[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-10-10":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-10-10","农业农村日报：AI赋能农业与绿色保鲜技术获突破",[7,8,9,10,11,12],"中国农科院农产品加工所研发无金属有机光催化材料，无牺牲剂下高效产生活性氧，对大肠杆菌抑菌率超90%，为食品保鲜与冷链物流提供新方案，成果发表于《德国应用化学》。","福建省农科院方少忠团队首次构建百合鳞茎单细胞转录组图谱，鉴定12种细胞类型，锁定维管束附近类分生组织细胞为再生起点，成果登《植物通讯》，助推百合种球国产化。","研究显示AI应用显著提升中国农业可持续消费效率，基于30省份2015—2024年面板数据，东中部及粮食主产区效果更突出，为SDG 12落地提供实证支撑。","全国供销合作社系统欢庆第九个中国农民丰收节，围绕“丰收开新局 实干促振兴”主题，将农事服务、供销大集、直播带货延伸至田头与城乡。","经济日报评论指出丰收节金秋消费季已累计带动农产品销售超1600亿元，呼吁把节庆流量沉淀为产业增量，做精乡土产品、借势短视频、分好增值收益。","多项前沿研究聚焦农业绿色转型：设施葡萄动态节能补光调控方法、印尼咖啡近九成面积符合欧盟零毁林法规、湄公河三角洲藜麦替代旱季水稻等。","本期聚焦农业人工智能与绿色技术前沿：AI提升农业可持续消费效率、无金属光催化材料助力食品保鲜、百合鳞茎再生机制获突破；同时关注丰收节供销系统行动与消费季1600亿元成效，并收录设施补光、零毁林咖啡、气候韧性作物等国际研究。\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。*",[15,115,187,228,275,348,409,463,540,602,694,775,846,893,953],{"id":16,"title":17,"url":18,"summary":19,"summary_zh":20,"content":21,"source_name":22,"source_url":18,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":27,"score_detail":28,"sources":36,"tags":38,"primary_topic":44,"secondary_topics":50,"classification":51,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":114},4507,"Nearly ninety percent of Indonesian coffee area is classified as deforestation-free under the European Union Deforestation Regulation (EUDR)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faeb1e2","Abstract Indonesia is the third-largest coffee producer globally and exports a substantial share of its production to European Union (EU) countries. Recent implementation of the EU Deforestation Regulation (EUDR), which requires deforestation-free supply chains, highlights the need for spatially-explicit coffee mapping to support the assessment of coffee-associated deforestation. In this study, we utilized multitemporal and multi-sensor remote sensing to produce annual maps of coffee distribution from 2016 to 2024. We analyzed the spatial patterns of annual coffee distribution during this period, and evaluated the potential implications of EUDR for coffee production in Indonesia. Our maps of coffee agroecosystems demonstrate an overall classification accuracy exceeding 80%, and are broadly consistent with independent coffee datasets (i.e., national statistics and coffee probability maps from the Forest Data Partnership). We detected a net increase of ~15% in coffee area over the eight-year period, although the patterns of change vary across provinces. Based on these maps, we estimate that 87.9% of the coffee area in 2024 meets the EUDR deforestation-free criterion, and only a relatively small proportion of agroecosystems are classified as non-deforestation-free. These findings highlight opportunities for spatially targeted EUDR implementation, where deforestation-free coffee agroecosystems can be supported towards meeting broader EUDR requirements, while areas associated with forest conversion can be supported towards other pathways, such as sustainable agricultural practices, restoration, and forest conservation.","摘要：印度尼西亚是全球第三大咖啡生产国，并将相当大比例的产量出口至欧盟（EU）国家。近期实施的《欧盟零毁林条例》（EUDR）要求供应链不涉及毁林，这凸显了开展空间显式咖啡制图以支持咖啡相关毁林评估的必要性。本研究利用多时相和多传感器遥感技术，制作了2016年至2024年咖啡分布的年度地图。我们分析了这一时期咖啡年度分布的空间格局，并评估了EUDR对印度尼西亚咖啡生产的潜在影响。我们的咖啡农业生态系统地图总体分类精度超过80%，并与独立咖啡数据集（即国家统计数据以及森林数据伙伴关系提供的咖啡概率图）大体一致。我们检测到八年间咖啡面积净增加约15%，但各省变化格局有所不同。基于这些地图，我们估计2024年87.9%的咖啡面积符合EUDR无毁林标准，仅有相对较小比例的农业生态系统被归类为非无毁林。这些发现凸显了在空间上有针对性地实施EUDR的机会，即可以支持无毁林咖啡农业生态系统满足EUDR更广泛的要求，同时可以支持与森林转化相关的区域转向其他路径，如可持续农业实践、恢复和森林保护。",null,"Environmental Research Letters","2026-10-08T00:00:00Z","论文",10,false,86,{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":35},22,23,18,14,9,1,"基于多时相多源遥感生成印尼咖啡分布图，量化EUDR合规比例，方法扎实、结论对咖啡出口与可持续供应链有直接参考价值。",[37],{"name":22,"url":18},[39,40,41,42,43],"遥感监测","咖啡产业","供应链溯源","EUDR","毁林零容忍",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":55},"classified","2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":59},[57,65,73,80],{"code":45,"score":58,"supported":59,"title":60,"abstract":62,"tags":64},15,true,[61],"coffee",[63,61],"咖啡",[40],{"code":66,"score":67,"supported":59,"title":68,"abstract":69,"tags":72},"I3",7,[],[70,71],"遥感","remote sensing",[39],{"code":74,"score":75,"supported":59,"title":76,"abstract":77,"tags":79},"H6",3,[],[78],"农业生态",[],{"code":81,"score":75,"supported":59,"title":82,"abstract":83,"tags":85},"H8",[],[84],"可持续农业",[],[87,88],"印尼 咖啡 遥感制图","EUDR 咖啡 毁林","印尼咖啡遥感制图-4507",0,"10.1088\u002F1748-9326\u002Faeb1e2",{"doi":91,"openalex_id":93,"authors":94,"venue":22,"cited_by_count":90,"oa_url":18,"card":107,"direction":111,"ingested_from":113},"W7221019615",[95,98,100,102,104],{"name":96,"orcid":97},"Tin Widyani Satriawan","https:\u002F\u002Forcid.org\u002F0000-0003-4580-9973",{"name":99,"orcid":21},"Xiangzhong (Remi) Luo",{"name":101,"orcid":21},"Shafira Nur Ramdhania",{"name":103,"orcid":21},"Agustín Escobar-López",{"name":105,"orcid":106},"Anggun Tridawati","https:\u002F\u002Forcid.org\u002F0000-0002-0070-883X",{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"利用多时相多源遥感绘制印尼2016-2024年咖啡分布图，评估EUDR合规性。","多时相多传感器遥感分类，结合国家统计与森林数据伙伴关系概率图验证。","印尼咖啡面积净增约15%，2024年87.9%咖啡区符合EUDR无毁林标准。","农业遥感与作物表型","可延伸研究：针对非合规小区域，结合高分辨率遥感与供应链追溯，设计精准干预与可持续转型路径。","openalex","2026-10-09T23:30:26.065388Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":21,"content":21,"source_name":120,"source_url":21,"published_at":121,"category":24,"cover_url":21,"hotness":25,"is_selected":59,"score":122,"score_detail":123,"sources":126,"tags":128,"primary_topic":134,"secondary_topics":140,"classification":141,"search_phrases":172,"slug":175,"view_count":90,"doi":176,"paper":177,"created_at":186},4548,"Artificial Intelligence and Sustainable Consumption Efficiency in Chinese Agriculture: Evidence from SDG 12（人工智能与中国农业可持续消费效率：来自可持续发展目标12的证据）","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002Faccepted","本研究从联合国2030年可持续发展目标12（SDG 12，确保可持续消费和生产模式）视角出发，识别AI技术对中国农业可持续消费效率的影响及其传导路径。