[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2917":3,"related-2917":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":33,"slug":36,"view_count":37,"doi":38,"paper":39,"created_at":72},2917,"Confronting policy gaps between antimicrobial resistance and climate change across LMIC animal production systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1831307","Background Climate change and antimicrobial resistance (AMR) represent intersecting risks to livestock and aquaculture systems, particularly in low- and middle-income countries (LMICs). Climate stressors drive increased antimicrobial reliance, while AMR undermines production resilience. Yet policy responses remain largely siloed: climate adaptation frameworks rarely address antimicrobial use, and AMR action plans give limited attention to climate-related drivers. This review assesses how national policies in LMICs integrate AMR control and climate change adaptation, identifying pathways to strengthen policy coherence. Methods A review of 204 national policy documents from 51 LMICs, sourced from the FAOLEX legal database and covering 2015–2025, was conducted. Documents were assessed using an intersectoral policy mention matrix across four integration levels: addressed in isolation, cross-referenced mentions, early integration, and full integration. Findings were complemented by 12 field- based community dialogues and site visits in the Philippines and 18 key informant interviews. Results Although regional patterns varied, most policies addressed AMR and climate change separately. Of 204 policies reviewed, 170 addressed the issues in isolation, 21 cross-referenced, and 13 showed early signs of integration; none met full integration. Even early integration was limited to specific activities and was not reflected across governance, financing, and monitoring. Common gaps included the absence of shared objectives, coordinated institutional responsibilities, and surveillance systems that jointly track climate variables, disease patterns, antimicrobial use, and resistance. The Philippine case reflected similar challenges, particularly in the absence of cross-sectoral coordination and surveillance systems, subnational implementation, and sustained financing, with the aquaculture sector remaining underrepresented. Discussion and policy implications Integration of AMR control and climate adaptation in animal production systems remain largely conceptual across LMICs. The One Health framework offers a practical institutional bridge, yet its environmental component remains underrepresented in policy and practice. Forthcoming revisions to National Action Plans on AMR, Nationally Determined Contributions, and National Adaptation Plans are timely entrypoints for aligning objectives, surveillance systems, and financing. Meaningful progress will require expanded One Health governance structures incorporating climate-AMR linkages, AMR projects within international climate finance mechanisms, and genuine engagement of local governments, communities, and private sector.","背景 气候变化和抗微生物药物耐药性（AMR）对畜牧和水产养殖系统构成交叉风险，尤其是在中低收入国家（LMICs）。气候胁迫因素推动抗微生物药物使用增加，而AMR则削弱生产韧性。然而，政策应对在很大程度上仍各自为政：气候适应框架很少涉及抗微生物药物使用，AMR行动计划对气候相关驱动因素的关注也有限。本综述评估了LMICs国家政策如何整合AMR防控与气候变化适应，并识别加强政策一致性的路径。方法 对来自51个LMICs的204份国家政策文件进行了综述，文件来源于FAOLEX法律数据库，覆盖2015—2025年。采用跨部门政策提及矩阵对文件进行评估，分为四个整合层级：孤立处理、交叉引用提及、早期整合和完全整合。研究结果还辅以在菲律宾开展的12次基于社区的对话和实地考察，以及18次关键知情人访谈。结果 尽管区域模式存在差异，但大多数政策将AMR和气候变化分开处理。在204份受审政策中，170份孤立处理这些问题，21份存在交叉引用，13份显示出早期整合迹象；没有任何政策达到完全整合。即便是早期整合也仅限于特定活动，并未体现在治理、融资和监测各层面。常见缺口包括缺乏共同目标、协调一致的机构职责，以及能够联合追踪气候变量、疾病模式、抗微生物药物使用和耐药性的监测系统。菲律宾案例反映了类似挑战，尤其是在缺乏跨部门协调和监测系统、地方层面实施和持续融资方面，水产养殖部门仍然代表性不足。讨论与政策启示 在LMICs的动物生产系统中，AMR防控与气候适应的整合在很大程度上仍停留在概念层面。