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2026-08-20",83,{"impact":28,"substance":71,"depth":31,"authority":31,"freshness":24,"relevant":33,"comment":282},"部级会议传达中央精神，涉及土地承包等三农重点任务，权威且时效性强，但内容多为传达学习，信息增量有限。",[284],{"name":279,"url":277},[286,287,288,289],"农业农村部","张柱","党组会议","江泽民诞辰100周年",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":292},{"candidates":293,"agricultural_evidence":25,"reason":56},[],[295,296],"江泽民诞辰100周年 农业农村部 党组会议 张柱","江泽民诞辰100周年 农业农村部","江泽民诞辰100周年农业农村部党组会议张柱-712","2026-08-19T23:57:26.113330Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":20,"content":304,"source_name":305,"source_url":20,"published_at":306,"category":307,"cover_url":20,"hotness":24,"is_selected":46,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":20,"secondary_topics":321,"classification":322,"search_phrases":335,"slug":338,"view_count":236,"doi":20,"paper":339,"created_at":343},746,"两套代表性农户调查中涉农数智技术采纳现状与挑战","https:\u002F\u002Ffinance.sina.com.cn\u002Fwm\u002F2026-08-11\u002Fdoc-inimyhkc7352205.shtml","北大黄季焜\u002F易红梅团队基于两套全国农户调查:农业主要决策者智能手机使用率76%,主要用于社交娱乐;52%有网购但农产品网络销售仅0.32%、网络贷款不足2%;非嵌入式数智技术呈'重生活轻生产'特征。","*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   [体育](http:\u002F\u002Fsports.sina.com.cn\u002F)\n*   [财经](http:\u002F\u002Ffinance.sina.com.cn\u002F)\n*   [娱乐](http:\u002F\u002Fent.sina.com.cn\u002F)\n*   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Adoption and Challenges of Digital and Intelligent Technologies in Agriculture: Evidence from Two Representative Household Surveys[J]. Smart Agriculture, 2026, 8(3): 51-66.\n\nDOI： 10.12133\u002Fj.smartag.SA202601001\n\n涉农数智技术采纳现状与现实挑战：基于两套具有代表性农户调查的研究\n\n易红梅1， 何煦1， 智华勇1， 黄季焜1*， 陈曦2\n\n（1.北京大学现代农学院\u002F中国农业政策研究中心，北京 100871，中国； 2.台州科技职业学院，浙江台州 318020，中国）\n\n摘要：［目的\u002F意义］评估涉农数智技术在中国农业农村微观生产主体中的采纳现状，识别涉农数智技术应用在农业农村未来发展中面临的主要挑战。［方法］基于两套具有代表性的农户调查数据，首先使用描述性统计方法刻画了各类涉农数智技术在中国农业农村的采纳情况；进一步结合文献综述，梳理了技术采纳的潜在影响及其作用机制，并进一步联系理论与实践，分析了这些数智技术在普及过程中面临的挑战及其成因。［结果和讨论］研究表明，非嵌入式数智技术在农业中的应用总体呈现“重生活、轻生产”的特征。农业主要决策者智能手机使用率已达76%，但其主要用于社交与娱乐（如微信、抖音、快手），在农业生产、销售及政策信息获取中的应用明显不足；约三分之二的受访者未关注农业类公众号，78%的受访者表示较少通过智能手机获取农业政策信息。在电商与数字金融领域，农户“消费端参与较多、生产端应用不足”的特征同样突出：2023年约52%的农户有网购行为，但通过网络销售[农产品](https:\u002F\u002Ffinance.sina.com.cn\u002Frealstock\u002Fcompany\u002Fsz000061\u002Fnc.shtml)(5.790, -0.33, -5.39%)的比例仅为0.32%，开展网络贷款的农户占比亦不足2%。与之形成对比的是，嵌入式数智技术在农业生产中的扩散相对较快。以无人机和北斗导航为代表的技术已在关键生产环节中得到应用：2023年约8%的农户在植保环节使用无人机，粮食主产区无人机植保作业覆盖耕地面积占比达39.3%，且主要依托社会化服务体系实现；配备北斗导航的农机占比按农户数量与耕地面积加权分别达到8%和12.5%。进一步分析表明，非嵌入式数智技术应用不足，主要受制于农户认知与数字素养约束、基础设施条件及政策支持不足；而嵌入式技术的加速扩散，则与其成本下降、社会化服务组织发展以及行业标准逐步完善密切相关。［结论］总体来看，中国农业农村数智化转型正在起步阶段。推动农业农村数智化转型，需积极提升农户数字素养、培育高人力资本水平的新型农业经营主体，继续推动农业适度规模化发展，培育社会化服务主体，加强服务供给能力，从而实现更广泛、更有效的数智技术普及与农业现代化提升。\n\n关键词：数智技术；数智化转型；农业社会化服务；农村电商；数字金融；北斗导航；无人机\n\n文章图片\n\n![Image 19: 图1　农业主要决策者智能手机上常用的App分布](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F268\u002Fw1080h788\u002F20260811\u002F2b06-b497b6973b6bbcaa1c0c9257c6992cc2.jpg)图1 农业主要决策者智能手机上常用的App分布\n\nFig. 1 Distribution of the frequently used smartphone apps among primary agricultural decision-makers\n\n![Image 20: 图2　各区域农业主要决策者中常用App为微信的比例](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F224\u002Fw1080h744\u002F20260811\u002F737f-bdf77360fd64d77f5aaf4b0f4f923473.jpg)图2 各区域农业主要决策者中常用App为微信的比例\n\nFig. 2 Ratio of primary agricultural decision-makers using WeChat frequently in each region\n\n![Image 21: 图3　各区域使用微信的农业主要决策者中使用朋友圈和关注朋友圈中销售信息的比例](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F126\u002Fw1080h646\u002F20260811\u002F3bc7-65b5371c3baca0d453fb7c7fc75f8cf0.jpg)图3 各区域使用微信的农业主要决策者中使用朋友圈和关注朋友圈中销售信息的比例\n\nFig. 3 Proportions of major agricultural decision-makers using WeChat in each region who browse Moments and pay attention to sales information posted on Moments\n\n![Image 22: 图4　各区域农业主要决策者对“用手机查看农业信息对农业生产是否有帮助”的看法](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F244\u002Fw1080h764\u002F20260811\u002Fd4c2-c1dc5dc46226deba52be867c4bf9248b.jpg)图4 各区域农业主要决策者对“用手机查看农业信息对农业生产是否有帮助”的看法\n\nFig. 4 Self-reported opinions on usefulness of accessing agricultural information via smartphones by primary agricultural decision-makers in each region\n\n![Image 23: 图5　不同网贷渠道占所有网贷批次的比例](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F29\u002Fw1080h549\u002F20260811\u002Fe005-c55d1201e2245d9005735338bea25cda.jpg)图5 不同网贷渠道占所有网贷批次的比例\n\nFig. 5 Distribution of online loan transactions across credit channels\n\n![Image 24: 图6　