[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-09-27":3},{"date":4,"title":5,"highlights":6,"content":12,"items":13},"2026-09-27","农业农村日报：六部门完善乡村振兴投入机制，杂交水稻制种亩产首破千斤",[7,8,9,10,11],"农业农村部等六部门联合印发《坚持农业农村优先发展 完善乡村振兴投入机制实施方案》，明确一般公共预算优先投向农业农村，耕地地力保护补贴、农机购置与应用补贴、稻谷小麦最低收购价继续托底，并推广畜禽活体、农业设施抵押贷及完全成本保险。","湖南邵阳绥宁200亩'爽两优138'制种示范片实测平均亩产530.4公斤，首次突破千斤大关，创我国杂交水稻制种大面积高产新纪录，较一般制种田产量高约一倍。","农业农村部就'十五五'国家重点研发计划5个重点专项2026年度项目申报指南征求意见，覆盖主要粮食与经济作物单产提升、果蔬绿色生产、畜禽健康养殖、农产品加工储运、海上牧场与淡水渔业全链条。","中国农科院基因组所商连光团队在《分子植物》发表自进化AI科学家'绿洲'（OASIS），融合组学数据与文献知识助力作物耐盐碱研究，为植物抗逆研究提供新范式。","研究显示数字乡村建设对脱贫地区个体技能提升与未来信心具有显著正向作用，揭示数字赋能的扶智扶志效应及协同机制。","本期聚焦乡村振兴投入机制完善与'十五五'重点专项申报两大政策动向，报道杂交水稻制种亩产首破千斤的产业突破，并收录AI科学家、数字乡村、智能播种装备、农业物联网安全、干旱脆弱性及作物模型数据同化等前沿研究，覆盖政策、产业与科技全链条。\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。*",[14,49,79,103,147,177,226,276,317,369,395,437,476,523,559],{"id":15,"title":16,"url":17,"summary":18,"summary_zh":19,"content":20,"source_name":21,"source_url":19,"published_at":22,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":26,"score_detail":27,"sources":35,"tags":37,"search_phrases":43,"slug":46,"view_count":47,"doi":19,"paper":19,"created_at":48},3596,"中国农科院基因组所商连光团队开发自进化AI科学家'绿洲'助力作物耐盐碱研究——Molecular Plant在线发表","https:\u002F\u002Fagis.caas.cn\u002Fxwzx\u002Fkyjz\u002Fb0960d967c964346b50cb50c4886c209.htm","2026年9月18日，中国农科院基因组所（大鹏湾实验室）商连光团队联合崖州湾国家实验室、盐碱地综合利用国家技术创新中心，在《分子植物（Molecular Plant）》上在线发表题为'OASIS, a self-evolving AI scientist that integrates omics data and literature knowledge for plant stress research'的研究论文。该研究开发了面向植物逆境研究的自进化AI科学家'绿洲'（OASIS）。系统将大语言模型、多智能体协作、植物逆境文献知识库以及组学和遗传数据纳入同一科研流程。'绿洲'将复杂科研任务拆解为多个子任务，由6个AI智能体分工协作完成。研究团队进一步以水稻耐盐为案例，对'绿洲'提出的候选基因开展实验验证，形成了从科学问题到候选假说再到实验验证的完整研究路径。",null,"2026年9月18日，中国农科院基因组所（大鹏湾实验室）商连光团队联合崖州湾国家实验室、盐碱地综合利用国家技术创新中心，在《分子植物（Molecular Plant）》上在线发表题为“OASIS, a self-evolving AI scientist that integrates omics data and literature knowledge for plant stress research”的研究论文。\n\n![Image 1](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002Fefa2de3e737741ea8e2ae4ceeed0695f.png)\n\n该研究开发了面向植物逆境研究的自进化AI科学家“绿洲”（OASIS）。系统将大语言模型、多智能体协作、植物逆境文献知识库以及组学和遗传数据纳入同一科研流程，可围绕一个科学问题自主完成任务规划、文献检索、数据查询、文献与组学数据融合推理和结果审查，并能够从历史任务中的失败与纠错中学习可复用经验。研究团队进一步以水稻耐盐为案例，对“绿洲”提出的候选基因开展实验验证，形成了从科学问题到候选假说再到实验验证的完整研究路径。\n\n植物抗逆研究往往不是“缺少信息”，而是证据分散在不同载体中：机制研究主要沉淀在论文里，基因表达、遗传定位、群体变异和种质资源则分布在多个数据库中。研究人员通常需要在大量文献和多类数据之间反复检索、比对和判断，才能把一个科学问题逐步收敛为可供实验验证的假说。通用大语言模型擅长理解和生成文本，但仅依靠模型自身知识，仍难以稳定完成跨数据库、跨数据类型的连续科研分析。\n\n**从“读文献”到“查数据”，让AI进入真实科研工作流**\n\n针对这一问题，“绿洲”将复杂科研任务拆解为多个子任务，由6个AI智能体分工协作完成。系统先识别研究意图并制定任务计划，再根据需要调用文献检索、数据分析和科研数据库等工具，最终由审查与综合模块检查证据覆盖范围和任务完成情况后生成结论。任务规划、文献片段、数据库查询结果和分析记录都会保存在共享工作空间中，便于研究人员回溯和核对。整个过程类似于一个小型科研团队围绕同一科学问题持续协作。\n\n![Image 2](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002Ff4ecb718f8d942b89d22b99880d0dbe3.png)\n\n图1 | “绿洲”多智能体协作科研框架。系统围绕科学问题进行任务拆解，并协调文献检索、数据分析、结果审查和综合生成，使不同来源的科研证据能够在同一工作流中被连续调用和融合推理。\n\n支撑这一流程的是专门整理的植物逆境知识和数据资源。“绿洲”目前覆盖拟南芥、水稻、玉米、小麦、大豆和陆地棉6个物种，以及盐、干旱、高温和低温4类主要非生物胁迫。文献知识库收录超过15万篇相关论文摘要和1.5万篇开放获取全文，并持续同步新发表研究；同时接入植物逆境表达数据、GWAS\u002FQTL、群体变异、eQTL和种质资源等结构化信息，使系统不仅能够回答“文献中有哪些报道”，还能够进一步判断“数据是否支持这一推断”。\n\n**OASIS的优势，体现在数据驱动的研究任务中**\n\n为评估系统在真实科研任务中的表现，研究团队构建了面向植物逆境研究的基准测试集PlantStressQA，包含200个贴近真实科研过程的问题，覆盖机制证据、基因ID映射、表达分析、遗传位点分析、品种筛选和群体变异六类任务。\n\n在与多种通用大语言模型的比较中，“绿洲”总体得分为84.0分，参评通用模型得分为33.8至50.1分。在需要实际调用并整合结构化数据的任务中，“绿洲”优势明显，其中群体变异和遗传位点分析两类任务分别比表现最好的通用模型高55.6分和52.3分，在基因ID映射和表达证据分析中也表现出明显优势。\n\n![Image 3](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002Fc2b2782477a64a55a3366b44219320d5.png)\n\n图2 | “绿洲”在PlantStressQA上的系统评价。系统在基因ID转换、表达数据查询、遗传位点分析和群体变异分析等数据密集型任务中表现出更明显的优势。\n\n这些结果说明，“绿洲”的核心并不是单纯依赖语言模型“记住更多知识”，而是通过组织科研工具和数据资源，在研究过程中主动查找数据、完成格式转换、比较不同来源的证据，并将这些步骤串联为可追溯的分析过程。\n\n**做错一次之后，下一次还会不会犯同样的错误？**\n\n真实科研分析中，数据库字段不匹配、基因ID格式错误、工具调用顺序不合理等问题并不少见。如果系统每次遇到相似任务都从头试错，不仅效率低，也会造成额外的计算资源消耗。为此，“绿洲”设计了自进化经验学习模块（SEEL），从历史任务中识别“先失败、随后被成功纠正”的执行轨迹，对比错误路径和正确路径，提炼可复用的操作经验，并通过重新执行真实任务检验其有效性。只有验证通过的经验才会进入技能库，供后续相似任务调用。\n\n![Image 4](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002F72cd8d054dc14f94b7b9461f4a7f8c46.png)\n\n图3 | SEEL自进化经验学习流程及效果。系统从历史任务的失败与纠错过程中提炼可复用经验，经验证后纳入技能库。\n\n研究团队从PlantStressQA的历史轨迹中筛选出86个包含可复用经验的任务。应用这些经验后，工具调用失败、数据库错误、重复操作和模型处理文本量均有所减少。在一个表达分析任务中，大模型调用次数从75次降至42次，模型处理文本量从112万降至31万，运行时间从476秒缩短到236秒，数据库错误和失败工具调用则从多次降为0次。随着有效经验持续积累，“绿洲”能够在后续相似任务中减少重复试错并优化执行过程。\n\n**最关键的一步：AI提出的候选基因，实验能不能验证？**\n\n基准测试可以衡量AI系统是否“会做题”，但科研价值最终还要回到能否帮助提出值得验证的科学假说。研究团队以水稻耐盐为案例，在未预设具体候选基因的情况下，通过多轮提问引导“绿洲”开展分析。系统先从已有研究中归纳离子稳态与Na⁺\u002FK⁺运输、ABA信号与渗透调节、ROS稳态、MAPK级联和盐感知\u002FCa²⁺信号等主要耐盐调控模块，并整理出284个有文献证据支持的拟南芥调控基因；随后开展跨物种同源映射和功能筛选，再整合水稻耐盐GWAS\u002FQTL等遗传证据，最终逐步确定5个优先候选基因。\n\n![Image 5](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002Fa9ba4a0ff48e43bc8239834881fb034c.png)\n\n图4 | “绿洲”辅助筛选水稻耐盐候选基因的多轮对话示例。系统整合耐盐机制文献、水稻同源基因信息和群体遗传证据，逐步筛选出5个优先候选基因。\n\n研究团队根据“绿洲”给出的证据链，选择此前未被直接报道参与水稻耐盐调控的OsPP2a（LOC_Os12g04290）开展实验验证。该基因的拟南芥同源基因TAP46与ABA信号相关，OsPP2a本身位于水稻耐盐meta-QTL区间内，并与一个eQTL信号共定位。两个独立的Ospp2a突变材料在150 mM NaCl处理后均表现出更明显的盐敏感表型，恢复存活率降低，同时Na⁺积累增加、Na⁺\u002FK⁺平衡发生改变。RT-qPCR结果还显示，离子稳态相关基因OsSOS1和OsHKT1;5的表达发生变化，为OsPP2a参与水稻盐胁迫响应提供了遗传和分子层面的证据。\n\n![Image 6](https:\u002F\u002Fagis.caas.cn\u002Fimages\u002F2026-09\u002Fe698f656e5c8488fb80baadceaab1bf0.png)\n\n图5 | “绿洲”优先推荐基因OsPP2a的耐盐功能验证。两个独立突变材料在盐胁迫下表现出更低的恢复存活率和更高的Na⁺\u002FK⁺比值，支持OsPP2a参与水稻耐盐响应。\n\n在这一案例中，研究人员通过多轮提问引导“绿洲”完成机制梳理、跨物种证据迁移、遗传数据查询和候选基因优先级排序，再由实验进一步检验候选假说，形成了“科学问题→证据融合→候选假说→实验验证”相衔接的研究路径。\n\n**从“AI回答科学”到“AI参与科学”**\n\n“绿洲”的目标并不是用AI替代科研人员作出判断，而是把过去需要在多篇文献、多个数据库和不同分析工具之间往返完成的工作，组织成一条可追溯、可复核的科研流程。通过多智能体协作、文献与组学数据融合推理以及自进化经验学习，系统能够帮助研究人员更高效地汇集证据、缩小候选范围并提出更有依据的科学假说。\n\n这项工作为人工智能参与植物抗逆研究提供了一个从“回答问题”走向“参与分析”的实践框架。未来，随着文献、组学数据和科研工具持续扩展，“绿洲”有望进一步服务于作物抗逆机制解析、关键基因挖掘和后续实验研究，为耐盐碱等作物抗逆改良提供智能化研究工具。\n\n基因组所（大鹏湾实验室）商连光研究员和崖州湾国家实验室钱前院士为论文共同通讯作者，基因组所（大鹏湾实验室）博士后韩漾和副研究员魏华为论文共同第一作者。该研究得到国家重点研发计划和国家自然科学基金的资助。