[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5458":3,"related-5458":66},{"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":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":65},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。",null,"中国农机化学报 2026-10-20","2026-10-20T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,18,14,9,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字经济","农业高质量发展","区域异质性","农业产业结构","新疆农业",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":48,"reason":49},[38,43],{"code":39,"score":21,"supported":13,"title":40,"abstract":41,"tags":42},"I6",[],[],[26],{"code":44,"score":21,"supported":13,"title":45,"abstract":46,"tags":47},"J2",[],[],[27],true,"缺少足够的主题证据",[51,52],"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",0,"10.13733\u002Fj.jcam.issn.2095-5553.2026.10.041",{"doi":55,"openalex_id":8,"authors":57,"venue":8,"cited_by_count":54,"oa_url":8,"card":58,"direction":62,"ingested_from":64},[],{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"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","数字乡村与农业信息化","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。","agent","2026-10-21T00:10:08.003418Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,116,158,201,297,362],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":8,"source_name":74,"source_url":8,"published_at":75,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":76,"score_detail":77,"sources":81,"tags":83,"primary_topic":8,"secondary_topics":86,"classification":87,"search_phrases":103,"slug":106,"view_count":54,"doi":107,"paper":108,"created_at":115},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。","中国林业经济(2026,4)","2026-10-15T00:00:00Z",80,{"impact":18,"substance":78,"depth":18,"authority":19,"freshness":79,"relevant":21,"comment":80},22,8,"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。",[82],{"name":74,"url":72},[26,27,84,85],"空间溢出","大数据试验区",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":88},{"candidates":89,"agricultural_evidence":48,"reason":49},[90,95,99],{"code":91,"score":21,"supported":13,"title":92,"abstract":93,"tags":94},"I4",[],[],[84],{"code":39,"score":21,"supported":13,"title":96,"abstract":97,"tags":98},[],[],[26],{"code":44,"score":21,"supported":13,"title":100,"abstract":101,"tags":102},[],[],[27],[104,105],"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","10.13691\u002Fj.cnki.cn23-1539\u002Ff.2026.04.001",{"doi":107,"openalex_id":8,"authors":109,"venue":8,"cited_by_count":54,"oa_url":8,"card":110,"direction":62,"ingested_from":64},[],{"tldr":111,"method":112,"finding":113,"direction":62,"opportunity":114},"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。","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。","2026-10-19T00:07:48.514777Z",{"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":18,"substance":78,"depth":18,"authority":19,"freshness":20,"relevant":21,"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},[129,130,26,27,131],"数字乡村","农业新质生产力","省级面板数据",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":134},{"candidates":135,"agricultural_evidence":48,"reason":49},[136,141],{"code":39,"score":137,"supported":13,"title":138,"abstract":139,"tags":140},2,[],[],[129,26],{"code":44,"score":137,"supported":13,"title":142,"abstract":143,"tags":144},[],[],[130,27],[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":54,"oa_url":8,"card":152,"direction":62,"ingested_from":64},[],{"tldr":153,"method":154,"finding":155,"direction":62,"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":163,"source_name":164,"source_url":8,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":8,"secondary_topics":178,"classification":179,"search_phrases":196,"slug":199,"view_count":79,"doi":8,"paper":8,"created_at":200},542,"Can the Digital Economy Enhance the Export Competitiveness of Agricultural Products? — An Empirical Analysis Based on Panel Data from 30 Chinese Provinces","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280","基于 2014—2023 年中国 30 个省份面板数据,采用双向固定效应模型考察数字经济与农产品出口竞争力的关系,并用中介效应模型与门槛效应模型揭示机制与非线性。结果显示数字经济显著正向提升农产品出口竞争力;农业产业结构升级、农业技术创新、农业劳动力转移构成三条中介路径;效应在中部地区与粮食主产区更强,且呈非线性关系。