[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-08-23":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-08-23","农业科技多点突破，数智赋能乡村振兴",[7,8,9,10,11,12],"中国农科院发布棉花黄萎病新型病圃，首次明确8种致病型，为抗病育种提供精准靶标。","高蛋白玉米品种中单126破解饲料蛋白短缺，订单农业全产业链模式助力粮食安全。","浙江、福建发布十五五农业农村现代化规划，明确农业科技进步贡献率目标，布局数智农业体系。","智慧农业研究揭示数据要素与物质资本协同驱动，农业数字化与粮食安全耦合协调度受四大障碍制约。","岭南黄鸡3号农业行业标准发布，为种源保护、引种鉴别等提供统一技术依据。","生物育种新突破：ZmHPATR1基因调控微生物组增强玉米抗病性，开辟抗病育种新路径。","本期日报涵盖棉花、玉米、青稞等作物育种前沿，智慧农业与数字化研究，浙江、福建等省份的农业农村现代化规划，以及畜禽标准发布等政策动态，全面展示农业科技与政策的最新进展。\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。*",[15,90,151,200,249,298,350,400,452,482,522,568,609,648,687],{"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":45,"secondary_topics":51,"classification":58,"search_phrases":84,"slug":87,"view_count":88,"doi":20,"paper":20,"created_at":89},857,"中国农科院植保所联合西部中心等发布棉花黄萎病新型病圃:全球最大48亩16隔离区,首次明确8种致病型其中4种侵染棉花","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fa4f6ba290a7b453aa6bedba59a290a67.htm","中国农科院植物保护研究所联合西部农业研究中心、辽宁省农科院经济作物研究所、石河子大学、新疆农科院植保所等多家单位,在新疆昌吉向科研院所高校、新疆自治区和新疆兵团农技推广系统及棉花育种企业等展示精准靶向黄萎病菌致病型新型病圃构建及棉花品种抗性鉴定与应用解决新方案。科研团队完成全国11000余株黄萎病菌系统收集鉴定,结合3624株大规模群体基因组学测序与遗传进化研究,首次明确黄萎病菌分化为8种致病型、侵染棉花的主要有4种;病圃2023年开始建设、总面积48亩、分16个隔离区,经三年连续播菌建成精准靶向各种致病型的新型病圃,是全球迄今面积最大、覆盖致病型最全的人工黄萎病病圃,已被纳入农业农村部主推技术。",null,"近日，中国农业科学院植物保护研究所联合中国农业科学院西部农业研究中心在新疆昌吉向科研院所高校、新疆自治区和新疆兵团农技推广系统及棉花育种企业等单位展示了精准靶向黄萎病菌致病型新型病圃构建及棉花品种抗性鉴定与应用解决新方案。\n\n科研团队通过全国农作物黄萎病菌菌种（11000余株）系统收集与鉴定，结合大规模群体基因组学测序（3624株）与遗传进化研究，首次明确了黄萎病菌分化为8种致病型，其中侵染棉花的主要有4种。\n\n根据黄萎病菌致病型组成特征，科研团队提出了精准靶向黄萎病菌致病型病圃构建思路，并与中国农业科学院西部农业研究中心、辽宁农业科学院经济作物研究所、石河子大学、新疆维吾尔自治区农业科学院植物保护研究所等国内多家科研院所和高校在老龙河试验站联合共建新型棉花黄萎病人工病圃。病圃于2023年开始建设，总面积达到48亩，分为16个隔离区，选取各致病型代表性菌株进行规模化扩繁并分别播入隔离区，历经三年连续播菌最终建成精准靶向黄萎病菌各种致病型的新型病圃。\n\n据科研团队介绍，该病圃是全世界迄今为止面积最大、覆盖致病型最全的人工黄萎病病圃，利用该病圃可以获得棉花对各种黄萎病菌致病型的抗性谱，具有广阔的应用前景。应用于抗性品种布局，可以根据各地田间黄萎病菌致病型检测结果，选择种植相应抗性的品种，解决品种田间抗性不稳的产业难题；解析棉花对不同致病型的抗性机制并挖掘抗病基因，应用于棉花抗黄萎病遗传改良；对抗不同致病型的棉花品种进行杂交选育，聚合选育抗多种致病型的品种；利用病圃黄萎病发病稳定的特征，应用于防控产品防效验证和评价；不同品种在病圃对不同致病型的病症差异显著，可用于棉花黄萎病无人机高光谱等早期监测预警技术研发与应用。同时，也将为其他作物黄萎病抗性特征鉴定、优异基因挖掘与遗传改良及早期监测预警提供平台支撑。\n\n据悉，该技术已被纳入农业农村部主推技术并依托项目“新疆棉花病虫害演替规律与全程绿色防控技术体系集成示范”在新疆棉区推广，为有效控制棉花黄萎病提供了有力技术支撑。","中国农业科学院 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中国科学报 2026年8月19日",92,{"impact":29,"substance":99,"depth":100,"authority":32,"freshness":33,"relevant":34,"comment":101},24,18,"国家级科研机构主导的高蛋白玉米重大突破，全产业链协同，对保障粮食安全有重要战略意义，信息详实，权威性高。",[103],{"name":96,"url":93},[105,106,107,108,109,110],"高蛋白玉米","饲料蛋白","中国农科院作科所","中单126","李新海团队","订单农业",[],{"status":113,"locked":60,"version":61,"method":62,"evidence":114},"needs_review",{"candidates":115,"agricultural_evidence":26,"reason":144},[116,122,128,134,139],{"code":117,"score":66,"supported":26,"title":118,"abstract":120,"tags":121},"A3",[119],"玉米",[119],[105],{"code":123,"score":66,"supported":26,"title":124,"abstract":126,"tags":127},"C4",[125],"饲料",[125],[106],{"code":129,"score":79,"supported":26,"title":130,"abstract":131,"tags":133},"A4",[],[132],"大豆",[],{"code":135,"score":34,"supported":60,"title":136,"abstract":137,"tags":138},"E5",[],[],[110],{"code":140,"score":34,"supported":60,"title":141,"abstract":142,"tags":143},"G4",[],[],[110],"多个产业证据相当，需要确认主归属",[146,147],"中国农科院作科所 李新海团队 高蛋白玉米 订单农业","中国农科院作科所 李新海团队","中国农科院作科所李新海团队高蛋白玉米订单农业-856",9,"2026-08-23T00:08:11.875706Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":20,"content":156,"source_name":157,"source_url":20,"published_at":158,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":172,"secondary_topics":177,"classification":183,"search_phrases":194,"slug":197,"view_count":198,"doi":20,"paper":20,"created_at":199},859,"中国农科院农业环境与可持续发展研究所智慧气象团队提出塑性感知基因组选择框架:50%基因型3环境数据即可精准预测小麦育种资源","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F64e83ccdab5c432e90d4dc3461f47743.htm","中国农业科学院农业环境与可持续发展研究所智慧气象与农业气候资源利用创新团队提出塑性感知基因组选择框架,实现跨环境小麦基因组预测与育种资源高效配置。相关研究成果发表在《农业计算机与电子学(Computers and Electronics in Agriculture)》。传统多环境品种试验需多地点多年开展、周期长且成本高;科研团队以重组自交系群体为材料、在8个环境中调查产量、千粒重、穗粒数、穗数和株高,研究发现仅使用50%基因型在3个环境中的表型数据,株高、穗粒数和千粒重的预测准确度即可分别达到0.70、0.54和0.59。该框架将小麦对环境变化的响应量化为特征指标,无需复杂的环境描述数据即可开展跨环境预测,可为试验设计、亲本选配提供数字化决策支持。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 2](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 3](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 4](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fcaas.cn\u002Findex.htm)\n\n*   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003.png](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fb0181f9c105a4685bf740c3031178147.png)\n\n传统多环境品种试验需要在多个地点和年份开展，虽然能够评价育种材料的适应性，但试验周期长且成本高。\n\n科研团队以重组自交系群体为材料，在8个环境中调查产量、千粒重、穗粒数、穗数和株高。研究发现，仅使用50%基因型在3个环境中的表型数据，株高、穗粒数和千粒重的预测准确度即可分别达到0.70、0.54和0.59，显示出减少试验规模的潜力。该框架将小麦对环境变化的响应量化为特征指标，无需复杂的环境描述数据，即可开展跨环境预测，可为试验设计、亲本选配提供数字化决策支持。\n\n该研究得到国家重点研发计划、国家自然科学基金等项目支持。（通讯员 王佳）\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2025.111371\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [泛基因组研究揭示黄瓜\"走遍全球\"的基因秘密](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1583ba500276409a9de0b852e1214852.htm)2026-08-12  \n*   [花生抗青枯病种质实现抗病优质聚合新突破](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2234b1e6560d4b92ba362f575c37dfdd.htm)2026-08-12  \n*   [构建苦荞泛基因组 找回苦荞中丢失的“高原密码”](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Ftpxw\u002F2a36a8ce6a0844adaadd8aff36bfe467.htm)2026-08-11  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2026年8月","2026-08-22T00:00:00Z",89,{"impact":30,"substance":161,"depth":100,"authority":32,"freshness":149,"relevant":34,"comment":162},22,"国家级科研机构发布小麦育种基因组选择新框架，方法创新且数据规模大，对智慧农业和育种领域有重要影响。",[164],{"name":157,"url":154},[166,167,168,169,170,171],"中国农科院","小麦育种","基因组选择","智慧气象","数字化决策","Computers and Electronics in Agriculture",{"code":173,"slug":174,"name":175,"path":176,"parent_code":50},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[178],{"code":179,"slug":180,"name":181,"path":182,"parent_code":57},"F2","biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology",{"status":59,"locked":60,"version":61,"method":62,"evidence":184},{"candidates":185,"agricultural_evidence":26},[186,190],{"code":173,"score":66,"supported":26,"title":187,"abstract":188,"tags":189},[175],[175],[167],{"code":179,"score":66,"supported":26,"title":191,"abstract":192,"tags":193},[168],[168],[168],[195,196],"中国农科院 基因组选择 数字化决策 小麦育种","中国农科院 基因组选择","中国农科院基因组选择数字化决策小麦育种-859",6,"2026-08-23T00:08:11.995796Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":20,"content":20,"source_name":205,"source_url":20,"published_at":206,"category":207,"cover_url":20,"hotness":25,"is_selected":26,"score":208,"score_detail":209,"sources":212,"tags":214,"primary_topic":221,"secondary_topics":227,"classification":228,"search_phrases":244,"slug":247,"view_count":149,"doi":20,"paper":20,"created_at":248},846,"浙江发布十五五农业农村现代化规划:2030年率先基本实现农业农村现代化,农业科技进步贡献率达74%","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260823\u002F20260823_1\u002Fnmrb_20260823_13385_1_2091261036783046668.html","浙江省政府办公厅正式印发《浙江省十五五农业农村现代化规划》:作为千万工程重要发源地,浙江明确到2030年率先基本实现农业农村现代化,农业科技进步贡献率达74%、和美乡村覆盖率90%以上、城乡居民收入倍差缩小至1.77。规划提出推动企业深度参与科技攻关,培育100家农业科技企业、组建10家创新联合体,布局概念验证—中试熟化—示范应用全链条,完善先用后转机制;打造粮油等十大千亿级产业集群,到2030年建成超百亿级全产业链20条、十亿级全产业链180条;推行一股份两平台林地资源盘活模式,全面推进三电一码(电子票据\u002F合同\u002F印章+资产浙农码)数字化改革。","农民日报 2026年8月23日 要闻1版","2026-08-23T00:00:00Z","政策",87,{"impact":99,"substance":210,"depth":100,"authority":66,"freshness":25,"relevant":34,"comment":211},23,"浙江省级重磅规划，目标明确、数据详实，对农业现代化具有示范意义。",[213],{"name":205,"url":203},[215,216,217,218,219,220],"浙江","十五五规划","农业农村现代化","农民日报","农业科技进步贡献率","千万工程",{"code":222,"slug":223,"name":224,"path":225,"parent_code":226},"J2","modernization","农业强国与现代化","\u002Ftopic\u002Fpolicy\u002Fmodernization","J",[],{"status":59,"locked":60,"version":61,"method":62,"evidence":229},{"candidates":230,"agricultural_evidence":26},[231,235,239],{"code":222,"score":66,"supported":26,"title":232,"abstract":233,"tags":234},[217],[217],[217],{"code":140,"score":34,"supported":60,"title":236,"abstract":237,"tags":238},[],[],[220],{"code":240,"score":34,"supported":60,"title":241,"abstract":242,"tags":243},"J1",[],[],[216],[245,246],"农业科技进步贡献率 农业农村现代化 十五五规划 农民日报","农业科技进步贡献率 农业农村现代化","农业科技进步贡献率农业农村现代化十五五规划农民日报-846","2026-08-23T00:08:10.054197Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":20,"content":254,"source_name":255,"source_url":20,"published_at":256,"category":257,"cover_url":20,"hotness":25,"is_selected":26,"score":258,"score_detail":259,"sources":262,"tags":264,"primary_topic":272,"secondary_topics":278,"classification":279,"search_phrases":292,"slug":295,"view_count":296,"doi":20,"paper":20,"created_at":297},877,"热孜燕瓦卡斯教授团队提出智慧农业发展综合评价体系:数据要素与农业物质资本协同驱动,空间上以竞争效应为主导","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7676046820790829611","新疆农业大学热孜燕瓦卡斯教授团队在《智慧农业(中英文)》2026年第3期发表论文,基于2015—2023年中国省级面板数据,采用层次分析法与熵权法相结合的组合赋权方式构建智慧农业发展综合评价指数并进行地形与主导产业校正。研究发现:(1)智慧农业呈现梯度发展格局,区域间增长动力与智慧化水平存在系统性错配;(2)核心驱动机制在于数据要素与农业物质资本的结构性互补,二者协同效应显著;技术资本发挥关键中介作用且促进作用具有非线性门槛特征;(3)空间上以竞争效应为主导:产业结构相似省份间呈现明显的虹吸效应;数据-资本协同模式具有正向空间溢出性。","![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**引用格式**：房洪伟, 胡然然, 热孜燕·瓦卡斯. 智慧农业发展综合评价体系构建、实践格局与驱动机制[J]. 智慧农业(中英文), 2026, 8(3): 26-39.\n\nCitation:FANG Hongwei, HU Ranran, REZIYAN·Wakasi . Construction of A Comprehensive Evaluation System, Practice Pattern, and Driving Mechanism for Smart Agriculture Development[J]. Smart Agriculture, 2026, 8(3): 26-39.