[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5308":3,"related-5308":107},{"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,"primary_topic":32,"secondary_topics":38,"classification":51,"search_phrases":85,"slug":88,"view_count":22,"doi":89,"paper":90,"created_at":106},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。",null,"Journal of Smart Farming and Food Security","2026-10-19T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"系统综述梳理地缘政治与粮食安全风险，并强调智慧农业的韧性价值，具政策与产业参考意义，但属综述性论文，非突破性成果。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业供应链","粮食安全","精准农业","地缘政治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":52,"locked":14,"version":53,"method":54,"evidence":55},"classified","2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":59},[57,64,71,78],{"code":33,"score":58,"supported":59,"title":60,"abstract":62,"tags":63},15,true,[61],"food security",[29,61],[29],{"code":65,"score":66,"supported":59,"title":67,"abstract":68,"tags":70},"I1",11,[],[27,30,69],"precision agriculture",[27,30],{"code":40,"score":21,"supported":59,"title":72,"abstract":73,"tags":77},[],[74,75,76],"气候变化","干旱","climate change",[],{"code":46,"score":21,"supported":59,"title":79,"abstract":80,"tags":84},[],[81,82,83],"物联网","internet of things","IoT",[],[86,87],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":103,"ingested_from":105},"W7222902031",[93,95],{"name":94,"orcid":9},"Parameswaran Mahendran",{"name":96,"orcid":97},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","智慧农业 \u002F 农业物联网","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","openalex","2026-10-19T23:30:07.850994Z",{"total":108,"page":22,"page_size":108,"items":109},6,[110,177,286,348,471,538],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":127,"classification":134,"search_phrases":158,"slug":161,"view_count":162,"doi":163,"paper":164,"created_at":176},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":120},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[122],{"name":116,"url":113},[27,124,81,125,30],"农业人工智能","数字孪生",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},[128,133],{"code":129,"slug":130,"name":131,"path":132,"parent_code":50},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":52,"locked":14,"version":53,"method":54,"evidence":135},{"candidates":136,"agricultural_evidence":59},[137,143,148,153],{"code":46,"score":17,"supported":59,"title":138,"abstract":139,"tags":142},[82],[81,140,82,83,141],"边缘计算","edge computing",[81],{"code":65,"score":144,"supported":59,"title":145,"abstract":146,"tags":147},12,[],[30,125,69],[125,27,30],{"code":129,"score":144,"supported":59,"title":149,"abstract":151,"tags":152},[150],"artificial intelligence",[150],[124],{"code":40,"score":21,"supported":59,"title":154,"abstract":155,"tags":157},[],[74,76,156],"climate resilience",[],[159,160],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058",2,"10.1016\u002Fj.nxsust.2026.100535",{"doi":163,"openalex_id":165,"authors":166,"venue":116,"cited_by_count":98,"oa_url":113,"card":170,"direction":103,"ingested_from":105},"W7216004668",[167],{"name":168,"orcid":169},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":195,"classification":203,"search_phrases":251,"slug":254,"view_count":216,"doi":255,"paper":256,"created_at":285},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation","2026-09-22T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":187,"freshness":13,"relevant":22,"comment":188},14,"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[190],{"name":183,"url":180},[27,29,192,74,193],"植物病害","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[196,197],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":52,"locked":14,"version":53,"method":54,"evidence":204},{"candidates":205,"agricultural_evidence":59},[206,211,215,221,226,233,240,247],{"code":40,"score":17,"supported":59,"title":207,"abstract":208,"tags":210},[76],[74,75,76,209],"drought",[74],{"code":33,"score":58,"supported":59,"title":212,"abstract":213,"tags":214},[61],[29,61],[29],{"code":198,"score":216,"supported":59,"title":217,"abstract":219,"tags":220},8,[218],"food