文章为强化数字基础设施、培育农业数字人才与优化要素配置以释放数字经济潜力提供了政策依据。","## 1. Introduction\n\nAs the new generation of information technology revolution—represented by big data, artificial intelligence, and the Internet of Things—sweeps across the globe, the digital economy is reshaping the landscape and trajectory of the world economy, becoming a core engine driving global economic recovery and growth [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B1-sustainability-18-08280)]. In recent years, China has attached great importance to the enabling role of the digital economy in agriculture, successively issuing guiding documents such as the Strategic Outline for the Development of Digital Villages and the Digital Rural Agricultural Development Plan (2022–2025). Against this backdrop, China’s agricultural economy has achieved leapfrog development. However, according to data published in the China Agricultural Products Import and Export Annual Statistical Report, China’s agricultural product imports reached as high as USD 215.16 billion in 2024, while exports amounted to only USD 103.01 billion, resulting in a trade deficit of USD 112.15 billion—nearly double that of 2018. This indicates that China’s agricultural products have a high degree of external dependence, and the prospects for export trade and international competitiveness remain far from optimistic. Moreover, as modern agricultural production transitions from a phase prioritizing output to one prioritizing quality, people have placed higher demands on the quality of agricultural products. The contradiction between the traditional low-value-added agricultural production structure and the international consumer market has become increasingly prominent [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B2-sustainability-18-08280)], severely hindering the realization of China’s strategic goal of transforming from a “large trading nation” to a “strong trading nation.” Therefore, how to harness the disruptive force of the digital economy to enable the high-quality development of agricultural product exports—and to solve the long-standing problems of low value addition, information asymmetry, and tightening environmental constraints, thereby building sustained and stable international competitive advantages—has become a major issue of both theoretical value and practical significance.\n\n## 2. Literature Review\n\n### 2.1. Research on the Digital Economy\n\nSince Tapscott (1996) [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B3-sustainability-18-08280)] introduced the concept of the “digital economy,” its core has consistently revolved around the restructuring of information generation, transmission, and processing methods through digital forms, aiming to enhance efficiency and reduce transaction costs (Ren and He, 2022) [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B4-sustainability-18-08280)]. The digital economy is essentially a structural transformation resulting from the deep embedding of digital technologies into various sectors of the national economy, manifesting as a new economic form characterized by digital infrastructure as the foundation, digital platforms as the carrier, and data elements as the core driving force (Xu and Zhang, 2020) [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B5-sustainability-18-08280)]. The G20 Digital Economy Development and Cooperation Initiative identifies digitized knowledge and information as key factors of production, a definition that has been widely adopted.\n\nDue to differences in statistical calibers and the virtuality and high permeability of the digital economy (Wen et al., 2019) [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B6-sustainability-18-08280)], there is no unified methodology for its measurement, and it faces technical constraints. Existing research primarily follows two paradigms: calculating value added based on national economic accounting, and constructing multi-dimensional indicator systems for comprehensive evaluation. Domestic studies predominantly adopt the comprehensive index method, such as Guan et al. (2020) constructing a provincial-level evaluation system [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B7-sustainability-18-08280)]; the Digital Financial Inclusion Index (Guo et al., 2020) is also widely used [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B8-sustainability-18-08280)]. Indicator weighting generally employs the entropy method or principal component analysis (Zhang and Ye, 2017) [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B9-sustainability-18-08280)]. This paper, referencing existing studies and considering the specific implementation pathways of agricultural digitization, constructs an indicator system from three dimensions: digital infrastructure, digital industrialization, and industrial digitization.