[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5300":3,"related-5300":69},{"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":8,"secondary_topics":29,"classification":30,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":68},5300,"张捷\u002F李林飞《中国林业经济》:数字经济政策对农业高质量发展的影响研究——基于国家大数据综合试验区的准自然实验","https:\u002F\u002Fzglyjj.nefu.edu.cn\u002Farticle\u002Fdoi\u002F10.13691\u002Fj.cnki.cn23-1539\u002Ff.2026.04.001","东北林业大学经济管理学院张捷、李林飞基于2011-2023年中国31个省级行政区面板数据,以国家大数据综合试验区建设为准自然实验,运用多期双重差分模型评估数字经济政策对农业高质量发展的影响;研究发现数字经济政策显著促进农业高质量发展且具滞后性和累积性;对本地区农业高质量发展具显著正向直接效应,但对其他地区产生负向空间溢出,呈现\"本地促进、外部抑制\"特征;非粮食主产区估计效应相对更强。",null,"中国林业经济(2026,4)","2026-10-15T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,8,1,"基于31省面板数据与国家大数据综合试验区的准自然实验，方法规范、结论有政策参考价值，但属学术论文且非时效热点，可入精选备选。",[23],{"name":9,"url":6},[25,26,27,28],"数字经济","农业高质量发展","空间溢出","大数据试验区",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"unclassified","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":51,"reason":52},[36,41,46],{"code":37,"score":20,"supported":13,"title":38,"abstract":39,"tags":40},"I4",[],[],[27],{"code":42,"score":20,"supported":13,"title":43,"abstract":44,"tags":45},"I6",[],[],[25],{"code":47,"score":20,"supported":13,"title":48,"abstract":49,"tags":50},"J2",[],[],[26],true,"缺少足够的主题证据",[54,55],"国家大数据综合试验区 农业高质量发展","数字经济政策 双重差分","国家大数据综合试验区农业高质量发展-5300",0,"10.13691\u002Fj.cnki.cn23-1539\u002Ff.2026.04.001",{"doi":58,"openalex_id":8,"authors":60,"venue":8,"cited_by_count":57,"oa_url":8,"card":61,"direction":65,"ingested_from":67},[],{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"用多期DID评估国家大数据综合试验区对农业高质量发展的影响。","2011-2023年31省面板数据，多期双重差分模型。","政策显著促进本地农业高质量发展，但对其他地区有负向空间溢出。","数字乡村与农业信息化","可探究政策负向溢出的机制与跨区协同路径，及非粮食主产区的差异化政策设计。","agent","2026-10-19T00:07:48.514777Z",{"total":70,"page":20,"page_size":70,"items":71},6,[72,116,158,212,255,314],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":8,"source_name":77,"source_url":8,"published_at":78,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":79,"score_detail":80,"sources":85,"tags":87,"primary_topic":8,"secondary_topics":91,"classification":92,"search_phrases":103,"slug":106,"view_count":57,"doi":107,"paper":108,"created_at":115},5458,"数字经济对农业高质量发展的影响——以新疆14地州市为例 (Impact of Digital Economy on the High-Quality Development of Agriculture: Evidence from the 14 Prefectures and Cities of Xinjiang)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.041","基于2010—2022年新疆14地州市面板数据，熵值法测度双指数，中介与固定效应模型分析。数字经济对农业高质量发展影响系数0.132且显著，农业产业结构升级中介效应0.0649。区域异质性为东疆最强、南疆次之、北疆最弱。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",78,{"impact":81,"substance":82,"depth":16,"authority":18,"freshness":83,"relevant":20,"comment":84},16,21,9,"基于新疆14地州市面板数据的实证研究，方法规范、结论具体，对区域数字农业政策有参考价值，但属区域性学术成果，影响层级有限。",[86],{"name":77,"url":75},[25,26,88,89,90],"区域异质性","农业产业结构","新疆农业",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":93},{"candidates":94,"agricultural_evidence":51,"reason":52},[95,99],{"code":42,"score":20,"supported":13,"title":96,"abstract":97,"tags":98},[],[],[25],{"code":47,"score":20,"supported":13,"title":100,"abstract":101,"tags":102},[],[],[26],[104,105],"新疆 数字经济 农业高质量发展","新疆 14地州市 面板数据","新疆数字经济农业高质量发展-5458","10.13733\u002Fj.jcam.issn.2095-5553.2026.10.041",{"doi":107,"openalex_id":8,"authors":109,"venue":8,"cited_by_count":57,"oa_url":8,"card":110,"direction":65,"ingested_from":67},[],{"tldr":111,"method":112,"finding":113,"direction":65,"opportunity":114},"基于新疆14地州市面板数据，实证检验数字经济对农业高质量发展的影响及中介机制。","2010—2022年新疆14地州市面板数据，熵值法测度指数，中介与固定效应模型。","数字经济显著促进农业高质量发展（系数0.132），农业产业结构升级起中介作用，效应东疆最强、南疆次之","可延伸至县域尺度、微观农户数据及数字基础设施异质性下的机制识别与政策分区研究。","2026-10-21T00:10:08.003418Z",{"id":117,"title":118,"url":119,"summary":120,"summary_zh":8,"content":8,"source_name":121,"source_url":8,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":123,"score_detail":124,"sources":126,"tags":128,"primary_topic":8,"secondary_topics":132,"classification":133,"search_phrases":145,"slug":148,"view_count":149,"doi":8,"paper":150,"created_at":157},3436,"《数字经济赋能农业高质量发展的实证分析》——基于2012—2022年省级面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174646.html","耿俊婷、徐梓钦、张朝辉基于2012—2022年省级面板数据，运用熵值法测算数字经济、农业新质生产力并用SBM-GML模型测算农业高质量发展，使用双向固定效应模型、中介效应模型等方法探究数字经济对农业高质量发展的影响及农业新质生产力的作用机制。