[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1717":3,"related-1717":102},{"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":23,"tags":27,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":80,"slug":83,"view_count":21,"doi":84,"paper":85,"created_at":101},1717,"AI-Driven Smart Agriculture And Food Security In Developing Countries: A Comparative Perspective","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303847","Agriculture remains a cornerstone of economic development and food security in many developing countries. However, the sector continues to face challenges such as climate change, declining natural resources, labour shortages, and increasing production costs. Recent advances in artificial intelligence (AI) and smart agriculture provide new opportunities to improve agricultural productivity, optimize resource use, and strengthen food security. This study examines the role of AI-driven smart agriculture in enhancing food security through a comparative analysis of India, China, Brazil, Vietnam, Kenya, and Indonesia. The findings reveal considerable differences in AI readiness and the adoption of smart agricultural technologies among the countries. Nations with stronger digital infrastructure and higher AI readiness demonstrate greater adoption of precision farming practices and better food security outcomes. The study concludes that expanding digital infrastructure, improving farmers' access to AI-based technologies, strengthening technical capacity, and encouraging collaboration among governments, research institutions, and agritech firms are essential for achieving sustainable agricultural development and improving food security in developing countries. The novelty of this study lies in integrating AI readiness, digital infrastructure, precision farming adoption, and food security into a comparative analysis of six developing countries using 2024 secondary data.","农业仍然是许多发展中国家经济发展和粮食安全的基石。然而，该部门持续面临气候变化、自然资源衰退、劳动力短缺以及生产成本上升等挑战。人工智能（AI）和智慧农业的最新进展为提高农业生产率、优化资源利用以及增强粮食安全提供了新的机遇。本研究通过对印度、中国、巴西、越南、肯尼亚和印度尼西亚的比较分析，探讨了AI驱动的智慧农业在提升粮食安全方面的作用。研究结果显示，各国在AI就绪度和智慧农业技术采用方面存在显著差异。数字基础设施更完善、AI就绪度更高的国家，在精准农业实践的采用率和粮食安全成果方面表现更优。研究得出结论：扩大数字基础设施、提升农民获取AI技术的机会、加强技术能力，并鼓励政府、研究机构和农业科技企业之间的合作，对于实现发展中国家的可持续农业发展及改善粮食安全至关重要。本研究的创新之处在于，利用2024年二手数据，将AI就绪度、数字基础设施、精准农业采用率及粮食安全整合到对六个发展中国家的比较分析中。",null,"International Journal for Research Trends in Social Science & Humanities.","2026-09-04T00:00:00Z","论文",25,false,69,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,10,3,1,"跨国比较研究，提供AI农业应用与粮食安全的新数据，但时效性较低。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303846",[28,29,30,31],"智慧农业","农业人工智能","粮食安全","发展中国家",{"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",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,59,67,74],{"code":46,"score":17,"supported":47,"title":48,"abstract":50,"tags":52},"I1",true,[49],"smart agriculture",[28,51,49],"精准农业",[28],{"code":33,"score":54,"supported":47,"title":55,"abstract":57,"tags":58},15,[56],"food security",[30,56],[30],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"H7",6,[],[64,65],"气候变化","climate change",[],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":73},"I2",4,[],[72],"artificial intelligence",[29],{"code":75,"score":20,"supported":47,"title":76,"abstract":77,"tags":79},"H8",[],[78],"可持续农业",[],[81,82],"农业人工智能 发展中国家 智慧农业 粮食安全","农业人工智能 发展中国家","农业人工智能发展中国家智慧农业粮食安全-1717","10.5281\u002Fzenodo.22303847",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":92,"oa_url":6,"card":93,"direction":99,"ingested_from":100},"W7208717815",[88,90],{"name":89,"orcid":9},"Dr.V. KALEESWARI",{"name":91,"orcid":9},"Dr. P. Anita",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"比较六国AI驱动智慧农业对粮食安全的影响，发现数字基础设施和AI准备度是关键。","基于2024年二手数据，对印度、中国等六国进行AI准备度、数字基础设施与精准农业","数字基础设施强、AI准备度高的国家，精准农业采纳多，粮食安全结果更好。","农业人工智能与决策模型","可深入研究发展中国家数字基础设施与AI准备度对精准农业采纳的因果机制，或构建评估框架指导政策。","智慧农业 \u002F 农业物联网","openalex","2026-09-05T23:30:08.783371Z",{"total":61,"page":21,"page_size":61,"items":103},[104,185,254,320,386,456],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":112,"score_detail":113,"sources":118,"tags":120,"primary_topic":123,"secondary_topics":124,"classification":136,"search_phrases":168,"slug":171,"view_count":20,"doi":172,"paper":173,"created_at":184},3613,"AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1723457","High-volume food manufacturing in Saudi Arabia is increasingly exposed to volatile demand, short product life cycles, promotion-driven peaks, imported input dependencies, capacity bottlenecks, and the operational consequences of seasonal and religious events. These conditions make demand forecasting and production scheduling inseparable rather than sequential planning tasks. This review synthesizes research published between 2020 and 2025 on artificial intelligence-assisted demand forecasting, perishable-product planning, finite-capacity scheduling, digital-twin-enabled decision support, and food supply-chain resilience. The study develops an integrated framework in which demand sensing produces probabilistic forecasts that are translated into production decisions through capacity, shelf-life, sanitation, changeover, labour, inventory, and service constraints. Evidence indicates that machine-learning and deep-learning methods can improve forecast accuracy when promotions, weather, calendar events, regional variation, and censored demand are represented appropriately, while optimization models can convert these signals into feasible schedules that reduce changeovers, waste, shortages, and unstable resource use [1-8]. The review also identifies a persistent implementation gap: forecasting studies often stop at predictive accuracy, whereas scheduling studies frequently assume demand inputs are fixed and reliable. The proposed framework closes this gap by linking forecast uncertainty, rolling-horizon scheduling, execution feedback, and food-security outcomes. For Saudi manufacturers, the value of this integration lies not only in lower cost, but also in improving product availability, freshness, localized processing capacity, shock recovery, and evidence-based management under Vision 2030. The paper concludes with a staged implementation roadmap and research priorities for explainability, cross-site scheduling, event-aware forecasting, data governance, and resilient planning.","沙特阿拉伯的大规模食品制造日益面临需求波动、产品生命周期短、促销驱动的峰值、进口原料依赖、产能瓶颈以及季节性和宗教事件的运营影响。这些条件使得需求预测与生产调度成为不可分割的任务，而非顺序规划任务。本文综述了2020年至2025年间发表的相关研究，涵盖人工智能辅助需求预测、易腐产品规划、有限产能调度、数字孪生赋能的决策支持以及食品供应链韧性。研究构建了一个集成框架，其中需求感知产生概率预测，并通过产能、保质期、卫生、换产、劳动力、库存和服务约束转化为生产决策。证据表明，当促销、天气、日历事件、区域差异和截尾需求得到适当表征时，机器学习和深度学习方法能够提高预测精度，而优化模型可将这些信号转化为可行的调度方案，从而减少换产、浪费、缺货和不稳定的资源使用[1-8]。本综述还指出了一个持续存在的实施缺口：预测研究往往止步于预测精度，而调度研究则常常假设需求输入是固定且可靠的。所提出的框架通过将预测不确定性、滚动时域调度、执行反馈和食品安全结果联系起来，弥合了这一缺口。对于沙特制造商而言，这种集成的价值不仅在于降低成本，还在于改善产品可得性、新鲜度、本地化加工能力、冲击恢复能力以及“2030愿景”下的循证管理。本文最后提出了分阶段实施路线图以及可解释性、跨厂区调度、事件感知预测、数据治理和韧性规划方面的研究优先事项。","Iconic Research and Engineering Journals","2026-09-25T00:00:00Z",60,{"impact":114,"substance":17,"depth":115,"authority":61,"freshness":116,"relevant":21,"comment":117},12,16,8,"综述性论文，提出AI需求预测与生产排程整合框架，对智慧农业与粮食安全有参考价值，但属境外研究且期刊权威性一般。",[119],{"name":110,"url":107},[28,29,30,121,122],"供应链韧性","生产调度",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[125,131],{"code":126,"slug":127,"name":128,"path":129,"parent_code":130},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":68,"slug":132,"name":133,"path":134,"parent_code":135},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":47},[139,145,152,157,162],{"code":126,"score":140,"supported":47,"title":141,"abstract":143,"tags":144},11,[142],"food