[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4153":3,"related-4153":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":81},4153,"中国农业农村数字化绿色化协同发展的时空格局及其增收效应","https:\u002F\u002Fcjournal.hep.com.cn\u002F1007-4333\u002FCN\u002F10.11841\u002Fj.issn.1007-4333.2026.10.06","蔡雪玲（中国农业大学学报2026年第10期，兰州财经大学会计学院）。文章立足于农业农村「生产-管理-生态-创新-生活」数绿协同的内在逻辑构建测度指标体系，采用熵值法、改进的耦合协调度模型、Kernel密度估计与空间Markov链转移矩阵测算农业农村数绿协同发展水平并刻画其时空演进特征，构建固定效应模型与空间计量模型检验农业农村数绿协同发展对农村居民收入的影响关系与空间溢出效应。结果表明：全国及各省份农业农村数绿协同发展水平缓慢上升，尚处于基本协同阶段。",null,"中国农业大学学报2026年第10期","2026-10-01T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,8,1,"核心期刊论文，构建数绿协同测度体系并实证检验其增收效应与空间溢出，方法规范、结论有政策参考价值，但属学术研究而非产业动态，适合主题聚合而非每日精选头条。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字乡村","农业绿色发展","农村居民收入","耦合协调度","空间溢出效应",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,55,60],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},5,true,[],[48],"空间计量",[29,28],{"code":51,"score":20,"supported":13,"title":52,"abstract":53,"tags":54},"H4",[],[],[26],{"code":56,"score":20,"supported":13,"title":57,"abstract":58,"tags":59},"H8",[],[],[26],{"code":61,"score":20,"supported":13,"title":62,"abstract":63,"tags":64},"I6",[],[],[25],[66,67],"农业农村 数字化绿色化 协同发展","农村居民收入 空间溢出效应","农业农村数字化绿色化协同发展-4153",4,"10.11841\u002Fj.issn.1007-4333.2026.10.06",{"doi":70,"openalex_id":8,"authors":72,"venue":8,"cited_by_count":73,"oa_url":8,"card":74,"direction":78,"ingested_from":80},[],0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"测度中国农业农村数字化绿色化协同水平并检验其时空格局与增收效应。","熵值法、耦合协调度模型、Kernel密度、空间Markov链与空间计量模型。","全国及各省数绿协同水平缓慢上升，仍处基本协同阶段，且对农村居民收入有影响与空间溢出。","数字乡村与农业信息化","可深入探究数绿协同增收效应的区域异质性与微观农户传导机制，识别关键短板维度。","agent","2026-10-04T00:09:09.731896Z",{"total":83,"page":20,"page_size":83,"items":84},6,[85,167,234,297,370,437],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":8,"source_name":91,"source_url":88,"published_at":92,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":93,"score_detail":94,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":135,"slug":138,"view_count":69,"doi":139,"paper":140,"created_at":166},3342,"Impact of agricultural greening on food production resilience-empirical evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1938875","The Food Production System is generally confronted with external uncertain shocks and rigid constraints from ecological factors. How to strengthen the system’s adaptability and resilience through green transition pathways is a crucial research topic in the field of modern agricultural governance. With the provincial panel data from China, the entropy method is adopted in this paper to measure agricultural greening and food production resilience, and then the two-way fixed-effect model, the moderating-effect model, the threshold-effect model and spatial econometric models are applied for empirical testing. According to the results, the degree of agricultural greening has promoted food production resilience; at the same time, regions show heterogeneity across geographical regions and grain functional zones. Green finance and rural scientific and technological input will also be effective driving forces. There is a single threshold for economic development level; after exceeding it, the marginal promoting effect on resilience weakens, but remains significantly positive. Food production resilience also exhibits significant spatial dependence, and agricultural greening generates positive spillover effects on neighboring provinces under the adjacency-based spatial specification. This paper will provide new ideas for the theory of resilience enhancement and offer some references to help optimize the distribution of agricultural green factors and foster long-term stable grain production.","粮食生产系统普遍面临外部不确定冲击与生态因素的刚性约束。