“同一健康”框架提供了实际的制度桥梁，但其环境组成部分在政策和实践中仍然代表性不足。即将开展的AMR国家行动计划、《国家自主贡献》和国家适应计划的修订，是协调目标、监测系统和融资的及时切入点。要取得实质性进展，需要扩大“同一健康”治",null,"Frontiers in Sustainable Food Systems","2026-09-18T00:00:00Z","论文",10,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,19,13,8,1,"基于51个中低收入国家204份政策文件的实证综述，揭示气候适应与抗菌药物耐药治理的政策割裂，对畜牧与水产养殖系统具有较强政策参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"水产养殖","气候变化","畜牧养殖","政策协同","One Health","抗菌药物耐药性",[34,35],"LMIC 畜牧 抗菌药物耐药性 气候政策","FAOLEX 国家政策 抗菌药物 气候变化","LMIC畜牧抗菌药物耐药性气候政策-2917",0,"10.3389\u002Ffsufs.2026.1831307",{"doi":38,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":37,"oa_url":6,"card":65,"direction":69,"ingested_from":71},"W7213549810",[42,44,46,49,51,54,56,59,61,63],{"name":43,"orcid":9},"Percival Ethan Lao",{"name":45,"orcid":9},"Noelle Anne Cubacub",{"name":47,"orcid":48},"Kristina Osbjer","https:\u002F\u002Forcid.org\u002F0000-0002-0902-8232",{"name":50,"orcid":9},"Sunday Ochai",{"name":52,"orcid":53},"Cèlia Ventura-Gabarró","https:\u002F\u002Forcid.org\u002F0000-0002-4937-3874",{"name":55,"orcid":9},"Sophie Caroline Fridman",{"name":57,"orcid":58},"Mohammed Dahiru Aminu","https:\u002F\u002Forcid.org\u002F0000-0003-1649-081X",{"name":60,"orcid":9},"Hazel Ann Fajardo",{"name":62,"orcid":9},"Arabelle Iza Barbin",{"name":64,"orcid":9},"Geminn Louis Apostol",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"综述51个中低收入国家204份政策，评估动物生产中抗微生物耐药与气候变化政策的整合程度。","审查FAOLEX 2015-2025年204份政策文件，辅以菲律宾社区对话和关键","多数政策将AMR与气候变化分开处理，无一实现完全整合，缺乏共同目标、协调责任和联合监测。","农业绿色发展与碳","可研究One Health框架下AMR与气候政策协同监测指标及国家行动计划衔接机制。","openalex","2026-09-19T23:30:08.301375Z",{"total":74,"page":22,"page_size":74,"items":75},6,[76,113,138,184,220,264],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":9,"source_name":10,"source_url":79,"published_at":82,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":83,"score_detail":84,"sources":89,"tags":91,"search_phrases":95,"slug":98,"view_count":37,"doi":99,"paper":100,"created_at":112},2026,"Antimicrobial use and antimicrobial resistance (AMR) in aquaculture: a One Health evidence-chain review of the “use–environment–food–human” continuum","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1801220","Background Antimicrobial resistance (AMR) is a major global challenge to public health and sustainable development. Its emergence and spread are shaped by antimicrobial use (AMU) in human healthcare and animal production, including aquaculture, and by environmental pathways. The One Health framework emphasizes the interdependence of human, animal, and ecosystem health and supports integrated assessment of AMR risk and coordinated mitigation. Objective Within a One Health framework, this review synthesizes evidence linking AMU in aquaculture to AMR and maps the transmission continuum from on-farm use → aquatic environments → aquatic products and processing → human exposure and health risks, highlighting actionable mitigation strategies and research priorities. Methods We conducted an evidence-chain systematic mapping review following PRISMA 2020 guidance. PubMed, Web of Science Core Collection, and Scopus were searched, alongside relevant grey literature from WHO, FAO, and WOAH websites, for studies published between 1 January 2000 and 31 January 2026. Eligible records addressed AMU in aquaculture and\u002For AMR\u002Fantimicrobial resistance genes (ARGs) across the use–environment–food–human continuum. Two reviewers independently screened titles\u002Fabstracts and full texts using predefined criteria. Study quality was assessed with design-appropriate checklists, and findings were synthesized by production system, environmental compartment, food-chain stage, and human exposure pathway. Results AMU in