文章构建包含人工智能基础设施部署、AI技术应用广度与应用深度三维一体的指标体系，运用双向固定效应模型、机制检验模型与门槛模型，对中国30个省份2015-2024年的面板数据展开系统实证。研究发现：AI应用显著提升了农业可持续消费效率，且在东中部、粮食主产区效应更突出；机制检验表明，AI通过促进农业生产要素重组、推动农产品加工链条低碳化、引导农村消费结构升级三条路径释放效率红利；门槛分析进一步显示，存在以农技推广覆盖率为单一门槛的\"低、高\"两种区制，AI边际效应在低覆盖率地区先升后降。研究对完善农业人工智能政策、推进农村消费革命具有重要意义，为\"十五五\"数字乡村建设背景下农业新质生产力发展提供经验依据。","Frontiers in Sustainable Food Systems 2026 年 10 月 06 日 Volume 10 Sec. Climate-Smart Food Systems (Accepted)","2026-10-05T16:00:00Z",85,{"impact":29,"substance":30,"depth":31,"authority":124,"freshness":33,"relevant":34,"comment":125},13,"基于30省十年面板数据的实证研究，方法扎实、结论具体，对数字乡村与农业AI政策有参考价值，值得进入每日精选。",[127],{"name":120,"url":118},[129,130,131,132,133],"数字乡村","农业人工智能","农业新质生产力","农技推广","可持续消费",{"code":135,"slug":136,"name":137,"path":138,"parent_code":139},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":142},{"candidates":143,"agricultural_evidence":59},[144,150,156,161,167],{"code":135,"score":145,"supported":59,"title":146,"abstract":148,"tags":149},12,[147],"artificial intelligence",[130],[130],{"code":151,"score":152,"supported":59,"title":153,"abstract":154,"tags":155},"G1",4,[],[132],[132],{"code":157,"score":152,"supported":59,"title":158,"abstract":159,"tags":160},"I6",[],[129],[129],{"code":162,"score":75,"supported":59,"title":163,"abstract":164,"tags":166},"E1",[],[165],"农产品加工",[],{"code":168,"score":34,"supported":26,"title":169,"abstract":170,"tags":171},"J2",[],[],[131],[173,174],"农业人工智能 可持续消费效率","SDG 12 中国农业 AI","农业人工智能可持续消费效率-4548","10.3389\u002Ffsufs.2026.xxxxx",{"doi":176,"openalex_id":21,"authors":178,"venue":21,"cited_by_count":90,"oa_url":21,"card":179,"direction":183,"ingested_from":185},[],{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"实证检验AI对中国农业可持续消费效率的影响及传导路径。","构建AI三维指标体系，用双向固定效应、机制与门槛模型分析30省面板数据。","AI显著提升农业可持续消费效率，通过要素重组、加工低碳化、消费升级传导，存在农技推广门槛。","农业人工智能与决策模型","可探究AI应用深度与农技推广覆盖率的协同机制，及低覆盖率区AI效应先升后降的微观成因。","agent","2026-10-10T00:15:03.509764Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":21,"content":192,"source_name":193,"source_url":21,"published_at":194,"category":195,"cover_url":21,"hotness":25,"is_selected":59,"score":196,"score_detail":197,"sources":199,"tags":201,"primary_topic":21,"secondary_topics":207,"classification":208,"search_phrases":223,"slug":226,"view_count":34,"doi":21,"paper":21,"created_at":227},4537,"福建省农科院方少忠团队破译百合鳞茎再生密码——首次构建百合鳞茎单细胞转录组图谱，成果发表于《植物通讯》","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-10\u002Fdoc-iniustfr6275804.shtml","福建省农业科学院生物技术研究所方少忠研究员领衔、杨成龙博士牵头的百合创新团队联合中国农业大学吴健教授团队，在植物学国际顶级期刊《植物通讯》（Plant Communications）在线发表最新研究成果，从细胞起源和分子机制维度破译百合鳞茎再生密码，有力推动百合种球国产化繁育进程。研究团队将百合鳞茎内的细胞核经过测序与鉴定细分为12种细胞类型，发现维管束附近的\"类分生组织细胞\"是百合鳞片启动再生的核心细胞起源。进一步研究定位到两个关键调控因子——BELL家族转录因子LdBLR和14-3-3蛋白LdGRF12，两者通过相互作用显著提高细胞增殖水平和小鳞茎再生率。该团队已构建\"原原种—原种—商品种\"三级繁育技术体系，在福建南平延平区、新疆玛纳斯县、木垒县等地规模化推广。","金秋时节的南平市延平区王台镇溪后村，迎来花田喜事。设施大棚内，稻农卢远祥刚收割完水稻便化身花农，忙着耕整地，计划无缝衔接原地播种百合种球。\n\n“这些形似大蒜的种球，就是百合的鳞茎，也就是用于贮藏养分的地下根茎。”卢远祥说，在生产上，百合主要靠鳞茎无性繁殖，“别看它们不过乒乓球大小，要繁育出一粒用于鲜切花生产的商品种球，需要3～4年时间”。\n\n正是因为繁育难，我国百合商品种球长期依赖进口。作为全球百合鲜切花生产与消费第一大国，我国年均进口商品种球超4亿粒，产业发展受制于国际种球供应量与价格波动。\n\n为改变这一局面，方少忠团队历经20多年攻关，构建了“原原种—原种—商品种”三级繁育技术体系：母球经过剥片、消毒等处理后，扦插入专用基质中，随后进入诱导库。经过一年的变温处理后，鳞片便被诱导出小鳞茎，并逐渐膨大、生根。紧接着，小鳞茎转场至温室大棚，半年后长成围径10厘米左右的籽球。这些籽球后续还要经过一年多的大田复壮，才能成为生产上可用的商品球。\n\n目前，这套技术体系已经在南平市延平区落地。后者是我国第三大百合鲜切花主产区，年产量8000多万枝，年产值超6亿元。百合种球正加速国产化，逐渐扭转商品种球受制于人、成本居高不下的困境。然而，要真正实现百合种球自由，还要进一步破解繁育周期长、效率低等难题。毕竟，一粒母球只能剥出约20瓣鳞片，一瓣鳞片只能再生出2～3个小鳞茎。","新浪财经 2026 年 10 月 10 日 转福建省农业科学院生物技术研究所","2026-10-09T23:30:00Z","报道",84,{"impact":29,"substance":29,"depth":31,"authority":124,"freshness":33,"relevant":34,"comment":198},"国家级科研机构在《植物通讯》发表百合鳞茎单细胞转录组图谱，属种业振兴领域实质性突破，兼具产业背景与数据增量，值得进入每日精选。",[200],{"name":193,"url":190},[202,203,204,205,206],"种业振兴","花卉产业","单细胞转录组","国产种球","百合种球",[],{"status":209,"locked":26,"version":53,"method":54,"evidence":210},"unclassified",{"candidates":211,"agricultural_evidence":59,"reason":222},[212,217],{"code":213,"score":34,"supported":26,"title":214,"abstract":215,"tags":216},"B4",[],[],[203],{"code":218,"score":34,"supported":26,"title":219,"abstract":220,"tags":221},"F1",[],[],[202],"缺少足够的主题证据",[224,225],"福建省农科院 百合 鳞茎再生","百合 单细胞转录组 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葡萄","设施葡萄动态补光节能调控-4549","10.12133\u002Fj.smartag.sa202605021",{"doi":399,"openalex_id":21,"authors":401,"venue":21,"cited_by_count":90,"oa_url":21,"card":402,"direction":406,"ingested_from":185},[],{"tldr":403,"method":404,"finding":405,"direction":406,"opportunity":407},"提出基于光合最佳效能点的设施葡萄动态补光调控方法，实现节能与增产。","光合-光响应曲线拟合、光合日累积模型、天空辐照预测与ROS仿真验证。","较传统补光电耗降24.6%，光合速率仅降5.8%，亩产增7.2%。","智慧农业 \u002F 农业物联网","可探索多作物通用光合效能点模型及边缘部署的实时闭环补光控制。","2026-10-10T00:15:03.677653Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":21,"content":414,"source_name":415,"source_url":21,"published_at":416,"category":195,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":417,"sources":421,"tags":423,"primary_topic":428,"secondary_topics":434,"classification":435,"search_phrases":458,"slug":461,"view_count":90,"doi":21,"paper":21,"created_at":462},4533,"经济日报评论：变丰收\"流量\"为产业增量","http:\u002F\u002Fbgimg.