[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-10-08":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-10-08","农业农村日报：智能农机与生物育种双轮驱动",[7,8,9,10,11,12],"农业农村部启动2027年度农业主导品种、主推技术和重大引领性技术征集，优先推荐农业人工智能、生物育种、农机装备等领域引领性技术。","「三秋」生产高端智能农机加快投入：18公斤\u002F秒谷物联合收获机、85公斤载荷植保无人机产业化，丘陵山地拖拉机与混动、纯电动拖拉机投入一线。","中国农科院油料所ARC生物耦合技术示范田大豆亩产265.49公斤、增产17.40%，农田氧化亚氮减排50.7%，2026年全国推广超1000万亩。","中国农科院基因组所在《Cell》揭示飞蝗母传共生菌合成2,4,6-三甲基吡啶，兼具聚集产卵与驱避天敌双重功能，为迁飞害虫绿色防控提供分子靶标。","黑龙江「佳家农」智慧农服APP覆盖耕地超700万亩、亩均增产约3%；山东农大发布小麦育种具身机器人WheatX，推动育种智能化。","中央网信办等三部门印发《数字乡村高质量发展行动计划（2026-2030年）》，农用无人机年作业面积突破4亿亩，农村网络零售额首破3万亿元。","本期聚焦农业政策征集与「三秋」智能农机应用，报道大豆ARC生物耦合技术、飞蝗产卵机制、异嗪皮啶合成等前沿成果，并关注黑龙江智慧农业、重庆数字乡村及小麦育种机器人等产业实践。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；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。*",[15,105,148,207,246,303,361,397,446,490,536,582,683,772,838],{"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":52,"search_phrases":100,"slug":103,"view_count":60,"doi":20,"paper":20,"created_at":104},4376,"农业农村部办公厅征集 2027 年度农业主导品种、主推技术和重大引领性技术","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7693543001843204643","农业农村部科学技术司发布通知,2026-09-30 至 11-13 期间在全国范围内征集 2027 年度农业主导品种、主推技术与重大引领性技术;主导品种覆盖农作物、饲草、畜禽、水产及名特优新品种,主推技术覆盖良田、良种、良机、良法融合集成技术,优先推荐农业人工智能、生物育种、农机装备等领域的引领性技术。",null,"近日，农业农村部科学技术司发布关于开展2027年度农业主导品种、主推技术和重大引领性技术申报推荐工作的通知。\n\n**关于开展2027年度农业主导品种、主推技术和重大引领性技术申报推荐工作的通知**\n\n**各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司，有关部直属单位，国家现代农业产业技术体系，有关单位：**\n\n为加快先进适用的农业科技成果大面积推广应用，发挥科技对粮油作物大面积单产提升和农业高质量发展的支撑作用，根据《农业农村部关于修订农业主导品种和主推技术推介发布办法的通知》（农科技发〔2025〕10号）的规定，我部决定在全国范围内征集推介2027年度农业主导品种、主推技术和重大引领性技术。现将有关事项通知如下。\n\n**一、推荐时间**\n\n2026年9月30日至2026年11月13日。\n\n**二、推荐范围**\n\n（一）主导品种\n\n主要农作物、饲草、畜禽、水产及重要名特优新品种等，具有增产潜力大、适应性广、抗性强、品质优、产量高等特点。\n\n（二）主推技术\n\n1. 农作物和饲草综合栽培技术及病虫草鼠害防治技术；\n\n2. 畜禽水产科学繁育、饲养及疫病综合防治技术；\n\n3. 重要名特优新品种的种养技术；\n\n4. 先进适用的农业机械化、设施化、智能化与农产品加工储运等农业产业化关键技术；\n\n5. 有利于农业防灾减灾、耕地质量提升、农产品质量安全、农业生物安全、农业资源高效利用和生态环境保护等可持续发展的先进适用技术。\n\n（三）重大引领性技术\n\n重大引领性技术是主推技术的前导性技术，重点推荐高效种养、生物育种、农业生物制造、农业人工智能、耕地保护与利用、防灾减灾、动植物疫病防控、农业资源环境、精深加工、绿色低碳、农机装备等领域技术。\n\n**三、推荐条件**\n\n（一）品种（配套系、遗传资源）须经全国或省级品种审定（登记）机构审定（登记、鉴定）。优先推荐在同等条件下获得品种权的农作物品种。\n\n（二）技术要点、操作规范和适宜区域清晰，须经国家农业技术推广机构试验示范，涉及的投入品、装备等符合我国法律法规要求。优先推荐良田、良种、良机、良法“四良”融合的集成技术。\n\n（三）成果权属清晰且无纠纷。\n\n（四）应具有较大的应用区域和规模且保持较快的增长速度，在增产、提质、增效、增收等方面潜力大，应用效果好；推荐的重大引领性技术应在一定区域示范应用中取得明显成效，具有重大引领作用和发展潜力。\n\n（五）在应用过程中得到用户、基层国家农业技术推广机构或地方政府的充分认可。\n\n（六）在相应技术领域发挥重要作用，代表轻简化、机械化、可持续的先进农业技术发展方向，适应产业规模化、信息化、智能化发展需要。\n\n（七）已经推介为主导品种、主推技术的，3年内再次申报推荐，应在条件（四）和（五）方面取得重大应用进展。\n\n**四、申报单位**\n\n申报单位为各级国家农业技术推广机构、农业科研单位、有关学校、涉农企业等。农业科研单位、有关学校、涉农企业等原则上应与国家农业技术推广机构联合申报。国家种业阵型企业可单独申报主导品种，鼓励与国家农业技术推广机构联合申报。\n\n**五、推荐单位和推荐数量**\n\n（一）主导品种、主推技术\n\n1、各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司推荐品种和技术各不超过10项。\n\n2、中国农业科学院推荐品种和技术各不超过15项，中国水产科学研究院、中国热带农业科学院、农业农村部规划设计研究院推荐各不超过10项；农业农村部农业生态与资源保护总站、全国农业技术推广服务中心、农业农村部农业机械化总站、全国畜牧总站、中国动物疫病预防控制中心、中国农垦经济发展中心、全国水产技术推广总站等推广机构推荐本单位作为成果完成单位或技术依托单位的各不超过5项。\n\n3、国家现代农业产业技术体系每个体系推荐品种和技术各不超过5项。\n\n（二）重大引领性技术\n\n1、各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司推荐不超过2项。\n\n2、中国农业科学院推荐不超过3项；中国水产科学研究院、中国热带农业科学院、农业农村部规划设计研究院推荐不超过2项；农业农村部农业生态与资源保护总站、全国农业技术推广服务中心、农业农村部农业机械化总站、全国畜牧总站、中国动物疫病预防控制中心、中国农垦经济发展中心、全国水产技术推广总站等推广机构推荐本单位作为技术依托单位的不超过2项。\n\n3、农业生物育种重大专项、农业农村部作为主责单位的国家重点研发计划等农业科技重大项目推荐数量各不超过2项，由有关项目管理专业机构组织推荐。\n\n**六、材料报送**\n\n（一）推荐单位在10月底前完成申报推荐与公示，并于10月26日—11月13日期间组织拟推荐主导品种、主推技术和重大引领性技术的申报单位将相关材料录入“农业主导品种主推技术和重大引领性技术管理系统”（简称“管理系统”，网址另行通知）。\n\n（二）推荐单位出具推荐函，内容应包括申报推荐过程、公示情况和结果等。11月13日前将加盖公章的推荐函及《2027年度农业主导品种（农作物）推荐汇总表》（附件1）、《2027年度农业主导品种（畜禽水产）推荐汇总表》（附件2）、《2027年度农业主推技术推荐汇总表》（附件3）、《2027年度农业重大引领性技术推荐汇总表》（附件4）提交管理系统。\n\n（三）拟推荐的主导品种申报单位，在管理系统中填写《2027年度农业主导品种（农作物）申报表》（附件5）或《2027年度农业主导品种（畜禽水产）申报表》（附件6）、《2027年度农业主导品种介绍材料（模板）》（附件7）。