\n\n### 2.2. Research on the Export Competitiveness of Agricultural Products\n\nThe measurement methods for the export competitiveness of agricultural products mainly include the Diamond Model, the Constant Market Share Model, and comprehensive evaluation indicator systems. International market share, Revealed Comparative Advantage index, and Trade Competitiveness index are widely adopted (Pan et al., 2021) [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B10-sustainability-18-08280)]. In terms of measurement results, Yu et al. (2016) found that China’s exports to South Asia are at a disadvantage [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B11-sustainability-18-08280)]; Yang and Tian (2022), through a comparison among China, Japan, and South Korea, found that China’s exports are primarily low- and medium-technology products [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B12-sustainability-18-08280)]; Shuai et al. (2003) pointed out that while exports are growing, competitiveness is weakening [[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B13-sustainability-18-08280)]; in terms of product categories, vegetables and fruits outperform grains (Mao and Feng, 2007) [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B14-sustainability-18-08280)]. Regarding influencing factors, the marginal contribution of traditional factors is diminishing, while the enabling effects of modern factors such as technology, information, and data are becoming prominent (Wu et al., 2023) [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B15-sustainability-18-08280)]. Trade policy adjustments exert significant impacts (Narayan and Bhattacharya, 2019) [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B16-sustainability-18-08280)], and technological progress is a key pathway (Li and Ding, 2016) [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B17-sustainability-18-08280)]. Although it may increase production costs in the short term, it has a significant positive promoting effect in the long run (Zhang and Hu, 2013) [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B18-sustainability-18-08280)]. Upon reviewing the relevant literature, it is evident that existing studies primarily analyze the impact mechanisms of agricultural product exports through their constituent elements, lacking research that incorporates the digital economy, influencing factors, and conditional factors into a unified analytical framework.\n\n### 2.3. Research on the Impact of the Digital Economy on the Export Competitiveness of Agricultural Products\n\nExisting studies have focused on the promoting role of digital tools in agricultural trade. Fan (2020) found that Internet popularization can shorten customs clearance time and reduce costs [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B19-sustainability-18-08280)]; Tang and Li (2023) found that digital trade platforms can generate positive incentives [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B20-sustainability-18-08280)]; Carlos Arnade (2017) found that digital platforms can reduce fixed entry costs for exports [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B21-sustainability-18-08280)]; Qi et al. (2023) found that networked sales platforms can enhance production and sales efficiency [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B22-sustainability-18-08280)]. However, most of these studies analyze the digital economy as a whole variable, with fewer conducting differentiated internal structural tests. In terms of transmission pathways, Zhu et al. (2019) confirmed that informatization levels have a positive impact on agricultural total factor productivity [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B23-sustainability-18-08280)]; Ma et al. (2019) confirmed that digital platforms can reduce agricultural product transaction costs [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B24-sustainability-18-08280)]; Bai and Huang (2021) argued that digital platforms can generate quality premiums for agricultural products [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B25-sustainability-18-08280)].\n\nAlthough the existing literature on the digital economy enabling agricultural product exports provides valuable insights, certain limitations remain: a lack of research incorporating the digital economy, mediation mechanisms, and threshold effects into a unified framework; and insufficient systematic testing of regional differences and conditional factors. Future research needs to integrate threshold effect models with regional heterogeneity analysis to deeply reveal the differentiated characteristics and threshold conditions of the digital economy in enabling the export competitiveness of agricultural products.\n\nA review of the existing literature reveals that previous studies have left room for further exploration in the following aspects: (1) Most studies have focused on the linear association between the digital economy and agricultural trade, with few embedding it within a unified analytical framework of “influencing factors—transmission pathways—boundary conditions” to examine its indirect mechanisms and nonlinear characteristics. (2) Discussions on regional heterogeneity have largely been confined to mean comparisons across the eastern, central, and western macro-regions, lacking a nuanced examination of structural dimensions in agricultural production, such as grain functional zones. (3) Constrained by the narrowness of research perspectives, few studies have integrated mediation mechanisms and threshold effects into a coherent empirical framework, making it difficult to comprehensively uncover the “black box” of how the digital economy is associated with agricultural export competitiveness.