如何通过绿色转型路径增强系统的适应性与韧性，是现代农业农村治理领域的重要研究课题。本文基于中国省级面板数据，采用熵值法测度农业绿色化与粮食生产韧性，进而运用双向固定效应模型、调节效应模型、门槛效应模型及空间计量模型进行实证检验。结果表明，农业绿色化程度提升了粮食生产韧性；同时，不同地理区域与粮食功能区呈现出异质性。绿色金融与农村科技投入也是有效的驱动力量。经济发展水平存在单一门槛，跨越门槛后对韧性的边际促进效应减弱，但仍显著为正。粮食生产韧性还表现出显著的空间依赖性，在邻接空间设定下，农业绿色化对邻近省份产生正向溢出效应。本文将为韧性提升理论提供新思路，并为优化农业绿色要素配置、促进粮食长期稳定生产提供一定参考。","Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z",79,{"impact":16,"substance":17,"depth":16,"authority":95,"freshness":19,"relevant":20,"comment":96},13,"基于中国省级面板数据的实证研究，方法扎实、结论有政策参考价值，但属学术论文而非产业级事件，适合进入主题聚合而非头条精选。",[98],{"name":91,"url":88},[26,100,101,102,29],"粮食生产韧性","农业科技投入","绿色金融",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":106},{"candidates":107,"agricultural_evidence":45},[108,112,117,122,126,130],{"code":31,"score":69,"supported":45,"title":109,"abstract":110,"tags":111},[],[48],[29],{"code":113,"score":20,"supported":13,"title":114,"abstract":115,"tags":116},"A8",[],[],[100],{"code":118,"score":20,"supported":13,"title":119,"abstract":120,"tags":121},"G3",[],[],[102],{"code":51,"score":20,"supported":13,"title":123,"abstract":124,"tags":125},[],[],[26],{"code":56,"score":20,"supported":13,"title":127,"abstract":128,"tags":129},[],[],[26],{"code":131,"score":20,"supported":13,"title":132,"abstract":133,"tags":134},"J2",[],[],[101],[136,137],"农业绿色化 粮食生产韧性","绿色金融 农村科技投入","农业绿色化粮食生产韧性-3342","10.3389\u002Ffsufs.2026.1938875",{"doi":139,"openalex_id":141,"authors":142,"venue":91,"cited_by_count":73,"oa_url":88,"card":159,"direction":163,"ingested_from":165},"W7214113879",[143,145,148,150,152,154,157],{"name":144,"orcid":8},"Huimin Shao",{"name":146,"orcid":147},"Wenjuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5062-1292",{"name":149,"orcid":8},"Qiong Peng",{"name":151,"orcid":8},"Haichuan Liu",{"name":153,"orcid":8},"Liangyan Lu",{"name":155,"orcid":156},"Mcxin Tee","https:\u002F\u002Forcid.org\u002F0000-0001-7990-8377",{"name":158,"orcid":8},"Qing Jin",{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"基于中国省级面板数据，实证检验农业绿色化对粮食生产韧性的提升作用。","熵值法测度指标，双向固定效应、调节效应、门槛效应与空间计量模型。","农业绿色化显著提升粮食生产韧性，存在区域异质性、经济门槛及正向空间溢出。","农业绿色发展与碳","可探究绿色金融与科技投入的协同机制，及跨区域溢出路径的微观落地。","openalex","2026-09-24T23:30:07.857044Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":8,"source_name":173,"source_url":170,"published_at":174,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":175,"score_detail":176,"sources":178,"tags":180,"primary_topic":185,"secondary_topics":190,"classification":192,"search_phrases":215,"slug":218,"view_count":219,"doi":220,"paper":221,"created_at":233},4102,"Application of GIS and Remote Sensing Techniques in Geospatial Mapping of Gully Statuses of an Alfisols in Yola South Local Government Area, Adamawa State","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v10.no1.2026.pg155.165","This research work saddled to study the application of GIS and Remote Sensing (RS) Techniques in geospatial mapping of gully erosion statuses of an Alfisols in Yola South LGA. Bole farmland was selected where expansion and development of gully was geospatially mapped out for the period of 20 years (2004-2024) at interval of five (5) years using GIS and RS techniques. The results reveal a concerning trend of increasing gully erosion, with significant growth in affected areas over the years. The highest percentage of increase occurred at 2014-2019 with about 58,069.00 m2 (65.5 %) and in 2019-2024was increased with 14,154.00 m2 (15.97 %). The total area affected in the area within the study period was 88,627.00 m2 caused by heavy rainfall and deforestation. Therefore, there are needs for effective land management practices, policy interventions, and community engagement to mitigate the effects of gully erosion.","本研究旨在探讨GIS与遥感（RS）技术在约拉南部地方政府区淋溶土冲沟侵蚀状况地理空间制图中的应用。