各区域使用无人机进行植保作业的农户比例](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F124\u002Fw1080h644\u002F20260811\u002F1f7b-baab351dc374031ad45a719a310fb433.jpg)图6 各区域使用无人机进行植保作业的农户比例\n\nFig. 6 Ratio of households using drones for pesticide application in each region\n\n![Image 25: 图7　各区域2023年使用无人机进行除草、施肥和播种作业的农户比例](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F24\u002Fw1080h544\u002F20260811\u002F7ebf-24cf0a8341c680938bfff316a87fe7be.jpg)图7 各区域2023年使用无人机进行除草、施肥和播种作业的农户比例\n\nFig. 7 Share of households using drones for herbicide, fertilizer, and sowing in 2023 in each region\n\n作者简介\n\n![Image 26](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F573\u002Fw229h344\u002F20260811\u002F0991-79e1f6c2193100c00a8a987b33df6cbb.png)\n\n黄季焜 教授\n\n黄季焜，北京大学博雅讲席教授，长江学者特聘教授。现任北京大学现代农学院院长、新农村发展研究院院长、中国农业政策研究中心名誉主任。发展中国家科学院院士。国际农业经济学家学会终身荣誉会士、美国农业和应用经济学会会士，亚洲农经学会前会长。兼任中国农业经济学会副会长，担任30个国内外期刊主编或编委，全国政协参政议政人才库特聘专家、“十五五”国家发展规划专家委员、农业农村部乡村振兴专家委员会和科技创新战略咨询委员会、五个省部委专家咨询委员会委员。长期从事农业经济与农村发展研究，主持国家自科基金委杰青、创新群体及科技部\"973\"等项目。获教育部科学优秀成果一等奖和多项省部级科技进步奖，获孙冶方经济学奖、中国青年科学家奖、管理学杰出贡献奖、国际水稻所成立50周年杰出校友等荣誉。在Science、Nature和经济管理等顶级期刊发表系列创新成果，至2025年Google Scholar总引5万多次、h-指数113。\n\n![Image 27: 来源：《智慧农业（中英文）》2026年第3期](https:\u002F\u002Fn.sinaimg.cn\u002Fspider20260811\u002F364\u002Fw1080h84\u002F20260811\u002F167b-143257c7d21f6c5f7056060f2fb60476.png)来源：《智慧农业（中英文）》2026年第3期\n\n[![Image 28: 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15:00\n\n##### 富国基金：金价重回4400美元！黄金后市怎么看？\n\n![Image 44](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F53743bfaed91d67cdc17c112b06af6c4.png)开播时间 | 8-19 15:00\n\n##### 易方达基金 机器人现场考察——第一视角解析机器人进化浪潮\n\n![Image 45](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F128606b1a0a1539326e84dd9241708a0.png)开播时间 | 8-19 14:00\n\n##### 2026世界机器人大会：开放合作\n\n![Image 46](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F4bbb48498d2328c495583efe1c8ec1f3.png)开播时间 | 8-19 20:00\n\n##### 浙商固收 | 银行二永债投资框架和最新思考\n\n![Image 47](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F80c1ec591c31e8edb00f62ca821d2950.png)开播时间 | 8-19 16:00\n\n##### 南方基金 【南方稳健多策略FOF】 多元布局 【稳健】如【磐】震荡市中的稳健之选\n\n![Image 48](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F57858e81e26fa6f8bd451127d41768a3.png)开播时间 | 8-19 16:00\n\n##### 光大证券：商业航天新突破，朱雀三号成功回收\n\n![Image 49](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002Fac6156b9e185e8e3bf039c04dfb9d70a.png)开播时间 | 8-19 15:30\n\n##### 博时基金|震荡市应对之道：构建“高增长、低估值、低波动”铁三角\n\n![Image 50](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002Fb6887b573f457f7927dbe23d49a8c255.png)开播时间 | 8-19 15:30\n\n##### 华安基金：指数大学堂：“芯光”璀璨，AI算力下半年怎么投？\n\n![Image 51](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002Fc05530c6d1a6c421f30c9a76543e9fa5.png)开播时间 | 8-19 15:15\n\n##### 国泰海通：低息时代，如何寻找更从容的投资方案?\n\n![Image 52](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F82682b8f6f58e5a267bcc48d4598e6e2.png)开播时间 | 8-19 15:00\n\n##### 中信期货：地缘与宏观博弈下 贵金属趋势展望\n\n![Image 53](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F3c058a4697f830c92aa4e803bdc223ed.png)开播时间 | 8-19 15:00\n\n##### 富国基金：金价重回4400美元！黄金后市怎么看？\n\n![Image 54](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F53743bfaed91d67cdc17c112b06af6c4.png)开播时间 | 8-19 15:00\n\n##### 易方达基金 机器人现场考察——第一视角解析机器人进化浪潮\n\n![Image 55](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F128606b1a0a1539326e84dd9241708a0.png)开播时间 | 8-19 14:00\n\n##### 2026世界机器人大会：开放合作\n\n[上一页](javascript:;)[下一页](javascript:;)\n\n1\u002F10\n\n## 热门推荐\n\n我国首次实现火箭陆地回收骄傲转发！_收起_\n\n![Image 56: 四川长宁县一升学宴突发事故致5人死亡](https:\u002F\u002Ffinance.sina.com.cn\u002Fwm\u002F2026-08-11\u002Fdoc-inimyhkc7352205.shtml)\n\n#### 我国首次实现火箭陆地回收骄傲转发！\n\n*   2026年08月19日\n*   00:41\n*   APP专享\n*   扒圈小记\n*   ![Image 57](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002Fpctianyi_wdg\u002Fsrc\u002Fimages\u002Ficon-cmnt.png)28,770\n\nAPP专享\n\n![Image 58: 四川长宁县一升学宴突发事故致5人死亡](https:\u002F\u002Ffinance.sina.com.cn\u002Fwm\u002F2026-08-11\u002Fdoc-inimyhkc7352205.shtml)\n\n#### 杭州酒局事件另一关键人物也被免职\n\n*   2026年08月19日\n*   02:55\n*   APP专享\n*   扒圈小记\n*   ![Image 59](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002Fpctianyi_wdg\u002Fsrc\u002Fimages\u002Ficon-cmnt.png)5,512\n\nAPP专享\n\n![Image 60: 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  [AI制药：从算法叙事到管线价值，临床兑现与商业化爆发在即！