[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5195":3,"related-5195":114},{"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":39,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":113},5195,"Nanoagricultural revolution under global warming: optimizing growth and nutrient use efficiency","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01904167.2026.2742923","Global food security faces growing threats from climate change, population growth, and environmental degradation, which reduce crop yield and quality. Innovative solutions are needed beyond conventional farming. This review examines nanotechnology’s potential to transform agriculture under climate stress, focusing on metallic, carbon-based, biogenic, and polymeric nanomaterials. These materials act as nanofertilizers, nanopesticides, nanosensors, and soil amendments. They enhance seed germination, photosynthetic efficiency, hormone regulation, gene expression, and antioxidant defenses. They also mitigate drought and salinity by improving water uptake, ion balance, osmolyte accumulation, and stress-gene regulation. Nanofertilizers enable controlled nutrient release, reducing emissions and fertilizer waste, while nanopesticides provide targeted pest control. Nanosensors support precision agriculture through real-time monitoring. Overall, nanotechnology improves soil quality, carbon sequestration, and crop resilience. However, safe application requires optimizing concentrations and methods to avoid toxicity. Future work should address molecular mechanisms, ecological impacts, cost-effective production, and regulatory frameworks. When integrated sustainably, nanotechnology offers a transformative path toward global food security under climate uncertainty.","全球粮食安全正面临气候变化、人口增长和环境退化日益加剧的威胁，这些因素导致作物产量和品质下降。需要超越传统农业的创新解决方案。本综述探讨了纳米技术在气候胁迫下变革农业的潜力，重点关注金属基、碳基、生物源和聚合物纳米材料。这些材料可作为纳米肥料、纳米农药、纳米传感器和土壤改良剂。它们增强种子萌发、光合效率、激素调节、基因表达和抗氧化防御。它们还通过改善水分吸收、离子平衡、渗透调节物质积累和胁迫基因调控来缓解干旱和盐胁迫。纳米肥料可实现养分的控释，减少排放和肥料浪费，而纳米农药则提供靶向病虫害防治。纳米传感器通过实时监测支持精准农业。总体而言，纳米技术改善土壤质量、碳固存和作物抗逆性。然而，安全应用需要优化浓度和方法以避免毒性。未来工作应关注分子机制、生态影响、经济高效的生产和监管框架。在可持续整合的前提下，纳米技术为气候不确定性下的全球粮食安全提供了一条变革性路径。",null,"Journal of Plant Nutrition","2026-10-15T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述纳米材料在气候胁迫下提升作物生长与养分利用效率的机制与应用前景，信息增量与专业深度较好，但属综述类论文、非突破性成果，适合进入主题聚合而非头条精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","气候变化","纳米农业","纳米肥料",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,53,59,67,74,81,88],{"code":33,"score":13,"supported":46,"title":47,"abstract":48,"tags":52},true,[],[29,49,50,51],"干旱","climate change","drought",[29],{"code":54,"score":21,"supported":46,"title":55,"abstract":56,"tags":58},"I1",[],[28,57],"precision agriculture",[27,28],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"A8",6,[],[64,65],"粮食安全","food security",[],{"code":68,"score":61,"supported":46,"title":69,"abstract":70,"tags":73},"F3",[],[71,72],"肥料","fertilizer",[],{"code":75,"score":61,"supported":46,"title":76,"abstract":77,"tags":80},"H2",[],[78,79],"土壤","soil",[],{"code":82,"score":83,"supported":46,"title":84,"abstract":85,"tags":87},"F4",3,[],[86],"农药",[],{"code":89,"score":83,"supported":46,"title":90,"abstract":91,"tags":93},"H4",[],[92],"carbon sequestration",[],[95,96],"纳米肥料 养分利用效率","纳米传感器 精准农业","纳米肥料养分利用效率-5195",0,"10.1080\u002F01904167.2026.2742923",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":98,"oa_url":9,"card":106,"direction":110,"ingested_from":112},"W7222281865",[103],{"name":104,"orcid":105},"Azam