manufacturing",[142],[],{"code":68,"score":19,"supported":47,"title":146,"abstract":147,"tags":151},[],[148,149,72,150],"深度学习","机器学习","decision support",[29],{"code":33,"score":153,"supported":47,"title":154,"abstract":155,"tags":156},9,[56],[],[30],{"code":46,"score":69,"supported":47,"title":158,"abstract":159,"tags":161},[],[160],"数字孪生",[28],{"code":163,"score":20,"supported":47,"title":164,"abstract":165,"tags":167},"E4",[],[166],"食品安全",[],[169,170],"沙特阿拉伯 食品制造 需求预测","Vision 2030 粮食安全","沙特阿拉伯食品制造需求预测-3613","10.64388\u002Firev10i3-1723457",{"doi":172,"openalex_id":174,"authors":175,"venue":110,"cited_by_count":92,"oa_url":178,"card":179,"direction":99,"ingested_from":100},"W7214295236",[176],{"name":177,"orcid":9},"Atif Maqbool Syed","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723457.pdf",{"tldr":180,"method":181,"finding":182,"direction":97,"opportunity":183},"综述2020-2025年AI需求预测与生产调度研究，提出沙特食品制造整合框架以保障Vision 20","文献综述2020-2025年AI预测、易腐品规划、有限产能调度与数字孪生研究。","预测研究止于精度、调度研究假设需求固定，整合框架可弥合实施鸿沟并提升粮食安全。","可研究事件感知预测与滚动调度耦合、跨工厂协同及可解释AI在食品供应链韧性中的应用。","2026-09-27T23:30:14.516185Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":204,"classification":210,"search_phrases":236,"slug":239,"view_count":240,"doi":241,"paper":242,"created_at":253},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":195,"authority":196,"freshness":116,"relevant":21,"comment":197},17,13,"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。",[199],{"name":191,"url":188},[28,29,30,201,202],"非洲农业","气候适应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[205],{"code":60,"slug":206,"name":207,"path":208,"parent_code":209},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":47},[213,217,221,227,231],{"code":60,"score":17,"supported":47,"title":214,"abstract":215,"tags":216},[65],[64,202,65],[202],{"code":33,"score":54,"supported":47,"title":218,"abstract":219,"tags":220},[56],[30,56],[30],{"code":46,"score":222,"supported":47,"title":223,"abstract":224,"tags":226},7,[],[51,225],"precision agriculture",[28],{"code":68,"score":69,"supported":47,"title":228,"abstract":229,"tags":230},[],[72],[29],{"code":232,"score":21,"supported":14,"title":233,"abstract":234,"tags":235},"J3",[],[],[201],[237,238],"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":241,"openalex_id":243,"authors":244,"venue":191,"cited_by_count":92,"oa_url":247,"card":248,"direction":99,"ingested_from":100},"W7213462904",[245],{"name":246,"orcid":9},"Izuchukwu Adamaagashi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 9 2025\u002FClimate Change and Food Security 79-102.pdf",{"tldr":249,"method":250,"finding":251,"direction":97,"opportunity":252},"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":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":193,"score_detail":262,"sources":264,"tags":266,"primary_topic":269,"secondary_topics":270,"classification":272,"search_phrases":298,"slug":301,"view_count":302,"doi":303,"paper":304,"created_at":319},1614,"Artificial intelligence adoption and food security in Saudi Arabia: the critical role of water-use efficiency management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1955058","Introduction Ensuring food security and increasing water-use efficiency have become strategic priorities for Saudi Arabia due to water scarcity, climate change, and rising food demand. Artificial intelligence (AI) advances significant opportunities to improve agricultural productivity and resource management; however, empirical evidence on its dynamic relationship with food security and water-use efficiency remains limited. Methods This study investigates the dynamic relationships among Information and Communication Technology (ICT) goods imports as an indirect