全球六大布洛芬原料药生产企业之一，制剂+ CDMO 转型；中药+创新药+独家品种](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002Ff5443bd63a8b4e858b9225ffa008a6c6\u002F304468eda6d9)08-19 12:36[会选股会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002Ff5443bd63a8b4e858b9225ffa008a6c6)\n*   [国家队持股+险资重仓+高股息+绿色电力+国企改革，机构认为电价超预期回升驱动 Q2 盈利超预期，给予目标价 6.15 元！](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002F54a2c39021ef4ec89125cd681601b84d\u002F7f05f05664d8)08-19 12:00[机构内参会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002F54a2c39021ef4ec89125cd681601b84d)\n\n## [](http:\u002F\u002Flive.finance.sina.com.cn\u002F)[股市直播](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n*   图文直播间\n*   视频直播间\n\n*   [![Image 63: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 今天 23:52:59 2026.8.20【中线波段持股策略】（业绩+趋势+风口）1、科技成长方向：中长期继续看好硬科技赛道，AI算力硬件、半导体、人形机器人（sz300024）、商业航天、创新药，固态电池。2、中线标准持仓50%，保守40%，激进60%。3、采用震荡策略，小跌小买，大跌大买，小涨不动，大涨减仓策略。风险第一，赚钱第二。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 64: 数字江恩](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002Fac791ff8\u002F20201116\u002FShuZiJiangEn.png\u002Fw120h120f1q80liv.png)### 数字江恩 今天 23:47:28 只看现在的结果，其实昨日就是韩股下跌，然后A股大跌，并且A股管科技的科创50指数、科创100指数、创业板指数（sz399006），跌幅都大于韩国综合指数。别的无论欧洲市场、美国市场都没有发生大跌。那么昨日是错跌吗？看本人分析的朋友都知道，这次下跌是早就预期的，上周四盘后收评的标题就是《翻脸的前奏》。A股反复一高开或稍拉高就出货，是上周二就开始了的，不是现在才开始。所以A股昨日的下跌并不是错跌，但昨日的下跌力度过于夸张，不应该是那样的力度。有了这些分析，接下来就很明确了。那就是小反弹抵抗，然后再跌，减缓昨日的下跌速度。换句话来说，昨日跌的空间太多，那么接下来反复震荡缓跌，减缓平均下跌速度。所以今日盘中哪怕有些超短线反弹，也只是下跌结构中的反弹，不可以反弹起来追高。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2342117515)\n*   [![Image 65: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 今天 23:42:32 【三星因AI芯片需求激增上调代工价格 部分先进制程涨幅最高达15%】财联社8月19日电，据报道，随着人工智能（AI）芯片需求推高产能紧张程度，三星电子已针对部分先进晶圆代工服务的新订单上调价格，涨幅最高达到15%。这一变化发生在长期由台积电主导的晶圆代工市场，三星正受益于AI芯片需求外溢带来的产能紧缺。三星于7月提高了采用4纳米制程（SF4）生产芯片的价格。客户的SF4订单价格较上月上涨10%至15%。此外，三星5纳米SF5制程晶圆价格上涨10%至15%，较老的8纳米制程价格上涨近10%。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 66: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 今天 23:41:49 【罗博特科（sz300757）：签订1.09亿元量产化耦合设备合同 适用于光通讯激光泵源模块技术路线核心环节】财联社8月19日电，罗博特科（sz300757）(300757.SZ)公告称，公司与一家纳斯达克上市公司的子公司签订日常经营重大合同，金额约1620.65万美元(折合人民币1.09亿元)，占2025年度经审计营收的11.50%，合同标的为量产化耦合设备及相关服务订单。本次交易合同标的为适用于光通讯激光泵源模块技术路线核心环节的量产化耦合设备及服务，该设备具备全球领先的技术优势。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 67: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 今天 23:40:58 【中微公司（sh688012）：上半年净利润同比增长300%】财联社8月19日电，中微公司（sh688012）(688012.SH)发布2026年半年度报告，实现营业收入66.91亿元，同比增长34.89%；归属于上市公司股东的净利润28.25亿元，同比增长300.22%。公司针对先进逻辑和存储器件制造中关键刻蚀工艺的高端产品新增付运量显著提升，在先进逻辑器件和先进存储器件中多种关键刻蚀工艺实现大规模量产。公司本期出售了部分持有的拓荆科技（sh688072）股份有限公司股票，产生投资收益约9.53亿元。小财注：公司Q2净利润18.95亿，Q1净利","智慧农业(中英文)2026,8(3):51-66 2026-08","2026-08-11T00:00:00Z","论文",81,{"impact":70,"substance":29,"depth":71,"authority":310,"freshness":311,"relevant":33,"comment":312},13,8,"基于两套代表性农户调查的学术论文，提供涉农数智技术采纳现状与挑战的新数据和新结论，来源权威，时效性较好。",[314],{"name":305,"url":302},[316,317,318,319,320],"数字乡村","数智技术","采纳","农户","黄季焜",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":323},{"candidates":324,"agricultural_evidence":25,"reason":56},[325,330],{"code":326,"score":33,"supported":46,"title":327,"abstract":328,"tags":329},"I1",[],[],[317],{"code":331,"score":33,"supported":46,"title":332,"abstract":333,"tags":334},"I6",[],[],[316],[336,337],"数字乡村 数智技术 黄季焜 农户","数字乡村 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头版)","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260819\u002F20260819_1\u002Fnmrb_20260819_13381_1_2089823318253342737.html","浙江省杭州市建德市打造新时代'千万工程'践行平台,3年间推出'乡村梦想家'行动。下涯镇之江村落户全省首家聚焦低空经济+AI技术深度融合的农事服务平台,年处理5000多万亩农田数据;李家镇化石村与中国科学院南京地质古生物研究所合作开发28种研学课程、年接待研学团队1.2万人次、带动户均增收上万元;创新'兜底+分成''投资+分成''销售+分成''管理+分成'多元机制,涌现11名扎根乡村的青年代表。","农民日报记者 王飞 2026-08-19",{"impact":29,"substance":70,"depth":156,"authority":310,"freshness":24,"relevant":33,"comment":351},"浙江建德乡村梦想家行动，聚焦青年返乡与乡村产业创新，含具体数据与多元机制，央媒报道，时效性强，具有省级示范意义。",[353],{"name":349,"url":347},[355,356,357,358,359],"千万工程","乡村梦想家","浙江建德","低空+AI","农事服务",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":362},{"candidates":363,"agricultural_evidence":25,"reason":56},[364,368],{"code":178,"score":33,"supported":46,"title":365,"abstract":366,"tags":367},[],[],[359],{"code":52,"score":33,"supported":46,"title":369,"abstract":370,"tags":371},[],[],[355],[373,374],"乡村梦想家 农事服务 千万工程 浙江建德","乡村梦想家 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Introduction\n\nDeveloping agricultural new quality productive forces is an important pathway for shifting the drivers of agricultural development from conventional factor inputs toward scientific and technological innovation and optimized factor allocation, thereby improving the quality of agricultural development. Compared with