[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2040":3,"related-2040":122},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":101,"slug":104,"view_count":22,"doi":105,"paper":106,"created_at":121},2040,"Climate Change and Agricultural Insect Pests: Ecological Mechanisms, Crop Productivity Impacts and Climate Adaptation Strategies","https:\u002F\u002Fdoi.org\u002F10.47495\u002Fokufbed.2000761","Climate change has emerged as one of the most significant challenges affecting agricultural production systems worldwide. Rising temperatures, altered precipitation patterns, increasing atmospheric carbon dioxide concentrations, and the growing frequency of extreme weather events are substantially influencing the biology, ecology, distribution, and population dynamics of agricultural pests. These changes contribute to increased pest abundance, expanded geographical ranges, higher overwintering success, accelerated development rates, and greater numbers of generations per year. Consequently, pest-induced crop losses are expected to increase, posing serious threats to crop productivity, agricultural sustainability, and global food security. In addition to direct effects on pest populations, climate change disrupts plant–pest–natural enemy interactions and weakens biological control mechanisms, further increasing pest pressure within agricultural ecosystems. This review examines the effects of climate change on agricultural insect pest populations and evaluates their implications for crop productivity. Particular attention is given to temperature increases, changes in precipitation and humidity regimes, geographical distribution shifts, invasive species expansion, and phenological mismatches. Furthermore, innovative adaptation strategies including integrated pest management, artificial intelligence-based forecasting systems, precision agriculture technologies, climate-smart agriculture approaches, and remote sensing applications are discussed as potential tools for enhancing agricultural resilience under changing climatic conditions. The findings indicate that sustainable management of climate-related pest risks requires multidisciplinary approaches integrating climate information, pest monitoring, ecological processes, and advanced decision-support technologies. Developing climate-resilient, technology-supported and ecologically based pest management strategies will be essential for safeguarding agricultural productivity and long-term global food security.","气候变化已成为影响全球农业生产系统的最重大挑战之一。气温上升、降水模式改变、大气二氧化碳浓度增加以及极端天气事件日益频繁，正在显著影响农业害虫的生物学、生态学、分布和种群动态。这些变化导致害虫丰度增加、地理分布范围扩大、越冬成功率提高、发育速率加快以及每年世代数增多。因此，害虫引起的作物损失预计将增加，对作物生产力、农业可持续性和全球粮食安全构成严重威胁。除对害虫种群的直接影响外，气候变化还扰乱了植物—害虫—天敌之间的相互作用，削弱了生物防治机制，进一步加剧了农业生态系统内的害虫压力。本文综述了气候变化对农业害虫种群的影响，并评估了其对作物生产力的意义。特别关注了温度升高、降水和湿度状况变化、地理分布转移、入侵物种扩散以及物候错配等方面。此外，还讨论了创新性适应策略，包括有害生物综合治理、基于人工智能的预测系统、精准农业技术、气候智慧型农业方法以及遥感应用，作为在气候变化条件下增强农业韧性的潜在工具。研究结果表明，气候相关害虫风险的可持续管理需要多学科方法，整合气候信息、害虫监测、生态过程和先进决策支持技术。开发气候韧性、技术支撑和基于生态的害虫管理策略，对于保障农业生产力和长期全球粮食安全至关重要。",null,"Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi","2026-09-09T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,12,9,1,"系统综述气候变化对农业害虫生态机制与作物生产力的影响，并整合IPM、AI预测、精准农业与遥感等适应策略，对智慧植保与气候韧性农业有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","粮食安全","病虫害防控","气候变化","遥感监测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,61,68,75,82,89,95],{"code":34,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},15,true,[50],"climate change",[31,50],[31],{"code":54,"score":13,"supported":48,"title":55,"abstract":56,"tags":60},"I1",[],[57,58,59],"精准农业","smart agriculture","precision agriculture",[27],{"code":62,"score":63,"supported":48,"title":64,"abstract":65,"tags":67},"A8",7,[],[29,66],"food security",[29],{"code":69,"score":63,"supported":48,"title":70,"abstract":71,"tags":74},"I3",[],[72,73],"遥感","remote sensing",[32],{"code":76,"score":77,"supported":48,"title":78,"abstract":79,"tags":81},"I2",4,[],[80],"artificial