研究发现：数字经济在推动农业实现高质量发展方面具有显著的正向效应且存在显著的时间异质性；农业新质生产力是数字经济赋能农业迈向高质量发展阶段的重要机制；在粮食主销区和产销平衡区存在显著正向影响。","《河南农业大学学报》2026年第2期","2026-09-20T00:00:00Z",81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":83,"relevant":20,"comment":125},"基于2012—2022年省级面板数据的实证研究，方法规范、结论有新意，对数字乡村与农业高质量发展政策制定有参考价值。",[127],{"name":121,"url":119},[129,130,25,26,131],"数字乡村","农业新质生产力","省级面板数据",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":134},{"candidates":135,"agricultural_evidence":51,"reason":52},[136,141],{"code":42,"score":137,"supported":13,"title":138,"abstract":139,"tags":140},2,[],[],[129,25],{"code":47,"score":137,"supported":13,"title":142,"abstract":143,"tags":144},[],[],[130,26],[146,147],"数字经济 农业高质量发展","农业新质生产力 中介效应","数字经济农业高质量发展-3436",3,{"doi":8,"openalex_id":8,"authors":151,"venue":8,"cited_by_count":57,"oa_url":8,"card":152,"direction":65,"ingested_from":67},[],{"tldr":153,"method":154,"finding":155,"direction":65,"opportunity":156},"基于2012—2022年省级面板数据，实证检验数字经济对农业高质量发展的影响及作用机制。","熵值法测度数字经济与农业新质生产力，SBM-GML测度农业高质量发展，双向固定效","数字经济显著正向促进农业高质量发展且具时间异质性，农业新质生产力为重要中介机制，效应在粮食主销区和产","可细化数字经济子维度（如数字基础设施、电商）对农业高质量发展的差异化路径，并补充微观农户或县域层面证据。","2026-09-25T00:09:33.774119Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":8,"content":8,"source_name":163,"source_url":8,"published_at":164,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":165,"score_detail":166,"sources":170,"tags":172,"primary_topic":174,"secondary_topics":180,"classification":181,"search_phrases":199,"slug":202,"view_count":137,"doi":8,"paper":203,"created_at":211},1961,"数字经济如何影响农业生态效率?——基于超效率SBM与省级面板数据的中介与门槛分析","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F162974.html","李宽、史磊（农业农村部规划设计研究院）等基于2011—2022年省级数据，采用超效率SBM模型对各省份农业生态效率进行测度，通过空间杜宾模型、中介效应模型和门槛回归模型实证分析数字经济对农业生态效率的影响。结果表明：(1)数字经济对农业生态效率存在显著促进作用且具有空间溢出特征。(2)土地规模经营、农村人力资本积累和农业技术进步发挥中介作用。(3)东部和西部地区各省份数字经济发展对周边地区农业生态效率提升存在促进作用，西部地区最为明显。研究建议在继续放大\"数字红利\"的同时要注意区域间协调发展。","生态经济 2026年第01期","2026-01-01T00:00:00Z",74,{"impact":17,"substance":167,"depth":16,"authority":168,"freshness":20,"relevant":20,"comment":169},20,13,"基于省级面板数据的实证研究，揭示数字经济对农业生态效率的促进与空间溢出效应，对区域协调发展有参考价值，但时效性较低。",[171],{"name":163,"url":161},[25,27,173,131],"农业生态效率",{"code":175,"slug":176,"name":177,"path":178,"parent_code":179},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[],{"status":182,"locked":13,"version":32,"method":33,"evidence":183},"classified",{"candidates":184,"agricultural_evidence":51},[185,191,195],{"code":175,"score":186,"supported":51,"title":187,"abstract":189,"tags":190},11,[188],"农业生态",[188],[],{"code":37,"score":20,"supported":13,"title":192,"abstract":193,"tags":194},[],[],[27],{"code":42,"score":20,"supported":13,"title":196,"abstract":197,"tags":198},[],[],[25],[200,201],"农业生态效率 省级面板数据 数字经济 空间溢出","农业生态效率 省级面板数据","农业生态效率省级面板数据数字经济空间溢出-1961",{"doi":8,"openalex_id":8,"authors":204,"venue":8,"cited_by_count":57,"oa_url":8,"card":205,"direction":209,"ingested_from":67},[],{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"基于省级面板数据，实证分析数字经济对农业生态效率的影响及机制。","超效率SBM测度效率，空间杜宾、中介、门槛模型分析。","数字经济显著促进农业生态效率，有空间溢出，经土地规模经营等中介。","农业绿色发展与碳","可探索数字经济对农业生态效率的非线性门槛效应及区域异质性驱动因素。","2026-09-09T00:03:56.949812Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":8,"content":217,"source_name":218,"source_url":8,"published_at":219,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":165,"score_detail":220,"sources":222,"tags":224,"primary_topic":226,"secondary_topics":232,"classification":233,"search_phrases":249,"slug":252,"view_count":19,"doi":253,"paper":8,"created_at":254},114,"Low-Altitude Economy Development and High-Quality Agricultural Development in China: Spatial and Nonlinear Evidence","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1873173\u002Ffull","Xiangyang Wu, Xiaowen Cheng, Wang Xiongjun研究低空经济发展与中国农业高质量发展的关系,基于空间与非线性的实证证据,发现低空经济通过提升农业生产效率、改善农产品流通等机制助推农业高质量发展。","## Abstract\n\nUsing panel data for 30 provincial-level