[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1266":3,"related-1266":109},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":73,"slug":76,"view_count":63,"doi":77,"paper":78,"created_at":108},1266,"Measurement, Modelling, and Mitigation of Methane Emissions in Agricultural Systems for Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i92133","Methane is a high-priority agricultural greenhouse gas because substantial emissions arise from enteric fermentation, anaerobic manure management, and flooded rice cultivation, while its relatively short atmospheric lifetime creates an opportunity for comparatively rapid climate benefits from sustained abatement. Yet mitigation claims are only as credible as the measurements and models used to establish baselines, attribute sources, quantify intervention effects, and detect leakage or trade-offs. This critical narrative review integrates evidence on agricultural methane measurement, emission modelling, and mitigation in a climate-smart agriculture framework. Literature was selected from agricultural, biomedical, environmental and multidisciplinary scholarly indexes, with emphasis on peer-reviewed evidence published from 2000 to 24 June 2026 and retention of earlier foundational work when necessary. The synthesis shows that no measurement method is universally superior: respiration chambers and tracer approaches offer controlled animal-level estimation, automated head-chamber systems increase throughput, micrometeorological and inverse methods enlarge the measurement footprint, and chamber, eddy-covariance and emerging remote-sensing approaches address contrasting scales in rice and manure systems. Model uncertainty often originates as much from activity data, dry matter intake, temperature, storage history, water regime and management assumptions as from model structure itself. Among mitigation options, 3-nitrooxypropanol has the most consistent evidence for substantial enteric methane suppression under controlled feeding, whereas red seaweed, nitrate, lipids and genetic selection show context-dependent promise with distinct safety, delivery or scalability constraints. Manure acidification, shortened anaerobic storage and well-managed anaerobic digestion can reduce emissions, but methane leakage can erode expected benefits. In rice, alternate wetting and drying is strongly supported for methane and water reduction, although yield and nitrous oxide responses depend on threshold, soil and management. Climate-smart deployment therefore requires scale-matched measurement, transparent uncertainty, multi-gas and productivity accounting, and verification systems capable of reconciling farm-level interventions with facility and landscape emissions. The strongest pathway is an integrated measurement-model-management architecture rather than reliance on any single technology.",null,"Archives of Current Research International","2026-08-29T00:00:00Z","论文",10,false,71,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,12,3,1,"系统综述农业甲烷测量、建模与减排路径，对气候智慧农业实践有参考价值。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","气候智慧农业","甲烷减排","农业温室气体",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"needs_review","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":38,"reason":72},[36,43,49,55,61,67],{"code":37,"score":18,"supported":38,"title":39,"abstract":41,"tags":42},"I1",true,[40],"smart agriculture",[40],[25],{"code":44,"score":19,"supported":38,"title":45,"abstract":46,"tags":48},"A1",[],[47],"rice",[],{"code":50,"score":19,"supported":38,"title":51,"abstract":52,"tags":54},"B7",[],[53],"greenhouse",[],{"code":56,"score":19,"supported":38,"title":57,"abstract":58,"tags":60},"H2",[],[59],"soil",[],{"code":62,"score":63,"supported":13,"title":64,"abstract":65,"tags":66},"H4",2,[],[],[28,27],{"code":68,"score":20,"supported":13,"title":69,"abstract":70,"tags":71},"H7",[],[],[26],"多个产业证据相当，需要确认主归属",[74,75],"农业温室气体 