选取博莱农田为研究区，利用GIS与遥感技术，以5年为间隔，对2004—2024年共20年间冲沟的扩展与发育进行了地理空间制图。结果表明，冲沟侵蚀呈令人担忧的加剧趋势，受影响面积逐年显著增长。增幅最大的时段为2014—2019年，约增加58 069.00 m²（65.5%）；2019—2024年增加14 154.00 m²（15.97%）。研究期内该区域受影响总面积为88 627.00 m²，主要由强降雨和森林砍伐所致。因此，需要采取有效的土地管理措施、政策干预和社区参与，以减轻冲沟侵蚀的影响。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-09-30T00:00:00Z",54,{"impact":19,"substance":16,"depth":18,"authority":83,"freshness":19,"relevant":20,"comment":177},"基于GIS与遥感的20年沟蚀动态监测，数据具体、结论明确，但属区域案例研究，公共影响有限。",[179],{"name":173,"url":170},[25,181,182,183,184],"遥感","土壤侵蚀","GIS","水土保持",{"code":186,"slug":187,"name":188,"path":189,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[191],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":193},{"candidates":194,"agricultural_evidence":45},[195,201,206,211],{"code":186,"score":196,"supported":45,"title":197,"abstract":199,"tags":200},15,[198],"remote sensing",[181,198],[181],{"code":31,"score":202,"supported":45,"title":203,"abstract":204,"tags":205},12,[183],[183],[183],{"code":207,"score":20,"supported":13,"title":208,"abstract":209,"tags":210},"H6",[],[],[184],{"code":61,"score":20,"supported":13,"title":212,"abstract":213,"tags":214},[],[],[25],[216,217],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102",2,"10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":220,"openalex_id":222,"authors":223,"venue":173,"cited_by_count":73,"oa_url":226,"card":227,"direction":231,"ingested_from":165},"W7214920253",[224],{"name":225,"orcid":8},"Mohammed Pakra Umar","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 10 NO. 1 2026\u002FAPPLICATION OF GIS AND REMOTE 156-165.pdf",{"tldr":228,"method":229,"finding":230,"direction":231,"opportunity":232},"用GIS和遥感技术对尼日利亚Yola South地区20年沟蚀动态进行空间制图与监测。","基于GIS与遥感影像，按5年间隔提取2004-2024年沟蚀范围并计算面积变化。","沟蚀面积持续扩大，2014-2019年增幅最大达65.5%，20年累计影响88,627平方米。","农业遥感与作物表型","可结合多源高分辨率遥感与机器学习，构建沟蚀风险预警模型并评估保护性耕作等治理措施效果。","2026-10-03T23:30:37.949646Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":8,"content":8,"source_name":239,"source_url":8,"published_at":240,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":241,"score_detail":242,"sources":246,"tags":248,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":285,"slug":288,"view_count":44,"doi":8,"paper":289,"created_at":296},3320,"人工智能对农业新质生产力的驱动机制与效应——基于278个地级市面板数据（2010—2024）","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165477.html","《农业经济与管理》2026年第03期。钟文、郑林泽、严芝清（江西理工大学）研究表明：人工智能技术创新对农业新质生产力具有显著直接提升效应。机制检验表明，人工智能主要通过强化农业科技创新能力、激发农村地区创业活力、深化农业生产数字化水平三条核心路径驱动农业新质生产力发展。异质性分析表明，人工智能的促进作用具有普遍性但存在显著梯度差异，受区域资源禀赋、发展阶段及农业生产模式多样性深刻影响。空间计量检验进一步发现，人工智能技术创新存在显著正向空间溢出效应，但农村人力资本水平构成关键调节变量，其不足将显著制约溢出效能的有效释放。","《农业经济与管理》2026年第03期","2026-09-17T00:00:00Z",86,{"impact":17,"substance":243,"depth":244,"authority":18,"freshness":19,"relevant":20,"comment":245},23,19,"基于278个地级市面板数据的实证研究，方法规范、结论有新意，对农业AI政策制定具参考价值，值得进入每日精选。",[247],{"name":239,"url":237},[249,250,251,29,252],"农业人工智能","农业新质生产力","农业数字化","农村创业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":256},{"candidates":257,"agricultural_evidence":45},[258,262,267,271,276,281],{"code":31,"score":69,"supported":45,"title":259,"abstract":260,"tags":261},[],[48],[29],{"code":263,"score":20,"supported":13,"title":264,"abstract":265,"tags":266},"G4",[],[],[252],{"code":35,"score":20,"supported":13,"title":268,"abstract":269,"tags":270},[],[],[251],{"code":272,"score":20,"supported":13,"title":273,"abstract":274,"tags":275},"I1",[],[],[251],{"code":277,"score":20,"supported":13,"title":278,"abstract":279,"tags":280},"I2",[],[],[249],{"code":131,"score":20,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[250],[286,287],"人工智能 