\n\n平台入口：[https:\u002F\u002Fwww.oasis.ac.cn](https:\u002F\u002Fwww.oasis.ac.cn\u002F)\n\n原文链接：[https:\u002F\u002Fwww.cell.com\u002Fmolecular-plant\u002Fabstract\u002FS1674-2052(26)00305-9](https:\u002F\u002Fwww.cell.com\u002Fmolecular-plant\u002Fabstract\u002FS1674-2052(26)00305-9)","中国农科院基因组研究所 2026-09-18","2026-09-18T00:00:00Z","报道",10,true,90,{"impact":28,"substance":29,"depth":30,"authority":31,"freshness":32,"relevant":33,"comment":34},26,24,19,15,6,1,"国家级科研机构在《分子植物》发表的自进化AI科学家系统，实现文献与组学数据融合推理并经水稻耐盐基因实验验证，属农业人工智能与智慧育种领域的标志性突破。",[36],{"name":21,"url":17},[38,39,40,41,42],"农业人工智能","水稻","多智能体","耐盐碱","智慧育种",[44,45],"中国农科院基因组所 绿洲 OASIS","商连光 水稻耐盐 OsPP2a","中国农科院基因组所绿洲OASIS-3596",0,"2026-09-27T00:05:16.998310Z",{"id":50,"title":51,"url":52,"summary":53,"summary_zh":19,"content":54,"source_name":55,"source_url":19,"published_at":56,"category":57,"cover_url":19,"hotness":24,"is_selected":25,"score":58,"score_detail":59,"sources":65,"tags":67,"search_phrases":74,"slug":77,"view_count":47,"doi":19,"paper":19,"created_at":78},3572,"农业农村部等六部门联合印发《坚持农业农村优先发展 完善乡村振兴投入机制实施方案》——财政优先保'三农'，耕地地力保护补贴、农机购置与应用补贴、稻谷小麦最低收购价继续发力","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689083769965412864\u002F","农业农村部、中央农办、国家发展改革委、财政部、中国人民银行、金融监管总局六部门联合印发《坚持农业农村优先发展 完善乡村振兴投入机制实施方案》，明确一般公共预算优先投农业农村，土地出让收入用于农业农村的政策继续落实；耕地地力保护补贴、农机购置与应用补贴（有机优补、有进有出）、稻谷小麦最低收购价继续托底；对脱贫人口落实帮扶小额信贷，推广畜禽活体、农业设施抵押贷；用好稻谷、小麦、玉米、大豆完全成本保险和种植收入保险；村集体可资源发包、物业出租、参股经营，符合条件的涉农项目可发基础设施REITs。","农业农村部等六部门印发**《坚持农业农村优先发展 完善乡村振兴投入机制实施方案》**，核心就一句：往农村投的钱要更多、更准、更见效。这跟咱农民、种粮大户到底有啥关系？阿业一条条给你捋明白。\n\n1.   **谁发的**。农业农村部、中央农办、国家发展改革委、财政部、中国人民银行、金融监管总局**六部门**联合，落款**2026年8月18日**（2026年9月7日发布），**全国通用**的正式文件。\n2.   **财政优先保\"三农\"**。一般公共预算优先投农业农村；土地出让收入用于农业农村的政策继续落实；加大对粮食主产区的利益补偿，完善产粮大县奖励政策——种粮的地方和种粮的人，都更有奔头。\n3.   **几项\"老补贴\"接着发、发得更准**。方案点名要完善**耕地地力保护补贴**、实施好**农机购置与应用补贴**（有机优补、有进有出）；稻谷、小麦最低收购价继续托底，让粮价有底线。\n4.   **贷款更好贷了**。对脱贫人口发展生产，落实**帮扶小额信贷**；推广畜禽活体、农业设施抵押贷；用\"信贷直通车\"、首贷、信用贷，让新型农业经营主体借钱不再难。\n5.   **保险帮你兜风险**。稻谷、小麦、玉米、大豆的**完全成本保险和种植收入保险**要用好用足；地方特色农产品保险继续支持；大灾理赔也更实在。\n6.   **农村\"沉睡资产\"能盘活**。村集体可以资源发包、物业出租、参股经营；农村地区还能合规搞光伏、风电、水电、生物质能等能源项目；符合条件的涉农项目可以发基础设施REITs。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F58b8c211ca1c40fb97d1312845a294a3~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791072341&x-signature=k3%2BaCdW5dFTZ3x6KXQjRzrXnvgE%3D)\n\n**避坑提醒**\n\n1.   这是\"投入机制\"的顶层设计，不是直接给个人发钱——补贴还是按原有官方渠道发，别误以为是新红包。\n2.   警惕\"乡村振兴内部名额\"\"扶贫基金认购\"类诈骗，所有补贴、贷款只走官方渠道，不交任何\"手续费\"。\n3.   具体补贴标准、信贷产品以当地最新通知和金融机构为准。\n\n这是**全国通用**的正式文件，已公开发布、非征求意见稿。这份方案落款2026年8月18日、2026年9月7日公开发布，本文是截至2026年9月24日对该文件的通俗梳理，并非新发布政策；具体补贴、信贷、保险以当地最新官方通知为准。觉得有用就收藏转发，关注本号，后续落地细则出来我再给你讲大白话。\n\n信息来源：[农业农村部 中央农办 国家发展改革委 财政部 中国人民银行 金融监管总局关于印发《坚持农业农村优先发展 完善乡村振兴投入机制实施方案》的通知](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002FCWS\u002F202609\u002Ft20260907_6487329.htm)\n\n```\n【适用范围：全国通用】\n【政策状态：正式落地文件（已公开发布、非征求意见稿、非未来才生效）】\n【免责声明：本文仅为官方政策通俗科普，仅供参考】\n```\n\n[#农业#](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689083769965412864\u002F)[#乡村#](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689083769965412864\u002F)[#民生关注#](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689083769965412864\u002F)","微观三农（今日头条转载）2026-09-25","2026-09-25T00:00:00Z","政策",88,{"impact":60,"substance":61,"depth":62,"authority":63,"freshness":32,"relevant":33,"comment":64},28,22,18,14,"六部门联合印发的全国性乡村振兴投入机制文件，条款具体、信源权威，虽非当日发布但仍在时效窗口内，值得进入每日精选。",[66],{"name":55,"url":52},[68,69,70,71,72,73],"乡村振兴","农业保险","财政支农","农村金融","最低收购价","农业补贴",[75,76],"六部门 乡村振兴投入机制 实施方案","耕地地力保护补贴 农机购置补贴","六部门乡村振兴投入机制实施方案-3572","2026-09-27T00:05:13.815434Z",{"id":80,"title":81,"url":82,"summary":83,"summary_zh":19,"content":84,"source_name":85,"source_url":19,"published_at":56,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":86,"score_detail":87,"sources":90,"tags":92,"search_phrases":98,"slug":101,"view_count":47,"doi":19,"paper":19,"created_at":102},3582,"530.4公斤！我国杂交水稻制种亩产首破千斤大关——湖南邵阳绥宁'爽两优138'200亩示范片实测","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689431215912256042\u002F","9月7日湖南省邵阳市绥宁县武阳镇200亩超级稻'爽两优138'制种示范片现场测产结果显示，平均制种亩产达530.4公斤，首次突破1000斤，创造了我国杂交水稻制种大面积高产新纪录。湖南省农学会联合中国水稻研究所、武汉大学等多所高校和单位知名专家学者组成的测产团队随机抽取3块田，采用收割机实割测产，比一般的杂交水稻制种田产量高出一倍左右。福建农科院研究员、中国科学院院士谢华安表示，对杂交水稻的制种更加有信心。","2026-09-25 19:20·[红网邵阳站](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAjHavG9gatHn0y7kbbD-mJ6_I5zjCcPhWT9h8NWwzEbY\u002F?source=tuwen_detail)\n\n视频加载中...\n\n**红网时刻新闻9月7日邵阳讯**（记者 谭卫丰 袁树勋 李俏 通讯员 向海 宋三龙 陶雯 唐远程）9月7日，湖南省邵阳市绥宁县武阳镇200亩超级稻“爽两优138”制种示范片现场测产结果显示，平均制种亩产达530.4公斤，首次突破1000斤，创造了我国杂交水稻制种大面积高产新纪录。\n\n7日上午，湖南省农学会联合中国水稻研究所、武汉大学等多所高校和单位知名专家学者组成的测产团队在200亩连片示范田中随机抽取3块田，采用收割机实割测产。刚过白露，武阳镇稻浪金黄，沉甸甸的稻穗压弯了稻秆，收割机在田间往来穿梭。经严格脱粒、去杂、称重、折算标准含水量后，3块田平均亩产定格在530.4公斤，比一般的杂交水稻制种田产量高出一倍左右。\n\n“从今天这个现场看，我们的品种的异交率很高，而且穗子又比较大，结实率也高，总体上都是非常平衡的，确实是一片丰收景象。”中国科学院院士、福建省农业科学院研究员谢华安表示，“看了今天两系稻爽两优138的制种现场，让人感到心情确实很爽。这让我们大家对杂交水稻的制种更加有信心。”\n\n长期以来，杂交水稻制种产量低、种子成本偏高，是制约产业发展的突出瓶颈。湖南杂交水稻研究中心以创制适于超高产制种的种源为突破口，集成创新杂交水稻大面积超高产制种栽培技术体系，历经20余年系统攻关，取得重大阶段性成果。\n\n国家杂交水稻工程技术研究中心主任邓华凤表示，今天恰逢袁隆平院士96周年诞辰，这次的创新纪录是对恩师最好的怀念和最好的告慰。下一步将全力推进实现全制种的全程机械化，继续把产量提得更高，把成本降下去。让更多的技术走出去，让更多的企业走出去，让袁隆平院士杂交水稻覆盖全球梦早日实现。","红网邵阳站（今日头条转载）2026-09-25",87,{"impact":60,"substance":29,"depth":62,"authority":24,"freshness":88,"relevant":33,"comment":89},7,"我国杂交水稻制种亩产首破千斤的全国性重大突破，数据翔实、院士与国家级机构背书，产业价值突出，值得进入每日精选。",[91],{"name":85,"url":82},[93,94,95,96,97],"种业振兴","水稻育种","杂交水稻","农业科技","制种高产",[99,100],"爽两优138 绥宁 制种","湖南杂交水稻研究中心 制种亩产","爽两优138绥宁制种-3582","2026-09-27T00:05:15.724182Z",{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":19,"source_name":109,"source_url":106,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":111,"score_detail":112,"sources":116,"tags":118,"search_phrases":124,"slug":127,"view_count":47,"doi":128,"paper":129,"created_at":146},3535,"Policy coordination of public data openness and supercomputing center for agricultural chain resilience","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1882123","Strengthening agricultural supply chain resilience is critical for safeguarding food security and promoting high-quality agricultural development in the face of escalating external shocks, including climate extremes, market volatility, and geopolitical uncertainties. Although public data openness and supercomputing infrastructure represent critical pillars of digital transformation, how their coordinated deployment enhances agricultural resilience remains both theoretically underdeveloped and empirically underexamined. Using provincial panel data from 31 Chinese provinces over 2011–2024, we exploit the staggered