agriculturally advanced countries, China still has considerable scope to improve agricultural labor productivity, the allocation of scientific and technological resources, and the commercialization of agricultural research outputs. Jiang [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B1-agriculture-16-01751)] estimated that, in 2020, agricultural labor productivity in the United States, Canada, and Israel was approximately 17.3, 19.8, and 25 times that of China, respectively. Meanwhile, increasingly stringent constraints on cultivated land, water resources, and the ecological environment have reduced the marginal returns of the conventional agricultural growth model, which relies primarily on expanding land, labor, machinery, and chemical inputs. A shift in development momentum through technological innovation, the reorganization of production factors, and changes in production modes is therefore urgently needed. The application of emerging technologies, including biological breeding, intelligent agricultural machinery, and artificial intelligence, has created new conditions for restructuring agricultural production functions and upgrading the form of agricultural productive forces, while drawing greater attention to the coordination between the upgrading of agricultural productive forces and resource–environmental performance.\n\nAgricultural new quality productive forces refer to an advanced form of agricultural productive forces led by agricultural scientific and technological innovation, grounded in improvements in worker quality, the modernization of the means of labor, and the optimization of the objects of labor, and formed through the reorganization of production factors and changes in production modes. They are characterized by high technology intensity, high efficiency, high quality, and greenness [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B2-agriculture-16-01751)]. Their “new quality” lies primarily in changes in factor quality, technological forms, and allocation patterns, rather than in a rejection of the basic components of conventional agricultural productive forces. Xi [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B3-agriculture-16-01751)] noted that green development is the defining feature of high-quality development and that new quality productive forces are inherently green productive forces, indicating an intrinsic consistency in the value orientation of the upgrading of productive forces and green development. Agricultural green development, in turn, emphasizes improving resource-use efficiency and reducing environmental burdens while maintaining agricultural output. The two concepts reflect the transformation of agricultural production modes from the perspectives of factor upgrading and resource–environmental performance improvement, respectively; however, their development levels and rates of evolution are not necessarily synchronized.\n\nIn recent years, China has made overall progress in agricultural green development. The composite agricultural green development index reported in the China Agricultural Green Development Report 2024 [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B4-agriculture-16-01751)] indicates that the national level of agricultural green development in 2023 was higher than both the previous year and 2015. Nevertheless, China’s agricultural green transition still faces practical constraints, including relatively low input-use efficiency. Data released by the Department of Science, Technology and Education of the Ministry of Agriculture and Rural Affairs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B5-agriculture-16-01751)] show that, in 2020, the fertilizer- and pesticide-use efficiencies of China’s three major grain crops were 40.2% and 40.6%, respectively. These indicators represent the shares of applied fertilizer nutrients absorbed by crops in the current season and of pesticides effectively used against target pests. Thus, although agricultural output has continued to grow, substantial room remains to improve resource-use efficiency and control environmental burdens. Whether the upgrading of agricultural productive forces can remain synchronized with improvements in resource–environmental performance has therefore become an important issue in transforming the agricultural development mode.