administrative regions in China from 2012 to 2022, this paper examines the relationship between low-altitude economy development (LAEDL) and high-quality agricultural development (HQAD), with particular attention to spatial linkages and nonlinear features. The empirical analysis combines a two-way fixed-effects model, a spatial Durbin model (SDM), and a panel threshold model. The results show that the LAEDL is significantly and positively associated with HQAD, and this finding remains robust across a series of robustness checks, instrumental-variable estimation, and a difference-in-differences design that exploits the 2016 national general-aviation policy. Further analysis indicates that HQAD exhibits significant positive spatial autocorrelation across provinces. The spatial spillover of LAEDL itself, however, is not statistically robust once its local and spatially lagged terms are considered, so we refrain from strong claims about cross-provincial spillovers. The nonlinear analysis suggests that the positive association between the low-altitude economy and HQAD is stronger in provinces with higher levels of economic development and urbanization, whereas the association weakens at high levels of LAEDL itself, a pattern consistent with diminishing marginal returns; the statistical evidence for threshold effects should be interpreted with caution. The moderation analysis further suggests that industrial upgrading and urbanization strengthen the contribution of the LAEDL to HQAD. By incorporating spatial dependence and conditional heterogeneity, this study provides new empirical evidence on the role of emerging aviation-based technologies in agricultural transformation.\n\n## 1 Introduction\n\nGlobal agriculture has been rapidly shifting toward greater precision, intelligence, and environmental sustainability, and technological progress has become a central driver of productivity growth and agricultural modernization (; ). In this context, low-altitude technologies—particularly drones, remote sensing, and low-altitude logistics—have increasingly emerged as an important component of agricultural innovation because of their applications in precision crop protection, field monitoring, and agricultural product distribution (; ; ). In a broader sense, the low-altitude economy extends well beyond the use of drones alone. It encompasses general aviation manufacturing, drone R&D and operations, low-altitude logistics networks, and the associated infrastructure and governance systems, all of which are becoming more deeply integrated into agricultural production systems.\n\nThe existing literature on technological progress and agricultural development has mainly focused on the Green Revolution, the diffusion of improved varieties, and the impact of information and communication technologies on agricultural production and market efficiency. , , for example, emphasize the importance of technology adoption and learning mechanisms in agricultural growth. and show that mobile communication technologies can improve agricultural market performance by reducing information asymmetry. , , and further show that broadband Internet, e-commerce, and digital transformation have significant effects on employment, the rural economy, and agricultural productivity. Compared with this literature on digital technologies, however, there is still little macro-level empirical evidence on whether emerging aviation-based technologies—particularly the low-altitude economy as a more comprehensive techno-industrial system—can significantly improve HQAD. Existing studies have concentrated more on application scenarios for agricultural drones, crop monitoring, and farmers’ adoption behavior (; ; ), while regional-level economic consequences remain underexplored.