Ranjbar","https:\u002F\u002Forcid.org\u002F0000-0001-8650-7395",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"综述纳米材料在气候胁迫下提升作物生长与养分利用效率的潜力与风险。","综述金属、碳基、生物源及聚合物纳米材料作为纳米肥料、农药和传感器的作用。","纳米材料可增强抗逆性、控释养分并支持精准农业，但需优化浓度以避免毒性。","农业绿色发展与碳","可研究纳米材料在田间尺度下的分子机制、生态风险与低成本规模化生产及监管标准。","openalex","2026-10-17T23:31:02.249682Z",{"total":61,"page":22,"page_size":61,"items":115},[116,212,284,367,428,488],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":140,"search_phrases":192,"slug":195,"view_count":98,"doi":9,"paper":196,"created_at":211},5312,"Agricultural Production under Climate Change in Afghanistan: Impacts, Challenges and Adaptation Options (A Review)","https:\u002F\u002Fesrj.edu.af\u002Fesrj\u002Farticle\u002Fview\u002F123","Climate change poses serious challenges to agriculture in Afghanistan, a country largely dependent on rainfed farming and highly vulnerable to water scarcity, drought, and temperature extremes. Understanding the impacts of climate variability and identifying effective adaptation strategies are essential to sustain agricultural productivity and ensure food security. This systematic review synthesizes empirical, modeling, and review studies on climate change effects on Afghan agriculture, assesses adaptation measures, and integrates regional experiences from Pakistan, Iran, Tajikistan, and Uzbekistan. A structured literature search was conducted using peer-reviewed articles, institutional reports, and regional studies published between 2000 and 2025. Studies were selected based on relevance to Afghan agro-ecological systems, climate stressors, and adaptation strategies. We extracted data using a structured table format following PRISMA guidelines, combining national and regional evidence. Findings indicate that water scarcity, prolonged drought, rising temperatures, and erratic precipitation are the main stressors affecting major crops, including wheat, barley, chickpea, maize, and cotton. Yield reductions ranged from 12% to 25% across provinces. Adaptation strategies such as Climate-Smart Agriculture (CSA), efficient irrigation (drip and sprinkler), rainwater harvesting, mulching, and drought- or heat-tolerant crop varieties were most effective. Regional evidence confirms the transferability of these measures under similar agro-climatic conditions. In conclusion, climate change significantly threatens agricultural productivity in Afghanistan. Evidence-based adaptation strategies, supported by coordinated policy and capacity-building initiatives, can enhance resilience, protect rural livelihoods, and ensure sustainable food security.","气候变化对阿富汗农业构成严峻挑战，该国农业在很大程度上依赖雨养耕作，极易受到水资源短缺、干旱和极端温度的影响。理解气候变异的影响并确定有效的适应策略，对于维持农业生产力和保障粮食安全至关重要。本系统综述综合了关于气候变化对阿富汗农业影响的实证研究、模拟研究和综述研究，评估了适应措施，并整合了巴基斯坦、伊朗、塔吉克斯坦和乌兹别克斯坦的区域经验。采用结构化文献检索方法，检索了2000年至2025年间发表的同行评审论文、机构报告和区域研究。研究依据其与阿富汗农业生态系统、气候胁迫因子和适应策略的相关性进行筛选。我们遵循PRISMA指南，采用结构化表格格式提取数据，综合了国家层面和区域层面的证据。研究结果表明，水资源短缺、持续干旱、气温上升和降水不稳定是影响主要作物（包括小麦、大麦、鹰嘴豆、玉米和棉花）的主要胁迫因子。各省产量降幅在12%至25%之间。气候智慧型农业（CSA）、高效灌溉（滴灌和喷灌）、雨水收集、覆盖栽培以及耐旱或耐热作物品种等适应策略最为有效。区域证据证实了这些措施在相似农业气候条件下的可推广性。总之，气候变化显著威胁阿富汗的农业生产力。