aquaculture can select for resistant bacteria and enrich ARGs within farms. Resistance determinants may spread to surrounding waters and sediments via effluents, sediment disturbance, and the movement of personnel and equipment. In environmental compartments, co-occurrence of ARGs with mobile genetic elements (MGEs) may facilitate horizontal gene transfer, contributing to an environmental resistome. Aquatic products and processing steps can act as vehicles for resistant bacteria\u002FARGs, while human exposure may occur through food handling and consumption, occupational contact, and environment-related routes. Conclusion The AMU–AMR nexus in aquaculture shows typical One Health characteristics. Effective governance should combine: (1) reducing unnecessary AMU, (2) strengthening prevention and biosecurity, (3) harmonizing AMU\u002FAMR surveillance, (4) improving effluent and discharge management, and (5) enhancing hygiene along the food chain and risk communication. Future research should prioritize quantitative attribution, standardized cross-sector monitoring, and integrative synthesis linking molecular AMR indicators to human health outcomes.","背景 抗菌药物耐药性（AMR）是公共卫生和可持续发展面临的重大全球性挑战。其产生和传播受到人类医疗保健和动物生产（包括水产养殖）中抗菌药物使用（AMU）以及环境途径的影响。“同一健康”（One Health）框架强调人类、动物和生态系统健康的相互依存关系，支持对AMR风险进行综合评估和协调减缓。目的 在“同一健康”框架下，本综述综合了水产养殖中AMU与AMR相关联的证据，并绘制了从养殖场使用→水生环境→水产品及加工→人类暴露与健康风险的传播连续谱，重点阐述了可操作的减缓策略和研究优先事项。方法 我们遵循PRISMA 2020指南开展了一项证据链系统映射综述。检索了PubMed、Web of Science核心合集和Scopus，以及WHO、FAO和WOAH网站的相关灰色文献，纳入2000年1月1日至2026年1月31日发表的研究。符合条件的文献涉及水产养殖中的AMU和\u002F或AMR\u002F抗菌药物耐药基因（ARGs）在使用—环境—食品—人类连续谱中的相关问题。两名评审员依据预设标准独立筛选标题\u002F摘要和全文。采用与设计相适应的清单评估研究质量，并按生产系统、环境介质、食物链环节和人类暴露途径对研究结果进行综合。结果 水产养殖中的AMU可选择性富集耐药细菌并增加养殖场内ARGs的丰度。耐药决定因子可通过废水排放、沉积物扰动以及人员和设备的流动传播至周边水体和沉积物。在环境介质中，ARGs与可移动遗传元件（MGEs）的共现可能促进水平基因转移，从而促成环境耐药组（resistome）的形成。水产品及加工环节可作为耐药细菌\u002FARGs的传播载体，而人类暴露可能通过食品处理和消费、职业接触以及与环境相关的途径发生。结论 水产养殖中的AMU–AMR关系呈现出典型的“同一健康”特征。有效治理应结合以下方面：(1) 减少不必要的AMU；(2) 加强预防和生物安全；(3) 统一AMU\u002FAMR监测；(4) 改善废水和排放管理；(5) 加强食物链沿线的卫生管理和风险沟通。未来研究应","2026-09-09T00:00:00Z",81,{"impact":85,"substance":17,"depth":85,"authority":86,"freshness":87,"relevant":22,"comment":88},18,14,9,"以One Health证据链系统梳理水产养殖抗菌药使用到人类健康的传播路径，方法规范、结论具治理指向，对水产绿色养殖与食品安全监管有参考价值。",[90],{"name":10,"url":79},[27,92,93,94,31],"食品安全","环境治理","抗生素耐药",[96,97],"抗生素耐药 水产养殖 环境治理 食品安全","抗生素耐药 水产养殖","抗生素耐药水产养殖环境治理食品安全-2026","10.3389\u002Ffsufs.2026.1801220",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":22,"oa_url":79,"card":107,"direction":69,"ingested_from":71},"W7211999607",[103,105],{"name":104,"orcid":9},"Lingfu Kong",{"name":106,"orcid":9},"Guangzhen Jiang",{"tldr":108,"method":109,"finding":110,"direction":69,"opportunity":111},"系统综述水产养殖抗菌药使用与耐药性沿“使用—环境—食品—人”链的传播证据。","遵循PRISMA 2020的证据链系统映射综述，检索三大数据库及WHO\u002FFAO\u002F","养殖用药可筛选耐药菌并富集ARGs，经废水、沉积物和食品链传播至人，呈典型One Health特征。","可延伸至水产养殖耐药基因环境扩散的定量风险评估与减排干预效果研究。","2026-09-10T23:30:07.044846Z",{"id":114,"title":115,"url":116,"summary":117,"summary_zh":9,"content":118,"source_name":119,"source_url":9,"published_at":120,"category":121,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":126,"tags":128,"search_phrases":133,"slug":136,"view_count":37,"doi":9,"paper":9,"created_at":137},2989,"江苏里下河地区农科院\"稻渔综合种养技术开发及应用\"拟申报江苏省科技创新协会科技创新奖","https:\u002F\u002Fyz.jaas.ac.cn\u002Fxww\u002Ftzgg\u002Fart\u002F2026\u002Fart_2c5c75f92f73496db27610e7142ee3c0.html","江苏里下河地区农业科学研究所9-15发布公示，\"稻渔综合种养技术开发及应用\"项目拟申报2026年度江苏省科技创新协会科技创新成果转化奖，公示期9月15日至9月21日。主要完成单位为江苏里下河地区农业科学研究所、中国水产科学研究院淡水渔业研究中心、江苏省渔业技术推广中心、盱眙县龙虾产业发展服务中心。","关于申报2026年度江苏省科技创新协会科技创新奖的公示\n\n作者：科研管理科 文章来源：里下河所 点击数：次 更新时间：2026-09-15 16:24\n\n我所“稻渔综合种养技术开发及应用”项目拟申报2026年度江苏省科技创新协会科技创新奖，现予以公示。\n\n公示时间： 2026年9月15日至2026年9月21日。公示期内，如有异议，请于2026年9月21日18：00前，以实名书面或电话形式向科研处反映，并提供证明材料。