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202610\u002Ft20261005_3247948.shtml","经济日报评论指出，丰收节不只是庆丰收的文化盛景，更是拉动消费、激活产业的经济契机。丰收节金秋消费季开展以来已累计带动销售农产品超1600亿元。文章呼吁以节为媒，把短视频、乡村旅游等新业态与乡土文化资源结合起来，让节庆\"流量\"沉淀为产业增量、转化为农民增收，并提出四点建议：做精乡土产品夯实供给基础、借势短视频引来市场流量、做实线下消费和分好增值收益。文章援引两组关键数据做支撑：截至2025年底全国共设立非遗工坊1.49万家；2025年全国农产品网络零售额达7833.1亿元、同比增长9.9%；目前全国已建成1597个国家级乡村旅游重点村镇、超6万个行政村开展乡村旅游经营，全年农村居民国内出游15.26亿人次、出游花费突破1万亿元。文章以此论证乡村产业的系统性重构正在持续释放乘数效应。","## 变丰收“流量”为产业增量\n\n2026年10月05日 06:08 来源：经济日报 冯鑫燚\n\n随着我国秋粮陆续迎来大面积收获，各地区各部门开展了以“丰收开新局、实干促振兴”为主题的中国农民丰收节系列活动。丰收节不仅是庆丰收的文化盛景，更成为拉动消费、激活产业的经济契机。丰收节活动金秋消费季开展以来，已累计带动销售农产品超1600亿元。\n\n今年6月份印发的《加快农业农村现代化“十五五”规划》提出，推动农业与文化、旅游、教育、康养、体育等产业深度融合。这启示我们，要以节为媒，把短视频、乡村旅游等新业态与乡土文化资源结合起来，让节庆“流量”沉淀为产业增量、转化为农民增收。其中，做精乡土产品是基础，引来市场流量是关键，做实线下消费是路径，分好增值收益是根本，四者贯通是促进农村一二三产业融合发展的务实之举。\n\n做精乡土产品，夯实供给基础。农时节气、乡土饮食、传统技艺和民俗故事，承载着鲜明地域特色，蕴藏着可开发的产业价值。截至2025年底，全国共设立非遗工坊1.49万家。可依托这类载体，把技艺传习、产品制作与体验服务结合起来，让“指尖技艺”变成“指尖经济”：围绕优势种养产业提升原料品质，发展精深加工和综合利用；将传统技艺与现代设计相结合，开发具有地方特色的食品、手工艺品和体验课程，推动土特产精品化、品牌化发展，为乡村旅游和线上销售备足好货。\n\n借势短视频，引来市场流量。乡村不缺好物产、好风景、好故事，缺的是被看见的渠道。手机直播和短视频降低了传播门槛：农户展示种植采收，返乡青年记录村庄变迁，让特色农产品直达城市餐桌。2025年，全国农产品网络零售额达7833.1亿元，同比增长9.9%。线上热度要变成持久销路，关键在质量与信誉：以稳定品质和可靠供应链承接传播效果，完善产地预冷、分级包装等配套，推动初级农产品升级为承载地域特色和市场信誉的特色品牌，让“网红”变“长红”。\n\n发展乡村旅游，做实线下消费。农事体验、稻田音乐会、丰收市集、非遗展示等活动，让游客在参与中感受乡土文化，也带动了食宿、购物、研学等消费。目前全国已建成1597个国家级乡村旅游重点村镇，超6万个行政村开展乡村旅游经营；全年农村居民国内出游15.26亿人次，出游花费突破1万亿元——城乡双向奔赴，乡村消费潜力加速释放。更重要的是，乡村旅游不止于餐饮住宿的短期增长，更贯通游客消费与本地生产。要引导民宿、餐饮经营主体与家庭农场、合作社、加工企业建立稳定供货关系，让一桌农家饭带动农产品采购，让一次研学带动食品加工，“接二连三”延展增值空间。\n\n健全联农带农，分好增值收益。产业融合的成色，最终要看农民是否受益。要通过合理的组织方式和分配机制，把产品销售、旅游服务、文化体验形成的收益落到农民手中：推广订单农业和保底收购，让农民分享加工增值；培育家庭农场、合作社和龙头企业组成的新型经营主体，让农民就近就业获得工资性收入；完善股份合作、保底分红等利益联结机制，让农民以土地、技艺、房屋等资源入股，分享产业发展收益。同时，保障乡村工匠、非遗代表性传承人在技艺展示和内容创作中的合理报酬，让农民既当乡村文化的创造者、传播者，也当融合发展的参与者、受益者。\n\n丰收，既是对耕耘的礼赞，也是向未来的蓄力。庆祝丰收，不只是庆贺五谷丰登，更是为乡村产业强起来、农民腰包鼓起来积蓄动能。要坚持农民主体、农业主线、农村主场，让资源变产品、让流量变销量、让增值变收入，在加快建设农业强国的新征程上，结出更多富民强村的硕果。（本文来源：经济日报 作者：冯鑫燚）\n\n(责任编辑：王炬鹏)\n\n推荐阅读\n\n中国经济网版权及免责声明\n\n*   1、凡本网注明 '来源：中国经济网' 或 '来源：经济日报-中国经济网' 的所有作品，版权均属于中国经济网（本网另有声明的除外）；未经本网授权，任何单位及个人不得转载、摘编或以其它方式使用上述作品；已经与本网签署相关授权使用协议的单位及个人，应注意该等作品中是否有相应的授权使用限制声明，不得违反该等限制声明，且在授权范围内使用时应注明 '来源：中国经济网' 或 '来源：经济日报-中国经济网'。违反前述声明者，本网将追究其相关法律责任。\n*   2、本网所有的图片作品中，即使注明'来源：中国经济网'及\u002F或标有'中国经济网(www.ce.cn)'水印，但并不代表本网对该等图片作品享有许可他人使用的权利；已经与本网签署相关授权使用协议的单位及个人，仅有权在授权范围内使用该等图片中明确注明'中国经济网记者XXX摄'或'经济日报社-中国经济网记者XXX摄'的图片作品，否则，一切不利后果自行承担。\n*   3、凡本网注明 '来源：XXX（非中国经济网）' 的作品，均转载自其它媒体，转载目的在于传递更多信息，并不代表本网赞同其观点和对其真实性负责。\n*   4、如因作品内容、版权和其它问题需要同本网联系的，请在30日内进行。\n*   ※ 网站总机:010-81025111有关作品版权事宜请联系:010-81025135 邮箱:![Image 1](http:\u002F\u002Fwww.ce.cn\u002Fmaterial\u002Fimages\u002Fflsm_email.jpg)","《经济日报》2026 年 10 月 05 日 06 版 评论","2026-10-04T22:08:00Z",{"impact":261,"substance":285,"depth":418,"authority":58,"freshness":419,"relevant":34,"comment":420},17,5,"央媒评论以丰收节消费季1600亿元、农产品网络零售7833.1亿元等数据为支撑，系统论述流量转产业增量的四条路径，观点与数据兼具，但属评论性文章且发布已逾一周。",[422],{"name":415,"url":412},[424,425,426,427,308],"联农带农","三产融合","农产品电商","乡村旅游",{"code":429,"slug":430,"name":431,"path":432,"parent_code":433},"G4","rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry","G",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":436},{"candidates":437,"agricultural_evidence":59},[438,443,447,453],{"code":429,"score":33,"supported":59,"title":439,"abstract":440,"tags":442},[],[441,427],"乡村产业",[425,427,424],{"code":297,"score":152,"supported":59,"title":444,"abstract":445,"tags":446},[],[308],[308],{"code":448,"score":75,"supported":59,"title":449,"abstract":450,"tags":452},"G6",[],[451],"农民增收",[],{"code":454,"score":34,"supported":26,"title":455,"abstract":456,"tags":457},"E5",[],[],[426],[459,460],"中国农民丰收节 金秋消费季","农产品网络零售额 7833.1亿元","中国农民丰收节金秋消费季-4533","2026-10-10T00:14:53.505538Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":468,"content":21,"source_name":469,"source_url":466,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":470,"sources":472,"tags":474,"primary_topic":479,"secondary_topics":483,"classification":484,"search_phrases":509,"slug":512,"view_count":34,"doi":513,"paper":514,"created_at":539},4503,"FUSING DEEP LEARNING AND REMOTE SENSING DATA TO ESTIMATE NON-OPTICALLY ACTIVE WATER QUALITY VARIABLES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23243307","The efficient monitoring of inland water bodies such as rivers and lakes is crucial not only for ensuring human health and ecosystem balance, but also for the effective management of water resources. Two water quality variables of high importance and interest are Dissolved Oxygen (DO) and pH. The former variable is an ecosystem health index as it is connected to hypoxia events, while the latter one is linked to biochemical processes and the acidity of water. Currently, conventional water quality monitoring methods (e.g., via in-situ point sampling) are accurate for the particular point, but costly and time-consuming. On the other hand, satellite remote sensing provides a remedy, as it offers high spatial and temporal coverage. Most of the water quality applications of remote sensing data have focused on optically active parameters like Chlorophyll-a and Colored Dissolved Organic Matter (CDOM), but the estimation of non-optically active variables such as pH and DO poses a significant challenge. This study introduces a novel approach, that fuses Earth Observation (EO) and in-situ data, in a deep learning context, to provide estimations of non-optically active variables, characterized by high accuracy, high spatial coverage and low latency. The approach also considers the data imbalance challenge, which is common in environmental datasets. The approach is applied to Lake Yliki in central Greece. The Boeotikos Kifissos river basin, which lies upstream of Lake Yliki, is dominated by intense agricultural activity. The lake is also part of Athens’ water distribution system, making its efficient monitoring even more important.","对河流、湖泊等内陆水体的高效监测，不仅对保障人类健康和生态系统平衡至关重要，也对水资源的有效管理不可或缺。