不平等","Frontiers Sustain Food Syst",{"code":153,"slug":416,"name":417,"path":418,"parent_code":142},"branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":421},{"candidates":422,"agricultural_evidence":26},[423,427],{"code":153,"score":57,"supported":26,"title":424,"abstract":425,"tags":426},[411],[411],[411],{"code":205,"score":34,"supported":51,"title":428,"abstract":429,"tags":430},[],[],[412],[432,433],"收入不平等 农村电商 数字技术 Frontiers Sustain Food Syst","收入不平等 农村电商","收入不平等农村电商数字技术FrontiersSustainFoodSyst-1147","10.3389\u002Ffsufs.2026.1937169\u002Ffull","2026-08-31T00:08:57.293928Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":20,"content":442,"source_name":443,"source_url":20,"published_at":444,"category":24,"cover_url":20,"hotness":25,"is_selected":51,"score":405,"score_detail":445,"sources":447,"tags":449,"primary_topic":20,"secondary_topics":452,"classification":453,"search_phrases":458,"slug":461,"view_count":114,"doi":20,"paper":20,"created_at":462},1138,"江苏农科院2026年度国家自然科学基金立项实现历史性跨越 获批79项 直接经费2889万元","https:\u002F\u002Fjaas.ac.cn\u002Fznbmxwlsml\u002Fltxc\u002Fkyc\u002Fart\u002F2026\u002Fart_b0b4f8b1a12a495ea0339e9e1c9499b5.html","8月26日,国家自然科学基金委员会公布2026年度集中接收申请项目评审结果,江苏农科院共获批79项,直接经费2889万元,立项数量和经费总额均创历史新高。青年科学基金项目(C类)53项,面上项目26项。全院立项数较2025年增长68%,青年科学基金项目数量实现翻倍增长。优势学科:环境地球科学、兽医学、农学基础与作物学、园艺学与植物营养学、植物保护学等,立项数占总立项数80%以上。","8月26日，国家自然科学基金委员会公布年度集中接收申请项目评审结果。我院共获批国家自然科学基金资助项目79项，直接经费2889万元，立项数量和经费总额均创历史新高。\n\n在获批项目中，青年科学基金项目（C类）53项，面上项目26项。全院立项数较2025年增长68%，青年科学基金项目数量实现翻倍增长。此次立项实现专业所全覆盖，泰州所首次获得国家自然科学基金项目资助。\n\n从学科分布上看，环境地球科学、兽医学、农学基础与作物学、园艺学与植物营养学、植物保护学等优势学科表现突出，立项数占总立项数80%以上；食品科学、化学工程与工业化学等交叉学科也取得立项突破。","江苏省农业科学院","2026-08-27T00:00:00Z",{"impact":127,"substance":330,"depth":85,"authority":57,"freshness":33,"relevant":34,"comment":446},"省级农科院科研立项突破，数据详实，对农业科技领域有参考价值，但非全国性政策或产业级事件。",[448],{"name":443,"url":440},[450,335,336,451],"江苏省农科院","历史新高",[],{"status":454,"locked":51,"version":52,"method":53,"evidence":455},"unclassified",{"candidates":456,"agricultural_evidence":26,"reason":457},[],"缺少足够的主题证据",[459,460],"国家自然科学基金 江苏省农科院 历史新高 立项","国家自然科学基金 江苏省农科院","国家自然科学基金江苏省农科院历史新高立项-1138","2026-08-31T00:08:56.134717Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":20,"content":468,"source_name":469,"source_url":20,"published_at":403,"category":404,"cover_url":20,"hotness":25,"is_selected":51,"score":470,"score_detail":471,"sources":474,"tags":476,"primary_topic":480,"secondary_topics":486,"classification":487,"search_phrases":512,"slug":515,"view_count":224,"doi":20,"paper":20,"created_at":516},1148,"人工智能在土壤表征与农业中的应用:技术、模型与应用系统综述(Agronomy 16(13) 1241)","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241","发表于《Agronomy》(Basel) 2026年第16卷第13期1241:基于PRISMA 2020框架,对Scopus数据库2018-2026年196项合格研究进行系统综述,聚焦土壤理化性质、数字土壤制图、灌溉管理和作物产量预测。研究显示机器学习与深度学习模型主导近期研究,随机森林、支持向量机、梯度提升、人工神经网络、卷积神经网络、长短期记忆架构是最常报告的技术。主要数据来源包括原位传感器、实验室测量、遥感影像和环境协变量,常通过多源数据融合框架集成。结果表明基于树的集成模型在不同土壤属性上提供稳健性能,深度学习模型对时空预测和遥感应用特别有效。挑战涉及数据质量与可用性、跨区域模型可迁移性和复杂模型有限的可解释性。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agronomy-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagronomy-logo.png?010a42cd82f83819)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagronomy)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Systematic Review\n\nby \nCesar Augusto Navarro Rubio\n\n[](mailto:cnavarro15@alumnos.uaq.mx)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0009-0003-7541-5841), \nHugo Martínez Ángeles\n\n *[](mailto:hmartinez15@alumnos.uaq.mx)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-8552-705X), \nMario Trejo Perea\n\n *[](mailto:mtp@uaq.mx)[![Image 4: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-7014-3379), \nJosé Luis Reyes Araiza\n\n[](mailto:lreyes@uaq.mx)[![Image 5: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-2609-102X), \nGuillermo Ronquillo-Lomeli\n\n[](mailto:gronquillo@cidesi.edu.mx)[![Image 6: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-2459-1602), \nIvan Gonzalez-Garcia\n\n[](mailto:ivan.glez@uaq.mx)[![Image 7: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0001-6331-5761), \nEusebio Ventura Ramos\n\n[](mailto:eventura@uaq.mx)[![Image 8: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-4414-7875) and \nJosé Gabriel Ríos Moreno\n\n[](mailto:riosg@uaq.mx)[![Image 9: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-8988-2729)\n\nFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico\n\n*\n\nAuthors to whom correspondence should be addressed.\n\nSubmission received: 22 May 2026 \u002F Revised: 19 June 2026 \u002F Accepted: 24 June 2026 \u002F Published: 26 June 2026\n\n## Abstract\n\nArtificial Intelligence (AI) has become a key enabler in soil science and agriculture, supporting advanced modeling, monitoring, and decision-making processes. This systematic review synthesizes recent developments in AI-based soil characterization and agricultural applications, with emphasis on soil physicochemical properties, digital soil mapping, irrigation management, and crop yield prediction. Following the PRISMA 2020 framework, a structured search of the Scopus database identified 196 eligible studies published between 2018 and 2026. The reviewed literature reveals a clear transition toward data-driven approaches, with machine learning and deep learning models dominating recent research. Random Forest, Support Vector Machines, gradient boosting methods, artificial neural networks, Convolutional Neural Networks, and Long Short-Term Memory architectures were the most frequently reported techniques. The primary data sources included in situ sensors, laboratory measurements, remote sensing imagery, and environmental covariates, often integrated through multi-source data fusion frameworks. The results indicate that tree-based ensemble models provide robust performance across diverse soil properties, whereas deep learning models are particularly effective for spatiotemporal prediction and remote sensing applications. AI-driven systems are increasingly used to support precision agriculture through irrigation optimization, crop yield forecasting, digital soil mapping, and soil health monitoring. However, challenges remain regarding data quality and availability, model transferability across regions, and the limited interpretability of complex models. The findings highlight current research trends, methodological challenges, and future opportunities for the development of reliable and scalable AI-driven soil and agricultural systems.\n\n## 1. Introduction\n\nSoil is a fundamental component of terrestrial ecosystems and plays a critical role in agricultural productivity, environmental sustainability, and land management. The accurate characterization of soil properties such as moisture content, texture, salinity, temperature, and nutrient availability is essential for optimizing agricultural practices and ensuring efficient resource utilization. These properties are inherently variable in both space and time, making soil systems highly complex and challenging to model using traditional analytical approaches.