\n\nExisting studies have examined the relationship between ANQPF and agricultural green development, but the coordination differences among the constituent dimensions of productive forces and the dynamic evolution of coordination states have not yet been systematically investigated. Because agricultural green development encompasses economic growth, resource use, ecological protection, social welfare, and governance institutions, this study does not construct a composite measure of agricultural green development in the broad sense. Instead, it focuses on the resource–environmental performance dimension and uses agricultural green total factor productivity (AGTFP) under resource and environmental constraints to represent AGDP. Against this background, the objective of this study is to characterize the temporal evolution, dimension-specific coordination, and state-transition patterns between ANQPF and AGDP across Chinese provinces. Specifically, using data for 30 Chinese provincial-level regions from 2013 to 2023, this study addresses three questions: (1) What annual evolutionary patterns characterize ANQPF and AGDP? (2) How does the coordination between AGDP and ANQPF as a whole, as well as its three constituent dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ? (3) How persistent are different coordination states, and in which directions do they transition?\n\n## 2. Literature Review\n\nResearch on agricultural green development has focused mainly on its conceptual connotation, influencing factors, and measurement methods. Existing studies generally define agricultural green development as the coordinated pursuit of agricultural economic growth, resource conservation, and ecological and environmental protection while ensuring an effective supply of agricultural products [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B6-agriculture-16-01751)]. Related research has further examined the determinants of agricultural green development from the perspectives of scientific and technological innovation, digital transformation, industrial restructuring, land-factor allocation, and agricultural socialized services, suggesting that regional resource endowments, technological foundations, and institutional conditions shape the effectiveness of the green transition [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B7-agriculture-16-01751)]. In terms of measurement, two principal approaches are used: composite indicator evaluation and green total factor productivity. The former captures the multidimensional objectives of agricultural green development, whereas the latter incorporates resource inputs, desirable outputs, and undesirable outputs into a unified production frontier and focuses on input–output performance under resource and environmental constraints [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B8-agriculture-16-01751),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B9-agriculture-16-01751),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B10-agriculture-16-01751)]. Because this study examines the coordination between the upgrading of productive forces and resource–environmental performance, AGTFP is used to represent AGDP, but it is not treated as equivalent to agricultural green development in the broad sense.\n\nAgricultural new quality productive forces are rooted in the Marxist theory of productive forces and represent a contextual development of this theory amid a new round of technological revolution and agricultural modernization. Workers, means of labor, and objects of labor remain the basic components of agricultural productive forces. ANQPF do not alter this fundamental logic; rather, they reflect the upgrading of factor quality, technological forms, and factor combinations under the leadership of scientific and technological innovation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B11-agriculture-16-01751)]. Compared with conventional agricultural productive forces, which depend largely on quantitative expansion of land, labor, machinery, and chemical inputs, ANQPF place greater emphasis on technological innovation, the application of digital technologies, and optimized factor combinations [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B12-agriculture-16-01751)]. Existing studies have mainly addressed conceptual definition, functional effects, and level measurement. They generally characterize ANQPF as an advanced form of productive forces led by scientific and technological innovation and formed through qualitative upgrading