[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4337":3,"related-4337":91},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":90},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",null,"iScience","2026-10-07T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},18,16,17,14,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","气候变化","农业科技","作物抗旱","植物微生物互作",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},12,true,[47],"drought",[47],[26],{"code":51,"score":44,"supported":45,"title":52,"abstract":54,"tags":55},"I1",[53],"smart agriculture",[53],[25],[57,58],"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",3,"10.1016\u002Fj.isci.2026.117422",{"doi":61,"openalex_id":63,"authors":64,"venue":9,"cited_by_count":87,"oa_url":6,"card":8,"direction":88,"ingested_from":89},"W7220703638",[65,67,69,72,75,77,79,82,84],{"name":66,"orcid":8},"Wenlong Wang",{"name":68,"orcid":8},"Zijian He",{"name":70,"orcid":71},"Jun Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2362-5359",{"name":73,"orcid":74},"Risheng Ding","https:\u002F\u002Forcid.org\u002F0000-0001-8565-7623",{"name":76,"orcid":8},"王兰香",{"name":78,"orcid":8},"Moxian Chen",{"name":80,"orcid":81},"Jian Kang","https:\u002F\u002Forcid.org\u002F0000-0001-6956-0485",{"name":83,"orcid":8},"Ling Tong",{"name":85,"orcid":86},"Taisheng Du","https:\u002F\u002Forcid.org\u002F0000-0002-6146-0217",0,"智慧农业 \u002F 农业物联网","openalex","2026-10-07T23:30:08.477531Z",{"total":92,"page":20,"page_size":92,"items":93},6,[94,194,274,361,454,539],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":8,"source_name":100,"source_url":97,"published_at":101,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":102,"score_detail":103,"sources":106,"tags":108,"primary_topic":112,"secondary_topics":113,"classification":119,"search_phrases":174,"slug":177,"view_count":87,"doi":8,"paper":178,"created_at":193},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":16,"substance":104,"depth":18,"authority":44,"freshness":92,"relevant":20,"comment":105},20,"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。",[107],{"name":100,"url":97},[25,109,26,110,111],"粮食安全","适应性策略","水资源短缺",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[114],{"code":115,"slug":116,"name":117,"path":118,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":38,"locked":13,"version":39,"method":40,"evidence":120},{"candidates":121,"agricultural_evidence":45},[122,128,135,142,149,156,163,168],{"code":31,"score":16,"supported":45,"title":123,"abstract":125,"tags":127},[124],"climate change",[26,126,124,47],"干旱",[26],{"code":115,"score":129,"supported":45,"title":130,"abstract":131,"tags":134},9,[],[132,133,116],"灌溉","水资源",[],{"code":136,"score":137,"supported":45,"title":138,"abstract":139,"tags":141},"A8",7,[],[109,140],"food security",[109],{"code":143,"score":92,"supported":45,"title":144,"abstract":145,"tags":148},"A2",[],[146,147],"小麦","wheat",[],{"code":150,"score":92,"supported":45,"title":151,"abstract":152,"tags":155},"A3",[],[153,154],"玉米","maize",[],{"code":157,"score":92,"supported":45,"title":158,"abstract":159,"tags":162},"A6",[],[160,161],"棉花","cotton",[],{"code":51,"score":164,"supported":45,"title":165,"abstract":166,"tags":167},4,[],[53],[25],{"code":169,"score":60,"supported":45,"title":170,"abstract":171,"tags":173},"H6",[],[172],"农业生态",[],[175,176],"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":8,"openalex_id":179,"authors":180,"venue":100,"cited_by_count":87,"oa_url":97,"card":187,"direction":88,"ingested_from":89},"W7222907396",[181,183,185],{"name":182,"orcid":8},"Mohammad Shafiq Faieq",{"name":184,"orcid":8},"Mustafa Abid",{"name":186,"orcid":8},"Abdul khalil Afghani",{"tldr":188,"method":189,"finding":190,"direction":191,"opportunity":192},"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。","农业绿色发展与碳","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":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":8,"source_name":200,"source_url":197,"published_at":201,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":202,"score_detail":203,"sources":207,"tags":209,"primary_topic":213,"secondary_topics":214,"classification":215,"search_phrases":257,"slug":260,"view_count":87,"doi":261,"paper":262,"created_at":273},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。","Journal of Plant