\n\nAmong the various soil parameters investigated in the literature, soil electrical resistivity (SER) has gained increasing attention due to its sensitivity to multiple physicochemical properties and its suitability for data-driven analysis. Electrical conduction in soils occurs primarily through the pore fluid and dissolved ions, making resistivity strongly dependent on moisture content, salinity, temperature, and soil structure. As a result, SER is widely used as an indirect proxy for assessing soil conditions in applications ranging from geophysical exploration to precision agriculture and environmental monitoring [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)].\n\nThe relationship between soil electrical resistivity and environmental variables is highly nonlinear and influenced by complex interactions among multiple parameters. Moisture content has been consistently identified as the dominant factor controlling resistivity, exhibiting a strong inverse correlation (r ≈\n\n$- 0.88$\n), while temperature and soil suction generally show weaker positive relationships [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)]. In addition, soil properties such as clay content, plasticity, and dry unit weight significantly affect electrical conduction mechanisms, highlighting the need for multivariable modeling approaches capable of capturing these interactions [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241)]. Furthermore, electrical conductivity, which represents the inverse of resistivity, is closely linked to soil salinity and chemical properties such as pH and nutrient concentration, enabling its use in irrigation management and soil fertility assessment [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B5-agronomy-16-01241)].\n\nTraditional methods for soil characterization and resistivity measurement, including laboratory testing and field-based techniques such as the Wenner four-electrode method, provide reliable results but are often labor-intensive, time-consuming, and limited in spatial resolution. These limitations become particularly significant in agricultural contexts, where large-scale monitoring and real-time decision-making are required. Consequently, there has been a growing interest in developing data-driven approaches capable of estimating soil properties based on easily measurable variables.\n\nIn this context, Artificial Intelligence (AI) has emerged as a powerful tool for modeling complex soil systems. Machine learning (ML) and deep learning (DL) algorithms have demonstrated strong capabilities in capturing nonlinear relationships between soil variables and predicting key properties with high accuracy. Models such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) have been widely applied for soil resistivity and property prediction. Some studies have reported coefficients of determination (R 2) values approaching 0.99 under specific experimental settings, controlled conditions, or dataset characteristics; however, model performance varies substantially depending on soil type, environmental conditions, data quality, and validation procedures [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B6-agronomy-16-01241)]. In addition, advanced deep learning architectures, including Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU), have shown strong performance in modeling temporal and spatial variability in soil systems, particularly for moisture prediction and hydraulic property estimation [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B7-agronomy-16-01241),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B8-agronomy-16-01241),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B9-agronomy-16-01241)].\n\nThe integration of AI with sensing technologies has further enhanced soil characterization capabilities. Real-time monitoring systems based on electrical resistivity measurements, such as robotic Wenner arrays, enable rapid spatial mapping of soil properties across agricultural fields [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)]. Similarly, Electrical Resistivity Tomography (ERT) combined with soil moisture measurements has been successfully applied to monitor irrigation processes and analyze wetting–drying cycles in crops such as cotton and tomatoes [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B10-agronomy-16-01241),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B11-agronomy-16-01241)]. Moreover, multi-sensor platforms incorporating electrical, dielectric, optical (Vis–NIR), and acoustic measurements, coupled with AI models, have enabled the development of high-resolution digital representations of soil systems, often referred to as “digital soil twins” [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241)].\n\nBeyond resistivity-focused applications, AI techniques have been widely applied in broader agricultural contexts, including soil fertility mapping, crop yield prediction, crop recommendation systems, and smart irrigation management. These applications leverage large-scale datasets combining soil, climate, and remote sensing information to support data-driven decision-making in precision agriculture [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B13-agronomy-16-01241),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B14-agronomy-16-01241),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B15-agronomy-16-01241),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B16-agronomy-16-01241),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B17-agronomy-16-01241)].\n\nDespite these advances, several challenges remain in the application of AI to soil characterization and agricultural systems. Soil heterogeneity, environmental variability, and the dependence of models on site-specific conditions limit the generalizability of AI approaches [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241)]. Additionally, the availability of high-quality labeled data remains a major constraint, particularly when integrating field measurements, laboratory analyses, and remote sensing data at different spatial and temporal scales [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B14-agronomy-16-01241)]. To address these issues, recent studies have explored synthetic data generation techniques, such as variational autoencoders, and multi-sensor data fusion strategies to improve model robustness and predictive performance [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B16-agronomy-16-01241)]. Furthermore, there is a growing emphasis on the development of hybrid models that combine physical principles with AI techniques, as well as interpretable machine learning approaches, to enhance both predictive accuracy and model transparency [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B5-agronomy-16-01241)].\n\nDespite the rapidly expanding body of research in this field, the existing literature remains fragmented due to variations in datasets, modeling approaches, soil types, environmental conditions, and application domains. Many studies focus on specific regions or individual soil properties, while others emphasize particular AI techniques, making it difficult to establish a unified understanding of current advancements and limitations.