[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5476":3,"related-5476":111},{"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":89,"slug":92,"view_count":93,"doi":94,"paper":95,"created_at":110},5476,"Digital Transformation of Haryana Agriculture: Strengthening Climate Resilience and Advancing the Sustainable Development Goals","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23491288","Climate change has surfaced as one of the most pivotal challenges confronting the agricultural sector, especially in agrarian regions vulnerable to resource degradation, water scarcity, and climate uncertainties. Haryana, a leading agricultural state in India, has faced significant environmental pressures in recent years, including declining groundwater levels, erratic rainfall patterns, rising temperatures, and soil degradation, necessitating the adoption of climate-resilient agricultural strategies. Against this backdrop, Information and Communication Technology has gained considerable importance as a transformative mechanism for strengthening agricultural sustainability, adaptive capacity, and informed decision-making among farmers. ICT-enabled interventions such as mobile-based advisory services, precision farming technologies, digital marketplaces, weather forecasting systems, remote sensing, and various e-governance platforms have increasingly contributed to improving agricultural efficiency and resilience. The present study aims to evaluate the role of ICT-enabled agricultural practices in Haryana and their contribution to climate resilience and to achieve the Sustainable Development Goals. This study applies a descriptive and analytical approach, drawing on secondary data and narrative review methodology. The study utilizes secondary data from government reports, policy documents, research articles, institutional publications, and international development reports on digital agriculture, climate change, and sustainable development, published between 2016 and 2026. The findings indicate that ICT-based agricultural innovations have substantially enhanced access to climate information, improved resource-use efficiency, increased market connectivity, and enabled adaptive agricultural management practices. Furthermore, ICT interventions have significant potential to support SDG 2: Zero Hunger, SDG 9: Industry, Innovation and Infrastructure, and SDG 13: Climate Action. However, challenges such as digital illiteracy, infrastructural disparities, limited technological accessibility among smallholders, and inadequate institutional support continue to hinder widespread adoption. The study emphasizes the need for inclusive digital policies, strengthened rural ICT infrastructure, farmer-centric skill development, and integrated climate-smart agricultural frameworks to facilitate sustainable, resilient agricultural development in Haryana.","气候变化已成为农业部门面临的最关键挑战之一，在易受资源退化、水资源短缺和气候不确定性影响的农业区域尤为如此。哈里亚纳邦是印度领先的农业邦，近年来面临显著的环境压力，包括地下水位下降、降雨模式不稳定、气温上升和土壤退化，因而亟需采取气候韧性农业策略。在此背景下，信息与通信技术（ICT）作为增强农业可持续性、适应能力以及农民知情决策的变革性机制，其重要性日益凸显。基于ICT的干预措施，如移动端咨询服务、精准农业技术、数字市场、天气预报系统、遥感以及各类电子治理平台，已日益为提升农业效率和韧性作出贡献。本研究旨在评估哈里亚纳邦基于ICT的农业实践所发挥的作用及其对气候韧性和实现可持续发展目标的贡献。本研究采用描述性与分析性方法，依托二手数据和叙述性综述方法。研究使用的二手数据来源于2016年至2026年间发布的政府报告、政策文件、研究论文、机构出版物以及关于数字农业、气候变化和可持续发展的国际发展报告。研究结果表明，基于ICT的农业创新大幅提升了气候信息的获取能力，改善了资源利用效率，增强了市场连通性，并促成了适应性农业管理实践。此外，ICT干预措施在支持可持续发展目标2：零饥饿、目标9：产业、创新和基础设施以及目标13：气候行动方面具有显著潜力。然而，数字文盲、基础设施差距、小农户技术可及性有限以及机构支持不足等挑战仍阻碍其广泛采用。研究强调，需要制定包容性数字政策、加强农村ICT基础设施、开展以农民为中心的技能培养以及构建综合气候智慧型农业框架，以促进哈里亚纳邦可持续且具有韧性的农业发展。