[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1796":3,"related-1796":59},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":54,"slug":57,"view_count":43,"doi":8,"paper":8,"created_at":58},1796,"经信所编制的《广东省农业品牌发展报告（2026年）》正式发布","https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1337434.html","近日，广东省农产品质量安全协会第四届第二次会员大会暨成立十五周年交流活动在广州举行。会上由广东省农业科学院经信所联合广东省农产品质量安全协会共同编制的行业智库报告《广东省农业品牌发展报告（2026年）》正式发布。报告锚定全省现代农业高质量发展大局，落实\"百千万工程\"工作部署，系统梳理广东农业品牌建设实践成效与典型案例。",null,"[![Image 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15](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzjc.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)\n\n[网站纠错](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)","广东省农业科学院2026年9月4日","2026-09-04T00:00:00Z","报道",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,16,12,5,1,"省级农业品牌发展报告发布，内容系统，对行业有参考价值，但时效性一般。",[24],{"name":10,"url":6},[26,27,28],"广东","农业品牌","智库报告",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},3,true,[],[47],"农产品质量",[],{"code":50,"score":21,"supported":14,"title":51,"abstract":52,"tags":53},"E5",[],[],[27],[55,56],"农业品牌 智库报告 广东","农业品牌 智库报告","农业品牌智库报告广东-1796","2026-09-07T00:04:39.176133Z",{"total":60,"page":21,"page_size":60,"items":61},6,[62,111,153,255,300,342],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":8,"content":8,"source_name":67,"source_url":8,"published_at":68,"category":69,"cover_url":8,"hotness":70,"is_selected":44,"score":71,"score_detail":72,"sources":77,"tags":88,"primary_topic":93,"secondary_topics":94,"classification":95,"search_phrases":106,"slug":109,"view_count":60,"doi":8,"paper":8,"created_at":110},2809,"三部门联合印发《农业品牌保护专项行动实施方案(2026—2027年)》","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260918\u002F20260918_1\u002Fnmrb_20260918_13408_1_2100697050475761675.html","近日,农业农村部、市场监管总局、国家知识产权局联合印发《方案》,提出带动一批特色品牌、壮大一批品牌主体,集中力量查办一批侵权违法案件,完善部门协同保护机制。围绕品牌农产品质量安全监管、涉农商标专利违法行为、不正当竞争行为、涉农知识产权行政保护四大重点任务,专项行动自印发之日起至2027年12月31日,分动员部署、组织实施、总结提升三阶段推进。","农民日报 2026年9月18日 头版","2026-09-18T00:00:00Z","政策",55,93,{"impact":73,"substance":74,"depth":17,"authority":75,"freshness":13,"relevant":21,"comment":76},27,23,15,"三部门联合印发的全国性农业品牌保护专项行动方案，任务分工与时间节点明确，政策增量足、权威性强，值得进入每日精选。",[78,79,82,85],{"name":67,"url":65},{"name":80,"url":81},"新华社·人民网","https:\u002F\u002Ffinance.people.com.cn\u002FBIG5\u002Fn1\u002F2026\u002F0917\u002Fc1004-40800714.html",{"name":83,"url":84},"央广网 2026-10-10","https:\u002F\u002Fwww.cnr.cn\u002Fmspd\u002Fjrhm\u002F20261010\u002Ft20261010_527837739.shtml",{"name":86,"url":87},"中国青年网 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专项行动","知识产权保护专项行动农业品牌品牌强农-2809","2026-09-18T00:03:24.162035Z",{"id":112,"title":113,"url":114,"summary":115,"summary_zh":8,"content":116,"source_name":117,"source_url":8,"published_at":118,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":124,"tags":126,"primary_topic":132,"secondary_topics":133,"classification":134,"search_phrases":147,"slug":150,"view_count":151,"doi":8,"paper":8,"created_at":152},5517,"上海农科院代表团赴马来西亚与新西兰 深化农产品质量安全与溯源合作","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57130","10月7日至12日院党委委员姚训率代表团一行4人出访，与马来西亚国立大学签署合作谅解备忘录并出席食品质量安全联合实验室揭牌，交流农业新兴污染物监测与农产品多源指纹真实性鉴别成果。