同时提供品种审定、鉴定、登记等证明文件，获奖证书，种子、种苗及品种产出商品图片（图片大小不低于1.0M），品种推广应用规模等情况的证明材料。\n\n（四）拟推荐的主推技术、重大引领性技术申报单位，分别按照《2027年度农业主推技术介绍材料（模板）》（附件8）、《2027年度农业重大引领性技术介绍材料（模板）》（附件9）在管理系统中提交相应技术介绍材料。材料字数不超过5000字，插入3~5幅有助于理解该项技术的高清图片（图片大小不低于1.0M），并附图注。同时提供知识产权、获奖证书（成果鉴定）、技术推广应用规模等情况的证明材料。\n\n**七、有关要求**\n\n（一）各推荐单位按照本通知要求组织申报和推荐工作，对拟推荐的材料要严格审核把关。拟推荐名单须按程序进行公示，接受社会监督，公示期为5个工作日。申报单位和技术（品种）持有者对所提交的技术（品种）信息及相关证明材料的真实性和规范性负责，证明文件的出具单位（机构）应具有权威性和代表性，推荐品种2024年和2025年生产应用面积（规模）数据应与种业信息管理部门的统计数据相符。\n\n（二）主导品种应列出国家农业技术推广机构，主推技术、重大引领性技术应将国家农业技术推广机构作为技术依托单位。推荐的重大引领性技术应有配套的试验示范场所和经费支持，能够组织开展技术集成熟化和示范推广等任务。\n\n（三）农业农村部直属单位的成果经相关行业司局同意后推荐；国家现代农业产业技术体系推荐工作由体系首席科学家牵头开展，成果经执行专家组一致同意并签字后推荐，由体系首席科学家所在单位组织报送。\n\n（四）同一品种、技术严格按照单一渠道推荐；同一技术不得同时申报主推技术和重大引领性技术；往年已经入选主推技术的，不得推荐为重大引领性技术。\n\n（五）主推技术、重大引领性技术的申报单位，须在管理系统填报项目基本情况，上传技术介绍材料word版和pdf版；技术介绍材料pdf版须加盖技术所列全部依托单位公章，系统填报的基础信息须与上传的技术介绍材料内容一致。\n\n（六）强化科研诚信与监督。对存在材料夸大应用效果，提供虚假数据、虚假证明、虚假合同等虚假材料，打招呼、请托等行为，技术内容包含特定商品品牌投入品以及知识产权权属不清、职务发明权属不清、存在纠纷等情形的，取消本次推介资格。存在违纪违规行为的，视情形轻重给予约谈、通报、列入科研失信档案等处理。\n\n（七）未按上述要求申报推荐和提供材料的，不予推介。\n\n**八、联系方式**\n\n**（一）中国农学会对外合作处**\n\n联系人：韩 溦\n\n电 话：010−59194213\n\n邮 箱：nxhgjc@126.com\n\n**（二）农业农村部科学技术司科技推广处**\n\n联系人：高修歌张 良\n\n电 话：010−59193003\n\n**（三）管理系统技术支持**\n\n联系人：陈金超 王菲菲\n\n电 话：010−81128410 010−81128409\n\n**附件：**\n\n_1．2027年度农业主导品种（农作物）推荐汇总表_\n\n_2．2027年度农业主导品种（畜禽水产）推荐汇总表_\n\n_3．2027年度农业主推技术推荐汇总表_\n\n_4．2027年农业重大引领性技术推荐汇总表_\n\n_5．2027年度农业主导品种（农作物）申报表_\n\n_6．2027年度农业主导品种（畜禽水产）申报表_\n\n_7．2027年度农业主导品种介绍材料（模板）_\n\n_8．2027年度农业主推技术介绍材料（模板）_\n\n_9．2027年农业重大引领性技术介绍材料（模板）_\n\n**农业农村部科学技术司**\n\n**2026年9月29日**\n\n![Image 1: 图片](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ffe995aaa8db04f25a288853d804aef59~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022771&x-signature=u8hH91qQKJNGwZ8gdDk9tGE%2BZVA%3D)\n来源丨农业农村部\n\n编辑 | 农财君\n\n联系农财君请加微信：18565265490\n\n新时代 新种业\n\n南方农村报丨农财宝典","今日头条 农财网种业宝典 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Systems","论文",{"impact":216,"substance":216,"depth":31,"authority":160,"freshness":257,"relevant":34,"comment":311},"基于中国10省肉鸭养殖户实证数据，量化智慧农业对畜牧生态效率的影响，方法规范、结论可靠，对智慧畜牧与绿色养殖具有参考价值。",[313],{"name":308,"url":306},[315,316,317,318,319],"数字农业","智慧农业","畜牧养殖","农业生态效率","肉鸭养殖",{"code":321,"slug":322,"name":323,"path":324,"parent_code":65},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry",[],{"status":53,"locked":54,"version":55,"method":56,"evidence":327},{"candidates":328,"agricultural_evidence":26},[329,335,340],{"code":65,"score":330,"supported":26,"title":331,"abstract":333,"tags":334},11,[332],"livestock",[332],[],{"code":321,"score":330,"supported":26,"title":336,"abstract":338,"tags":339},[337],"duck",[337],[],{"code":341,"score":188,"supported":54,"title":342,"abstract":343,"tags":344},"I1",[],[],[315,316],[346,347],"肉鸭养殖 智慧农业 生态效率","中国十省 肉鸭养殖 实证分析","肉鸭养殖智慧农业生态效率-4327","10.1016\u002Fj.agsy.2026.105003",{"doi":349,"openalex_id":351,"authors":352,"venue":308,"cited_by_count":244,"oa_url":306,"card":20,"direction":20,"ingested_from":359},"W7220714215",[353,355,357],{"name":354,"orcid":20},"Guiren Tao",{"name":356,"orcid":20},"Yuhao Wang",{"name":358,"orcid":20},"Lingzhi Liu","openalex","2026-10-07T23:30:03.988823Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":20,"content":366,"source_name":367,"source_url":20,"published_at":368,"category":113,"cover_url":20,"hotness":25,"is_selected":54,"score":369,"score_detail":370,"sources":372,"tags":374,"primary_topic":380,"secondary_topics":384,"classification":385,"search_phrases":392,"slug":395,"view_count":188,"doi":20,"paper":20,"created_at":396},4390,"中国农科院基因组所李伟团队揭示被子植物异嗪皮啶生物合成与演化规律—《Nature Communications》","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7691749797241324072","中国农科院基因组所(大鹏湾实验室)李伟团队联合王丽团队在《Nature Communications》发表研究,完成草珊瑚染色体水平高质量基因组组装(基因组大小 4.23 Gb),鉴定出活性成分异嗪皮啶生物合成的关键 O-甲基转移酶 SgOMT3 及关键氨基酸残基 Leu127;揭示 OMT 在被子植物远缘科属中的功能保守性与分化轨迹,为传统中药现代分子育种提供关键基因靶点。","近日，中国农科院基因组所（大鹏湾实验室） **李伟团队**联合**王丽团队**在 《自然通讯（_Nature Communications_）》 在线发表了题为“_Sarcandra glabra_ genome unveils functional conservation and divergence of O-methyltransferases catalyzing isofraxidin biosynthesis in angiosperms”的研究论文。