农业新质生产力 地级市面板","农业新质生产力 农业人工智能 空间溢出效应 农业数字化","人工智能农业新质生产力地级市面板-3320",{"doi":8,"openalex_id":8,"authors":290,"venue":8,"cited_by_count":73,"oa_url":8,"card":291,"direction":78,"ingested_from":80},[],{"tldr":292,"method":293,"finding":294,"direction":78,"opportunity":295},"基于278个地级市面板数据，实证检验人工智能对农业新质生产力的驱动机制与空间溢出效应。","278个地级市2010—2024年面板数据，机制检验、异质性分析与空间计量模型。","人工智能显著直接提升农业新质生产力，经三条路径驱动，并具正向空间溢出，但受农村人力资本调节制约。","可深入探究农村人力资本门槛下AI空间溢出的非线性衰减机制及跨区域协同提升路径。","2026-09-24T00:04:02.275354Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":8,"source_name":303,"source_url":300,"published_at":174,"category":11,"cover_url":8,"hotness":304,"is_selected":13,"score":305,"score_detail":306,"sources":310,"tags":314,"primary_topic":317,"secondary_topics":321,"classification":323,"search_phrases":354,"slug":357,"view_count":69,"doi":358,"paper":359,"created_at":369},2333,"Empowering India's Digital Future through Artificial Intelligence in Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705830","The convergence of Geographic Information Systems (GIS), high-resolution Earth observation (EO) systems, and Artificial Intelligence (AI) has established a new paradigm: Geospatial Artificial Intelligence (GeoAI). In the context of India's rapid digital transformation, GeoAI serves as a core technology driving sustainable urbanization, precision agriculture, disaster risk reduction, and multi-modal logistics planning. Anchored by strategic national frameworks such as the National Geospatial Policy (NGP), PM Gati Shakti National Master Plan, and the SVAMITVA scheme, this paper examines how AI-integrated spatial analysis accelerates India's digital governance. Through technical methodologies, statistical hypothesis testing, applied case studies, and systemic evaluation, this study demonstrates the pivotal role of GeoAI in building resilient spatial data infrastructure and offers strategic policy recommendations for scalable national adoption.","地理信息系统（GIS）、高分辨率对地观测（EO）系统与人工智能（AI）的融合，已确立了一种新范式：地理空间人工智能（GeoAI）。在印度快速数字化转型的背景下，GeoAI成为推动可持续城市化、精准农业、灾害风险降低和多式联运物流规划的核心技术。本文以国家地理空间政策（NGP）、PM Gati Shakti国家总体规划以及SVAMITVA计划等战略性国家框架为依托，考察了AI集成的空间分析如何加速印度的数字治理。通过技术方法、统计假设检验、应用案例研究和系统性评估，本研究表明GeoAI在构建韧性空间数据基础设施中发挥关键作用，并为全国范围的可扩展应用提供了战略性政策建议。","Zenodo (CERN European Organization for Nuclear Research)",25,72,{"impact":16,"substance":307,"depth":16,"authority":95,"freshness":308,"relevant":20,"comment":309},20,3,"论文系统阐述GeoAI在印度精准农业、数字治理中的应用与政策建议，方法扎实但发布日期在未来，时效性存疑。",[311,312],{"name":303,"url":300},{"name":303,"url":313},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705829",[25,315,249,181,316],"智慧农业","地理信息",{"code":277,"slug":318,"name":319,"path":320,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[322],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":324},{"candidates":325,"agricultural_evidence":45},[326,331,339,346,350],{"code":277,"score":202,"supported":45,"title":327,"abstract":329,"tags":330},[328],"artificial intelligence",[328],[249],{"code":31,"score":332,"supported":45,"title":333,"abstract":334,"tags":338},9,[],[316,335,183,336,337],"空间分析","spatial analysis","geographic information",[],{"code":272,"score":340,"supported":45,"title":341,"abstract":342,"tags":345},7,[],[343,344],"精准农业","precision