and optimized combinations of production factors, and they construct evaluation systems either around the three elements of productive forces or around functional dimensions such as technology, digitalization, and greenness [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B15-agriculture-16-01751),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751)]. Common measurement methods include the entropy-weight method, entropy-weight TOPSIS, and Markov chains. These studies generally find that ANQPFs in China have increased overall, although substantial regional disparities remain [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B17-agriculture-16-01751)].\n\nResearch on the relationship between ANQPF and green performance has followed two main analytical approaches: unidirectional effects and system coordination. Econometric studies primarily examine the effects of ANQPF on agricultural green production efficiency, carbon-emission efficiency, and sustainable-development performance, treating technological innovation, digital transformation, industrial upgrading, and factor optimization as potential channels [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B19-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)]. Other studies adopt a system-coordination perspective to investigate the relationships between new quality productive forces and agricultural green development, between digitalization and greening, and between ANQPF and green grain-production capacity. They generally find that coordination has improved, although regional disparities remain pronounced [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B21-agriculture-16-01751),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B22-agriculture-16-01751),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B23-agriculture-16-01751)]. Consistent with these findings, research on sustainable intensification, precision agriculture, digital agriculture, and agricultural innovation systems indicates that technological upgrading can improve resource use and environmental performance, but the extent to which these benefits materialize depends on infrastructure, human capital, service systems, and the institutional environment [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B24-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B27-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751),[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B29-agriculture-16-01751)]. The upgrading of agricultural productive forces and improvements in green performance are therefore not necessarily synchronized.\n\nOverall, the existing literature has three limitations. First, most studies focus on the unidirectional effect of ANQPF on green performance and pay insufficient attention to the synchronization and structural alignment of the two systems. Second, some studies include greening indicators in both ANQPF and agricultural green development assessments, leaving the measurement boundaries between the two systems insufficiently defined. Third, existing coordination studies primarily examine overall levels and spatial disparities, while giving limited attention to coordination differences among the three dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—or to the persistence, upgrading, and decline of coordination states. To address these gaps, this study uses AGTFP to represent AGDP, examines the coupling coordination between AGDP and both ANQPF as a whole and its three constituent dimensions, and applies a Markov chain to analyze the dynamic evolution of coordination states.\n\nThe marginal contributions of this study are threefold. First, AGTFP is used to represent AGDP, distinguishing this approach from composite assessments that incorporate economic, social, and ecological dimensions and thereby clarifying the measurement boundaries between ANQPF and AGDP. Second, the analysis extends from overall coordination to the three dimensions of new-quality means of labor, new-quality labor force, and new-quality objects of labor, allowing differences in the alignment between distinct elements of productive forces and AGDP to be compared. Third, in addition to examining coordination levels and spatial distributions, a Markov chain is introduced to identify the persistence, upgrading, and decline of coordination states. The coupling coordination degree reflects the synchronization, alignment, and coordination of the two systems; it is not used to identify the direction of influence or a causal relationship between them.