\n\n联系人：盖玉芳 0514-87303751\n\n江苏里下河地区农业科学研究所\n\n2026年9月15日\n\n附：2026年度江苏省科技创新协会科技创新奖 拟申报项目基本信息\n\n项目 名称 稻渔综合种养技术开发及应用\n申报奖励类别 江苏省科技创新协会科技创新成果转化奖\n主要完成人 寇祥明、徐 荣、李 冰、杨思雨、杨 婷、王守红、马林杰、施冠玉、张诚信、张家宏\n主要完成单位 江苏里下河地区农业科学研究所、中国水产科学研究院淡水渔业研究中心、江苏省渔业技术推广中心、盱眙县龙虾产业发展服务中心","江苏省农科院里下河地区农业科学研究所","2026-09-15T00:00:00Z","报道",46,{"impact":124,"substance":13,"depth":74,"authority":124,"freshness":74,"relevant":22,"comment":125},12,"省级科研机构奖项申报公示，属常规程序性信息，行业参考价值有限，不建议进入每日精选。",[127],{"name":119,"url":116},[129,27,130,131,132],"成果转化","稻渔综合种养","里下河","科技创新奖",[134,135],"江苏里下河农科所 稻渔综合种养","江苏省科技创新协会 科技创新奖","江苏里下河农科所稻渔综合种养-2989","2026-09-20T00:03:03.443595Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":83,"score_detail":145,"sources":147,"tags":149,"search_phrases":154,"slug":157,"view_count":37,"doi":158,"paper":159,"created_at":183},2941,"Drought dynamics and climatic drivers in the Tarim Basin using remote sensing indices and pixel-wise machine learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72142-5","Abstract Drought characterization in hyper-arid endorheic basins requires multi-index approaches that capture distinct hydrometeorological processes. This study investigates spatio-temporal drought dynamics in the Tarim Basin (TB)—China’s largest inland arid region—using two complementary remote sensing indices: the Temperature Vegetation Dryness Index (TVDI) for landscape-scale moisture and the Crop Water Stress Index (CWSI) for agricultural drought. Based on 2000–2024 remote sensing and meteorological data, we employed a pixel-wise Random Forest framework with spatial cross-validation and permutation importance analysis to quantify climatic drivers across the TB. Results reveal a fundamental “core-periphery” dichotomy: TVDI identifies persistent extreme drought in the Taklamakan Desert core, while CWSI reveals alleviating water stress in peripheral oasis farmlands (73.21% showing significant decrease, p \u003C 0.05). Despite regional warming-wetting trends, TVDI exhibited an insignificant decrease (54.72% of the basin), contrasting with CWSI's significant agricultural drought alleviation. Vapor Pressure Deficit (VPD)—a key atmospheric dryness indicator—exhibited high relative permutation importance for both drought indices (72–75%), considerably exceeding the values obtained for precipitation (6–8%) within the Tarim Basin. Secondary drivers diverge by land surface type: TVDI responds to Relative Humidity (8.2%) and Precipitation (6.1%), while CWSI is modulated by Land Surface Temperature (9.4%) and Sunshine Hours (7.8%). Partial correlation analyses controlling for topography and temperature confirm VPD’s independent effect on drought severity. Large-scale climate oscillations, particularly the Arctic Oscillation (AO) and ENSO-PDO interactions, significantly modulate interannual drought variability (r = 0.74–0.75, p \u003C 0.01). This study provides the first pixel-scale quantification of the relative dominance of atmospheric water demand over precipitation in driving drought evolution in the Tarim Basin, with VPD contributing 72–75% of the total permutation importance compared to 6–8% for precipitation. This quantitative benchmark offers actionable parameters for drought monitoring systems in arid regions and underscores the need to integrate VPD and large-scale climate signals into early warning frameworks.","摘要 极端干旱内流盆地的干旱特征刻画需要能够捕捉不同水文气象过程的多指标方法。本研究利用两个互补的遥感指数——用于景观尺度土壤湿度的温度植被干旱指数（TVDI）和用于农业干旱的作物水分胁迫指数（CWSI）——探讨了塔里木盆地（TB）——中国最大的内陆干旱区——干旱的时空动态。基于2000—2024年遥感与气象数据，我们采用逐像元随机森林框架，结合空间交叉验证和置换重要性分析，量化了塔里木盆地气候驱动因子的作用。结果揭示了一种根本性的“核心—边缘”二分格局：TVDI识别出塔克拉玛干沙漠核心区持续存在的极端干旱，而CWSI则显示外围绿洲农田的水分胁迫正在缓解（73.21%呈显著下降，p \u003C 0.05）。