intelligence",[28],{"code":83,"score":84,"supported":48,"title":85,"abstract":86,"tags":88},"F4",3,[],[87],"生物防治",[],{"code":90,"score":84,"supported":48,"title":91,"abstract":92,"tags":94},"H6",[],[93],"农业生态",[],{"code":96,"score":84,"supported":48,"title":97,"abstract":98,"tags":100},"J3",[],[99],"全球农业",[],[102,103],"农业人工智能 病虫害防控 智慧农业 气候变化","农业人工智能 病虫害防控","农业人工智能病虫害防控智慧农业气候变化-2040","10.47495\u002Fokufbed.2000761",{"doi":105,"openalex_id":107,"authors":108,"venue":10,"cited_by_count":112,"oa_url":6,"card":113,"direction":119,"ingested_from":120},"W7212022123",[109],{"name":110,"orcid":111},"Ekrem ASLAN","https:\u002F\u002Forcid.org\u002F0000-0001-8829-7301",0,{"tldr":114,"method":115,"finding":116,"direction":117,"opportunity":118},"综述气候变化对农业害虫生态机制、作物生产力影响及气候适应策略。","文献综述，整合气候数据、害虫监测与AI预测、遥感等技术。","气候变暖扩大害虫分布、增加世代与危害，削弱生物防治，威胁粮食安全。","农业人工智能与决策模型","可构建融合气候、遥感与AI的害虫风险预警决策模型，填补多尺度动态预测空白。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.295781Z",{"total":123,"page":22,"page_size":123,"items":124},6,[125,208,313,401,488,551],{"id":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":137,"tags":139,"primary_topic":141,"secondary_topics":146,"classification":148,"search_phrases":191,"slug":194,"view_count":22,"doi":195,"paper":196,"created_at":207},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)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":135,"depth":47,"authority":20,"freshness":21,"relevant":22,"comment":136},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。",[138],{"name":131,"url":128},[27,28,140,31,32],"可持续发展",{"code":76,"slug":142,"name":143,"path":144,"parent_code":145},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[147],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":149},{"candidates":150,"agricultural_evidence":48},[151,159,163,167,171,178,184],{"code":76,"score":17,"supported":48,"title":152,"abstract":153,"tags":158},[80],[154,155,156,157,80],"深度学习","机器学习","machine learning","deep learning",[28],{"code":34,"score":47,"supported":48,"title":160,"abstract":161,"tags":162},[50],[31,50],[31],{"code":54,"score":63,"supported":48,"title":164,"abstract":165,"tags":166},[],[57,59],[27],{"code":69,"score":63,"supported":48,"title":168,"abstract":169,"tags":170},[],[72,73],[32],{"code":172,"score":123,"supported":48,"title":173,"abstract":174,"tags":177},"H4",[],[175,176],"碳排放","carbon emission",[],{"code":90,"score":123,"supported":48,"title":179,"abstract":180,"tags":183},[],[181,182],"生物多样性","biodiversity",[],{"code":185,"score":123,"supported":48,"title":186,"abstract":187,"tags":190},"I5",[],[188,189],"物联网","internet of things",[],[192,193],"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":195,"openalex_id":197,"authors":198,"venue":131,"cited_by_count":112,"oa_url":128,"card":201,"direction":119,"ingested_from":120},"W7222092855",[199],{"name":200,"orcid":9},"Deepmala Suryavanshi",{"tldr":202,"method":203,"finding":204,"direction":205,"opportunity":206},"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。","农业绿色发展与碳","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":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"primary_topic":224,"secondary_topics":229,"classification":237,"search_phrases":279,"slug":282,"view_count":251,"doi":283,"paper":284,"created_at":312},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":218,"freshness":13,"relevant":22,"comment":219},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。",[221],{"name":214,"url":211},[27,29,223,31,32],"植物病害",{"code":62,"slug":225,"name":226,"path":227,"parent_code":228},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[230,231],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":232,"slug":233,"name":234,"path":235,"parent_code":236},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":41,"locked":14,"version":42,"method":43,"evidence":238},{"candidates":239,"agricultural_evidence":48},[240,246,250,256,260,264,269,275],{"code":34,"score":17,"supported":48,"title":241,"abstract":242,"tags":245},[50],[31,243,50,244],"干旱","drought",[31],{"code":62,"score":47,"supported":48,"title":247,"abstract":248,"tags":249},[66],[29,66],[29],{"code":232,"score":251,"supported":48,"title":252,"abstract":254,"tags":255},8,[253],"food