\n\nDOI： 10.12133\u002Fj.smartag.SA202510009\n\nScience\n\nScience\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**智慧农业发展综合评价体系构建、实践格局与驱动机制**\n\n**摘要：****［目的\u002F意义］**发展智慧农业是构建农业新质生产力的核心路径。针对当前发展中存在的区域异质性与动力错配问题，本研究旨在构建一个融合地理与产业异质性的综合评价体系，实证分析数据要素与农业物质资本的协同驱动机制，并揭示其在省域空间关联中的作用，从而为优化农业空间布局与差异化政策制定提供理论依据。**［方法］**基于2015—2023年中国省级面板数据，首先采用层次分析法与熵权法相结合的组合赋权方式构建智慧农业发展综合评价指数，并进行地形与主导产业校正。进一步，通过空间自相关检验与空间杜宾模型识别智慧农业发展的空间关联特征与溢出模式；运用广义矩估计模型检验数据要素的驱动作用；并通过中介效应与调节效应模型，系统验证数据要素与物质资本的协同机制及其影响路径。**［结果和讨论］**（1）智慧农业呈现梯度发展格局，区域间增长动力与智慧化水平存在系统性错配。（2）核心驱动机制在于数据要素与农业物质资本的结构性互补，二者协同效应显著；技术资本发挥关键中介作用，且其促进作用具有非线性门槛特征。（3）空间上以竞争效应为主导：产业结构相似省份间呈现明显的“虹吸效应”；“数据-资本”协同模式具有正向空间溢出性，可形成可效仿的区域发展路径。异质性分析表明，上述机制的效果因农业功能区划与发展阶段不同而存在差异。**［结论］**本研究为理解智慧农业发展的要素协同机制与空间竞争逻辑提供了直接经验证据，有助于识别区域差异化驱动路径，从而为促进智慧农业均衡发展与政策精准实施提供参考。\n\n**关键词：**智慧农业；数据要素；空间溢出；空间杜宾模型；区域异质性；评价体系\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Feec06177dbb109d8eb5acc661bd95049~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=ofwZac3wiNpsQK3FyvZPEgfwO5g%3D)![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**0 引 言**\n\n2025年中共中央、国务院《关于进一步深化农村改革扎实推进乡村全面振兴的意见》首次明确提出发展农业新质生产力，并将“支持发展智慧农业”作为重要的战略突破口。这一最新部署，与此前《“十四五”推进农业农村现代化规划》将智慧农业列为农业高质量发展关键路径的方针一脉相承。系列顶层设计表明，智慧农业已成为引领农业现代化进程的核心力量。然而，如何有效解决智慧技术采纳成本高、应用场景适配性不足、政策支持力度区域差异等难题，缩小“东部领跑、中西部跟跑、东北滞后”的区域发展差距，确保国家智慧农业战略有效落地并赋能全域农业新质生产力发展，已成为实现农业数字化转型和区域协同发展的关键议题。因此，深入研究智慧农业发展的内在机制，特别是其面临的技术门槛约束、空间溢出规律，以及区域差异化突破路径，具有重要的理论价值和实践意义。\n\n尽管现有研究为理解智慧农业的区域发展提供了重要基础，但其区域异质性的形成机制仍是尚未突破的关键瓶颈，尤其体现在以下三个方面。首先，区域趋同研究具有局限性，既有探讨多聚焦于农业现代化的辐射效应与劳动生产率的趋同两大维度，并初步识别出技术适配性优化、地方政府竞争等驱动机制。但此类研究存在明显局限。其一，普遍忽略智慧农业技术扩散过程中天然存在的空间关联性，以及伴随的非线性门槛效应，致使地理邻近性、经济关联度对技术扩散的深层作用机制未能得到充分揭示；其二，未能清晰阐释技术鸿沟如何加剧区域发展失衡，具体而言，低技术水平区域因适配成本高而陷入困境，而高技术水平区域则因规模效应形成良性循环，这一核心机制尚未得到深入探讨。其次，智慧农业测度体系的不足。现有理论范式已从“信息技术与农业生态融合”演进为“技术-产业-制度多维度协同”，但对应测度体系尚未形成统一规范，导致区域资源禀赋约束下的异质性赋能水平难以得到精准刻画。具体而言，部分研究未能充分彰显“智慧”的核心要义，仅停留在技术应用的表层描述；另有研究忽略地形条件、主导产业类型等核心区域禀赋对技术应用成本与效益的调节效应，无法体现不同地域的适配性差异；更有研究仅以智慧农业相关企业数量、硬件设备装机量等单一指标开展衡量，缺乏对赋能效能的多维度解构。最后，现有研究对智慧农业空间溢出的区域异质性缺乏有力解释，未能从技术适配性与数据要素驱动这两个关键维度深入揭示其区域差异化的成因。\n\n针对上述研究缺口，本研究旨在构建一个整合要素协同、中介机制与空间竞争的综合分析框架，系统考察智慧农业发展的驱动机制与空间关联模式。为此，基于2015—2023年中国省级面板数据进行整合评价、机制与空间的实证研究，核心内容包括：1）构建融合地理与产业异质性的智慧农业发展综合评价体系，采用层次分析法（Analytic Hierarchy Process, AHP）熵权组合赋权法确定指标权重，测算省级智慧农业发展指数，以科学刻画其时空分异格局；2）实证检验“数据要素-农业物质资本”的结构性互补效应这一核心驱动机制，并揭示技术资本在其中的关键中介作用及其非线性门槛特征；3）运用空间杜宾模型与多种空间权重矩阵，系统辨析智慧农业发展的空间互动模式，重点揭示以经济竞争为主导的“虹吸效应”，以及成功协同模式产生的正向“示范效应”，并从农业功能区划与发展阶段两个维度进行深入的异质性检验，以厘清驱动机制与空间效应的情境依赖特征。本研究旨在系统解决以下核心问题：第一，中国省域智慧农业发展呈现何种时空格局，其区域增长动力与智慧化水平是否存在系统性错配；第二，驱动智慧农业发展的核心机制是什么，数据要素与农业物质资本是否确实存在结构性互补，且这一过程是否通过技术资本的中介并受制于非线性门槛；第三，智慧农业发展的空间关联是竞争性的还是合作性的，其溢出效应在不同类型的区域关联（地理邻近、资源相似、经济结构相似）中如何表现，并受哪些异质性因素调节。最终，本研究试图为识别区域差异化的发展瓶颈与路径、制定促进要素协同与区域协调的政策提供系统的经验证据与决策依据。\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)![Image 6](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**1 数据收集与处理**\n\n![Image 7](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**1.1 数据来源与样本说明**\n\n本研究旨在构建一个整合要素协同与空间关联的分析框架，系统考察中国智慧农业的发展机制。基于研究目标与数据可得性，分析样本覆盖中国大陆30个省份（不含西藏自治区及港澳台地区），时间跨度为2015年至2023年，构成一个平衡面板数据集。所有原始数据均来源于权威的官方统计出版物、专业数据库及公开研究报告，主要涵盖《中国统计年鉴》《中国农村统计年鉴》《中国农业机械工业年鉴》、EPS（Express Professional Superior）数据库、国家知识产权局专利数据库等。为确保数据可比性，对所有以货币计量的变量均以2012年为基期进行了价格平减处理；对于个别年份的缺失值，采用线性插值法予以补充。核心变量的具体来源与处理方式详见表1。\n\n表1 智慧农业核心变量数据来源与处理（2015—2023）\n\nTable 1 Data sources and processing of core smart agriculture variables （2015—2023）\n\n![Image 8](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fd9885daaeab9a6d2b95c28c20e06b81b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=m6CNEcrn5X3PsaPCwG2y6E47M10%3D)\n注：EPS数据库网址为：https:\u002F\u002Fwww.epsnet.com.cn。\n\n**1.2 变量定义与测度**\n\n**1.2.1 被解释变量**\n\n被解释变量为智慧农业应用水平。为准确识别数据要素的驱动效应，本研究在构建该变量时遵循一个关键原则：确保其与核心解释变量数字基础设施在测度上完全独立。具体构建遵循“适配区域禀赋”与“主客观赋权结合”原则，步骤如下。1）基于熵值法与主成分分析法，测算智慧农机作业强度等关键衍生指标的基础指数；2）采用等权重法将基础指数合并为子指标综合指数，并运用熵值法确定基础权重；3）将基础权重与各省“农林牧渔产值占比”结合计算加权权重，从而合成结构要素总指数，其中，种植业子指标引入地形修正系数以适配地理禀赋；4）智慧农业应用水平通过AHP法确定主观权重，并结合农林牧渔产值占比加权得出。\n\n![Image 9](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**1.2.2 核心解释变量**\n\n![Image 10](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n参考既有文献，选择以“每平方公里移动电话基站数”的密度衡量数据要素，反映数据采集与传输的基础网络覆盖水平；选择研究与开发投入强度（Research and Development Intensity, R&D）作为衡量技术资本的指标，反映技术创新与消化能力；选择“单位农业总产值对应的农林牧渔业固定资产投资额”衡量农业物质资本，反映传统有形资本的投入强度；构造“数据要素”与“农业物质资本”的中心化交互项，用以捕捉二者间的结构性互补效应。\n\n![Image 11](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**1.2.3 控制变量**\n\n![Image 12](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n参考既有研究，控制一系列可能影响智慧农业发展的区域特征变量，包括：人均农业产值（生产力水平）、受灾面积占比（自然风险）、土地流转率（经营规模）、农村劳动力平均受教育年限（人力资本），以及人均农林水事务财政支出（政策支持力度）。所有连续型控制变量在回归前均进行标准化处理。\n\n**1.2.4 工具变量**\n\n为缓解核心解释变量可能存在的内生性偏误（如双向因果关系），参考既有研究［10］，选取一个历史地理变量作为工具变量：各省在1984年的邮局数量与上一年全国信息技术服务总收入的交互项，并取对数。该选择满足相关性与外生性要求：历史上邮电基础设施的密度影响了后续信息技术扩散的路径，但与当前智慧农业发展的随机误差项无关。弱工具变量检验（Cragg-Donald Wald F统计量）表明该工具变量有效（见后文）。本研究的主要变量描述性统计结果详见表2。\n\n表2 智慧农业变量描述性统计（2015—2023）\n\nTable 2 Descriptive statistics of smart agriculture variables （2015—2023）\n\n![Image 13](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F7d3f444f47687ccc157e2e2971d241c1~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=SrifVXl7oUgmxUTqTRyX7ok84xo%3D)![Image 14](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)![Image 15](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2 研究方法**\n\n![Image 16](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2.1 智慧农业综合指数评价体系的构建**\n\n![Image 17](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n综合考虑数字农业与智慧农业在技术基础、产业形态与政策导向的共性与差异后，本研究借鉴李娜等的研究思路，构建了一套适用于2015—2023年中国智慧农业发展水平的综合指数体系。为准确反映农业生产的区域差异性，本研究在指标处理阶段引入了地形修正系数与主导产业权重，对原始指标进行了校正。在此基础上，采用AHP与熵权法相结合的组合赋权法确定指标权重。其中，AHP部分为确保权重的科学性与代表性，研究组建了一个由15位长期从事智慧农业相关领域研究的新疆农业大学经济管理学院专家团队（含8名教授、7名副教授，其中6名为博导）进行独立打分。所有专家判断矩阵均通过了一致性检验（一致性比率 （Consistency Ratio, CR）均小于0.1），符合可接受标准。该方法平衡了理论经验与客观数据，相较于主成分分析、因子分析和等权重法更契合本研究的综合评价需求。为验证结论的可靠性，本研究进一步采用优劣解距离法（Technique for Order Preference by Similarity to Ideal Solution, TOPSIS）模型对校正后的指标进行重新评估，结果显示各省份发展水平排序与核心结论高度一致，证实了研究发现的稳健性。指标体系的具体构成与说明见表3。\n\n![Image 18](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n表3 智慧农业发展评价指标体系\n\nTable 3 Evaluation indicator system for smart agriculture development\n\n![Image 19](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fb151d196adfa0b8e7addde86fd7c2a07~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=9sTLmz%2FsD16sIpRFfk6sA0oP27E%3D)\n**2.2****理论框架与研究假设**\n\n智慧农业的区域协同发展，本质上是技术创新、数据要素与农业资本在空间适配性约束下寻求动态均衡的过程。有文献强调了单一要素的贡献或线性溢出，却未能厘清要素因属性差异形成的结构性依赖，以及区域间因集聚力量而产生的竞争性关系。本研究基于不完全契约与资产专用性理论、内生增长理论和新经济地理学，构建了一个包含要素内生协同与空间关联传导的双维理论框架，其核心论点是：在要素协同维度，数据要素的价值实现高度依赖于与专用性农业资本的互补与锁定；在空间关联维度，要素流动并非简单的辐射扩散，而是在集聚经济规律下，呈现出以竞争效应为主导、溢出效应为条件的复杂图景。为系统阐明上述要素协同与空间关联的交互机制，本研究构建了理论分析框架，具体见图1。\n\n![Image 20](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F44d23d374bd9070009501a35b26e18cc~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=j7eDKmMT3wPEEUrePO7toSfyKPo%3D)\n图1 智慧农业发展的理论分析框架\n\nFig.1 Theoretical analytical framework for the development of smart agriculture\n\n![Image 21](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2.2.1 基于不完全契约理论的价值生成与锁****定机制**\n\n![Image 22](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n通用性要素与专用性资产的结合是价值创造与留存的基础。根据不完全契约理论，投资的价值取决于其与互补资产的匹配程度。数据基础设施（如通信网络、云平台）具有显著的通用性，其产生的信息流具有非竞争性和低转移成本，易于在开放区域中扩散。相反，农业物质资本（如适应特定地块的智能农机、与本地气候绑定的高端温室）则具有强烈的地理专用性与物理专用性。当通用性数据要素与专用性物质资本结合时，便形成了一个“专用性资产组合”。这种组合产生了双边锁定效应：专用性物质资本需要持续、特定的数据流以维持最优效能；同时，数据要素的价值实现也深度依赖于这些专用性实体载体。这种相互依赖显著提升了要素整体的套牢程度，将易流动的数据“锚定”在本地，转化为难以被竞争对手复制的场景化生产力。因此，单纯增加通用性投入，在区域竞争下可能因价值外溢而导致本地净收益受限；唯有通过与专用性资本的深度协同，才能有效锁定价值，实现其促进本地发展的潜力。这构成了本研究的第一个理论支柱。\n\n![Image 23](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2.2.2 技术资本的内生积累与采纳门槛：基于内生增长与技术扩散理论的阐释**\n\n![Image 24](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n从要素投入到经济增长，关键的转化媒介是本土化知识。基于内生增长理论，真正驱动长期、可持续增长的不是原始数据或简单设备，而是通过研发与“干中学”积累的技术资本（如专利、定制算法、本土农艺模型等）。这一观点进一步指出，技术资本具有高度的隐默性与场景依赖性，其形成过程强化了知识积累的内生性，并构成了区域核心竞争力的来源。然而，技术资本的生成与应用并非线性过程。依据技术扩散理论，智慧农业技术体系的复杂性导致其采纳存在显著的临界规模阈值。在达到阈值前，高昂的初始固定成本、互补性技能的短缺，以及应用场景的碎片化，将使投资面临“适配困境”，边际收益增长缓慢；一旦跨越阈值，网络效应、学习效应与规模经济将共同触发采纳进程的良性循环，实现回报的跃升。因此，技术资本不仅是联结要素投入与最终产出的核心中介，其自身的积累也遵循一条非线性的演化路径，这深刻影响了区域智慧农业发展的节奏与效能。\n\n![Image 25](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2.2.3 空间分异与竞争机制：基于新经济地理学的解释**\n\n![Image 26](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n要素与产出在区域间的分布并非由匀质的扩散力决定，而是由集聚经济的向心力与竞争效应塑造。根据新经济地理学，智慧农业所依赖的高技能劳动力、数据资源与风险资本，在空间上具有强烈的规模报酬递增与集聚倾向。这导致初期具有微弱优势或更优政策环境的地区，会通过循环累积因果效应，形成吸引要素流入的“中心区”，决定了智慧农业的发展必然呈现显著的空间异质性，也为理解区域间的发展差距提供了微观基础。当省份间产业结构相似、发展目标同质时（即经济关联度高），它们实质上面向同一池流动性高端要素进行竞争。在此情境下，区域间的空间互动主要表现为零和博弈式的虹吸效应：一地的快速发展，往往以吸引走邻地的关键要素为代价。据此，提出以下研究假说。\n\nH1：数据要素与农业物质资本之间存在结构性互补，二者的协同投入对智慧农业产出具有显著的正向协同效应。\n\nH2：技术资本在数据要素驱动智慧农业的过程中发挥关键中介作用，且技术资本对智慧农业产出的促进作用存在基于临界规模的非线性特征。\n\nH3：智慧农业的发展在省域间存在以竞争效应为主导的空间关联，尤其在产业结构相似的省份之间，虹吸效应显著。\n\n![Image 27](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F88c234fb2977490d875d78a0fee454c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=L0PJhKvrK6ZLplPU59hLwv1vP1k%3D)\n**2****.3 实证策略与模型设定**\n\n为系统检验数据要素对智慧农业发展的驱动作用，并深入揭示其空间溢出机制与影响路径，本研究构建了一个涵盖基准回归、空间计量、机制检验及稳健性分析的综合性实证框架。具体模型选取与设定如下。\n\n**2.3.1 基准模型**\n\n首先，为考察数据要素对智慧农业发展的基础影响，并控制省份异质性与时间趋势，本研究建立静态面板固定效应模型作为基准。该模型旨在初步识别驱动效应的存在性与方向，为后续深入分析奠定基础。如公式（1）所示。