safety",[],[],{"code":65,"score":222,"supported":59,"title":223,"abstract":224,"tags":225},7,[],[30,69],[27],{"code":227,"score":222,"supported":59,"title":228,"abstract":229,"tags":232},"I3",[],[230,231],"遥感","remote sensing",[193],{"code":234,"score":108,"supported":59,"title":235,"abstract":236,"tags":239},"F4",[],[237,238],"生物防治","crop protection",[],{"code":241,"score":242,"supported":59,"title":243,"abstract":244,"tags":246},"H2",3,[],[245],"soil",[],{"code":129,"score":242,"supported":59,"title":248,"abstract":249,"tags":250},[],[150],[],[252,253],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":255,"openalex_id":257,"authors":258,"venue":183,"cited_by_count":98,"oa_url":180,"card":278,"direction":284,"ingested_from":105},"W7213972258",[259,261,263,266,269,272,275],{"name":260,"orcid":9},"Sindiswa Khawula",{"name":262,"orcid":9},"Siyabonga Ntshalitshali",{"name":264,"orcid":265},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":267,"orcid":268},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":270,"orcid":271},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":273,"orcid":274},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":276,"orcid":277},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":279,"method":280,"finding":281,"direction":282,"opportunity":283},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","农业人工智能与决策模型","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","农业遥感与作物表型","2026-09-22T23:30:22.790623Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":297,"tags":299,"primary_topic":302,"secondary_topics":303,"classification":305,"search_phrases":330,"slug":333,"view_count":334,"doi":335,"paper":336,"created_at":347},2939,"Climate Change and Food Security in Africa: Harnessing Responsible AI for Sustainable Agricultural Transformation","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no9.2025.pg79.102","Africa stands at the crossroads of two defining 21st-century challenges: climate change and food insecurity. The continents agricultural systems are increasingly disrupted by rising temperatures, erratic rainfall, and extreme weather events, exacerbating hunger, poverty, and rural vulnerability. At the same time, Artificial Intelligence (AI) presents transformative potential to support climate adaptation, improve agricultural resilience, and enhance food systems governance across Africa. This study undertakes a qualitative content analysis and critical synthesis of 25 peer-reviewed articles, institutional reports, and working papers to examine how AI is currently applied and could be more effectively leveraged for climate-resilient agriculture and food security on the continent. Findings reveal a growing body of innovations, from AIdriven weather forecasting and early warning systems to precision agriculture and supply chain optimization. Yet the study identifies persistent structural limitations, including unreliable data infrastructure, low digital capacity in rural areas, and governance gaps around ethical deployment, data ownership, and equitable access. Through African case studies, the paper explores how localized AI solutions when supported by inclusive policies, multi-stakeholder collaboration, and responsible innovation frameworks can mitigate climate-induced food shocks and drive sustainable development. The paper argues for a strategic alignment between AI deployment and national adaptation plans, emphasizing the need for stronger public-private partnerships, investments in AI-ready infrastructure, and the development of ethical and contextsensitive governance mechanisms. In doing so, it offers a roadmap for policymakers, development agencies, and researchers seeking to harness AI not just as a technological tool, but as a catalyst for systemic transformation in African food systems in the era of climate change.","非洲正处在21世纪两大决定性挑战的交汇点：气候变化与粮食不安全。