\n\nAccordingly, the marginal contributions of this paper are threefold. First, we construct a multiple-mediation framework of “digital economy → (industrial structure upgrading\u002Ftechnological innovation\u002Flabor transfer) → agricultural export competitiveness,” thereby opening the “black box” of the transmission pathways through which the digital economy is associated with export competitiveness. Second, we introduce trade openness as a threshold variable to reveal the “nonlinear leap” in this association as external market conditions evolve. Third, we conduct heterogeneity analyses from both geographic regional and grain functional zone perspectives, providing more granular empirical evidence for differentiated policy design. These three contributions together constitute the incremental value of this paper relative to the existing literature, helping to deepen the systematic understanding of the relationship between the digital economy and agricultural trade, while offering an extensible analytical framework for future research.\n\n## 3. Theoretical Analysis and Research Hypotheses\n\n### 3.1. The Direct Impact of the Digital Economy on the Export Competitiveness of Agricultural Products\n\nAgainst the backdrop of the rapid penetration of agricultural digitization, the digital economy has become an endogenous driving force for rural economic development. Its direct impact on the export competitiveness of agricultural products is primarily reflected in the following three aspects: (1) In agricultural production, the digital economy can promote the intelligent, intensive, and precise development of traditional agriculture [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B26-sustainability-18-08280)]. Internet of Things technologies, characterized by information perception, can accurately and promptly collect agricultural production data, enabling farmers to extract effective information from production data and achieve precise regulation of agricultural production. Meanwhile, the digital economy enhances the level of agricultural mechanization and automation, greatly improving production efficiency, reducing labor intensity and production costs, and ensuring the stability and consistency of agricultural product quality. (2) In agricultural capital circulation, the digital economy effectively alleviates the difficulties of costly and inaccessible financing faced by agricultural production through inclusive finance, mobile payments, and online credit. This enables agricultural enterprises to invest in advanced technologies, equipment, and high-quality inputs, breaking through capital constraints and avoiding agricultural cyclical problems, thereby providing the necessary financial support for enhancing the export competitiveness of agricultural products [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B27-sustainability-18-08280)]. (3) In agricultural product export circulation, the digital economy breaks through traditional temporal and spatial constraints, enabling exporting enterprises to directly reach global consumers and realize a series of trade processes such as online precision marketing, trade negotiations, contract signing, export customs declaration, and inspection and quarantine. Simultaneously, the development of the digital economy enhances market transparency, facilitating farmers and agricultural exporting enterprises to adjust production strategies in a timely manner, changing their status as “price takers” and enhancing their bargaining power in factor and product markets, thereby improving the export competitiveness of agricultural products.\n\nFurthermore, the enhancing effect of the digital economy on the export competitiveness of agricultural products may exhibit heterogeneity. First, provinces (municipalities) in different geographical locations differ in terms of policy support and resource allocation. The eastern and central regions tend to receive more policy support and resource allocation, helping them establish more comprehensive infrastructure and service systems in the digital economy field, thereby enhancing the quality and quantity of agricultural products. In contrast, the western region may be at a disadvantage in resource acquisition, facing issues such as digital technology monopolies, imperfect infrastructure, and information trade barriers. However, it may also exhibit unique advantages in agricultural production, operation, and circulation due to shorter decision-making chains and more flexible policy