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,15,12,9,1,"基于二手数据的综述性研究，系统梳理印度哈里亚纳邦ICT农业与气候韧性、SDGs的关系，结论可靠但方法新颖性一般，对国内智慧农业与气候适应有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","可持续发展","气候韧性","农业ICT",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,53,61,69,76,83],{"code":33,"score":18,"supported":46,"title":47,"abstract":49,"tags":52},true,[48],"climate resilience",[50,51,48],"气候变化","climate change",[30],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":60},"I1",11,[],[58,27,59],"精准农业","digital agriculture",[27,28],{"code":62,"score":63,"supported":46,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":63,"supported":46,"title":71,"abstract":72,"tags":75},"I3",[],[73,74],"遥感","remote sensing",[],{"code":77,"score":78,"supported":46,"title":79,"abstract":80,"tags":82},"G2",3,[],[81],"小农户",[],{"code":84,"score":78,"supported":46,"title":85,"abstract":86,"tags":88},"H3",[],[87],"水资源",[],[90,91],"Haryana 数字化转型","ICT 气候韧性","Haryana数字化转型-5476",0,"10.5281\u002Fzenodo.23491288",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":93,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7223011965",[98,100],{"name":99,"orcid":9},"Himani Rathi",{"name":101,"orcid":9},"Pardeep Kumar Duhan",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"评估印度哈里亚纳邦ICT农业实践对气候韧性与可持续发展目标的贡献。","描述性与分析性方法，基于2016-2026年政府报告等二手数据与叙事综述。","ICT显著提升气候信息获取、资源效率与市场连接，助力SDG 2\u002F9\u002F13，但数字素养与基础设施不足制","数字乡村与农业信息化","可研究小农户数字素养与ICT采纳门槛的实证机制，及包容性数字政策在气候智慧农业中的落地路径。","智慧农业 \u002F 农业物联网","openalex","2026-10-21T23:30:08.814085Z",{"total":63,"page":22,"page_size":63,"items":112},[113,208,267,351,426,487],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":10,"source_url":116,"published_at":119,"category":12,"cover_url":9,"hotness":120,"is_selected":14,"score":121,"score_detail":122,"sources":126,"tags":130,"primary_topic":131,"secondary_topics":132,"classification":133,"search_phrases":190,"slug":193,"view_count":124,"doi":194,"paper":195,"created_at":207},1619,"Agricultural Technologies, Food Systems and Climate-Resilient Entrepreneurship in Kwara State, Nigeria: Opportunities, Challenges and Policy Imperatives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22268616","The agricultural sector which contributes approximately 19-25% of the national GDP is facing increasing challenges from climate changes, food shortages and demographic expansion in Nigeria. The state of Kwara, which is found in Nigeria’s North-Central Agro-ecological region, offers a fascinating case study of how Agri-technology, climate-smart methods, and entrepreneurial creativity may interact to change food system. This paper examines the intersection of climate-smart business, food system resilience, and agricultural technology in Kwara State. The study's findings are supported by empirical research, government policy documents and industry reports that Kwara state is endowed with agricultural potential for commercial production of rice, casava, maize, soybeans and yam. It also revealed that climate change has reduced food accessibility for 94.8% of the farming households, increased food prices by 91.8% and limited access to market for 74.7% of the households. The study outlines the emergence of digital agricultural platforms, state-led mechanization programs and climate adaptation partnerships, such as the Kwara ACReSAL-IITA collaboration. Despite these developments, transformative effects are limited by persistent constraints; such as funding gaps, poor extension services, infrastructural deficits, insecurity and low adaptation of climate-smart technologies. The study recommends a wide-ranging policy framework focusing on blended financing, digital infrastructure, youth agripreneurship, and institutional collaboration to fast-track Kwara state’s transition into a climate-resilient, technology-led agricultural economy. The findings contribute to the broader discussion on agricultural transformation in sub-Saharan Africa and provide useful guidance for development experts, investors, and decision makers. Keywords: Agri-Tch, Climate-Smart Agriculture, Food System, Entrepreneurship, Kwara State, Nigeria, Digital Agriculture, Smallholder Farmers.","农业部门约占尼日利亚国内生产总值的19%至25%，正面临气候变化、粮食短缺和人口扩张带来的日益严峻的挑战。