随着气温上升、降雨异常和极端天气事件频发，非洲大陆的农业系统日益受到扰乱，饥饿、贫困和农村脆弱性问题不断加剧。与此同时，人工智能（AI）展现出变革性潜力，可支持非洲的气候适应、提升农业韧性并改善粮食系统治理。本研究对25篇同行评审论文、机构报告和工作论文进行了定性内容分析与批判性综合，考察AI当前在非洲大陆的应用方式，以及如何更有效地将其用于气候韧性农业和粮食安全。研究发现，相关创新不断涌现，涵盖AI驱动的天气预报和预警系统、精准农业以及供应链优化等领域。然而，研究也识别出持续存在的结构性制约，包括数据基础设施不可靠、农村地区数字能力薄弱，以及围绕伦理部署、数据所有权和公平获取的治理缺口。通过非洲案例研究，本文探讨了在包容性政策、多利益相关方协作和负责任创新框架支持下，本地化的AI解决方案如何能够缓解气候引发的粮食冲击并推动可持续发展。本文主张将AI部署与国家适应计划进行战略对接，强调需要加强公私伙伴关系、投资于AI就绪型基础设施，并建立合乎伦理且情境敏感的治理机制。由此，本文为政策制定者、发展机构和研究人员提供了一份路线图，旨在气候变化时代将AI不仅作为技术工具，更作为非洲粮食系统系统性转型的催化剂加以利用。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-16T00:00:00Z",76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":216,"relevant":22,"comment":296},"系统综述25篇文献，梳理AI在非洲气候韧性农业中的应用与治理缺口，对农业信息化有参考价值，但属境外区域研究，公共影响有限。",[298],{"name":292,"url":289},[27,124,29,300,301],"非洲农业","气候适应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[304],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":52,"locked":14,"version":53,"method":54,"evidence":306},{"candidates":307,"agricultural_evidence":59},[308,312,316,320,325],{"code":40,"score":17,"supported":59,"title":309,"abstract":310,"tags":311},[76],[74,301,76],[301],{"code":33,"score":58,"supported":59,"title":313,"abstract":314,"tags":315},[61],[29,61],[29],{"code":65,"score":222,"supported":59,"title":317,"abstract":318,"tags":319},[],[30,69],[27],{"code":129,"score":321,"supported":59,"title":322,"abstract":323,"tags":324},4,[],[150],[124],{"code":326,"score":22,"supported":14,"title":327,"abstract":328,"tags":329},"J3",[],[],[300],[331,332],"非洲 农业人工智能 粮食安全","AI 气候韧性农业 非洲","非洲农业人工智能粮食安全-2939",5,"10.56201\u002Fijaes.vol.11.no9.2025.pg79.102",{"doi":335,"openalex_id":337,"authors":338,"venue":292,"cited_by_count":98,"oa_url":341,"card":342,"direction":103,"ingested_from":105},"W7213462904",[339],{"name":340,"orcid":9},"Izuchukwu Adamaagashi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 9 2025\u002FClimate Change and Food Security 79-102.pdf",{"tldr":343,"method":344,"finding":345,"direction":282,"opportunity":346},"通过定性分析25篇文献，探讨如何负责任地利用AI推动非洲气候适应型农业与粮食安全。","对25篇同行评议论文、机构报告和工作论文进行定性内容分析与批判性综合。","AI在天气预报、精准农业等方面有创新，但面临数据基础设施差、农村数字能力低和治理缺口等结构性限制。","可研究非洲本地化AI解决方案的伦理治理框架与公私合作模式，填补数据所有权和公平获取的研究空白。","2026-09-19T23:30:19.482449Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":9,"source_name":354,"source_url":351,"published_at":355,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":361,"tags":363,"primary_topic":365,"secondary_topics":366,"classification":369,"search_phrases":436,"slug":439,"view_count":22,"doi":440,"paper":441,"created_at":470},1380,"Harnessing Digital Twin (DT) Technology for Food Security and Climate Resilience in Sub-Saharan Africa (SSA)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102532","Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility criteria. Forty peer-reviewed studies published between 2019 and 2025 were included. Structured extraction captured bibliometric, geographic, methodological, technological, and technology readiness level (TRL) data, analysed using frequency tabulation and thematic clustering. DT agriculture publications roughly tripled between 2019 and 2023 (from three to ten studies), yet 57.5% of studies remain at TRL 2–3. Europe and East Asia dominate the evidence