以协调政策和能力建设举措为支撑的循证适应策略能够增强韧性、保护农村生计并确保可持续的粮食安全。","ESRJ","2026-10-16T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":126,"freshness":61,"relevant":22,"comment":127},12,"系统综述整合2000-2025年证据，量化减产12%-25%并提出可迁移的适应策略，对区域农业气候韧性研究有参考价值，但非国内三农政策或产业事件，时效性一般。",[129],{"name":122,"url":119},[27,64,29,131,132],"适应性策略","水资源短缺",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[135],{"code":136,"slug":137,"name":138,"path":139,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":141},{"candidates":142,"agricultural_evidence":46},[143,147,154,159,166,173,180,186],{"code":33,"score":17,"supported":46,"title":144,"abstract":145,"tags":146},[50],[29,49,50,51],[29],{"code":136,"score":148,"supported":46,"title":149,"abstract":150,"tags":153},9,[],[151,152,137],"灌溉","水资源",[],{"code":60,"score":155,"supported":46,"title":156,"abstract":157,"tags":158},7,[],[64,65],[64],{"code":160,"score":61,"supported":46,"title":161,"abstract":162,"tags":165},"A2",[],[163,164],"小麦","wheat",[],{"code":167,"score":61,"supported":46,"title":168,"abstract":169,"tags":172},"A3",[],[170,171],"玉米","maize",[],{"code":174,"score":61,"supported":46,"title":175,"abstract":176,"tags":179},"A6",[],[177,178],"棉花","cotton",[],{"code":54,"score":181,"supported":46,"title":182,"abstract":183,"tags":185},4,[],[184],"smart agriculture",[27],{"code":187,"score":83,"supported":46,"title":188,"abstract":189,"tags":191},"H6",[],[190],"农业生态",[],[193,194],"阿富汗 气候变化","气候智慧型农业 灌溉","阿富汗气候变化-5312",{"doi":9,"openalex_id":197,"authors":198,"venue":122,"cited_by_count":98,"oa_url":119,"card":205,"direction":210,"ingested_from":112},"W7222907396",[199,201,203],{"name":200,"orcid":9},"Mohammad Shafiq Faieq",{"name":202,"orcid":9},"Mustafa Abid",{"name":204,"orcid":9},"Abdul khalil Afghani",{"tldr":206,"method":207,"finding":208,"direction":110,"opportunity":209},"系统综述气候变化对阿富汗农业的影响、挑战与适应策略。","按PRISMA检索2000-2025年文献，整合阿富汗及周边四国区域证据。","水资源短缺与干旱致主要作物减产12%-25%，气候智慧型农业与节水灌溉最有效。","可研究气候智慧型农业措施在阿富汗及周边干旱区的本地化适配与推广机制。","智慧农业 \u002F 农业物联网","2026-10-19T23:30:11.285828Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":233,"classification":241,"search_phrases":265,"slug":268,"view_count":22,"doi":269,"paper":270,"created_at":283},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":19,"authority":20,"freshness":148,"relevant":22,"comment":222},"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。",[224],{"name":218,"url":215},[27,226,64,28,227],"农业供应链","地缘政治",{"code":60,"slug":229,"name":230,"path":231,"parent_code":232},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[234,235],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":242},{"candidates":243,"agricultural_evidence":46},[244,249,254,258],{"code":60,"score":245,"supported":46,"title":246,"abstract":247,"tags":248},15,[65],[64,65],[64],{"code":54,"score":250,"supported":46,"title":251,"abstract":252,"tags":253},11,[],[27,28,57],[27,28],{"code":33,"score":148,"supported":46,"title":255,"abstract":256,"tags":257},[],[29,49,50],[],{"code":236,"score":148,"supported":46,"title":259,"abstract":260,"tags":264},[],[261,262,263],"物联网","internet of things","IoT",[],[266,267],"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":269,"openalex_id":271,"authors":272,"venue":218,"cited_by_count":98,"oa_url":215,"card":278,"direction":210,"ingested_from":112},"W7222902031",[273,275],{"name":274,"orcid":9},"Parameswaran