[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5172":3,"related-5172":96},{"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":95},5172,"Forest green-up in the Pearl River Basin: trends, driving factors, and relationships with net primary productivity","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2738487","The spring green-up date (GUD), a critical indicator of ecosystem response to climate change, profoundly influences terrestrial productivity. However, the complex drivers of GUD remain elusive, while the contentious relationship between GUD and net primary productivity (NPP) challenges the universal validity of the hypothesis that earlier green-up enhances productivity. Using MODIS NDVI data (2000-2020), we quantified forest GUD dynamics in the Pearl River Basin and combined random forest, SHAP, and structural equation modelling to identify drivers and assess GUD-NPP relationships. GUD was advanced by approximately 5 days, with 81.8% of forestland pixels advancing and 18.2% delaying. Only 5.39% of advances showed significant trends, whereas 30.45% of delays were concentrated near croplands and developed areas. The random forest model explained 58.2% of GUD variation. Elevation, precipitation, and soil potassium and phosphorus were dominant; elevation-induced thermal gradients outweighed ambient temperature effects, with elevations above 1,200 m exhibiting markedly delayed GUD. Spatially, GUD had a positive correlation with NPP, indicating that later GUD was associated with higher forest NPP, possibly because of early-spring cold snaps and frost. Temporally, areas with stronger GUD delays tended to show greater NPP increases. This study identified key controls on subtropical forest phenology and revealed a GUD-NPP trade-off.","春季返青期（GUD）是生态系统响应气候变化的关键指标，深刻影响陆地生产力。然而，GUD的复杂驱动机制仍不明确，且GUD与净初级生产力（NPP）之间存在争议性关系，挑战了“提前返青提高生产力”这一假说的普适性。本研究利用MODIS NDVI数据（2000—2020年），量化了珠江流域森林GUD动态，并结合随机森林、SHAP和结构方程模型识别驱动因子并评估GUD-NPP关系。GUD整体提前约5天，其中81.8%的林地像元提前、18.2%推迟。仅5.39%的提前像元呈显著趋势，而30.45%的推迟像元集中在农田和建成区附近。随机森林模型解释了58.2%的GUD变异。海拔、降水以及土壤钾和磷为主导因子；海拔引起的热梯度效应超过环境温度的影响，海拔1200 m以上区域GUD显著推迟。空间上，GUD与NPP呈正相关，表明GUD越晚森林NPP越高，这可能与早春寒潮和霜冻有关。时间上，GUD推迟越强的区域往往表现为NPP增加越大。本研究识别了亚热带森林物候的关键控制因子，揭示了GUD-NPP之间的权衡关系。",null,"International Journal of Digital Earth","2026-10-15T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,8,1,"基于MODIS长时序数据的区域森林物候研究，方法组合新颖、结论有增量，但属区域基础研究，产业与政策影响有限，可作专业补充而非每日精选头条。",[25],{"name":10,"url":6},[27,28,29,30,31],"气候变化","遥感监测","植被生产力","森林物候","珠江流域",{"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":47},[45,52,60],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[27,50],"climate change",[27],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I3",[],[],[28],[66,67],"珠江流域 森林 返青期","MODIS NDVI 植被生产力","珠江流域森林返青期-5172",0,"10.1080\u002F17538947.2026.2738487",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":69,"oa_url":87,"card":88,"direction":92,"ingested_from":94},"W7221897424",[74,77,79,81,83,85],{"name":75,"orcid":76},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":78,"orcid":9},"Zhaoju Zheng",{"name":80,"orcid":9},"Xueming Zhao",{"name":82,"orcid":9},"Zhipeng Zhang",{"name":84,"orcid":9},"Xiaohui Cui",{"name":86,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"基于MODIS NDVI量化珠江流域森林返青期动态，并解析其驱动因子及与NPP的关系。","MODIS