赴新期间访问威世物流新西兰公司、新西兰生物经济科学研究所与梅西大学，调研有机食品认证与追溯管理经验。","2026年10月7-12日，院党委委员、纪委书记姚训率代表团一行4人赴马来西亚和新西兰参加国际学术会议并开展访问交流。代表团成员包括院农业生物安全研究中心副主任董茂锋、刘星副研究员和王献礼助理研究员。此次出访重点围绕农产品质量安全、真实性与产地溯源、农业新兴污染物监测及农食产品跨境供应链管理等领域，深化与两国高校、科研机构及企业的交流合作。\n\n7-9日，姚训代表我院参加与马来西亚国立大学合作谅解备忘录（MOU）交换仪式，出席双方食品质量安全联合实验室揭牌仪式并致辞。联合实验室的揭牌标志着双方合作进一步深化，为推动食品质量安全领域的联合科研、人才培养和成果转化搭建了合作平台。双方围绕科研项目合作、人员互访、研究生联合培养及科研资源共享等事项进行了深入交流。\n\n代表团参加了第三届马来西亚国立大学-宋卡王子大学亚洲科学技术国际研讨会（ISSTUP-Asia 2026）。会议期间，我院宋卫国研究员以线上方式作学术报告，介绍农业新兴污染物环境行为与食品安全风险防控方面的研究进展；刘星现场分享了农产品多源指纹真实性鉴别与产地溯源研究成果；王献礼通过学术墙报展示了绿色超分子溶剂在全氟和多氟烷基物质（PFAS）提取分析中的研究成果，并与参会专家开展交流。此外，代表团还参访了马来西亚国立大学食品科学相关实验室、甜食技术创新中心及马来西亚可可局可可创新与技术中心，并调研当地有机食品市场，了解食品质量安全检测、产品研发、成果转化及质量追溯管理等方面的实践经验。\n\n10-12日，代表团赴新西兰开展科研与产业交流。代表团访问了威世物流新西兰公司，围绕农产品跨境运输、冷链仓储、质量控制及供应链追溯等开展交流，了解新西兰农食产品出口及进入中国市场的物流与质量管理经验。代表团还与我院柔性引进专家Karyne Rogers教授就农产品真实性鉴别与产地溯源等领域的合作进展及后续研究计划进行交流。随后，代表团访问新西兰生物经济科学研究所及梅西大学马纳瓦图校区相关科研平台，围绕农产品质量安全、环境污染物监测研究及科研成果转化等开展学术交流，并探讨联合研究与人才培养等合作方向。访问期间，代表团还调研了新西兰有机食品市场的认证、质量保障及追溯管理情况。\n\n此次出访进一步深化了我院与马来西亚、新西兰相关高校、科研机构和企业的交流合作，展示了我院在农产品质量安全、真实性与产地溯源及农业新兴污染物研究方面的科技创新成果。下一步，我院将以食品质量安全联合实验室等合作平台为依托，推动联合科研、人才交流及成果转化，促进国际合作取得更多实质性成效，为提升农业科技创新能力提供支持。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z",65,{"impact":18,"substance":121,"depth":122,"authority":122,"freshness":60,"relevant":21,"comment":123},17,13,"省级农科院出访交流，有联合实验室揭牌与学术报告等实质内容，但属机构外事动态，公共影响有限。",[125],{"name":117,"url":114},[127,128,129,130,131],"国际合作","农产品质量安全","产地溯源","农业污染物","跨境供应链",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":135},{"candidates":136,"agricultural_evidence":44},[137,142],{"code":30,"score":138,"supported":44,"title":139,"abstract":140,"tags":141},9,[47],[],[128],{"code":143,"score":21,"supported":14,"title":144,"abstract":145,"tags":146},"J3",[],[],[127],[148,149],"上海农科院 马来西亚 联合实验室","农产品 真实性鉴别 产地溯源","上海农科院马来西亚联合实验室-5517",0,"2026-10-22T00:07:04.117432Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":8,"source_name":159,"source_url":156,"published_at":118,"category":160,"cover_url":8,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":167,"tags":169,"primary_topic":175,"secondary_topics":181,"classification":183,"search_phrases":226,"slug":229,"view_count":151,"doi":230,"paper":231,"created_at":254},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","论文",80,{"impact":17,"substance":163,"depth":17,"authority":164,"freshness":165,"relevant":21,"comment":166},22,14,8,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[168],{"name":159,"url":156},[170,171,172,173,174],"智慧农业","农业人工智能","机器学习","食品安全","文献计量",{"code":176,"slug":177,"name":178,"path":179,"parent_code":180},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[182],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":184},{"candidates":185,"agricultural_evidence":44},[186,197,202,208,215,221],{"code":176,"score":187,"supported":44,"title":188,"abstract":190,"tags":196},19,[189],"artificial