溶解氧（Dissolved Oxygen, DO）和pH值是两项备受关注且极为重要的水质变量。前者作为生态系统健康指标，与缺氧事件密切相关；后者则与生物化学过程及水体酸度相关。目前，传统的水质监测方法（如原位定点采样）对特定点位而言较为准确，但成本高昂且耗时费力。相比之下，卫星遥感提供了一种解决方案，因其具备较高的时空覆盖能力。遥感数据在水质方面的应用大多集中于光学活性参数，如叶绿素a和有色溶解有机物（Colored Dissolved Organic Matter, CDOM），而对pH值和溶解氧等非光学活性变量的估算则构成重大挑战。本研究提出了一种新方法，在深度学习框架下融合地球观测（Earth Observation, EO）数据与原位数据，以实现对非光学活性变量的高精度、高空间覆盖度和低延迟估算。该方法还考虑了环境数据集中常见的数据不平衡问题。该方法已应用于希腊中部的伊利基湖（Lake Yliki）。位于伊利基湖上游的维奥蒂亚基菲索斯河流域（Boeotikos Kifissos river basin）以密集的农业活动为主。该湖也是雅典供水系统的组成部分，因此对其开展高效监测尤为重要。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":31,"substance":29,"depth":31,"authority":124,"freshness":25,"relevant":34,"comment":471},"将深度学习与遥感、地面实测数据融合估算内陆水体非光学活性水质参数，方法新颖且面向农业流域与饮用水源监测，具备较高专业参考价值。",[473],{"name":469,"url":466},[475,476,70,477,478],"深度学习","农业面源污染","水资源管理","水质监测",{"code":139,"slug":480,"name":481,"path":482,"parent_code":21},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":485,"locked":26,"version":53,"method":54,"evidence":486},"partial",{"candidates":487,"agricultural_evidence":59,"reason":508},[488,493,497,503],{"code":135,"score":58,"supported":59,"title":489,"abstract":491,"tags":492},[490],"deep learning",[475,490],[475],{"code":66,"score":58,"supported":59,"title":494,"abstract":495,"tags":496},[71],[70,71],[70],{"code":498,"score":152,"supported":59,"title":499,"abstract":500,"tags":502},"H3",[],[501],"水资源",[477],{"code":504,"score":34,"supported":26,"title":505,"abstract":506,"tags":507},"H5",[],[],[476],"一级领域明确，二级方向需要细分",[510,511],"Yliki湖 水质 遥感","溶解氧 pH 遥感 估算","Yliki湖水质遥感-4503","10.5281\u002Fzenodo.23243307",{"doi":513,"openalex_id":515,"authors":516,"venue":469,"cited_by_count":90,"oa_url":466,"card":534,"direction":111,"ingested_from":113},"W7220943541",[517,520,523,526,528,531],{"name":518,"orcid":519},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":521,"orcid":522},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":524,"orcid":525},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":527,"orcid":21},"Konstantinos Peroulis",{"name":529,"orcid":530},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":532,"orcid":533},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":535,"method":536,"finding":537,"direction":111,"opportunity":538},"融合深度学习与遥感及实地数据，估算湖泊非光学活性水质变量（溶解氧和pH）。","深度学习融合地球观测与实地数据，处理数据不平衡，应用于希腊Yliki湖。","该方法能高精度、高覆盖、低延迟地估算非光学活性水质变量。","可探索将此类方法迁移至农业流域水质监测，结合农业活动数据提升预警能力。","2026-10-09T23:30:25.773729Z",{"id":541,"title":542,"url":543,"summary":544,"summary_zh":545,"content":21,"source_name":546,"source_url":543,"published_at":547,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":548,"sources":550,"tags":552,"primary_topic":555,"secondary_topics":559,"classification":560,"search_phrases":580,"slug":583,"view_count":339,"doi":584,"paper":585,"created_at":601},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。","Scientific Reports","2026-10-07T00:00:00Z",{"impact":31,"substance":29,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":549},"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[551],{"name":546,"url":543},[361,130,70,553,554],"NDVI","土地利用",{"code":66,"slug":556,"name":557,"path":558,"parent_code":139},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":561},{"candidates":562,"agricultural_evidence":59},[563,568,572,576],{"code":66,"score":124,"supported":59,"title":564,"abstract":566,"tags":567},[565],"multispectral",[565],[553,70],{"code":135,"score":67,"supported":59,"title":569,"abstract":570,"tags":571},[],[475,490],[130],{"code":74,"score":34,"supported":26,"title":573,"abstract":574,"tags":575},[],[],[554],{"code":387,"score":34,"supported":26,"title":577,"abstract":578,"tags":579},[],[],[361],[581,582],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497","10.1038\u002Fs41598-026-67985-x",{"doi":584,"openalex_id":586,"authors":587,"venue":546,"cited_by_count":90,"oa_url":595,"card":596,"direction":406,"ingested_from":113},"W7220869344",[588,590,592],{"name":589,"orcid":21},"Neeraj Kumar",{"name":591,"orcid":21},"Navin Rajpal",{"name":593,"orcid":594},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":597,"method":598,"finding":599,"direction":111,"opportunity":600},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","2026-10-09T23:30:12.234854Z",{"id":603,"title":604,"url":605,"summary":606,"summary_zh":607,"content":21,"source_name":608,"source_url":605,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":609,"sources":611,"tags":613,"primary_topic":619,"secondary_topics":625,"classification":626,"search_phrases":655,"slug":658,"view_count":90,"doi":659,"paper":660,"created_at":693},4485,"Quinoa as an alternative dry season climate resilient crop in the Mekong Delta Vietnam","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1897910","Saline intrusion, drought, and water shortages are causing rice production