\n\nThis fragmentation highlights the need for a systematic and comprehensive synthesis of the existing literature on AI applications in soil characterization and agriculture. A structured review is essential to identify dominant research trends, evaluate the performance of different AI models, and determine the most relevant soil parameters for predictive modeling.\n\nAlthough several review studies have examined specific aspects of AI in agriculture, such as crop yield prediction, remote sensing applications, digital soil mapping, or precision agriculture technologies, a comprehensive synthesis integrating AI methodologies, soil properties, sensing technologies, and agricultural applications within a unified framework remains limited. Addressing these interconnected dimensions is essential to better understand how AI supports soil characterization and agricultural decision-making across multiple scales and application domains.\n\nAccordingly, this study conducts a systematic review of the applications of Artificial Intelligence in soil characterization and agricultural systems, with particular emphasis on soil electrical resistivity, multivariable modeling approaches, and precision agriculture applications.\n\nTo structure this systematic analysis, the following research questions are defined. Addressing these questions provides insights into the current state of knowledge on AI-driven soil analysis and agricultural applications, while acknowledging that the findings are conditioned by the scope and characteristics of the selected studies.\n\n*   What types of AI models are most commonly used for soil characterization and agricultural applications?\n\n*   Which soil properties are most frequently modeled using AI techniques?\n\n*   What sensing technologies and data sources are employed in AI-driven soil studies?\n\n*   What are the main agricultural applications of AI in soil analysis, including precision agriculture and irrigation management?\n\n*   What limitations, challenges, and research gaps exist in current AI-based soil modeling approaches?\n\nThis systematic review is not hypothesis-driven; rather, it is structured around predefined research questions that provide a framework for synthesizing and analyzing the existing literature. The objective is to analyze and compare AI-based methodologies, identify key soil variables influencing predictive performance, and evaluate their applicability in agricultural systems.\n\nThis paper is structured as follows. [Section 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec1-agronomy-16-01241) presents the Introduction, describing the importance of soil characterization, the role of electrical resistivity, and the emergence of AI techniques in agricultural applications. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec2-agronomy-16-01241) describes the Materials and Methods, including the systematic review protocol, search strategy, and selection criteria. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec3-agronomy-16-01241) presents the Results, summarizing the main AI techniques, soil properties, and application domains identified in the literature. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec4-agronomy-16-01241) provides the Discussion, analyzing methodological trends, limitations, and future research directions. Finally, [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec5-agronomy-16-01241) presents the Conclusions, highlighting the main findings and contributions of this study.\n\n## 2. Materials and Methods\n\n### 2.1. Systematic Review Protocol\n\nThis systematic review was conducted following the principles established in the PRISMA 2020 framework [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B18-agronomy-16-01241)], which provides a standardized methodology for identifying, screening, and selecting scientific literature. The application of this protocol ensures a transparent, structured, and reproducible workflow throughout the review process.\n\nThe methodological procedure was organized into four main phases: (i) identification of relevant studies through database searching, (ii) screening of records based on titles and abstracts, (iii) eligibility assessment through full-text evaluation, and (iv) final inclusion of studies that met the predefined criteria. This sequential approach allows for a clear traceability of the selection process while minimizing potential biases associated with subjective study selection.\n\nThe adoption of the PRISMA methodology enables a systematic synthesis of the existing literature on Artificial Intelligence applications in soil characterization and agricultural systems, with particular attention to soil electrical resistivity, multivariable soil analysis, and precision agriculture technologies. The PRISMA 2020 checklist is provided as [Supplementary Material](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#app1-agronomy-16-01241) to ensure compliance with reporting standards.\n\nTo further support transparency and reproducibility, the review protocol was registered in the Open Science Framework (OSF) platform (registration DOI: [https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F6ACSK](https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F6ACSK), accessed on 30 March 2026). The literature search, screening, and study selection processes were completed prior to the final preparation of the manuscript, and the year 2026 represents the final time boundary defined in the search strategy. The OSF access date corresponds solely to the consultation and verification of the registered review protocol.\n\n### 2.2. Search Strategy and Database Selection\n\nThe literature search was performed using the Scopus database, which was selected due to its extensive coverage of peer-reviewed journals, robust indexing system, and advanced search capabilities. Scopus offers a comprehensive representation of research across multiple disciplines, including soil science, agriculture, environmental engineering, and artificial intelligence, making it particularly suitable for the multidisciplinary scope of this study.\n\nAlthough other databases such as Web of Science and Google Scholar also provide valuable sources of scientific information, Scopus was chosen to ensure consistency in metadata, reproducibility of results, and reliable citation tracking. Nevertheless, it is acknowledged that relying on a single database may lead to the omission of some relevant studies, particularly those published in regional or non-indexed journals.\n\nTo mitigate this limitation, the search strategy was carefully designed to capture a broad range of relevant publications by combining keywords related to artificial intelligence, soil characterization, agricultural applications, and soil electrical resistivity.