safety",[],[],{"code":54,"score":63,"supported":48,"title":257,"abstract":258,"tags":259},[],[57,59],[27],{"code":69,"score":63,"supported":48,"title":261,"abstract":262,"tags":263},[],[72,73],[32],{"code":83,"score":123,"supported":48,"title":265,"abstract":266,"tags":268},[],[87,267],"crop protection",[],{"code":270,"score":84,"supported":48,"title":271,"abstract":272,"tags":274},"H2",[],[273],"soil",[],{"code":76,"score":84,"supported":48,"title":276,"abstract":277,"tags":278},[],[80],[],[280,281],"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":283,"openalex_id":285,"authors":286,"venue":214,"cited_by_count":112,"oa_url":211,"card":306,"direction":311,"ingested_from":120},"W7213972258",[287,289,291,294,297,300,303],{"name":288,"orcid":9},"Sindiswa Khawula",{"name":290,"orcid":9},"Siyabonga Ntshalitshali",{"name":292,"orcid":293},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":295,"orcid":296},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":298,"orcid":299},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":301,"orcid":302},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":304,"orcid":305},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":307,"method":308,"finding":309,"direction":117,"opportunity":310},"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。","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":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":9,"source_name":319,"source_url":316,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":329,"secondary_topics":330,"classification":336,"search_phrases":382,"slug":385,"view_count":112,"doi":9,"paper":386,"created_at":400},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":20,"freshness":123,"relevant":22,"comment":323},"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。",[325],{"name":319,"url":316},[27,29,31,327,328],"适应性策略","水资源短缺",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[331],{"code":332,"slug":333,"name":334,"path":335,"parent_code":38},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":41,"locked":14,"version":42,"method":43,"evidence":337},{"candidates":338,"agricultural_evidence":48},[339,343,349,353,360,367,374,378],{"code":34,"score":17,"supported":48,"title":340,"abstract":341,"tags":342},[50],[31,243,50,244],[31],{"code":332,"score":21,"supported":48,"title":344,"abstract":345,"tags":348},[],[346,347,333],"灌溉","水资源",[],{"code":62,"score":63,"supported":48,"title":350,"abstract":351,"tags":352},[],[29,66],[29],{"code":354,"score":123,"supported":48,"title":355,"abstract":356,"tags":359},"A2",[],[357,358],"小麦","wheat",[],{"code":361,"score":123,"supported":48,"title":362,"abstract":363,"tags":366},"A3",[],[364,365],"玉米","maize",[],{"code":368,"score":123,"supported":48,"title":369,"abstract":370,"tags":373},"A6",[],[371,372],"棉花","cotton",[],{"code":54,"score":77,"supported":48,"title":375,"abstract":376,"tags":377},[],[58],[27],{"code":90,"score":84,"supported":48,"title":379,"abstract":380,"tags":381},[],[93],[],[383,384],"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":387,"authors":388,"venue":319,"cited_by_count":112,"oa_url":316,"card":395,"direction":119,"ingested_from":120},"W7222907396",[389,391,393],{"name":390,"orcid":9},"Mohammad Shafiq Faieq",{"name":392,"orcid":9},"Mustafa Abid",{"name":394,"orcid":9},"Abdul khalil