losses in the Mekong River Delta dry season. Quinoa is a drought tolerant halophyte that could be an alternative crop option to rice or fallow in the dry season. The novelty of growing quinoa in the delta requires an understanding of variety selection and crop optimisation in conditions experienced in the dry season. A range of greenhouse and field trials screened five quinoa varieties (2-want, Sluga, Titicaca, 42- test, and Atlas) for salinity tolerance, fertiliser nitrogen response, and sowing density. The higher yielding varieties, Titicaca and 2-want, had lower biomass and improved water use efficiency in saline and water limited conditions. Nitrogen fertiliser rates of 120 kg N\u002Fha produced the greatest biomass and yield, however lodging occurred, and the 2-want variety recorded negligible yield. Sowing densities between 15 plants\u002Fm 2 and 50 plants\u002Fm 2 had no significant effect on lodging rate and an uncharacteristic heatwave during anthesis causing no measurable yield. Though these experiments identify Titicaca as suitable variety of quinoa based on tolerance to salinity and agronomic performance, sensitivity to temperature has been identified as a risk factor. Manipulating the growing calendar so that saline and water limited tolerant varieties like Titicaca avoid peaks in temperature during anthesis may provide farmers with a suitable dry season alternative crop to rice.","盐水入侵、干旱和水资源短缺正导致湄公河三角洲旱季水稻减产。藜麦（quinoa）是一种耐旱的盐生植物，可作为旱季水稻或休耕的替代作物选择。在三角洲种植藜麦的新颖性要求了解旱季条件下的品种选择和作物优化。通过一系列温室和田间试验，对五个藜麦品种（2-want、Sluga、Titicaca、42-test和Atlas）进行了耐盐性、氮肥响应和播种密度的筛选。高产量的品种Titicaca和2-want在盐渍化和水资源受限条件下具有较低的生物量和较高的水分利用效率。氮肥施用量为120 kg N\u002Fha时产生了最大的生物量和产量，但发生了倒伏，且2-want品种的产量可忽略不计。播种密度在15株\u002Fm²至50株\u002Fm²之间对倒伏率无显著影响，且开花期遭遇的异常热浪未造成可测量的产量损失。尽管这些实验基于耐盐性和农艺表现将Titicaca确定为适宜的藜麦品种，但温度敏感性已被确定为风险因素。调整种植日历，使像Titicaca这样耐盐渍化和水资源受限的品种避开开花期的高温峰值，可能为农民提供一种适宜的旱季水稻替代作物。","Frontiers in Sustainable Food Systems",{"impact":31,"substance":29,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":610},"针对湄公河三角洲旱季盐渍化问题筛选耐盐藜麦品种，数据扎实、结论明确，对气候韧性种植有参考价值。",[612],{"name":608,"url":605},[614,615,616,617,618],"气候韧性农业","藜麦","湄公河三角洲","耐盐碱作物","旱季替代作物",{"code":620,"slug":621,"name":622,"path":623,"parent_code":624},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":627},{"candidates":628,"agricultural_evidence":59},[629,635,642,647,651],{"code":620,"score":67,"supported":59,"title":630,"abstract":631,"tags":634},[],[632,633],"干旱","drought",[614],{"code":636,"score":313,"supported":59,"title":637,"abstract":638,"tags":641},"A1",[],[639,640],"水稻","rice",[],{"code":381,"score":313,"supported":59,"title":643,"abstract":644,"tags":646},[],[384,645],"greenhouse",[],{"code":326,"score":152,"supported":59,"title":648,"abstract":649,"tags":650},[],[615],[615],{"code":498,"score":75,"supported":59,"title":652,"abstract":653,"tags":654},[],[501],[],[656,657],"湄公河三角洲 藜麦 耐盐","Titicaca 藜麦 旱季","湄公河三角洲藜麦耐盐-4485","10.3389\u002Ffsufs.2026.1897910",{"doi":659,"openalex_id":661,"authors":662,"venue":608,"cited_by_count":90,"oa_url":605,"card":687,"direction":691,"ingested_from":113},"W7221048629",[663,665,667,669,671,673,675,678,681,684],{"name":664,"orcid":21},"Duong Hay",{"name":666,"orcid":21},"Binh Duy Nguyen",{"name":668,"orcid":21},"Truong Vinh Hai",{"name":670,"orcid":21},"Tran Phuong Ly",{"name":672,"orcid":21},"Nguyen Long",{"name":674,"orcid":21},"Nguyen Van Loc",{"name":676,"orcid":677},"Châu Minh Khôi","https:\u002F\u002Forcid.org\u002F0000-0002-8533-5752",{"name":679,"orcid":680},"Dang Duy Minh","https:\u002F\u002Forcid.org\u002F0009-0004-5985-1106",{"name":682,"orcid":683},"Jason Condon","https:\u002F\u002Forcid.org\u002F0000-0001-8300-0927",{"name":685,"orcid":686},"Brooke Kaveney","https:\u002F\u002Forcid.org\u002F0000-0001-8132-8961",{"tldr":688,"method":689,"finding":690,"direction":691,"opportunity":692},"在越南湄公河三角洲旱季筛选藜麦品种并优化氮肥与播期，以替代水稻应对盐旱胁迫。","温室与田间试验，测试5个藜麦品种的耐盐性、氮肥响应和播种密度。","Titicaca和2-want耐盐节水且高产，但花期高温敏感；120 kg N\u002Fha增产却易倒伏。","农业绿色发展与碳","可研究调整播期使耐盐品种避开花期高温，并开发抗倒伏的氮肥管理方案。","2026-10-09T23:30:06.421585Z",{"id":695,"title":696,"url":697,"summary":698,"summary_zh":699,"content":21,"source_name":608,"source_url":697,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":700,"sources":702,"tags":704,"primary_topic":710,"secondary_topics":716,"classification":717,"search_phrases":749,"slug":752,"view_count":90,"doi":753,"paper":754,"created_at":774},4483,"Biofortified feed crops: bridging plant biotechnology and animal-derived functional foods","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1963997","Micronutrient deficiency continues to be a significant global nutritional problem, especially in populations relying on staple diets with limited micronutrient diversity. Biofortification presents a sustainable way to enhance crop nutritional quality through methods like agronomic practices, traditional breeding, microbial interventions, nanotechnology, transgenic techniques, and genome editing. This review critically explores these strategies, focusing on their application to feed and forage crops, which are often neglected in biofortification discussions. It examines factors affecting micronutrient uptake, accumulation, transport, and bioavailability in plants, as well as their transfer from biofortified feed to animals and subsequently into milk, meat, and eggs. The review also discusses how nutrient-enriched feed impacts animals