base; no study was led by an SSA institution, and no study reported SSA-based deployment on priority crops such as maize, cassava, sorghum, or cowpea. Internet of Things (IoT) sensor infrastructure (92.5%) and artificial intelligence\u002Fmachine learning (AI\u002FML) algorithms (52.5%) constitute the dominant technology stack. Five thematic clusters were identified: smart farming infrastructure, precision water and nutrient management, controlled environment agriculture, agrifood supply chains, and livestock and aquaculture systems. Government-funded studies tend to achieve higher TRL scores than academically funded counterparts. Synthesising these findings into a system-level, deployment-oriented analysis, we derive a conceptual SSA-DT framework. It combines offline-first edge-computing architecture, hybrid crop modelling, and multi-stakeholder interfaces, with dietary diversity and climate resilience as the two key performance indicators (KPIs), aligned with IITA’s 2024–2030 Strategy. North–South research partnerships and targeted CGIAR investment in DT technology are identified as priority actions for closing the SSA evidence gap.","撒哈拉以南非洲（SSA）尽管拥有全球约60%的未开垦耕地，却长期面临粮食不安全问题。田间试验虽能提供实证，但成本高昂且规模不足以应对日益加剧的气候与人口压力。数字孪生（DT）技术，定义为利用实时数据对物理系统进行持续、双向的虚拟复制，可支持农业食品系统的监测、建模与优化。然而，据我们所知，尚无已发表的综述从SSA粮食系统或小农生产现实的角度审视DT农业研究。本研究遵循PRISMA规范，在书目数据库中采用预定义的布尔检索和适应性PICOS资格标准进行系统综述，纳入2019至2025年间发表的40篇同行评审研究。结构化提取涵盖文献计量、地理、方法、技术及技术就绪水平（TRL）数据，并采用频次制表和主题聚类进行分析。DT农业出版物在2019至2023年间约增长两倍（从三篇增至十篇），但57.5%的研究仍处于TRL 2–3级。欧洲和东亚主导了证据基础；没有一项研究由SSA机构牵头，也没有研究报告在SSA地区针对玉米、木薯、高粱或豇豆等优先作物进行部署。物联网（IoT）传感器基础设施（92.5%）和人工智能\u002F机器学习（AI\u002FML）算法（52.5%）构成主导技术栈。研究识别出五个主题聚类：智慧农业基础设施、精准水肥管理、受控环境农业、农业食品供应链以及畜牧和水产养殖系统。政府资助的研究往往比学术资助的研究达到更高的TRL评分。将这些发现综合为系统层面、面向部署的分析，我们提出了一个概念性的SSA-DT框架。该框架结合离线优先的边缘计算架构、混合作物建模和多利益相关方接口，以膳食多样性和气候韧性作为两项关键绩效指标（KPI），与国际热带农业研究所（IITA）2024–2030年战略保持一致。南北研究伙伴关系和CGIAR对DT技术的定向投资被确定为缩小SSA证据差距的优先行动。","Smart Agricultural Technology","2026-09-01T00:00:00Z",79,{"impact":17,"substance":358,"depth":359,"authority":20,"freshness":222,"relevant":22,"comment":360},22,19,"系统综述数字孪生技术在撒哈拉以南非洲的应用，揭示研究空白并提出框架，对全球农业信息化有参考价值。",[362],{"name":354,"url":351},[27,364,29,125],"非洲",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[367,368],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":129,"slug":130,"name":131,"path":132,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":370},{"candidates":371,"agricultural_evidence":59},[372,377,381,385,391,395,402,409,416,423,430],{"code":65,"score":359,"supported":59,"title":373,"abstract":375,"tags":376},[374],"digital twin",[27,125,374],[125,27],{"code":40,"score":66,"supported":59,"title":378,"abstract":379,"tags":380},[156],[156],[],{"code":33,"score":21,"supported":59,"title":382,"abstract":383,"tags":384},[61],[],[29],{"code":129,"score":21,"supported":59,"title":386,"abstract":387,"tags":390},[],[388,389,150],"机器学习","machine learning",[],{"code":46,"score":21,"supported":59,"title":392,"abstract":393,"tags":394},[],[81,140,82,83],[],{"code":396,"score":108,"supported":59,"title":397,"abstract":398,"tags":401},"A3",[],[399,400],"玉米","maize",[],{"code":403,"score":108,"supported":59,"title":404,"abstract":405,"tags":408},"A5",[],[406,407],"高粱","sorghum",[],{"code":410,"score":108,"supported":59,"title":411,"abstract":412,"tags":415},"C",[],[413,414],"畜牧","livestock",[],{"code":417,"score":108,"supported":59,"title":418,"abstract":419,"tags":422},"D",[],[420,421],"水产","aquaculture",[],{"code":424,"score":108,"supported":59,"title":425,"abstract":426,"tags":429},"I7",[],[427,428],"文献计量","bibliometric",[],{"code":431,"score":242,"supported":59,"title":432,"abstract":433,"tags":435},"G2",[],[434],"smallholder",[],[437,438],"数字孪生 智慧农业 粮食安全 非洲","数字孪生 