\n\nThe final search query used was as follows:\n\nTITLE-ABS-KEY ((“machine learning” OR “deep learning” OR “artificial intelligence”) AND (“soil characte","《Agronomy》2026, 16(13), 1241",70,{"impact":32,"substance":330,"depth":84,"authority":472,"freshness":72,"relevant":34,"comment":473},13,"系统综述总结AI在土壤与农业应用，方法全面，但时效性稍差，影响限于学术领域。",[475],{"name":469,"url":466},[477,183,478,479],"系统综述","土壤表征","Agronomy",{"code":481,"slug":482,"name":483,"path":484,"parent_code":485},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":488},{"candidates":489,"agricultural_evidence":26},[490,495,500,506],{"code":481,"score":100,"supported":26,"title":491,"abstract":493,"tags":494},[492],"土壤",[492],[],{"code":199,"score":33,"supported":26,"title":496,"abstract":497,"tags":499},[],[498,202],"深度学习",[183],{"code":501,"score":63,"supported":26,"title":502,"abstract":503,"tags":505},"H3",[],[504],"灌溉",[],{"code":507,"score":63,"supported":26,"title":508,"abstract":509,"tags":511},"I3",[],[510],"遥感",[],[513,514],"人工智能 土壤表征 系统综述 Agronomy","人工智能 土壤表征","人工智能土壤表征系统综述Agronomy-1148","2026-08-31T00:08:57.336490Z",{"id":518,"title":519,"url":520,"summary":521,"summary_zh":20,"content":522,"source_name":523,"source_url":20,"published_at":524,"category":24,"cover_url":20,"hotness":25,"is_selected":51,"score":525,"score_detail":526,"sources":528,"tags":530,"primary_topic":534,"secondary_topics":535,"classification":536,"search_phrases":549,"slug":552,"view_count":114,"doi":20,"paper":20,"created_at":553},1132,"内蒙古日报:AI下田地 开启“算天吃饭”新模式 8台“八爪”智能除草机器人落地巴彦淖尔","https:\u002F\u002Fszb.northnews.cn\u002Fnmgrb\u002Fhtml\u002F2026-08\u002F26\u002Fcontent_57933_286458.htm","巴彦淖尔市磴口县圣牧高科生态草业基地引进的6台“八爪”智能除草机器人,可精准判别12种田间常见农作物和杂草,效率是人工除草的40倍,全程物理除草不施化学药剂。呼和浩特市玉泉区姜家营村落地自治区首个MAP农业种植应用大模型,实现智能监测、数字管理和田间农事深度融合。《内蒙古自治区“人工智能+”行动实施方案(2026—2028年)》提出加快农牧业智慧化进程,推动传统育种技术与人工智能深度融合。","-->　　智能除草机器人列队驶过田垄，灵活的机械臂精准拔除田间杂草……这般高效智能的农耕场景，并非科幻影片，而是巴彦淖尔市磴口县田间地头的真实日常。圣牧高科生态草业基地引进的6台“八爪”智能除草机器人，可精准判别12种田间常见农作物和杂草，效率是人工除草的40倍，全程物理除草不施化学药剂，从源头上提升牛羊养殖原料质量。\n\n如今，AI干农活，不再是展会上的样机、PPT里的概念，而是真真切切扎进了内蒙古大地的泥土里，从单点应用走向全产业渗透。\n\n过去农耕看天吃饭，现在越来越多的农田已然开启“算天吃饭”的新模式。呼和浩特市玉泉区姜家营村落地自治区首个MAP农业种植应用大模型，将智能监测、数字管理和田间农事深度融合，助力农业生产提质增效、绿色低碳发展。AI改变的不只是种植方式，更是决策逻辑。过去蹲田头看长势、捏土块，估摸估摸“该浇水了”“该施肥了”，现在算法根据土壤墒情、气象数据、苗情长势自动生成处方图，人只需要点一下确认，就能实现科学田管。在呼伦贝尔农垦特泥河农牧场公司示范基地，“云耕一号”智能灌溉机器人下田，小麦、油菜等作物开启农作物无人化、精准化、绿色化灌溉新模式。有了这样的智能装备，人不再是驾驶着机器跑的劳动者，而是站在屏幕前的决策者。AI种地的本质，是把经验变成数据、用算力替代体力。\n\n如果说田间地头的变化是看得见的，那么更深的变化，发生在整条农牧产业链上。AI不只是在田间牧场干活，它还渗透到从种子到餐桌的每一个环节。《内蒙古自治区“人工智能+”行动实施方案（2026—2028年）》，为全区现代农牧业发展勾勒出清晰蓝图：加快农牧业智慧化进程，推动传统育种技术与人工智能深度融合；推广应用人工智能实现主要粮油作物的智能灌溉、精准施肥播种，加大病虫害人工智能识别与预警；推广应用智能项圈、无人机巡牧、精准饲喂系统，实现牲畜个体识别、行为监测、疫病早筛、繁育管控的全流程智能化。从育种到种养、从加工到监管，AI正在嵌入农牧业生产的每一个关键环节。\n\n产业链上的变革，不只发生在生产端。最近，内蒙古数据交易中心线上平台便进行了这样一笔数据交易，农牧业数据从“副产品”变成了可交易的生产要素。内蒙古益牧智联科技有限公司提供相关数据支撑，内蒙古工业大学提供算法，并研发出智慧牧业智能分析模型，包括牛羊生长及养殖成本预测、肉产品分级与溯源等功能。该模型的落地，让养殖场和冷库迎来了“数字化副场长”“数字化车间主任”，让每一头牛羊都有了可追溯的数字档案。过去种地和养殖靠化肥、农药、饲料、机械，现在还要靠数据、算法、模型。数据越用越值钱，模型越训越聪明，这就是农牧业领域实实在在的新质生产力。\n\n放眼未来，AI赋能农牧业前景广阔、大有可为。这场从田间到餐桌、从经验到数据、从体力到算力的变革，正在重塑内蒙古农牧业的生产方式、产业形态和价值链条。更值得期待的是，随着数据成为新农资、手机成为新农具，越来越多懂技术、会经营的年轻人回到田间牧场，借力AI技术赋能乡村发展，农牧业就有了源源不断的新活力，内蒙古也将在智慧农牧业的新赛道上跑出更远的未来。","《内蒙古日报》2026年8月26日","2026-08-26T01:00:00Z",69,{"impact":84,"substance":330,"depth":128,"authority":25,"freshness":114,"relevant":34,"comment":527},"省级官媒报道AI农业落地案例，涉及智能除草、灌溉及数据交易，内容具体，但时效性稍差。",[529],{"name":523,"url":520},[183,531,532,533],"MAP大模型","智能除草机器人","算天吃饭",{"code":296,"slug":297,"name":298,"path":299,"parent_code":300},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":537},{"candidates":538,"agricultural_evidence":26},[539,544],{"code":296,"score":100,"supported":26,"title":540,"abstract":542,"tags":543},[541],"除草",[541],[],{"code":199,"score":72,"supported":26,"title":545,"abstract":546,"tags":548},[],[547],"大模型",[183],[550,551],"智能除草机器人 人工智能 算天吃饭 MAP大模型","智能除草机器人 人工智能","智能除草机器人人工智能算天吃饭MAP大模型-1132","2026-08-31T00:08:54.752849Z",{"id":555,"title":556,"url":557,"summary":558,"summary_zh":20,"content":559,"source_name":560,"source_url":20,"published_at":561,"category":404,"cover_url":20,"hotness":25,"is_selected":51,"score":562,"score_detail":563,"sources":565,"tags":567,"primary_topic":20,"secondary_topics":572,"classification":573,"search_phrases":582,"slug":585,"view_count":34,"doi":20,"paper":20,"created_at":586},1146,"数字乡村发展韧性评估:基于浙江省90个县(市、区)的实证研究","https:\u002F\u002Ffsdjournal.org\u002Findex.php\u002Fojs\u002Farticle\u002Fview\u002F324","Frontiers in Sustainable Development 2026年第6卷第8期25日发表论文:基于韧性理论,构建数字乡村发展水平评估框架,应用于浙江省90个县(市、区)。使用2022年统计数据,采用熵权法和综合评价方法测度县级数字乡村韧性。结果显示,浙江省数字乡村韧性存在显著区域差异,高韧性地区主要集中在经济发达的东北部地区,而低韧性地区主要位于西部和山区。基于研究结果,提出增强县级数字乡村韧性和促进区域协调发展的政策建议。","## Authors\n\n*    Yongjia Zhao  School of Public Administration, Sichuan University, Chengdu 610000, China \n\n## DOI:\n\n[https:\u002F\u002Fdoi.org\u002F10.54691\u002Fjjd8xg45](https:\u002F\u002Fdoi.org\u002F10.54691\u002Fjjd8xg45)\n## Keywords:\n\n Digital Rural Areas; Resilience Theory; Zhejiang Province. \n## Abstract\n\nDrawing on resilience theory, this study develops an evaluation framework for assessing the development level of digital rural areas and applies it to 90 counties (districts and county-level cities) in Zhejiang Province. Using statistical data from 2022, the entropy weight method and a comprehensive evaluation approach are employed to measure county-level digital rural resilience. The results reveal significant regional disparities in digital rural resilience across Zhejiang Province. High-resilience areas are primarily concentrated in the economically developed northeastern region, whereas low-resilience areas are mainly located in western and mountainous counties. Based on these findings, this study proposes policy recommendations to enhance county-level digital rural resilience and promote coordinated regional development.\n\n## Downloads\n\nDownload data is not yet available.\n\n## References\n\n[1] Cyberspace Administration of China. (2023). China digital rural development report (2022). [https:\u002F\u002Fwww.cac.gov.cn\u002F2023-03\u002F01\u002Fc_1679309718486615.htm](https:\u002F\u002Fwww.cac.gov.cn\u002F2023-03\u002F01\u002Fc_1679309718486615.htm)\n\n[2] Zeng, Y. W., Song, Y. X., Lin, X. Z., et al. (2021). Several issues concerning digital rural development in China. Chinese Rural Economy, (4), 21 35.\n\n[3] Shen, F. W., & Ye, W. X. (2021). Digital rural development: A strategic choice for high quality rural revitalization. Journal of Nanjing Agricultural University (Social Sciences Edition), 21(5), 41 53.\n\n[4] Wang, S., Yu, N., & Fu, R. (2021). Digital rural development: Mechanisms, practical challenges and implementation strategies. Reform, (4), 45 59.\n\n[5] Xing, Z. J. (2021). The national logic of digital rural development. Journal of Jishou University (Social Sciences), 42(6), 58 68.\n\n[6] Hu, W. W., & Lu, Y. N. (2023). Formation and operation of the digital rural governance community: Evidence from China's first greenhouse village. Journal of Public Management, 20(1), 133 143.\n\n[7] Costopoulou, C., Ntaliani, M., & Karetsos, S. Studying mobile apps for agriculture.\n\n[8] Ding, B. (2022). Digital governance: A new model of village governance in digital rural areas. Journal of Northwest A&F University (Social Sciences Edition), 22(2), 9 15.