Mahendran",{"name":276,"orcid":277},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":279,"method":280,"finding":281,"direction":210,"opportunity":282},"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":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":290,"source_url":287,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":295,"tags":297,"primary_topic":301,"secondary_topics":306,"classification":308,"search_phrases":351,"slug":354,"view_count":22,"doi":355,"paper":356,"created_at":366},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)",70,{"impact":17,"substance":293,"depth":245,"authority":126,"freshness":148,"relevant":22,"comment":294},16,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[296],{"name":290,"url":287},[27,298,299,29,300],"农业人工智能","可持续发展","遥感监测",{"code":302,"slug":303,"name":304,"path":305,"parent_code":240},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[307],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":309},{"candidates":310,"agricultural_evidence":46},[311,320,324,328,335,341,347],{"code":302,"score":17,"supported":46,"title":312,"abstract":314,"tags":319},[313],"artificial intelligence",[315,316,317,318,313],"深度学习","机器学习","machine learning","deep learning",[298],{"code":33,"score":245,"supported":46,"title":321,"abstract":322,"tags":323},[50],[29,50],[29],{"code":54,"score":155,"supported":46,"title":325,"abstract":326,"tags":327},[],[28,57],[27],{"code":329,"score":155,"supported":46,"title":330,"abstract":331,"tags":334},"I3",[],[332,333],"遥感","remote sensing",[300],{"code":89,"score":61,"supported":46,"title":336,"abstract":337,"tags":340},[],[338,339],"碳排放","carbon emission",[],{"code":187,"score":61,"supported":46,"title":342,"abstract":343,"tags":346},[],[344,345],"生物多样性","biodiversity",[],{"code":236,"score":61,"supported":46,"title":348,"abstract":349,"tags":350},[],[261,262],[],[352,353],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":355,"openalex_id":357,"authors":358,"venue":290,"cited_by_count":98,"oa_url":287,"card":361,"direction":210,"ingested_from":112},"W7222092855",[359],{"name":360,"orcid":9},"Deepmala Suryavanshi",{"tldr":362,"method":363,"finding":364,"direction":110,"opportunity":365},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":9,"content":9,"source_name":372,"source_url":370,"published_at":373,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":378,"tags":380,"primary_topic":384,"secondary_topics":385,"classification":386,"search_phrases":397,"slug":400,"view_count":83,"doi":401,"paper":402,"created_at":427},4337,"Revolutionizing drought defense: The water-plant-microbe synergy in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isci.2026.117422","Revolutionizing drought defense: The water-plant-microbe synergy in climate-smart agriculture。iScience","iScience","2026-10-07T00:00:00Z",75,{"impact":17,"substance":293,"depth":19,"authority":376,"freshness":13,"relevant":22,"comment":377},14,"核心期刊发表的抗旱协同机制综述，专业性强但偏基础研究，产业落地影响有限。",[379],{"name":372,"url":370},[27,29,381,382,383],"农业科技","作物抗旱","植物微生物互作",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":387},{"candidates":388,"agricultural_evidence":46},[389,393],{"code":33,"score":126,"supported":46,"title":390,"abstract":391,"tags":392},[51],[51],[29],{"code":54,"score":126,"supported":46,"title":394,"abstract":395,"tags":396},[184],[184],[27],[398,399],"iScience 水-植物-微生物 抗旱","植物微生物互作 作物抗旱 农业科技 智慧农业","iScience水-植物-微生物抗旱-4337","10.1016\u002Fj.isci.2026.117422",{"doi":401,"openalex_id":403,"authors":404,"venue":372,"cited_by_count":98,"oa_url":370,"card":9,"direction":210,"ingested_from":112},"W7220703638",[405,407,409,412,415,417,419,422,424],{"name":406,"orcid":9},"Wenlong Wang",{"name":408,"orcid":9},"Zijian