NDVI（2000-2020）、随机森林、SHAP与结构方程模型。","返青期整体提前约5天，但延迟区NPP更高，揭示返青期与NPP的权衡关系。","农业遥感与作物表型","可探究早春寒潮霜冻对返青-NPP权衡的调控机制，并拓展至其他亚热带流域验证普适性。","openalex","2026-10-17T23:30:29.750744Z",{"total":54,"page":22,"page_size":54,"items":97},[98,216,305,359,423,526],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":118,"classification":127,"search_phrases":187,"slug":190,"view_count":69,"doi":191,"paper":192,"created_at":215},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":108,"substance":19,"depth":109,"authority":17,"freshness":21,"relevant":22,"comment":110},13,15,"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[112],{"name":104,"url":101},[27,114,28,115,116],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[119,122],{"code":53,"slug":58,"name":120,"path":121,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":123,"slug":124,"name":125,"path":126,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":128},{"candidates":129,"agricultural_evidence":47},[130,136,140,147,153,160,167,174,181],{"code":33,"score":19,"supported":47,"title":131,"abstract":132,"tags":135},[50],[27,133,134,50],"气候适应","干旱",[27],{"code":53,"score":109,"supported":47,"title":137,"abstract":138,"tags":139},[58],[57,58],[114],{"code":123,"score":13,"supported":47,"title":141,"abstract":142,"tags":146},[],[143,144,124,145],"灌溉","水资源","evapotranspiration",[115],{"code":61,"score":46,"supported":47,"title":148,"abstract":149,"tags":152},[],[150,151],"遥感","remote sensing",[28],{"code":154,"score":54,"supported":47,"title":155,"abstract":156,"tags":159},"A8",[],[157,158],"粮食安全","food security",[],{"code":161,"score":54,"supported":47,"title":162,"abstract":163,"tags":166},"I1",[],[164,165],"精准农业","precision agriculture",[],{"code":168,"score":54,"supported":47,"title":169,"abstract":170,"tags":173},"I4",[],[171,172],"地理信息","geographic information",[],{"code":175,"score":176,"supported":47,"title":177,"abstract":178,"tags":180},"F3",3,[],[179],"养分管理",[],{"code":182,"score":176,"supported":47,"title":183,"abstract":184,"tags":186},"J1",[],[185],"农业政策",[],[188,189],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":191,"openalex_id":193,"authors":194,"venue":104,"cited_by_count":69,"oa_url":101,"card":210,"direction":92,"ingested_from":94},"W7222999158",[195,197,199,201,203,205,208],{"name":196,"orcid":9},"Asifa Naz",{"name":198,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":200,"orcid":9},"Syed Asghar Hussain Shah",{"name":202,"orcid":9},"Shahrish Nawaz",{"name":204,"orcid":9},"Muhmmad Irfan",{"name":206,"orcid":207},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":209,"orcid":9},"Muhammad Nouman Khalid",{"tldr":211,"method":212,"finding":213,"direction":92,"opportunity":214},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":228,"tags":230,"primary_topic":234,"secondary_topics":240,"classification":242,"search_phrases":287,"slug":290,"view_count":22,"doi":291,"paper":292,"created_at":304},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":19,"substance":225,"depth":109,"authority":17,"freshness":226,"relevant":22,"comment":227},16,9,"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。",[229],{"name":222,"url":219},[231,232,233,27,28],"智慧农业","农业人工智能","可持续发展",{"code":235,"slug":236,"name":237,"path":238,"parent_code":239},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[241],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":243},{"candidates":244,"agricultural_evidence":47},[245,254,258,262,266,273,280],{"code":235,"score":19,"supported":47,"title":246,"abstract":248,"tags":253},[247],"artificial intelligence",[249,250,251,252,247],"深度学习","机器学习","machine learning","deep learning",[232],{"code":33,"score":109,"supported":47,"title":255,"abstract":256,"tags":257},[50],[27,50],[27],{"code":161,"score":46,"supported":47,"title":259,"abstract":260,"tags":261},[],[164,165],[231],{"code":61,"score":46,"supported":47,"title":263,"abstract":264,"tags":265},[],[150,151],[28],{"code":267,"score":54,"supported":47,"title":268,"abstract":269,"tags":272},"H4",[],[270,271],"碳排放","carbon emission",[],{"code":274,"score":54,"supported":47,"title":275,"abstract":276,"tags":279},"H6",[],[277,278],"生物多样性","biodiversity",[],{"code":281,"score":54,"supported":47,"title":282,"abstract":283,"tags":286},"I5",[],[284,285],"物联网","internet of things",[],[288,289],"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":291,"openalex_id":293,"authors":294,"venue":222,"cited_by_count":69,"oa_url":219,"card":297,"direction":303,"ingested_from":94},"W7222092855",[295],{"name":296,"orcid":9},"Deepmala Suryavanshi",{"tldr":298,"method":299,"finding":300,"direction":301,"opportunity":302},"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。