Nutrition","2026-10-15T00:00:00Z",76,{"impact":16,"substance":104,"depth":18,"authority":204,"freshness":205,"relevant":20,"comment":206},13,8,"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。",[208],{"name":200,"url":197},[25,210,26,211,212],"精准农业","纳米农业","纳米肥料",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":216},{"candidates":217,"agricultural_evidence":45},[218,222,227,231,238,245,251],{"code":31,"score":12,"supported":45,"title":219,"abstract":220,"tags":221},[],[26,126,124,47],[26],{"code":51,"score":205,"supported":45,"title":223,"abstract":224,"tags":226},[],[210,225],"precision agriculture",[25,210],{"code":136,"score":92,"supported":45,"title":228,"abstract":229,"tags":230},[],[109,140],[],{"code":232,"score":92,"supported":45,"title":233,"abstract":234,"tags":237},"F3",[],[235,236],"肥料","fertilizer",[],{"code":239,"score":92,"supported":45,"title":240,"abstract":241,"tags":244},"H2",[],[242,243],"土壤","soil",[],{"code":246,"score":60,"supported":45,"title":247,"abstract":248,"tags":250},"F4",[],[249],"农药",[],{"code":252,"score":60,"supported":45,"title":253,"abstract":254,"tags":256},"H4",[],[255],"carbon sequestration",[],[258,259],"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","10.1080\u002F01904167.2026.2742923",{"doi":261,"openalex_id":263,"authors":264,"venue":200,"cited_by_count":87,"oa_url":8,"card":268,"direction":191,"ingested_from":89},"W7222281865",[265],{"name":266,"orcid":267},"Azam Ranjbar","https:\u002F\u002Forcid.org\u002F0000-0001-8650-7395",{"tldr":269,"method":270,"finding":271,"direction":191,"opportunity":272},"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。","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。","2026-10-17T23:31:02.249682Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":8,"source_name":280,"source_url":277,"published_at":201,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":285,"tags":287,"primary_topic":291,"secondary_topics":297,"classification":299,"search_phrases":345,"slug":348,"view_count":20,"doi":349,"paper":350,"created_at":360},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":16,"substance":17,"depth":283,"authority":44,"freshness":129,"relevant":20,"comment":284},15,"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。",[286],{"name":280,"url":277},[25,288,289,26,290],"农业人工智能","可持续发展","遥感监测",{"code":292,"slug":293,"name":294,"path":295,"parent_code":296},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[298],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":300},{"candidates":301,"agricultural_evidence":45},[302,311,315,319,326,332,338],{"code":292,"score":16,"supported":45,"title":303,"abstract":305,"tags":310},[304],"artificial intelligence",[306,307,308,309,304],"深度学习","机器学习","machine learning","deep learning",[288],{"code":31,"score":283,"supported":45,"title":312,"abstract":313,"tags":314},[124],[26,124],[26],{"code":51,"score":137,"supported":45,"title":316,"abstract":317,"tags":318},[],[210,225],[25],{"code":320,"score":137,"supported":45,"title":321,"abstract":322,"tags":325},"I3",[],[323,324],"遥感","remote sensing",[290],{"code":252,"score":92,"supported":45,"title":327,"abstract":328,"tags":331},[],[329,330],"碳排放","carbon emission",[],{"code":169,"score":92,"supported":45,"title":333,"abstract":334,"tags":337},[],[335,336],"生物多样性","biodiversity",[],{"code":339,"score":92,"supported":45,"title":340,"abstract":341,"tags":344},"I5",[],[342,343],"物联网","internet of things",[],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":280,"cited_by_count":87,"oa_url":277,"card":355,"direction":88,"ingested_from":89},"W7222092855",[353],{"name":354,"orcid":8},"Deepmala Suryavanshi",{"tldr":356,"method":357,"finding":358,"direction":191,"opportunity":359},"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":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":8,"source_name":367,"source_url":364,"published_at":368,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":369,"score_detail":370,"sources":373,"tags":375,"primary_topic":378,"secondary_topics":381,"classification":383,"search_phrases":419,"slug":422,"view_count":423,"doi":424,"paper":425,"created_at":453},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":16,"substance":371,"depth":16,"authority":19,"freshness":205,"relevant":20,"comment":372},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。",[374],{"name":367,"url":364},[25,153,26,376,377],"作物模型","适应策略",{"code":150,"slug":154,"name":153,"path":379,"parent_code":380},"\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[382],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":384},{"candidates":385,"agricultural_evidence":45},[386,390,394,398,405,409,415],{"code":31,"score":16,"supported":45,"title":387,"abstract":388,"tags":389},[124],[26,126,124,47],[26],{"code":150,"score":283,"supported":45,"title":391,"abstract":392,"tags":393},[154],[153,154],[153],{"code":136,"score":92,"supported":45,"title":395,"abstract":396,"tags":397},[],[109,140],[],{"code":399,"score":92,"supported":45,"title":400,"abstract":401,"tags":404},"I7",[],[402,403],"文献计量","bibliometric",[],{"code":51,"score":164,"supported":45,"title":406,"abstract":407,"tags":408},[],[53],[25],{"code":410,"score":60,"supported":45,"title":411,"abstract":412,"tags":414},"H8",[],[413],"conservation