智慧农业","数字孪生智慧农业粮食安全非洲-1380","10.1016\u002Fj.atech.2026.102532",{"doi":440,"openalex_id":442,"authors":443,"venue":354,"cited_by_count":98,"oa_url":463,"card":464,"direction":468,"ingested_from":105},"W7204898011",[444,447,449,452,454,457,459,461],{"name":445,"orcid":446},"Henri E. Z. Tonnang","https:\u002F\u002Forcid.org\u002F0000-0002-9424-9186",{"name":448,"orcid":9},"Francis Chianu",{"name":450,"orcid":451},"Siyabusa Mkuhlani","https:\u002F\u002Forcid.org\u002F0000-0001-7872-2036",{"name":453,"orcid":9},"Francis Muthoni",{"name":455,"orcid":456},"John Choptiany","https:\u002F\u002Forcid.org\u002F0009-0008-9961-4606",{"name":458,"orcid":9},"Franck B.N. Tonle",{"name":460,"orcid":9},"Bonoukpoe M. Sokame",{"name":462,"orcid":9},"Mercy Lung’aho","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007574\u002Fpdf",{"tldr":465,"method":466,"finding":467,"direction":468,"opportunity":469},"系统综述数字孪生技术在撒哈拉以南非洲农业的应用，提出SSA-DT框架。","PRISMA系统综述，检索2019-2025年40篇研究，频率统计和主题聚类。","DT农业研究增长但TRL低，SSA无主导研究，提出离线优先边缘计算框架。","数字乡村与农业信息化","SSA地区数字孪生农业研究空白，可探索离线优先架构与本地作物模型结合。","2026-09-02T23:30:04.414055Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":9,"content":9,"source_name":476,"source_url":474,"published_at":477,"category":12,"cover_url":9,"hotness":478,"is_selected":14,"score":479,"score_detail":480,"sources":483,"tags":487,"primary_topic":490,"secondary_topics":491,"classification":498,"search_phrases":521,"slug":524,"view_count":22,"doi":525,"paper":526,"created_at":537},1255,"Optimizing Water Management in Agriculture: The Role of IoT-Powered Smart Irrigation Systems in Addressing Global Food Security and Water Scarcity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.21413238","Objective: This study aims to explore the impact of IoT-powered smart irrigation systems on optimizing water management in agriculture, with a specific focus on addressing global food security and mitigating water scarcity challenges. Materials and Methods: The research employs a mixed-methods approach, combining quantitative data analysis from agricultural case studies and qualitative interviews with farmers and agricultural experts. The study assesses the effectiveness of smart irrigation technologies, including soil moisture sensors, weather forecasting tools, and automated irrigation systems, in improving water use efficiency and crop yields. Results and Discussion: The findings indicate that IoT-powered smart irrigation systems significantly enhance water management practices in agriculture. These systems result in a reduction of up to 30% in water usage, while simultaneously increasing crop productivity by 15-20%. Additionally, the integration of real-time data analytics allows for more precise irrigation scheduling, which contributes to sustainable agricultural practices and improved food security. Conclusions: This research contributes to the existing literature by providing empirical evidence of the benefits of IoT technologies in agriculture. It highlights the potential of smart irrigation systems as a viable solution to the dual challenges of food security and water scarcity. The findings emphasize the need for policy interventions and investment in technology to facilitate the adoption of these systems in farming practices worldwide.","Zenodo (CERN European Organization for Nuclear