\n\nMore specifically, three limitations remain in the literature when identifying the agricultural effects of the low-altitude economy. First, most studies treat regions as independent units and pay limited attention to the spatial connectedness of agricultural development and technology diffusion, even though spatial econometric research has long shown that ignoring spatial dependence may lead to incomplete or biased inference (; ; ). In agricultural economics, prior work has shown that agricultural development, technology adoption, and green transformation may all involve substantial spatial spillovers (; ; ). Second, existing analyses typically assume homogeneous and linear technology effects, whereas the benefits of technology diffusion often depend on regional absorptive capacity and development stage, implying potentially strong conditional heterogeneity (; ; ). Third, from an identification perspective, the literature still lacks a systematic framework that combines endogeneity treatment, robustness checks, spatial analysis, and nonlinear testing for the low-altitude economy–agriculture nexus.\n\nAgainst this backdrop, we use panel data for 30 Chinese provincial-level administrative regions over 2012–2022 to investigate the relationship between LAEDL and HQAD, with a further focus on spatial spillovers and nonlinear features. More specifically, the study addresses three questions: (i) Is the low-altitude economy positively associated with HQAD? (ii) Does this association display cross-regional spatial linkages? (iii) And does the strength of the association vary with regional economic development and urbanization? To answer these questions, we construct a two-way fixed-effects model, a spatial Durbin model, and a panel threshold model, thereby providing a more systematic empirical assessment of how emerging aviation-based technologies shape agricultural transformation (; ; ).\n\nThis paper contributes to the literature in three respects. First, it extends the literature on technological progress and agricultural transformation by examining the agricultural role of the low-altitude economy from a provincial, macro-regional perspective. While prior research has focused primarily on the Green Revolution, mobile communication, the Internet, e-commerce platforms, and digital transformation (, ; ; ; ; ; ), work on the low-altitude economy has largely remained at the level of drone applications and micro-level adoption decisions (; ; ). This paper adds macro-regional evidence on how a broader aviation-based industrial form matters for agriculture.\n\nSecond, the paper introduces a spatial econometric framework into the analysis of the low-altitude economy and HQAD. Spatial economics has shown that regional economic activities often exhibit substantial dependence and spillovers, implying that non-spatial models may misstate the relationships of interest (; ; ). Related studies in agricultural economics likewise document spatial correlation in technology diffusion, agricultural ecological efficiency, and green development (; ). Building on this literature, the paper examines not only the local association between the low-altitude economy and HQAD but also its potential spatial spillovers; ultimately, we document robust spatial dependence in HQAD but no statistically robust spillover of LAEDL itself.\n\nThird, the paper examines the nonlinear and conditional nature of the relationship between the low-altitude economy and agricultural development. The absorptive-capacity literature stresses that the economic payoff to new technologies depends on the recipient’s existing knowledge base, institutional setting, and resource endowments (). At the macro level, technology gains may also vary across development stages (; ). Prior studies have widely used threshold models to identify nonlinear effects in agriculture and the digital economy (; ; ). This paper accordingly investigates the threshold and moderating roles of economic development, urbanization, and industrial structure in shaping the agricultural effect of the low-altitude economy.