位于尼日利亚中北部农业生态区的夸拉州，为农业技术、气候智能型方法和创业创新如何相互作用以改变粮食体系提供了一个引人注目的案例研究。本文考察了夸拉州气候智能型商业、粮食体系韧性与农业技术之间的交汇点。研究结果得到实证研究、政府政策文件和行业报告的支持，表明夸拉州具备商业化生产水稻、木薯、玉米、大豆和山药等农产品的农业潜力。研究还揭示，气候变化已导致94.8%的农户家庭粮食可及性下降，使91.8%的家庭面临粮价上涨，并限制了74.7%的家庭进入市场的机会。研究概述了数字农业平台、国家主导的机械化项目和气候适应伙伴关系（如夸拉州ACReSAL-IITA合作项目）的出现。尽管取得了这些进展，但资金缺口、推广服务薄弱、基础设施不足、不安全因素以及气候智能型技术采用率低等持续存在的制约因素，限制了变革性效果的发挥。研究建议建立一个广泛的政策框架，重点关注混合融资、数字基础设施、青年农业创业和机构协作，以加快夸拉州向气候韧性、技术主导型农业经济的转型。研究结果为撒哈拉以南非洲农业转型的更广泛讨论做出了贡献，并为发展专家、投资者和决策者提供了有益的指导。关键词：农业技术、气候智能型农业、粮食体系、创业、夸拉州、尼日利亚、数字农业、小农户。","2026-09-03T00:00:00Z",25,56,{"impact":20,"substance":18,"depth":17,"authority":123,"freshness":124,"relevant":22,"comment":125},8,2,"聚焦尼日利亚Kwara州农业技术与气候适应创业，提供实证数据，但地域性强且时效性低。",[127,128],{"name":10,"url":116},{"name":10,"url":129},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22268615",[27,28,81,30],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":134},{"candidates":135,"agricultural_evidence":46},[136,141,146,152,159,166,172,178,184],{"code":54,"score":55,"supported":46,"title":137,"abstract":138,"tags":140},[],[27,139,59],"smart agriculture",[27,28],{"code":33,"score":13,"supported":46,"title":142,"abstract":143,"tags":145},[],[50,144,51],"气候适应",[30],{"code":77,"score":147,"supported":46,"title":148,"abstract":149,"tags":151},7,[],[81,150],"smallholder",[81],{"code":153,"score":63,"supported":46,"title":154,"abstract":155,"tags":158},"A1",[],[156,157],"水稻","rice",[],{"code":160,"score":63,"supported":46,"title":161,"abstract":162,"tags":165},"A3",[],[163,164],"玉米","maize",[],{"code":167,"score":78,"supported":46,"title":168,"abstract":169,"tags":171},"A4",[],[170],"大豆",[],{"code":173,"score":78,"supported":46,"title":174,"abstract":175,"tags":177},"F5",[],[176],"机械化",[],{"code":179,"score":78,"supported":46,"title":180,"abstract":181,"tags":183},"H6",[],[182],"农业生态",[],{"code":185,"score":78,"supported":46,"title":186,"abstract":187,"tags":189},"J3",[],[188],"非洲农业",[],[191,192],"数字农业 智慧农业 气候韧性 小农户","数字农业 智慧农业","数字农业智慧农业气候韧性小农户-1619","10.5281\u002Fzenodo.22268616",{"doi":194,"openalex_id":196,"authors":197,"venue":10,"cited_by_count":93,"oa_url":116,"card":202,"direction":108,"ingested_from":109},"W7207600170",[198,200],{"name":199,"orcid":9},"Idowu Oluwafemi Sunday",{"name":201,"orcid":9},"Samuel Afuajemi Jonathan",{"tldr":203,"method":204,"finding":205,"direction":106,"opportunity":206},"探讨尼日利亚夸拉州农业技术、气候韧性创业与食品系统的互动，提出政策建议。","基于实证研究、政府政策文件和行业报告进行分析。","气候变化严重影响农户，数字农业平台涌现但受资金、基础设施等制约。","可研究非洲小农气候韧性转型中数字农业平台与混合融资模式的结合路径。","2026-09-04T23:30:09.960124Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":9,"source_name":213,"source_url":9,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":220,"tags":222,"primary_topic":225,"secondary_topics":231,"classification":233,"search_phrases":254,"slug":257,"view_count":22,"doi":9,"paper":258,"created_at":266},5304,"Front. Sustain. Food Syst.:数字农业技术推广与贵州辣椒种植户极端气候适应技术采纳","https:\u002F\u002Fscienmag.com\u002Fdigital-agricultural-extension-linked-to-climate-adaptation-among-chili-farmers-in-mountainous-china","Gao H, Han H (2026) 在Frontiers in Sustainable Food Systems发表论文,以贵州辣椒种植户为研究对象,发现数字农业技术推广(DATP)对极端气候适应技术采纳呈显著正相关,通过更广泛信息渠道、信息信任和应用能力发挥作用,益处与风险预期确认起调节作用;数字信息素养较高种植户DATP效应更强(系数0.326、0.408 vs. 