agriculture",[],{"code":292,"score":20,"supported":13,"title":416,"abstract":417,"tags":418},[],[],[376],[420,421],"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",2,"10.3390\u002Fagriculture16192123",{"doi":424,"openalex_id":426,"authors":427,"venue":367,"cited_by_count":87,"oa_url":364,"card":448,"direction":88,"ingested_from":89},"W7214894179",[428,431,434,436,439,442,445],{"name":429,"orcid":430},"Magdoline Mustafa Ahmed Osman","https:\u002F\u002Forcid.org\u002F0009-0003-5743-9161",{"name":432,"orcid":433},"Ronald Kuunya","https:\u002F\u002Forcid.org\u002F0009-0007-3502-0297",{"name":435,"orcid":8},"Erastus Wasikoyo",{"name":437,"orcid":438},"András Tamás","https:\u002F\u002Forcid.org\u002F0000-0001-8175-3300",{"name":440,"orcid":441},"Árpád Illés","https:\u002F\u002Forcid.org\u002F0000-0001-6548-2926",{"name":443,"orcid":444},"Adrienn Széles","https:\u002F\u002Forcid.org\u002F0000-0003-4101-3177",{"name":446,"orcid":447},"Tamás Rátonyi","https:\u002F\u002Forcid.org\u002F0000-0001-9436-5446",{"tldr":449,"method":450,"finding":451,"direction":191,"opportunity":452},"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",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":8,"source_name":460,"source_url":457,"published_at":461,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":102,"score_detail":462,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":474,"classification":476,"search_phrases":509,"slug":512,"view_count":423,"doi":513,"paper":514,"created_at":538},3477,"Ratoon rice under a changing climate: Physiological responses and management strategies","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16473","Ratoon rice has gained attention as a sustainable strategy for maintaining rice productivity under changing climatic conditions, where rising temperatures, irregular rainfall, droughts, floods and other extreme weather events threaten crop productivity and global food security. Ratoon rice has emerged as a resource efficient and climate smart production method as it makes use of the regenerative ability of plants to generate a second harvest from stubble of the main crop. Compared to conventional rice cultivation, ratoon rice requires less labor, reduced inputs and a shorter growing period while improving land, water and nutrient use efficiency. However, the physiological responses of rice plants to changing environmental conditions are important for the success of ratoon crops. Bud regeneration, carbohydrate reserve mobilisation, hormonal regulation, photosynthetic efficiency and source sink relationship play a critical role in determining ratoon establishment and yield under climatic stress. Ratoon rice is more resilient to heat, drought, flooding and other environmental challenges when the appropriate management techniques such as optimal stubble height, balanced nutrition, effective irrigation and plant growth regulators are used. Furthermore, improving soil health through organic amendments and beneficial microbial interactions contributes to greater stress tolerance and sustainable productivity. Ratoon rice production is still limited despite its potential due to automation issues, yield instability and climate related hazards. To enhance the adaptation under shifting climatic conditions, future research should concentrate on creating climate-resilient ratoon cultivars, precision nutrient and water management technologies and decision support systems. This review summarises the physiological response of ratoon rice to climate change and highlights effective management strategies that can enhance productivity, resource use efficiency and sustainability, thereby supporting resilient rice production and long term food security.","再生稻作为一种在气候变化条件下维持水稻生产力的可持续策略而受到关注，气温升高、降雨不规律、干旱、洪涝及其他极端天气事件正威胁作物生产力和全球粮食安全。再生稻已成为一种资源高效且气候智慧型的生产方式，因为它利用植物的再生能力，从主作物的稻桩上获得第二次收获。与传统水稻栽培相比，再生稻所需劳动力更少、投入更低、生育期更短，同时提高了土地、水分和养分利用效率。