该研究整合基因组、转录组和代谢组等多组学分析，并结合体外酶活性验证与体内毛状根功能验证，鉴定出活性成分异嗪皮啶生物合成的关键O-甲基转移酶，并通过该酶及其同源基因的演化分析和定点突变揭示了异嗪皮啶生物合成相关O-甲基转移酶（OMT）在被子植物远缘科属中的功能保守性与分化轨迹，为传统中药现代分子育种提供了关键基因靶点和理论技术支撑。\n\n”\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F52d32e7001fc448d94441a59ef287f02~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022824&x-signature=FnezJUtYgnT72O7Dc6V4nEcVwcc%3D)\n研究首先根据物质结构推测了异嗪皮啶的生物合成途径：秦皮素前体在O-甲基转移酶的催化下分别生成异嗪皮啶和嗪皮啶。代谢分析结果表明异嗪皮啶在根组织中含量最高。利用PacBio HiFi联合Hi-C技术完成草珊瑚染色体水平高质量基因组组装，基因组大小4.23 Gb（图1）。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F05dad0e3958a4e5094a1c1b0d34a80f6~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022824&x-signature=CQZfDN4OTsqt0Ickpd%2BxmniqsQQ%3D)\n**图1 | 异嗪皮啶生物合成途径、组织特异性与草珊瑚基因组组装分析**\n\n基于草珊瑚转录组与异嗪皮啶含量的相关性分析，筛选出4个与异嗪皮啶合成高度相关的OMT基因。体外蛋白酶活结果表明SgOMT3能够催化底物秦皮素生成异嗪皮啶，而SgOMT3的旁系同源基因均已丧失催化生成异嗪皮啶的能力（图2）。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F0694156a6d9741fd9920619f75b3d229~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022824&x-signature=dOPTVX0AR0cVSkEFZWgTMCR1hik%3D)\n**图2 | 草珊瑚中异嗪皮啶合成关键OMT酶的筛选与催化活性鉴定**\n\n对来源于双子叶和金粟兰科的11个SgOMT3直系同源蛋白进行比对，筛选决定异嗪皮啶催化合成活性的关键氨基酸位点。结合蛋白质结构预测、分子动力学模拟、定点突变与活性测定，表明Leu127是SgOMT3催化合成异嗪皮啶的关键氨基酸残基（图3）。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002Fb2100fb1615047eb806ee34b82a91844~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022824&x-signature=oghmmFr5e4jTZ6XAeE4AZ9AB8LQ%3D)\n**图3 | SgOMT3催化异嗪皮啶合成的关键氨基酸残基鉴定**\n\n8个SgOMT3直系同源基因酶活结果表明，在双子叶植物的演化过程中，异嗪皮啶合成特异性逐渐增强，而作为进化早期的金粟兰科OMT蛋白保留了较为原始的催化特征。随后通过多序列比对、分子动力学模拟与活性解析，表明Leu307是决定异嗪皮啶合成特异性的关键氨基酸残基（图4）。\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F05a5aac2ad94414ebfe3dd5ff0426a6d~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022824&x-signature=Y4QKmNTIMQvHsHDEFgaJ0yz%2B7no%3D)\n> 图4 | 被子植物中异嗪皮啶合成OMT酶功能保守性与区域选择性差异\n\n基因组所（大鹏湾实验室） **李伟**研究员和**王丽**研究员为论文的**共同通讯作者**。基因组所助理研究员**谢德金**博士、中国科学院大学博士研究生**余熙恺**和深势科技**陈家楠**博士为论文**共同第一作者**。该研究得到了国家重点研发计划、国家自然科学基金、中国农科院青年英才和深圳市科技计划项目和中国农业科学院重大科技任务等项目的支持。\n\nhttps:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-026-77631-9","中国科学报 今日头条 2026-10-02","2026-10-02T00:00:00Z",83,{"impact":216,"substance":158,"depth":117,"authority":160,"freshness":161,"relevant":34,"comment":371},"国家级科研机构在核心期刊发表的中药材基因组与合成生物学突破，方法扎实、结论可靠，对中药分子育种有实质指导价值。",[373],{"name":367,"url":364},[375,376,377,378,379],"分子育种","基因组学","合成生物学","草珊瑚","中药材育种",{"code":89,"slug":381,"name":382,"path":383,"parent_code":133},"biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology",[],{"status":53,"locked":54,"version":55,"method":56,"evidence":386},{"candidates":387,"agricultural_evidence":26},[388],{"code":89,"score":118,"supported":26,"title":389,"abstract":390,"tags":391},[],[375],[375,377,376],[393,394],"中国农科院基因组所 草珊瑚 异嗪皮啶","SgOMT3 异嗪皮啶 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智慧农业","佳家农农服APP-4379","2026-10-08T00:06:06.718728Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":20,"content":451,"source_name":452,"source_url":20,"published_at":23,"category":113,"cover_url":20,"hotness":25,"is_selected":54,"score":453,"score_detail":454,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":468,"classification":469,"search_phrases":485,"slug":488,"view_count":34,"doi":20,"paper":20,"created_at":489},4385,"绘就数字乡村新图景 — 重庆日报评论员文章","https:\u002F\u002Fwww.cqrb.cn\u002Fcomment\u002F2026-10-06\u002F2790066_pc.html","重庆日报评论员文章指出我国数字乡村建设正在从「技术示范」走向「产业常态」;2026 年 9 月,中央网信办、农业农村部、工信部联合印发《数字乡村高质量发展行动计划(2026-2030 年)》明确优化数字乡村发展学科专业布局、强化政策支持、培育乡村数字人才,吸引数字游民等新群体投身乡村;农用无人机年作业面积已突破 4 亿亩、农村网络零售额首次突破 3 万亿元。","数字乡村建设是乡村振兴战略的重要发展方向。