agriculture",[315],{"code":186,"score":20,"supported":13,"title":347,"abstract":348,"tags":349},[],[],[181],{"code":61,"score":20,"supported":13,"title":351,"abstract":352,"tags":353},[],[],[25],[355,356],"农业人工智能 地理信息 数字乡村 智慧农业","农业人工智能 地理信息","农业人工智能地理信息数字乡村智慧农业-2333","10.5281\u002Fzenodo.22705830",{"doi":358,"openalex_id":360,"authors":361,"venue":303,"cited_by_count":73,"oa_url":300,"card":364,"direction":78,"ingested_from":165},"W7212270621",[362],{"name":363,"orcid":8},"Lalitkumar G. Thakur",{"tldr":365,"method":366,"finding":367,"direction":231,"opportunity":368},"本文探讨地理空间人工智能（GeoAI）如何推动印度数字治理与可持续发展。","结合GIS、高分辨率对地观测与AI，采用案例研究、统计检验和政策评估。","GeoAI在精准农业、灾害减灾等领域关键，需国家政策框架支持规模化应用。","可研究GeoAI在印度小农精准农业中的适配性、数据鸿沟与政策落地机制。","2026-09-13T23:30:29.245252Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":8,"source_name":376,"source_url":373,"published_at":377,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":378,"score_detail":379,"sources":382,"tags":384,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":410,"slug":413,"view_count":219,"doi":414,"paper":415,"created_at":436},1993,"Multi-scale OPGD uncovers spatial pattern and influencing factors of rural settlements in the Minjiang River Basin","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69084-3","Rapid urbanization and industrialization are reshaping rural systems, necessitating a thorough analysis of their spatial patterns and driving forces. This study conducted cross-sectional analysis of rural settlement in the Minjiang River Basin (MRB) for the year 2020, using rural-settlement land data derived from annual China Land Cover Dataset (CLCD). Settlement scale was defined as the total area of rural settlement land within each analysis grid cell. was characterized using spatial analysis methods. Spatial analysis methods including Average Nearest Neighbor (ANN), Kernel Density Estimation (KDE), and Hot Spot Analysis applied to characterize the spatial pattern of rural settlements. An optimal analysis grid was identified through scale sensitivity testing, and the Optimal Parameter Geodetector (OPGD) was subsequently applied to examine the associated mechanisms of spatial differentiation in settlement scale. The results indicate that: (1) Rural settlements in the MRB exhibit a significant clustered distribution at the global scale (R = 0.698, Z = − 30.08), manifesting a \"one primary core and multiple sub-cores\" structure and a distinct Northwest-Southeast axial, with the spatial centroid notably shifted toward coastal lowlands. (2) A 4 km grid size produced the highest mean q-value among the six tested scales. (3) Factor detection reveals that the Normalized Difference Vegetation Index (NDVI, q = 0.116) and road network density ( q = 0.091) showed the strongest spatial stratification in driving settlement scale differentiation. (4) All factor interactions exhibit enhancement effects; notably, the nonlinear enhancement between road network density and topographic relief is the most significant ( q = 0.147). These findings provide decision support for differentiated rural planning in the MRB and offer a valuable reference for spatial analysis and influential factor research in regions with complex terrain.","快速城市化与工业化正在重塑乡村系统，亟需对其空间格局及驱动机制进行深入分析。本研究基于年度中国土地覆盖数据集（CLCD）提取的农村居民点用地数据，对2020年岷江流域（MRB）农村居民点进行了横截面分析。居民点规模定义为每个分析网格单元内农村居民点用地的总面积，并采用空间分析方法刻画其特征。运用平均最近邻指数（ANN）、核密度估计（KDE）和热点分析等空间分析方法表征农村居民点的空间格局。通过尺度敏感性测试确定最优分析网格，进而应用最优参数地理探测器（OPGD）探究居民点规模空间分异的关联机制。结果表明：（1）岷江流域农村居民点在全球尺度上呈现显著的集聚分布格局（R = 0.698，Z = −30.08），表现为“一主核多副核”的空间结构及明显的西北—东南轴向特征，空间重心显著偏向沿海低地。