气候智慧农业 智慧农业 甲烷减排","农业温室气体 气候智慧农业","农业温室气体气候智慧农业智慧农业甲烷减排-1266","10.9734\u002Facri\u002F2026\u002Fv26i92133",{"doi":77,"openalex_id":79,"authors":80,"venue":9,"cited_by_count":99,"oa_url":6,"card":100,"direction":106,"ingested_from":107},"W7204641378",[81,83,85,87,89,91,93,95,97],{"name":82,"orcid":8},"M. N. Karthik",{"name":84,"orcid":8},"V. Chandrika",{"name":86,"orcid":8},"D. Subramanyam",{"name":88,"orcid":8},"P. Venkata Subbaiah",{"name":90,"orcid":8},"P. Latha",{"name":92,"orcid":8},"S. S. T. Aarthi",{"name":94,"orcid":8},"K. B. Hazeera",{"name":96,"orcid":8},"G.P. Sathwik",{"name":98,"orcid":8},"K. Deepasri",0,{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"综述农业甲烷测量、建模与减排，提出综合框架以支持气候智慧农业。","批判性综述2000-2026年文献，整合测量、模型与减排证据。","无普适测量法，减排需综合测量-模型-管理架构。","农业绿色发展与碳","可探索多尺度测量与模型耦合，及减排措施在不同农业系统中的协同效应与验证体系。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:11.854117Z",{"total":110,"page":20,"page_size":110,"items":111},6,[112,203,256,331,412,493],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":8,"source_name":118,"source_url":115,"published_at":119,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":137,"classification":138,"search_phrases":185,"slug":188,"view_count":99,"doi":189,"paper":190,"created_at":202},5232,"Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02540-1","Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.","人工智能（AI）正在重塑农业，通过在作物生产系统中实现数据驱动的决策，并支持向更可持续、更具韧性和气候智能型农业转型。本综述批判性地综合了AI应用的进展，涵盖精准灌溉、养分与土壤管理、产量预测、病虫害监测、计算机视觉、自主农业机器人以及基于大语言模型（LLM）的决策支持系统。除总结技术发展外，本综述还评估了其实际部署情况，强调农业AI的成功不仅取决于预测准确性，还取决于模型泛化能力、可解释性、经济可行性、互操作性以及与现有农学知识的整合。文中讨论了包括视觉Transformer、基础模型、数字孪生、多模态AI、检索增强生成以及联邦学习和持续学习在内的新兴技术，将其视为克服数据稀缺、环境变异性和田间可扩展实施等局限的重要方向。本综述强调，负责任的AI采用需要透明的治理框架，以解决数据所有权、隐私、算法公平性、网络安全和公平获取等问题，尤其是对小农耕作系统而言。尽管众多AI模型报告了卓越的性能，但农业领域的广泛采用仍受限于跨区域验证、碎片化数据集、基础设施要求以及现实世界部署不足。未来的进展将取决于多学科协作，整合人工智能、传感技术、机器人技术、作物科学和农业工程领域的进展，以开发稳健、可信且以农民为中心的决策支持系统。通过批判性评估技术创新和实施挑战，本综述提供了一个理解AI在农业中现状的框架，同时确定了将塑造下一代可持续和气候韧性农业系统的研究优先事项。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",83,{"impact":122,"substance":122,"depth":16,"authority":18,"freshness":123,"relevant":20,"comment":124},22,9,"该综述系统梳理了AI在农业中的应用与落地瓶颈，覆盖精准灌溉、机器人、大模型决策支持等方向，信息密度高，但属综述类论文，产业级突破性有限，可入精选。",[126],{"name":118,"url":115},[25,128,129,130,26],"农业人工智能","大语言模型","数字孪生",{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":139,"locked":13,"version":32,"method":33,"evidence":140},"classified",{"candidates":141,"agricultural_evidence":38},[142,152,157,164,169,175,181],{"code":132,"score":143,"supported":38,"title":144,"abstract":146,"tags":151},19,[145],"artificial intelligence",[147,145,148,149,150],"计算机视觉","computer vision","large language model","decision support",[128,129],{"code":56,"score":110,"supported":38,"title":153,"abstract":154,"tags":156},[],[155,59],"土壤",[],{"code":158,"score":110,"supported":38,"title":159,"abstract":160,"tags":163},"H3",[],[161,162],"灌溉","irrigation",[],{"code":37,"score":165,"supported":38,"title":166,"abstract":167,"tags":168},5,[],[130],[130,25],{"code":170,"score":19,"supported":38,"title":171,"abstract":172,"tags":174},"F6",[],[173],"农业机器人",[],{"code":176,"score":19,"supported":38,"title":177,"abstract":178,"tags":180},"G2",[],[179],"smallholder",[],{"code":68,"score":20,"supported":13,"title":182,"abstract":183,"tags":184},[],[],[26],[186,187],"气候智慧农业 AI","农业人工智能 气候智慧农业 大语言模型 