它以物联网、大数据、人工智能、5G等现代信息技术为支撑，深刻影响着农业生产、流通与服务方式，为广大农业从业人员提供新的发展机遇。\n\n当前，我国数字乡村建设正在从“技术示范”走向“产业常态”。2026年9月，中央网信办、农业农村部、工业和信息化部联合印发的《数字乡村高质量发展行动计划（2026—2030年）》明确提出：优化数字乡村发展相关学科专业布局，推动学科交叉融合和创新发展。强化政策支持，培育乡村数字人才，吸引数字游民等新群体投身乡村创业就业。乡村已经成为立业逐梦的赛场，数字乡村的产业形态与发展格局，正在为青年群体解锁多样、宽阔的成长赛道。\n\n数字改变农业，产业升级催生新兴岗位。当前，我国农业生产信息化率持续提升，智能农机装备加快普及，农用无人机年作业面积已突破4亿亩，农村网络零售额首次突破3万亿元，农产品网络零售额近8000亿元。数字赋能让农业生产从“经验决策”走向“数据驱动”，让农产品销售从“层层批发”走向“产地直发”。伴随着数字技术重塑农业生产全链条，田间地头的岗位形态也在持续迭代更新。田间无人机飞手、农业大数据分析师、农产品溯源运维员、智慧农机维保师、乡村新媒体运营师、农村电商孵化师等新兴岗位蓬勃兴起，为致力于农业生产的从业人员铺开了全新的职业赛道与成长沃土。对广大青年来说，一个“把数字能力变成产业能力”的新空间正在打开。\n\n面对这样的历史机遇，该如何作为？这既需要青年群体主动转换角色、练好内功，也需要各方协同搭台、优化生态。具体而言，可以从以下几个方面发力：","重庆日报 2026-10-06",82,{"impact":116,"substance":256,"depth":455,"authority":456,"freshness":33,"relevant":34,"comment":457},16,13,"央媒评论员文章，紧扣三部委数字乡村行动计划，含信息化率、无人机作业面积、农村网络零售额等新数据，政策与产业价值兼具，值得进入每日精选。",[459],{"name":452,"url":449},[461,409,39,462,463],"农村电商","农用无人机","乡村数字人才",{"code":437,"slug":465,"name":466,"path":467,"parent_code":50},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":53,"locked":54,"version":55,"method":56,"evidence":470},{"candidates":471,"agricultural_evidence":26},[472,476,481],{"code":437,"score":182,"supported":26,"title":473,"abstract":474,"tags":475},[409],[409],[409],{"code":477,"score":34,"supported":54,"title":478,"abstract":479,"tags":480},"E5",[],[],[461],{"code":46,"score":34,"supported":54,"title":482,"abstract":483,"tags":484},[],[],[39],[486,487],"农用无人机 作业面积 4亿亩","乡村数字人才 农业人工智能 农用无人机 农村电商","农用无人机作业面积4亿亩-4385","2026-10-08T00:06:07.318997Z",{"id":491,"title":492,"url":493,"summary":494,"summary_zh":20,"content":495,"source_name":496,"source_url":20,"published_at":23,"category":113,"cover_url":20,"hotness":25,"is_selected":54,"score":453,"score_detail":497,"sources":500,"tags":502,"primary_topic":505,"secondary_topics":510,"classification":511,"search_phrases":531,"slug":534,"view_count":118,"doi":20,"paper":20,"created_at":535},4382,"山东农业大学研发小麦育种具身机器人 WheatX 首次亮相","http:\u002F\u002Fwww.shandong.gov.cn\u002Fart\u002F2026\u002F10\u002F6\u002Fart_97564_765520.html","山东农业大学张亮团队联合山东省农业科学院、中国农业大学等单位研发小麦育种具身机器人 WheatX,集感知、决策与选育功能于一体,可完成大田麦穗快速筛选、对量化达标麦穗喷涂颜料原位标记,实现「感知—决策—执行」全流程闭环;团队已与极智生物达成合作,将首款小麦育种大模型 iwheat 嵌入机器人,推动从原型机向产品级升级。","小麦试验田里，一台长宽高均超过两米的“大家伙”在麦株前稳稳停住。机械臂移动滑台探出，前端圆环装置精准套住一株麦穗缓缓上滑，环内八枚微型摄像头从多个角度对其精细成像。随后，穗粒数、穗长等性状的量化数据全部生成。\n\n这是由山东农业大学、小麦育种全国重点实验室王宏伟、张亮教授团队联合研发的小麦育种具身机器人原型机，近日在该校举办的小麦智能育种研讨会上首次公开亮相。\n\n王宏伟介绍，传统小麦育种中，育种家经年积累了大量宝贵育种经验，能从成千上万的杂交后代里选育出性状优良的品系。但在大田环境下，人工筛选不仅劳动强度大，筛选结果还容易受主观经验差异影响。同时，现有田间装备多以被动扫描和影像采集为主，能够完成“看”和“测”，但在自主判断、主动干预和选育执行等方面仍存在不足。该研究主要聚焦传统小麦育种主要依赖人工、选育效率低等行业共性问题，将为大田育种智能化提供新的装备与技术路径。\n\n“在模型训练时加入大量育种家先验经验是该款机器人特色之一。”王宏伟说，团队的目标，是让机器人不仅能够“看得见”，还能够“作判断、去操作”，在田间完成类似育种家的选育动作。\n\n为让机器人“学会”育种，团队种植了2000份不同的小麦种质资源，逐一拍摄影像后，请育种家按标准打分标注，再将标注结果输入系统，育种家凭多年经验作出的判断被转化为机器可学习的数据。机器人结合育种家经验构建智能决策模型，具备了初步的自主选育判断能力。\n\n同时，该机器人采用RTK与视觉融合导航技术，可在试验田小区间自主移动作业；搭载滑台高清相机，对田间小麦进行连续影像采集，并利用视觉算法自动量化株高、穗部性状等表型参数。在此基础上，系统结合智能决策模型，可对田间单株进行高通量初筛，自主识别具有进一步考察价值的候选植株。\n\n“对于初筛获得的目标植株，机器人还可通过自主研发的多功能机械臂开展进一步精细作业，包括单株抓取、环形多角度精细成像等操作。”张亮解释，对于量化后确认符合标准的麦穗，机器人会像育种家在田间“绑绳子做标记”一样，喷涂颜料原位标记，完成从大范围快速筛选到目标植株精细评价和标记的连续作业，实现“感知—决策—执行”的全流程闭环。\n\n该研究获山东省重点研发计划项目支持，并联合山东省农业科学院、中国农业大学等单位协同攻关。国家小麦产业技术体系副首席曹新有表示，该机器人集感知、决策与选育功能于一体，为智能育种由算法模型走向田间自主作业提供了关键技术支撑。\n\n“用机器人辅助人工选择，这是很难但很有意义的方向。”张亮坦言，大田场景麦穗密度高、类型多，场景极其复杂，加之天气影响，对机器人精准作业都是极大考验，系统在抓取精准度、学习经验深度等指标上还需进一步升级改良。