低素养0.084、0.133),规模化种植户效应同样更强。","Frontiers in Sustainable Food Systems(2026,Article 1914305)","2026-10-13T00:00:00Z",74,{"impact":17,"substance":217,"depth":18,"authority":218,"freshness":63,"relevant":22,"comment":219},21,13,"基于贵州辣椒种植户实证数据，揭示数字农业推广对气候适应技术采纳的调节机制，结论具体且具政策参考价值，值得入选。",[221],{"name":213,"url":211},[27,28,223,144,224],"数字素养","辣椒种植",{"code":226,"slug":227,"name":228,"path":229,"parent_code":230},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[232],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":234},{"candidates":235,"agricultural_evidence":46},[236,240,244,249],{"code":54,"score":218,"supported":46,"title":237,"abstract":238,"tags":239},[27],[27],[27,28],{"code":33,"score":20,"supported":46,"title":241,"abstract":242,"tags":243},[144],[144],[144],{"code":226,"score":55,"supported":46,"title":245,"abstract":247,"tags":248},[246],"辣椒",[246],[],{"code":250,"score":22,"supported":14,"title":251,"abstract":252,"tags":253},"I6",[],[],[223],[255,256],"贵州 辣椒种植户 数字农业技术推广","极端气候适应 技术采纳 数字素养","贵州辣椒种植户数字农业技术推广-5304",{"doi":9,"openalex_id":9,"authors":259,"venue":9,"cited_by_count":93,"oa_url":9,"card":260,"direction":106,"ingested_from":265},[],{"tldr":261,"method":262,"finding":263,"direction":106,"opportunity":264},"研究数字农业技术推广对贵州辣椒种植户采纳极端气候适应技术的影响。","基于贵州辣椒种植户调查数据，采用回归分析检验中介与调节效应。","数字农业技术推广显著促进适应技术采纳，信息渠道、信任与应用能力起中介作用，数字素养和规模起调节作用。","可探究数字推广服务对不同作物、不同气候区农户适应行为的差异化机制与长期效果。","agent","2026-10-19T00:07:48.767992Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":10,"source_url":270,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":274,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":287,"classification":289,"search_phrases":334,"slug":337,"view_count":22,"doi":338,"paper":339,"created_at":350},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。","2026-10-15T00:00:00Z",{"impact":18,"substance":17,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":275},"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。",[277],{"name":10,"url":270},[28,279,29,50,280],"农业人工智能","遥感监测",{"code":282,"slug":283,"name":284,"path":285,"parent_code":286},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[288],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":290},{"candidates":291,"agricultural_evidence":46},[292,301,305,310,314,321,327],{"code":282,"score":18,"supported":46,"title":293,"abstract":295,"tags":300},[294],"artificial intelligence",[296,297,298,299,294],"深度学习","机器学习","machine learning","deep learning",[279],{"code":33,"score":19,"supported":46,"title":302,"abstract":303,"tags":304},[51],[50,51],[50],{"code":54,"score":147,"supported":46,"title":306,"abstract":307,"tags":309},[],[58,308],"precision agriculture",[28],{"code":70,"score":147,"supported":46,"title":311,"abstract":312,"tags":313},[],[73,74],[280],{"code":315,"score":63,"supported":46,"title":316,"abstract":317,"tags":320},"H4",[],[318,319],"碳排放","carbon emission",[],{"code":179,"score":63,"supported":46,"title":322,"abstract":323,"tags":326},[],[324,325],"生物多样性","biodiversity",[],{"code":328,"score":63,"supported":46,"title":329,"abstract":330,"tags":333},"I5",[],[331,332],"物联网","internet of things",[],[335,336],"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":338,"openalex_id":340,"authors":341,"venue":10,"cited_by_count":93,"oa_url":270,"card":344,"direction":108,"ingested_from":109},"W7222092855",[342],{"name":343,"orcid":9},"Deepmala