\n\n![Image 28](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0a7473564000639b4ccdf740c36b83ad~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=rARy%2Fd2yEvdfe6yl3yCpJg6DcnA%3D)\n式中：SA it 为为智慧农业应用水平；X i 为数据要素；Controlsit为一系列控制变量；μ i 与 λ t 与分别为省份固定效应、时间固定效应；εit为随机误差项。\n\n**2.3.2 空间计量模型设定**\n\n为考察空间溢出效应，本研究进一步引入空间计量方法。该部分分析遵循“相关性检验-模型选择-估计与分解”的流程。\n\n1）空间自相关检验。采用全局莫兰指数（Moran's I）检验被解释变量的空间依赖性，计算如公式（2）所示。这是使用空间模型的前提。\n\n![Image 29](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F6a7e5f516cf7498c7e4eba56483766e5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788048527&x-signature=T%2BtPNU3FqA9YZVHkrQoD0LqUHiw%3D)\n式中：N = 30，为样本省份数量；x i、x j 为第 i、j 省的智慧农业发展指数；x ˉ 为指数均值；W ij 为空间权重矩阵\n\n2）空间计量模型选取与设定。本研究旨在运用空间计量方法分析智慧农业发展的区域特征。为确保模型设定的严谨性，研究首先选择能够同时捕捉被解释变量与解释变量空间依赖性的空间杜宾模型（Spatial Durbin Model, SDM）作为一般性框架，并通过似然比检验（Likelihood Ratio, LR），实证判别其能否简化为更特殊的空间自回归模型（Spatial Autoregressive Model, SAR）或空间误差模型（Spatial Error Model, SEM）。模型的具体选择与检验结果如表4所示，最终采用的SDM模型设定详见公式（3）。\n\n式中：ρ 为空间自回归系数，反映相邻区域智慧农业发展的联动效应；θ为解释变量的空间滞后系数，衡量要素跨区域溢出强度；μi、λt分别为省份固定效应、时间固定效应，控制个体异质性与宏观政策冲击。\n\n表4 智慧农业发展的空间计量模型选择检验结果\n\nTable 4 Test results for spatial econometric model selection in smart agriculture development\n\n为精准识别发展的区域异质性，本研究借鉴了权威的区域划分方案，依据作物结构、经营模式与生态条件，将全国划分为东北粮食主产区、黄淮海粮经区等五大典型农业区域，并计划从发展水平与增长动力两个维度进行后续综合分析。在空间关联的刻画上，本研究构建了三类空间权重矩阵以多角度反映省域间的空间联系：其一为农业资源环境分区权重，基于一级农业生态区划构建，旨在捕捉生态条件相似性带来的关联；其二为农业经济结构权重，基于省域间第一产业占比的相似性构建，以反映经济结构上的联系；其三为地理邻接权重，以捕捉纯粹的地理邻近效应。\n\n（3）空间溢出效应分解。为深入解析核心变量的空间作用机制，本研究遵循LESAGE and Pace的方法，对基于不同空间权重矩阵（农业资源环境分区权重与农业经济结构相似性权重）估计的空间杜宾模型结果进行了效应分解。该分解能够将解释变量的影响系统区分为直接效应、间接效应与总效应，从而精确量化要素投入的本地影响与空间溢出。\n\n**2.3.3 机制检验与稳健性估计**\n\n在完成核心空间效应分析后，本研究通过构建中介效应模型与调节效应模型，对数据要素驱动智慧农业发展的具体路径与边界条件进行检验。此外，为缓解模型可能存在的内生性问题（如互为因果），本研究进一步采用动态面板系统广义矩估计（System Generalized Method of Moments, System GMM）模型进行估计，以确保基准结论的稳健性。模型设定详见公式（4）。\n\n式中：SA i，t - 1 为智慧农业应用水平的一阶滞后项，用于控制其动态惯性；其余变量含义与基准模型一致。\n\n**3 结果与分析**\n\n**3.1 智慧农业发展的区域特征**\n\n**3.1.1 典型农业区域划分下智慧农业发展水平与增长动力的特征分析**\n\n图2所示的智慧农业发展指数揭示了数字化转型进程中的梯度格局。具体而言，黄淮海粮经区的智慧农业发展水平持续领先，这与其作为农业经济核心区所享有的资源禀赋与市场区位优势密切相关，为技术率先应用与扩散提供了有利条件。尤其值得注意的是，西南山地农业区也保持了较高的发展水平，这说明地形限制反而催生了对智慧技术的迫切需求。南方水稻经济林区呈现稳健发展态势。而东北与西北地区智慧化水平相对滞后，反映其仍以传统发展路径为主，数字化转型尚待深化。全国平均水平表明整体发展仍处于中期阶段，区域差距明显。从增长动力视角看，各区域增幅变化呈现出显著的“后发追赶”特征。其中，东北粮食主产区增幅呈现出爆发式增长，后期增速遥遥领先，体现其规模化农业模式的增长潜力。黄淮海粮经区增幅保持稳步快速上升。西北旱作区在经历短期调整后增幅快速回升，展现出其在资源环境约束下寻求技术突破的增长韧性。反观西南山地农业区，受制于细碎化的地形条件，其增幅始终较为平缓。全国平均增幅则呈现稳健的上升趋势。\n\n综合图2可见，各区域在增长动力与智慧化水平上存在系统性错配：东北表现为“高增长-低智慧化”的规模驱动模式；黄淮海呈现“高增长-高智慧化”的全面发展格局；西南属于“中增长-中高智慧化”的需求引致型路径；西北则展现“中增长-低智慧化”的技术改良轨迹。\n\n图2 区域智慧农业发展指数与增速表\n\nFig. 2 Regional smart agriculture development index and growth rate\n\n**3.1.2 全国趋同动态演变**\n\n表5显示，2015—2023年中国省际智慧农业结构趋同进程呈现鲜明的三阶段演化特征。初期表现为显著的分化扩张，结构指数变异系数（Coefficient of Variation, CV）在0.76~0.85区间波动，省际差异持续拉大。此阶段东部地区依托技术与资本要素优势，结构优化推进较快；中西部及东北部分省份则受制于薄弱的经济基础与有限的技术吸纳能力，升级进程明显滞后。转折点出现在2019—2020年，CV值从0.741骤降至0.555。国家层面乡村振兴战略的全面推进及其配套措施的密集落地，有效促进了数字技术向中西部及东北地区的下沉和专项扶持的精准实施，显著加速了这些区域的结构优化进程，省际差距快速收敛。然而，2021—2022年分化态势重现，CV值回升至0.740。这一反弹可归因于多重压力：东部地区在人工智能决策、智能农机等前沿领域的技术迭代加速，拉大了与中西部技术吸收滞后区域的技术梯度；宏观经济下行压力传导至农业领域，导致部分省份公共投入与私人投资缩减；同时，部分地区智慧农业技术方案与本地自然禀赋、生产需求的适配性不足，抑制了增长动能。\n\n表5 省级智慧农业结构趋同动态研究的变异系数与莫兰指数\n\nTable 5 Coefficient of variation and Moran's I in the study on dynamic convergence of provincial smart agriculture structure\n\n注：***、**和*分别表示1%、5%和10%的显著水平。\n\n空间维度上，邻接省份的结构差异演变呈现先降后升的悖论性轨迹。如图3所示，2019—2020年的低谷期，反映了地理邻近区域通过技术模仿、要素流动及政策协同实现的局部协同效应达到峰值。然而，2021年后的差异回升，凸显了空间溢出的局限性：尽管邻近区域能实现短期相对收敛，但技术高地与技术洼地间的绝对发展水平差距依然存在，印证了“边缘区相对溢出效率较高但绝对水平滞后”的核心困境。与此形成鲜明对比的是，基于农业GDP聚类的同质经济组内部差异在2017—2022年间持续扩大。这揭示了更深层次的分化机制：即使经济基础相似的省份，也因产业政策导向差异、资源配置效率悬殊等制度与能力因素，在智慧农业转型路径上出现显著分化。部分固守传统模式的省份与积极推动智慧化转型的同类省份差距持续拉大，这种同质经济组内部的发展路径分异现象，对基于经济收敛假说的传统趋同理论构成了重要挑战。\n\n注：该图基于自然资源部标准地图服务网站下载的审图号为GS（2024）0650号的标准地图制作，底图边界无修改；数据来源：AHP+熵权法综合指数评估，灰色区域表示数据缺失。\n\n图3 中国省域智慧农业指数空间分布格局及演变\n\nFig. 3 Spatial distribution and evolution of China's provincial smart agriculture index\n\n**3.1.3 技术扩散阈值**\n\n为进一步检验技术扩散是否存在门槛效应，本研究首先基于各省份样本初期（即样本期第一年）的数字服务综合指数来测度其初始技术水平；其中，数字服务综合指数是由农村宽带接入用户、电商企业占比、移动电话普及率以及研发人员全时当量四项指标经熵值法加权合成的综合评价指数，反映各省智慧农业发展的数字技术基础。随后，采用Logistic扩散模型拟合各省技术指数的时间演化，提取技术扩散速度，并以初始技术水平作为门槛变量，通过Hansen面板门槛回归模型估计门槛值，得到门槛值为0.228，据此将30个省级行政区划分为低技术组（初始技术水平 ≤ 0.228）与高技术组（初始技术水平 > 0.228）。分组相关性分析显示：低技术组内初始技术水平与扩散速度呈弱正相关且未通过统计显著性检验；高技术组内两者则呈显著正相关。这表明，当技术积累跨越该门槛后，技术投入与扩散速度之间开始形成良性互动，技术应用的规模效应与网络效应逐步显现，推动智慧农业技术进入规模化应用与快速扩散阶段。\n\n**3.2 空间溢出机制**\n\n**3.2.1 空间自回归检验结果**\n\n表6为数据要素驱动智慧农业发展的基准回归、机制检验及稳健性估计结果。表6列（1）的固定效应模型结果为本研究提供了基准参照。数据要素对智慧农业发展的估计系数显著为正，这印证了数据要素是推动农业智能化转型的基础性驱动力。表6列（4）的调节效应模型为识别核心机制提供了关键证据。数据要素与农业物质资本的交互项系数在较高的统计水平上显著为正。这一发现有力证明了，数据要素与农业物质资本之间存在密切的互补效应。这意味着，智慧农业的发展并非依赖于单一要素的线性投入，而是取决于数字技术与实体资本能否深度融合、相互适配。二者的协同能够释放出超越其独立边际贡献的系统合力，从而验证了本研究的假说H1，即两者之间存在结构性互补，协同投入能产生显著的正向协同效应。\n\n进一步地，为剖析上述协同作用的具体传导路径，研究检验了技术资本的中介角色。表6列（2）与列（3）的结果共同表明，数据要素的发展显著促进了技术资本的积累，而技术资本又对智慧农业产出具有显著的积极影响。这构成了一个清晰的部分中介路径，证实了“数据要素→技术资本→智慧农业”这一传导机制的存在，验证了假说H2。\n\n综上所述，基准回归与机制识别结果表明，数据要素对智慧农业的驱动，核心在于其与农业物质资本的结构性互补，而这一互补效应的实现，部分经由技术资本的生成与转化来完成。这为理解智慧农业发展的微观动力机制提供了实证依据。\n\n表6 数据要素与农业物质资本协同驱动智慧农业发展的实证检验结果\n\nTable 6 Empirical test results on the synergistic drivers of smart agriculture development by data elements and agricultural material\n\n注：***、**和*分别表示1%、5%和10%的显著性水平，括号内为稳健标准误。\n\n**3.2.3 内生性处理与动态效应检验**\n\n首先，为缓解潜在的内生性与动态偏差，本研究采用系统GMM方法进行再估计。如表6列（5）所示，在控制被解释变量的滞后项后，核心解释变量的系数符号与统计显著性未发生根本变化，假说H1与H2的结论保持稳健。模型通过了序列相关检验与过度识别检验，表明工具变量有效，估计结果具有一致性。\n\n其次，为规避核心变量测度方法可能带来的误差，本研究采用TOPSIS法对“智慧农业应用水平”进行重新测度，并代入模型进行估计。如表6列（6）所示，替换测度指标后的结果与基准回归结论高度一致。\n\n最后，考虑到智慧农业发展的多维性，本研究进一步使用“传感器企业数量”这一实体产业指标作为其代理变量进行检验。传感器是感知层的关键硬件与数据入口，其产业规模能直观反映技术落地与资本投入程度。如表6列（7）所示，基于该代理变量的估计结果同样支持前述核心发现。\n\n上述三项检验分别从估计方法、变量测度与指标替代等不同层面验证了结论的稳健性。\n\n**3.2.4 空间计量分析**\n\n考虑到地区间的相互关联，引入空间计量经济学方法。根据LR检验结果，在农业资源环境分区权重与农业经济结构权重矩阵下选用SDM，而在地理权重矩阵下选用SAR，结果分别见表7。\n\n表7 数据要素与物质资本协同效应的空间计量分析结果\n\nTable 7 Spatial econometric analysis results of the synergistic effects between data elements and agricultural physical capital\n\n注：***、**和*分别表示1%、5%和10%的显著性水平，括号内为稳健标准误。\n\n核心估计结果直接回应了本研究的理论假说。首先，在所有空间模型设定中，“数据要素×农业物质资本”交互项的系数均显著为正。这从空间维度证实，即使在控制了区域间的空间关联性之后，数据要素与农业物质资本之间的互补协同效应依然稳健存在，假说H1在空间分析中再次得到支持。\n\n其次，空间自回归系数（Spatial Autoregression Coefficient, Rho）的估计值揭示了区域关联模式的差异。在经济结构权重矩阵下，Rho值在10%的水平上显著为负，这表明在经济结构与水平相近的省份之间，智慧农业的发展存在显著的负向空间关联，即“虹吸效应”，假说H3因此获得实证支持。而在农业分区权重与地理邻接权重下，Rho值未通过显著性检验，意味着基于自然禀赋相似性或单纯地理邻近的空间溢出效应并不明显。\n\n此外，一个关键发现是，在空间回归中“数据要素”与“农业物质资本”两个变量的独立主效应系数均显著为负。这与基准回归中数据要素显著为正的结果形成了对比。结合交互项显著为正，共同表明在开放的区域竞争环境中未能形成有效协同的孤立要素投入，其创造的价值可能面临外流风险，本地净收益受限；而“数据-资本”协同则是将发展红利内部化、抵御要素虹吸的关键机制。\n\n综上所述，空间计量分析不仅验证了核心协同机制的稳健性，也揭示出中国智慧农业发展的空间互动呈现出以经济竞争为主导的鲜明特征。\n\n**3.2.5 空间溢出效应分解**\n\n为揭示核心变量的空间作用机制，本研究进一步对模型结果进行了效应分解。表8呈现了基于农业资","智慧农业(中英文)2026年第3期 2026年8月20日","2026-08-20T00:00:00Z","论文",86,{"impact":30,"substance":161,"depth":100,"authority":260,"freshness":33,"relevant":34,"comment":261},14,"核心期刊论文，构建智慧农业综合评价体系，实证分析数据要素与物质资本协同驱动及空间竞争效应，具有较高学术价值和政策参考意义。",[263],{"name":255,"url":252},[265,266,267,268,269,270,271],"数据要素","智慧农业发展","综合评价体系","农业物质资本","协同驱动","热孜燕瓦卡斯","智慧农业中英文",{"code":273,"slug":274,"name":275,"path":276,"parent_code":277},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":59,"locked":60,"version":61,"method":62,"evidence":280},{"candidates":281,"agricultural_evidence":26},[282,286],{"code":273,"score":66,"supported":26,"title":283,"abstract":284,"tags":285},[265],[265],[265],{"code":287,"score":72,"supported":26,"title":288,"abstract":290,"tags":291},"I1",[289],"智慧农业",[289],[],[293,294],"智慧农业中英文 农业物质资本 智慧农业发展 热孜燕瓦卡斯","智慧农业中英文 农业物质资本","智慧农业中英文农业物质资本智慧农业发展热孜燕瓦卡斯-877",4,"2026-08-23T00:08:13.574818Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":20,"content":303,"source_name":304,"source_url":20,"published_at":23,"category":257,"cover_url":20,"hotness":25,"is_selected":60,"score":305,"score_detail":306,"sources":308,"tags":310,"primary_topic":318,"secondary_topics":323,"classification":324,"search_phrases":344,"slug":347,"view_count":348,"doi":20,"paper":20,"created_at":349},873,"30省面板数据揭示农业数字化与粮食安全耦合协调度:数字化技术应用数字发展活力资源安全质量安全为四大障碍因素","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2","学者Gao等在《Nature》子刊《Humanities and Social Sciences Communications》发表论文,基于2010—2022年30个省份的面板数据,采用熵值法测算农业数字化与粮食安全水平,运用耦合协调度模型与相对发展模型识别协调类型及演化特征;基于Dagum基尼系数和Moran I指数考察耦合协调度的空间分异特征;最后运用障碍度模型识别约束耦合协调度提升的障碍因素。研究发现:(1)耦合协调度与相对发展水平呈上升趋势,呈粮食主销区大于粮食主产区大于粮食产销平衡区格局;(2)空间动态差异方面,总体差距逐步缩小,三大功能区之间差距是主要来源;(3)主要障碍因素为数字化技术应用、数字发展活力、资源安全与质量安全。","*   Article\n*   [Open access](https:\u002F\u002Fwww.springernature.com\u002Fgp\u002Fopen-science\u002Fabout\u002Fthe-fundamentals-of-open-access-and-open-research)\n*   Published: 19 August 2026\n\n*   [Qun Gao](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#auth-Qun-Gao-Aff1)[1](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#Aff1), \n*   [Yawen Dou](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#auth-Yawen-Dou-Aff1)[1](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#Aff1), \n*   [Yuxuan Wang](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#auth-Yuxuan-Wang-Aff1)[1](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#Aff1)&\n*   …\n*   [Ming Zeng](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#auth-Ming-Zeng-Aff2)[2](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#Aff2)\n\n[_Humanities and Social Sciences Communications_](https:\u002F\u002Fwww.nature.com\u002Fpalcomms) (2026) [Cite this article](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41599-026-08646-2#citeas)\n\nWe’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.