目前，团队已与极智生物科技有限公司达成合作意向，将我国首款小麦育种大模型iwheat嵌入WheatX育种机器人，联合研发小麦智能育种系统，推动原型机迭代升级向产品级迈进。（记者 刘姝彤 通讯员 屈越）","山东省人民政府网 2026-10-06",{"impact":216,"substance":216,"depth":159,"authority":456,"freshness":498,"relevant":34,"comment":499},8,"国内首款小麦育种具身机器人原型机亮相，实现感知—决策—执行闭环，属智慧育种领域实质性技术突破，值得进入每日精选。",[501],{"name":496,"url":493},[316,39,40,503,504],"小麦育种","具身机器人",{"code":506,"slug":507,"name":508,"path":509,"parent_code":176},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[],{"status":53,"locked":54,"version":55,"method":56,"evidence":512},{"candidates":513,"agricultural_evidence":26},[514,518,523,527],{"code":506,"score":182,"supported":26,"title":515,"abstract":516,"tags":517},[508],[508],[503],{"code":46,"score":60,"supported":26,"title":519,"abstract":520,"tags":522},[],[521],"大模型",[39],{"code":84,"score":34,"supported":54,"title":524,"abstract":525,"tags":526},[],[],[40],{"code":341,"score":34,"supported":54,"title":528,"abstract":529,"tags":530},[],[],[316],[532,533],"山东农业大学 小麦育种机器人 WheatX","小麦智能育种 iwheat 大模型","山东农业大学小麦育种机器人WheatX-4382","2026-10-08T00:06:07.051280Z",{"id":537,"title":538,"url":539,"summary":540,"summary_zh":20,"content":541,"source_name":154,"source_url":20,"published_at":112,"category":113,"cover_url":20,"hotness":25,"is_selected":54,"score":542,"score_detail":543,"sources":545,"tags":547,"primary_topic":551,"secondary_topics":552,"classification":553,"search_phrases":577,"slug":580,"view_count":188,"doi":20,"paper":20,"created_at":581},4393,"良种良法良机良田精准赋能国产大豆提质增效 — 中国农科院作科所","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572491.shtm","中国农业科学院作物科学研究所在河南新乡召开「四良融合」促进大豆大面积单产提升观摩交流会,展示「中黄301」大豆品种集成免耕覆秸精量播种、水肥精准调控、病虫草害绿色防控、低损高质量机械收获等关键技术;依托免耕覆秸精量播种,大豆亩株数由 1.6 万株提高到 1.8-2.0 万、亩增产 15% 以上,为黄淮海大豆规模化标准化生产提供成熟技术方案。","与会专家详细听取国家大豆产业技术体系首席科学家、作科所研究员吴存祥就大豆大面积单产提升核心目标的介绍，实地观摩了大豆品种“中黄301”的良种特性及田间长势，以及该品种集成配套免耕覆秸精量播种、水肥精准调控、病虫草害绿色防控、低损高质量机械收获等提产和减损关键技术。\n\n与会者围绕密植高产群体调控，农机农艺深度融合，抗逆提质栽培等大豆单产提升路径开展交流研讨，并一致表示，作科所创建的大豆“四良深度融合”高产栽培综合技术体系，可有效降低黄淮海地区由于为麦后免耕播种出苗质量参差不齐、田间群体结构失衡、水肥资源利用效率偏低、产量与品质协同性不足、种植综合效益薄弱等突出问题，为黄淮海大豆规模化标准化生产提供了成熟技术方案。\n\n近年来，作科所持续发挥国家作物育种与栽培技术科研主力军作用，深耕大豆良种培育、技术集成、示范推广全链条。该所育成的大豆品种“中黄301”入选“十四五”国家育种攻关大豆突破性品种名录，集成的黄淮海夏大豆免耕覆秸机械化生产技术连续多年入选农业农村部主推技术。依托免耕覆秸精量播种技术，配套密植水肥精准调控实现大豆提匀增密，当地大豆亩株数由1.6万株提高到1.8~2.0万，亩增产15%以上。\n\n下一步，作科所将继续依托“双千双百”科技推广行动，持续聚焦大豆产业关键技术难题，持续优化四良融合技术体系，加大示范推广和科技成果转化落地，为助力全国大豆大面积单产提升，以及我国粮油作物产能稳步提升注入强劲科技动力。\n\n全国农技服务推广中心，中国农科院院办公室、转化局、重任局、作科所、油料所、中原中心负责同志参加会议。\n\n版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。",81,{"impact":30,"substance":256,"depth":455,"authority":32,"freshness":118,"relevant":34,"comment":544},"国家级科研机构发布的大豆四良融合技术体系报道，含具体品种、增产数据与主推技术，产业指导价值较高。",[546],{"name":154,"url":539},[40,548,549,166,550],"单产提升","农机农艺融合","良种良法",{"code":172,"slug":173,"name":174,"path":175,"parent_code":176},[],{"status":53,"locked":54,"version":55,"method":56,"evidence":554},{"candidates":555,"agricultural_evidence":26},[556,560,565,569,573],{"code":172,"score":182,"supported":26,"title":557,"abstract":558,"tags":559},[166],[166],[166],{"code":561,"score":60,"supported":26,"title":562,"abstract":563,"tags":564},"A8",[],[548],[548],{"code":129,"score":66,"supported":26,"title":566,"abstract":567,"tags":568},[],[141],[],{"code":84,"score":188,"supported":54,"title":570,"abstract":571,"tags":572},[],[],[40,550],{"code":95,"score":34,"supported":54,"title":574,"abstract":575,"tags":576},[],[],[549],[578,579],"中国农科院作科所 中黄301","黄淮海 大豆 