尽管区域呈现暖湿化趋势，TVDI却表现出不显著的下降（占流域面积的54.72%），这与CWSI所反映的农业干旱显著缓解形成对比。饱和水汽压差（VPD）——一个关键的大气干燥度指标——对两个干旱指数均表现出较高的相对置换重要性（72%—75%），远超塔里木盆地降水所对应的值（6%—8%）。次要驱动因子因地表类型而异：TVDI响应相对湿度（8.2%）和降水（6.1%），而CWSI受地表温度（9.4%）和日照时数（7.8%）调控。控制地形和温度后的偏相关分析证实了VPD对干旱严重程度的独立影响。大尺度气候振荡，尤其是北极涛动（AO）和ENSO-PDO相互作用，显著调控着年际干旱变率（r = 0.74—0.75，p \u003C 0.01）。本研究首次在像元尺度上量化了大气需水量相对于降水在驱动塔里木盆地干旱演变中的相对主导地位，其中VPD贡献了总置换重要性的72%—75%，而降水仅贡献6%—8%。这一定量基准为干旱区干旱监测系统提供了可操作的参数，并凸显了将VPD和大尺度气候信号纳入预警框架的必要性。","Scientific Reports",{"impact":85,"substance":18,"depth":85,"authority":86,"freshness":21,"relevant":22,"comment":146},"首次在像元尺度量化VPD对干旱的主导作用，方法新颖、数据跨度长，对干旱预警系统建设有实质参考价值。",[148],{"name":144,"url":141},[150,28,151,152,153],"农业遥感","遥感监测","干旱预警","塔里木盆地",[155,156],"塔里木盆地 遥感 干旱","TVDI CWSI 干旱监测","塔里木盆地遥感干旱-2941","10.1038\u002Fs41598-026-72142-5",{"doi":158,"openalex_id":160,"authors":161,"venue":144,"cited_by_count":37,"oa_url":141,"card":177,"direction":181,"ingested_from":71},"W7213539719",[162,164,166,168,171,173,175],{"name":163,"orcid":9},"Mutallip Sattar",{"name":165,"orcid":9},"Alim Abbas",{"name":167,"orcid":9},"Sardar Parhat",{"name":169,"orcid":170},"Alimujiang Yasen","https:\u002F\u002Forcid.org\u002F0000-0002-9860-7921",{"name":172,"orcid":9},"Muhemaiti Wahafu",{"name":174,"orcid":9},"Akida Salam",{"name":176,"orcid":9},"Batur Bake",{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"基于遥感指数与逐像元机器学习，量化塔里木盆地2000—2024年干旱动态及气候驱动因子。","TVDI与CWSI双指数，逐像元随机森林、空间交叉验证与置换重要性分析。","干旱呈核心—边缘分异，VPD贡献72–75%远超降水的6–8%，主导干旱演变。","农业遥感与作物表型","可将VPD与大尺度气候振荡纳入干旱预警，并拓展至其他干旱内陆盆地的逐像元归因研究。","2026-09-19T23:30:32.698746Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":9,"source_name":10,"source_url":187,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":196,"search_phrases":201,"slug":204,"view_count":37,"doi":205,"paper":206,"created_at":219},2920,"Breeding rice for optimal maturity across diverse sowing windows under future climate change scenarios in Chongqing area","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1899019","Introduction It is of great significance to optimize rice cultivars for different sowing dates under future climate change for rice sustainable production in Chongqing. Methods In this study, using the APSIM-Rice model and Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) scenarios, we investigated the changes of yield, water consumption and water use efficiency (WUE) across six sowing dates and three cultivars (early, normal and late-maturing cultivars) under baseline period (1981 – 2010) and future climate 2scenarios (2031-2100, SSP2-4.5 and SSP5-8.5). In this study, the climate model BCC-CSM2-MR was selected due to its reliable simulation of China’s climate and compatibility with the APSIM-Rice model. Results Results showed that rice yield with normal cultivar in the baseline period peaked on March 30, with the value of 6,310 kg ha− −1 , and reached its minimum on March 1, with the value of 4,580 kg ha− −1 . Water consumption during the rice growing period increased with the delayed sowing dates (212 mm on March 1 to 276 mm on April 20). The response trend of water use efficiency (WUE) to different sowing dates was identical to that of yield, with the maximum WUE of 23.30 kg ha− −1 mm −1 achieved when sown on March 30. Under future SSP2-4.5 and SSP5-8.5 scenarios, early sowing (March 1–20) consistently enhanced yield and WUE for normal cultivars (maximum increments were 25.40% and 29.00% for yield and WUE), while late sowing (March 30–April 20) caused severe losses (up to 26.90% and 34.60% for yield and WUE for April 10 under SSP5-8.5 in the 2060s), with water consumption rising across all sowing dates. Early-maturing cultivars reduced yield (10.60%–44.60%) and WUE (7.70%–43.90%) across all sowing dates under