数字孪生","气候智慧农业AI-5232","10.1007\u002Fs44163-026-02540-1",{"doi":189,"openalex_id":191,"authors":192,"venue":118,"cited_by_count":99,"oa_url":115,"card":196,"direction":106,"ingested_from":107},"W7222823256",[193],{"name":194,"orcid":195},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":197,"method":198,"finding":199,"direction":200,"opportunity":201},"综述农业人工智能在精准灌溉、产量预测、病虫害监测等应用及落地挑战。","文献综述，覆盖视觉Transformer、基础模型、数字孪生、多模态AI与联邦学","农业AI落地受限于跨区域验证、数据碎片化与基础设施，需兼顾可解释性与公平治理。","农业人工智能与决策模型","可研究小农户场景下跨区域可泛化、可解释且低成本的农业大模型与联邦学习部署。","2026-10-18T23:30:07.666127Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":8,"source_name":209,"source_url":206,"published_at":210,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":211,"score_detail":212,"sources":217,"tags":219,"primary_topic":223,"secondary_topics":227,"classification":228,"search_phrases":239,"slug":242,"view_count":99,"doi":243,"paper":244,"created_at":255},4654,"The Role of Indigenous Knowledge in Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23283885","The Role of Indigenous Knowledge in Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda。Zenodo (CERN European Organization for Nuclear Research)","本土知识在气候智慧型农业中的作用：乌干达天气预报案例研究。Zenodo（CERN欧洲核子研究组织）","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",67,{"impact":18,"substance":16,"depth":213,"authority":214,"freshness":215,"relevant":20,"comment":216},16,13,8,"非洲本土知识与气候智慧农业结合的案例研究，对国内农业气象信息化有借鉴价值，但地域相关性有限。",[218],{"name":209,"url":206},[25,26,220,221,222],"乌干达","传统知识","气象预报",{"code":37,"slug":224,"name":225,"path":226,"parent_code":136},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":139,"locked":13,"version":32,"method":33,"evidence":229},{"candidates":230,"agricultural_evidence":38},[231,235],{"code":37,"score":18,"supported":38,"title":232,"abstract":233,"tags":234},[40],[40],[25],{"code":68,"score":20,"supported":13,"title":236,"abstract":237,"tags":238},[],[],[26],[240,241],"乌干达 气象预报 传统知识","气候智慧农业 土著知识","乌干达气象预报传统知识-4654","10.5281\u002Fzenodo.23283885",{"doi":243,"openalex_id":245,"authors":246,"venue":209,"cited_by_count":99,"oa_url":206,"card":249,"direction":106,"ingested_from":107},"W7221309644",[247],{"name":248,"orcid":8},"Academians Publishers",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"研究乌干达土著知识在气候智慧型农业天气预测中的作用。","案例研究，结合土著知识与气候智慧型农业。","土著天气预测知识对气候智慧型农业有重要价值。","数字乡村与农业信息化","可将土著天气预测知识与现代气象数据融合，开发本地化农业决策支持系统。","2026-10-11T23:30:07.345273Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":8,"source_name":262,"source_url":259,"published_at":263,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":211,"score_detail":264,"sources":267,"tags":269,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":295,"slug":298,"view_count":99,"doi":299,"paper":300,"created_at":330},4423,"A stage-weighted environmental risk and visual severity integration model for climate-smart management of cassava anthracnose in humid tropical systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffphgy.2026.1898224","Colletotrichum anthracnose is a persistent constraint on cassava root yield and planting material quality in humid tropical systems, where calendar-based management often leads to poorly timed or excessive fungicide use. This study developed and pilot-tested the Cassava Anthracnose Risk Index (CARI), a stage-sensitive, persistence-adjusted early-warning framework integrating low-cost environmental sensing with structured visual severity scoring for climate-smart, input-reduced disease management. Microclimate sensors were deployed at CSIR-CRI Fumesua, Ghana, across a 13-month rainfed crop cycle (March 2025 – April 2026) using the susceptible landrace Debo . A stage-weighted Environmental Risk Subscore (ERS; 0–600 points, ERS%) was constructed from six environmental components, moderated by a persistence factor requiring sustained infection-favorable conditions, and stratified across three growth stages. A Visual Symptom Subscore (VSS; 0–40) was derived from standardised weekly severity assessments of 15 sentinel plants, integrated into a composite CARI (0–100) with stage-specific alert thresholds. Across 73 valid Stage 3 monitoring days, CARI classified all observations as Low Risk and issued no Amber or Red alerts, including on 37 of 73 days with sustained