He",{"name":410,"orcid":411},"Jun Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2362-5359",{"name":413,"orcid":414},"Risheng Ding","https:\u002F\u002Forcid.org\u002F0000-0001-8565-7623",{"name":416,"orcid":9},"王兰香",{"name":418,"orcid":9},"Moxian Chen",{"name":420,"orcid":421},"Jian Kang","https:\u002F\u002Forcid.org\u002F0000-0001-6956-0485",{"name":423,"orcid":9},"Ling Tong",{"name":425,"orcid":426},"Taisheng Du","https:\u002F\u002Forcid.org\u002F0000-0002-6146-0217","2026-10-07T23:30:08.477531Z",{"id":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":434,"source_url":431,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":439,"tags":441,"primary_topic":443,"secondary_topics":444,"classification":447,"search_phrases":469,"slug":472,"view_count":473,"doi":474,"paper":475,"created_at":487},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":148,"relevant":22,"comment":438},"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。",[440],{"name":434,"url":431},[27,298,261,442,28],"数字孪生",{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},[445,446],{"code":302,"slug":303,"name":304,"path":305,"parent_code":240},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":448},{"candidates":449,"agricultural_evidence":46},[450,456,460,464],{"code":236,"score":17,"supported":46,"title":451,"abstract":452,"tags":455},[262],[261,453,262,263,454],"边缘计算","edge computing",[261],{"code":54,"score":126,"supported":46,"title":457,"abstract":458,"tags":459},[],[28,442,57],[442,27,28],{"code":302,"score":126,"supported":46,"title":461,"abstract":462,"tags":463},[313],[313],[298],{"code":33,"score":148,"supported":46,"title":465,"abstract":466,"tags":468},[],[29,50,467],"climate resilience",[],[470,471],"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":474,"openalex_id":476,"authors":477,"venue":434,"cited_by_count":98,"oa_url":431,"card":481,"direction":210,"ingested_from":112},"W7216004668",[478],{"name":479,"orcid":480},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":482,"method":483,"finding":484,"direction":485,"opportunity":486},"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":489,"title":490,"url":491,"summary":492,"summary_zh":493,"content":9,"source_name":494,"source_url":491,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":500,"tags":502,"primary_topic":505,"secondary_topics":507,"classification":509,"search_phrases":545,"slug":548,"view_count":473,"doi":549,"paper":550,"created_at":578},3878,"Climate Change and Sustainable Maize Production: Risks, Response and Resilience Pathways","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192123","Climate change significantly threatens maize (Zea mays L.) production through rising temperatures, erratic precipitation, and extreme weather events, thereby jeopardising global food security. This review synthesises current knowledge on the impacts of climate change, abiotic stresses, crop simulation models, and adaptation strategies for maize. A bibliometric analysis of 485 articles published between 2015 and 2025 and indexed in the Web of Science was conducted using VOSviewer. The results showed a significant increase in publications (Mann–Kendall test, p = 0.0001; R2 = 0.8207), with dominant research themes shifting toward climate-smart agriculture and sustainable intensification. Heat stress, drought, and nutrient deficiencies were identified as the primary abiotic constraints. DSSAT-CERES-Maize and APSIM