rollout of public data platforms and supercomputing centers as quasi-natural experiments, constructing a synergistic difference-in-differences (DID) model to isolate their coordinated policy effects. The causal identification is further fortified using event-study parallel trend tests, placebo simulations, propensity score matching (PSM-DID), and double\u002Fdebiased machine learning (DDML). Our baseline estimates demonstrate that policy synergy between public data openness and supercomputing infrastructure substantially enhances agricultural supply chain resilience, with effects remaining robust across multiple specifications and economically significant. Mechanistic analysis reveals two primary transmission channels through which policy synergy strengthens resilience: (i) industrial structure rationalization, and (ii) agricultural electrification enhancement. Specifically, policy coordination (1) reduces informational barriers between agricultural and related industries, (2) facilitates optimal factor reallocation across sectors, and (3) accelerates the adoption of electricity-intensive precision agriculture technologies. Heterogeneity analysis reveals that policy effectiveness is substantially greater in central and western regions (coefficient ~0.089) compared to eastern regions (coefficient ~0.036), suggesting a ‘technology compensation effect’ whereby digital infrastructure offsets traditional regional disparities. Policy effects remain robustly positive across both major grain-producing and grain-consuming zones, indicating broad applicability. By unpacking the complementary and sequential dynamics between data factors and computing capacity, this study moves beyond the traditional “black box” treatment of the digital economy, providing critical policy insights for the coordinated deployment of digital public goods to secure modern food systems.","在气候极端事件、市场波动和地缘政治不确定性等外部冲击日益加剧的背景下，增强农业供应链韧性对于保障粮食安全和推动农业高质量发展至关重要。尽管公共数据开放和超算基础设施是数字化转型的关键支柱，但二者协同部署如何增强农业韧性，在理论上尚不成熟，在实证上也缺乏充分检验。本文利用2011—2024年中国31个省份的面板数据，将公共数据平台和超算中心的交错 rollout 作为准自然实验，构建协同双重差分（DID）模型以识别其协调政策效应。因果识别进一步通过事件研究平行趋势检验、安慰剂模拟、倾向得分匹配（PSM-DID）以及双重\u002F去偏机器学习（DDML）加以强化。基准估计表明，公共数据开放与超算基础设施之间的政策协同显著增强了农业供应链韧性，该效应在多种设定下保持稳健且具有经济显著性。机制分析揭示了政策协同增强韧性的两条主要传导渠道：（i）产业结构合理化，以及（ii）农业电气化水平提升。具体而言，政策协调（1）降低了农业与相关产业之间的信息壁垒，（2）促进了要素在部门间的优化再配置，（3）加速了电力密集型精准农业技术的采用。异质性分析表明，政策效果在中西部地区（系数约0.089）显著大于东部地区（系数约0.036），暗示存在“技术补偿效应”，即数字基础设施抵消了传统的区域差距。政策效应在粮食主产区和主销区均保持稳健为正，表明其具有广泛适用性。通过揭示数据要素与算力之间的互补性和序贯动态，本研究超越了数字经济传统的“黑箱”处理方式，为协调部署数字公共产品以保障现代粮食体系提供了关键政策启示。","Frontiers in Sustainable Food Systems","论文",85,{"impact":61,"substance":113,"depth":62,"authority":63,"freshness":114,"relevant":33,"comment":115},23,8,"基于31省面板数据的准自然实验，证实公共数据开放与超算中心协同显著提升农业供应链韧性，方法严谨、结论有政策价值，值得进入每日精选。",[117],{"name":109,"url":106},[119,120,121,122,123],"数字乡村","农业供应链","农业数字化","公共数据开放","超算中心",[125,126],"公共数据开放 超算中心 农业供应链韧性","农业供应链韧性 准自然实验","公共数据开放超算中心农业供应链韧性-3535","10.3389\u002Ffsufs.2026.1882123",{"doi":128,"openalex_id":130,"authors":131,"venue":109,"cited_by_count":47,"oa_url":106,"card":139,"direction":143,"ingested_from":145},"W7214402306",[132,134,136],{"name":133,"orcid":19},"Zhaoqun Chen",{"name":135,"orcid":19},"Jiewen Zheng",{"name":137,"orcid":138},"Cheng Chi","https:\u002F\u002Forcid.org\u002F0000-0002-3823-6735",{"tldr":140,"method":141,"finding":142,"direction":143,"opportunity":144},"基于中国省级面板数据，用交错DID识别公共数据开放与超算中心协同对农业供应链韧性的因果效应。","2011–2024年31省面板数据，交错DID、PSM-DID、DDML与事件研","政策协同显著提升农业供应链韧性，经产业结构合理化与农业电气化传导，中西部效应更强。","数字乡村与农业信息化","可探究数据开放与算力协同的时序互补机制，并下沉到县域或产业链微观主体验证技术补偿效应。","openalex","2026-09-26T23:30:07.920756Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":19,"content":19,"source_name":152,"source_url":19,"published_at":153,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":154,"score_detail":155,"sources":157,"tags":159,"search_phrases":164,"slug":167,"view_count":47,"doi":19,"paper":168,"created_at":176},3599,"数字赋能何以激发内生动力？——数字乡村建设的扶智扶志效应及其协同机制研究","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689001773884834319","周迪、李书曼、王雪芹在《数量经济技术经济研究》2026年第9期发表。文章基于2018年与2020年县域数字乡村指数与中国家庭追踪调查（CFPS）的匹配数据，构建包含数字乡村建设、人力资本积累与个体努力程度的世代交叠理论模型，运用数值模拟呈现不同数字化水平下个体技能与信心的动态演化路径，并采用双重机器学习模型进行因果推断。研究发现：数字乡村建设整体上对脱贫地区个体的技能提升与未来信心均具有显著正向作用；扶智效应主要通过拓宽信息渠道与促进非农就业实现，扶志效应借助改善职业环境与提升收入达成；对低技能、原深度连片贫困县和山区县的群体产生更为明显的扶志扶智效应。","三农直通车（今日头条）2026-09-24","2026-09-24T00:00:00Z",84,{"impact":61,"substance":113,"depth":30,"authority":63,"freshness":32,"relevant":33,"comment":156},"核心期刊论文，基于CFPS与县域数字乡村指数匹配数据，方法新颖、结论可靠，对数字乡村激发内生动力具有政策参考价值。",[158],{"name":152,"url":150},[119,160,161,162,163],"双重机器学习","非农就业","CFPS","扶智扶志",[165,166],"数字乡村 扶智扶志 CFPS","县域数字乡村指数 双重机器学习","数字乡村扶智扶志CFPS-3599",{"doi":19,"openalex_id":19,"authors":169,"venue":19,"cited_by_count":47,"oa_url":19,"card":170,"direction":143,"ingested_from":175},[],{"tldr":171,"method":172,"finding":173,"direction":143,"opportunity":174},"基于县域数字乡村指数与CFPS数据，实证检验数字乡村建设对脱贫地区个体的扶智扶志效应及协同机制。","世代交叠模型数值模拟与双重机器学习因果推断，匹配2018、2020年县域数字乡村","数字乡村建设显著提升脱贫地区个体技能与未来信心，扶智靠信息渠道与非农就业，扶志靠职业环境与收入改善。","可探究数字乡村扶智扶志效应的长期动态演化及不同数字化水平的门槛与空间溢出效应。","agent","2026-09-27T00:05:18.097742Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":19,"source_name":183,"source_url":180,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":184,"score_detail":185,"sources":189,"tags":191,"search_phrases":196,"slug":199,"view_count":47,"doi":200,"paper":201,"created_at":225},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy",82,{"impact":61,"substance":186,"depth":62,"authority":187,"freshness":114,"relevant":33,"comment":188},21,13,"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[190],{"name":183,"url":180},[192,38,193,194,195],"智慧农业","智能农机","精准农业","智能播种",[197,198],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":200,"openalex_id":202,"authors":203,"venue":183,"cited_by_count":47,"oa_url":180,"card":218,"direction":224,"ingested_from":145},"W7214297818",[204,206,208,210,212,215],{"name":205,"orcid":19},"Yuting Dong",{"name":207,"orcid":19},"Yapeng Wu",{"name":209,"orcid":19},"Shiguo Wang",{"name":211,"orcid":19},"Xiaohu Guo",{"name":213,"orcid":214},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":216,"orcid":217},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":219,"method":220,"finding":221,"direction":222,"opportunity":223},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","智慧农业 \u002F 农业物联网","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","农业人工智能与决策模型","2026-09-26T23:30:47.821171Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":19,"source_name":232,"source_url":229,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":184,"score_detail":233,"sources":236,"tags":238,"search_phrases":242,"slug":245,"view_count":47,"doi":246,"paper":247,"created_at":275},3548,"Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fa19100827","Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence\u002Fabsence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.","农业物联网（IoT）生态系统日益将传感器、无人机、边缘设备和云平台连接起来，以支持精准农业，然而网络安全、隐私以及所提出保障措施的现实适用性在文献中仍呈现碎片化状态。