nocturnal high-humidity conditions that would have triggered a precautionary alert under a simple threshold approach. In the single confirmed disease episode observed, an approximately one-day interval separated the highest environmental risk signal from confirmed symptom onset; this is not presented as a validated lag value, a preceding 25-day sensor outage means antecedent conditions cannot be characterised. CARI did not issue an intervention recommendation during this episode (11–24 February 2026) because concurrent environmental risk remained below the Amber threshold. Published decision support systems for Colletotrichum anthracnose in other pathosystems report fungicide reductions of 30–70% relative to calendar-based approaches; these figures are cited only for benchmarking and were not measured here, as no comparative fungicide trial was conducted. These results provide proof of concept for the technical operability of CARI and identify the dataset and analyses required for formal predictive evaluation in a planned multi-season study; they should not be interpreted as evidence of CARI’s predictive accuracy or input-reduction performance.","炭疽病（Colletotrichum anthracnose）是湿润热带地区木薯块根产量和种植材料质量的持续制约因素，当地基于日历的防治方式常导致杀菌剂施用时机不当或过量。本研究开发并初步测试了木薯炭疽病风险指数（Cassava Anthracnose Risk Index, CARI），这是一个阶段敏感、持续性校正的预警框架，将低成本环境传感与结构化视觉严重度评分相结合，服务于气候智慧型、减投入的病害管理。微气候传感器部署于加纳CSIR-CRI Fumesua试验站，覆盖13个月雨养作物周期（2025年3月–2026年4月），供试品种为感病地方品种Debo。研究构建了阶段加权环境风险子评分（Environmental Risk Subscore, ERS；0–600分，ERS%），由六个环境组分构成，并受持续性因子调节——该因子要求感染有利条件持续存在——同时按三个生长阶段进行分层。视觉症状子评分（Visual Symptom Subscore, VSS；0–40）来源于对15株哨兵植株的标准化每周严重度评估，并与ERS整合为综合CARI（0–100），设定阶段特异性警报阈值。在73个有效的第3阶段监测日中，CARI将所有观测归类为低风险，未发出琥珀色或红色警报，其中包括73天中的37天存在持续性夜间高湿条件——若采用简单阈值方法，这些条件本会触发预防性警报。在观察到的唯一一次确诊病害事件中，最高环境风险信号与确诊症状出现之间相隔约一天；此值不作为经验证的滞后值呈现，因为此前25天的传感器中断意味着无法表征先行条件。CARI在该事件期间（2026年2月11–24日）未发出干预建议，因为同期环境风险仍低于琥珀色阈值。其他病理系统中针对炭疽病（Colletotrichum anthracnose）已发表的决策支持系统报告，相对于基于日历的方法，杀菌剂用量减少30–70%；这些数据仅用于基准参照，本研究并未测定，因为未开展比较性杀菌剂试验。上述结果为CARI的技术可操作性提供了概念验证，并明确了在计划中的多季节研究中开展正式预测评估所需的数据集和分析；不应将其解读为CARI预测准确性或减投入性能的证据。","Frontiers in Plant Physiology","2026-10-06T00:00:00Z",{"impact":215,"substance":17,"depth":265,"authority":214,"freshness":123,"relevant":20,"comment":266},17,"方法新颖、数据翔实的木薯炭疽病早期预警概念验证研究，但仅为单点单季试点、无预测精度与减药效果验证，公共影响有限，适合作为技术前沿条目而非每日精选头条。",[268],{"name":262,"url":259},[25,270,26,271,272],"木薯","病害预警","环境传感",{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},[],{"status":139,"locked":13,"version":32,"method":33,"evidence":276},{"candidates":277,"agricultural_evidence":38},[278,282,287,291],{"code":132,"score":19,"supported":38,"title":279,"abstract":280,"tags":281},[],[150],[],{"code":283,"score":20,"supported":13,"title":284,"abstract":285,"tags":286},"A5",[],[],[270],{"code":68,"score":20,"supported":13,"title":288,"abstract":289,"tags":290},[],[],[26],{"code":37,"score":20,"supported":13,"title":292,"abstract":293,"tags":294},[],[],[25],[296,297],"CSIR-CRI 木薯 炭疽病","木薯炭疽病 风险指数 CARI","CSIR-CRI木薯炭疽病-4423","10.3389\u002Ffphgy.2026.1898224",{"doi":299,"openalex_id":301,"authors":302,"venue":262,"cited_by_count":99,"oa_url":324,"card":325,"direction":106,"ingested_from":107},"W7220664728",[303,306,309,311,313,316,318,320,322],{"name":304,"orcid":305},"Cevdet Bulut","https:\u002F\u002Forcid.org\u002F0000-0003-1614-7990",{"name":307,"orcid":308},"Kwaku Onwona-Hwesofour Asante","https:\u002F\u002Forcid.org\u002F0000-0003-3539-3505",{"name":310,"orcid":8},"Gordon Akon-Yamga",{"name":312,"orcid":8},"Monica Ode Adu-Gyamfi",{"name":314,"orcid":315},"Zippora Appiah-Kubi","https:\u002F\u002Forcid.org\u002F0000-0001-5653-5707",{"name":317,"orcid":8},"Sarah Odame Afari",{"name":319,"orcid":8},"Josua Brett",{"name":321,"orcid":8},"Kwame Onwona Simpe",{"name":323,"orcid":8},"Reuben Nutefe