Suryavanshi",{"tldr":345,"method":346,"finding":347,"direction":348,"opportunity":349},"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":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":358,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":375,"search_phrases":404,"slug":407,"view_count":22,"doi":408,"paper":409,"created_at":425},3531,"Extreme climate shocks and agricultural net carbon sinks: the moderating role of supply chain resilience in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940685","Introduction Extreme climate shocks increasingly challenge the ability of agricultural systems to sustain crop carbon fixation while controlling production-related emissions. Methods Using panel data for 240 Chinese cities from 2002 to 2023, this study examines the relationships between four climate shocks—extreme high temperature (HTD), extreme low temperature (LTD), extreme rainfall (ERD), and extreme drought (EED)—and agricultural net carbon sinks (NCS), together with the moderating role of agricultural supply chain resilience. NCS is measured as annual crop-biomass carbon fixation net of selected agricultural production emissions, thereby providing an integrated, production-based indicator of agricultural carbon performance. Results Two-way fixed-effects estimates show that all four climate shocks are negatively associated with NCS, and the direction of these relationships remains stable across alternative accounting boundaries, sample adjustments, leave-one-province-out tests, and double machine learning specifications. Agricultural supply chain resilience significantly conditions the climate–carbon relationship. Its resistance, recovery, and reorientation capacities attenuate the adverse associations of high-temperature, extreme-rainfall, and drought shocks with NCS. The low-temperature result further indicates that the effectiveness of resilience depends on matching supply chain functions with the biological mechanisms and intervention windows of specific climate hazards. Heterogeneity analyses confirm that climate sensitivity and resilience requirements vary with regional location, agricultural productivity, modernization, and irrigation conditions. Complementary machine-learning analysis identifies nonlinear predictive patterns and reinforces the importance of differentiated adaptation. Discussion These findings extend agricultural climate research from production and emissions to an integrated carbon-balance perspective and demonstrate that hazard-specific supply chain resilience can support both climate adaptation and low-carbon agricultural development.","引言 极端气候冲击日益挑战农业系统在维持作物碳固定的同时控制生产相关排放的能力。方法 本研究利用2002年至2023年中国240个城市的面板数据，考察四种气候冲击——极端高温（HTD）、极端低温（LTD）、极端降雨（ERD）和极端干旱（EED）——与农业净碳汇（NCS）之间的关系，以及农业供应链韧性的调节作用。NCS以年度作物生物量碳固定量扣除部分农业生产排放量来衡量，从而提供一个基于生产的综合性农业碳绩效指标。结果 双向固定效应估计表明，四种气候冲击均与NCS呈负相关，且这些关系的方向在替代核算边界、样本调整、逐一剔除省份检验和双重机器学习设定下均保持稳定。农业供应链韧性显著调节气候—碳关系。其抵抗能力、恢复能力和重新定向能力减弱了高温、极端降雨和干旱冲击与NCS之间的不利关联。低温结果进一步表明，韧性的有效性取决于供应链功能与特定气候灾害的生物机制及干预窗口的匹配。异质性分析证实，气候敏感性和韧性需求因区域位置、农业生产率、现代化水平和灌溉条件而异。补充性机器学习分析识别出非线性预测模式，并强化了差异化适应的重要性。讨论 这些发现将农业气候研究从生产和排放拓展至综合碳平衡视角，并表明针对特定灾害的供应链韧性能够同时支持气候适应和农业低碳发展。","Frontiers in Sustainable Food Systems","2026-09-25T00:00:00Z",80,{"impact":18,"substance":361,"depth":18,"authority":218,"freshness":21,"relevant":22,"comment":362},22,"基于240城22年面板数据的实证研究，方法扎实、结论稳健，对农业气候适应与低碳发展有参考价值，但属学术论文，公共传播性有限。",[364],{"name":357,"url":354},[28,366,367,30,368],"农业供应链","低碳农业","农业碳汇",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[371],{"code":315,"slug":372,"name":373,"path":374,"parent_code":37},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",{"status":40,"locked":14,"version":41,"method":42,"evidence":376},{"candidates":377,"agricultural_evidence":46},[378,384,390,396,400],{"code":33,"score":13,"supported":46,"title":379,"abstract":380,"tags":383},[],[144,381,382],"干旱","drought",[30],{"code":315,"score":123,"supported":46,"title":385,"abstract":386,"tags":389},[],[387,388],"碳汇","农业碳",[367,368],{"code":84,"score":63,"supported":46,"title":391,"abstract":392,"tags":395},[],[393,394],"灌溉","irrigation",[],{"code":282,"score":63,"supported":46,"title":397,"abstract":398,"tags":399},[],[297,298],[],{"code":54,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[28],[405,406],"中国 