emerged as the most widely used models for assessing climate impacts and the effectiveness of adaptation strategies. Effective adaptation measures include the use of resilient varieties, optimised water management, conservation agriculture, and diversified cropping systems. Future research should focus on integrated adaptation approaches, improved model validation, and long-term field studies to enhance maize resilience. This study provides a comprehensive overview of the research landscape and highlights critical pathways for ensuring sustainable maize production under changing climate scenarios.","气候变化通过气温升高、降水异常和极端天气事件，严重威胁玉米（Zea mays L.）生产，进而危及全球粮食安全。本文综述了气候变化影响、非生物胁迫、作物模拟模型及玉米适应策略的现有知识。利用VOSviewer对2015至2025年间Web of Science收录的485篇文献进行了文献计量分析。结果表明，发文量显著增加（Mann–Kendall检验，p = 0.0001；R2 = 0.8207），主导研究主题转向气候智慧型农业和可持续集约化。热胁迫、干旱和养分缺乏被确定为主要的非生物限制因素。DSSAT-CERES-Maize和APSIM是评估气候影响及适应策略效果最广泛使用的模型。有效的适应措施包括使用抗逆品种、优化水分管理、保护性农业和多样化种植制度。未来研究应聚焦于综合适应方法、改进模型验证和长期田间试验，以增强玉米的恢复力。本研究全面概述了该领域的研究格局，并指出了在气候变化情景下确保玉米可持续生产的关键路径。","Agriculture","2026-09-30T00:00:00Z",80,{"impact":17,"substance":498,"depth":17,"authority":376,"freshness":21,"relevant":22,"comment":499},22,"基于485篇文献的计量综述，系统梳理气候变化下玉米生产的风险与适应路径，方法规范、结论可靠，对农业科技与智慧农业领域有较高参考价值。",[501],{"name":494,"url":491},[27,170,29,503,504],"作物模型","适应策略",{"code":167,"slug":171,"name":170,"path":506,"parent_code":232},"\u002Ftopic\u002Ffield-crops\u002Fmaize",[508],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":510},{"candidates":511,"agricultural_evidence":46},[512,516,520,524,531,535,541],{"code":33,"score":17,"supported":46,"title":513,"abstract":514,"tags":515},[50],[29,49,50,51],[29],{"code":167,"score":245,"supported":46,"title":517,"abstract":518,"tags":519},[171],[170,171],[170],{"code":60,"score":61,"supported":46,"title":521,"abstract":522,"tags":523},[],[64,65],[],{"code":525,"score":61,"supported":46,"title":526,"abstract":527,"tags":530},"I7",[],[528,529],"文献计量","bibliometric",[],{"code":54,"score":181,"supported":46,"title":532,"abstract":533,"tags":534},[],[184],[27],{"code":536,"score":83,"supported":46,"title":537,"abstract":538,"tags":540},"H8",[],[539],"conservation agriculture",[],{"code":302,"score":22,"supported":14,"title":542,"abstract":543,"tags":544},[],[],[503],[546,547],"玉米 气候变化 适应策略","DSSAT APSIM 玉米","玉米气候变化适应策略-3878","10.3390\u002Fagriculture16192123",{"doi":549,"openalex_id":551,"authors":552,"venue":494,"cited_by_count":98,"oa_url":491,"card":573,"direction":210,"ingested_from":112},"W7214894179",[553,556,559,561,564,567,570],{"name":554,"orcid":555},"Magdoline Mustafa Ahmed Osman","https:\u002F\u002Forcid.org\u002F0009-0003-5743-9161",{"name":557,"orcid":558},"Ronald Kuunya","https:\u002F\u002Forcid.org\u002F0009-0007-3502-0297",{"name":560,"orcid":9},"Erastus Wasikoyo",{"name":562,"orcid":563},"András Tamás","https:\u002F\u002Forcid.org\u002F0000-0001-8175-3300",{"name":565,"orcid":566},"Árpád Illés","https:\u002F\u002Forcid.org\u002F0000-0001-6548-2926",{"name":568,"orcid":569},"Adrienn Széles","https:\u002F\u002Forcid.org\u002F0000-0003-4101-3177",{"name":571,"orcid":572},"Tamás Rátonyi","https:\u002F\u002Forcid.org\u002F0000-0001-9436-5446",{"tldr":574,"method":575,"finding":576,"direction":110,"opportunity":577},"综述气候变化对玉米生产的影响，通过文献计量分析揭示研究趋势与适应策略。","文献计量分析485篇文献，使用VOSviewer和DSSAT、APSIM模型。","热胁迫、干旱和营养缺乏是主要限制，气候智慧型农业和可持续集约化成为研究热点。","可探索综合适应策略的长期田间验证及模型改进，以增强玉米气候韧性。","2026-10-01T23:30:09.232711Z"]