Afghani",{"tldr":396,"method":397,"finding":398,"direction":205,"opportunity":399},"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。","2026-10-19T23:30:11.285828Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":414,"tags":416,"primary_topic":418,"secondary_topics":422,"classification":425,"search_phrases":463,"slug":466,"view_count":467,"doi":468,"paper":469,"created_at":487},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":17,"substance":411,"depth":412,"authority":218,"freshness":251,"relevant":22,"comment":413},22,19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[415],{"name":407,"url":404},[27,29,155,32,417],"农业干旱",{"code":69,"slug":419,"name":420,"path":421,"parent_code":145},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[423,424],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":76,"slug":142,"name":143,"path":144,"parent_code":145},{"status":41,"locked":14,"version":42,"method":43,"evidence":426},{"candidates":427,"agricultural_evidence":48},[428,432,436,440,444,449,455,459],{"code":69,"score":47,"supported":48,"title":429,"abstract":430,"tags":431},[73],[72,73],[32],{"code":34,"score":218,"supported":48,"title":433,"abstract":434,"tags":435},[244],[243,244],[],{"code":76,"score":13,"supported":48,"title":437,"abstract":438,"tags":439},[],[154,155,156,157,80],[155],{"code":62,"score":63,"supported":48,"title":441,"abstract":442,"tags":443},[],[29,66],[29],{"code":270,"score":123,"supported":48,"title":445,"abstract":446,"tags":448},[],[447,273],"土壤",[],{"code":450,"score":84,"supported":48,"title":451,"abstract":452,"tags":454},"D",[],[453],"水产",[],{"code":90,"score":84,"supported":48,"title":456,"abstract":457,"tags":458},[],[93],[],{"code":54,"score":22,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[27],[464,465],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":468,"openalex_id":470,"authors":471,"venue":407,"cited_by_count":112,"oa_url":481,"card":482,"direction":119,"ingested_from":120},"W7221081979",[472,475,478],{"name":473,"orcid":474},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":476,"orcid":477},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":479,"orcid":480},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":483,"method":484,"finding":485,"direction":311,"opportunity":486},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"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":504,"secondary_topics":505,"classification":506,"search_phrases":529,"slug":532,"view_count":22,"doi":533,"paper":534,"created_at":550},4309,"Assessing agricultural drought intensification and spatiotemporal pattern in the central Rift Valley using multi-source indices","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cliser.2026.100737","Drought is a complex and multifaceted phenomenon that includes meteorological, agricultural, hydrological, and socio-economic components. Both single and composite drought indices were used in this study to assess agricultural drought in the East Shewa Zone of Central Ethiopia. The Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI) were used as single indices. To improve drought characterization, three composite indices—the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Optimized Vegetation Dryness Index (OVDI)—were created using empirical weighting and limited optimization techniques. The association of both single and composite indices with the multi-timescale Standardized Precipitation Index (SPI), a widely used meteorological drought measure, was evaluated using in-sample correlation analysis. The results showed that the composite indices (OVDI, VHI, and SDCI) exhibited stronger associations with the multi-timescale SPI than the individual indices, although the strength of these associations varied across seasons and SPI timescales. Among the single indices, the PCI showed the strongest in-sample correlation with the short-term SPI ( r = 0.764 with SPI-1). In contrast, the VCI and SMCI were more strongly correlated with longer-term SPI scales, with SMCI showing in-sample correlations of ( r = 0.577 with SPI-3) and ( r = 0.467 with SPI-6). Spatiotemporal analysis indicated an increase in drought severity, with the area classified as severely dry expanding from 1% to 11% between 2002 and 2015. The findings provide insights into the use of remote sensing indices for drought monitoring and may contribute to the development of improved drought monitoring and early warning approaches in the region.","干旱是一种复杂且多层面的现象，包括气象、农业、水文和社会经济等组成部分。