\n\n[9] Wang, W. B., & Zhao, L. Z. (2023). Digital rural governance transformation: Structural adjustment, functional enhancement and practical path. E Government, (5), 29 37.\n\n[10] Shen, F. W., & Ye, W. X. (2022). Pathways to digital rural development: An exploration based on smart growth theory. Humanities Magazine, (4), 69 80.\n\n[11] Liu, T. Y., & Tian, B. H. (2022). Practical dilemmas and optimization paths of digital rural development from the perspective of governance modernization. Jianghan Tribune, (3), 116 123.\n\n[12] Feng, C. R., & Xu, H. Y. (2021). Practical dilemmas and breakthrough paths for digital rural development. Journal of Yunnan Normal University (Philosophy and Social Sciences), 53(5), 93 102.\n\n[13] Li, L. L., Zeng, Y. W., & Guo, H. D. (2023). Digital rural development: Underlying logic, practical misunderstandings and optimization paths. Chinese Rural Economy, (1), 77 92.\n\n[14] Zhang, H., & Wang, L. (2023). Measurement and promotion paths of digital rural development in western China. East China Economic Management, 37(11), 70 78.\n\n[15] Shen, J. B., & Wang, Y. K. (2019). Evaluation index system of agricultural informatization in China. Transactions of the Chinese Society of Agricultural Engineering, 35(24), 162 172.\n\n[16] Cui, K., & Feng, X. (2020). Indicator system design for the rural digital economy from the perspective of digital rural development. Research of Agricultural Modernization, 41(6), 899 909.\n\n[17] Zhu, H. G., & Chen, H. (2023). Measurement, spatiotemporal evolution and promotion paths of digital rural development in China. Issues in Agricultural Economy, (3), 21 33.\n\n[18] Xu, J. H., Wang, N. Q., & Guo, F. L. (2023). Construction and empirical analysis of an evaluation index system for digital rural development. Statistics & Decision, 39(2), 73 77.\n\n[19] Liu, Q. (2023). Measurement and spatiotemporal evolution of digital rural development: Evidence from Henan Province. Statistics & Decision, 39(9), 85 90.\n\n[20] Chen, T., & Wang, P. (2020). Information divide and digital rural development. E Government, (12), 2 12.\n\n[21] Chen, L., & Fan, Z. Q. (2022). Influencing factors of digital rural development based on the TOE framework: Evidence from 115 counties in Sichuan Province. Western Economic Management Forum, 33(6), 10 17.\n\n[22] Yang, Y. J., Peng, P., He, S., et al. (2024). Regional characteristics and influencing factors of county level digital rural development in Sichuan Province. Journal of Natural Science of Hunan Normal University.\n\n[23] Liu, C. M., Wang, R., & Shao, M. J. (2024). Spatial pattern and driving factors of county level digital rural development in China. Tropical Geography, 44(1), 79 91.\n\n[24] Heijman, W. J. M., & Hagelaar, J. L. F. (2007). Rural resilience as a new development concept. General Information, 383 396.\n\n[25] Sharifi, A. (2016). A critical review of selected tools for assessing community resilience. Ecological Indicators, 66, 629 647.\n\n[26] Wilson, A. G., Hu, Z., & Rahman, S. (2018). Community resilience in rural China: The case of Hu Village, Sichuan Province. Journal of Rural Studies, 60, 130 140.\n\n[27] Tang, R. W., & Guo, W. J. (2018). Evolutionary resilience of rural revitalization and its internal governance logic. Reform, (8), 64 72.\n\n[28] Lei, X. K., & Wang, J. (2020). Paths and strategies for resilience governance in poor rural areas under rural revitalization. Journal of University of Jinan (Social Sciences), 30(1), 92 99.","《Frontiers in Sustainable Development》2026, 6(8)","2026-08-25T00:00:00Z",67,{"impact":32,"substance":330,"depth":84,"authority":57,"freshness":224,"relevant":34,"comment":564},"基于浙江省90个县市数据的数字乡村韧性评估，方法严谨，结论有区域差异，但时效性较低。",[566],{"name":560,"url":557},[568,569,570,571],"数字乡村","浙江","韧性","Frontiers in Sustainable Development",[],{"status":340,"locked":51,"version":52,"method":53,"evidence":574},{"candidates":575,"agricultural_evidence":51,"reason":581},[576],{"code":577,"score":57,"supported":26,"title":578,"abstract":579,"tags":580},"I6",[568],[568],[568],"农业相关性需要复核",[583,584],"数字乡村 浙江 韧性 Frontiers in Sustainable Development","数字乡村 浙江","数字乡村浙江韧性FrontiersinSustainableDevelopmen-1146","2026-08-31T00:08:57.260082Z",{"id":588,"title":589,"url":590,"summary":591,"summary_zh":20,"content":592,"source_name":443,"source_url":20,"published_at":593,"category":24,"cover_url":20,"hotness":25,"is_selected":51,"score":562,"score_detail":594,"sources":596,"tags":598,"primary_topic":602,"secondary_topics":603,"classification":604,"search_phrases":617,"slug":620,"view_count":621,"doi":20,"paper":20,"created_at":622},1137,"江苏农科院承办国家重点研发计划「长江下游稻作区杂草全程绿色防控」现场观摩评议会","https:\u002F\u002Fjaas.ac.cn\u002Fxww\u002Fxsky\u002Fart\u002F2026\u002Fart_1cccab0519d5494abe5a63fc1c28573f.html","8月17日,国家重点研发计划项目「稻田草害演替规律与全程绿色防控技术体系集成示范(2023YFD1401100)」课题5子任务「长江下游稻作区杂草全程绿色防控技术体系示范」现场观摩评议会在江苏泰州召开,江苏农科院植保所承办。项目团队在姜堰、兴化等地的示范基地集中展示「栽前封闭+栽后早期封杀」「两次适期轻简化施药」等多种除草模式,并结合无人机撒施\u002F滴施新型除草剂拌肥技术,实现除草剂减量10%-15%;同时现场演示60分钟出结果的杂草抗药性快检技术。","为推进国家重点研发计划项目顺利实施及相关科研成果的落地应用，8月17日，国家重点研发计划项目“稻田草害演替规律与全程绿色防控技术体系集成示范（2023YFD1401100）”课题5子任务“长江下游稻作区杂草全程绿色防控技术体系示范”现场观摩评议会在江苏省泰州市顺利召开。活动邀请了中国农科院植保所首席科学家李香菊研究员、沈阳农业大学纪明山教授、省植保站副站长吴佳文、安徽农科院植保质安所所长张勇、广东农科院植保所张纯研究员、泰州市植保站站长王晓兵、广西农科院植保所王彦辉研究员担任评议专家。会议由我院植保所承办，副所长王红春主持会议。项目首席、湖南农业大学潘浪教授，课题主持人黑龙江农科院植保所黄元炬研究员，我院植保所李贵研究员、资源所杨霞研究员，以及来自云南农业大学、华南农业大学、中国水稻研究所、湖南农业科学院等单位的项目参加人员、种植大户、农业合作社技术负责人40余人参加此次活动。\n\n在观摩活动中，项目团队在姜堰、兴化等地的示范基地集中展示了“栽前封闭+栽后早期封杀”“两次适期轻简化施药”及不同栽培方式下的全程控草等多种除草模式，并结合无人机撒施\u002F滴施新型除草剂拌肥技术，实现除草剂减量10%—15%；同时现场演示了60分钟出结果的杂草抗药性快检技术，为精准选药提供快速支撑。\n\n在评议会上，项目成员李贵汇报了重点研发项目子任务的执行情况，重点介绍了无人机场景下机插秧田除草剂减量高效使用技术。专家组对农田草害防控创新团队在杂草全程绿色防控技术体系示范方面取得的进展给予充分肯定，并对项目子任务所取得的阶段性成果表示高度认可。专家评议认为，项目实施中在泰州姜堰、兴化等地建立多个核心示范区，集成应用了稻田杂草智能识别、抗药性快检、新型除草剂组合及无人机精准施药等系列成果，创新形成的“两封”施药模式较传统方式可实现除草剂减量，整体技术高效安全、省工节本，专家组建议在长江下游稻作区加大推广应用力度。\n\n此次现场观摩评议会的成功召开，不仅为项目后续研究和推广工作奠定了扎实基础，也为推动农业绿色低碳转型、保障国家粮食安全、助力乡村全面振兴提供了更为坚实的科技支撑。","2026-08-18T00:00:00Z",{"impact":84,"substance":84,"depth":32,"authority":57,"freshness":72,"relevant":34,"comment":595},"国家级科研项目现场会，展示绿色防控技术集成应用，有实质进展，但时效性一般。",[597],{"name":443,"url":590},[450,599,600,601],"杂草防控","国家重点研发","无人机施药",{"code":296,"slug":297,"name":298,"path":299,"parent_code":300},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":605},{"candidates":606,"agricultural_evidence":26},[607,612],{"code":296,"score":84,"supported":26,"title":608,"abstract":610,"tags":611},[609],"绿色防控",[307,609,541],[599],{"code":44,"score":63,"supported":26,"title":613,"abstract":614,"tags":616},[],[615],"稻田",[],[618,619],"国家重点研发 江苏省农科院 无人机施药 杂草防控","国家重点研发 江苏省农科院","国家重点研发江苏省农科院无人机施药杂草防控-1137",0,"2026-08-31T00:08:56.092870Z",{"id":624,"title":625,"url":626,"summary":627,"summary_zh":20,"content":628,"source_name":629,"source_url":20,"published_at":444,"category":404,"cover_url":20,"hotness":25,"is_selected":51,"score":630,"score_detail":631,"sources":633,"tags":635,"primary_topic":638,"secondary_topics":643,"classification":644,"search_phrases":651,"slug":654,"view_count":224,"doi":20,"paper":20,"created_at":655},1145,"PNS-WINGS-AHP–EWM集成框架:数字农业多准则决策分析方法","https:\u002F\u002Fwww.mdpi.com\u002F2073-8994\u002F18\u002F9\u002F1435","山东农业大学经济管理学院与山东女子大学商学院发表于《Symmetry》2026年第18卷第9期1435(7月7日投稿、8月21日修订、8月25日接收、8月27日发表)。