Kuavedzi","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-physiology\u002Farticles\u002F10.3389\u002Ffphgy.2026.1898224\u002Fpdf",{"tldr":326,"method":327,"finding":328,"direction":106,"opportunity":329},"构建木薯炭疽病阶段加权风险指数CARI，融合环境传感与目测病情，实现气候智慧型预警。","13个月微气候传感监测加15株每周病情目测，构建ERS与VSS复合指数。","CARI全程判为低风险、未发预警，仅一次发病且环境风险信号领先症状约一天。","需多季节数据验证预警阈值与滞后关系，并开展减药对比试验评估实际投入削减效果。","2026-10-08T23:30:11.101422Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":8,"source_name":337,"source_url":334,"published_at":338,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":339,"score_detail":340,"sources":342,"tags":344,"primary_topic":348,"secondary_topics":354,"classification":355,"search_phrases":395,"slug":398,"view_count":63,"doi":399,"paper":400,"created_at":411},3826,"From Dry Fields To Smart Resilience: Climate-Smart Agriculture, Digital Early-Warning Systems And Household Food Security In Chivi District, Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.36923\u002Fsq.v3i1.435","This paper examines how climate-smart agriculture (CSA) and digital early-warning systems can jointly support household food-security resilience in Chivi District, a drought-prone area of south-eastern Zimbabwe dominated by rain-fed smallholder agriculture. Using a structured qualitative evidence synthesis with a district-centred case orientation, the study reviews 47 academic, policy and institutional sources published from 2021 to 14 September 2026. The study prioritises direct Chivi evidence, while using Zimbabwean, southern African, and wider African evidence comparatively where district-specific studies are limited. The synthesis identifies six interrelated themes concerning climate shocks, CSA adoption, small grains and water management, digital early warning, gender and institutional capacity, and integrated resilience. The findings indicate that CSA can strengthen food security when extension, inputs, finance, and markets support conservation agriculture, crop diversification, small grains, soil-fertility management, and water harvesting. However, digital early-warning information contributes to resilience only when it is timely, trusted, localised and translated into feasible action. Accordingly, the paper's principal contribution is an integrated smart-resilience framework centred on an information-to-action pathway: climate information can influence household food-security resilience only when farmers have viable adaptation options and the material, social and institutional capacity to act. The paper therefore treats digital information, agronomic adaptation, gender, household resources and local institutions as interdependent rather than separate interventions.","本文考察气候智慧型农业（climate-smart agriculture, CSA）与数字预警系统如何共同支撑津巴布韦东南部干旱易发区奇维（Chivi）地区以雨养小农农业为主的家庭粮食安全韧性。研究采用以地区为中心的案例导向结构化定性证据综合方法，梳理了2021年至2026年9月14日期间发表的47份学术、政策与机构文献。研究优先采用奇维地区的直接证据，在地区性专门研究有限时，则比较性地参考津巴布韦、南部非洲及更广泛非洲的证据。该综合识别出六个相互关联的主题，涉及气候冲击、气候智慧型农业采纳、小粒谷物与水资源管理、数字预警、性别与制度能力，以及综合韧性。研究结果表明，当推广服务、投入品、金融和市场支持保护性农业、作物多样化、小粒谷物、土壤肥力管理和集水时，气候智慧型农业能够增强粮食安全。然而，数字预警信息只有在及时、可信、本地化并转化为可行行动时，才能促进韧性。因此，本文的主要贡献在于提出了一个以信息到行动路径为核心的综合智慧韧性框架：气候信息只有在农民拥有可行的适应选项以及采取行动所需的物质、社会和制度能力时，才能影响家庭粮食安全韧性。据此，本文将数字信息、农艺适应、性别、家庭资源和地方制度视为相互依存的干预措施，而非彼此分离的干预措施。","Sustainability Quest","2026-09-29T00:00:00Z",56,{"impact":215,"substance":16,"depth":213,"authority":110,"freshness":215,"relevant":20,"comment":341},"以津巴布韦Chivi区为案例的定性证据综述，提出信息到行动的气候智慧农业韧性框架，对数字预警落地条件有参考价值，但属区域案例研究，公共影响有限。",[343],{"name":337,"url":334},[25,345,346,26,347],"粮食安全","小农户","数字预警",{"code":349,"slug":350,"name":351,"path":352,"parent_code":353},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":139,"locked":13,"version":32,"method":33,"evidence":356},{"candidates":357,"agricultural_evidence":38},[358,364,368,375,379,384,389],{"code":349,"score":359,"supported":38,"title":360,"abstract":362,"tags":363},15,[361],"food