免耕覆秸","中国农科院作科所中黄301-4393","2026-10-08T00:06:56.321627Z",{"id":583,"title":584,"url":585,"summary":586,"summary_zh":587,"content":20,"source_name":588,"source_url":585,"published_at":23,"category":309,"cover_url":20,"hotness":25,"is_selected":26,"score":542,"score_detail":589,"sources":591,"tags":593,"primary_topic":597,"secondary_topics":603,"classification":610,"search_phrases":653,"slug":656,"view_count":34,"doi":657,"paper":658,"created_at":682},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering",{"impact":31,"substance":216,"depth":31,"authority":160,"freshness":257,"relevant":34,"comment":590},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[592],{"name":588,"url":585},[316,39,594,595,596],"智能灌溉","精准农业","温室种植",{"code":598,"slug":599,"name":600,"path":601,"parent_code":602},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[604],{"code":605,"slug":606,"name":607,"path":608,"parent_code":609},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":53,"locked":54,"version":55,"method":56,"evidence":611},{"candidates":612,"agricultural_evidence":26},[613,619,625,630,637,643,649],{"code":605,"score":31,"supported":26,"title":614,"abstract":615,"tags":618},[606],[616,617,606],"灌溉","水资源",[594],{"code":598,"score":32,"supported":26,"title":620,"abstract":622,"tags":624},[621],"greenhouse",[623,621],"温室",[596],{"code":341,"score":498,"supported":26,"title":626,"abstract":627,"tags":629},[],[595,628],"precision agriculture",[316,595],{"code":631,"score":118,"supported":26,"title":632,"abstract":633,"tags":636},"H2",[],[634,635],"土壤","soil",[],{"code":638,"score":66,"supported":26,"title":639,"abstract":640,"tags":642},"B1",[],[641],"番茄",[],{"code":644,"score":66,"supported":26,"title":645,"abstract":646,"tags":648},"H7",[],[647],"气候变化",[],{"code":46,"score":34,"supported":54,"title":650,"abstract":651,"tags":652},[],[],[39],[654,655],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":657,"openalex_id":659,"authors":660,"venue":588,"cited_by_count":244,"oa_url":585,"card":675,"direction":681,"ingested_from":359},"W7220746994",[661,664,667,669,672],{"name":662,"orcid":663},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":665,"orcid":666},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":668,"orcid":20},"Abdelkader Mami",{"name":670,"orcid":671},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":673,"orcid":674},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":676,"method":677,"finding":678,"direction":679,"opportunity":680},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","智慧农业 \u002F 农业物联网","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","农业人工智能与决策模型","2026-10-07T23:30:43.848308Z",{"id":684,"title":685,"url":686,"summary":687,"summary_zh":688,"content":20,"source_name":689,"source_url":686,"published_at":23,"category":309,"cover_url":20,"hotness":25,"is_selected":26,"score":542,"score_detail":690,"sources":692,"tags":694,"primary_topic":698,"secondary_topics":701,"classification":707,"search_phrases":735,"slug":738,"view_count":34,"doi":739,"paper":740,"created_at":771},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data",{"impact":31,"substance":216,"depth":31,"authority":160,"freshness":257,"relevant":34,"comment":691},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[693],{"name":689,"url":686},[316,39,695,696,697],"高光谱","遥感","土壤监测",{"code":631,"slug":635,"name":699,"path":700,"parent_code":609},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[702],{"code":703,"slug":704,"name":705,"path":706,"parent_code":50},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":53,"locked":54,"version":55,"method":56,"evidence":708},{"candidates":709,"agricultural_evidence":26},[710,714,719,723,729],{"code":631,"score":160,"supported":26,"title":711,"abstract":712,"tags":713},[635],[634,635],[],{"code":703,"score":456,"supported":26,"title":715,"abstract":717,"tags":718},[716],"hyperspectral",[716],[696,695],{"code":341,"score":33,"supported":26,"title":720,"abstract":721,"tags":722},[],[595,628],[316],{"code":46,"score":33,"supported":26,"title":724,"abstract":725,"tags":728},[],[726,727],"机器学习","machine