intelligence",[191,172,192,193,194,189,195],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[171,172],{"code":30,"score":75,"supported":44,"title":198,"abstract":200,"tags":201},[199],"food 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Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","openalex","2026-10-20T23:30:31.567802Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":8,"content":8,"source_name":260,"source_url":8,"published_at":261,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":266,"tags":268,"primary_topic":274,"secondary_topics":275,"classification":276,"search_phrases":295,"slug":298,"view_count":21,"doi":8,"paper":8,"created_at":299},5294,"云南省农科院质标所举办\"特色农产品数字化品控及标准体系构建\"成果推介会","https:\u002F\u002Fwww.yn.xinhuanet.com\u002F20261016\u002F4e2d2462c42e41f8b2d9d5bf525cbed7\u002Fc.html","10月16日云南省农科院质标所发布由云南省科技厅支持的\"特色农产品全产业链可信数字化技术研究\"项目下产出的3个数字化平台系统(产地环境质量监测调控系统、特色农产品质量可信追溯系统、特色农产品可信供应订单定制系统),覆盖产地环境、质量追溯、订单供应三大环节;相关数字化成果已在元阳梯田红米、绥江半边红李、香格里拉草莓、盈江优质软米中应用。","新华网云南频道(2026-10-16)","2026-10-16T00:00:00Z",72,{"impact":17,"substance":264,"depth":164,"authority":19,"freshness":165,"relevant":21,"comment":265},20,"省级科研机构发布三大数字化平台并落地多品类应用，信息增量与实操价值较高，值得进入每日精选。",[267],{"name":260,"url":258},[269,270,271,272,273],"数字乡村","标准体系","农产品追溯","数字化品控","一物一码",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":277},{"candidates":278,"agricultural_evidence":44},[279,284,290],{"code":30,"score":280,"supported":44,"title":281,"abstract":282,"tags":283},4,[],[47],[271],{"code":285,"score":43,"supported":44,"title":286,"abstract":287,"tags":289},"B1",[],[288],"草莓",[],{"code":291,"score":21,"supported":14,"title":292,"abstract":293,"tags":294},"I6",[],[],[269],[296,297],"云南省农科院 质标所 数字化品控","特色农产品 可信追溯 元阳梯田红米","云南省农科院质标所数字化品控-5294","2026-10-19T00:07:42.258537Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":8,"content":8,"source_name":305,"source_url":8,"published_at":306,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":310,"tags":312,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":337,"slug":340,"view_count":151,"doi":8,"paper":8,"created_at":341},5060,"云南省农科院质标所参与2026年云南省食品安全宣传周主场活动 自主研发快检技术现场演示","https:\u002F\u002Fwww.yn.xinhuanet.com\u002F20261010\u002F59bfd10da16b4a36ad8de9272595ce5e\u002Fc.html","10月9日,云南省食品安全宣传周在昆明启动,云南省农科院质标所受邀参加主场活动,围绕\"从农田到餐桌\"的源头检测提供技术支撑。