health, productivity, and the nutritional value of animals products. Importantly, it highlights gaps in understanding the efficiency of nutrient transfer across the soil–plant–feed–animal–food–human pathway, especially how increased micronutrient levels in forage improve animals mineral status and human nutrition. Finally, the review points to emerging opportunities to integrate crop biofortification with animals nutrition efforts, including precision breeding, genome editing, microbial methods, and targeted nutrient management. An integrated approach like this could extend biofortification’s benefits beyond human food crops, supporting better feed quality, enhanced animals productivity, healthier animal-derived functional foods, and more sustainable, nutritionally resilient food systems.","微量营养素缺乏仍然是一个重大的全球性营养问题，尤其是在依赖主食膳食且微量营养素多样性有限的人群中。生物强化提供了一种可持续的方式来提升作物营养品质，其方法包括农艺措施、传统育种、微生物干预、纳米技术、转基因技术和基因组编辑等。本综述批判性地探讨了这些策略，重点关注其在饲料和饲草作物中的应用，而这些作物在生物强化讨论中往往被忽视。文章考察了影响植物中微量营养素吸收、积累、运输和生物利用度的因素，以及这些营养素从生物强化饲料向动物、进而向牛奶、肉和蛋转移的过程。综述还讨论了营养强化饲料如何影响动物健康、生产性能以及动物产品的营养价值。重要的是，文章指出了在理解养分沿土壤—植物—饲料—动物—食品—人体路径转移效率方面存在的空白，尤其是饲草中微量营养素水平提高如何改善动物矿物质状况和人类营养。最后，综述指出了将作物生物强化与动物营养工作相结合的新兴机遇，包括精准育种、基因组编辑、微生物方法和靶向养分管理。这种综合方法可以将生物强化的益处扩展到人类食用作物之外，支持更好的饲料品质、提高动物生产性能、生产更健康的动物源功能性食品，以及构建更可持续、营养更具韧性的食物系统。",{"impact":31,"substance":29,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":701},"系统综述生物强化饲料作物向动物源功能食品的养分传递路径，提出精准育种与微生物干预等整合方向，对农业科技与营养型食物系统有参考价值。",[703],{"name":608,"url":697},[705,706,707,708,709],"功能食品","生物强化","动物营养","基因组编辑","饲料作物",{"code":711,"slug":712,"name":713,"path":714,"parent_code":715},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":718},{"candidates":719,"agricultural_evidence":59},[720,727,734,739,744],{"code":711,"score":33,"supported":59,"title":721,"abstract":722,"tags":726},[],[723,724,725],"饲料","饲草","forage",[],{"code":728,"score":313,"supported":59,"title":729,"abstract":730,"tags":733},"H2",[],[731,732],"土壤","soil",[],{"code":332,"score":75,"supported":59,"title":735,"abstract":736,"tags":738},[],[737],"养分管理",[],{"code":740,"score":339,"supported":26,"title":741,"abstract":742,"tags":743},"F2",[],[],[708,706],{"code":745,"score":34,"supported":26,"title":746,"abstract":747,"tags":748},"E2",[],[],[705],[750,751],"生物强化 饲料作物 功能食品","Frontiers 生物强化 动物营养","生物强化饲料作物功能食品-4483","10.3389\u002Ffsufs.2026.1963997",{"doi":753,"openalex_id":755,"authors":756,"venue":608,"cited_by_count":90,"oa_url":697,"card":769,"direction":691,"ingested_from":113},"W7221036499",[757,760,762,764,766],{"name":758,"orcid":759},"Shailja Dhiman","https:\u002F\u002Forcid.org\u002F0000-0001-7659-3891",{"name":761,"orcid":21},"Pooja Bhowmick",{"name":763,"orcid":21},"Pankaj Kumar",{"name":765,"orcid":21},"Pankti Patel",{"name":767,"orcid":768},"Dhruvansh Patni","https:\u002F\u002Forcid.org\u002F0009-0007-7321-5693",{"tldr":770,"method":771,"finding":772,"direction":691,"opportunity":773},"综述生物强化饲料作物策略，探讨其向动物源功能食品的营养传递与整合路径。","文献综述，聚焦农艺、育种、基因编辑、微生物及纳米技术。","土壤-植物-饲料-动物-食品-人路径的营养传递效率尚不明确，需整合研究。","可量化饲料生物强化向动物产品转移效率，结合精准育种与微生物方法优化全链条营养。","2026-10-09T23:30:06.312063Z",{"id":776,"title":777,"url":778,"summary":779,"summary_zh":780,"content":21,"source_name":608,"source_url":778,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":355,"score_detail":781,"sources":783,"tags":785,"primary_topic":788,"secondary_topics":793,"classification":794,"search_phrases":822,"slug":825,"view_count":339,"doi":826,"paper":827,"created_at":845},4481,"Climate policy uncertainty and agricultural digital-intelligent transformation in China: evidence from provincial panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1909691","Introduction Climate policy uncertainty can affect long-term investment decisions in agriculture, yet its influence on agricultural digital-intelligent transformation remains insufficiently understood. This study examines how lagged climate policy uncertainty affects agricultural digital-intelligent transformation and whether rural finance and agricultural service policies can moderate this relationship. Methods Using panel data from 31 Chinese provinces from 2013 to 2022, we construct an agricultural digital-intelligent transformation index. A two-way fixed effects model, instrumental variable estimation, heterogeneity analysis, and moderation tests are adopted for empirical analysis. Results The results show that climate policy uncertainty significantly inhibits agricultural digital-intelligent transformation, and this negative effect remains evident after addressing potential endogeneity. Heterogeneity analysis indicates that the inhibitory effect is significant only in regions with moderately developed digital finance and in major grain-consuming areas. Further analysis shows that loans to agriculture, forestry, animal husbandry and fishery industries and the pilot policy for full-process socialized agricultural services positively moderate this relationship and weaken the adverse effect of climate policy uncertainty. Discussion These findings contribute to the literature on climate policy uncertainty and sustainable agricultural transformation by linking policy expectations, agriculture-related loans, and digital-smart agriculture development. The results suggest that stabilizing climate policy expectations, improving rural credit support, and strengthening agricultural service systems are important for promoting climate-resilient and regionally differentiated digital transformation in agriculture.","引言 气候政策不确定性会影响农业的长期投资决策，但其对农业数字化智能化转型的影响尚缺乏充分认识。