learning",[39],{"code":187,"score":118,"supported":26,"title":730,"abstract":731,"tags":734},[],[732,733],"施肥","fertilization",[],[736,737],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":739,"openalex_id":741,"authors":742,"venue":689,"cited_by_count":244,"oa_url":764,"card":765,"direction":679,"ingested_from":359},"W7220738771",[743,746,749,752,755,758,761],{"name":744,"orcid":745},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":747,"orcid":748},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":750,"orcid":751},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":753,"orcid":754},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":756,"orcid":757},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":759,"orcid":760},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":762,"orcid":763},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":766,"method":767,"finding":768,"direction":769,"opportunity":770},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","农业遥感与作物表型","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":773,"title":774,"url":775,"summary":776,"summary_zh":777,"content":20,"source_name":778,"source_url":775,"published_at":23,"category":309,"cover_url":20,"hotness":25,"is_selected":26,"score":542,"score_detail":779,"sources":781,"tags":783,"primary_topic":789,"secondary_topics":794,"classification":795,"search_phrases":812,"slug":815,"view_count":188,"doi":816,"paper":817,"created_at":837},4331,"Does rural land transfer promote high-quality agriculture development? Empirical evidence from 30 provinces in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940003","As a fundamental pathway toward agricultural-scale operations, rural land transfer (RLT) has emerged as an important institutional mechanism for promoting high-quality agricultural development (HQAD). However, how to effectively unleash the productive potential of land resources and align RLT with the objectives of high-quality development remains an important research question. Treating the 2010 national regulatory intensification of rural land transfer as a common policy milestone, this study employs a continuous difference-in-differences (DID) model and a mediating effect model to examine the relationship between RLT intensity and HQAD and to explore the potential transmission role of new agricultural business entities (NABE). The results show that the overall level of HQAD in China has steadily improved, although substantial spatial and temporal disparities remain, with HQAD generally decreasing from the eastern to the central and western regions. The continuous DID estimates indicate a significant positive association between RLT intensity and HQAD after the 2010 national regulatory-intensification milestone, and this relationship remains stable across the reported robustness checks. The mediation analysis provides evidence consistent with a potential transmission pathway through new agricultural business entities (NABE). Furthermore, the estimated association between RLT intensity and HQAD is stronger in the central region and in non-major grain-producing areas.","作为农业规模化经营的基础路径，农地流转(rural land transfer, RLT)已成为推动农业高质量发展(high-quality agricultural development, HQAD)的重要制度安排。然而，如何有效释放土地资源的生产潜力，使农地流转与高质量发展目标相契合，仍是一个重要的研究问题。本研究以2010年国家加强农地流转监管为共同政策节点，采用连续型双重差分(continuous difference-in-differences, DID)模型和中介效应模型，考察农地流转强度与农业高质量发展之间的关系，并探讨新型农业经营主体(new agricultural business entities, NABE)可能的中介传导作用。