本研究系统综述了2015年至2025年间发表的农业物联网研究中的网络安全威胁、隐私问题、人工智能驱动及传统保障措施以及证据缺口。遵循Kitchenham和Charters方法论，从五个数据库检索到的2535条记录中筛选出103项研究。STRIDE和LINDDUN被回溯性应用为威胁与隐私分类的互补框架。由于编码方案为多标签，可靠性在类别层面通过20项研究样本的存在\u002F缺失判定进行评估，STRIDE的观察一致率为75%至95%，LINDDUN为95%至100%，可解释的Cohen's κ值分别为0.348至0.794和0.875至1.000。所有纳入研究还接受了质量评价和补充性生态效度评估。拒绝服务、篡改和欺骗是报告最频繁的威胁，集中在设备层、网络层和云层，而边缘层仍未被充分考察。基于人工智能和机器学习的入侵检测以及联邦学习等隐私保护方法成为突出的保障措施，但农业人工智能模型的对抗性操纵受到的关注有限。隐私研究仍以保密性为导向，90.3%的研究未引用任何适用的监管框架。最重要的是，103项研究中仅有8项（7.8%）获得高生态效度评级，表明证据基础鲜有扎根于真实农业田间条件。本综述将田间实证评估、对抗鲁棒人工智能、隐私治理和网络韧性确定为未来农业物联网安全研究的优先事项。","Algorithms",{"impact":62,"substance":113,"depth":30,"authority":187,"freshness":234,"relevant":33,"comment":235},9,"系统综述103项研究，揭示农业物联网安全证据多脱离田间实际，对智慧农业安全研究有较高参考价值。",[237],{"name":232,"url":229},[192,38,239,240,241],"农业物联网","隐私保护","数据安全",[243,244],"农业物联网 网络安全","农业AI 隐私保护 联邦学习","农业物联网网络安全-3548","10.3390\u002Fa19100827",{"doi":246,"openalex_id":248,"authors":249,"venue":232,"cited_by_count":47,"oa_url":229,"card":270,"direction":222,"ingested_from":145},"W7214403401",[250,253,256,259,262,265,267],{"name":251,"orcid":252},"Emmanuel Kojo Gyamfi","https:\u002F\u002Forcid.org\u002F0009-0002-0441-2830",{"name":254,"orcid":255},"Jess Kropczynski","https:\u002F\u002Forcid.org\u002F0000-0002-7458-6003",{"name":257,"orcid":258},"Jacques Bou Abdo","https:\u002F\u002Forcid.org\u002F0000-0002-3482-9154",{"name":260,"orcid":261},"Joseph Samuel Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2555-8142",{"name":263,"orcid":264},"Mustapha Awinsongya Yakubu","https:\u002F\u002Forcid.org\u002F0009-0005-6623-0858",{"name":266,"orcid":19},"Anthony Tsetse",{"name":268,"orcid":269},"Gertrude Kaneah Abagale","https:\u002F\u002Forcid.org\u002F0009-0009-1502-3188",{"tldr":271,"method":272,"finding":273,"direction":222,"opportunity":274},"系统综述2015-2025年农业物联网的网络安全、隐私威胁与防护措施及证据缺口。","Kitchenham系统综述法，筛选103项研究，用STRIDE与LINDDUN","拒绝服务、篡改、欺骗威胁最多，边缘层研究不足，仅7.8%研究具高生态效度。","农业AI模型的对抗鲁棒性、边缘层安全、隐私治理与真实田间条件下的韧性评估是明显空白。","2026-09-26T23:30:15.415998Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":19,"source_name":282,"source_url":279,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":184,"score_detail":283,"sources":285,"tags":287,"search_phrases":293,"slug":296,"view_count":47,"doi":297,"paper":298,"created_at":316},3542,"Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pclm.0001038","Climate change is intensifying drought vulnerability in northern Ghana, posing serious risks to crop production, food security, and rural livelihoods in semi-arid regions. This study develops an empirical modelling framework to assess drought vulnerability and impacts at three temporal horizons, 2026–2050, 2051–2075, and 2076–2100, under SSP1-2.6 and SSP5-8.5 scenarios. We integrated bias-corrected outputs from five CMIP6 models, MRI-ESM2–0, EC-Earth3, IPSL-CM6A-LR, CESM2, and HadGEM3-GC31-LL, downscaled to 0.25° resolution, with drought indices including the Standardized Precipitation Index SPI, Reconnaissance Drought Index RDI, Normalized Difference Drought Index NDDI, and Soil Moisture Index SMI. These were combined with MODIS-derived Normalized Difference Vegetation Index NDVI, population density, and land use\u002Fland cover using GIS-based tools to generate composite drought vulnerability maps. CMIP6 models were validated against reference datasets and showed strong performance for annual precipitation, r = 0.95, RMSE = 3.4 mm, and temperature, r = 0.99, RMSE = 0.47–0.74°C. Results project increasing drought severity, with mean temperature rises of 2.0–3.9°C and rainfall declines up to 19% by 2100. Crop yields for maize, millet, sorghum, and groundnuts are expected to fall by 25–60% by 2050, with maize showing the strongest negative correlation to drought severity, r = –0.72, p \u003C 0.01, confirmed by Mann-Kendall trends at the 95% confidence level. About 84% of the study area is classified as highly vulnerable due to limited adaptive capacity, over 80% dependence on rain-fed agriculture, more than 49% household food insecurity, less than 23% cropland use, and under 5% water body coverage. Significant uncertainties remain in precipitation projections, with some models showing divergent temperature trends, highlighting challenges in modelling climate impacts. The findings underscore the urgent need for district-level drought preparedness, solar-powered irrigation, index insurance, climate-smart agriculture, and Disaster Risk Reduction aligned with UNDRR and Sendai Framework priorities to build resilience in northern Ghana.","气候变化正在加剧加纳北部的干旱脆弱性，对半干旱地区的作物生产、粮食安全和农村生计构成严重风险。本研究构建了一个经验建模框架，用于评估2026—2050年、2051—2075年和2076—2100年三个时间尺度下SSP1-2.6和SSP5-8.5情景中的干旱脆弱性及其影响。我们将五个CMIP6模型（MRI-ESM2–0、EC-Earth3、IPSL-CM6A-LR、CESM2和HadGEM3-GC31-LL）的偏差校正输出降尺度至0.25°分辨率，并与标准化降水指数（SPI）、侦察干旱指数（RDI）、归一化差值干旱指数（NDDI）和土壤水分指数（SMI）等干旱指数相结合。这些数据与MODIS衍生的归一化差值植被指数（NDVI）、人口密度以及土地利用\u002F土地覆盖数据通过基于GIS的工具相结合，生成综合干旱脆弱性地图。CMIP6模型经参考数据集验证，在年降水量（r = 0.95，RMSE = 3.4 mm）和温度（r = 0.99，RMSE = 0.47–0.74°C）方面表现出色。结果预测干旱严重程度将加剧，到2100年平均气温上升2.0–3.9°C，降雨量下降高达19%。到2050年，玉米、小米、高粱和花生的作物产量预计将下降25–60%，其中玉米与干旱严重程度呈最强负相关（r = –0.72，p \u003C 0.01），并经Mann-Kendall趋势检验在95%置信水平上得到证实。约84%的研究区域被归类为高度脆弱，原因包括适应能力有限、超过80%依赖雨养农业、超过49%的家庭粮食不安全、不足23%的耕地利用率以及低于5%的水体覆盖率。降水预测仍存在显著不确定性，部分模型显示出不同的温度趋势，凸显了气候影响建模的挑战。