proxy for AI adoption, food security, and water-use efficiency for various purposes in Saudi Arabia, utilizing annual data from 2000 to 2023. The analysis employs a Vector Autoregression (VAR) framework alongside Granger causality tests, impulse response functions (IRFs), forecast error variance decomposition (FEVD), and historical decomposition (HD). Results The results reveal that ICT goods imports, as an indirect proxy for AI adoption, significantly influence food security and industrial water-use efficiency; likewise, agricultural and service-sector water-use efficiency, in turn, promote AI adoption. The IRF results indicate that positive AI shocks improve food security and industrial water-use efficiency in the short run. However, the FEVD results show that the agricultural and service sectors will become increasingly significant factors in long-term food security. The HD outcomes support the robustness of the study results. Conclusion The study concludes that integrating AI technologies with sustainable water management can boost food security and Structural enhance resource efficiency. These outcomes provide valuable policy insights for advancing Saudi Vision 2030 through AI-driven agricultural innovation and sustainable water resource management. The study contributes to the literature by integrating ICT goods imports, as an indirect proxy for AI adoption, food security, and sector-specific water-use efficiency into a dynamic framework, providing new evidence on the potential role of digital technology diffusion in enhancing food security through improved water-use efficiency.","引言：由于水资源短缺、气候变化及粮食需求不断增长，保障粮食安全和提高用水效率已成为沙特阿拉伯的战略优先事项。人工智能（AI）的进步为提升农业生产力和资源管理提供了重大机遇；然而，关于其与粮食安全和用水效率之间动态关系的实证证据仍然有限。方法：本研究利用2000年至2023年的年度数据，探讨沙特阿拉伯信息和通信技术（ICT）产品进口（作为AI采用的间接代理指标）、粮食安全及各部门用水效率之间的动态关系。分析采用向量自回归（VAR）框架，并结合格兰杰因果关系检验、脉冲响应函数（IRFs）、预测误差方差分解（FEVD）和历史分解（HD）。结果：结果表明，ICT产品进口作为AI采用的间接代理指标，显著影响粮食安全和工业用水效率；同样，农业和服务业的用水效率反过来也促进了AI的采用。脉冲响应函数结果显示，正向的AI冲击在短期内改善了粮食安全和工业用水效率。然而，预测误差方差分解结果表明，农业和服务业将成为长期粮食安全中日益重要的因素。历史分解结果支持了研究结果的稳健性。结论：研究得出结论，将AI技术与可持续水资源管理相结合，可以提升粮食安全并增强资源效率。这些成果为通过AI驱动的农业创新和可持续水资源管理推进沙特2030愿景提供了有价值的政策启示。本研究通过将ICT产品进口（作为AI采用的间接代理指标）、粮食安全和分部门用水效率整合到一个动态框架中，为文献做出了贡献，为数字技术扩散通过改善用水效率增强粮食安全的潜在作用提供了新的证据。","Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":196,"freshness":222,"relevant":21,"comment":263},"研究AI采用与水资源效率对沙特粮食安全的影响，方法严谨，结论具政策参考价值。",[265],{"name":260,"url":257},[28,29,30,267,268],"水资源管理","沙特",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[271],{"code":68,"slug":132,"name":133,"path":134,"parent_code":135},{"status":40,"locked":14,"version":41,"method":42,"evidence":273},{"candidates":274,"agricultural_evidence":47},[275,279,283,290,294],{"code":33,"score":54,"supported":47,"title":276,"abstract":277,"tags":278},[56],[30,56],[30],{"code":68,"score":114,"supported":47,"title":280,"abstract":281,"tags":282},[72],[72],[29],{"code":284,"score":222,"supported":47,"title":285,"abstract":286,"tags":289},"H3",[],[287,288],"水资源","water resource",[267],{"code":60,"score":61,"supported":47,"title":291,"abstract":292,"tags":293},[],[64,65],[],{"code":46,"score":21,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[28],[299,300],"农业人工智能 水资源管理 智慧农业 粮食安全","农业人工智能 水资源管理","农业人工智能水资源管理智慧农业粮食安全-1614",2,"10.3389\u002Ffsufs.2026.1955058",{"doi":303,"openalex_id":305,"authors":306,"venue":260,"cited_by_count":92,"oa_url":313,"card":314,"direction":97,"ingested_from":100},"W7207821862",[307,309,311],{"name":308,"orcid":9},"Raga M. Elzaki",{"name":310,"orcid":9},"Omar A. Alkhateeb",{"name":312,"orcid":9},"Abdulelah