本研究考察滞后气候政策不确定性如何影响农业数字化智能化转型，以及农村金融和农业服务政策能否调节这一关系。方法 基于2013—2022年中国31个省份的面板数据，构建农业数字化智能化转型指数，采用双向固定效应模型、工具变量估计、异质性分析和调节效应检验进行实证分析。结果 结果表明，气候政策不确定性显著抑制农业数字化智能化转型，且在处理潜在内生性后这一负向效应依然明显。异质性分析表明，该抑制效应仅在数字金融中等发达地区及粮食主销区显著。进一步分析显示，农、林、牧、渔业贷款和农业生产全程社会化服务试点政策对这一关系具有正向调节作用，削弱了气候政策不确定性的不利影响。讨论 这些发现通过将政策预期、涉农贷款与数字智慧农业发展联系起来，为气候政策不确定性与农业可持续转型的文献作出了贡献。结果表明，稳定气候政策预期、完善农村信贷支持、健全农业服务体系，对于促进农业气候韧性型和区域差异化的数字化转型具有重要意义。",{"impact":31,"substance":29,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":782},"基于31省十年面板数据的实证研究，揭示气候政策不确定性对农业数字化智能化转型的抑制效应及农村金融、农业社会化服务的调节作用，方法规范、结论有政策参考价值，值得进入每日精选。",[784],{"name":608,"url":778},[129,361,786,290,787],"农村金融","气候政策",{"code":789,"slug":790,"name":791,"path":792,"parent_code":433},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":795},{"candidates":796,"agricultural_evidence":59},[797,802,807,812,818],{"code":789,"score":67,"supported":59,"title":798,"abstract":799,"tags":801},[],[786,800],"rural finance",[786],{"code":387,"score":67,"supported":59,"title":803,"abstract":804,"tags":806},[],[361,805],"smart agriculture",[361],{"code":338,"score":152,"supported":59,"title":808,"abstract":809,"tags":811},[],[810],"社会化服务",[290],{"code":813,"score":75,"supported":59,"title":814,"abstract":815,"tags":817},"D",[],[816],"渔业",[],{"code":157,"score":34,"supported":26,"title":819,"abstract":820,"tags":821},[],[],[129],[823,824],"气候政策不确定性 农业数字化 省级面板","农业数字化智能化转型 农村金融","气候政策不确定性农业数字化省级面板-4481","10.3389\u002Ffsufs.2026.1909691",{"doi":826,"openalex_id":828,"authors":829,"venue":608,"cited_by_count":90,"oa_url":778,"card":839,"direction":406,"ingested_from":113},"W7221013071",[830,833,835,837],{"name":831,"orcid":832},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":834,"orcid":21},"Yuanyi Yang",{"name":836,"orcid":21},"Yanru Yu",{"name":838,"orcid":21},"Yanwei Zhang",{"tldr":840,"method":841,"finding":842,"direction":843,"opportunity":844},"研究气候政策不确定性如何抑制中国农业数字化智能化转型，并检验农村金融与服务政策的调节作用。","2013-2022年31省面板数据，构建农业数字化智能化转型指数，用双向固定效应","气候政策不确定性显著抑制农业数字化智能化转型，该效应仅在数字金融中等发达地区和粮食主销区显著。","数字乡村与农业信息化","可深入探究气候政策预期稳定机制与数字金融、农业社会化服务的协同路径，及区域差异化转型策略。","2026-10-09T23:30:06.185798Z",{"id":847,"title":848,"url":849,"summary":850,"summary_zh":21,"content":851,"source_name":852,"source_url":21,"published_at":235,"category":195,"cover_url":21,"hotness":25,"is_selected":26,"score":853,"score_detail":854,"sources":856,"tags":858,"primary_topic":862,"secondary_topics":866,"classification":867,"search_phrases":888,"slug":891,"view_count":339,"doi":21,"paper":21,"created_at":892},4545,"上海市农科院智慧农业科研团队在国际主流期刊Computers and Electronics in Agriculture发表论文——多资源约束下猪场长期行为分析云边协同系统","https:\u002F\u002Fwww.saas.sh.cn\u002Fxx\u002Fxwzx\u002Fttxw\u002Fcontent_54686","上海市农业科学院农业科技信息研究所\u002F智慧农业研究中心科研团队在Computers and Electronics in Agriculture（中科院一区，影响因子8.9）发表题为\"Research on a Cloud-Edge Collaborative System for Long-Term Behavior Analysis in Intensive Pig Farms Under Multi-Resource Constraints\"的研究论文。团队针对边云协同框架下猪只行为分析系统多重资源约束下\"持续采集—稳定上传—及时推理\"难兼顾的难题，在国际上率先提出适度降低视频分辨率、帧率并采用队列上传的融合策略。评估显示，相对基线方案单位视频源CPU开销降至约1\u002F3、视频数据生成大小降至约1\u002F11.6、平均传输码率降至约1\u002F39.7；云端窗口吞吐量提升约9倍。第一作者郭孝坤。","近日，院农业科技信息研究所\u002F智慧农业研究中心科研团队在农业工程领域国际主流期刊 Computers and Electronics in Agriculture（中科院一区IF=8.9）上发表了题为“Research on a Cloud-Edge Collaborative System for Long-Term Behavior Analysis in Intensive Pig Farms Under Multi-Resource Constraints”的研究论文。\n\n团队在研究和部署应用边云协同框架下的猪只行为分析系统时发现，系统应用时受制于边缘端算力、上行带宽、存储与云端 GPU 吞吐等多重资源约束，使“持续采集-稳定上传-及时推理”难以兼顾。研究团队经过多轮讨论并通过实际验证，在国际上率先提出了在不影响识别精度的前提下采用适度降低视频分辨率和帧率、裁剪视频大小和基于队列上传的融合策略，极大地减少了数据冗余、降低了带宽压力，同时提升数据传输鲁棒性。\n\n系统实际部署应用与实验评估表明，相对基线方案，源端减量与两级ROI设计将单位视频源的 CPU开销降至约1\u002F3、视频数据生成大小降至约1\u002F11.6、平均传输码率降至约1\u002F39.7；同时，传输侧的队列式缓存管理与带宽感知上传在网络波动条件下保障了上传过程的稳定性并提升系统鲁棒性；云端窗口吞吐量实现了约9倍的处理能力提升。\n\n《Computers and Electronics in Agriculture》是农林科学领域的国际知名学术期刊，旨在汇集最先进和高质量的研究成果，促进了包括农学、农业计算机交叉科学等重要领域的研究和应用发展。该期刊发表有关人工智能在动植物农业生产中的应用进展，包括但不限于农业土壤、水、虫害、受控环境，以及植物和动物本身等，2025年影响因子为8.9。\n\n上海市农业科学院农业科技信息研究所郭孝坤为论文的第一作者，徐识溥和王运圣为共同通讯作者。该项研究得到上海市农业科学院AI赋能农业专项（沪农科AI-A（2025）009）、上海市科技兴农攻关项目（2025-02-08-00-12-F00037）和上海市农业科学院2026年度农业科技创新支撑领域研究专项（Grant No.2026 (07)）支持。","上海市农业科学院 2026 年 10 月 09 日 农业科技信息研究所",80,{"impact":31,"substance":29,"depth":31,"authority":32,"freshness":237,"relevant":34,"comment":855},"中科院一区论文，提出多资源约束下猪场行为分析云边协同系统，数据详实、方法新颖，具行业参考价值。",[857],{"name":852,"url":849},[361,130,859,860,861],"边缘计算","云边协同","生猪养殖",{"code":387,"slug":863,"name":864,"path":865,"parent_code":139},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":868},{"candidates":869,"agricultural_evidence":59},[870,874,879,883],{"code":387,"score":145,"supported":59,"title":871,"abstract":872,"tags":873},[361],[361],[361],{"code":875,"score":34,"supported":26,"title":876,"abstract":877,"tags":878},"C1",[],[],[861],{"code":135,"score":34,"supported":26,"title":880,"abstract":881,"tags":882},[],[],[130],{"code":884,"score":34,"supported":26,"title":885,"abstract":886,"tags":887},"I5",[],[],[859],[889,890],"上海农科院 猪场 云边协同","Computers and Electronics in Agriculture 生猪行为分析","上海农科院猪场云边协同-4545","2026-10-10T00:14:58.383720Z",{"id":894,"title":895,"url":896,"summary":897,"summary_zh":898,"content":21,"source_name":899,"source_url":896,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":853,"score_detail":900,"sources":902,"tags":904,"primary_topic":908,"secondary_topics":913,"classification":914,"search_phrases":933,"slug":936,"view_count":152,"doi":937,"paper":938,"created_at":952},4520,"Agricultural