future scenarios, whereas late-maturing cultivars synergized with early sowing to boost yield (up to 25.62%) and WUE (up to 32.85%) but exacerbated losses for late sowing, with water consumption increasing significantly (up to 56.30% under SSP5-8.5 in the 2060s). Discussion These findings provide critical scientific support for optimizing sowing date and cultivar combinations, thereby enhancing the climate resilience and sustainability of rice production in Chongqing and similar subtropical monsoon regions. However, in the future, more climate models, extreme climate impacts, and agronomic factors should be considered","引言 优化不同播期下的水稻品种对重庆未来气候变化背景下的水稻可持续生产具有重要意义。方法 本研究利用APSIM-Rice模型和耦合模式比较计划第六阶段（CMIP6）共享社会经济路径（SSP）情景，研究了基准期（1981—2010年）和未来气候情景（2031—2100年，SSP2-4.5和SSP5-8.5）下6个播期和3个品种（早熟、中熟和晚熟品种）的产量、耗水量和水分利用效率（WUE）变化。本研究选择气候模式BCC-CSM2-MR，因其对中国气候的模拟可靠且与APSIM-Rice模型兼容。结果 结果表明，基准期中熟品种水稻产量在3月30日达到峰值，为6 310 kg ha⁻¹，在3月1日降至最低，为4 580 kg ha⁻¹。水稻生育期耗水量随播期推迟而增加（3月1日的212 mm增至4月20日的276 mm）。水分利用效率（WUE）对不同播期的响应趋势与产量一致，3月30日播种时WUE最高，为23.30 kg ha⁻¹ mm⁻¹。在未来SSP2-4.5和SSP5-8.5情景下，早播（3月1—20日）持续提高中熟品种的产量和WUE（产量和WUE的最大增幅分别为25.40%和29.00%），而晚播（3月30日—4月20日）造成严重损失（在2060年代SSP5-8.5情景下，4月10日播种的产量和WUE损失分别高达26.90%和34.60%），且所有播期的耗水量均增加。在未来情景下，早熟品种在所有播期均降低产量（10.60%—44.60%）和WUE（7.70%—43.90%），而晚熟品种与早播协同提高产量（最高25.62%）和WUE（最高32.85%），但加剧了晚播的损失，耗水量显著增加（在2060年代SSP5-8.5情景下最高达56.30%）。讨论 这些发现为优化播期和品种组合提供了关键科学支撑，从而增强重庆及类似亚热带季风区水稻生产的气候韧性和可持续性。然而，未来应考虑更多气候模式、极端气候影响和农艺因素。",78,{"impact":192,"substance":17,"depth":85,"authority":20,"freshness":87,"relevant":22,"comment":193},16,"基于APSIM-Rice与CMIP6情景模拟重庆水稻播期与品种组合，数据扎实、结论对区域气候适应性育种有参考价值，但属细分领域研究，影响力有限。",[195],{"name":10,"url":187},[197,198,28,199,200],"智慧农业","水稻","品种选育","播期优化",[202,203],"APSIM-Rice 水稻 播期","重庆 水稻 品种 气候","APSIM-Rice水稻播期-2920","10.3389\u002Ffsufs.2026.1899019",{"doi":205,"openalex_id":207,"authors":208,"venue":10,"cited_by_count":37,"oa_url":187,"card":213,"direction":217,"ingested_from":71},"W7213555399",[209,211],{"name":210,"orcid":9},"Jianzhao Tang",{"name":212,"orcid":9},"Jianping Zhang",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"用APSIM-Rice与CMIP6情景模拟重庆不同播期和品种水稻产量、耗水与水分利用效率。","APSIM-Rice模型结合CMIP6 SSP2-4.5\u002FSSP5-8.5情景及","未来早播配晚熟品种可增产提效，晚播则大幅减产，各播期耗水均上升。","农业人工智能与决策模型","可引入多模型集合、极端气候与氮肥管理等农艺因素，优化播期-品种组合的适应策略。","2026-09-19T23:30:08.567885Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":10,"source_url":223,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":231,"tags":233,"search_phrases":237,"slug":240,"view_count":37,"doi":241,"paper":242,"created_at":263},2913,"A quantum-inspired multi-objective learning framework for real-time sustainable aquaculture water quality prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827991","Management of sustainable aquaculture necessitates accurate prediction of water quality parameters, since environmental variability is an important factor in determining aquatic productivity and ecological stability. Traditional machine learning systems are usually limited by inefficient parameter optimization, feature overlap, and limited adaptability to non-linear and time-varying environmental trends. To address these issues, a new concept is proposed, which is called Quantum-Inspired Aquaculture Optimization Network (Q-AQUAOptNet). To improve the process of feature selection, hyperparameter optimization, and model stability before temporal prediction, the proposed architecture combines sustainability index modeling and quantum-inspired multi-objective evolutionary optimization. The optimization approach balances exploration and exploitation, enhancing the learning ability of the prediction model. Implementation of the framework was done through Python-based simulation tools in