security",[345,361],[345],{"code":37,"score":18,"supported":38,"title":365,"abstract":366,"tags":367},[40],[40],[25],{"code":68,"score":369,"supported":38,"title":370,"abstract":371,"tags":374},7,[],[372,373],"干旱","drought",[26],{"code":56,"score":110,"supported":38,"title":376,"abstract":377,"tags":378},[],[155,59],[],{"code":176,"score":380,"supported":38,"title":381,"abstract":382,"tags":383},4,[],[179],[346],{"code":158,"score":19,"supported":38,"title":385,"abstract":386,"tags":388},[],[387],"水资源",[],{"code":390,"score":19,"supported":38,"title":391,"abstract":392,"tags":394},"H8",[],[393],"conservation agriculture",[],[396,397],"气候智慧农业 数字预警 智慧农业 粮食安全","气候智慧农业 数字预警","气候智慧农业数字预警智慧农业粮食安全-3826","10.36923\u002Fsq.v3i1.435",{"doi":399,"openalex_id":401,"authors":402,"venue":337,"cited_by_count":99,"oa_url":334,"card":406,"direction":106,"ingested_from":107},"W7214743351",[403],{"name":404,"orcid":405},"Victor Madziyauswa","https:\u002F\u002Forcid.org\u002F0000-0002-6122-6666",{"tldr":407,"method":408,"finding":409,"direction":253,"opportunity":410},"综述津巴布韦Chivi地区气候智慧农业与数字预警系统如何共同提升家庭粮食安全韧性。","结构化定性证据综合，分析47份2021-2026年学术、政策与机构文献。","数字预警须及时、可信、本地化并转化为可行行动，且需配套适应选项与能力才能提升韧性。","可实证检验“信息—行动”路径，研究数字预警与农户适应能力、性别和制度条件的交互机制。","2026-09-30T23:30:09.849166Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":8,"source_name":418,"source_url":415,"published_at":419,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":420,"score_detail":421,"sources":424,"tags":426,"primary_topic":430,"secondary_topics":435,"classification":436,"search_phrases":463,"slug":466,"view_count":19,"doi":467,"paper":468,"created_at":492},3773,"Precision seed placement to advanced seed delivery: evolution, innovations, and future perspectives of seed tape systems for climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1952898","Seed placement is a critical determinant of crop establishment, resource use efficiency, and agricultural productivity. Traditional sowing methods are associated with poor seed uniformity, high sowing rates, and high labor requirements, creating a need for more sustainable sowing technologies. Seed tape technology has been demonstrated as a precision seed placement approach, particularly through paper, cellulose, natural fiber, and biodegradable polymer-based carriers. This review highlights the evolution of seed tapes from these established precision placement systems toward multifunctional and potentially intelligent seed delivery platforms for climate-smart agriculture. The review demonstrates technologies from emerging and future concepts, including established applications such as biodegradable carrier materials, precision seed placement, and mechanized seed tape systems. Emerging developments include functionalized and selected nano-enabled seed tapes incorporating hydrogels, nutrients, biological inputs, and nanomaterials, as well as smart sensing, AI, IoT, robotics, autonomous deployment, and real-time digital decision support. A technology-readiness framework is used to evaluate the maturity, agronomic relevance, sustainability potential, and commercialization prospects of these systems. Major challenges include manufacturing scale-up, formulation stability, regulatory requirements, economic viability, and field validation of advanced systems. Future research should focus on validating multifunctional materials and biological inputs, improving nano-enabled formulations, and developing digital and autonomous technologies for intelligent seed delivery. The transition from demonstrated precision seed placement to intelligent seed delivery should therefore be viewed as a technology-development trajectory rather than as a currently established integrated system. This review provides a perspective on the evolution of seed tape technology toward multifunctional and potentially intelligent seed delivery for sustainable and climate-smart crop production.","种子位置是决定作物定植、资源利用效率和农业生产力的关键因素。