implementation [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B28-sustainability-18-08280)]. Second, different provinces (municipalities) differ in terms of economic development levels and agricultural production modes. Major grain-producing areas typically possess abundant cultivated land resources, comprehensive infrastructure, and mature agricultural production systems, and also frequently benefit from key national policy support, further promoting the enhancing effect of the digital economy on the export competitiveness of agricultural products. Non-major grain-producing areas, constrained by geographical location and other factors, are at a disadvantage in terms of production scale, industrial structure, and product quality [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8280#B29-sustainability-18-08280)]. Based on this, this paper proposes the following hypothesis:\n\n**H1.**\n\nThe digital economy can enhance the export competitiveness of agricultural products, and this enhancing effect exhibits regional heterogeneity.\n\n### 3.2. Transmission Mechanisms of the Digital Economy’s Impact on the Export Competitiveness of Agricultural Products\n\nThe digital economy can indirectly enhance the export competitiveness of agricultural products through three pathways: upgrading of the agricultural industrial structure, agricultural technological innovation, and agricultural labor transfer.\n\nFirst, there is a positive synergistic relationship with the upgrading of agricultural industrial structure. The digital economy enhances the efficiency of information allocation, strengthens the market’s regulatory capacity over agricultural factors, alleviates information gaps between producers and consumers, reduces factor misallocation, and promotes the coordinated development of various sub-sectors within agriculture, laying the foundation for the rationalization of the industrial structure. Meanwhile, the deep application of technologies such as the Internet of Things and big data drives the entire agricultural industry chain toward digital and intelligent evolution, breaks down traditional industrial barriers, fosters the development of new business forms, and promotes the upgrading of agriculture toward high value addition. The rationalization and upgrading of the industrial structure jointly provide solid support for the export competitiveness of agricultural products.\n\nSecond, there is a statistically positive association with the enhancement of agricultural technological innovation. The digital economy reduces the costs of information acquisition and transactions for technological innovation, enabling innovation entities to more conveniently access cutting-edge technologies and market dynamics. Digital village construction enhances the digital literacy of agricultural practitioners and promotes the implementation and transformation of technological innovation outcomes. Technological innovation, on the one hand, enhances the level of agricultural mechanization, automation, and intelligence, strengthens the synergy among various industries, and promotes the evolution of agricultural product exports toward higher quality; on the other hand, it extends the industrial chain, promotes the transformation of agricultural products toward deep processing and high value addition, and significantly enhances their international market competitiveness.\n\nThird, there is a statistically positive association with the expansion of agricultural labor transfer. Digital technologies break down information barriers in the labor market and reduce job search costs. Flexible employment opportunities provided by Internet platforms accelerate the transfer of agricultural labor to non-agricultural sectors. Meanwhile, new business forms fostered by the digital economy create abundant employment demand, promote the optimal allocation of production factors, and enhance agricultural production efficiency. The income growth effect brought by labor transfer, through the income elasticity mechanism, stimulates the upgrading of agricultural product consumption, indirectly promoting the evolution of the agricultural industrial structure toward high value addition and enhancing export competitiveness.\n\nIn summary, this paper proposes hypothesis H2: The digital economy indirectly enhances the export competitiveness of agricultural products by promoting the upgrading of the agricultural industrial structure, stimulating agricultural technological innovation, and facilitating labor transfer. Based on this, this paper proposes the following hypothesis:\n\n**H2.**\n\nThe digital economy enhances the export competitiveness of agricultural products by promoting the upgrading of the agricultural industrial structure, stimulating agricultural technological innovation, and facilitating labor transfer.