\n\n## 3. Theoretical Analysis\n\nThis study draws on the theory of productive forces, system-coupling theory, and evolutionary and path-dependence theory to examine the relationship between ANQPF and AGDP at three levels: overall coordination, dimension-specific coordination, and state transitions. The purpose is to evaluate the synchronization and structural alignment of the two systems rather than to identify causal relationships among variables. Accordingly, the following theoretical propositions concern coordination states and their evolution and do not presuppose a causal direction.\n\n### 3.1. Coupling Coordination Between Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nSystem-coupling theory holds that the coordination state between systems depends on the relative alignment of their development levels, evolutionary rates, and internal structures. ANQPF and AGDP are jointly embedded in agricultural production and operation. New-quality means of labor, new-quality labor force, and new-quality objects of labor reflect qualitative upgrading in production conditions, production actors, and production objects, whereas AGDP captures changes in aggregate output under resource and environmental constraints. Changes in technological equipment, digital infrastructure, and human capital occur within the same production system as changes in agriculture-related environmental outputs. The development states of the two systems may therefore correspond, but they are not necessarily synchronized. Differences in technological suitability, resource endowments, and development stages may generate divergence in their levels and rates of change: some regions may accumulate new-quality factors rapidly while AGDP lags, whereas others may exhibit the opposite pattern [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B30-agriculture-16-01751)]. The coupling coordination degree measured in this study is intended to reflect the synchronization and alignment of the two systems rather than to identify causal effects. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 1****(H1).**\n\nANQPF and AGDP exhibit a general trend of joint improvement, but their growth trajectories are not fully synchronized; consequently, the overall level of coupling coordination increases over time.\n\n### 3.2. Differentiated Coordination Between the Dimensions of Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nThe theory of productive forces regards workers, means of labor, and objects of labor as the basic components of productive forces. ANQPF thus represent the qualitative upgrading and recombination of these three components driven by scientific and technological innovation [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B31-agriculture-16-01751)]. Because the foundations, evolutionary cycles, and regional dependence of the three dimensions differ, each may form a distinct coordination relationship with AGDP. New-quality means of labor reflect upgrades in technological equipment and digital infrastructure. Although technological renewal is relatively rapid, its diffusion is uneven, and its rate of change may be faster or slower than that of AGDP, producing different alignment states [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751)]. The new-quality labor force reflects the accumulation of human capital and skills. Its formation and allocation require longer periods, while the efficiency with which it is transformed into productive outcomes depends on training and extension systems [[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751)]; its coordination with AGDP may therefore differ from that of the means-of-labor dimension. New-quality objects of labor reflect changes in how production objects are developed and utilized. They are strongly dependent on resources and location [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)], and their optimization and value realization exhibit marked path dependence, generating coordination characteristics distinct from those of the other two dimensions. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 2****(H2).**\n\nThe coordination levels and evolutionary pathways between AGDP and the three dimensions of ANQPF—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ.\n\n### 3.3. Dynamic Evolution of Coupling Coordination States\n\nEvolutionary and path-dependence theory indicates that technologies, knowledge, and institutional rules are cumulative. System transformation evolves incrementally along established trajectories but may stagnate or regress in response to external shocks [[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B32-agriculture-16-01751)]. Coordination states are built on existing technological and institutional arrangements. Technology diffusion and human-capital development are generally long-term processes, making cross-stage transitions difficult to achieve in the short run. Interdependence between technologies and institutions creates path inertia, and transformation usually occurs through incremental reconfiguration [[27](https","MDPI Agriculture 16(16):1751 2026-08-15",76,{"impact":70,"substance":29,"depth":71,"authority":88,"freshness":236,"relevant":33,"comment":525},"研究中国农业新质生产力与绿色发展绩效的耦合协调度，具有全国性政策意义，数据详实，但时效性稍差。",[527],{"name":522,"url":519},[316,529,530,531,532],"新质生产力","AGTFP","耦合协调度","30省",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":535},{"candidates":536,"agricultural_evidence":25,"reason":56},[537,542,546],{"code":538,"score":33,"supported":46,"title":539,"abstract":540,"tags":541},"I4",[],[],[531],{"code":331,"score":33,"supported":46,"title":543,"abstract":544,"tags":545},[],[],[316],{"code":547,"score":33,"supported":46,"title":548,"abstract":549,"tags":550},"J2",[],[],[529],[552,553],"新质生产力 耦合协调度 数字乡村 30省","新质生产力 耦合协调度","新质生产力耦合协调度数字乡村30省-743","2026-08-19T23:57:32.301681Z",{"id":557,"title":558,"url":559,"summary":560,"summary_zh":20,"content":20,"source_name":561,"source_url":20,"published_at":104,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":523,"score_detail":562,"sources":564,"tags":566,"primary_topic":572,"secondary_topics":576,"classification":577,"search_phrases":589,"slug":592,"view_count":376,"doi":20,"paper":20,"created_at":593},718,"两大地理标志产品有了\"数据资源身份证\"——湖南隆回金银花、龙牙百合获数据产权登记证书(农民日报8-18)","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260818\u002F20260818_8\u002Fnmrb_20260818_13380_8_2089469102808731759.html","湖南隆回县城发大数据产业发展有限公司在第二届'早安隆回'银花百合交易展销会开幕式上发布特色农业数据资产化成果,获湖南大数据交易所核发的'隆回金银花产业链数据集'与'隆回龙牙百合产业链数据集'两份数据产权登记证书。隆回金银花种植面积22万亩、年产干花1.2万吨、占全国总产50%以上、综合产值超40亿元;龙牙百合种植面积5万亩、综合产值超30亿元。数据产品上架后首位采购方惠农网现场签约,标志湖南省农业数据资产化的标志性突破。","农民日报记者 戴瑾昕 宁炳炎 陈斌 2026-08-18",{"impact":70,"substance":70,"depth":31,"authority":88,"freshness":32,"relevant":33,"comment":563},"农业数据资产化标志性突破，具全国示范意义，信息详实，来源权威。",[565],{"name":561,"url":559},[567,568,569,570,571],"数据要素","农业数据资产化","隆回","金银花","龙牙百合",{"code":132,"slug":573,"name":574,"path":575,"parent_code":216},"branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce",[],{"status":219,"locked":46,"version":47,"method":48,"evidence":578},{"candidates":579,"agricultural_evidence":25},[580,585],{"code":132,"score":311,"supported":25,"title":581,"abstract":583,"tags":584},[582],"地理标志",[],[],{"code":331,"score":33,"supported":46,"title":586,"abstract":587,"tags":588},[],[],[567],[590,591],"农业数据资产化 数据要素 龙牙百合 金银花","农业数据资产化 数据要素","农业数据资产化数据要素龙牙百合金银花-718","2026-08-19T23:57:26.291847Z",{"id":595,"title":596,"url":597,"summary":598,"summary_zh":20,"content":20,"source_name":599,"source_url":20,"published_at":104,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":523,"score_detail":600,"sources":602,"tags":604,"primary_topic":20,"secondary_topics":610,"classification":611,"search_phrases":619,"slug":622,"view_count":61,"doi":20,"paper":20,"created_at":623},717,"把实验室\"种\"进大山里——武陵山现代农业科技创新基地观摩会的现场与背后(农民日报8-18)","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260818\u002F20260818_3\u002Fnmrb_20260818_13380_3_2089469102808731671.html","重庆市农业农村委、市农科院、黔江区政府在武陵山区邻鄂、白土、独水3镇同步布局3个海拔梯度(300米\u002F1000米\u002F1500米)的现代农业科技创新基地,总投资超1200万元;白土乡海拔1500米基地为全国13个智能育种统一标准实验点之一,30亩试验田切成20平方米小区统一密度水肥管理;武陵山分院副院长柯剑鸿介绍今年3月同步启动以来3基地同步落地,首次实现山区品种试验'单点管全域'向'多海拔协同'跨越。","农民日报记者 邓俐 2026-08-18",{"impact":70,"substance":70,"depth":31,"authority":88,"freshness":32,"relevant":33,"comment":601},"报道了武陵山区多海拔梯度现代农业科技创新基地的落地，涉及智能育种和协同试验，具有省级产业级影响，信息具体，来源权威，时效性好。",[603],{"name":599,"url":597},[605,606,607,608,609],"重庆","智能育种","观摩会","武陵山","海拔梯度",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":612},{"candidates":613,"agricultural_evidence":25,"reason":56},[614],{"code":615,"score":33,"supported":46,"title":616,"abstract":617,"tags":618},"F2",[],[],[606],[620,621],"智能育种 海拔梯度 武陵山 观摩会","智能育种 海拔梯度","智能育种海拔梯度武陵山观摩会-717","2026-08-19T23:57:26.266197Z"]