\n\n## Abstract\n\nThe profound integration of agricultural digitalization and food security stands as a cornerstone strategy for achieving agricultural modernization and enhancing national food security. Based on the provincial panel data from 2010 to 2022, this study first employs the entropy method to measure the levels of agricultural digitalization and food security, and then utilizes the coupling coordination degree and relative development models to identify the coordination types and their evolutionary characteristics between the two. Subsequently, based on the Dagum Gini coefficient and Moran’s _I_ index, this study investigates the spatial disparity features of their coupling coordination degree. Finally, the obstacle degree model is applied to identify the obstacle factors constraining the enhancement of their coupling coordination degree. The research findings are as follows: (1) During the study period, both the coupling coordination degree and the relative development level between agricultural digitalization and food security exhibited an upward trend, manifesting as “major grain-consuming areas > major grain-producing regions > the areas with balanced grain production and consumption.” (2) From the perspective of spatial dynamic disparities, the overall gap in the coupling coordination degree gradually narrowed. The gap among the three functional zones emerged as the primary source of disparity, with the coefficient of disparity showing an upward trend. From the perspective of spatial dynamic correlations, a positive spatial correlation was observed in their coupling coordination degree. (3) The primary obstacle factors influencing their coupling coordination are the application of digital technology, digital development vitality, resource security, and quality security. This study offers compelling empirical support for exploring effective coupling strategies between agricultural digitalization and food security.\n\n## Acknowledgements\n\nSincere thanks go to National Social Science Foundation of China (24AGL039), National Natural Science Foundation of China (72563022), and Social Science Foundation Project of Jiangxi Province (SZ241004) for the generous funding support.\n\n## Author information\n\n### Authors and Affiliations\n\n1.   School of Public Policy and Administration, Nanchang University, Nanchang, China\n\nQun Gao,Yawen Dou&Yuxuan Wang\n\n2.   School of Law and Public Administration, Nanjing University of Information Science & Technology, Nanjing, China\n\nMing Zeng\n\nAuthors\n\n1.   Qun Gao\n2.   Yawen Dou\n3.   Yuxuan Wang\n4.   Ming Zeng\n\n### Corresponding author\n\nCorrespondence to [Ming Zeng](mailto:003256@nuist.edu.cn).\n\n## Ethics declarations\n\n### Competing interests\n\nThe authors declare no competing interests.\n\n### Ethical approval and informed consent\n\nNo involved.\n\n## Additional information\n\n**Publisher’s note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\n## Rights and permissions\n\n**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F](http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F).\n\n[Reprints and permissions](https:\u002F\u002Fs100.copyright.com\u002FAppDispatchServlet?title=Research%20on%20spatial%20disparities%20and%20obstacle%20factors%20in%20the%20coupling%20coordination%20between%20agricultural%20digitalization%20and%20food%20security%3A%20evidence%20from%20panel%20data%20of%2030%20provinces%20in%20China&author=Qun%20Gao%20et%20al&contentID=10.1057%2Fs41599-026-08646-2&copyright=The%20Author%28s%29&publication=2662-9992&publicationDate=2026-08-19&publisherName=SpringerNature&orderBeanReset=true&oa=CC%20BY)\n\n## About this article\n\n[![Image 1: Check for updates. Verify currency and authenticity via CrossMark](blob:http:\u002F\u002Flocalhost\u002Fdf43c82f6cb6dcd2a87db38f317bf9f2)](https:\u002F\u002Fcrossmark.crossref.org\u002Fdialog\u002F?doi=10.1057\u002Fs41599-026-08646-2)\n\n### Cite this article\n\nGao, Q., Dou, Y., Wang, Y. _et al._ Research on spatial disparities and obstacle factors in the coupling coordination between agricultural digitalization and food security: evidence from panel data of 30 provinces in China. _Humanit Soc Sci Commun_ (2026). https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-08646-2\n\n[Download citation](https:\u002F\u002Fcitation-needed.springer.com\u002Fv2\u002Freferences\u002F10.1057\u002Fs41599-026-08646-2?format=refman&flavour=citation)\n\n*   Received: 26 July 2025\n\n*   Accepted: 31 July 2026\n\n*   Published: 19 August 2026\n\n*   DOI: https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-08646-2","Humanities and Social Sciences Communications 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API平台。多模态API支持Chat Completions、Messages、Responses三种格式调用，可以方便接入各类Agent工具中使用，支持图文混合输入，支持base64内联、外部URL、Files API三种图片传入方式。（新华财经）\n\n•紫金矿业发布2026年半年度报告，营收同比增长15.78%至1941.78亿元，净利润同比增长68.17%至391.70亿元，拟每10股派发现金红利4.2元（含税）。紫金矿业称，“第三增长极”锂板块已形成规模贡献，实现当量碳酸锂产量4.4万吨，同比大幅增长；钼、钨、锡等稀贵战略金属产能顺势释放，硫精矿、硫酸等硫资源综合利用效益显著，成为新的利润增长亮点。（新华财经）\n\n•中际旭创发布2026年半年度报告，营收同比增长182.49%至417.78亿元，净利润同比增长241.70%至136.51亿元，拟每10股派12元（含税），合计派发现金红利约14.04亿元（含税）。中际旭创称，1.6T硅光模块进入规模放量阶段，出货量逐季攀升，成为拉动收入增长的核心产品。（新华财经）\n\n•东山精密发布2026年半年度报告，营收同比增长63.95%至277.98亿元，净利润同比增长290.09%至29.57亿元。东山精密拟对子公司索尔思光电及其子公司、维信泰国、盐城东山在常州、泰国、盐城等地实施的光芯片及光模块扩建项目新增投资额5亿美元，项目总投资额由12亿美元调增至17亿美元。（新华财经）\n\n•长飞光纤发布2026年半年度报告，营收同比增长53.64%至98.09亿元，净利润同比增长888.88%至29.25亿元，拟每10股派发现金红利10.60元（含税），合计派发现金红利约8.78亿元（含税）。长飞光纤称，业绩增长主要得益于算力数据中心加速建设，新型光纤光缆产品需求持续增长，产品销量及平均价格均有提升。（新华财经）\n\n•锦龙股份拟转让所持东莞证券20%股份（3亿股），预计构成重大资产重组。（新华财经）\n\n•景旺电子拟调整珠海金湾基地扩产投资计划，将“关键工序产能强化投资项目”变更为“超高多层PCB智能制造项目”，预计项目总投资43.88亿元。（新华财经）\n\n•兆易创新于8月21日首次回购A股股份24.70万股，占公司总股本的比例为0.04%，购买的最高价格为409.95元\u002F股，最低价格为400.30元\u002F股，已支付的总金额约1亿元。（新华财经）\n\n•荷兰数据保护局21日宣布，对美国网约车服务运营商优步公司处以8.25亿欧元罚款，理由是优步公司在未充分履行相关告知和人工审核义务的情况下，通过自动化系统停用司机账户，违反欧盟《通用数据保护条例》的相关规定。（新华社）\n\n•美国总统特朗普21日表示，美国将在未来90天内允许免关税进口最多30万吨碎牛肉，以降低国内消费价格。特朗普称，这一举措“旨在降低美国民众的消费价格，同时也为美国畜牧养殖业恢复增长提供空间”。（新华社）\n\n•据多家美国媒体20日报道，美国副总统万斯在当天播出的一档播客访谈节目中称，美国与伊朗战事“进入新阶段”，对伊朗施加经济压力是“最有效手段”，但这是一种“微妙的博弈”。（新华社）\n\n•巴西总统卢拉21日与美国总统特朗普通电话，反对美国政府近期对巴西商品加征关税。据巴西政府发布的声明，在1小时20分钟的通话中，卢拉和特朗普讨论了双边贸易、公共安全合作及国际热点问题。（新华社）\n\n•日本总务省21日公布的数据显示，剔除生鲜食品后，日本7月核心消费价格指数（CPI）为102.1，同比上涨1.8%，较6月的1.6%涨幅进一步扩大。至此，日本核心CPI已连续59个月同比上涨。（新华社）\n\n•据日本共同社21日报道，日本政府部门和机构2027财年（2027年4月至2028年3月）预算申请总额预计将超过130万亿日元（1美元约合159日元），相比本财年的122万亿日元显著增加。（新华财经）\n\n•巴拿马运河管理局20日宣布，受厄尔尼诺现象导致运河流域降雨减少影响，将从9月起下调船闸每日船舶通行量，并调整每日通行名额竞拍机制。（新华社）\n\n•21日，国际贵金属价格再度走强，现货黄金自5月15日以来再次站上4600美元关口。（新华财经）","新华社 中国金融信息网 2026年8月22日",{"impact":30,"substance":31,"depth":32,"authority":32,"freshness":149,"relevant":34,"comment":460},"中央网信委印发行动计划，将数字乡村列入重要应用场景，属于全国性政策，对农业信息化有直接指导意义，信息权威且时效性强。",[462],{"name":458,"url":455},[362,464,465,466,467],"高质量发展","网信企业","中央网信委","行动计划",[],{"status":113,"locked":60,"version":61,"method":62,"evidence":470},{"candidates":471,"agricultural_evidence":60,"reason":476},[472],{"code":273,"score":66,"supported":26,"title":473,"abstract":474,"tags":475},[362],[362],[362],"农业相关性需要复核",[478,479],"中央网信委 高质量发展 数字乡村 网信企业","中央网信委 高质量发展","中央网信委高质量发展数字乡村网信企业-849","2026-08-23T00:08:10.172173Z",{"id":483,"title":484,"url":485,"summary":486,"summary_zh":20,"content":487,"source_name":488,"source_url":20,"published_at":489,"category":24,"cover_url":20,"hotness":25,"is_selected":60,"score":490,"score_detail":491,"sources":493,"tags":495,"primary_topic":500,"secondary_topics":504,"classification":505,"search_phrases":517,"slug":520,"view_count":34,"doi":20,"paper":20,"created_at":521},858,"中国农科院作科所联合西藏农牧科学院召开2026年青稞产业科技成果观摩研讨会:十五五体系研发重点聚焦稳产保供","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fe4dc11625b2146979a18c5b4a325b244.htm","8月15日至17日,国家大麦青稞产业技术体系2026年青稞产业科技成果观摩研讨会在西藏拉萨召开,以共探科创产创融合新机遇、助推青稞产业高质量发展为主题,设田间成果观摩、十五五体系研发重点工作专题研讨、青年学术交流、产企供需对接四大板块。专家团队深入白朗县嘎东镇贵热村青稞新品种展示示范基地,实地调研13个青稞新品种长势。十五五期间体系将聚焦稳产保供、提质增效核心目标,扎实干好四件事:集成关键技术、精准赋能县域经济、健全应急响应机制、锤炼过硬农业科技创新国家队。","青稞是西藏特色优势作物，更是保障高原粮食安全的核心支撑。8月15日至17日，国家大麦青稞产业技术体系2026年青稞产业科技成果观摩研讨会在西藏自治区拉萨市召开。会议以“共探科创产创融合新机遇、助推青稞产业高质量发展”为主题，会议设置田间成果现场观摩、“十五五”体系研发重点工作专题研讨、青年学术交流、产企供需对接四大核心板块，集中展示了青藏区青稞新品种、新技术、新装备、新产品及新业态模式。与会代表系统总结各地青稞稳产增产、节本增效、产业服务的典型实践经验，全面梳理种业培育、田间标准化种植、加工流通销售全链条痛点堵点。\n\n会议期间，专家团队深入白朗县嘎东镇贵热村青稞新品种（系）展示示范基地，实地观摩调研新品种长势、示范模式落地成效及机械化应用情况。据介绍，该基地承担着区域青稞新品种适应性测试、高产栽培技术示范等关键职能，累计展示“藏青2000”“藏青3000”“藏青17”“喜马拉22号”等自治区主推品种，以及“13-6927”“08-1277”等新品系共13个青稞新品种（系），为当地良种推广提供数据支撑。\n\n西藏自治区农牧科学院党组书记曹永寿表示，自治区农牧科学院作为全区青稞科技创新核心平台，多年来深度联动国家大麦青稞产业技术体系与自治区青稞产业技术体系，围绕全产业链布局科研攻关，聚焦种质资源保护创新、突破性新品种选育、绿色轻简栽培、病虫害绿色防控、农机农艺融合、营养品质提升、精深加工转化等核心方向持续攻坚，大力推进“良种、良法、良机、良田”一体化示范落地，全力打通从实验室到田间地头、从科研成果到市场产品的转化通道。\n\n“十五五”时期是我国农业产业转型升级、特色种业高质量发展的关键阶段。国家大麦青稞产业技术体系首席科学家、中国农业科学院作物科学研究所副所长郭刚刚表示，“十五五”期间，体系将聚焦青稞稳产保供、大麦提质增效核心目标，以问题需求导向、科创产创融合为核心思维，扎实干好“四件事”：一是干大事，集成关键技术，保障粮食安全和重要农产品供给；二是干实事，精准赋能县域经济高质量发展，强化科技支撑；三是干急事，健全应急响应机制，高效应对产业各类突发性风险挑战；四是干要事，锤炼过硬农业科技创新国家队，夯实产业发展人才与技术根基。\n\n明晰年度目标与“十五五”青稞产业发展任务，必将有力推动青稞领域科技创新与产业创新深度融合，西藏自治区农业农村厅副厅长蒲正学指出，要持续抓实良种推广应用，夯实青稞稳产根基；强化技术集成示范，打通科技进村入户通道；全力推动产学研用深度融合，补齐产业链短板；健全上下联动协作机制，凝聚产业发展合力，为高原粮食安全与特色产业高质量发展注入强劲动力。\n\n在专题研讨交流环节，国家大麦青稞产业技术体系各岗位专家围绕西藏青稞育种攻关、大麦青稞高产高效与农机农艺融合栽培、产品研发等领域分别汇报了最新研究进展。产业要发展，良种须先行。