preprocessing, optimization, and performance evaluation. The created system had a Root Mean Square Error of 0.24, a Mean Absolute Error of 0.21, and an R 2 of 0.97, which means that it has effective predictive power and a great ability to explain the variance. Integrating evolutionary optimization and temporal learning improves generalization performance and reduces prediction uncertainty. The findings demonstrate that Q-AQUAOptNet provides effective predictive performance within a simulation-based framework and shows potential as a sustainability-oriented intelligent water-quality monitoring and decision support system for aquaculture applications.","可持续水产养殖管理需要准确预测水质参数，因为环境变异性是决定水生生产力和生态稳定性的重要因素。传统机器学习系统通常受限于参数优化效率低、特征重叠以及对非线性和时变环境趋势的适应能力有限。为解决这些问题，提出了一种新概念，称为量子启发水产养殖优化网络（Quantum-Inspired Aquaculture Optimization Network，Q-AQUAOptNet）。为在时间预测之前改进特征选择、超参数优化和模型稳定性，所提出的架构结合了可持续性指数建模和量子启发多目标进化优化。该优化方法平衡了探索与利用，增强了预测模型的学习能力。该框架的实现通过基于Python的仿真工具完成，涵盖预处理、优化和性能评估。所构建系统的均方根误差为0.24，平均绝对误差为0.21，R²为0.97，这意味着其具有有效的预测能力和较强的方差解释能力。将进化优化与时间学习相结合，提高了泛化性能并降低了预测不确定性。研究结果表明，Q-AQUAOptNet在基于仿真的框架内提供了有效的预测性能，并显示出作为面向可持续性的智能水质监测与决策支持系统应用于水产养殖的潜力。",71,{"impact":124,"substance":228,"depth":229,"authority":20,"freshness":87,"relevant":22,"comment":230},20,17,"提出量子启发多目标优化网络用于水产养殖水质预测，仿真指标较优，属智慧渔业细分领域的方法学进展，但尚处仿真阶段、缺乏真实场景验证。",[232],{"name":10,"url":223},[197,234,27,235,236],"农业人工智能","多目标优化","水质预测",[238,239],"Q-AQUAOptNet 水产养殖 水质预测","农业人工智能 多目标优化 智慧农业 水产养殖","Q-AQUAOptNet水产养殖水质预测-2913","10.3389\u002Ffsufs.2026.1827991",{"doi":241,"openalex_id":243,"authors":244,"venue":10,"cited_by_count":37,"oa_url":223,"card":257,"direction":261,"ingested_from":71},"W7213532503",[245,248,251,253,255],{"name":246,"orcid":247},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":249,"orcid":250},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":252,"orcid":9},"Sara A. Ghorashi",{"name":254,"orcid":9},"Betty Wan Niu Voon",{"name":256,"orcid":9},"Mohamed Hafez",{"tldr":258,"method":259,"finding":260,"direction":261,"opportunity":262},"提出量子启发多目标优化网络Q-AQUAOptNet，实现水产养殖水质实时预测。","量子启发多目标进化优化结合时间预测与可持续指数建模，Python仿真。","RMSE 0.24、MAE 0.21、R² 0.97，预测精度高且泛化好。","智慧农业 \u002F 农业物联网","可探索真实养殖场部署与多源传感器融合，验证量子启发优化在边缘端的实时性。","2026-09-19T23:30:07.985707Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":37,"score_detail":272,"sources":274,"tags":276,"search_phrases":280,"slug":283,"view_count":37,"doi":284,"paper":285,"created_at":309},2867,"From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpubh.2026.1922265","Background Antimicrobial resistance (AMR) is a leading global health threat requiring coordinated surveillance across human, animal, environmental, and genomic systems. Machine learning is increasingly applied to AMR data, yet its contribution to actionable surveillance intelligence, rather than prediction alone, remains poorly defined. Objective To map how machine-learning approaches generate AMR surveillance intelligence, to characterise their validation and implementation maturity, and to propose a framework distinguishing technical prediction from actionable surveillance intelligence. Methods We conducted a scoping review following JBI methodology and PRISMA-ScR reporting. PubMed\u002FMEDLINE, Scopus, and Web of Science were searched from January 2015 to May 2026 for studies applying machine learning or related methods to AMR surveillance intelligence. Two reviewers independently screened and charted records. Of 1,985 records, 66 met eligibility and formed the working evidence base; 41 studies (40 core empirical and one supporting preprint) were appraised against TRIPOD+AI- and PROBAST-aligned reporting, validation, and implementation-readiness domains. Results Machine learning was applied across