研究结果强调迫切需要开展县级干旱备灾、太阳能灌溉、指数保险、气候智慧型农业以及与UNDRR和仙台框架优先事项一致的风险减灾工作，以增强加纳北部的韧性。","PLOS Climate",{"impact":62,"substance":113,"depth":62,"authority":63,"freshness":234,"relevant":33,"comment":284},"基于CMIP6多模型与遥感指数的干旱脆弱性实证研究，数据扎实、结论明确，对半干旱区农业防灾与气候适应有参考价值。",[286],{"name":282,"url":279},[288,289,290,291,292],"粮食安全","防灾减灾","气候变化","遥感监测","干旱风险",[294,295],"加纳北部 干旱 玉米","CMIP6 干旱脆弱性 作物产量","加纳北部干旱玉米-3542","10.1371\u002Fjournal.pclm.0001038",{"doi":297,"openalex_id":299,"authors":300,"venue":282,"cited_by_count":47,"oa_url":279,"card":310,"direction":222,"ingested_from":145},"W7214399028",[301,304,307],{"name":302,"orcid":303},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":305,"orcid":306},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":308,"orcid":309},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":311,"method":312,"finding":313,"direction":314,"opportunity":315},"构建干旱脆弱性经验模型，评估加纳北部未来气候情景下作物与生计影响。","集成5个CMIP6模型、SPI\u002FRDI\u002FNDDI\u002FSMI指数与MODIS NDV","至2100年升温2.0-3.9°C、降水降19%，2050年作物减产25-60%，84%区域高度脆弱","农业遥感与作物表型","可结合遥感干旱指数与作物模型，在数据稀缺区开展多情景脆弱性动态评估与适应策略优化。","2026-09-26T23:30:10.153994Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":19,"source_name":323,"source_url":320,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":25,"score":184,"score_detail":324,"sources":326,"tags":328,"search_phrases":332,"slug":335,"view_count":47,"doi":336,"paper":337,"created_at":368},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。","Precision Agriculture",{"impact":62,"substance":61,"depth":30,"authority":63,"freshness":234,"relevant":33,"comment":325},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[327],{"name":323,"url":320},[192,329,330,291,331],"玉米","作物模型","数据同化",[333,334],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":336,"openalex_id":338,"authors":339,"venue":323,"cited_by_count":47,"oa_url":320,"card":363,"direction":314,"ingested_from":145},"W7214405444",[340,342,345,348,351,353,355,358,360],{"name":341,"orcid":19},"Manuela Montella",{"name":343,"orcid":344},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":346,"orcid":347},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":349,"orcid":350},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":352,"orcid":19},"Jean François Iffly",{"name":354,"orcid":19},"Thomas Udelhoven",{"name":356,"orcid":357},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":359,"orcid":19},"Zoltan Szantoi",{"name":361,"orcid":362},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":364,"method":365,"finding":366,"direction":314,"opportunity":367},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":19,"content":374,"source_name":375,"source_url":19,"published_at":376,"category":57,"cover_url":19,"hotness":24,"is_selected":25,"score":377,"score_detail":378,"sources":381,"tags":383,"search_phrases":390,"slug":393,"view_count":47,"doi":19,"paper":19,"created_at":394},3570,"农业农村部就'十五五'国家重点研发计划5个重点专项2026年度项目申报指南征求意见——主要粮食与经济作物大面积单产提升、果蔬园艺作物优质绿色生产、畜禽健康养殖、农产品加工储运与食品制造、海上牧场与淡水渔业","https:\u002F\u002Fservice.most.gov.cn\u002Fkjjh_tztg_all\u002F20260922\u002F5884.html","农业农村部近日就'十五五'国家重点研发计划'主要粮食与经济作物大面积单产提升技术''果蔬等园艺作物优质绿色生产技术创新''畜禽健康养殖与高效生产''农产品加工储运与食品制造''海上牧场与淡水渔业'等5个重点专项2026年度项目申报指南向社会公开征求意见，公示期9月22日至9月26日。5个专项基本覆盖种植业、畜牧业、加工业、渔业全链条，瞄准大面积单产提升、园艺作物绿色生产、畜禽高效生产、农产品加工储运和渔业绿色循环发展等核心方向。","![Image 2](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_wenhao.png)\n\n在线帮助\n\n![Image 3](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_xiaochengxu.png)\n\n![Image 4](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Fvx_code.png)\n\n![Image 5](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_quanzhandaohang.png)\n\n全站导航\n\n![Image 6](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_user.png)\n\n![Image 7](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_user_login_info.png)\n\n单位名称：\n\n用户角色：\n\n登录时间：\n\n登录IP：\n\n![Image 8](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_repass.png)修改密码\n\n![Image 9](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_loginout.png)退出登录\n\n![Image 10](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_user.png)登录\u002F注册\n\n![Image 11](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_logo.png)![Image 12](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Fpopup_close.png)\n\n![Image 13](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_logo_m.png)\n\n![Image 14](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_search.png)\n\n![Image 15](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_home.png)\n\n 首页 \n\n公开公示\n\n项目申报\n\n在线服务\n\n监督评估\n\n伦理登记平台\n\n![Image 16](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fcy_tools\u002Fpopup\u002Fpopup_warning.png)系统维护提示 \n\n![Image 17](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fcy_tools\u002Fpopup\u002Fcy_popup_close.png)\n\n确定\n\n![Image 18](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Ferror\u002Ferror.png)\n\n##### 您查看的内容已到期\n\n返回首页\n\n![Image 19](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fcy_tools\u002Fpopup\u002Fpopup_warning.png)浏览器兼容 \n\n![Image 20](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fcy_tools\u002Fpopup\u002Fcy_popup_close.png)\n\n![Image 21](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fbrowser\u002F360.png)\n\n360\n\n9.0及以上版本\n\n![Image 22](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fbrowser\u002Fchrome.png)\n\nChrome\n\n52.0及以上版本\n\n![Image 23](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Fbrowser\u002Fie.png)\n\nIE\n\n10.0及以上版本\n\n建议使用以上浏览器的最新版本\n\n确定\n\n![Image 24](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_footer_phone.png)\n\n 为使您的咨询问题及时得到答复，请您务必  \n拨打以下技术支持电话，请勿拨打其它电话：  \n010-58882999（中继线） \n\n![Image 25](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_footer_mail.png)\n\n 如电话繁忙请通过以下邮箱进行咨询：  \n[program@istic.ac.cn](mailto:program@istic.ac.cn)\n\n![Image 26](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Ficon_footer_fax.png)\n\n 传真请发送至：010-58882370 \n\n![Image 27](https:\u002F\u002Fservice.most.gov.cn\u002Fui\u002Fimages\u002Findex\u002Fvx_code.png)\n\n中华人民共和国科学技术部 © 2015 \n\n 京ICP备05022684号 \n\n查看浏览器兼容版本","国家科技管理信息系统公共服务平台 