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":312,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":313,"sources":315,"tags":317,"primary_topic":321,"secondary_topics":322,"classification":323,"search_phrases":338,"slug":341,"view_count":22,"doi":342,"paper":343,"created_at":358},4756,"ديناميكيات وحساسية المناخ والغطاء النباتي في العراق باستخدام بيانات رصد الأرض طويلة الأمد","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fy7543b35","تؤدي تغيرات المناخ إلى تكثيف الإجهاد الحراري والمائي عبر المناطق الجافة، مما يزيد من الحاجة إلى رصد متسق مكانياً وطويل الأمد لاستجابات الغطاء النباتي. العراق معرض بشكل خاص لهذه التغيرات لأن التدرجات المناخية القوية، والجفاف المتكرر، ومحدودية توافر المياه تتزامن مع مساحات شاسعة من الأراضي المطرية والمروية. قامت هذه الدراسة بتقييم ديناميكيات المناخ والغطاء النباتي في العراق من عام 2001 إلى 2025 باستخدام مؤشر الاختلاف الغطائي النباتي الطبيعي (MODIS NDVI)، وبيانات هطول الأمطار (CHIRPS)، وبيانات درجة حرارة الهواء على ارتفاع مترين (ERA5-Land). تم تحليل متوسط هطول الأمطار للفترة من أكتوبر إلى مايو، واتجاهات مؤشر (NDVI) للفترة من يناير إلى مايو، وارتباطات المناخ مع مؤشر (NDVI) بعد إزالة الاتجاه (detrended) على شبكة مشتركة بدقة 0.1 درجة.تم بعد ذلك تصنيف البكسلات النباتية على أنها: حساسة لهطول الأمطار، أو حساسة للحرارة، أو حساسة لكليهما، أو لا تظهر علاقة متوقعة قوية، أو تظهر اتجاهاً قوياً آخر. أظهر هطول الأمطار في الموسم الرطب تدرجاً واضحاً من إجماليات عالية جداً في شمال وشمال شرق العراق إلى إجماليات منخفضة ومنخفضة جداً عبر الغرب والجنوب الغربي والجنوب. كان الاخضرار ذو الدلالة الإحصائية أكثر انتشاراً بشكل ملحوظ من الاسمرار، حيث تركزت أقوى اتجاهات مؤشر (NDVI) الإيجابية في المناطق الزراعية في الشمال والشمال الشرقي والوسط. بعد إزالة الاتجاهات الخطية، كانت ارتباطات هطول الأمطار مع مؤشر (NDVI) إيجابية في الغالب وواسعة النطاق مكانياً، في حين كانت ارتباطات درجة الحرارة مع مؤشر (NDVI) ذات دلالة سلبية بشكل رئيسي ومركّزة في شمال وشرق وجنوب شرق العراق. هيمنت حساسية هطول الأمطار على التصنيف المتكامل، بينما شكلت الحساسية المزدوجة لهطول الأمطار والحرارة مجموعات متميزة في شمال-وسط وجنوب شرق البلاد. توفر هذه النتائج خط أساس مكانياً وطنياً لتحديد مناطق الغطاء النباتي المرتبطة بتقلبات المياه والحرارة بين السنوات. وتمثل فئات الحساسية ارتباطات إحصائية وليست عزوًا سببيًا.","气候变化正在加剧干旱地区的高温与水分胁迫，从而增加了对植被响应进行空间一致、长期监测的需求。伊拉克尤其容易受到这些变化的影响，因为强烈的气候梯度、频繁的干旱以及有限的水资源可用性，与大片雨养和灌溉土地并存。本研究利用MODIS归一化植被指数（NDVI）、降雨数据（CHIRPS）以及2米高度气温数据（ERA5-Land），评估了2001年至2025年伊拉克的气候与植被动态。研究在0.1度分辨率的统一网格上，分析了10月至5月的平均降雨量、1月至5月的NDVI趋势，以及去趋势（detrended）后气候与NDVI的相关性。随后将植被像元分类为：对降雨敏感、对温度敏感、对两者均敏感、未表现出强预期关系，或表现出其他强趋势。湿季降雨呈现出从伊拉克北部和东北部极高总量向西、西南和南部低及极低总量的明显梯度。具有统计显著性的绿化现象明显比褐化更为广泛，其中最强的NDVI正趋势集中在北部、东北部和中部的农业区。去除线性趋势后，降雨与NDVI的相关性大多为正且在空间上广泛分布，而温度与NDVI的相关性主要为显著负相关，集中在伊拉克北部、东部和东南部。降雨敏感性在综合分类中占主导地位，而降雨与温度的双重敏感性在该国中北部和东南部形成了独特的集群。这些结果为识别与年际水热波动相关的植被区域提供了全国尺度的空间基线。