国家大麦青稞产业技术体系遗传改良研究室主任、青海省农林科学院研究员吴昆仑介绍，“十四五”期间，我国已打通大麦青稞种源创新、良种繁育、产业推广全链条，实现种质资源自主专用、品种覆盖百万亩级规模化示范推广，有力推动大麦青稞产业高质量发展。\n\n本次汇聚国家大麦青稞产业技术体系科研团队、农技人员、涉农企业代表、青年专家及自治区体系相关团队等100余人参会，为深化产学研用对接、加速青稞领域科技创新与产业创新融合奠定了良好基础。","中国农业科学院 农民日报 2026年8月15-17日","2026-08-17T00:00:00Z",81,{"impact":30,"substance":31,"depth":32,"authority":260,"freshness":33,"relevant":34,"comment":492},"全国性青稞产业体系会议，聚焦十五五稳产保供，信息详实，权威性高，但时效性稍弱。",[494],{"name":488,"url":485},[496,107,497,498,499],"十五五","青稞产业","国家大麦青稞产业技术体系","西藏",{"code":140,"slug":501,"name":502,"path":503,"parent_code":382},"rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry",[],{"status":59,"locked":60,"version":61,"method":62,"evidence":506},{"candidates":507,"agricultural_evidence":26},[508,513],{"code":140,"score":79,"supported":26,"title":509,"abstract":510,"tags":512},[],[511],"县域经济",[],{"code":240,"score":34,"supported":60,"title":514,"abstract":515,"tags":516},[],[],[496],[518,519],"国家大麦青稞产业技术体系 中国农科院作科所 青稞产业 十五五","国家大麦青稞产业技术体系 中国农科院作科所","国家大麦青稞产业技术体系中国农科院作科所青稞产业十五五-858","2026-08-23T00:08:11.952749Z",{"id":523,"title":524,"url":525,"summary":526,"summary_zh":20,"content":527,"source_name":528,"source_url":20,"published_at":158,"category":257,"cover_url":20,"hotness":25,"is_selected":60,"score":529,"score_detail":530,"sources":533,"tags":535,"primary_topic":20,"secondary_topics":543,"classification":544,"search_phrases":563,"slug":566,"view_count":34,"doi":20,"paper":20,"created_at":567},872,"长江流域115个地级市面板数据揭示:数字驱动农业新质生产力与耕地多功能性存在显著正向关联,但呈门槛效应","https:\u002F\u002Fwww.mdpi.com\u002F2673-6470\u002F6\u002F3\u002F70","四川农业大学李新营等学者在《MDPI Digital》发表论文,基于2013—2023年中国长江流域115个地级市面板数据,实证检验数字驱动农业新质生产力(ANQPF)与耕地多功能性(CLM)的关联。研究发现ANQPF与CLM呈显著正向关联且经多重稳健性检验;中介机制表明,该正向关联通过提升规模以上农产品加工企业主营业务收入、增加农业技术专利数量、提升农业社会化服务水平三条路径实现;分样本分析显示,非粮食主产区、人均GDP较低地区以及长江流域上中游地区关联更强;门槛效应分析显示,ANQPF超过临界水平后正向关联呈边际递减。","Previous Article in Journal\n\n[A Semantic Clustering Framework for Discovering Latent Offense Patterns: A Case Study of Thai Police 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__\n\nBackground:\n\n**This is an early access version, the complete PDF, HTML, and XML versions will be available soon.**\n\nOpen Access Article\n\n# Digitally Driven Agricultural New Quality Productive Forces and Cultivated Land Multifunctionality in the Yangtze River Basin\n\n by \n\n Xinying Li\n\n![Image 7](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Xinying Li\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5386315?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Xinying%20Li)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Xinying+Li%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Xinying+Li)\n\n 1[](mailto:202302018@stu.sicau.edu.cn), \n\n Zhanpeng Qu\n\n![Image 8](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Zhanpeng Qu\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5334809?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Zhanpeng%20Qu)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Zhanpeng+Qu%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Zhanpeng+Qu)\n\n 1[](mailto:202301799@stu.sicau.edu.cn), \n\n Shuohuan Yan\n\n![Image 9](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Shuohuan 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Wang\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002Fauthor\u002FMmVXTHQ4djhiQTdVYU5uYnN1VENhaU1FM24vdVlQZ0oxRjJtODFBV2NaVT0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Shanni%20Wang)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Shanni+Wang%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Shanni+Wang)\n\n 1[](mailto:202302584@stu.sicau.edu.cn), \n\n Haozhaoxing Liao\n\n![Image 12](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Haozhaoxing Liao\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002Fauthor\u002FQWpLeUJqWDhVZTZ2RFF4bTVEN3FPNUhPZithMmtlK0VHSm9jMXA4OXlRaz0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Haozhaoxing%20Liao)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Haozhaoxing+Liao%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Haozhaoxing+Liao)\n\n 2[](mailto:lhzx2004@foxmail.com), \n\n Yue Zhang\n\n![Image 13](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Yue Zhang\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5509645?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Yue%20Zhang)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Yue+Zhang%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Yue+Zhang)\n\n 3[](mailto:m202510967@xs.ustb.edu.cn), \n\n Siyuan Li\n\n![Image 14](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Siyuan Li\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5513179?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Siyuan%20Li)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Siyuan+Li%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Siyuan+Li)\n\n 1[](mailto:202302912@stu.sicau.edu.cn) and \n\n Yue Wang\n\n![Image 15](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Yue Wang\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F2350850?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Yue%20Wang)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Yue+Wang%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Yue+Wang)\n\n 1,*[](mailto:wangyue@sicau.edu.cn)\n\n1\n\nCollege of Economics, Sichuan Agricultural University, Chengdu 611130, China\n\n2\n\nSchool of Finance, Nankai University, Beijing 300353, China\n\n3\n\nSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China\n\n*\n\nAuthor to whom correspondence should be addressed. \n\n_Digital_**2026**, _6_(3), 70; https:\u002F\u002Fdoi.org\u002F10.3390\u002Fdigital6030070 (registering DOI) \n\nSubmission received: 6 July 2026 \u002F Revised: 13 August 2026 \u002F Accepted: 20 August 2026 \u002F Published: 22 August 2026\n\n[Download _keyboard\\_arrow\\_down_](https:\u002F\u002Fwww.mdpi.com\u002F2673-6470\u002F6\u002F3\u002F70)\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2673-6470\u002F6\u002F3\u002F70\u002Fpdf?version=1787391194)\n\n[Versions Notes](https:\u002F\u002Fwww.mdpi.com\u002F2673-6470\u002F6\u002F3\u002F70\u002Fnotes)\n\n## Abstract\n\n Transitioning cultivated land from a narrowly defined production resource into a coordinated multifunctional asset is a cornerstone of agricultural modernization. Despite this imperative, current land utilization in China remains largely constrained by a singular production focus, resulting in suboptimal multifunctionality. Although digitally driven agricultural new quality pr","MDPI Digital 6(3):70 2026年8月22日",80,{"impact":31,"substance":161,"depth":100,"authority":66,"freshness":531,"relevant":34,"comment":532},8,"研究揭示数字农业与耕地多功能性的关联，数据详实，但发表于MDPI期刊，权威性中等。",[534],{"name":528,"url":525},[536,537,538,539,540,541,542],"农业新质生产力","门槛效应","长江流域","地级市面板","耕地多功能性","MDPI Digital","四川农大",[],{"status":113,"locked":60,"version":61,"method":62,"evidence":545},{"candidates":546,"agricultural_evidence":26,"reason":144},[547,553,559],{"code":548,"score":79,"supported":26,"title":549,"abstract":550,"tags":552},"E1",[],[551],"农产品加工",[],{"code":554,"score":79,"supported":26,"title":555,"abstract":556,"tags":558},"G2",[],[557],"社会化服务",[],{"code":222,"score":34,"supported":60,"title":560,"abstract":561,"tags":562},[],[],[536],[564,565],"农业新质生产力 耕地多功能性 地级市面板 四川农大","农业新质生产力 耕地多功能性","农业新质生产力耕地多功能性地级市面板四川农大-872","2026-08-23T00:08:13.415304Z",{"id":569,"title":570,"url":571,"summary":572,"summary_zh":20,"content":573,"source_name":574,"source_url":20,"published_at":575,"category":24,"cover_url":20,"hotness":25,"is_selected":60,"score":529,"score_detail":576,"sources":578,"tags":580,"primary_topic":587,"secondary_topics":590,"classification":592,"search_phrases":604,"slug":607,"view_count":198,"doi":20,"paper":20,"created_at":608},868,"生物育种安徽省实验室李晓玉教授团队揭示ZmHPATR1基因调控鞘氨醇单胞菌增强玉米弯孢叶斑病抗性:基于植物微生物组的抗病育种新路径","http:\u002F\u002Fswyz.aaas.org.cn\u002F43505232\u002F70788327.html","2026年8月3日,生物育种安徽省实验室双聘人员、安徽农业大学李晓玉教授团队在《New Phytologist》在线发表题为ZmHPATR1 orchestrates phyllosphere organic acid flux to recruit Sphingomonas and enhance resistance of maize to Curvularia leaf spot的研究论文。该研究揭示玉米ZmHPATR1基因通过调控叶片有机酸积累,特异性招募有益菌鞘氨醇单胞菌(Sphingomonas),后者分泌胞外多糖威兰胶(Welan gum)直接抑制病原菌生长,从而增强玉米弯孢叶斑病抗性的全新机制,为基于植物微生物组的抗病育种提供了理论依据和技术路径。研究团队通过EMS诱变群体筛选获得抗弯孢叶斑病突变材料并利用MutMap定位方法鉴定出候选基因。","为解析ZmHPATR1调控微生物组介导抗病性的机制，研究团队结合代谢组和叶际微生物组等分析发现，ZmHPATR1功能缺失能够重塑玉米叶片代谢状态，促进富马酸、叶酸和四氢叶酸等关键有机酸积累，这些有机酸可特异性招募有益细菌鞘氨醇单胞菌定殖富集；从富集叶际分离鉴定的鞘氨醇单胞菌可显著抑制新月弯孢菌，分析发现鞘氨醇单胞菌产生的胞外多糖威兰胶可直接抑制病原菌生长、孢子萌发，破坏病原菌细胞结构，从而增强玉米弯孢叶斑病抗性，构建了“植物基因—代谢物—微生物—病原菌”的四级抗病调控网络（图2）。进一步研究证实ZmHPATR1功能缺失突变体也可增强玉米对小斑病抗性。