A. Alasfour","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1955058\u002Fpdf",{"tldr":315,"method":316,"finding":317,"direction":97,"opportunity":318},"研究沙特AI采用与粮食安全、水资源效率的动态关系，发现AI提升短期粮食安全与工业用水效率。","使用2000-2023年数据，VAR模型、Granger因果、脉冲响应、方差分解","AI采用显著影响粮食安全与工业用水效率，农业和服务业用水效率促进AI采用，长期农业和服务业对粮食安全","可探索AI与农业用水效率的因果机制，或扩展到其他干旱地区，考虑气候变量和实际AI技术指标。","2026-09-04T23:30:08.321918Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":327,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":336,"secondary_topics":337,"classification":344,"search_phrases":367,"slug":370,"view_count":21,"doi":371,"paper":372,"created_at":385},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。","Journal of Smart Farming and Food Security","2026-10-19T00:00:00Z",77,{"impact":17,"substance":18,"depth":195,"authority":196,"freshness":153,"relevant":21,"comment":330},"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。",[332],{"name":326,"url":323},[28,334,30,51,335],"农业供应链","地缘政治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[338,339],{"code":60,"slug":206,"name":207,"path":208,"parent_code":209},{"code":340,"slug":341,"name":342,"path":343,"parent_code":135},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":345},{"candidates":346,"agricultural_evidence":47},[347,351,355,360],{"code":33,"score":54,"supported":47,"title":348,"abstract":349,"tags":350},[56],[30,56],[30],{"code":46,"score":140,"supported":47,"title":352,"abstract":353,"tags":354},[],[28,51,225],[28,51],{"code":60,"score":153,"supported":47,"title":356,"abstract":357,"tags":359},[],[64,358,65],"干旱",[],{"code":340,"score":153,"supported":47,"title":361,"abstract":362,"tags":366},[],[363,364,365],"物联网","internet of things","IoT",[],[368,369],"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":371,"openalex_id":373,"authors":374,"venue":326,"cited_by_count":92,"oa_url":323,"card":380,"direction":99,"ingested_from":100},"W7222902031",[375,377],{"name":376,"orcid":9},"Parameswaran Mahendran",{"name":378,"orcid":379},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":381,"method":382,"finding":383,"direction":99,"opportunity":384},"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。","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。","2026-10-19T23:30:07.850994Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":393,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":403,"secondary_topics":404,"classification":405,"search_phrases":439,"slug":442,"view_count":21,"doi":443,"paper":444,"created_at":455},4600,"Optimization model of agricultural economic structure based on LSTM","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09594-x","With the development of artificial intelligence (AI) and agricultural big data, the adjustment of agricultural economic structure has shifted from empirical judgment to data-driven decision-making. This study uses the provincial panel data of agricultural economy in Hubei Province from 2010 to 2023 to construct a comprehensive framework of prediction and optimization. The forecasting layer uses the Long Short-Term Memory (LSTM) model to capture the dynamic relationship among agricultural GDP growth rate, main crop output, rural employment structure, agricultural product price index, financial subsidies and agricultural infrastructure investment. The optimization layer uses the prediction results as parameters. It defines three objectives. overall agricultural benefit, structural deviation and food security risk. It also includes restrictions on cultivated land area, budget, labor supply and food security. Based on these backgrounds, the adjustment scheme of agricultural economic structure was formulated. The results show that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and r of LSTM model are 0.32, 0.45 and 0.91 respectively. Its prediction performance is better than autoregressive moving average (ARIMA), back propagation neural network (BPNN), support vector regression (SVR) and random forest (RF). These findings show that the combination of multivariate collaborative forecasting and structural optimization constraints can improve the efficiency of agricultural resource allocation. This method also provides quantitative support for rural revitalization, food security and digital agriculture policy design.","随着人工智能（AI）与农业大数据的发展，农业经济结构调整已从经验判断转向数据驱动决策。