然而，水稻植株对环境条件变化的生理响应对于再生稻作的成功至关重要。在气候胁迫下，芽再生、碳水化合物储备动员、激素调控、光合效率及源库关系在决定再生稻建成和产量方面发挥着关键作用。当采用适宜的管理技术，如最佳留桩高度、平衡营养、有效灌溉和植物生长调节剂时，再生稻对高温、干旱、洪涝及其他环境挑战具有更强的适应力。此外，通过有机改良和有益微生物互作改善土壤健康，有助于提高抗逆性和可持续生产力。尽管再生稻生产潜力巨大，但由于自动化问题、产量不稳定及气候相关灾害，其发展仍然受限。为增强在不断变化的气候条件下的适应性，未来研究应集中于培育气候韧性再生稻品种、精准养分和水分管理技术及决策支持系统。本综述总结了再生稻对气候变化的生理响应，并重点介绍了能够提高生产力、资源利用效率和可持续性的有效管理策略，从而支持具有韧性的水稻生产和长期粮食安全。","Plant Science Today","2026-09-23T00:00:00Z",{"impact":283,"substance":104,"depth":18,"authority":204,"freshness":205,"relevant":20,"comment":463},"综述系统梳理再生稻应对气候变化的生理机制与管理策略，专业性强但属学术综述，产业即时影响有限。",[465],{"name":460,"url":457},[25,109,467,26,468],"再生稻","水稻栽培",{"code":470,"slug":471,"name":472,"path":473,"parent_code":380},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[475],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":477},{"candidates":478,"agricultural_evidence":45},[479,483,488,492,496,500,505],{"code":470,"score":16,"supported":45,"title":480,"abstract":481,"tags":482},[471],[472,467,471],[467],{"code":31,"score":12,"supported":45,"title":484,"abstract":485,"tags":487},[],[26,126,486,124,47],"洪涝",[26],{"code":136,"score":137,"supported":45,"title":489,"abstract":490,"tags":491},[],[109,140],[109],{"code":239,"score":92,"supported":45,"title":493,"abstract":494,"tags":495},[],[242,243],[],{"code":115,"score":92,"supported":45,"title":497,"abstract":498,"tags":499},[],[132,116],[],{"code":292,"score":60,"supported":45,"title":501,"abstract":502,"tags":504},[],[503],"decision support",[],{"code":51,"score":20,"supported":13,"title":506,"abstract":507,"tags":508},[],[],[25],[510,511],"再生稻 气候变化 栽培管理","Plant Science Today 再生稻","再生稻气候变化栽培管理-3477","10.14719\u002Fpst.16473",{"doi":513,"openalex_id":515,"authors":516,"venue":460,"cited_by_count":87,"oa_url":532,"card":533,"direction":88,"ingested_from":89},"W7214098380",[517,520,523,526,529],{"name":518,"orcid":519},"R Abirami","https:\u002F\u002Forcid.org\u002F0009-0002-8075-9908",{"name":521,"orcid":522},"R Vigneshwari","https:\u002F\u002Forcid.org\u002F0000-0002-4417-1034",{"name":524,"orcid":525},"K. Malarkodi","https:\u002F\u002Forcid.org\u002F0000-0002-8974-9389",{"name":527,"orcid":528},"N Sakthivel","https:\u002F\u002Forcid.org\u002F0009-0005-8248-3812",{"name":530,"orcid":531},"Koothan Vanitha","https:\u002F\u002Forcid.org\u002F0000-0001-8516-9275","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16473\u002F16260",{"tldr":534,"method":535,"finding":536,"direction":191,"opportunity":537},"综述再生稻在气候变化下的生理响应与栽培管理策略，以提升产量与可持续性。","文献综述，聚焦再生芽、碳储备、激素、光合及源库关系等生理机制。","优化留桩高度、水肥与生长调节剂可增强再生稻抗逆性，但自动化与产量稳定性仍受限。","可研发气候韧性再生稻品种及精准水肥决策支持系统，并探索微生物互作提升抗逆性。","2026-09-25T23:30:11.458343Z",{"id":540,"title":541,"url":542,"summary":543,"summary_zh":544,"content":8,"source_name":545,"source_url":542,"published_at":461,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":369,"score_detail":546,"sources":548,"tags":550,"primary_topic":551,"secondary_topics":552,"classification":554,"search_phrases":573,"slug":576,"view_count":577,"doi":578,"paper":579,"created_at":595},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures",{"impact":16,"substance":371,"depth":16,"authority":204,"freshness":129,"relevant":20,"comment":547},"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[549],{"name":545,"url":542},[25,288,289,26,402],{"code":292,"slug":293,"name":294,"path":295,"parent_code":296},[553],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":555},{"candidates":556,"agricultural_evidence":45},[557,561,565,569],{"code":292,"score":16,"supported":45,"title":558,"abstract":559,"tags":560},[304],[306,307,308,309,304],[288],{"code":31,"score":283,"supported":45,"title":562,"abstract":563,"tags":564},[124],[26,124],[26],{"code":399,"score":283,"supported":45,"title":566,"abstract":567,"tags":568},[403],[402,403],[402],{"code":51,"score":137,"supported":45,"title":570,"abstract":571,"tags":572},[],[25,53],[25],[574,575],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353",5,"10.1016\u002Fj.sftr.2026.102145",{"doi":578,"openalex_id":580,"authors":581,"venue":545,"cited_by_count":87,"oa_url":542,"card":589,"direction":88,"ingested_from":89},"W7214099405",[582,585,587],{"name":583,"orcid":584},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":586,"orcid":8},"Shiyan Zhai",{"name":588,"orcid":8},"Yuke Feng",{"tldr":590,"method":591,"finding":592,"direction":593,"opportunity":594},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","农业人工智能与决策模型","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z"]