敏感性类别代表统计关联，而非因果归因。","Journal Of Babylon Center for Humanities Studies","2026-10-09T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":54,"relevant":22,"comment":314},"基于MODIS、CHIRPS与ERA5-Land长时序数据评估伊拉克气候与植被敏感性，方法规范、结论有区域参考价值，但属境外基础研究，公共影响有限。",[316],{"name":311,"url":308},[27,318,28,319,320],"NDVI","干旱区农业","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":324},{"candidates":325,"agricultural_evidence":47},[326,330,334],{"code":33,"score":21,"supported":47,"title":327,"abstract":328,"tags":329},[],[27,134],[319,27],{"code":123,"score":54,"supported":47,"title":331,"abstract":332,"tags":333},[],[143,144],[],{"code":61,"score":176,"supported":14,"title":335,"abstract":336,"tags":337},[],[],[318,320,28],[339,340],"伊拉克 NDVI 植被动态","MODIS CHIRPS ERA5-Land 遥感","伊拉克NDVI植被动态-4756","10.66026\u002Fy7543b35",{"doi":342,"openalex_id":344,"authors":345,"venue":311,"cited_by_count":69,"oa_url":352,"card":353,"direction":92,"ingested_from":94},"W7221306668",[346,348,350],{"name":347,"orcid":9},"خولة‌ حمه‌رشید عمر",{"name":349,"orcid":9},"سایە دلاوەر محمد",{"name":351,"orcid":9},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":354,"method":355,"finding":356,"direction":92,"opportunity":357},"基于2001–2025年MODIS NDVI、CHIRPS降水与ERA5-Land温度数据，评估伊拉","MODIS NDVI、CHIRPS降水、ERA5-Land温度，0.1°网格去趋","植被绿化趋势多于褐化，降水敏感性主导，温度负相关集中于北部、东部和东南部。","可结合灌溉与土地覆盖数据，区分雨养与灌溉区植被对水热胁迫的差异化响应机制。","2026-10-12T23:30:27.995466Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":376,"secondary_topics":377,"classification":378,"search_phrases":394,"slug":397,"view_count":398,"doi":399,"paper":400,"created_at":422},3898,"Climate change and human activities reshape the water-food-ecology nexus and water-economy decoupling in oasis cities of the Hexi corridor, China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115599","In arid oasis systems, water allocation links agricultural production, ecological stability, and regional economic development, making the water-food-ecology (WFE) nexus highly sensitive to hydroclimatic stress and human activities. Yet county-scale evidence remains limited on how hydroclimatic and socioeconomic changes are associated with WFE interactions and water-economy decoupling in inland oasis cities. Focusing on the Hexi Corridor, we integrate multi-decadal hydroclimatic records, remote sensing ecological indicators, and county-level socioeconomic, water use, and crop data. Tapio decoupling, temporally purged and spatially blocked random-forest validation, Grey relational analysis (GRA), exploratory two-way fixed-effects piecewise path analysis, and scenario-based reliability-resilience-vulnerability assessment were combined to characterize water-economy relationships, crop–water associations, WFE interactions, and management outcomes. During 1981–2024, annual mean 2-m air temperature increased significantly by 0.038 °C\u002Fyear, annual precipitation showed a weak increase of 0.349 mm\u002Fyear, and December SPEI-12 decreased significantly by 0.0476\u002Fyear, indicating a deteriorating climatic water balance under pronounced warming. Strong water-economy decoupling emerged before 2020 and was associated with declining water use intensity, while industrial decoupling showed greater interannual variability during 2020–2022. Random-forest performance was moderate under temporal validation (R 2 = 0.673) and weaker under spatial validation (R 2 = 0.441), indicating stronger temporal generalization than spatial transferability. Crop composition showed pronounced spatial heterogeneity and county-specific similarity with agricultural water use. Piecewise path analysis revealed scale-dependent associations among socioeconomic activity, water use, hydroclimatic conditions, and vegetation. Within counties, land surface temperature (LST) was negatively associated with normalized difference vegetation index (NDVI), while surface water use was positively associated with vegetation condition and SPEI-12 was negatively associated with agricultural water use in the primary specification. Scenario results showed that agricultural-demand reduction and coordinated measures improved water-system reliability and resilience, whereas groundwater constraints applied alone increased unmet demand. These findings support differentiated local implementation within basin-scale governance and provide a transferable diagnostic workflow for comparable arid inland basins, although local parameters and management outcomes require region-specific recalibration.","在干旱绿洲系统中，水资源分配将农业生产、生态稳定与区域经济发展联系起来，使水—粮食—生态（WFE）纽带对水文气候胁迫和人类活动高度敏感。