\n\n### 3.3. Nonlinear Impact of the Digital Economy on the Export Competitiveness of Agricultural Products\n\nThe improvement of China’s agricultural product export competitiveness is not only related to the level of digital economy development but ","Sustainability","2026-08-11T22:00:00Z",72,{"impact":18,"substance":78,"depth":18,"authority":168,"freshness":137,"relevant":21,"comment":169},12,"基于省级面板数据的实证研究，探讨数字经济对农产品出口竞争力的影响，数据详实，结论有参考价值，但时效性较低。",[171],{"name":164,"url":161},[129,173,26,174,175,176,29,177],"乡村振兴","中介效应","门槛效应","农产品出口","中国30省",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":180},{"candidates":181,"agricultural_evidence":48,"reason":49},[182,186,191],{"code":39,"score":137,"supported":13,"title":183,"abstract":184,"tags":185},[],[],[129,26],{"code":187,"score":21,"supported":13,"title":188,"abstract":189,"tags":190},"E5",[],[],[176],{"code":192,"score":21,"supported":13,"title":193,"abstract":194,"tags":195},"G",[],[],[173],[197,198],"农业产业结构 农产品出口 中介效应 乡村振兴","农业产业结构 农产品出口","农业产业结构农产品出口中介效应乡村振兴-542","2026-08-15T00:02:55.799820Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":8,"source_name":207,"source_url":204,"published_at":208,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":209,"sources":213,"tags":215,"primary_topic":220,"secondary_topics":226,"classification":238,"search_phrases":269,"slug":272,"view_count":54,"doi":273,"paper":274,"created_at":296},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",{"impact":168,"substance":17,"depth":210,"authority":211,"freshness":20,"relevant":21,"comment":212},17,13,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[214],{"name":207,"url":204},[216,217,218,219,30],"智慧农业","农业人工智能","遥感","土壤盐渍化",{"code":221,"slug":222,"name":223,"path":224,"parent_code":225},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[227,233],{"code":228,"slug":229,"name":230,"path":231,"parent_code":232},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":234,"slug":235,"name":236,"path":237,"parent_code":225},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":239,"locked":13,"version":34,"method":35,"evidence":240},"classified",{"candidates":241,"agricultural_evidence":48},[242,248,253,258,264],{"code":221,"score":243,"supported":48,"title":244,"abstract":246,"tags":247},15,[245],"remote sensing",[218,245],[218],{"code":228,"score":19,"supported":48,"title":249,"abstract":250,"tags":252},[229],[251,229],"土壤",[],{"code":234,"score":20,"supported":48,"title":254,"abstract":256,"tags":257},[255],"deep learning",[],[217],{"code":259,"score":149,"supported":48,"title":260,"abstract":261,"tags":263},"H7",[],[262],"干旱",[],{"code":265,"score":21,"supported":13,"title":266,"abstract":267,"tags":268},"I1",[],[],[216],[270,271],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":273,"openalex_id":275,"authors":276,"venue":207,"cited_by_count":54,"oa_url":204,"card":289,"direction":293,"ingested_from":295},"W7221816183",[277,279,282,284,286],{"name":278,"orcid":8},"Mingjie Qian",{"name":280,"orcid":281},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":283,"orcid":8},"Peng Wang",{"name":285,"orcid":8},"Hangyuan Liu",{"name":287,"orcid":288},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":290,"method":291,"finding":292,"direction":293,"opportunity":294},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","农业遥感与作物表型","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","openalex","2026-10-15T23:30:29.667342Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":8,"source_name":303,"source_url":300,"published_at":304,"category":11,"cover_url":8,"hotness":12,"is_selected":48,"score":123,"score_detail":305,"sources":307,"tags":309,"primary_topic":314,"secondary_topics":319,"classification":320,"search_phrases":337,"slug":340,"view_count":137,"doi":341,"paper":342,"created_at":361},4331,"Does rural land transfer promote high-quality agriculture development? Empirical evidence from 30 provinces in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940003","As a fundamental pathway toward agricultural-scale operations, rural land transfer (RLT) has emerged as an important institutional mechanism for promoting high-quality agricultural development (HQAD). However, how to effectively unleash the productive potential of land resources and align RLT with the objectives of high-quality development remains an important research question. Treating the 2010 national regulatory intensification of rural land transfer as a common policy milestone, this study employs a continuous difference-in-differences (DID) model and a mediating effect model to examine the relationship between RLT intensity and HQAD and to explore the potential transmission role of new agricultural business entities (NABE). The results show that the overall level of HQAD in China has steadily