2026-09-22","2026-09-22T00:00:00Z",81,{"impact":28,"substance":62,"depth":379,"authority":31,"freshness":32,"relevant":33,"comment":380},16,"农业农村部就十五五重点研发计划5个涉农专项2026年度申报指南公开征求意见，属全国性政策信号，方向明确但正文为平台页面内容、条款细节有限。",[382],{"name":375,"url":372},[384,385,386,387,388,389],"十五五","单产提升","农产品加工","重点研发计划","渔业","畜禽健康养殖",[391,392],"农业农村部 十五五 重点研发计划 申报指南","主要粮食作物 大面积单产提升","农业农村部十五五重点研发计划申报指南-3570","2026-09-27T00:05:13.539928Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":19,"content":19,"source_name":400,"source_url":398,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":401,"score":377,"score_detail":402,"sources":404,"tags":406,"search_phrases":412,"slug":415,"view_count":47,"doi":416,"paper":417,"created_at":436},3527,"Greenhouse gas emissions assessment and mitigation potential in Pakistan's cattle and Buffalo sector: An IPCC tier 2 inventory method with probabilistic uncertainty modeling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104994","Greenhouse gas emissions assessment and mitigation potential in Pakistan's cattle and Buffalo sector: An IPCC tier 2 inventory method with probabilistic uncertainty modeling。Agricultural Systems","Agricultural Systems",false,{"impact":62,"substance":61,"depth":62,"authority":63,"freshness":234,"relevant":33,"comment":403},"该研究采用IPCC tier 2方法结合概率不确定性建模，评估巴基斯坦牛与水牛部门温室气体排放及减排潜力，方法新颖、数据扎实，对畜牧业绿色低碳发展有参考价值，但属区域性研究，影响范围有限。",[405],{"name":400,"url":398},[407,408,409,410,411],"畜牧业","温室气体","减排","巴基斯坦","IPCC方法",[413,414],"巴基斯坦 水牛 温室气体","IPCC tier 2 排放清单","巴基斯坦水牛温室气体-3527","10.1016\u002Fj.agsy.2026.104994",{"doi":416,"openalex_id":418,"authors":419,"venue":400,"cited_by_count":47,"oa_url":398,"card":19,"direction":19,"ingested_from":145},"W7214389301",[420,422,425,427,430,433],{"name":421,"orcid":19},"Syed Agha Armaghan Asad Abbas Naqvi",{"name":423,"orcid":424},"Muhammad Waqas Alam Chattha","https:\u002F\u002Forcid.org\u002F0000-0003-1087-0854",{"name":426,"orcid":19},"Syed Akbar",{"name":428,"orcid":429},"Khurram Yousaf","https:\u002F\u002Forcid.org\u002F0000-0002-8897-3629",{"name":431,"orcid":432},"Almazea Fatima","https:\u002F\u002Forcid.org\u002F0009-0005-1654-2503",{"name":434,"orcid":435},"L. A. González","https:\u002F\u002Forcid.org\u002F0000-0002-6400-2588","2026-09-26T23:30:05.095606Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":19,"source_name":443,"source_url":440,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":401,"score":444,"score_detail":445,"sources":447,"tags":449,"search_phrases":453,"slug":456,"view_count":47,"doi":457,"paper":458,"created_at":475},3569,"Efficient and explainable attention-based multitask learning for fruit type, ripeness, and disease classification","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2108","Precision agriculture increasingly relies on automated crop monitoring to support disease prevention and harvest decisions. However, most existing systems treat fruit type, ripeness, and disease classification as separate tasks, leading to high computational costs in real-world deployments. This paper proposes an efficient and explainable multitask learning (MTL) model for fruit monitoring that performs all three tasks simultaneously. The MTL model uses a shared lightweight MobileNetV2 backbone with a convolutional block attention module (CBAM) to extract both shared and discriminative features. Dedicated classification heads then utilize these features to generate predictions for each task. To address the scarcity of fully annotated agricultural datasets, we combine multiple public datasets and apply a partial label-masking strategy to handle missing annotations. In addition, the proposed model is evaluated using several backbone architectures, including ResNet-50, EfficientNetV2-M, and ViT-B\u002F16, under both single optimizer and separate optimizers. Experimental results demonstrate that the proposed MTL model achieves 99.39% (fruit type), 96.35% (ripeness), and 99.7% (disease) accuracy, while using only 5.5M parameters, outperforming single-task solutions. Furthermore, gradient-based explainability analysis provides transparency into the model's decision-making process.","精准农业日益依赖自动化作物监测，以支持病害预防和收获决策。然而，现有大多数系统将水果类型、成熟度和病害分类视为独立任务，导致实际部署中的计算成本较高。本文提出了一种高效且可解释的多任务学习（MTL）模型用于水果监测，可同时执行上述三项任务。该MTL模型采用共享的轻量级MobileNetV2主干网络，并结合卷积块注意力模块（CBAM）以提取共享特征和判别特征。随后，专用分类头利用这些特征为每个任务生成预测。为应对完全标注农业数据集稀缺的问题，我们整合了多个公开数据集，并采用部分标签掩码策略来处理缺失标注。此外，我们在单一优化器和独立优化器两种设置下，使用包括ResNet-50、EfficientNetV2-M和ViT-B\u002F16在内的多种主干架构对所提模型进行了评估。实验结果表明，所提MTL模型在仅使用5.5M参数的情况下，分别达到了99.39%（水果类型）、96.35%（成熟度）和99.7%（病害）的准确率，优于单任务方案。此外，基于梯度的可解释性分析为模型的决策过程提供了透明度。","Journal of Agricultural Engineering",80,{"impact":62,"substance":61,"depth":62,"authority":63,"freshness":114,"relevant":33,"comment":446},"提出轻量可解释多任务模型，同时识别水果种类、成熟度与病害，精度高、参数少，对智慧果园部署有实用价值。",[448],{"name":443,"url":440},[192,38,450,451,452],"病害识别","多任务学习","水果检测",[454,455],"MobileNetV2 CBAM 水果分类","多任务学习 果实成熟度 病害","MobileNetV2CBAM水果分类-3569","10.4081\u002Fjae.2026.2108",{"doi":457,"openalex_id":459,"authors":460,"venue":443,"cited_by_count":47,"oa_url":440,"card":470,"direction":224,"ingested_from":145},"W7214312987",[461,464,467],{"name":462,"orcid":463},"Rida El Chall","https:\u002F\u002Forcid.org\u002F0000-0002-7620-7767",{"name":465,"orcid":466},"Sarah Alayan","https:\u002F\u002Forcid.org\u002F0009-0007-5706-4688",{"name":468,"orcid":469},"Abed Ellatif Samhat","https:\u002F\u002Forcid.org\u002F0000-0002-1137-621X",{"tldr":471,"method":472,"finding":473,"direction":224,"opportunity":474},"提出轻量多任务模型，同时分类水果种类、成熟度和病害，并具可解释性。","MobileNetV2+CBAM共享主干，多数据集合并与部分标签掩码。","三任务准确率达99.39%、96.35%、99.7%，仅5.5M参数，优于单任务。","可探索多任务模型在田间实时部署与跨作物泛化，结合弱监督降低标注成本。","2026-09-26T23:30:57.555191Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":19,"source_name":482,"source_url":479,"published_at":56,"category":110,"cover_url":19,"hotness":483,"is_selected":401,"score":444,"score_detail":484,"sources":486,"tags":491,"search_phrases":495,"slug":498,"view_count":47,"doi":499,"paper":500,"created_at":522},3562,"Toward Common Prosperity: Spatially Heterogeneous Effects and Mechanisms of Digital Village Construction on the Urban–Rural Income Gap in Mountainous