重点演示薄层色谱法(2分钟快速鉴别肥料中隐性添加的禁限用农药)、自主研发的丙溴磷等金纳米抗体侧流层析核酸试纸条(灵敏度较商品试纸条提升10倍)以及合成诱导圆偏振发光探针和微针贴片技术(10秒内捕捉稳定信号)。","新华网云南频道 2026年10月10日","2026-10-10T00:00:00Z",68,{"impact":18,"substance":264,"depth":75,"authority":19,"freshness":20,"relevant":21,"comment":309},"省级食品安全宣传周活动，含自主研发快检技术现场演示，技术细节具体，具备一定行业参考价值。",[311],{"name":305,"url":303},[313,173,314,315],"云南省农科院","农业科技","快检技术",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":319},{"candidates":320,"agricultural_evidence":44},[321,325,331],{"code":30,"score":19,"supported":44,"title":322,"abstract":323,"tags":324},[173],[173],[173],{"code":326,"score":43,"supported":44,"title":327,"abstract":328,"tags":330},"F3",[],[329],"肥料",[],{"code":332,"score":43,"supported":44,"title":333,"abstract":334,"tags":336},"F4",[],[335],"农药",[],[338,339],"云南农科院 质标所 快检技术","食品安全宣传周 云南","云南农科院质标所快检技术-5060","2026-10-16T00:13:12.742863Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":8,"source_name":348,"source_url":345,"published_at":349,"category":160,"cover_url":8,"hotness":13,"is_selected":14,"score":350,"score_detail":351,"sources":353,"tags":355,"primary_topic":360,"secondary_topics":361,"classification":362,"search_phrases":382,"slug":385,"view_count":151,"doi":386,"paper":387,"created_at":403},4978,"Fixed compliance costs and producer selection in regional food production: evidence from China's 2006 agricultural product quality and safety law","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1959282","Food-safety law protects consumers, but the mandatory standards, input restrictions, record-keeping, and pre-sale testing it imposes are largely fixed costs that fall hardest on the smallest producers. Whether that incidence survives aggregation, that is, whether output shed by marginal producers is absorbed by survivors or genuinely lost to a region, remains unsettled. We study China's 2006 Agricultural Product Quality and Safety Law, a nationally synchronous mandate, across 284 prefecture-level cities from 2001 to 2018. A continuous-intensity difference-in-differences design links predetermined pre-law exposure to post-law changes in per-capita animal-source food output, measured at the point of production rather than consumption, treating 2006 as a transition year and 2007 as the first full year of implementation. In the baseline specification, more exposed cities produced roughly 5% less animal-source food after implementation, with the contraction emerging over the post-law period. Because exposure is built from a pre-law outcome level, we re-estimate the relationship on a window excluding the years used to measure exposure; the negative gradient persists. Three findings locate the burden. The contraction covers the activities the statute regulates, animal-source food, grain, and primary-sector value added alike, and is absent from secondary and tertiary activity, making a general regional decline less consistent with the observed pattern. Within animal-source food, it concentrates in aquaculture, the most compliance-intensive segment. Further, it weakens as baseline farm scale