同时，鞘氨醇单胞菌与威兰胶对玉米小斑病菌（Bipolaris maydis）、玉米大斑病菌（Exserohilum turcicum）、玉米茎基腐病菌（Fusarium verticillioides）等病原真菌均具有显著的拮抗抑制效果，显示其广谱抗病应用潜力。","安徽省农业科学院 生物育种安徽省实验室 2026年8月3日","2026-08-03T00:00:00Z",{"impact":161,"substance":99,"depth":31,"authority":66,"freshness":348,"relevant":34,"comment":577},"揭示植物基因-代谢物-微生物-病原菌四级调控网络，为抗病育种提供新路径，但发布已超两周，时效性低。",[579],{"name":574,"url":571},[581,582,583,584,585,586],"安徽省农科院","生物育种安徽省实验室","李晓玉","ZmHPATR1","鞘氨醇单胞菌","New Phytologist",{"code":117,"slug":588,"name":119,"path":589,"parent_code":50},"maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[591],{"code":179,"slug":180,"name":181,"path":182,"parent_code":57},{"status":59,"locked":60,"version":61,"method":62,"evidence":593},{"candidates":594,"agricultural_evidence":26},[595,599],{"code":117,"score":260,"supported":26,"title":596,"abstract":597,"tags":598},[119],[119,588],[],{"code":179,"score":72,"supported":26,"title":600,"abstract":602,"tags":603},[601],"生物育种",[601],[],[605,606],"生物育种安徽省实验室 安徽省农科院 鞘氨醇单胞菌 李晓玉","生物育种安徽省实验室 安徽省农科院","生物育种安徽省实验室安徽省农科院鞘氨醇单胞菌李晓玉-868","2026-08-23T00:08:12.457555Z",{"id":610,"title":611,"url":612,"summary":613,"summary_zh":20,"content":614,"source_name":615,"source_url":20,"published_at":256,"category":24,"cover_url":20,"hotness":25,"is_selected":60,"score":529,"score_detail":616,"sources":619,"tags":621,"primary_topic":627,"secondary_topics":633,"classification":634,"search_phrases":643,"slug":646,"view_count":34,"doi":20,"paper":20,"created_at":647},864,"山东省农科院资环所揭示酸性、盐碱和沙性土壤铵态氮高效利用机制与调控路径:基于菌根真菌固氮菌铵同化功能菌群重塑土壤氮循环微生物群落","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10721\u002F202608\u002F1947a91e-1a1a-4a89-9d43-95eda6ce4cd9.shtml","山东省农科院资环所在《农业科学学报(Journal of Integrative Agriculture)》发表论文,在酸性、盐碱和沙性土壤铵态氮高效利用机制与调控路径方面取得新进展。研究通过接种固氮菌、铵同化功能菌及菌根真菌,重塑土壤氮循环微生物群落;为不同类型障碍土壤培肥与作物改良提供差异化方案,为盐碱地、酸化耕地和沙性土壤实现稳产增产及养分高效利用提供理论依据与技术思路。论文第一作者为资环所土壤与农业环境创新团队研究生史效迪,通讯作者为李光杰研究员,得到国家自然科学基金面上项目、泰山学者工程青年专家项目等资助。","近日，资环所土壤与农业环境创新团队在中国科技期刊卓越行动计划英文领军期刊Journal of Integrative Agriculture（中科院1区，IF=5.7）发表题为《Ammonium utilization in constrained soils: Mechanisms and multi-dimensional regulation pathways》的研究成果，系统梳理了酸性、盐碱和沙性三类典型障碍土壤铵态氮利用效率低的成因，厘清了不同土壤条件下铵态氮流失与转化的差异化制约机制，并据此提出多维增效调控技术框架。\n\n氮素是作物生长最关键的限制性营养。铵态氮可直接被植物吸收利用，既是合成蛋白、核酸、光合物质的基础物质，也是调控根系发育、碳氮代谢基因表达的信号因子，决定作物氮利用效率与产量上限，适配未来高CO₂环境下C3作物养分供给需求。我国和全球大量酸性、盐碱、沙性障碍土壤保蓄铵态氮能力薄弱，极易引发氮素挥发、淋溶损失，不仅降低氮肥利用率，还带来农业面源污染，制约耕地稳产增产，亟须厘清差异化调控机制及改良策略。\n\n该论文指出酸性土壤中铝锰离子竞争铵态氮吸附位点，且硝化菌群活性受抑制；盐碱土因高pH引发氨挥发，同时钠离子拮抗根系对铵离子的吸收；沙性土因黏粒和有机质缺乏，铵态氮易随水分淋溶损失。文章明确根鞘作为根—土—微生物交互界面，可局部富集氮素，是提升铵利用率的关键潜在靶点。在此基础上，研究提出四类调控路径：精准配施缓释肥、硝化\u002F脲酶抑制剂、生物炭等改良物料，结合优化水肥管理，增强土壤固铵能力；配施钙、硅及纳米微量元素，缓解毒害离子对铵转运的抑制；筛选高表达AMT、GS\u002FGOGAT基因的作物基因型，或通过基因工程强化根系吸铵能力及体内解毒同化效率；接种固氮菌、铵同化功能菌及菌根真菌，重塑土壤氮循环微生物群落。该成果为不同类型障碍土壤的培肥与作物改良提供了差异化方案，并为盐碱地、酸化耕地和沙性土壤实现稳产增产及养分高效利用提供理论依据与技术思路。\n\n论文第一作者为资环所土壤与农业环境创新团队研究生史效迪，通讯作者为资环所李光杰研究员，资环所李彦研究员、山东农业大学崔秀敏教授、澳大利亚墨尔本大学Herbert J. Kronzucker教授参与指导。研究工作得到国家自然科学基金面上项目、泰山学者工程青年专家项目、山东省生态农业技术体系岗位科学家项目等资助。（撰稿：李光杰、史效迪，核稿：谭德水）","山东省农业科学院 2026年8月",{"impact":31,"substance":161,"depth":100,"authority":617,"freshness":33,"relevant":34,"comment":618},13,"省级农科院在核心期刊发表关于障碍土壤铵态氮利用机制的研究，提出差异化调控路径，对农业科技有实质贡献，但影响范围限于省级科研层面。",[620],{"name":615,"url":612},[622,623,624,625,626],"山东省农科院资环所","铵态氮高效利用","菌根真菌","土壤氮循环","李光杰",{"code":628,"slug":629,"name":630,"path":631,"parent_code":632},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":59,"locked":60,"version":61,"method":62,"evidence":635},{"candidates":636,"agricultural_evidence":26},[637],{"code":628,"score":260,"supported":26,"title":638,"abstract":640,"tags":642},[639],"土壤",[639,641],"盐碱地",[],[644,645],"山东省农科院资环所 铵态氮高效利用 土壤氮循环 菌根真菌","山东省农科院资环所 铵态氮高效利用","山东省农科院资环所铵态氮高效利用土壤氮循环菌根真菌-864","2026-08-23T00:08:12.212596Z",{"id":649,"title":650,"url":651,"summary":652,"summary_zh":20,"content":653,"source_name":654,"source_url":20,"published_at":158,"category":257,"cover_url":20,"hotness":25,"is_selected":60,"score":655,"score_detail":656,"sources":658,"tags":660,"primary_topic":667,"secondary_topics":668,"classification":669,"search_phrases":682,"slug":685,"view_count":34,"doi":20,"paper":20,"created_at":686},875,"新疆农业大学低成本快速构建田间棉花3D点云评估冠层性状:UAV航拍141张影像约20分钟重建,效率优于SfM-MVS和Instant-NGP","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619","新疆农业大学邱浩等学者在《MDPI Agronomy》发表论文,提出低成本快速构建田间棉花3D点云并评估冠层性状的技术堆栈。针对传统3D数据采集成本高、处理时间长、运营效率低等瓶颈,集成快速重建算法与基于地面控制点(GCP)的尺度恢复机制,能够以低成本和短时间从UAV航拍图像生成真实尺度3D点云。仅使用141张UAV影像和约20分钟重建时间即可高效重建两个5.5m×5.5m棉花小区的高质量尺度精确点云,在重建效率和点云完整性方面均显著优于SfM-MVS和Instant-NGP;研究进一步量化分析收获期冠层高度变异系数、孔隙度分布和冠层体积分数,为精准栽培管理与机械化兼容种植提供数据支持。","## 1. Introduction\n\nCotton, as one of the most economically important crops worldwide [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B1-agronomy-16-01619)], not only provides the core natural fiber raw material for the textile industry, but also serves as a significant oilseed and cash crop. Its industrial chain plays an irreplaceable supporting role in global rural employment and economic stability [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B2-agronomy-16-01619),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B3-agronomy-16-01619),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B4-agronomy-16-01619)]. Xinjiang, as the major cotton-producing region in China, holds a strategically irreplaceable role in ensuring the supply security of the national cotton industry [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B5-agronomy-16-01619)]. With the rapid advancement of agricultural mechanization and smart farming technologies, Xinjiang cotton has achieved a high degree of mechanization in key production processes such as sowing and harvesting, greatly improving production management efficiency and reducing labor dependence. However, the current analysis of canopy characteristics for field-grown cotton still relies primarily on traditional manual field survey methods, which suffer from high labor intensity, strong subjective bias in observation, limited sampling coverage, and insufficient spatial representativeness [[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B6-agronomy-16-01619),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B7-agronomy-16-01619)], making it difficult to achieve large-scale, refined, and time-series dynamic monitoring and quantitative evaluation of canopy characteristics at the field scale [[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B8-agronomy-16-01619),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B9-agronomy-16-01619)]. Therefore, it is necessary to develop efficient and scalable technical approaches for quantitative analysis and comprehensive evaluation of canopy structure and growth status in field-grown cotton populations, thereby providing scientific data support and technical foundations for growth diagnosis, cultivation regulation, and population optimization [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B10-agronomy-16-01619),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B11-agronomy-16-01619)].\n\nTo overcome the limitations of the aforementioned manual surveys, the rapid development of UAV remote sensing technology and three-dimensional (3D) reconstruction methods in recent years has brought new technical opportunities for crop canopy analysis. By equipping UAVs with RGB cameras, multispectral sensors, or even LiDAR, researchers can acquire both two-dimensional spectral information and three-dimensional spatial structural information of the canopy, and subsequently estimate key phenotypic parameters such as plant height, leaf area index, biomass, and canopy porosity [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B12-agronomy-16-01619),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B13-agronomy-16-01619),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B14-agronomy-16-01619),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B15-agronomy-16-01619)]. Among these, 3D point cloud data, owing to their ability to faithfully reconstruct the stereoscopic spatial configuration of the canopy, exhibit unique advantages in canopy vertical stratification analysis, light radiation interception assessment, population ventilation and light transmission diagnosis, and planting density optimisation [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B16-agronomy-16-01619)]. In recent years, deep learning-based 3D reconstruction methods have made significant progress, among which Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have become research hotspots in this field [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B17-agronomy-16-01619),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B18-agronomy-16-01619)]. NeRF can generate high-quality, photorealistic 3D reconstructions from sparse-view 2D images, demonstrating unique advantages in fine-scale plant structure modelling. 