China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101801","Mountainous regions are critical to promoting inclusive growth and reducing spatial inequality worldwide. Digital village construction offers a new pathway to overcome geographical constraints and reshape urban–rural income distribution in these areas. Using county-level data for 2020 and focusing on mountainous China, this study integrates the Theil index, IV-2SLS, MGWR, and mediation models to characterize the spatial pattern of the urban–rural income gap (URIG) and identify the spatially heterogeneous effects and potential mechanisms of digital village construction across different geomorphological settings. The results indicate that: (1) Large and highly dispersed URIG are concentrated in mountainous regions. On average, the gap is approximately twice as large as that in non-mountainous regions and varies markedly across geomorphological types. URIG is highest in mountain counties, shows the greatest variation in plateau counties, and tends to be lower in hill counties. (2) Digital village construction exerts a significant negative effect on URIG in mountainous China, with the effect being stronger than in non-mountainous regions and generally increasing in magnitude from east to west. (3) The effects of different DVI dimensions on URIG exhibit substantial spatial heterogeneity and vary in their spatial scales of influence. Rural economy digitalization shows widespread and consistently negative effects and emerges as the dominant dimension across most mountainous regions, while the effect of other dimensions is more localized, particularly rural life digitalization, which shows stronger negative effects in mountain counties, plateau counties, and underdeveloped mountainous counties. (4) Agricultural operation modernization and labor allocation optimization constitute potential channels underlying the association between digital village construction and a narrower URIG, with the estimated indirect effect being more pronounced through labor allocation optimization. These findings provide empirical evidence for advancing inclusive development in mountainous regions amid China’s common prosperity agenda, with broader implications for place-based digital village construction.","山区对于促进包容性增长和缩小全球空间不平等至关重要。数字乡村建设为这些地区突破地理约束、重塑城乡收入分配格局提供了新路径。本研究以2020年县级数据为基础，聚焦中国山区，综合运用泰尔指数、IV-2SLS、MGWR和中介模型，刻画城乡收入差距（URIG）的空间格局，并识别数字乡村建设在不同地貌条件下的空间异质性效应及其潜在机制。结果表明：（1）规模较大且高度分散的URIG集中于山区。其平均值约为非山区的两倍，且在不同地貌类型间差异显著。URIG在山区县最高，在高原县变异最大，在丘陵县则趋于较低水平。（2）数字乡村建设对中国山区URIG具有显著负向影响，其效应强于非山区，且总体上呈自东向西递增态势。（3）数字乡村建设不同维度对URIG的影响表现出显著的空间异质性，且其影响的空间尺度各异。农村经济数字化呈现出广泛且稳定的负向效应，并在大多数山区成为主导维度，而其他维度的效应则更具局部性，尤其是农村生活数字化，在山区县、高原县及欠发达山区县表现出更强的负向效应。（4）农业经营现代化和劳动力配置优化构成数字乡村建设与URIG缩小之间关联的潜在渠道，其中通过劳动力配置优化的间接效应更为明显。上述发现为中国共同富裕议程下推进山区包容性发展提供了经验证据，并对因地制宜推进数字乡村建设具有更广泛的启示。","Land",25,{"impact":62,"substance":61,"depth":62,"authority":187,"freshness":234,"relevant":33,"comment":485},"基于县域数据的实证研究，方法扎实、结论对山区数字乡村政策有参考价值，但属学术论文，公共传播热度有限。",[487,488],{"name":482,"url":479},{"name":489,"url":490},"MDPI Land 15(10), 1801 2026-09-25","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F10\u002F1801",[119,492,121,493,494],"县域经济","山区农业","城乡收入差距",[496,497],"数字乡村 城乡收入差距 山区","县域 数字乡村 泰尔指数","数字乡村城乡收入差距山区-3562","10.3390\u002Fland15101801",{"doi":499,"openalex_id":501,"authors":502,"venue":482,"cited_by_count":47,"oa_url":479,"card":517,"direction":143,"ingested_from":145},"W7214349448",[503,506,509,512,514],{"name":504,"orcid":505},"Xueting Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4633-2185",{"name":507,"orcid":508},"Xiaoping Qiu","https:\u002F\u002Forcid.org\u002F0000-0001-6310-5959",{"name":510,"orcid":511},"Fubiao Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-4606-3336",{"name":513,"orcid":19},"Mingkai Xu",{"name":515,"orcid":516},"Yun Xu","https:\u002F\u002Forcid.org\u002F0000-0002-2442-7792",{"tldr":518,"method":519,"finding":520,"direction":143,"opportunity":521},"研究数字乡村建设对中国山区城乡收入差距的空间异质性影响及机制。","用2020年县级数据，结合泰尔指数、IV-2SLS、MGWR与中介模型。","数字乡村建设显著缩小山区城乡收入差距，效应自东向西增强，且存在地貌与维度异质性。","可深入探究不同地貌下数字乡村各维度的空间尺度差异及劳动力配置中介的微观路径。","2026-09-26T23:30:38.665617Z",{"id":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":19,"source_name":529,"source_url":526,"published_at":153,"category":110,"cover_url":19,"hotness":24,"is_selected":401,"score":444,"score_detail":530,"sources":532,"tags":534,"search_phrases":539,"slug":542,"view_count":47,"doi":543,"paper":544,"created_at":558},3555,"Ecosystem Resilience and Land-Use Governance: Remote Sensing Insights for Environmental Management in a Tropical Dry Forest","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00267-026-02629-4","Seasonally Dry Tropical Forests (SDTFs), such as the Brazilian Caatinga, are highly vulnerable to climate change and chronic anthropogenic disturbances. Despite extensive land-use conversion, understanding how vegetation productivity and resilience vary across different land governance regimes remains a critical gap. Here, we integrated two decades (2004-2024) of remote sensing data to evaluate Net Primary Productivity (NPP) and Water Use Efficiency (WUE). We applied interannual climate anomaly analyses (Z-scores) and adapted a trend-based classification framework to assess ecosystem trajectories. These biophysical metrics were cross-referenced with the spatial boundaries of Conservation Units, Indigenous Lands, Quilombola Territories, and private properties, including a distance-decay edge effect analysis. While long-term temporal trends for NPP and WUE remained relatively stable over the two decades, standardized anomaly analyses revealed a strong, statistically significant inverse coupling between productivity and water-use efficiency (r = - 0.741, p \u003C 0.001), primarily mediated by surface moisture retention (LSWI) rather than immediate precipitation alone. Spatial analyses demonstrated that Conservation Units act as the main ecological refugia, maintaining peak restoration within a 0-2 km internal buffer. Notably, Indigenous Lands and Quilombola Territories exhibited remarkable edge-to-core resilience, maintaining homogeneous conservation levels that effectively buffer external degradation. To safeguard the Caatinga's structural integrity against desertification, public policies must move beyond reactive drought subsidies towards proactive landscape management that legally empowers traditional communities and incentivizes conservation on private lands.","季节性干旱热带森林（Seasonally Dry Tropical Forests, SDTFs），如巴西卡廷加（Caatinga），极易受到气候变化和长期人为干扰的影响。