rises, a gradient that persists under province-by-year fixed effects and under year-specific trends in baseline economic structure, both of which absorb the average exposure gradient entirely. Because the design works on regional quantities rather than producer histories, it identifies output a region no longer produces rather than the departure of identified producers. The findings are therefore consistent with uniform safety obligations having uneven production-side incidence across regions with different producer structures, which points to shared compliance infrastructure as a way to ease fixed compliance burdens without weakening substantive safety standards.","食品安全法保护消费者，但其规定的强制性标准、投入品限制、记录保存和售前检测在很大程度上属于固定成本，对最小规模的生产者冲击最大。这种负担分布能否在加总层面继续成立，即边际生产者减少的产出是被幸存者吸收还是真正从某一地区流失，仍无定论。我们研究中国2006年《农产品质量安全法》，这是一项全国同步实施的强制要求，覆盖2001年至2018年间284个地级市。我们采用连续强度双重差分设计，将法律实施前预先确定的暴露程度与法律实施后人均动物源食品产出的变化联系起来，产出在生产端而非消费端度量，并将2006年视为过渡年份、2007年视为首个完整实施年份。在基准设定中，暴露程度更高的城市在实施后动物源食品产量约减少5%，且收缩在法律实施后逐步显现。由于暴露程度基于法律实施前的产出水平构建，我们在排除用于度量暴露程度的年份的窗口上重新估计该关系；负向梯度依然存在。三项发现定位了负担所在。收缩覆盖了该法规所监管的活动，动物源食品、粮食和第一产业增加值均如此，而第二和第三产业则没有出现收缩，这使得一般性地区衰退与观察到的模式不太一致。在动物源食品内部，收缩集中于水产养殖，这是合规强度最高的部分。此外，随着基线农场规模上升，收缩减弱，这一梯度在省份-年份固定效应以及基线经济结构的年份特定趋势下依然存在，而这两者都完全吸收了平均暴露梯度。由于该设计基于地区总量而非生产者历史，它识别的是某一地区不再生产的产出，而非可识别生产者的退出。因此，这些发现与以下观点一致：统一的安全义务在不同生产者结构的地区之间产生了不均衡的生产端负担分布，这表明共享合规基础设施是一种在不削弱实质性安全标准的前提下缓解固定合规负担的途径。","Frontiers in Sustainable Food Systems","2026-10-14T00:00:00Z",86,{"impact":163,"substance":74,"depth":187,"authority":122,"freshness":138,"relevant":21,"comment":352},"基于284个地级市2001—2018年数据的准自然实验研究，揭示食品安全法规固定合规成本对区域畜牧水产产出的收缩效应，方法扎实、结论有政策含义，值得进入每日精选。",[354],{"name":348,"url":345},[128,356,357,358,359],"水产养殖","小农户","食品安全法","合规成本",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":363},{"candidates":364,"agricultural_evidence":44},[365,373,377],{"code":366,"score":367,"supported":44,"title":368,"abstract":369,"tags":372},"D",7,[],[370,371],"水产","aquaculture",[356],{"code":30,"score":367,"supported":44,"title":374,"abstract":375,"tags":376},[],[173,47],[128],{"code":378,"score":21,"supported":14,"title":379,"abstract":380,"tags":381},"G2",[],[],[357],[383,384],"中国 地级市 动物源食品 产出","农产品质量安全 食品安全法 合规成本 水产养殖","中国地级市动物源食品产出-4978","10.3389\u002Ffsufs.2026.1959282",{"doi":386,"openalex_id":388,"authors":389,"venue":348,"cited_by_count":151,"oa_url":345,"card":397,"direction":401,"ingested_from":253},"W7221767515",[390,393,395],{"name":391,"orcid":392},"Xi Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8652-5980",{"name":394,"orcid":8},"毕军涛",{"name":396,"orcid":8},"Mengwei Yang",{"tldr":398,"method":399,"finding":400,"direction":401,"opportunity":402},"研究中国2006年农产品质量安全法，发现合规固定成本使高暴露地区动物源食品产量下降约5%。","连续强度双重差分，2001-2018年284个地级市面板数据，以法律前暴露度识别","合规成本集中于水产养殖，且随农场规模上升而减弱，说明小生产者承担更重固定成本。","农业绿色发展与碳","可研究共享合规基础设施（如区域检测、记录平台）如何降低小农固定合规成本而不放松安全标准。","2026-10-15T23:30:06.488813Z"]