3DGS, on the other hand, explicitly represents scene geometry via Gaussian primitives, showing remarkable potential in reconstruction efficiency and scalability. These two types of methods have been applied in phenotypic studies of various crops, including wheat [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B19-agronomy-16-01619)] and pepper [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B20-agronomy-16-01619)], and have demonstrated promising application prospects in tasks such as plant organ segmentation, yield-related trait prediction, and large-scale field reconstruction [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B21-agronomy-16-01619)]. However, current mainstream methods for field 3D data acquisition still face significant technical bottlenecks and engineering obstacles. On one hand, although LiDAR systems based on ground or UAV platforms can directly acquire high-precision 3D point clouds, they suffer from high equipment costs, large data volumes, and complex pre-processing workflows [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B22-agronomy-16-01619),[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B23-agronomy-16-01619),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B24-agronomy-16-01619)]. Moreover, under field conditions, the multiple-return signals of LiDAR are susceptible to canopy occlusion and wind-induced disturbances, leading to data redundancy and local information loss [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B25-agronomy-16-01619)]. On the other hand, traditional point cloud generation methods based on Structure from Motion (SfM) require a large number of UAV aerial images and high overlap rates—typically hundreds or even thousands of images per experimental plot—resulting in lengthy reconstruction times and heavy computational resource consumption. Furthermore, under complex field illumination, mutual plant occlusion, and repetitive leaf texture conditions, these methods are prone to point cloud voids, excessive noise, and reduced geometric accuracy. These technical bottlenecks make it difficult for existing 3D canopy monitoring methods to achieve a balance among cost, efficiency, and accuracy, and thus they cannot truly meet the practical demands of rapid field monitoring, real-time diagnosis, and large-scale application [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B20-agronomy-16-01619),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B26-agronomy-16-01619),[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B27-agronomy-16-01619)].\n\nTo address the above issues, this study aims to explore a low-cost, high-efficiency, and few-image-input method for constructing 3D point clouds of field-grown cotton, and to carry out quantitative evaluation of canopy characteristics and population structure diagnosis based on high-quality point clouds. Using UAV aerial images as the sole data source, this study innovatively integrates the Splatfacto [[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B28-agronomy-16-01619),[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B29-agronomy-16-01619)] reconstruction algorithm with the 3DGS-to-PC combined point cloud generation technique [[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B30-agronomy-16-01619),[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B31-agronomy-16-01619),[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B32-agronomy-16-01619)], while introducing ground control points (GCPs) placed in the field as geometric scale references. This effectively corrects scale drift during reconstruction, restores the generated cotton canopy point clouds to true physical dimensions, and ensures the accuracy and comparability of subsequent phenotypic parameter calculations. Based on this technical pathway, we systematically conduct research on the low-cost and rapid construction of 3D point clouds for field-grown cotton [[33](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B33-agronomy-16-01619)]. Compared with traditional methods, this technical pathway significantly reduces the reliance on the number of images and computational resources, achieving a technical breakthrough in reconstructing with fewer input images and high efficiency while ensuring point cloud reconstruction accuracy and completeness, greatly improving the acquisition efficiency and engineering practicality of 3D data for field cotton. On the basis of obtaining high-fidelity, true-scale 3D point clouds, we further carry out systematic quantitative evaluation of the canopy characteristics of cotton populations at the harvest stage. By extracting key canopy phenotypic indicators from point cloud data and performing quantitative calculations and comparative analyses, we systematically reveal the current structural characteristics, spatial heterogeneity patterns, and weak links of the field population canopy structure [[34](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B34-agronomy-16-01619)], and identify the core shortcomings and optimisation directions of the population structure. This provides scientific and reliable data support for precision cultivation management, canopy structure optimisation, and mechanisation-adapted planting in cotton [[35](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B35-agronomy-16-01619)]. Ultimately, the findings of this study will contribute to the sustainable development of the cotton industry towards high quality, large scale, and intelligence, providing a novel, scalable, and readily deployable technical paradigm for field crop phenotyping and population optimisation in the era of smart agriculture. Nonetheless, the present validation has been carried out only in a single season, one growth stage, and two experimental plots, and further validation involving multiple seasons and varieties is already underway to strengthen the applicability of these results.\n\n## 2. Materials and Methods\n\n### 2.1. 3D Reconstruction Workflow for Field-Grown Cotton\n\n#### 2.1.1. Data Acquisition\n\nThe data acquisition area was located at Huaxing Farm, Changji City, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region, with geographic coordinates of 44°02′ N, 87°30′ E, as shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#fig_body_display_agronomy-16-01619-f001). The acquisition area is located in a large-scale commercial field planting zone for industrial crops. The cotton variety used in this study was Zhongmian 113GK (National Certified Cotton Variety 20253014), bred by the Institute of Cotton Research, Chinese Academy of Agricultural Sciences. This variety is a conventional cultivar of upland cotton (Gossypium hirsutum L.) with a well-defined genetic background and stable transgenic insect-resistant traits. It exhibits a relatively open plant architecture, with clear spatial distribution patterns of branching configuration and leaf area. These genotype-determined morphological characteristics directly shape the canopy spatial structure and height uniformity, providing a validation scenario with a clear biological benchmark for 3D reconstruction algorithms. Irrigation, fertilisation, and other field management practices were implemented uniformly in accordance with local standardised large-scale production protocols to ensure the reproducibility of data acquisition conditions. In this study, two plots with dimensions of 5.5 m × 5.5 m each were established, and each plot contained six standardized cotton rows with a row spacing of 0.6 m and a plant spacing of 0.1 m. The data acquisition campaign was conducted on 25 October 2025, during the cotton harvest period under clear skies, no precipitation, and stable illumination conditions, effectively ruling out the interference of meteorological variability on data quality. The average wind speed from ground level to take-off altitude was approximately 1 m\u002Fs (corresponding to Beaufort Force 1), falling within the calm to light air range. Despite the low wind speed, the cotton canopy leaves and bolls exhibited continuous slight oscillation, and since the 3D Gaussian Splatting (3DGS) reconstruction algorithm strictly relies on the static scene assumption, wind-induced deformation constituted a potential factor affecting reconstruction accuracy. The current algorithm does not explicitly model wind-induced deformation; instead, the acquisition strategy relied on two mitigation measures. First, the extremely short single exposure time (1\u002F2000 s) ensured that leaf displacement within this integration period was far smaller than the ground sampling distance, rendering motion blur within individual frames negligible. Second, the high overlap rate and short shooting intervals resulted in a baseline distance of approximately 0.5 m between adjacent along-track images, with each spatial point being covered by multiple overlapping views. The shooting interval between adjacent images was 1 s. If leaf positions exhibited observable changes due to wind during this interval, the 60% overlap rate ensured that every spatial point was recorded from a large number of views at different instants. The random positional deviations among views caused by wind were effectively averaged out through multi-constraint joint adjustment during the estimation of 3D Gaussian parameters, thereby mitigating their systematic impact on canopy structure reconstruction. A total of 141 RGB images were acquired in this campaign, all collected within a single day, ensuring the timeliness and spatial consistency of the dataset.\n\n**Figure 1.** Overview of image data acquisition for field-grown cotton. (**A**) Location map of the sampling site. (**B**) Photograph of the field experimental acquisition area. (**C**) Multi-view images of field-grown cotton acquired by the UAV.