农业净碳汇 极端气候","农业供应链韧性 气候适应","中国农业净碳汇极端气候-3531","10.3389\u002Ffsufs.2026.1940685",{"doi":408,"openalex_id":410,"authors":411,"venue":357,"cited_by_count":93,"oa_url":354,"card":420,"direction":348,"ingested_from":109},"W7214310732",[412,414,417],{"name":413,"orcid":9},"Chunyan Zhao",{"name":415,"orcid":416},"Jiajie Xia","https:\u002F\u002Forcid.org\u002F0000-0003-3225-0154",{"name":418,"orcid":419},"Guoping Ding","https:\u002F\u002Forcid.org\u002F0000-0002-4865-5044",{"tldr":421,"method":422,"finding":423,"direction":348,"opportunity":424},"基于240个城市2002-2023年面板数据，检验四类极端气候冲击对农业净碳汇的影响及供应链韧性的调","双向固定效应模型、双重机器学习，240城市面板数据，农业净碳汇指标。","四类极端气候冲击均降低农业净碳汇，供应链韧性可显著缓解高温、暴雨和干旱的负面影响。","可探究不同气候灾害下供应链韧性功能与生物机制匹配的差异化适应策略及非线性预测。","2026-09-26T23:30:07.569768Z",{"id":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":9,"source_name":432,"source_url":429,"published_at":433,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":434,"sources":436,"tags":438,"primary_topic":440,"secondary_topics":441,"classification":443,"search_phrases":464,"slug":467,"view_count":468,"doi":469,"paper":470,"created_at":486},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","2026-09-23T00:00:00Z",{"impact":18,"substance":361,"depth":18,"authority":218,"freshness":21,"relevant":22,"comment":435},"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。",[437],{"name":432,"url":429},[28,279,29,50,439],"文献计量",{"code":282,"slug":283,"name":284,"path":285,"parent_code":286},[442],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":444},{"candidates":445,"agricultural_evidence":46},[446,450,454,460],{"code":282,"score":18,"supported":46,"title":447,"abstract":448,"tags":449},[294],[296,297,298,299,294],[279],{"code":33,"score":19,"supported":46,"title":451,"abstract":452,"tags":453},[51],[50,51],[50],{"code":455,"score":19,"supported":46,"title":456,"abstract":458,"tags":459},"I7",[457],"bibliometric",[439,457],[439],{"code":54,"score":147,"supported":46,"title":461,"abstract":462,"tags":463},[],[28,139],[28],[465,466],"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":469,"openalex_id":471,"authors":472,"venue":432,"cited_by_count":93,"oa_url":429,"card":480,"direction":108,"ingested_from":109},"W7214099405",[473,476,478],{"name":474,"orcid":475},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":477,"orcid":9},"Shiyan Zhai",{"name":479,"orcid":9},"Yuke Feng",{"tldr":481,"method":482,"finding":483,"direction":484,"opportunity":485},"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",{"id":488,"title":489,"url":490,"summary":491,"summary_zh":492,"content":9,"source_name":493,"source_url":490,"published_at":494,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":495,"score_detail":496,"sources":500,"tags":502,"primary_topic":506,"secondary_topics":507,"classification":509,"search_phrases":555,"slug":558,"view_count":124,"doi":559,"paper":560,"created_at":572},2648,"Horticulture-Voltaic: The Earth's Solar Horticulture Turns Every Ray into Food and Power - A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.25125\u002Fijoear-sep-2026-8","Horticulture-voltaic systems - the co-location of photovoltaic (PV) electricity