[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1224":3,"related-1224":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":45,"slug":48,"view_count":21,"doi":49,"paper":50,"created_at":65},1224,"Can the low-altitude economy promote high-quality agricultural development? From the perspective of factor mobility","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1880856","The low-altitude economy, characterized by high-tech support, efficient operations, and high-quality development, has become a new engine for cultivating new quality productive forces in agriculture and accelerating the modernization of agriculture and rural areas. Based on the Chinese government’s new development philosophy, this study constructs a comprehensive index of high-quality agricultural development. Using panel data for 31 provinces in China from 2004 to 2024, this study employs a difference-in-differences model and a mediation-effect model to empirically examine the impact of the low-altitude economy on high-quality agricultural development. The results show that the low-altitude economy significantly promotes high-quality agricultural development. Further analysis indicates that the low-altitude economy indirectly drives high-quality agricultural development by promoting technology diffusion and labor mobility. Among the mediating channels, technology diffusion has the strongest mediating effect, while the coefficient of the mediating effect of capital flow is positive but statistically insignificant. The promotion effect of the low-altitude economy on high-quality agricultural development is more pronounced in major grain-consuming areas, eastern and central regions, and regions with higher levels of digital infrastructure. Accordingly, this study proposes policy recommendations in terms of rationally arranging agricultural low-altitude application scenarios, facilitating factor mobility, and implementing differentiated regional development paths for the low-altitude economy. These findings provide empirical references for relevant departments to scientifically formulate low-altitude economic development plans and promote high-quality agricultural development.",null,"Frontiers in Sustainable Food Systems","2026-08-30T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},25,22,18,12,2,1,"基于省级面板数据实证低空经济对农业高质量发展的促进作用，机制清晰，政策建议具体，具有较高参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"低空经济","农业高质量发展","要素流动","技术扩散","劳动力流动",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":43,"reason":44},[38],{"code":39,"score":21,"supported":13,"title":40,"abstract":41,"tags":42},"J2",[],[],[27],true,"缺少足够的主题证据",[46,47],"农业高质量发展 劳动力流动 低空经济 技术扩散","农业高质量发展 劳动力流动","农业高质量发展劳动力流动低空经济技术扩散-1224","10.3389\u002Ffsufs.2026.1880856",{"doi":49,"openalex_id":51,"authors":52,"venue":9,"cited_by_count":57,"oa_url":6,"card":58,"direction":62,"ingested_from":64},"W7204686905",[53,55],{"name":54,"orcid":8},"Fang Jiang",{"name":56,"orcid":8},"Qiong Zhang",0,{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"研究低空经济对农业高质量发展的影响，发现其通过技术扩散和劳动力流动促进农业发展。","使用2004-2024年中国31省面板数据，采用双重差分和中介效应模型。","低空经济显著促进农业高质量发展，技术扩散中介效应最强，资本流动不显著。","农业人工智能与决策模型","可探索低空经济在农业中的具体应用场景，如无人机植保、物流配送，及其对农业全要素生产率的影响。","openalex","2026-09-01T04:03:08.487112Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,118,197,246,292,335],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":74,"source_name":75,"source_url":8,"published_at":76,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":77,"score_detail":78,"sources":81,"tags":83,"primary_topic":85,"secondary_topics":91,"classification":92,"search_phrases":111,"slug":114,"view_count":115,"doi":116,"paper":8,"created_at":117},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",74,{"impact":18,"substance":17,"depth":18,"authority":79,"freshness":20,"relevant":21,"comment":80},14,"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。",[82],{"name":75,"url":72},[26,27,84],"空间溢出",{"code":86,"slug":87,"name":88,"path":89,"parent_code":90},"E5","branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce","E",[],{"status":93,"locked":13,"version":34,"method":35,"evidence":94},"classified",{"candidates":95,"agricultural_evidence":43},[96,102,107],{"code":86,"score":97,"supported":43,"title":98,"abstract":99,"tags":101},3,[],[100],"农产品流通",[],{"code":103,"score":21,"supported":13,"title":104,"abstract":105,"tags":106},"I4",[],[],[84],{"code":39,"score":21,"supported":13,"title":108,"abstract":109,"tags":110},[],[],[27],[112,113],"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",8,"10.3389\u002Ffsufs.2026.1873173\u002Ffull","2026-07-31T00:00:56.078125Z",{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":8,"source_name":124,"source_url":121,"published_at":125,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":126,"score_detail":127,"sources":131,"tags":133,"primary_topic":138,"secondary_topics":144,"classification":145,"search_phrases":162,"slug":165,"view_count":57,"doi":166,"paper":167,"created_at":196},5557,"Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1890862","Introduction