\n\n## 2 Literature review and theoretical analysis\n\n### 2.1 Digitalization, intelligent technologies, and agricultural development\n\nA growing body of research suggests that digital and intelligent technologies are becoming important drivers of agricultural modernization. argues that digital innovations can increase agricultural productivity while simultaneously reducing environmental footprints and improving resilience. Along the same line, examine the relationship between information and communication technologies and agricultural land and labor productivity from global and regional perspectives, and find a stable positive association between Information and Communication Technologies (ICT) diffusion and agricultural productivity. Using micro data from Chinese farmers, and further show that ICT use significantly improves the technical efficiency of smallholders. , using the policy shock generated by U.S. rural broadband grants, also find that improved digital infrastructure raises agricultural productivity. Taken together, these studies indicate that digital technologies do much more than change access to information; they may systematically affect agricultural development by reducing transaction costs, improving decision-making, and enhancing resource allocation.\n\nIn terms of technological form, agricultural digitalization is no longer confined to information access alone. Rather, it increasingly involves sensing, recognition, analysis, and execution throughout the production chain. argue that edge artificial intelligence can enhance efficiency, quality, and sustainability across agricultural production, processing, storage, and transportation. show that digital innovations are reshaping how sustainability in food systems is monitored, reported, and verified. Consistent with this line of work, show in a systematic review that precision agriculture has already accumulated relatively strong evidence of input reduction and environmental improvement. This implies that low-altitude technologies—such as drones, remote sensing, low-altitude monitoring, and data transmission—can be understood as a further deepening of digital and precision agriculture. Their effects are therefore unlikely to be confined to a single operational link and may instead influence productivity, green development, and industrial coordination simultaneously.\n\n### 2.2 Extending the literature: from digital agriculture to the low-altitude economy\n\nAlthough the literature has provided substantial evidence that digital technologies and precision agriculture improve agricultural performance, most existing studies focus on ICT use, digital infrastructure, extension design, or specific precision-agriculture tools. Much less is known about how a more complex technology–industry configuration affects HQAD at the regional level. emphasizes the broader opportunities and policy implications of digital innovation; and focus on the effects of ICT and broadband infrastructure on farm efficiency and agricultural productivity; , , and emphasize the sustainability value of digital sensing, intelligent recognition, and precision agriculture. Compared with these studies, the low-altitude economy is not a single technology. Rather, it is an integrated industrial form that combines low-altitude equipment, remote-sensing data, flight services, logistics organization, and infrastructure provision. Its influence is therefore likely to be more systemic and more regional in nature.\n\nIn the context of HQAD, the low-altitude economy may operate through at least three channels. First, in production, drone-based crop protection, low-altitude remote sensing, and intelligent monitoring can improve input precision, reduce labor costs, and enhance the timeliness of farm decision-making. Second, in circulation, the combination of low-altitude logistics and digital platforms may shorten delivery radii and response times, thus improving supply-chain coordination. Third, in governance and service provision, the LAEDL is often accompanied by parallel improvements in equipment supply, data processing, maintenance services, and institutional support, thereby offering broader support for agricultural quality upgrading. Accordingly, the impact of the low-altitude economy on HQAD should not be understood solely through the lens of single-point technology adoption, but rather within a broader framework in which technology, industry, and institutions are jointly coupled. This perspective is consistent with the systemic view of digital agriculture emphasized by , , and .