本研究利用2010—2023年湖北省农业经济省级面板数据，构建了预测与优化的综合框架。预测层采用长短期记忆（LSTM）模型，捕捉农业GDP增长率、主要作物产量、农村就业结构、农产品价格指数、财政补贴与农业基础设施投资之间的动态关系。优化层以预测结果作为参数，设定三个目标：农业整体效益、结构性偏差与粮食安全风险，并纳入耕地面积、预算、劳动力供给与粮食安全等约束条件。在此基础上，制定农业经济结构调整方案。结果表明，LSTM模型的平均绝对误差（MAE）、均方根误差（RMSE）与r分别为0.32、0.45和0.91，其预测性能优于自回归移动平均（ARIMA）、反向传播神经网络（BPNN）、支持向量回归（SVR）与随机森林（RF）。这些发现表明，多变量协同预测与结构优化约束的结合能够提高农业资源配置效率。该方法也为乡村振兴、粮食安全与数字农业政策设计提供了量化支持。","Discover Applied Sciences","2026-10-09T00:00:00Z",75,{"impact":115,"substance":18,"depth":195,"authority":196,"freshness":153,"relevant":21,"comment":396},"基于湖北省级面板数据构建LSTM预测与多目标优化框架，方法对比充分、结论有量化支撑，对数字农业与乡村振兴政策设计具参考价值。",[398],{"name":392,"url":389},[400,29,30,401,402],"数字农业","农业经济结构","LSTM预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":406},{"candidates":407,"agricultural_evidence":47},[408,412,417,421,427,433],{"code":33,"score":222,"supported":47,"title":409,"abstract":410,"tags":411},[],[30,56],[30],{"code":46,"score":222,"supported":47,"title":413,"abstract":414,"tags":416},[],[400,415],"digital agriculture",[400],{"code":68,"score":69,"supported":47,"title":418,"abstract":419,"tags":420},[],[72],[29],{"code":422,"score":20,"supported":47,"title":423,"abstract":424,"tags":426},"G",[],[425],"乡村振兴",[],{"code":428,"score":20,"supported":47,"title":429,"abstract":430,"tags":432},"I6",[],[431],"农业大数据",[],{"code":434,"score":20,"supported":47,"title":435,"abstract":436,"tags":438},"J1",[],[437],"农业政策",[],[440,441],"湖北 农业经济结构 LSTM","农业经济结构 优化模型","湖北农业经济结构LSTM-4600","10.1007\u002Fs42452-026-09594-x",{"doi":443,"openalex_id":445,"authors":446,"venue":392,"cited_by_count":92,"oa_url":389,"card":449,"direction":454,"ingested_from":100},"W7221088951",[447],{"name":448,"orcid":9},"Xue Gong",{"tldr":450,"method":451,"finding":452,"direction":97,"opportunity":453},"用LSTM预测湖北农业经济指标，并构建多目标优化模型调整农业结构。","LSTM预测2010-2023年湖北面板数据，结合多目标优化与约束条件。","LSTM预测精度优于ARIMA、BPNN等，优化方案可提升农业资源配置效率。","可探索将LSTM与多目标优化耦合框架推广至其他省份或作物类型，并引入实时数据流。","数字乡村与农业信息化","2026-10-10T23:30:34.807708Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":462,"source_url":459,"published_at":463,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":193,"score_detail":464,"sources":466,"tags":468,"primary_topic":473,"secondary_topics":474,"classification":480,"search_phrases":521,"slug":524,"view_count":92,"doi":525,"paper":526,"created_at":540},4488,"Harnessing Indigenous Knowledge for Climate-Smart Agri-Food Systems: A Review of Adaptation Strategies, Food Security, Ecosystem Services, Governance, and Digital Innovation in Sub-Saharan Africa","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fconservation6040120","Sub-Saharan Africa (SSA) faces escalating climate challenges that threaten agricultural productivity, food security, ecosystem sustainability, and rural livelihoods. Indigenous knowledge (IK), developed through generations of interaction with local environments, offers context-specific and culturally embedded solutions for climate adaptation and resilient agri-food systems. This scoping review synthesizes evidence on the role of indigenous knowledge in advancing climate-smart agriculture (CSA) and resilient agri-food systems across SSA. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), the review analyzed 17 studies published between 2000 and 2026. The findings reveal that indigenous knowledge contributes significantly to climate resilience through traditional weather forecasting, crop diversification, indigenous seed preservation, agroforestry, water harvesting, soil fertility management, and community-based natural resource governance. These practices enhance food security, livelihood resilience, biodiversity conservation, and ecosystem service provision while strengthening adaptive capacity among smallholder farmers. The review further highlights the importance of inclusive governance frameworks, knowledge co-production, and policy support for integrating indigenous knowledge into climate adaptation and agricultural development strategies. Emerging digital technologies and innovation platforms also present opportunities for documenting, preserving, and scaling indigenous agricultural knowledge. However, challenges related to weak policy recognition, inadequate documentation, knowledge erosion, and limited digital infrastructure continue to constrain its effective utilization. The study concludes that integrating indigenous knowledge with scientific innovation is essential for building inclusive, sustainable, and climate-resilient agri-food systems in SSA.","撒哈拉以南非洲（Sub-Saharan Africa, SSA）面临日益加剧的气候挑战，威胁着农业生产力、粮食安全、生态系统可持续性和农村生计。土著知识（Indigenous Knowledge, IK）经由世代与当地环境的互动而积累，为气候适应和韧性农业食品系统提供了契合具体情境且深植于文化的解决方案。本范围综述综合了关于土著知识在推动撒哈拉以南非洲气候智慧型农业（Climate-Smart Agriculture, CSA）和韧性农业食品系统中作用的证据。在系统综述和荟萃分析首选报告条目扩展版——范围综述（PRISMA-ScR）的指导下，本综述分析了2000年至2026年间发表的17项研究。研究结果表明，土著知识通过传统天气预报、作物多样化、土著种子保存、农林业、集水、土壤肥力管理和社区为基础的自然资源治理，对气候韧性作出了显著贡献。这些实践增强了粮食安全、生计韧性、生物多样性保护和生态系统服务供给，同时强化了小农户的适应能力。本综述进一步强调了包容性治理框架、知识共同生产以及政策支持对于将土著知识纳入气候适应和农业发展战略的重要性。新兴数字技术和创新平台也为记录、保存和推广土著农业知识提供了机遇。然而，政策认可薄弱、记录不足、知识流失和数字基础设施有限等挑战持续制约着其有效利用。研究结论认为，将土著知识与科学创新相结合，对于在撒哈拉以南非洲构建包容、可持续且具有气候韧性的农业食品系统至关重要。","Conservation","2026-10-08T00:00:00Z",{"impact":17,"substance":18,"depth":195,"authority":196,"freshness":116,"relevant":21,"comment":465},"系统综述揭示本土知识与数字创新融合路径，对气候适应型农业与农业信息化有参考价值，但属区域研究、非国内政策事件。",[467],{"name":462,"url":459},[28,469,30,470,471,472],"撒哈拉以南非洲","气候智慧型农业","本土知识","数字创新",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[475],{"code":476,"slug":477,"name":478,"path":479,"parent_code":209},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",{"status":40,"locked":14,"version":41,"method":42,"evidence":481},{"candidates":482,"agricultural_evidence":47},[483,487,494,499,506,513,517],{"code":33,"score":54,"supported":47,"title":484,"abstract":485,"tags":486},[56],[30,56],[30],{"code":476,"score":153,"supported":47,"title":488,"abstract":489,"tags":493},[],[490,491,477,492],"生物多样性","生态系统服务","ecosystem service",[],{"code":60,"score":222,"supported":47,"title":495,"abstract":496,"tags":498},[],[202,497],"climate resilience",[470],{"code":500,"score":61,"supported":47,"title":501,"abstract":502,"tags":505},"G2",[],[503,504],"小农户","smallholder",[],{"code":507,"score":61,"supported":47,"title":508,"abstract":509,"tags":512},"H2",[],[510,511],"土壤","soil",[],{"code":46,"score":69,"supported":47,"title":514,"abstract":515,"tags":516},[],[49],[28],{"code":232,"score":21,"supported":14,"title":518,"abstract":519,"tags":520},[],[],[469],[522,523],"撒哈拉以南非洲 本土知识 气候智慧型农业","撒哈拉以南非洲 气候智慧型农业 数字创新 智慧农业","撒哈拉以南非洲本土知识气候智慧型农业-4488","10.3390\u002Fconservation6040120",{"doi":525,"openalex_id":527,"authors":528,"venue":462,"cited_by_count":92,"oa_url":459,"card":535,"direction":99,"ingested_from":100},"W7220921430",[529,532],{"name":530,"orcid":531},"Adetomiwa Kolapo","https:\u002F\u002Forcid.org\u002F0000-0002-0808-1293",{"name":533,"orcid":534},"Wale Ayojimi","https:\u002F\u002Forcid.org\u002F0000-0003-0038-3462",{"tldr":536,"method":537,"finding":538,"direction":454,"opportunity":539},"综述撒哈拉以南非洲本土知识在气候智慧型农业与食物系统中的作用与挑战。","PRISMA-ScR范围综述，分析2000-2026年17项研究。","本土知识通过传统预报、作物多样化等提升气候韧性，但政策认可与数字化不足制约其应用。","可研究本土知识与数字技术协同的知识共生产机制，及适配小农的低成本数字化记录与传播工具。","2026-10-09T23:30:07.725864Z"]