结果表明，中国农业高质量发展总体水平稳步提升，但仍存在显著的空间和时间差异，农业高质量发展水平总体呈东、中、西部递减态势。连续型双重差分估计表明，在2010年国家加强监管这一政策节点之后，农地流转强度与农业高质量发展之间存在显著正相关关系，且该关系在各项稳健性检验中保持稳定。中介分析提供了与新型农业经营主体潜在传导路径相一致的证据。此外，农地流转强度与农业高质量发展之间的估计关联在中部地区和非粮食主产区更强。","Frontiers in Sustainable Food Systems",{"impact":31,"substance":216,"depth":31,"authority":160,"freshness":257,"relevant":34,"comment":780},"基于30省面板数据的连续DID实证研究，方法规范、结论有政策参考价值，但属学术论文而非产业动态，适合主题聚合而非头条精选。",[782],{"name":778,"url":775},[784,785,786,787,788],"农业高质量发展","新型农业经营主体","粮食主产区","双重差分","土地流转",{"code":432,"slug":790,"name":791,"path":792,"parent_code":793},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":53,"locked":54,"version":55,"method":56,"evidence":796},{"candidates":797,"agricultural_evidence":26},[798,803,807],{"code":432,"score":456,"supported":26,"title":799,"abstract":801,"tags":802},[800],"land transfer",[800],[788,785],{"code":561,"score":34,"supported":54,"title":804,"abstract":805,"tags":806},[],[],[786],{"code":808,"score":34,"supported":54,"title":809,"abstract":810,"tags":811},"J2",[],[],[784],[813,814],"农村土地流转 农业高质量发展","新型农业经营主体 中介效应","农村土地流转农业高质量发展-4331","10.3389\u002Ffsufs.2026.1940003",{"doi":816,"openalex_id":818,"authors":819,"venue":778,"cited_by_count":244,"oa_url":830,"card":831,"direction":835,"ingested_from":359},"W7220726312",[820,822,824,826,828],{"name":821,"orcid":20},"Tian Tian",{"name":823,"orcid":20},"Jiawei Yang",{"name":825,"orcid":20},"Tong Yu",{"name":827,"orcid":20},"李孟然",{"name":829,"orcid":20},"Zhen Xie","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1940003\u002Fpdf",{"tldr":832,"method":833,"finding":834,"direction":835,"opportunity":836},"用连续DID和中介模型检验农村土地流转对农业高质量发展的影响及新型经营主体的中介作用。","2010年政策节点，连续DID与中介效应模型，中国30省面板数据。","土地流转显著促进农业高质量发展，新型经营主体起中介作用，中西部及非主产区效应更强。","数字乡村与农业信息化","可结合数字技术或信息化服务，探究土地流转通过新型经营主体推动农业高质量发展的数字化路径。","2026-10-07T23:30:06.424093Z",{"id":839,"title":840,"url":841,"summary":842,"summary_zh":20,"content":20,"source_name":308,"source_url":841,"published_at":23,"category":309,"cover_url":20,"hotness":25,"is_selected":26,"score":542,"score_detail":843,"sources":845,"tags":847,"primary_topic":20,"secondary_topics":853,"classification":854,"search_phrases":866,"slug":869,"view_count":244,"doi":870,"paper":871,"created_at":882},4326,"Where should straw return be promoted? Spatial zoning of management suitability based on C\u002FN ratio optimization in Northeast China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104998","Where should straw return be promoted? Spatial zoning of management suitability based on C\u002FN ratio optimization in Northeast China。Agricultural Systems",{"impact":31,"substance":216,"depth":31,"authority":160,"freshness":257,"relevant":34,"comment":844},"基于碳氮比优化的东北秸秆还田空间分区研究，方法新颖、结论对区域秸秆管理有直接指导价值，值得进入每日精选。",[846],{"name":308,"url":841},[848,849,850,851,852],"黑土地保护","秸秆还田","东北农业","碳氮比","空间区划",[],{"status":227,"locked":54,"version":55,"method":56,"evidence":855},{"candidates":856,"agricultural_evidence":26,"reason":239},[857,862],{"code":858,"score":34,"supported":54,"title":859,"abstract":860,"tags":861},"H1",[],[],[848],{"code":231,"score":34,"supported":54,"title":863,"abstract":864,"tags":865},[],[],[849],[867,868],"东北 秸秆还田 空间区划","碳氮比 优化 秸秆还田","东北秸秆还田空间区划-4326","10.1016\u002Fj.agsy.2026.104998",{"doi":870,"openalex_id":872,"authors":873,"venue":308,"cited_by_count":244,"oa_url":841,"card":20,"direction":20,"ingested_from":359},"W7220696749",[874,876,879],{"name":875,"orcid":20},"Ziyue Yu",{"name":877,"orcid":878},"Hang Han","https:\u002F\u002Forcid.org\u002F0009-0001-6861-4987",{"name":880,"orcid":881},"Fan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4648-5851","2026-10-07T23:30:03.931608Z"]