本研究使用单一和复合干旱指数评估埃塞俄比亚中部东谢瓦区的农业干旱。采用降水条件指数（PCI）、温度条件指数（TCI）、植被条件指数（VCI）和土壤水分条件指数（SMCI）作为单一指数。为改进干旱表征，利用经验加权和有限优化技术构建了三个复合指数——尺度化干旱条件指数（SDCI）、植被健康指数（VHI）和优化植被干燥度指数（OVDI）。采用样本内相关分析评估了单一和复合指数与多时间尺度标准化降水指数（SPI）——一种广泛使用的气象干旱指标——的关联。结果表明，复合指数（OVDI、VHI和SDCI）与多时间尺度SPI的关联强于单一指数，尽管这些关联的强度因季节和SPI时间尺度而异。在单一指数中，PCI与短期SPI的样本内相关性最强（与SPI-1的r = 0.764）。相比之下，VCI和SMCI与较长期SPI尺度的相关性更强，SMCI的样本内相关性为（与SPI-3的r = 0.577）和（与SPI-6的r = 0.467）。时空分析表明干旱严重程度增加，2002年至2015年间被归类为严重干旱的面积从1%扩大至11%。研究结果为利用遥感指数进行干旱监测提供了见解，并可能有助于该地区改进干旱监测和预警方法的发展。","Climate Services","2026-10-05T00:00:00Z",71,{"impact":20,"substance":18,"depth":19,"authority":498,"freshness":21,"relevant":22,"comment":499},13,"基于多源遥感指数评估埃塞俄比亚中部裂谷农业干旱加剧趋势，方法组合与时空结论具参考价值，但属区域案例研究，公共影响有限。",[501],{"name":494,"url":491},[27,29,32,417,503],"干旱指数",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":507},{"candidates":508,"agricultural_evidence":48},[509,513,517,521,525],{"code":34,"score":47,"supported":48,"title":510,"abstract":511,"tags":512},[244],[243,244],[503],{"code":69,"score":63,"supported":48,"title":514,"abstract":515,"tags":516},[],[72,73],[32],{"code":270,"score":123,"supported":48,"title":518,"abstract":519,"tags":520},[],[447,273],[],{"code":62,"score":22,"supported":14,"title":522,"abstract":523,"tags":524},[],[],[29],{"code":54,"score":22,"supported":14,"title":526,"abstract":527,"tags":528},[],[],[27],[530,531],"埃塞俄比亚 中部裂谷 农业干旱","遥感 干旱指数 SPI","埃塞俄比亚中部裂谷农业干旱-4309","10.1016\u002Fj.cliser.2026.100737",{"doi":533,"openalex_id":535,"authors":536,"venue":494,"cited_by_count":112,"oa_url":544,"card":545,"direction":311,"ingested_from":120},"W7220385252",[537,539,541],{"name":538,"orcid":9},"Bekele Alemayehu",{"name":540,"orcid":9},"Zenebe Ayele",{"name":542,"orcid":543},"Hamere Yohannes","https:\u002F\u002Forcid.org\u002F0000-0002-6899-5150","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2405880726001056\u002Fpdf",{"tldr":546,"method":547,"finding":548,"direction":311,"opportunity":549},"用多源遥感指数评估埃塞俄比亚中部裂谷农业干旱加剧及时空格局。","PCI、TCI、VCI、SMCI及SDCI、VHI、OVDI复合指数，结合SPI","复合指数与SPI相关性更强，2002-2015年重旱面积从1%增至11%。","可探索多源指数优化加权与机器学习融合，提升区域干旱预警精度。","2026-10-06T23:30:20.023409Z",{"id":552,"title":553,"url":554,"summary":555,"summary_zh":556,"content":9,"source_name":557,"source_url":554,"published_at":558,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":559,"score_detail":560,"sources":563,"tags":565,"primary_topic":568,"secondary_topics":569,"classification":575,"search_phrases":625,"slug":628,"view_count":84,"doi":629,"paper":630,"created_at":647},3542,"Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pclm.0001038","Climate change is intensifying drought vulnerability in northern Ghana, posing serious risks to crop production, food security, and rural livelihoods in semi-arid regions. This study develops an empirical modelling framework to assess drought vulnerability and impacts at three temporal horizons, 2026–2050, 2051–2075, and 2076–2100, under SSP1-2.6 and SSP5-8.5 scenarios. We integrated bias-corrected outputs from five CMIP6 models, MRI-ESM2–0, EC-Earth3, IPSL-CM6A-LR, CESM2, and HadGEM3-GC31-LL, downscaled to 0.25° resolution, with drought indices including the Standardized Precipitation Index SPI, Reconnaissance Drought Index RDI, Normalized Difference Drought Index NDDI, and Soil Moisture Index SMI. These were combined with MODIS-derived Normalized Difference Vegetation Index NDVI, population density, and land use\u002Fland cover using GIS-based tools to generate composite drought vulnerability maps. CMIP6 models were validated against reference datasets and showed strong performance for annual precipitation, r = 0.95, RMSE = 3.4 mm, and temperature, r = 0.99, RMSE = 0.47–0.74°C. Results project increasing drought severity, with mean temperature rises of 2.0–3.9°C and rainfall declines up to 19% by 2100. Crop yields for maize, millet, sorghum, and groundnuts are expected to fall by 25–60% by 2050, with