Research)","2026-08-30T00:00:00Z",25,70,{"impact":17,"substance":358,"depth":481,"authority":144,"freshness":162,"relevant":22,"comment":482},16,"论文实证智能灌溉节水30%增产20%，对农业水资源管理有参考价值，但时效性较低。",[484,485],{"name":476,"url":474},{"name":476,"url":486},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.21413237",[27,29,488,489],"智能灌溉","水资源管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[492,497],{"code":493,"slug":494,"name":495,"path":496,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},{"status":52,"locked":14,"version":53,"method":54,"evidence":499},{"candidates":500,"agricultural_evidence":59},[501,505,509,513,517],{"code":493,"score":20,"supported":59,"title":502,"abstract":503,"tags":504},[494],[494],[488,489],{"code":33,"score":144,"supported":59,"title":506,"abstract":507,"tags":508},[61],[61],[29],{"code":46,"score":66,"supported":59,"title":510,"abstract":511,"tags":512},[83],[83],[],{"code":241,"score":242,"supported":59,"title":514,"abstract":515,"tags":516},[],[245],[],{"code":65,"score":22,"supported":14,"title":518,"abstract":519,"tags":520},[],[],[27],[522,523],"水资源管理 智慧农业 智能灌溉 粮食安全","水资源管理 智慧农业","水资源管理智慧农业智能灌溉粮食安全-1255","10.5281\u002Fzenodo.21413238",{"doi":525,"openalex_id":527,"authors":528,"venue":476,"cited_by_count":98,"oa_url":474,"card":532,"direction":103,"ingested_from":105},"W7169531097",[529],{"name":530,"orcid":531},"Mahdi Sabati Gavgani","https:\u002F\u002Forcid.org\u002F0000-0002-4585-9199",{"tldr":533,"method":534,"finding":535,"direction":103,"opportunity":536},"研究物联网智能灌溉系统优化农业用水，提升粮食安全与缓解水资源短缺。","混合方法：案例定量分析与农户专家定性访谈，评估传感器、天气预报和自动灌溉技术。","智能灌溉节水30%，增产15-20%，实时数据分析优化灌溉调度。","可探索智能灌溉系统在不同作物、气候和农户采纳障碍下的适应性，以及政策激励对推广的影响。","2026-09-01T04:03:11.304099Z",{"id":539,"title":540,"url":541,"summary":542,"summary_zh":543,"content":9,"source_name":544,"source_url":541,"published_at":545,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":546,"score_detail":547,"sources":549,"tags":551,"primary_topic":556,"secondary_topics":557,"classification":564,"search_phrases":601,"slug":604,"view_count":98,"doi":605,"paper":606,"created_at":622},5478,"Characterisation of Food Security Situation for Smallholder Farmers in Zvimba","https:\u002F\u002Fdoi.org\u002F10.71458\u002Frx0qpm30","This study provides a comprehensive analysis of the food security situation among smallholder farmers (SHFs) in Zvimba District, Zimbabwe. Smallholder agriculture is the bedrock of Zimbabwe's food system, yet it remains highly vulnerable to a complex array of shocks and stressors. Using a mixed-methods approach, this research investigates the prevalence, drivers and lived experiences of food insecurity within this community. A structured survey was administered to 412 randomly selected smallholder households across the district's wards. The quantitative data were supplemented by 12 Focus Group Discussions (FGDs) and 24 Key Informant Interviews (KIIs) with farmers, community leaders and agricultural extension officers to capture qualitative nuances. The study is grounded in the Food Security Theory (FST), which conceptualises food security through its four pillars: food availability, food access, food utilisation and food stability. Findings reveal a precarious food security situation. Quantitatively, 78.4% of households were found to be food insecure, with an average Hunger Months Index of 4.7 months per year. The study identifies a significant disconnect between maize production, the national staple and household consumption, with over 65% of households failing to produce enough maize to meet their annual requirements. Qualitative data illuminated the multi-faceted drivers of this insecurity. Key points to note include climate change whereby erratic rainfall and prolonged mid-season dry spells were consistently reported as the primary production challenges, limited access to farming inputs whereby high costs and unreliable supplies of certified seeds, fertilizers and agro[1]chemicals constrain productivity. Morelso, socio-economic factors such as poverty, lack of viable alternative livelihoods and limited access to formal credit perpetuate a cycle of vulnerability. In addition, institutional weaknesses, for example, challenges