five clusters: clinical and electronic-health-record risk prediction and decision support; genomic and whole-genome-sequencing prediction; MALDI-TOF-based rapid resistance prediction; wastewater and metagenomic surveillance; and environmental, animal, food-chain, and One Health early warning. Prediction and risk stratification predominated, but validation maturity was limited: most studies were retrospective or internally validated, with few using external, cross-country, temporal, prospective, or drift-focused evaluation. On appraisal, discrimination was reported in 31 of 41 studies (76%) and explainability in 26 (63%); by contrast, external or temporal validation was present in only 15 (37%), calibration in 5 (12%), prospective evaluation in 1 (2%), and operational deployment with measured clinical or public-health impact in a single study (2%). Conclusion Machine learning can support AMR surveillance intelligence across clinical, genomic, diagnostic, environmental, and One Health settings, but the evidence demonstrates technical feasibility far more convincingly than operational readiness. Realising this transition will require external and prospective validation, calibration and drift monitoring, transparent and equitable reporting, workflow integration, and explicit linkage of model outputs to clinical and public-health action. We propose a One Health AMR Surveillance Intelligence Framework to organise this shift from data generation toward actionable, adaptive surveillance intelligence.","背景 抗微生物药物耐药性（AMR）是主要的全球健康威胁，需要在人类、动物、环境和基因组系统之间开展协调监测。机器学习正越来越多地应用于AMR数据，但其对可操作监测情报的贡献，而非仅用于预测，仍界定不清。目的 梳理机器学习方法如何生成AMR监测情报，描述其验证和实施成熟度，并提出一个区分技术预测与可操作监测情报的框架。方法 我们按照JBI方法学和PRISMA-ScR报告规范开展了一项范围综述。检索PubMed\u002FMEDLINE、Scopus和Web of Science，时间范围为2015年1月至2026年5月，纳入将机器学习或相关方法应用于AMR监测情报的研究。两名综述者独立筛选并提取记录。在1，985条记录中，66项符合纳入标准并构成工作证据基础；41项研究（40项核心实证研究和1项支持性预印本）依据与TRIPOD+AI和PROBAST一致的报告、验证和实施准备度领域进行了评价。结果 机器学习应用于五个集群：临床和电子健康记录风险预测与决策支持；基因组和全基因组测序预测；基于MALDI-TOF的快速耐药预测；废水和宏基因组监测；以及环境、动物、食物链和“同一健康”早期预警。预测和风险分层占主导，但验证成熟度有限：大多数研究为回顾性或内部验证，少数采用外部、跨国、时间、前瞻性或聚焦漂移的评估。评价中，41项研究有31项（76%）报告了区分度，26项（63%）报告了可解释性；相比之下，仅15项（37%）进行了外部或时间验证，5项（12%）进行了校准，1项（2%）进行了前瞻性评价，仅1项研究（2%）进行了实际部署并测量了临床或公共卫生影响。结论 机器学习可在临床、基因组、诊断、环境和“同一健康”背景下支持AMR监测情报，但证据在技术可行性方面远比在操作准备度方面更具说服力。实现这一转变需要外部和前瞻性验证、校准和漂移监测、透明且公平的报告、工作流程整合，以及ex","Frontiers in Public Health","2026-09-17T00:00:00Z",{"impact":37,"substance":37,"depth":37,"authority":37,"freshness":37,"relevant":37,"comment":273},"该文为公共卫生领域抗微生物耐药监测的机器学习综述，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[275],{"name":270,"url":267},[277,31,278,279],"机器学习","抗微生物耐药","公共卫生监测",[281,282],"公共卫生监测 抗微生物耐药 机器学习 One Health","公共卫生监测 抗微生物耐药","公共卫生监测抗微生物耐药机器学习OneHealth-2867","10.3389\u002Ffpubh.2026.1922265",{"doi":284,"openalex_id":286,"authors":287,"venue":270,"cited_by_count":37,"oa_url":303,"card":304,"direction":181,"ingested_from":71},"W7213443344",[288,291,293,295,298,300],{"name":289,"orcid":290},"Syed Arman Rabbani","https:\u002F\u002Forcid.org\u002F0000-0002-8454-8158",{"name":292,"orcid":9},"Mohamed El-Tanani",{"name":294,"orcid":9},"Ismail I. Matalka",{"name":296,"orcid":297},"Shrestha Sharma","https:\u002F\u002Forcid.org\u002F0000-0001-8527-3419",{"name":299,"orcid":9},"Manita saini",{"name":301,"orcid":302},"Rakesh Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-8807-8421","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fpublic-health\u002Farticles\u002F10.3389\u002Ffpubh.2026.1922265\u002Fpdf",{"tldr":305,"method":306,"finding":307,"direction":217,"opportunity":308},"综述机器学习在One Health抗微生物耐药监测情报中的应用与成熟度。","遵循JBI与PRISMA-ScR的范围综述，检索三大数据库并评估66项研究。","ML多用于预测与风险分层，但外部验证、校准与落地应用严重不足。","可探索动物-环境-食品链AMR数据的跨域外部验证与漂移监测框架。","2026-09-18T23:30:21.367014Z"]