\n\n#### 2.1.2. UAV Flight Route Planning and Design\n\nIn this study, a DJI Mavic 3M UAV (DJI, Shenzhen, China) was employed for field image acquisition of field-grown cotton. The flight height was set to 5 m above ground level, and the flight speed was set to 0.5 m\u002Fs, which served as the core operational parameters for flight route planning. All acquired images were saved in JPG format, with a minimum resolution of 5280 × 3956 pixels per image. Each image was accompanied by GPS positioning, flight attitude, camera parameters, and absolute flight altitude information, providing comprehensive data support for high-precision 3D reconstruction of cotton from multiple viewpoints. A grid-based flight route was specifically designed for data acquisition in this study, and the route layout is illustrated in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#fig_body_display_agronomy-16-01619-f002). Although this grid route does not possess the capability of the CCO (crop canopy-oriented) route to penetrate the occlusion of upper canopy leaves and fully capture the detailed images of middle and lower plant organs [[36](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B36-agronomy-16-01619),[37](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B37-agronomy-16-01619)], our study aimed to construct point clouds of field-grown cotton rapidly and efficiently. Therefore, the grid route allowed for the rapid acquisition of images from four viewing angles with substantially higher collection efficiency. The camera inclination angle was set to 45°, with both heading and side overlap ratios set to 60%, and the shooting interval was 1 s, thereby enabling automatic temporal continuity and precise spatial alignment.\n\n**Figure 2.** Design of the UAV-based flight route for data acquisition. (**A**) Flight trajectory of the grid route. (**B**) Distribution of image capture points along the grid route.\n\n#### 2.1.3. GCP-Based Georeferencing and Scale Calibration for 3D Canopy Reconstruction\n\nThe acquired aerial images were imported into Agisoft Metashape Professional Software (Version 2.2.2) [[38](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B38-agronomy-16-01619)] to complete the full photogrammetric processing workflow. This software, based on the Structure from Motion (SfM) algorithm, performs image stitching and registration to construct a sparse 3D point cloud model of field-grown cotton. In this study, ground control points (GCPs) were placed at the four corners of the acquisition area as georeferencing references for photogrammetric processing. The GCPs provide absolute control for the UAV images, effectively correcting geometric distortions caused by flight attitude variations, camera lens distortion, and GPS positioning errors, eliminating cumulative drift errors generated during the aerial triangulation process of 3D reconstruction, and significantly improving the absolute positioning accuracy of the model in both planar and elevational directions. More critically, the GCPs also serve a core function in establishing the true physical dimensions for the reconstructed model. During the SfM processing stage, the GCPs deployed in this study have already enabled georeferencing and absolute scale calibration of the imagery. Through bundle adjustment optimisation, a sparse point cloud model with true physical dimensions and precise camera pose information was obtained. However, the subsequent Splatfacto reconstruction model, whose input consists of normalised camera poses and image features, outputs the scene’s 3D structure in a normalised representation, the reconstructed result is constrained within a normalised coordinate space, where geometric parameters including distances and volumes are expressed in relative proportions, inherently lacking a scale correspondence to the real physical world. Consequently, although the dense point cloud produced by Splatfacto reconstruction exhibits superior local geometric details and completeness compared to the SfM sparse point cloud, its spatial scale has deviated from true physical dimensions. To address this scale loss problem, this study leverages the precisely measured GCP coordinates obtained during the SfM stage to establish a correspondence between the GCPs in the normalised point cloud space and their true physical dimensions. Specifically, based on the known physical size of the GCP markers (80 cm × 80 cm) and the distances between their corresponding corner points in the Splatfacto-reconstructed point cloud, a global scale factor between the normalised space and the real physical space is calculated. This scale factor is then applied to the entire point cloud, transforming it from normalised coordinates to true physical coordinates, thereby restoring the geometric metrics such as distances, areas, and volumes to their actual field values. Through this GCP-based post-calibration strategy, the final cotton canopy 3D point clouds not only preserve the high-precision geometric details offered by Splatfacto, but also possess a dimensionally consistent basis that matches the actual field scale, providing a reliable scale reference for subsequent accurate quantitative analyses of phenotypic parameters such as canopy height, volume fraction, and porosity [[39](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B39-agronomy-16-01619),[40](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B40-agronomy-16-01619),[41](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1619#B41-agronomy-16-01619)]. Moreover, the large 80 cm × 80 cm size significantly enhances the detectability of GCPs in UAV aerial imagery, ensuring stable and repeatable corner detection even under challenging conditions such as high flight altitudes, varying illumination, or partial canopy occlusion. This effectively reduces uncertainties in scale recovery and georeferencing, further improving the robustness and engineering practicality of the overall technical workflow. The GCP coordinates were collected using a Qianxun SR3 Pro RTK receiver (Qianxun Spatial Intelligence Inc., Shanghai, China), and the layout of the control points is shown in [Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1","MDPI Agronomy 16(17):1619 2026年8月22日",79,{"impact":100,"substance":161,"depth":100,"authority":66,"freshness":149,"relevant":34,"comment":657},"新疆农大利用UAV与3D重建技术高效评估棉花冠层，方法新颖且数据详实，对智慧农业有实质贡献。",[659],{"name":654,"url":651},[661,662,663,664,665,666],"新疆农业大学","田间棉花","3D点云","冠层性状","SfM-MVS UAV MDPI Agronomy","邱浩",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},[],{"status":59,"locked":60,"version":61,"method":62,"evidence":670},{"candidates":671,"agricultural_evidence":26},[672,676],{"code":46,"score":72,"supported":26,"title":673,"abstract":674,"tags":675},[68],[68],[],{"code":677,"score":79,"supported":26,"title":678,"abstract":679,"tags":681},"F5",[],[680],"机械化",[],[683,684],"新疆农业大学 冠层性状 田间棉花 3D点云","新疆农业大学 冠层性状","新疆农业大学冠层性状田间棉花3D点云-875","2026-08-23T00:08:13.522331Z",{"id":688,"title":689,"url":690,"summary":691,"summary_zh":20,"content":692,"source_name":693,"source_url":20,"published_at":694,"category":24,"cover_url":20,"hotness":25,"is_selected":60,"score":655,"score_detail":695,"sources":697,"tags":699,"primary_topic":706,"secondary_topics":710,"classification":711,"search_phrases":729,"slug":732,"view_count":348,"doi":20,"paper":20,"created_at":733},871,"黑龙江省农科院四有成果转化模式获农业农村部体系赞誉:建院70周年十四五成果转化到账8.88亿元,较往期增长76%","https:\u002F\u002Fwww.haas.cn\u002Finfo\u002F1231\u002F335321.htm","8月18日中国农业科技管理研究会农业科技成果转化与知识产权保护工作委员会2026年年会在哈尔滨开幕,黑龙江省农业科学院四有成果转化模式获农业农村部体系赞誉。陈宇辉院长介绍四有即聚焦产业需求确保有的转、深化体制改革确保有人转、搭建多元平台确保有地转、创新转化模式确保有招转。建院70年来累计选育农作物新品种2465个,主导支撑黑龙江省主要农作物品种6次更新换代;自育水稻、大豆品种分别占黑龙江种植面积的72%、53%,全国粳稻和大豆种植面积四分之一的良种均出自该院。成果转化净收益重要贡献人员分配比例不低于70%,近三年73名科研人员拿到百万级转化收入;去年克豆91大豆转化金额高达3155万元。","8月18日，中国农业科技管理研究会农业科技成果转化与知识产权保护工作委员会2026年年会在哈尔滨开幕。与会人员聚焦“推动科技创新与产业创新深度融合”“科研项目管理创新”“强化企业创新主体地位”等话题开展深入研讨。中国农业科技管理研究会理事长、农业农村部科技发展中心主任杨礼胜主持开幕式。会上，唐华俊院士、张新友院士和黑龙江省农业科学院党组书记、院长陈宇辉分别以《农业发展千年之变与新时期农业发展战略》《践行产业科技理念，以科技创新引领产业创新——团队四十年花生育种与成果转化实践》和《构建“四有”成果转化模式，加速推进科技创新与产业创新深度融合》为题，作主旨报告。\n\n自1956年建院以来，70年来，黑龙江省农科院坚守“为国科研、为农科研、为民科研”的初心使命，秉持“论文写在大地上，成果留在农民家”的服务“三农”理念，累计选育出农作物新品种2465个，主导支撑了黑龙江省主要农作物品种6次更新换代。陈宇辉以构建“四有”成果转化模式为主线，重点介绍黑龙江省农科院如何深耕科技成果转化全链条，赋能农业新质生产力发展，为端牢“中国饭碗”、筑牢国家粮食安全压舱石贡献龙江农科力量。“四有”即聚焦产业需求，确保“有的转”；深化体制改革，确保“有人转”；搭建多元平台，确保“有地转”；创新转化模式，确保“有招转”。\n\n与会专家一致认为，当前农业科技已进入与产业需求深度融合的新阶段，必须打通从实验室到生产田的“最后一公里”，让科研成果真正转化为支撑粮食安全、驱动产业升级的现实生产力。\n\n北京市农林科学院院长燕继晔介绍，该院以产业需求为导向，围绕生产一线急需技术和产业链堵点卡点，凝练科研任务。双轮驱动强化产业需求，依托院级项目组织优势力量，开展应用基础研究、关键技术攻关和集成创新，做强成果转化和院属企业，做实京郊科技示范服务，实现从“田间—实验室—工厂—田间”的科研全过程。\n\n山东省农业科学院党委委员、副院长刘开昌介绍了该院在科研历史、人才引进、创新平台建设、成果转化等方面的工作和成就，重点提及了在粮食安全、农民增收、绿色低碳农业方面的努力和成果。他表示，山东省农业科学院坚持“一体两翼”发展思路，一手抓科技创新，一手抓成果转化。\n\n本次年会由中国农业科技管理研究会主办，中国农业科技管理研究会农业科技成果转化与知识产权保护工作委员会、黑龙江省农业科学院、黑龙江省龙科农业科技成果转移服务中心有限公司共同承办。来自全国各地的农业科技界专家学者300多人参加会议。\n\n今年适逢黑龙江省农业科学院建院70周年。会议期间，黑龙江省农业科学院通过举办系列学术会议，为寒地农业高质量发展“把脉问诊”，以农科智慧赋能龙江发展，为现代化大农业战略实施注入科技动能。","黑龙江省农业科学院 2026年8月18日","2026-08-18T00:00:00Z",{"impact":30,"substance":31,"depth":32,"authority":66,"freshness":33,"relevant":34,"comment":696},"省级农科院成果转化模式获部级赞誉，数据详实，具有行业参考价值。",[698],{"name":693,"url":690},[700,701,702,703,704,705],"黑龙江省农科院","克豆91","建院70周年","四有成果转化模式","陈宇辉","全国农牧渔业",{"code":78,"slug":707,"name":708,"path":709,"parent_code":382},"extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer",[],{"status":59,"locked":60,"version":61,"method":62,"evidence":712},{"candidates":713,"agricultural_evidence":26},[714,719,723],{"code":78,"score":72,"supported":26,"title":715,"abstract":717,"tags":718},[716],"成果转化",[716],[],{"code":129,"score":296,"supported":26,"title":720,"abstract":721,"tags":722},[],[132],[701],{"code":724,"score":79,"supported":26,"title":725,"abstract":726,"tags":728},"A1",[],[727],"水稻",[],[730,731],"四有成果转化模式 黑龙江省农科院 全国农牧渔业 建院70周年","四有成果转化模式 黑龙江省农科院","四有成果转化模式黑龙江省农科院全国农牧渔业建院70周年-871","2026-08-23T00:08:12.571161Z"]