传统播种方式存在种子均匀度差、播种率高和劳动力需求大等问题，因此需要更可持续的播种技术。种子带技术已被证明是一种精准播种方法，尤其是通过纸、纤维素、天然纤维和可生物降解聚合物基载体实现。本文综述了种子带从这些已建立的精准放置系统向多功能乃至潜在智能化种子递送平台的发展历程，以服务于气候智慧型农业。综述展示了新兴和未来概念中的技术，包括已确立的应用，如可生物降解载体材料、精准种子放置和机械化种子带系统。新兴发展包括功能化和精选的纳米赋能种子带，其整合了水凝胶、养分、生物投入品和纳米材料，以及智能传感、人工智能、物联网、机器人技术、自主部署和实时数字决策支持。本文采用技术成熟度框架评估这些系统的成熟度、农艺相关性、可持续性潜力和商业化前景。主要挑战包括制造规模化、配方稳定性、监管要求、经济可行性以及先进系统的田间验证。未来研究应侧重于验证多功能材料和生物投入品、改进纳米赋能配方，以及开发用于智能种子递送的数字化和自主技术。因此，从已证明的精准种子放置向智能种子递送的转变应被视为一个技术发展轨迹，而非当前已建立的集成系统。本文综述了种子带技术向多功能乃至潜在智能化种子递送演进的观点，以服务于可持续和气候智慧型作物生产。","Frontiers in Agronomy","2026-09-28T00:00:00Z",81,{"impact":16,"substance":122,"depth":16,"authority":422,"freshness":123,"relevant":20,"comment":423},14,"综述系统梳理种子带从精准排种到智能种子输送的技术演进，提出技术成熟度评估框架，对气候智慧型农业与智能播种装备研发有参考价值，但属概念性综述、缺乏一手数据，未达重大突破层级。",[425],{"name":418,"url":415},[25,427,26,428,429],"智能农机","精准播种","种子带",{"code":431,"slug":432,"name":433,"path":434,"parent_code":136},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":139,"locked":13,"version":32,"method":33,"evidence":437},{"candidates":438,"agricultural_evidence":38},[439,443,449,455,459],{"code":37,"score":18,"supported":38,"title":440,"abstract":441,"tags":442},[40],[40],[25],{"code":431,"score":110,"supported":38,"title":444,"abstract":445,"tags":448},[],[446,447],"物联网","IoT",[],{"code":450,"score":380,"supported":38,"title":451,"abstract":452,"tags":454},"F5",[],[453],"机械化",[427],{"code":132,"score":19,"supported":38,"title":456,"abstract":457,"tags":458},[],[150],[],{"code":68,"score":20,"supported":13,"title":460,"abstract":461,"tags":462},[],[],[26],[464,465],"种子带 精准播种 智能种子输送","气候智慧农业 智慧农业 智能农机 精准播种","种子带精准播种智能种子输送-3773","10.3389\u002Ffagro.2026.1952898",{"doi":467,"openalex_id":469,"authors":470,"venue":418,"cited_by_count":99,"oa_url":415,"card":487,"direction":106,"ingested_from":107},"W7214664548",[471,474,477,480,482,484],{"name":472,"orcid":473},"Sourav Paramanik","https:\u002F\u002Forcid.org\u002F0009-0001-0628-4337",{"name":475,"orcid":476},"Poonguzhali Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-4282-6148",{"name":478,"orcid":479},"Mahalakshmi Mani","https:\u002F\u002Forcid.org\u002F0009-0001-6257-0928",{"name":481,"orcid":8},"Jothika Manokaran",{"name":483,"orcid":8},"Divya Nagarajan",{"name":485,"orcid":486},"Karthika Rajendran","https:\u002F\u002Forcid.org\u002F0000-0003-4175-2463",{"tldr":488,"method":489,"finding":490,"direction":106,"opportunity":491},"综述种子带技术从精准排种向多功能智能播种平台演进的路径与前景。","文献综述，结合技术成熟度框架评估材料、智能感知与自主播种技术。","种子带正从精准排种向集成纳米材料、AI与物联网的智能播种系统演进，但尚处技术开发阶段。","可研究可降解种子带与AI\u002F物联网融合的智能播种系统，并开展田间验证与商业化评估。","2026-09-29T23:30:10.437986Z",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":8,"source_name":499,"source_url":496,"published_at":500,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":501,"score_detail":502,"sources":505,"tags":507,"primary_topic":510,"secondary_topics":511,"classification":512,"search_phrases":551,"slug":554,"view_count":63,"doi":555,"paper":556,"created_at":571},3546,"When Winters Fail: How Warming Winters Fuel Pest Resurgence and Threaten Food Security","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fpei3.70216","ABSTRACT The Mediterranean fruit fly ( Ceratitis capitata , Wiedemann; Diptera: Tephritidae), commonly known as the Medfly, re‐emerged in Mazandaran province, northern Iran—home to an estimated 25% of the nation's fruit crops and approximately 80% of its citrus production—following its apparent eradication in 1984, an outcome consistent with several years of pheromone‐trap surveillance without further captures. Its return in 2006 and subsequent spread is consistent with a broader phenomenon we term the erosion of climatic pest barriers: seasonal thermal conditions that have historically suppressed or excluded pest populations, and which appear to be weakening as