maize showing the strongest negative correlation to drought severity, r = –0.72, p \u003C 0.01, confirmed by Mann-Kendall trends at the 95% confidence level. About 84% of the study area is classified as highly vulnerable due to limited adaptive capacity, over 80% dependence on rain-fed agriculture, more than 49% household food insecurity, less than 23% cropland use, and under 5% water body coverage. Significant uncertainties remain in precipitation projections, with some models showing divergent temperature trends, highlighting challenges in modelling climate impacts. The findings underscore the urgent need for district-level drought preparedness, solar-powered irrigation, index insurance, climate-smart agriculture, and Disaster Risk Reduction aligned with UNDRR and Sendai Framework priorities to build resilience in northern Ghana.","气候变化正在加剧加纳北部的干旱脆弱性，对半干旱地区的作物生产、粮食安全和农村生计构成严重风险。本研究构建了一个经验建模框架，用于评估2026—2050年、2051—2075年和2076—2100年三个时间尺度下SSP1-2.6和SSP5-8.5情景中的干旱脆弱性及其影响。我们将五个CMIP6模型（MRI-ESM2–0、EC-Earth3、IPSL-CM6A-LR、CESM2和HadGEM3-GC31-LL）的偏差校正输出降尺度至0.25°分辨率，并与标准化降水指数（SPI）、侦察干旱指数（RDI）、归一化差值干旱指数（NDDI）和土壤水分指数（SMI）等干旱指数相结合。这些数据与MODIS衍生的归一化差值植被指数（NDVI）、人口密度以及土地利用\u002F土地覆盖数据通过基于GIS的工具相结合，生成综合干旱脆弱性地图。CMIP6模型经参考数据集验证，在年降水量（r = 0.95，RMSE = 3.4 mm）和温度（r = 0.99，RMSE = 0.47–0.74°C）方面表现出色。结果预测干旱严重程度将加剧，到2100年平均气温上升2.0–3.9°C，降雨量下降高达19%。到2050年，玉米、小米、高粱和花生的作物产量预计将下降25–60%，其中玉米与干旱严重程度呈最强负相关（r = –0.72，p \u003C 0.01），并经Mann-Kendall趋势检验在95%置信水平上得到证实。约84%的研究区域被归类为高度脆弱，原因包括适应能力有限、超过80%依赖雨养农业、超过49%的家庭粮食不安全、不足23%的耕地利用率以及低于5%的水体覆盖率。降水预测仍存在显著不确定性，部分模型显示出不同的温度趋势，凸显了气候影响建模的挑战。研究结果强调迫切需要开展县级干旱备灾、太阳能灌溉、指数保险、气候智慧型农业以及与UNDRR和仙台框架优先事项一致的风险减灾工作，以增强加纳北部的韧性。","PLOS Climate","2026-09-25T00:00:00Z",82,{"impact":17,"substance":561,"depth":17,"authority":218,"freshness":21,"relevant":22,"comment":562},23,"基于CMIP6多模型与遥感指数的干旱脆弱性实证研究，数据扎实、结论明确，对半干旱区农业防灾与气候适应有参考价值。",[564],{"name":557,"url":554},[29,566,31,32,567],"防灾减灾","干旱风险",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[570],{"code":571,"slug":572,"name":573,"path":574,"parent_code":228},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",{"status":41,"locked":14,"version":42,"method":43,"evidence":576},{"candidates":577,"agricultural_evidence":48},[578,582,589,593,597,601,605,611,615,621],{"code":34,"score":412,"supported":48,"title":579,"abstract":580,"tags":581},[244],[31,243,50,244],[31,566],{"code":571,"score":21,"supported":48,"title":583,"abstract":584,"tags":588},[],[585,586,587],"高粱","sorghum","millet",[],{"code":62,"score":63,"supported":48,"title":590,"abstract":591,"tags":592},[],[29,66],[29],{"code":361,"score":123,"supported":48,"title":594,"abstract":595,"tags":596},[],[364,365],[],{"code":270,"score":123,"supported":48,"title":598,"abstract":599,"tags":600},[],[447,273],[],{"code":332,"score":123,"supported":48,"title":602,"abstract":603,"tags":604},[],[346,333],[],{"code":606,"score":84,"supported":48,"title":607,"abstract":608,"tags":610},"A4",[],[609],"花生",[],{"code":54,"score":84,"supported":48,"title":612,"abstract":613,"tags":614},[],[58],[],{"code":616,"score":84,"supported":48,"title":617,"abstract":618,"tags":620},"I4",[],[619],"GIS",[],{"code":69,"score":22,"supported":14,"title":622,"abstract":623,"tags":624},[],[],[32],[626,627],"加纳北部 干旱 玉米","CMIP6 干旱脆弱性 作物产量","加纳北部干旱玉米-3542","10.1371\u002Fjournal.pclm.0001038",{"doi":629,"openalex_id":631,"authors":632,"venue":557,"cited_by_count":112,"oa_url":554,"card":642,"direction":119,"ingested_from":120},"W7214399028",[633,636,639],{"name":634,"orcid":635},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":637,"orcid":638},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":640,"orcid":641},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":643,"method":644,"finding":645,"direction":311,"opportunity":646},"构建干旱脆弱性经验模型，评估加纳北部未来气候情景下作物与生计影响。","集成5个CMIP6模型、SPI\u002FRDI\u002FNDDI\u002FSMI指数与MODIS NDV","至2100年升温2.0-3.9°C、降水降19%，2050年作物减产25-60%，84%区域高度脆弱","可结合遥感干旱指数与作物模型，在数据稀缺区开展多情景脆弱性动态评估与适应策略优化。","2026-09-26T23:30:10.153994Z"]