（2）在六种测试尺度中，4 km网格尺度产生的平均q值最高。（3）因子探测显示，归一化植被指数（NDVI，q = 0.116）和路网密度（q = 0.091）对居民点规模分异具有最强的空间分层解释力。（4）所有因子交互作用均呈现增强效应，其中路网密度与地形起伏度之间的非线性增强最为显著（q = 0.147）。上述发现为岷江流域差异化乡村规划提供了决策支持，也为复杂地形区域的空间格局及影响因素研究提供了有价值的参考。","Scientific Reports","2026-09-08T00:00:00Z",56,{"impact":19,"substance":16,"depth":380,"authority":202,"freshness":219,"relevant":20,"comment":381},16,"论文聚焦流域尺度乡村聚落空间格局与影响因素，方法严谨，对复杂地形区乡村规划有参考价值，但时效性较低。",[383],{"name":376,"url":373},[25,181,385,386,387],"乡村聚落","空间格局","地理探测器",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":391},{"candidates":392,"agricultural_evidence":45},[393,397,402,406],{"code":31,"score":83,"supported":45,"title":394,"abstract":395,"tags":396},[],[335,336],[],{"code":277,"score":308,"supported":45,"title":398,"abstract":399,"tags":401},[],[400],"decision support",[],{"code":186,"score":20,"supported":13,"title":403,"abstract":404,"tags":405},[],[],[181],{"code":61,"score":20,"supported":13,"title":407,"abstract":408,"tags":409},[],[],[25],[411,412],"地理探测器 乡村聚落 数字乡村 空间格局","地理探测器 乡村聚落","地理探测器乡村聚落数字乡村空间格局-1993","10.1038\u002Fs41598-026-69084-3",{"doi":414,"openalex_id":416,"authors":417,"venue":376,"cited_by_count":73,"oa_url":430,"card":431,"direction":231,"ingested_from":165},"W7211874041",[418,420,423,426,428],{"name":419,"orcid":8},"Shanghui Li",{"name":421,"orcid":422},"Zhipeng Zhu","https:\u002F\u002Forcid.org\u002F0000-0001-6825-3117",{"name":424,"orcid":425},"Xinxin Xia","https:\u002F\u002Forcid.org\u002F0009-0007-5767-3758",{"name":427,"orcid":8},"江道镨",{"name":429,"orcid":8},"Peng Yu","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-69084-3_reference.pdf",{"tldr":432,"method":433,"finding":434,"direction":78,"opportunity":435},"利用多尺度最优参数地理探测器分析闽江流域农村居民点空间格局及影响因素。","使用CLCD数据，结合最邻近指数、核密度、热点分析和最优参数地理探测器。","农村居民点呈集聚分布，NDVI和路网密度为主要驱动因素，交互作用增强。","可延伸至山区农村居民点优化布局，结合多源数据与多尺度分析，探索地形与交通交互影响。","2026-09-09T23:30:22.214502Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":8,"source_name":443,"source_url":440,"published_at":444,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":445,"score_detail":446,"sources":448,"tags":450,"primary_topic":452,"secondary_topics":453,"classification":454,"search_phrases":482,"slug":485,"view_count":73,"doi":486,"paper":487,"created_at":500},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":19,"substance":16,"depth":18,"authority":202,"freshness":332,"relevant":20,"comment":447},"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[449],{"name":443,"url":440},[181,451,182,183,184],"土地利用",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":455},{"candidates":456,"agricultural_evidence":45},[457,461,465,472,478],{"code":31,"score":12,"supported":45,"title":458,"abstract":459,"tags":460},[],[316,183,337],[183],{"code":186,"score":340,"supported":45,"title":462,"abstract":463,"tags":464},[],[181,198],[181],{"code":466,"score":83,"supported":45,"title":467,"abstract":468,"tags":471},"H2",[],[469,470],"土壤","soil",[],{"code":473,"score":308,"supported":45,"title":474,"abstract":475,"tags":477},"H3",[],[476],"灌溉",[],{"code":207,"score":219,"supported":13,"title":479,"abstract":480,"tags":481},[],[],[451,184],[483,484],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":486,"openalex_id":488,"authors":489,"venue":443,"cited_by_count":73,"oa_url":8,"card":495,"direction":231,"ingested_from":165},"W7223117733",[490,492],{"name":491,"orcid":8},"MERİÇ KURT",{"name":493,"orcid":494},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":496,"method":497,"finding":498,"direction":163,"opportunity":499},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z"]