针对数字农业多准则决策分析(MCDA)中难以表示不确定专家判断、捕捉因子间影响结构、整合互补权重信息的难题,构建集成Pythagorean Neutrosophic Sets(PNS)–Weighted Influence Non-linear Gauge System(WINGS)–Analytic Hierarchy Process(AHP)–Entropy Weight Method(EWM)框架。应用于数字农业案例识别关键影响因素并确定发展优先级,结果显示经济发展水平(W1)排名第一,其次是数字技术水平(W6)和数字基础设施(W5);WINGS分析揭示因子间感知影响结构,数字基础设施(W5)为网络中最强净影响因子。","Loading [MathJax]\u002Fjax\u002Foutput\u002FHTML-CSS\u002Fjax.js\nJournals Topics Information Author Services Initiatives About\nSign In \u002F Sign Up Submit\n \nSearch for Articles:\nSymmetry\nAll Article Types\nAdvanced\n \nJournals  Symmetry  Volume 18  Issue 9  10.3390\u002Fsym18091435 \nSubmit to this Journal Review for this Journal Propose a Special Issue\nArticle Menu\nAcademic Editor\nJie Yang\nRecommended Articles\nRelated Info Link\nMore by Authors Links\nArticle Views\n104\nTable of Contents\nAbstract\nIntroduction\nLiterature Review\nFundamental Concepts and Procedures\nPythagorean Neutrosophic WINGS-AHP–EWM Combination Method\nResults\nDiscussion\nConclusions\nAuthor Contributions\nFunding\nData Availability Statement\nConflicts of Interest\nAppendix A\nAppendix B\nReferences\nshare\nShare\nannouncement\nHelp\nformat_quote\nCite\nquestion_answer\nDiscuss in SciProfiles\nfirst_page\nsettings\nOrder Article Reprints\nOpen AccessArticle\nPNS-WINGS-AHP–EWM Integrated Framework for Multi-Criteria Decision Analysis: An Application-Oriented Decision Framework in Digital Agriculture\nby Yawen Wang 1 andKecheng Zhang 2,*\n1\nCollege of Economics and Management, Shandong Agricultural University, Tai’an 271018, China\n2\nSchool of Business Administration, Shandong Women’s University, Jinan 250300, China\n*\nAuthor to whom correspondence should be addressed.\nSymmetry 2026, 18(9), 1435; https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18091435\nSubmission received: 7 July 2026 \u002F Revised: 21 August 2026 \u002F Accepted: 25 August 2026 \u002F Published: 27 August 2026\n(This article belongs to the Special Issue Symmetry\u002FAsymmetry in Fuzzy Multi-Criteria Decision-Making: Application in Management and Engineering)\n\nDownload\nkeyboard_arrow_down\n Browse Figures Versions Notes\n\nAbstract\nDigital-agriculture development increasingly requires multi-criteria decision analysis (MCDA) approaches to evaluate complex and interrelated influencing factors. However, existing methods often face difficulties in representing uncertain expert judgments, capturing inter-factor influence structures, and integrating complementary weighting information. To address these challenges, this study develops an integrated Pythagorean Neutrosophic Sets (PNS)–Weighted Influence Non-linear Gauge System (WINGS)–Analytic Hierarchy Process (AHP)–Entropy Weight Method (EWM) framework for digital-agriculture factor evaluation. Specifically, PNS is employed to represent uncertain linguistic assessments through truth, indeterminacy, and falsity information; WINGS is applied to identify the direction and intensity of expert-perceived influence relationships among factors; and a coordinated weighting mechanism integrates AHP-based preference information with entropy-based cross-expert dispersion information. The proposed framework is applied to a digital-agriculture case to identify key influencing factors and determine development priorities. The results indicate that Economic Development Level (W1) ranks first in the final composite priority assessment, followed by Digital Technology Level (W6) and Digital Infrastructure (W5). Furthermore, the WINGS analysis reveals the perceived influence structure among factors and identifies Digital Infrastructure (W5) as the strongest net-influencing factor within the network. Comparative analyses suggest that the proposed framework provides a systematic representation of uncertainty, influence relationships, and complementary weighting information for digital-agriculture factor evaluation. This study highlights the application potential of integrating established MCDM techniques to support complex agricultural decision-making problems.\nKeywords: multi-criteria decision analysis; Pythagorean neutrosophic sets; weighted influence nonlinear gauge system; Analytic Hierarchy Process; Entropy Weight Method; digital agriculture\n\n1. Introduction\nDigital agriculture, emerging from the convergence of information technology and agricultural production, has become an important driver of agricultural transformation worldwide. Meanwhile, population growth and accelerated urbanization continue to increase the demand for agricultural products. However, agricultural systems face multiple challenges, including limited land availability, labor constraints, climate variability, and changing socioeconomic conditions. These challenges have accelerated the transition toward digitalization, intelligent management, and sustainable development, positioning digital agriculture as an important topic in both scientific research and policy discussions [1,2].\nTo support this transition, extensive studies have investigated the factors influencing digital-agriculture development. Existing research has identified several important determinants, including economic development level, social capital participation, policy support, human capital quality, digital infrastructure, digital technology level, operational scale, profitability, and openness to global markets. Understanding the relative importance of these factors is essential for designing effective development strategies. However, existing analytical approaches still face several limitations that restrict their ability to provide comprehensive decision support.\nFirst, many studies analyze influencing factors separately and treat them as independent criteria within conventional multi-criteria decision-making (MCDM) frameworks [3,4]. Such approaches may overlook the complex interactions, feedback effects, and interdependencies among factors in digital-agriculture systems. Consequently, the resulting rankings often provide a static evaluation of factor importance but may not reveal the relative influence positions of different factors during the digital transformation process [5]. Second, many existing methods rely on precise numerical evaluations and provide limited support for representing uncertainty, hesitation, and inconsistency in expert judgments [6]. As expert assessments of digital-agriculture development often involve subjective perceptions and incomplete information, directly converting linguistic judgments into crisp values may reduce the richness of the original evaluation information [7].