尽管土地利用转换广泛发生，但关于植被生产力与恢复力在不同土地治理制度下如何变化的认识仍存在关键空白。本研究整合了二十年（2004—2024年）的遥感数据，评估净初级生产力（Net Primary Productivity, NPP）和水分利用效率（Water Use Efficiency, WUE）。我们采用年际气候异常分析（Z分数），并改编了基于趋势的分类框架以评估生态系统轨迹。这些生物物理指标与保护单元、原住民土地、基隆博拉领地及私有财产的空间边界进行了交叉比对，包括距离衰减边缘效应分析。尽管NPP和WUE的长期时间趋势在二十年间保持相对稳定，但标准化异常分析揭示出生产力与水分利用效率之间存在强烈的、统计显著的反向耦合关系（r = -0.741，p \u003C 0.001），这一关系主要由地表水分保持（LSWI）而非仅由即时降水所介导。空间分析表明，保护单元是主要的生态避难所，在0—2 km的内部缓冲区内维持着最高的恢复水平。值得注意的是，原住民土地和基隆博拉领地表现出显著边缘到核心的恢复力，维持着均质的保护水平，有效缓冲了外部退化。为保障卡廷加的结构完整性以抵御荒漠化，公共政策必须超越被动的干旱补贴，转向积极的景观管理，在法律上赋权传统社区并激励私有土地上的保护行为。","Environmental Management",{"impact":62,"substance":61,"depth":62,"authority":63,"freshness":114,"relevant":33,"comment":531},"基于20年遥感数据揭示保护地与原住民领地生态缓冲作用，方法扎实、结论对土地治理有参考价值，但属区域案例研究，公共影响有限。",[533],{"name":529,"url":526},[291,535,536,537,538],"生态系统韧性","土地利用治理","热带干旱林","退化土地修复",[540,541],"巴西 Caatinga 遥感 植被生产力","保护地 原住民领地 生态韧性","巴西Caatinga遥感植被生产力-3555","10.1007\u002Fs00267-026-02629-4",{"doi":543,"openalex_id":545,"authors":546,"venue":529,"cited_by_count":47,"oa_url":552,"card":553,"direction":314,"ingested_from":145},"W7214117687",[547,550],{"name":548,"orcid":549},"Lucas  Nascimento da Silva","https:\u002F\u002Forcid.org\u002F0009-0004-5435-0986",{"name":551,"orcid":19},"Bartolomeu Israel de Souza","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00267-026-02629-4.pdf",{"tldr":554,"method":555,"finding":556,"direction":314,"opportunity":557},"整合20年遥感数据评估巴西卡廷加NPP与WUE，分析不同土地治理制度下的生态韧性。","2004-2024年遥感NPP\u002FWUE、气候异常Z评分、趋势分类与边缘衰减分析。","保护单元是主要生态避难所，原住民与Quilombola领地边缘到核心韧性突出。","可探究传统社区土地权属如何通过制度设计提升干旱森林韧性，并量化边缘效应的政策阈值。","2026-09-26T23:30:30.120235Z",{"id":560,"title":561,"url":562,"summary":563,"summary_zh":564,"content":19,"source_name":109,"source_url":562,"published_at":56,"category":110,"cover_url":19,"hotness":24,"is_selected":401,"score":444,"score_detail":565,"sources":567,"tags":569,"search_phrases":573,"slug":576,"view_count":47,"doi":577,"paper":578,"created_at":595},3531,"Extreme climate shocks and agricultural net carbon sinks: the moderating role of supply chain resilience in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940685","Introduction Extreme climate shocks increasingly challenge the ability of agricultural systems to sustain crop carbon fixation while controlling production-related emissions. Methods Using panel data for 240 Chinese cities from 2002 to 2023, this study examines the relationships between four climate shocks—extreme high temperature (HTD), extreme low temperature (LTD), extreme rainfall (ERD), and extreme drought (EED)—and agricultural net carbon sinks (NCS), together with the moderating role of agricultural supply chain resilience. NCS is measured as annual crop-biomass carbon fixation net of selected agricultural production emissions, thereby providing an integrated, production-based indicator of agricultural carbon performance. Results Two-way fixed-effects estimates show that all four climate shocks are negatively associated with NCS, and the direction of these relationships remains stable across alternative accounting boundaries, sample adjustments, leave-one-province-out tests, and double machine learning specifications. Agricultural supply chain resilience significantly conditions the climate–carbon relationship. Its resistance, recovery, and reorientation capacities attenuate the adverse associations of high-temperature, extreme-rainfall, and drought shocks with NCS. The low-temperature result further indicates that the effectiveness of resilience depends on matching supply chain functions with the biological mechanisms and intervention windows of specific climate hazards. Heterogeneity analyses confirm that climate sensitivity and resilience requirements vary with regional location, agricultural productivity, modernization, and irrigation conditions. Complementary machine-learning analysis identifies nonlinear predictive patterns and reinforces the importance of differentiated adaptation. Discussion These findings extend agricultural climate research from production and emissions to an integrated carbon-balance perspective and demonstrate that hazard-specific supply chain resilience can support both climate adaptation and low-carbon agricultural development.","引言 极端气候冲击日益挑战农业系统在维持作物碳固定的同时控制生产相关排放的能力。方法 本研究利用2002年至2023年中国240个城市的面板数据，考察四种气候冲击——极端高温（HTD）、极端低温（LTD）、极端降雨（ERD）和极端干旱（EED）——与农业净碳汇（NCS）之间的关系，以及农业供应链韧性的调节作用。NCS以年度作物生物量碳固定量扣除部分农业生产排放量来衡量，从而提供一个基于生产的综合性农业碳绩效指标。结果 双向固定效应估计表明，四种气候冲击均与NCS呈负相关，且这些关系的方向在替代核算边界、样本调整、逐一剔除省份检验和双重机器学习设定下均保持稳定。农业供应链韧性显著调节气候—碳关系。其抵抗能力、恢复能力和重新定向能力减弱了高温、极端降雨和干旱冲击与NCS之间的不利关联。低温结果进一步表明，韧性的有效性取决于供应链功能与特定气候灾害的生物机制及干预窗口的匹配。异质性分析证实，气候敏感性和韧性需求因区域位置、农业生产率、现代化水平和灌溉条件而异。补充性机器学习分析识别出非线性预测模式，并强化了差异化适应的重要性。讨论 这些发现将农业气候研究从生产和排放拓展至综合碳平衡视角，并表明针对特定灾害的供应链韧性能够同时支持气候适应和农业低碳发展。",{"impact":62,"substance":61,"depth":62,"authority":187,"freshness":234,"relevant":33,"comment":566},"基于240城22年面板数据的实证研究，方法扎实、结论稳健，对农业气候适应与低碳发展有参考价值，但属学术论文，公共传播性有限。",[568],{"name":109,"url":562},[192,120,570,571,572],"低碳农业","气候韧性","农业碳汇",[574,575],"中国 农业净碳汇 极端气候","农业供应链韧性 气候适应","中国农业净碳汇极端气候-3531","10.3389\u002Ffsufs.2026.1940685",{"doi":577,"openalex_id":579,"authors":580,"venue":109,"cited_by_count":47,"oa_url":562,"card":589,"direction":593,"ingested_from":145},"W7214310732",[581,583,586],{"name":582,"orcid":19},"Chunyan Zhao",{"name":584,"orcid":585},"Jiajie Xia","https:\u002F\u002Forcid.org\u002F0000-0003-3225-0154",{"name":587,"orcid":588},"Guoping Ding","https:\u002F\u002Forcid.org\u002F0000-0002-4865-5044",{"tldr":590,"method":591,"finding":592,"direction":593,"opportunity":594},"基于240个城市2002-2023年面板数据，检验四类极端气候冲击对农业净碳汇的影响及供应链韧性的调","双向固定效应模型、双重机器学习，240城市面板数据，农业净碳汇指标。","四类极端气候冲击均降低农业净碳汇，供应链韧性可显著缓解高温、暴雨和干旱的负面影响。","农业绿色发展与碳","可探究不同气候灾害下供应链韧性功能与生物机制匹配的差异化适应策略及非线性预测。","2026-09-26T23:30:07.569768Z"]