然而，关于内陆绿洲城市水文气候和社会经济变化如何与WFE相互作用及水—经济脱钩相关联的县域尺度证据仍然有限。本研究以河西走廊为对象，整合了多年代水文气候记录、遥感生态指标以及县级社会经济、用水和作物数据，综合运用Tapio脱钩模型、时间净化与空间分块的随机森林验证、灰色关联分析（GRA）、探索性双向固定效应分段路径分析以及基于情景的可靠性—恢复力—脆弱性评估，刻画水—经济关系、作物—水关联、WFE相互作用及管理成效。1981—2024年间，年平均2 m气温显著上升，速率为0.038 °C\u002F年；年降水量呈微弱增加趋势，为0.349 mm\u002F年；12月SPEI-12显著下降，速率为0.0476\u002F年，表明在显著增暖背景下气候水分平衡趋于恶化。2020年前出现了较强的水—经济脱钩，并与用水强度下降相关，而2020—2022年间产业脱钩表现出更大的年际变率。随机森林在时间验证下表现中等（R² = 0.673），在空间验证下表现较弱（R² = 0.441），表明其时间泛化能力强于空间可迁移性。作物组成表现出显著的空间异质性，并与农业用水存在县域特定的相似性。分段路径分析揭示了社会经济活动、用水、水文气候条件与植被之间尺度依赖的关联。在县域内部，地表温度（LST）与归一化植被指数（NDVI）呈负相关，而地表用水与植被状况呈正相关，且在基准设定中SPEI-12与农业用水呈负相关。情景结果表明，农业需求削减和协同措施提高了水系统的可靠性和恢复力，而单独实施地下水约束则增加了未满足需求。这些发现支持在流域尺度治理框架下进行差异化的地方实施。","Ecological Indicators","2026-09-30T00:00:00Z",78,{"impact":225,"substance":18,"depth":19,"authority":108,"freshness":226,"relevant":22,"comment":369},"基于多源数据与多种方法揭示河西走廊绿洲水-粮-生态关联及水经济脱钩，方法新颖、数据跨度长，对干旱区农业水资源管理有参考价值。",[371],{"name":365,"url":362},[27,373,28,374,375],"水资源管理","绿洲农业","农业用水",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":379},{"candidates":380,"agricultural_evidence":47},[381,385,389],{"code":33,"score":17,"supported":47,"title":382,"abstract":383,"tags":384},[50],[134],[27],{"code":61,"score":46,"supported":47,"title":386,"abstract":387,"tags":388},[],[150,151],[28],{"code":123,"score":390,"supported":47,"title":391,"abstract":392,"tags":393},4,[],[144],[373],[395,396],"河西走廊 绿洲城市 水粮生态","河西走廊 农业用水 脱钩","河西走廊绿洲城市水粮生态-3898",2,"10.1016\u002Fj.ecolind.2026.115599",{"doi":399,"openalex_id":401,"authors":402,"venue":365,"cited_by_count":69,"oa_url":362,"card":417,"direction":92,"ingested_from":94},"W7214916060",[403,405,408,410,412,414],{"name":404,"orcid":9},"Jinbo Zhang",{"name":406,"orcid":407},"Zhixiang Lu","https:\u002F\u002Forcid.org\u002F0000-0002-9954-9321",{"name":409,"orcid":9},"Jiali Xie",{"name":411,"orcid":9},"Yan Luo",{"name":413,"orcid":9},"Li Anyuan",{"name":415,"orcid":416},"Qi Feng","https:\u002F\u002Forcid.org\u002F0009-0004-6561-3331",{"tldr":418,"method":419,"finding":420,"direction":301,"opportunity":421},"整合多源数据揭示河西走廊绿洲城市水-粮-生态关系及水经济脱钩的时空特征。","多年代水文气候、遥感生态与县级社会经济数据，结合Tapio脱钩、随机森林、GRA","变暖下气候水平衡恶化，2020年前水经济强脱钩，农业需水减少与协同措施提升水系统可靠性。","可探索县域尺度水-粮-生态耦合机制与脱钩政策的跨区域可迁移性及地下水约束的优化路径。","2026-10-01T23:30:28.964236Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":440,"secondary_topics":441,"classification":448,"search_phrases":503,"slug":506,"view_count":176,"doi":507,"paper":508,"created_at":525},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":19,"substance":433,"depth":19,"authority":20,"freshness":226,"relevant":22,"comment":434},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。",[436],{"name":429,"url":426},[157,438,27,28,439],"防灾减灾","干旱风险",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[442],{"code":443,"slug":444,"name":445,"path":446,"parent_code":447},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",{"status":40,"locked":14,"version":41,"method":42,"evidence":449},{"candidates":450,"agricultural_evidence":47},[451,457,464,468,475,479,483,489,494,499],{"code":33,"score":452,"supported":47,"title":453,"abstract":455,"tags":456},19,[454],"drought",[27,134,50,454],[27,438],{"code":443,"score":226,"supported":47,"title":458,"abstract":459,"tags":463},[],[460,461,462],"高粱","sorghum","millet",[],{"code":154,"score":46,"supported":47,"title":465,"abstract":466,"tags":467},[],[157,158],[157],{"code":469,"score":54,"supported":47,"title":470,"abstract":471,"tags":474},"A3",[],[472,473],"玉米","maize",[],{"code":53,"score":54,"supported":47,"title":476,"abstract":477,"tags":478},[],[57,58],[],{"code":123,"score":54,"supported":47,"title":480,"abstract":481,"tags":482},[],[143,124],[],{"code":484,"score":176,"supported":47,"title":485,"abstract":486,"tags":488},"A4",[],[487],"花生",[],{"code":161,"score":176,"supported":47,"title":490,"abstract":491,"tags":493},[],[492],"smart