\nDespite considerable methodological progress, existing studies remain fragmented across four interconnected analytical dimensions. At the information representation level, expert judgments are frequently transformed into numerical scores, which may fail to preserve linguistic uncertainty and indeterminacy. At the structural analysis level, conventional weighting and ranking approaches generally assume criterion independence and therefore have limited capability to capture direct, indirect, and self-influence relationships among digital-agriculture factors [3,4,5]. At the weighting level, the Analytic Hierarchy Process (AHP) incorporates expert preferences but may be affected by subjective judgment, whereas the Entropy Weight Method (EWM) reflects data variation but does not consider expert preference information or network relationships [8,9]. At the integration level, uncertainty representation, influence analysis, and weight determination are often conducted separately. Therefore, existing approaches may identify factor importance or influence structures individually, but they rarely establish a continuous analytical process connecting uncertain expert assessments, inter-factor relationships, and final priority determination.\nMotivated by these limitations, this study integrates Pythagorean Neutrosophic Sets (PNS), the Weighted Influence Non-linear Gauge System (WINGS), AHP, and EWM into a sequential and functionally differentiated analytical framework. Rather than simply combining multiple techniques, the proposed framework assigns each method a specific analytical role. PNS is used at the input stage to represent linguistic evaluations through truth, indeterminacy, and falsity components. After aggregation and score transformation, the resulting influence matrix is used as the input for WINGS, which analyzes direct, indirect, and self-influence relationships and identifies network prominence and net-influence positions. Subsequently, AHP and EWM provide complementary perspectives on factor importance: AHP reflects expert preference information, whereas EWM captures cross-expert dispersion information derived from the individual AHP priority distributions. Their results are integrated through a combination-weighting procedure, and the resulting weights are further linked with WINGS-based network prominence to generate composite priorities.\nThe contribution of this study lies in establishing and applying this connected analytical workflow for digital-agriculture factor evaluation rather than developing entirely new individual methods. The framework provides both an expert-perceived influence structure and a composite priority ranking. Moreover, the potential effects of multi-stage processing, including possible information compression, are examined through subsequent comparative and sensitivity analyses.\nThe remainder of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 introduces the fundamental concepts of PNS. Section 4 presents the integrated methodology. Section 5 reports the empirical results and analysis. Section 6 discusses the comparative analysis, theoretical implications, policy implications, limitations, and future research directions. Finally, Section 7 summarizes the main findings and conclusions of this study.\n2. Literature Review\n2.1. Evolution of Digital Agriculture and Its Influencing Factors\nCertain stages in agricultural development have involved profound transformations and have therefore been described as agricultural revolutions, such as the Green Revolution [10,11]. The emergence of digital technologies is expected to promote a new stage of agricultural transformation by improving the efficiency, sustainability, and management capability of both crop production and livestock systems. This transition has attracted increasing attention from researchers, policymakers, and practitioners [12].\nThe concept of digital agriculture was introduced in 1997 as a data-driven agricultural development approach that integrates geospatial technologies and information-based systems [13]. By leveraging digital tools and resources, digital agriculture emphasizes the application of digital technologies throughout the agricultural production process, with the objectives of improving productivity, increasing farmers’ income, and enhancing the competitiveness of agricultural products [14]. In recent years, digital agriculture has also been associated with concepts such as precision agriculture and smart farming, which originated from technology-enabled crop management practices in the 1990s [15,16]. Accordingly, researchers have proposed various definitions of digital agriculture from different perspectives, reflecting its multidisciplinary characteristics [17,18,19].\nRecent studies have increasingly focused on identifying the factors that influence digital-agriculture development. Existing research has highlighted the importance of technological progress, economic conditions, digital infrastructure, policy support, operational scale, profitability, social capital participation, human capital quality, and market openness. Specifically, Shepherd emphasized the role of technological advancement in transforming traditional agricultural management models [20]. Shen demonstrated that regional economic conditions significantly influence the development level of digital agriculture [21]. Luo highlighted the importance of digital infrastructure in supporting digital transformation [22], while Tian examined the role of governmental policy support in facilitating agricultural digitalization [23]. Chen further suggested that the adoption of digital technologies is closely associated with the expansion of agricultural operation scale [24]. In addition, other studies have identified profitability, global market openness, social capital participation, and human capital quality as important determinants of digital-agriculture development [25,26,27,28].\nAlthough previous studies have identified numerous influencing factors, an important research question remains: how can the relative importance of these factors and their interrelationships be systematically evaluated? Existing studies often examine individual factors separately, providing limited insights into the interactions among multiple dimensions of digital-agriculture development. Therefore, a comprehensive quantitative framework is required to identify key factors and reveal their structural relationships. Furthermore, because evaluations of digital-agriculture development frequently involve expert knowledge, incomplete information, and subjective judgment, decision-making approaches capable of handling uncertainty are needed.\nIn this study, the decision problem is defined as a regional-level prioritization problem within the Chinese digital-agriculture context. The evaluation object is not an individual farm, enterprise, or specific technology project, but rather the regional enabling environment that supports digital-agriculture development. The primary decision stakeholders are regional agricultural authorities and public development agencies, which need to determine the relative priorities of policy attention, infrastructure investment, capability development, and implementation resources. The resulting ranking aims to support medium-term planning over an approximately three-to-five-year horizon by identifying structural development priorities rather than predicting short-term agricultural performance or determining specific budget allocations.