generation with horticultural crop production on the same land - are emerging as a central strategy for climate-resilient horticulture. This in-depth review examines the technology, engineering, intercultural operations and empirical evidence for horticulture-voltaics applied to fruit, vegetable and flower crops. We cover solar panel technologies, system architectures, the full design-component and engineering stack (mounting heights of 2.5-5 m, panel spacing and tilt, deep-buried armoured cables, structural safety under ASCE-7-16 wind loads, smart irrigation integration), the electrical architecture (module\u002Fstring voltage, inverter, battery, transformer, grid connection), and intercultural operations adapted for sub-array horticulture. Empirical evidence shows that shade relieves heat and drought stress, with grape +277% (Mediterranean, LER 3.54), tomato and chiltepin doubled or tripled (Arizona), and partial shade increasing floral abundance in flowers. Shading must stay below ~30% for fruit crops and 20-40% for most vegetables and flower crops. With continued research and engineering, horticulture-voltaics can deliver climate-adaptive, resource-efficient and energy-positive horticultural systems.","园艺光伏系统——在同一土地上将光伏（PV）发电与园艺作物生产相结合——正逐渐成为气候适应性园艺的核心策略。这篇深度综述考察了园艺光伏应用于水果、蔬菜和花卉作物的技术、工程、行间作业及实证证据。我们涵盖太阳能电池板技术、系统架构、完整的设计-组件与工程体系（安装高度2.5-5米、板间距与倾角、深埋铠装电缆、ASCE-7-16风荷载下的结构安全、智能灌溉集成）、电气架构（组件\u002F组串电压、逆变器、电池、变压器、电网连接），以及针对子阵列园艺适配的行间作业。实证证据表明，遮阴可缓解高温和干旱胁迫，葡萄增产+277%（地中海地区，LER 3.54），番茄和野生辣椒产量翻倍或增至三倍（亚利桑那州），部分遮阴增加了花卉的花量。水果作物的遮阴率须保持在约30%以下，大多数蔬菜和花卉作物为20-40%。通过持续的研究与工程实践，园艺光伏能够提供气候适应性、资源高效且能量正输出的园艺系统。","International Journal of Environmental and Agriculture Research","2026-09-15T00:00:00Z",85,{"impact":361,"substance":497,"depth":498,"authority":218,"freshness":123,"relevant":22,"comment":499},23,19,"系统综述农光互补园艺的技术架构与实证数据，提出遮光阈值等可操作结论，对设施农业与新能源融合具参考价值。",[501],{"name":493,"url":490},[28,30,503,504,505],"农光互补","设施园艺","光伏农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[508],{"code":226,"slug":227,"name":228,"path":229,"parent_code":230},{"status":40,"locked":14,"version":41,"method":42,"evidence":510},{"candidates":511,"agricultural_evidence":46},[512,516,523,530,534,540,546,551],{"code":33,"score":13,"supported":46,"title":513,"abstract":514,"tags":515},[],[144,381,382],[30],{"code":226,"score":21,"supported":46,"title":517,"abstract":518,"tags":522},[],[228,519,246,520,521],"番茄","vegetable","tomato",[],{"code":524,"score":63,"supported":46,"title":525,"abstract":526,"tags":529},"B2",[],[527,528],"葡萄","grape",[],{"code":84,"score":63,"supported":46,"title":531,"abstract":532,"tags":533},[],[393,394],[],{"code":535,"score":78,"supported":46,"title":536,"abstract":537,"tags":539},"A7",[],[538],"间作",[],{"code":541,"score":78,"supported":46,"title":542,"abstract":543,"tags":545},"B4",[],[544],"花卉",[],{"code":547,"score":124,"supported":14,"title":548,"abstract":549,"tags":550},"B7",[],[],[505,504],{"code":54,"score":22,"supported":14,"title":552,"abstract":553,"tags":554},[],[],[28],[556,557],"光伏农业 农光互补 智慧农业 气候韧性","光伏农业 农光互补","光伏农业农光互补智慧农业气候韧性-2648","10.25125\u002Fijoear-sep-2026-8",{"doi":559,"openalex_id":561,"authors":562,"venue":493,"cited_by_count":93,"oa_url":566,"card":567,"direction":108,"ingested_from":109},"W7213346284",[563],{"name":564,"orcid":565},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-SEP-2026-8.pdf",{"tldr":568,"method":569,"finding":570,"direction":348,"opportunity":571},"综述园艺光伏系统，即同一土地上光伏发电与园艺作物共址，涵盖技术、工程与实证。","综述光伏技术、系统架构、工程设计与间作操作，汇总多地实证数据。","适度遮阴缓解热旱胁迫，葡萄增产277%，番茄等倍增，遮阴需低于30%。","可探索不同气候区园艺光伏最优遮阴阈值与作物-光伏协同设计模型。","2026-09-16T23:30:12.350469Z"]