Industry Agglomeration and Carbon Emission Intensity: A Systems Perspective on Resilience and Digital Development","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsystems14101263","Decarbonizing agriculture depends not only on the efficiency of individual production activities but also on coordination among production, processing, and service activities. From a systems perspective, this study treats provincial agriculture as an interconnected industrial system and examines the relationship between agricultural industry agglomeration and agricultural carbon emission intensity. Moving beyond conventional measures of production concentration, agricultural industry agglomeration is measured as coordination between horizontal agglomeration in agricultural production and vertical agglomeration across production, processing, and agricultural services. Based on a balanced panel of 390 observations across 30 Chinese provinces from 2011 to 2023, the study uses province and year fixed effects, mediation and moderation analyses, robustness checks, and an endogeneity check. Results show that agricultural industry agglomeration is significantly associated with lower agricultural carbon emission intensity. The mediation results are consistent with a partial intermediary role for agricultural industry resilience in this relationship. The interaction and simple slope results show that the positive association between agricultural industry agglomeration and agricultural industry resilience is larger at higher levels of digital village development. These findings connect subsystem coordination, adaptive capacity, digital conditions, and carbon performance, thereby extending research on agricultural transformation toward lower carbon intensity from a systems perspective.","农业脱碳不仅取决于单个生产活动的效率，还取决于生产、加工和服务活动之间的协调。本研究从系统视角出发，将省级农业视为一个相互关联的产业系统，考察农业产业集聚与农业碳排放强度之间的关系。本研究超越传统的生产集中度衡量方式，将农业产业集聚界定为农业生产横向集聚与生产、加工及农业服务纵向集聚之间的协调。基于2011年至2023年中国30个省份390个观测值的平衡面板数据，本研究采用省份和年份固定效应、中介与调节分析、稳健性检验以及内生性检验。结果表明，农业产业集聚与较低的农业碳排放强度显著相关。中介结果表明，农业产业韧性在这一关系中发挥部分中介作用。交互效应和简单斜率结果表明，在数字乡村发展水平较高时，农业产业集聚与农业产业韧性之间的正向关联更强。这些发现将子系统协调、适应能力、数字条件与碳绩效联系起来，从而从系统视角拓展了农业向低碳强度转型的研究。","Systems",{"impact":31,"substance":29,"depth":31,"authority":124,"freshness":33,"relevant":34,"comment":901},"基于30省2011-2023年面板数据，从系统视角揭示农业产业集聚通过韧性降低碳排放强度，并受数字乡村发展调节，方法规范、结论有政策参考价值。",[903],{"name":899,"url":896},[129,361,905,906,907],"农业韧性","农业碳排放","农业产业集聚",{"code":909,"slug":910,"name":911,"path":912,"parent_code":624},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":915},{"candidates":916,"agricultural_evidence":59},[917,924,929],{"code":909,"score":31,"supported":59,"title":918,"abstract":920,"tags":923},[919],"carbon emission",[921,922,919],"碳排放","农业碳",[906],{"code":157,"score":67,"supported":59,"title":925,"abstract":926,"tags":928},[],[129,927],"digital village",[129],{"code":387,"score":34,"supported":26,"title":930,"abstract":931,"tags":932},[],[],[361],[934,935],"农业产业集聚 碳排放强度","数字乡村 农业韧性","农业产业集聚碳排放强度-4520","10.3390\u002Fsystems14101263",{"doi":937,"openalex_id":939,"authors":940,"venue":899,"cited_by_count":90,"oa_url":896,"card":947,"direction":843,"ingested_from":113},"W7220924322",[941,943,945],{"name":942,"orcid":21},"Jinghui Hu",{"name":944,"orcid":21},"Jun He",{"name":946,"orcid":21},"Junzhong He",{"tldr":948,"method":949,"finding":950,"direction":691,"opportunity":951},"从系统视角研究中国省级农业产业集聚对农业碳排放强度的影响及韧性与数字化的作用。","30省2011-2023年面板数据，固定效应、中介与调节分析。","农业产业集聚显著降低碳排放强度，产业韧性部分中介，数字乡村发展正向调节。","可探究产业集聚不同模式与数字化协同对碳减排的非线性及空间溢出效应。","2026-10-09T23:30:32.435467Z",{"id":954,"title":955,"url":956,"summary":957,"summary_zh":21,"content":21,"source_name":958,"source_url":21,"published_at":354,"category":195,"cover_url":21,"hotness":25,"is_selected":26,"score":959,"score_detail":960,"sources":963,"tags":965,"primary_topic":970,"secondary_topics":975,"classification":976,"search_phrases":992,"slug":995,"view_count":90,"doi":21,"paper":21,"created_at":996},4543,"云南省农科院参与组建榴莲科技攻关协作联合体——推动云南热区榴莲产业从实验示范向标准化、产业化发展","https:\u002F\u002Fwww.yn.news.cn\u002F20261008\u002Fdf1b23b5d1ba421d8e31b53e5da919c0\u002Fc.html","10月8日，由中国热带农业科学院和云南省农业科学院共同主办的云南省榴莲产业科技攻关协作会议在昆明召开，来自省内外20余家相关单位的100余名代表参加。会议聚焦品种筛选、适生栽培、种苗繁育等技术难题，筹划组建攻关协作组，打通科研到产业的转化链条。中国热带农业科学院、云南省农业科学院和云南省热带作物科学研究所分别签订\"云南省榴莲科技攻关合作协议\"和\"云南省榴莲专家工作站建站协议\"，多家榴莲种植企业与科研院所签订科企合作协议，并举行\"云南榴莲科技攻关协作联合体成员单位\"授牌仪式。云南榴莲科技攻关协作联合体未来3-5年系统攻关将构建省域榴莲联合创新体系，在种质资源、品种选育、种苗繁育、栽培技术、病虫害防控、采后加工、标准制定和人才培养等方面重点发力，推动云南榴莲产业从实验示范向产业化、标准化发展转变。","新华网云南频道 2026 年 10 月 08 日",79,{"impact":29,"substance":285,"depth":961,"authority":124,"freshness":237,"relevant":34,"comment":962},16,"省级科研机构牵头组建榴莲科技攻关联合体，聚焦品种选育与标准化，产业级实质进展，值得进入每日精选。",[964],{"name":958,"url":956},[202,966,967,968,969],"产学研合作","科技攻关","榴莲产业","热区农业",{"code":218,"slug":971,"name":972,"path":973,"parent_code":974},"seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":977},{"candidates":978,"agricultural_evidence":59},[979,984,988],{"code":218,"score":152,"supported":59,"title":980,"abstract":981,"tags":983},[],[982],"种质资源",[202],{"code":366,"score":34,"supported":26,"title":985,"abstract":986,"tags":987},[],[],[968],{"code":151,"score":34,"supported":26,"title":989,"abstract":990,"tags":991},[],[],[966],[993,994],"云南省农科院 榴莲 科技攻关","云南榴莲 联合体 标准化","云南省农科院榴莲科技攻关-4543","2026-10-10T00:14:58.242183Z"]