\n2.2. Methodological Foundation: From Fuzzy Sets to Pythagorean Neutrosophic Sets\nTo address uncertainty in complex decision-making problems, fuzzy set theory has been widely adopted as an effective approach for representing imprecise information. Zadeh introduced fuzzy sets in 1965, using membership functions to describe uncertain and vague information [29]. However, because fuzzy sets rely on a single membership degree, they may have limited capability in representing situations involving hesitation or conflicting information [30].\nTo overcome this limitation, Atanassov proposed intuitionistic fuzzy sets (IFS), which extend fuzzy sets by incorporating membership and non-membership degrees as well as hesitation information [31]. Nevertheless, IFS requires that the sum of membership and non-membership degrees does not exceed one, which may restrict its applicability in certain complex decision environments [32,33]. Subsequently, Yager introduced Pythagorean fuzzy sets (PFS), allowing the squared sum of membership and non-membership degrees to satisfy a less restrictive condition and thereby expanding the representation space of uncertain information [34,35,36,37]. Meanwhile, Smarandache proposed neutrosophic set theory, which explicitly introduces an indeterminacy dimension in addition to truth and falsity memberships, enabling a more comprehensive description of uncertain and incomplete information [38,39,40,41].\nBuilding on these developments, Jansi et al. introduced the concept of Pythagorean neutrosophic sets (PNS) in 2019 [7]. PNS integrates truth, falsity, and indeterminacy components, providing a flexible representation of information that involves uncertainty, ambiguity, and incomplete knowledge [42,43]. This characteristic is particularly relevant for digital-agriculture factor evaluation, where expert judgments regarding factor relationships may contain both supportive and uncertain perceptions. Compared with conventional fuzzy sets and intuitionistic fuzzy sets, PNS provides a broader representation space by explicitly incorporating indeterminacy information [44]. Therefore, PNS has been applied in various uncertain decision-making contexts, including multi-criteria decision analysis, artificial intelligence, and decision-support systems [45,46,47].\nExisting PNS research has gradually expanded from theoretical development toward practical applications. Early studies mainly focused on mathematical properties and operational rules [48,49], whereas recent studies have explored its application in complex decision-support problems [50]. By retaining multiple dimensions of uncertain information, PNS provides a useful foundation for decision frameworks that require the representation of expert uncertainty.\nRecent MCDM studies have also explored the integration of uncertainty modeling and structural analysis. Muyulema-Allaica et al. combined FAHP with DEMATEL to identify causal and effect factors in industrial symbiosis resource optimization [51]. Petrović et al. incorporated triangular fuzzy numbers into AHP to address uncertainty in weight determination under expert subjectivity [52]. Turgay et al. developed a decision model based on the T-spherical hesitant fuzzy rough set to represent incomplete and conflicting information using truth, falsity, and indeterminacy dimensions [53]. These studies demonstrate the importance of uncertainty-aware decision models. However, existing approaches generally focus on specific analytical aspects, such as uncertainty representation or influence analysis, rather than integrating uncertainty modeling, influence-network analysis, and hybrid weighting within a unified decision process.\nTo clarify the methodological positioning of the proposed framework, Table 1 compares representative MCDM approaches from the perspectives of uncertainty representation, influence analysis, and weight determination.\nTable 1. Analytical comparison of representative MCDM methods related to uncertainty quantification, influence-structure analysis, and hybrid weighting.\n2.3. Analytical Methods: WINGS, AHP, and EWM\nThe Decision-Making Trial and Evaluation Laboratory (DEMATEL) is a widely used MCDM method for analyzing complex systems through influence relationship modeling [54]. Building upon DEMATEL, Michnik proposed the Weighted Influence Non-linear Gauge System (WINGS), which extends the analysis by incorporating self-influence and considering broader network characteristics beyond pairwise relationships [55]. Previous studies have applied WINGS to analyze complex inter-factor relationships and identify influential elements in different decision contexts. For example, Wang et al. demonstrated its capability in analyzing interconnected factors and their internal relationships [56]. Kannan et al. applied WINGS to prioritize barriers in decision systems [57], while Kumar et al. used it to investigate factors affecting blockchain technology adoption in supply chains [58]. However, WINGS generally requires numerical input information and therefore has limited capability in directly handling linguistic uncertainty [59].\nThe Analytic Hierarchy Process (AHP) is a classical MCDM approach that combines qualitative judgments with quantitative analysis and has been widely applied in complex decision-making problems since the 1970s [60,61]. By incorporating expert preferences, AHP provides an effective approach for subjective weight determination. However, its results may be influenced by expert judgment and consistency issues [8].\nIn contrast, the Entropy Weight Method (EWM) determines indicator weights according to information entropy and the dispersion of observations [9]. Although EWM is commonly classified as an objective weighting method because its weights follow predefined mathematical rules once the input matrix is fixed, the informational meaning of the resulting weights depends on the nature of that input matrix. In the present study, the EWM input consists of expert-specific AHP priority distributions; therefore, EWM is interpreted as extracting cross-expert dispersion information rather than as introducing an independent objective data source. EWM has been applied in various evaluation and optimization problems, including industrial efficiency assessment, risk evaluation, ","《Symmetry》2026, 18(9), 1435",66,{"impact":57,"substance":330,"depth":84,"authority":57,"freshness":72,"relevant":34,"comment":632},"论文提出数字农业多准则决策新框架，方法新颖，但时效性稍差。",[634],{"name":629,"url":626},[181,636,637],"多准则决策","Pythagorean Neutrosophic Sets Symmetry",{"code":205,"slug":639,"name":640,"path":641,"parent_code":642},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":645},{"candidates":646,"agricultural_evidence":26},[647],{"code":205,"score":57,"supported":26,"title":648,"abstract":649,"tags":650},[181],[181],[181],[652,653],"多准则决策 数字农业 Pythagorean Neutrosophic Sets Symmetry","多准则决策 数字农业","多准则决策数字农业PythagoreanNeutrosophicSetsSymm-1145","2026-08-31T00:08:57.223880Z"]