\n\n### 2.3 Spatial linkages, technology diffusion, and regional externalities\n\nThe economic consequences of agricultural technologies are not confined to individual production units or single regions. They are often accompanied by diffusion effects and spatial externalities. , for example, use social-network changes generated by church mergers in the U.S. Midwest and show that expanded social ties significantly increase fertilizer adoption, highlighting the networked nature of technology diffusion. similarly show in a randomized trial from Myanmar that digitally supported extension improves farmers’ knowledge acquisition and input-use behavior, and that the effects vary across extension designs. These studies imply that the impacts of agricultural technologies depend not only on the technologies themselves, but also on information networks, organizational carriers, and regional linkages. Because flight services, remote sensing, and low-altitude logistics all exhibit strong cross-regional mobility, the agricultural effects of the low-altitude economy are likely to extend beyond any single province.\n\nImportantly, spatial connectedness does not necessarily imply one-way positive spillovers. show that participation in agri-environmental schemes affects not only enrolled farmland but also non-enrolled plots, generating spillovers. identify strong agglomeration externalities in seafood exports and show that regional clustering affects firms through different margins. These studies suggest that interregional linkages may produce knowledge diffusion, organizational coordination, and scale economies, but they may also generate resource competition, factor siphoning, and market crowding-out. This possibility is especially relevant for the low-altitude economy. Leading regions may stimulate surrounding agricultural development through technological diffusion and service radiance, yet they may also absorb scarce talent, capital, and infrastructure resources, thereby intensifying competitive pressure on neighboring regions. For this reason, both spatial spillovers and spatial competition need to be incorporated into the analytical framework.\n\nThese mechanisms can be organized around two established bodies of theory. Technology-diffusion theory holds that the productivity gains from a new technology spread gradually through space and social networks rather than appearing everywhere at once (; ), so the agricultural payoff of the low-altitude economy should depend on how quickly low-altitude equipment, operating standards, and shared service providers diffuse from leading provinces to their neighbors. Knowledge-spillover theory, in turn, shows that R&D and innovative activity generate geographically localized externalities (; ), which—together with the spatial-econometric tradition (; )—implies that provincial agricultural quality is unlikely to be spatially independent. Taken together, these theories motivate a causal pathway in which regional low-altitude industrial capacity raises local agricultural quality directly and, through diffusion and spillovers, is associated with the agricultural performance of neighboring regions.\n\n### 2.4 Conditional heterogeneity, absorptive capacity, and nonlinear effects\n\nThe literature also shows that the benefits of agricultural technologies are strongly conditional. Using randomized experimental data, find substantial heterogeneity in the economic impact of integrated pest management for mango fruit flies. likewise show that ICT improves technical efficiency more strongly among farmers who were initially less efficient. further demonstrate that different digitally supported extension designs generate different outcomes in terms of knowledge improvement and input use. These findings imply that technologies do not affect all regions or actors with equal intensity. Rather, their effects are conditioned by existing resource endowments, infrastructure, organizational capacity, and the institutional environment.