winters warm. Warming may facilitate Medfly persistence by relaxing overwintering limitation, although reinvasion pressure, host availability, fruit movement, and surveillance and management practices likely also contribute. In Mazandaran, growers report substantial on‐tree citrus losses during heavy infestation years, and pest‐induced fruit wounding may provide infection courts for common postharvest fungal pathogens, compounding losses for smallholder producers. Current management relies heavily on organophosphate insecticides, particularly malathion, whose hazard classification and associated risks are well documented, even though realized exposure‐related risk in practice depends on application conditions. We situate the Mazandaran case within a broader global pattern of climate‐associated pest range expansion and weakened biological control, and argue that resilience‐based alternatives—including the Sterile Insect Technique, entomopathogenic agents, and threshold‐guided digital monitoring, embedded within climate‐smart agriculture frameworks—offer a technically proven, though not yet regionally established, pathway forward. We propose that climatic pest barriers merit recognition as an ecosystem service whose erosion under warming carries implications for temperate agricultural systems beyond this case.","地中海实蝇（Ceratitis capitata，Wiedemann；双翅目：实蝇科），俗称地中海果蝇，在1984年明显被根除后，重新出现在伊朗北部的马赞德兰省——该省估计占全国水果作物的25%和柑橘产量的约80%——这一根除结果与多年信息素诱捕监测未再捕获到该虫的情况一致。其2006年的重现及随后的扩散，与我们所称的“气候性有害生物屏障侵蚀”这一更广泛现象相符：历史上抑制或排除有害生物种群的季节性热条件，似乎正随着冬季变暖而减弱。气候变暖可能通过放宽越冬限制而促进地中海实蝇的存续，尽管再入侵压力、寄主可获得性、水果调运以及监测和管理实践也可能起作用。在马赞德兰，种植者报告在严重侵染年份柑橘树上果实损失巨大，且有害生物造成的果实伤口可能为常见的采后真菌病原体提供侵染位点，加剧了小农户的损失。当前的管理严重依赖有机磷杀虫剂，尤其是马拉硫磷，其危害分类及相关风险已有充分记录，尽管实际中与暴露相关的现实风险取决于施药条件。我们将马赞德兰的案例置于气候相关的有害生物分布区扩张和生物防治减弱的更广泛全球格局中，并认为基于韧性的替代方案——包括昆虫不育技术、昆虫病原制剂和阈值引导的数字监测，嵌入气候智慧型农业框架内——提供了一条技术上经过验证但尚未在区域内建立的可行路径。我们提出，气候性有害生物屏障值得被认定为一种生态系统服务，其在变暖下的侵蚀对温带农业系统的影响超越了本案例。","Plant-Environment Interactions","2026-09-24T00:00:00Z",78,{"impact":16,"substance":503,"depth":16,"authority":214,"freshness":215,"relevant":20,"comment":504},21,"以伊朗马赞德兰地中海实蝇复现为案例，提出气候性害虫屏障退化概念，并给出昆虫不育技术、数字化阈值监测等气候智慧农业路径，对暖冬区农业植保与粮食安全具参考价值。",[506],{"name":499,"url":496},[25,345,26,508,509],"柑橘产业","害虫监测",{"code":349,"slug":350,"name":351,"path":352,"parent_code":353},[],{"status":139,"locked":13,"version":32,"method":33,"evidence":513},{"candidates":514,"agricultural_evidence":38},[515,519,526,530,537,541,547],{"code":349,"score":123,"supported":38,"title":516,"abstract":517,"tags":518},[361],[],[345],{"code":520,"score":110,"supported":38,"title":521,"abstract":522,"tags":525},"B2",[],[523,524],"柑橘","citrus",[],{"code":176,"score":110,"supported":38,"title":527,"abstract":528,"tags":529},[],[346,179],[],{"code":531,"score":110,"supported":38,"title":532,"abstract":533,"tags":536},"H6",[],[534,535],"生态系统服务","ecosystem service",[],{"code":37,"score":380,"supported":38,"title":538,"abstract":539,"tags":540},[],[40],[25],{"code":542,"score":19,"supported":38,"title":543,"abstract":544,"tags":546},"F4",[],[545],"生物防治",[],{"code":68,"score":20,"supported":13,"title":548,"abstract":549,"tags":550},[],[],[26],[552,553],"地中海实蝇 马赞德兰 柑橘","气候变暖 害虫越冬 粮食安全","地中海实蝇马赞德兰柑橘-3546","10.1002\u002Fpei3.70216",{"doi":555,"openalex_id":557,"authors":558,"venue":499,"cited_by_count":99,"oa_url":496,"card":566,"direction":106,"ingested_from":107},"W7214309725",[559,560,562,564],{"name":194,"orcid":195},{"name":561,"orcid":8},"Shahab Mirinejad",{"name":563,"orcid":8},"Mitra Moezipour",{"name":565,"orcid":8},"Mohammad Sadat‐Hosseini",{"tldr":567,"method":568,"finding":569,"direction":104,"opportunity":570},"以伊朗马赞德兰省地中海实蝇重现为例，提出气候害虫屏障侵蚀概念，探讨暖冬致害虫复苏威胁粮食安全。","结合害虫监测历史、农户损失报告与文献综述，分析暖冬对越冬限制的放松。","暖冬削弱气候害虫屏障，使地中海实蝇重新定殖并扩散，威胁柑橘等作物生产与粮食安全。","可量化气候害虫屏障作为生态系统服务的价值，并开发阈值引导的数字监测与气候智慧型害虫管理方案。","2026-09-26T23:30:12.754871Z"]