improved, although substantial spatial and temporal disparities remain, with HQAD generally decreasing from the eastern to the central and western regions. The continuous DID estimates indicate a significant positive association between RLT intensity and HQAD after the 2010 national regulatory-intensification milestone, and this relationship remains stable across the reported robustness checks. The mediation analysis provides evidence consistent with a potential transmission pathway through new agricultural business entities (NABE). Furthermore, the estimated association between RLT intensity and HQAD is stronger in the central region and in non-major grain-producing areas.","作为农业规模化经营的基础路径，农地流转(rural land transfer, RLT)已成为推动农业高质量发展(high-quality agricultural development, HQAD)的重要制度安排。然而，如何有效释放土地资源的生产潜力，使农地流转与高质量发展目标相契合，仍是一个重要的研究问题。本研究以2010年国家加强农地流转监管为共同政策节点，采用连续型双重差分(continuous difference-in-differences, DID)模型和中介效应模型，考察农地流转强度与农业高质量发展之间的关系，并探讨新型农业经营主体(new agricultural business entities, NABE)可能的中介传导作用。结果表明，中国农业高质量发展总体水平稳步提升，但仍存在显著的空间和时间差异，农业高质量发展水平总体呈东、中、西部递减态势。连续型双重差分估计表明，在2010年国家加强监管这一政策节点之后，农地流转强度与农业高质量发展之间存在显著正相关关系，且该关系在各项稳健性检验中保持稳定。中介分析提供了与新型农业经营主体潜在传导路径相一致的证据。此外，农地流转强度与农业高质量发展之间的估计关联在中部地区和非粮食主产区更强。","Frontiers in Sustainable Food Systems","2026-10-06T00:00:00Z",{"impact":18,"substance":78,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":306},"基于30省面板数据的连续DID实证研究，方法规范、结论有政策参考价值，但属学术论文而非产业动态，适合主题聚合而非头条精选。",[308],{"name":303,"url":300},[27,310,311,312,313],"新型农业经营主体","粮食主产区","双重差分","土地流转",{"code":315,"slug":316,"name":317,"path":318,"parent_code":192},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services",[],{"status":239,"locked":13,"version":34,"method":35,"evidence":321},{"candidates":322,"agricultural_evidence":48},[323,328,333],{"code":315,"score":211,"supported":48,"title":324,"abstract":326,"tags":327},[325],"land transfer",[325],[313,310],{"code":329,"score":21,"supported":13,"title":330,"abstract":331,"tags":332},"A8",[],[],[311],{"code":44,"score":21,"supported":13,"title":334,"abstract":335,"tags":336},[],[],[27],[338,339],"农村土地流转 农业高质量发展","新型农业经营主体 中介效应","农村土地流转农业高质量发展-4331","10.3389\u002Ffsufs.2026.1940003",{"doi":341,"openalex_id":343,"authors":344,"venue":303,"cited_by_count":54,"oa_url":355,"card":356,"direction":62,"ingested_from":295},"W7220726312",[345,347,349,351,353],{"name":346,"orcid":8},"Tian Tian",{"name":348,"orcid":8},"Jiawei Yang",{"name":350,"orcid":8},"Tong Yu",{"name":352,"orcid":8},"李孟然",{"name":354,"orcid":8},"Zhen Xie","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1940003\u002Fpdf",{"tldr":357,"method":358,"finding":359,"direction":62,"opportunity":360},"用连续DID和中介模型检验农村土地流转对农业高质量发展的影响及新型经营主体的中介作用。","2010年政策节点，连续DID与中介效应模型，中国30省面板数据。","土地流转显著促进农业高质量发展，新型经营主体起中介作用，中西部及非主产区效应更强。","可结合数字技术或信息化服务，探究土地流转通过新型经营主体推动农业高质量发展的数字化路径。","2026-10-07T23:30:06.424093Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":8,"content":8,"source_name":367,"source_url":8,"published_at":368,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":76,"score_detail":369,"sources":371,"tags":373,"primary_topic":376,"secondary_topics":380,"classification":381,"search_phrases":397,"slug":400,"view_count":149,"doi":8,"paper":401,"created_at":408},4155,"数字经济对粮食生产韧性的影响及作用机制检验","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F127630.html","刘威、吴芮、郑雪丽、朱书葳（统计与决策2026年第02期；河南工业大学管理学院、西北农林科技大学乡村振兴学院）。基于2012—2023年中国31个省份面板数据，从理论与实证两方面探究数字经济对粮食生产韧性的影响及作用机制。结果表明：数字经济能显著促进粮食生产韧性提升；异质性检验表明东部地区促进效应显著大于中西部地区，粮食主销区大于粮食产销平衡区，粮食主产区不显著；机制检验表明增强农产品销售能力与技术创新能力是数字经济赋能粮食生产韧性提升的重要渠道；门槛效应检验表明数字经济与粮食生产韧性间存在明显门槛效应。","统计与决策2026年第02期","2026-10-01T00:00:00Z",{"impact":18,"substance":78,"depth":18,"authority":19,"freshness":79,"relevant":21,"comment":370},"基于31省面板数据的实证研究，结论与机制清晰，对数字乡村与粮食安全议题有参考价值，但属学术论文，公共传播性一般。",[372],{"name":367,"url":365},[129,26,311,374,375],"粮食生产韧性","农业技术创新",{"code":192,"slug":377,"name":378,"path":379,"parent_code":8},"rural-services","农业服务与乡村发展","\u002Ftopic\u002Frural-services",[],{"status":382,"locked":13,"version":34,"method":35,"evidence":383},"partial",{"candidates":384,"agricultural_evidence":48},[385,389,393],{"code":192,"score":149,"supported":48,"title":386,"abstract":387,"tags":388},[],[173],[],{"code":329,"score":137,"supported":13,"title":390,"abstract":391,"tags":392},[],[],[311,374],{"code":39,"score":137,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[129,26],[398,399],"数字经济 粮食生产韧性","河南工业大学 粮食生产韧性","数字经济粮食生产韧性-4155",{"doi":8,"openalex_id":8,"authors":402,"venue":8,"cited_by_count":54,"oa_url":8,"card":403,"direction":62,"ingested_from":64},[],{"tldr":404,"method":405,"finding":406,"direction":62,"opportunity":407},"基于2012—2023年31省面板数据，实证检验数字经济对粮食生产韧性的促进效应与作用机制。","31省面板数据，固定效应与门槛回归、异质性及中介机制检验。","数字经济显著提升粮食生产韧性，东部与主销区效应更强，主产区不显著。","可深入探究主产区数字经济效应不显著的成因，并细化销售与技术创新渠道的微观机制。","2026-10-04T00:09:09.867912Z"]