with agricultural extension services and market inefficiencies, exacerbate the situation. The study concludes that food insecurity in Zvimba is not merely a problem of food production, but is deeply rooted in structural issues affecting food access, food utilisation, as well as food stability. Recommendations are proposed for a multi-sectoral approach, including promoting Climate-Smart Agriculture (CSA) and drought-resistant crops, strengthening input support systems and rural finance, diversifying livelihood strategies and enhancing the capacity of extension services. This research contributes to a deeper understanding of the localised dynamics of food security and provides evidence-based guidance for policymakers, development practitioners and the farmers of Zvimba themselves.","本研究对津巴布韦兹维姆巴区小农户（smallholder farmers, SHFs）的粮食安全状况进行了全面分析。小农农业是津巴布韦粮食体系的基石，但仍极易受到一系列复杂冲击和压力的影响。本研究采用混合方法，考察了该社区粮食不安全的普遍程度、驱动因素及生活体验。研究对该区各选区随机抽取的412户小农户进行了结构化问卷调查。定量数据辅以12次焦点小组讨论（FGDs）和24次关键知情人访谈（KIIs），访谈对象包括农民、社区领袖和农业推广人员，以捕捉定性层面的细微差异。本研究以粮食安全理论（Food Security Theory, FST）为基础，该理论通过四大支柱界定粮食安全：粮食可供性、粮食获取、粮食利用和粮食稳定性。研究结果揭示了严峻的粮食安全形势。定量数据显示，78.4%的家庭存在粮食不安全，年均饥饿月份指数为4.7个月。研究发现，玉米产量——该国主粮——与家庭消费之间存在显著脱节，超过65%的家庭生产的玉米不足以满足其年度需求。定性数据揭示了这一不安全状况的多重驱动因素。值得注意的要点包括：气候变化，即降雨不稳定和持续的中期干旱被一致报告为主要生产挑战；农业投入品获取受限，即认证种子、化肥和农用化学品成本高昂且供应不可靠，制约了生产力。此外，贫困、缺乏可行的替代生计以及正规信贷获取受限等社会经济因素使脆弱性循环持续存在。此外，制度薄弱环节，例如农业推广服务面临的挑战和市场效率低下，加剧了这一状况。研究得出结论，兹维姆巴的粮食不安全不仅仅是粮食生产问题，而是深深植根于影响粮食获取、粮食利用以及粮食稳定性的结构性问题。研究提出了采取多部门方法的建议，包括推广气候智能型农业（Climate-Smart Agriculture, CSA）和抗旱作物、加强投入品支持体系和农村金融、多样化生计策略以及增强","Ngenani The Zimbabwe Ezekiel Guti Journal of Community Engagement and Societal Transformations","2026-10-20T00:00:00Z",55,{"impact":216,"substance":17,"depth":58,"authority":108,"freshness":216,"relevant":22,"comment":548},"基于412户调查与混合方法的扎实实证研究，但属津巴布韦地方性案例，公共影响与信源权威度有限，可作参考而非每日精选。",[550],{"name":544,"url":541},[29,552,553,554,555],"小农户","气候智慧农业","农业推广","津巴布韦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[558,563],{"code":431,"slug":559,"name":560,"path":561,"parent_code":562},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":52,"locked":14,"version":53,"method":54,"evidence":565},{"candidates":566,"agricultural_evidence":59},[567,571,575,579,585,589,596],{"code":33,"score":58,"supported":59,"title":568,"abstract":569,"tags":570},[61],[29,61],[29],{"code":431,"score":58,"supported":59,"title":572,"abstract":573,"tags":574},[434],[552,434],[552],{"code":40,"score":13,"supported":59,"title":576,"abstract":577,"tags":578},[],[74,75,76,209],[553],{"code":580,"score":222,"supported":59,"title":581,"abstract":582,"tags":584},"G1",[],[554,583],"agricultural extension",[554],{"code":396,"score":108,"supported":59,"title":586,"abstract":587,"tags":588},[],[399,400],[],{"code":590,"score":108,"supported":59,"title":591,"abstract":592,"tags":595},"G3",[],[593,594],"农村金融","rural finance",[],{"code":65,"score":242,"supported":59,"title":597,"abstract":598,"tags":600},[],[599],"smart agriculture",[],[602,603],"Zvimba 小农户 粮食安全","津巴布韦 小农 玉米","Zvimba小农户粮食安全-5478","10.71458\u002Frx0qpm30",{"doi":605,"openalex_id":607,"authors":608,"venue":544,"cited_by_count":98,"oa_url":9,"card":617,"direction":103,"ingested_from":105},"W7223029383",[609,612,615],{"name":610,"orcid":611},"Casper Myambo","https:\u002F\u002Forcid.org\u002F0009-0007-8625-7526",{"name":613,"orcid":614},"Chipo Plaxedes Mubaya","https:\u002F\u002Forcid.org\u002F0000-0002-4123-0238",{"name":616,"orcid":9},"Patience Mutopo",{"tldr":618,"method":619,"finding":620,"direction":468,"opportunity":621},"调查津巴布韦Zvimba小农户粮食安全状况，分析其普遍性、成因与实际体验。","混合方法：412户问卷、12场焦点小组、24个关键人物访谈，基于粮食安全四支柱理","78.4%家庭粮食不安全，年均饥饿月4.7个月，主因是气候、投入、社会经济与制度等结构性问题。","可探索用移动端问卷与遥感产量监测构建小农户粮食安全动态预警与精准干预系统。","2026-10-21T23:30:08.937079Z"]