Agriculture remains central to Pakistan's economy, with Punjab accounting for the largest share of national crop production. Despite the rapid global advancement of artificial intelligence (AI)-enabled agricultural technologies, their diffusion across farms in developing economies remains uneven and underexplored. This study examines the extent of AI diffusion among farmers in Punjab, Pakistan, and assesses its associations with perceived farm productivity and profitability. Methods Drawing on Diffusion of Innovations (DoI) theory, complemented by the Resource-Based View (RBV) and the Technology-Organisation-Environment (TOE) framework, the study adopts a quantitative, cross-sectional design using survey data from 101 farmers. Results The findings indicate that while awareness and perceived benefits of AI are relatively high, actual adoption remains moderate (AI Diffusion: M = 18.27, SD = 3.71), indicating a gap between perceived value and implementation. AI diffusion was positively correlated with perceived farm productivity ( r = 0.617, p \u003C 0.001) and perceived farm profitability ( r = 0.607, p \u003C 0.001). The findings therefore demonstrate statistically significant associations between greater AI diffusion and stronger perceived farm performance, while not establishing causal effects. Discussion The observed perception-adoption gap is consistent with structural barriers identified in previous research, including high investment costs, limited digital infrastructure and technical capability constraints. The study contributes context-specific evidence on digital agriculture in a developing economy by jointly examining AI diffusion, productivity and profitability. The findings highlight the importance of infrastructure, institutional support and capability development in facilitating wider and more effective adoption of AI-enabled agricultural technologies.","引言 农业仍然是巴基斯坦经济的核心，其中旁遮普省占全国农作物产量的最大份额。尽管人工智能（AI）赋能农业技术在全球范围内快速发展，但其在发展经济体农场中的扩散仍不均衡且研究不足。本研究考察了巴基斯坦旁遮普省农民中AI扩散的程度，并评估其与感知农场生产力和盈利能力的关联。方法 本研究以创新扩散理论（DoI）为基础，辅以资源基础观（RBV）和技术—组织—环境（TOE）框架，采用定量横断面设计，使用来自101名农民的调查数据。结果 研究结果表明，尽管农民对AI的认知和感知收益相对较高，但实际采用程度仍处于中等水平（AI扩散：M = 18.27，SD = 3.71），表明感知价值与实施之间存在差距。AI扩散与感知农场生产力（r = 0.617，p \u003C 0.001）和感知农场盈利能力（r = 0.607，p \u003C 0.001）呈正相关。因此，研究结果证明了更高程度的AI扩散与更强的感知农场绩效之间存在统计学显著关联，但并未确立因果关系。讨论 所观察到的感知—采用差距与既有研究中识别的结构性障碍相一致，包括高投资成本、有限的数字基础设施和技术能力约束。本研究通过联合考察AI扩散、生产力和盈利能力，为发展中经济体的数字农业提供了情境化证据。研究结果凸显了基础设施、制度支持和能力建设在促进AI赋能农业技术更广泛、更有效采用方面的重要性。","Frontiers in Artificial Intelligence","2026-10-21T00:00:00Z",68,{"impact":19,"substance":18,"depth":128,"authority":129,"freshness":12,"relevant":21,"comment":130},15,13,"基于101户调查的实证研究，揭示AI认知与采纳落差及其与感知绩效的正相关，对发展中国家数字农业推广有参考价值，但样本小、非因果、地域性强，属细分领域进展。",[132],{"name":124,"url":121},[134,135,29,136,137],"智慧农业","农业人工智能","巴基斯坦","小农采纳",{"code":139,"slug":140,"name":141,"path":142,"parent_code":143},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":93,"locked":13,"version":34,"method":35,"evidence":146},{"candidates":147,"agricultural_evidence":43},[148,156],{"code":149,"score":150,"supported":43,"title":151,"abstract":152,"tags":155},"I1",7,[],[153,154],"数字农业","digital agriculture",[134],{"code":139,"score":157,"supported":43,"title":158,"abstract":159,"tags":161},4,[],[160],"artificial intelligence",[135],[163,164],"农业人工智能 小农采纳 巴基斯坦 技术扩散","农业人工智能 小农采纳","农业人工智能小农采纳巴基斯坦技术扩散-5557","10.3389\u002Ffrai.2026.1890862",{"doi":166,"openalex_id":168,"authors":169,"venue":124,"cited_by_count":57,"oa_url":188,"card":189,"direction":195,"ingested_from":64},"W7223153473",[170,172,174,177,180,182,184,186],{"name":171,"orcid":8},"Aeman Khalid",{"name":173,"orcid":8},"Colin Akhurst",{"name":175,"orcid":176},"Sayed Abdul Majid Gilani","https:\u002F\u002Forcid.org\u002F0000-0002-4340-1643",{"name":178,"orcid":179},"Firas Armosh","https:\u002F\u002Forcid.org\u002F0009-0006-6458-3331",{"name":181,"orcid":8},"Syeda Aqsa Zanib",{"name":183,"orcid":8},"Jack Boath",{"name":185,"orcid":8},"Richard Stanley Peel",{"name":187,"orcid":8},"Yohani Jinadasa","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1890862\u002Fpdf",{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"基于巴基斯坦旁遮普省101份农户调查，分析AI技术扩散与农场生产力、盈利能力的关系。","基于DoI、RBV、TOE框架的问卷调查与相关分析，样本101户。","AI认知高但采用率中等，AI扩散与感知生产力(r=0.617)和盈利能力(r=0.607)显著正相关","数字乡村与农业信息化","可延伸研究AI认知—采用鸿沟的成因机制，并开展纵向或准实验设计验证因果效应。","智慧农业 \u002F 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