agriculture",[],{"code":168,"score":176,"supported":47,"title":495,"abstract":496,"tags":498},[],[497],"GIS",[],{"code":61,"score":22,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[28],[504,505],"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":507,"openalex_id":509,"authors":510,"venue":429,"cited_by_count":69,"oa_url":426,"card":520,"direction":303,"ingested_from":94},"W7214399028",[511,514,517],{"name":512,"orcid":513},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":515,"orcid":516},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":518,"orcid":519},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":521,"method":522,"finding":523,"direction":92,"opportunity":524},"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",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":533,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":534,"score_detail":535,"sources":538,"tags":540,"primary_topic":542,"secondary_topics":546,"classification":554,"search_phrases":593,"slug":596,"view_count":21,"doi":597,"paper":598,"created_at":626},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":19,"substance":536,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":537},20,"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。",[539],{"name":532,"url":529},[231,157,541,27,28],"植物病害",{"code":154,"slug":543,"name":544,"path":545,"parent_code":447},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[547,548],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":549,"slug":550,"name":551,"path":552,"parent_code":553},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":40,"locked":14,"version":41,"method":42,"evidence":555},{"candidates":556,"agricultural_evidence":47},[557,561,565,570,574,578,585,589],{"code":33,"score":19,"supported":47,"title":558,"abstract":559,"tags":560},[50],[27,134,50,454],[27],{"code":154,"score":109,"supported":47,"title":562,"abstract":563,"tags":564},[158],[157,158],[157],{"code":549,"score":21,"supported":47,"title":566,"abstract":568,"tags":569},[567],"food safety",[],[],{"code":161,"score":46,"supported":47,"title":571,"abstract":572,"tags":573},[],[164,165],[231],{"code":61,"score":46,"supported":47,"title":575,"abstract":576,"tags":577},[],[150,151],[28],{"code":579,"score":54,"supported":47,"title":580,"abstract":581,"tags":584},"F4",[],[582,583],"生物防治","crop protection",[],{"code":53,"score":176,"supported":47,"title":586,"abstract":587,"tags":588},[],[58],[],{"code":235,"score":176,"supported":47,"title":590,"abstract":591,"tags":592},[],[247],[],[594,595],"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":597,"openalex_id":599,"authors":600,"venue":532,"cited_by_count":69,"oa_url":529,"card":620,"direction":92,"ingested_from":94},"W7213972258",[601,603,605,608,611,614,617],{"name":602,"orcid":9},"Sindiswa Khawula",{"name":604,"orcid":9},"Siyabonga Ntshalitshali",{"name":606,"orcid":607},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":609,"orcid":610},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":612,"orcid":613},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":615,"orcid":616},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":618,"orcid":619},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":621,"method":622,"finding":623,"direction":624,"opportunity":625},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","农业人工智能与决策模型","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","2026-09-22T23:30:22.790623Z"]