\n\nExtending this insight to the low-altitude economy suggests that its contribution to HQAD is also likely to be nonlinear. Regions with higher levels of economic development often possess better digital infrastructure, stronger equipment-support capacity, and more mature agricultural service systems, making it easier to convert low-altitude technologies into realized production gains. Regions with higher urbanization and more advanced industrial structures also tend to enjoy stronger factor mobility, better organizational coordination, and more complete complementary services, all of which facilitate the deeper integration of the low-altitude economy into agricultural production systems. , , and all highlight the importance of infrastructure, technological environments, and institutional support as prerequisites for the productive use of digital technologies. Hence, the effect of the low-altitude economy on HQAD is unlikely to be linear and homogeneous; rather, it is likely to depend strongly on regional absorptive capacity and development stage.\n\nThis conditional pattern is best understood through absorptive-capacity theory, which argues that the ability to convert external technology into realized gains depends on the recipient’s prior knowledge base, infrastructure, and complementary capabilities (). Applied to our setting, low-altitude industrial capacity should translate into agricultural quality only where provinces have crossed a threshold of digital infrastructure, human capital, and agricultural-service organization; below that threshold the same industrial capacity yields little measurable agricultural return. This logic provides the theoretical basis for the threshold and moderation specifications below, in which economic development, urbanization, and industrial upgrading govern the strength of the LAEDL–HQAD relationship.\n\n### 2.5 Theoretical framework and research hypotheses\n\n#### 2.5.1 Conceptual clarification\n\nBefore specifying the hypotheses, we clarify the core constructs to avoid conceptual ambiguity. We distinguish the low-altitude economy as an industrial sector from low-altitude agricultural technology as an application scenario. Following the emerging literature on the low-altitude economy (), we define the low-altitude economy as an integrated techno-industrial system organized around the use of civil low-altitude airspace, encompassing general-aviation and UAV manufacturing, drone R&D and operation, low-altitude logistics and flight services, and the supporting infrastructure and governance arrangements. Our explanatory variable, the LAEDL, accordingly measures the regional capacity of this industrial system, rather than the intensity of any single on-farm application such as drone-based crop protection. Low-altitude agricultural technology, by contrast, refers to the deployment of low-altitude tools within agricultural production and circulation, and is only one of the channels through which the low-altitude economy may influence agriculture.\n\nFollowing the now-standard operationalization in the Chinese agricultural-economics literature (; ), we define HQAD as agricultural development that is simultaneously innovation-driven, internally coordinated, environmentally green, externally open, and socially shared. This conceptualization, grounded in the new development philosophy, motivates the five-dimension measurement system reported in [Appendix A](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-foo","Frontiers in Sustainable Food Systems 2026-07-01","2026-07-01T01:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":137,"relevant":20,"comment":221},"论文探讨低空经济与农业高质量发展的关系，使用省级面板数据和多种计量方法，结论有政策参考价值，但发表时间较早，时效性不足。",[223],{"name":218,"url":215},[225,26,27],"低空经济",{"code":227,"slug":228,"name":229,"path":230,"parent_code":231},"E5","branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce","E",[],{"status":182,"locked":13,"version":32,"method":33,"evidence":234},{"candidates":235,"agricultural_evidence":51},[236,241,245],{"code":227,"score":149,"supported":51,"title":237,"abstract":238,"tags":240},[],[239],"农产品流通",[],{"code":37,"score":20,"supported":13,"title":242,"abstract":243,"tags":244},[],[],[27],{"code":47,"score":20,"supported":13,"title":246,"abstract":247,"tags":248},[],[],[26],[250,251],"农业高质量发展 低空经济 空间溢出","农业高质量发展 低空经济","农业高质量发展低空经济空间溢出-114","10.3389\u002Ffsufs.2026.1873173\u002Ffull","2026-07-31T00:00:56.078125Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":8,"source_name":261,"source_url":258,"published_at":262,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":123,"score_detail":263,"sources":265,"tags":267,"primary_topic":272,"secondary_topics":278,"classification":279,"search_phrases":295,"slug":298,"view_count":57,"doi":299,"paper":300,"created_at":313},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications","2026-10-21T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":83,"relevant":20,"comment":264},"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[266],{"name":261,"url":258},[268,269,27,270,271],"数字农业","新质生产力","区域协调","农业数字化转型",{"code":273,"slug":274,"name":275,"path":276,"parent_code":277},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":182,"locked":13,"version":32,"method":33,"evidence":280},{"candidates":281,"agricultural_evidence":51},[282,287,291],{"code":273,"score":12,"supported":51,"title":283,"abstract":285,"tags":286},[284],"digital agriculture",[],[271,268],{"code":37,"score":20,"supported":13,"title":288,"abstract":289,"tags":290},[],[],[27],{"code":47,"score":20,"supported":13,"title":292,"abstract":293,"tags":294},[],[],[269],[296,297],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":299,"openalex_id":301,"authors":302,"venue":261,"cited_by_count":57,"oa_url":258,"card":307,"direction":65,"ingested_from":312},"W7221723385",[303,305],{"name":304,"orcid":8},"Qingqing Huang",{"name":306,"orcid":8},"Wenjing Guo",{"tldr":308,"method":309,"finding":310,"direction":65,"opportunity":311},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","openalex","2026-10-22T11:20:47.354744Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":8,"content":8,"source_name":319,"source_url":8,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":8,"secondary_topics":330,"classification":331,"search_phrases":342,"slug":345,"view_count":57,"doi":346,"paper":347,"created_at":354},5461,"Research on the multidimensional impact effects of the rural digital economy on the resilience of agricultural development: evidence from 30 provinces in China（农村数字经济对农业发展韧性的多维影响效应：来自中国30个省份的证据）","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1057\u002Fs41599-026-09360-9","基于2011—2022年30个省份数据，熵值法测度农村数字经济与农业韧性，用空间杜宾模型检验直接效应、空间溢出与技术进步中介。直接效应在西部与非粮食主产区更显著，溢出在东部与主产区更显著，技术进步仅有本地中介。","Humanities and Social Sciences Communications 2026-10-17","2026-10-17T00:00:00Z",82,{"impact":17,"substance":17,"depth":16,"authority":18,"freshness":70,"relevant":20,"comment":323},"基于30省11年面板数据的实证研究，方法规范、结论有区域异质性价值，属数字乡村与农业经济交叉领域的高质量论文，值得进入每日精选。",[325],{"name":319,"url":317},[129,327,27,328,329],"农业韧性","省级面板","农村数字经济",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":332},{"candidates":333,"agricultural_evidence":51,"reason":52},[334,338],{"code":37,"score":20,"supported":13,"title":335,"abstract":336,"tags":337},[],[],[27],{"code":42,"score":20,"supported":13,"title":339,"abstract":340,"tags":341},[],[],[129],[343,344],"农村数字经济 农业韧性","空间杜宾模型 省级面板","农村数字经济农业韧性-5461","10.1057\u002Fs41599-026-09360-9",{"doi":346,"openalex_id":8,"authors":348,"venue":8,"cited_by_count":57,"oa_url":8,"card":349,"direction":65,"ingested_from":67},[],{"tldr":350,"method":351,"finding":352,"direction":65,"opportunity":353},"基于2011—2022年30省数据，检验农村数字经济对农业发展韧性的多维影响效应。","熵值法测度农村数字经济与农业韧性，空间杜宾模型检验直接、溢出与中介效应。","直接效应在西部与非粮食主产区更显著，溢出效应在东部与主产区更显著，技术进步仅具本地中介。","可深入探究技术进步跨区溢出缺失的机制，并识别主产区与非主产区差异化数字乡村政策路径。","2026-10-21T00:10:08.312922Z"]