[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-109":3,"related-109":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":49,"slug":52,"view_count":53,"doi":54,"paper":8,"created_at":55},109,"Does Digital Literacy Promote Farmers' Socialized Agricultural Service Adoption? Evidence from China's Thousand Villages Survey","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull","Li & Wu基于中国千村调查数据,实证检验数字素养对农户社会化农业服务采纳行为的影响。研究发现数字素养显著提升农户对社会化农业服务的采纳概率,且在不同代际、不同经营规模、不同地区之间存在异质性效应。",null,"## Abstract\n\nThe rapid development of digital technologies is reshaping rural production, daily life, rural governance, and patterns of social interaction. Digital tools such as smartphones make farmers obtain market information and engage in rural governance. Digital literacy (DL) works as a crucial capability empowering farmers to engage in production and managerial decision-making, thereby affecting socialized agricultural service adoption. However, few studies have directly examined the influence of DL upon farmers’ adoption of socialized agricultural services. Our research investigated the effect of DL upon farmers’ socialized agricultural service adoption, using microdata from the 2023 China Thousand Villages Survey. It utilized a logit model to analyze the data and employed instrumental variables and other methods to conduct robustness checks. The study found that DL dramatically raised the probability that farmers adopted socialized agricultural services. As indicated by mechanism analysis, DL propels farmers’ socialized agricultural service adoption mainly through three channels: broadening information access, alleviating financing constraints, and facilitating the transformation of agricultural production and management practices. Moderation analysis showed that the higher the dependence of DL upon agricultural operating income and the degree of marketization in agricultural production and management, the stronger the driving force. However, if the level of digital village governance is higher, its marginal effect will be lower. In heterogeneity analysis, the effect of DL was more evident among male-headed households, households headed by individuals aged over 50 years, and households headed by individuals with junior high school education or below. These findings expand the analytical framework for the determinants of socialized agricultural service adoption, while providing evidence and policy perceptions of introducing modern agriculture based on division of labor into small farms through digital empowerment.\n\n## 1 Introduction\n\nIn many developing countries, agricultural production is experiencing deep-going transformation. With rapid urbanization, continuous rural labor outflow, an aging agricultural workforce, and rising household labor opportunity costs, traditional small farming faces increasingly severe constraints on labor supply, production efficiency, technology adoption, and risk management (; ; ; ). Socialized agricultural services, a critical institutional arrangement linking smallholders to modern agriculture, can help farmers overcome the constraints arising from lack of household labor, machinery, and professional expertise by offering services such as mechanized field operations, agricultural input procurement, pest and disease control, technical guidance, harvesting, drying, storage, transportation, and full process management. Thus, they improve production efficiency and reduce production risks (; ). However, because of factors such as limited access to information, ability-to-pay constraints, difficulties in assessing service quality, and traditional production habits, farmers’ potential demand for socialized agricultural services often cannot be fully translated into actual adoption behavior (; ).\n\nChina forges a representative context, aimed at examining this issue. As a major agricultural country with a large number of smallholders (), China’s agricultural modernization partially depends upon land consolidation and farm expansion. Instead, it increasingly emphasizes integrating smallholders into the trajectory of modern agricultural development through socialized agricultural services, enabling them to indirectly share the benefits of mechanization, specialization, and scaled-up operations without having to transfer their land or transform themselves into large-scale farming entities (; ). Therefore, socialized agricultural service development is regarded as an important pathway to organically integrate smallholders into modern agriculture. Although the market for socialized agricultural services has rapidly expanded recently, the extent of adoption significantly differ among farmers; the key factors underlying these differences are yet to be fully identified. Clarifying these factors is of great significance to facilitate better integration of smallholders into modern agriculture based on division of labor and effectively transform their service needs into actual adoption behaviors.\n\nThe rapid development of digital technologies is reshaping rural production, daily life, rural governance, and patterns of social interaction (; ; ). Digital tools such as smartphones, the mobile Internet, digital finance, e-commerce platforms, short-video applications, and online government service platforms have gradually become embedded in rural society, offering farmers novel approaches to obtaining market information, obtain financial services, compare service providers, connect with agricultural business organizations, and participate in rural governance. However, the dividends of digital technology do not automatically translate into improvements in farmers’ production and operational decisions; the key lies in whether farmers are able to effectively access, understand, access, and utilize digital information (). Digital literacy (DL), a crucial capability that enables farmers to engage in production and managerial decision-making in the digital era, may shape the process through which digital resources are translated into actual farming practices, thereby affecting socialized agricultural service adoption. Against this backdrop, our research examined whether digital literacy empirically promotes farmers’ socialized agricultural service adoption.\n\nExisting studies have mainly examined the determinants of farmers’ socialized agricultural service adoption from the following viewpoints: household resource endowments, farm size, labor allocation, risk perception, social networks, dependence on agricultural income, and village-level service supply conditions. argue that the shrinking size of household labor has become a key factor in driving smallholders’ utilization of socialized agricultural services. Surveying 1,174 smallholders in Jiangsu Province, China. A sample survey of farmers in Ecuador show that income functions as a pivotal factor in impacting agricultural socialized service adoption (). According to investigation data from 787 farm households in Jiangxi, Fujian, and Zhejiang, China, risk perception dramatically inhibits farmers’ socialized agricultural service adoption (). According to survey information from China’s 638 farm households, reveal that farm size exerts a nonlinear impact on socialized agricultural service adoption; an expansion in farm size significantly promotes large-scale farmers’ actual purchase behavior but suppresses that of smallholders. In addition, the study finds that household income exerts a remarkably positive effect upon adopting socialized agricultural services; if the household burden is heavier and the degree of land fragmentation is greater, farmers will trend less to accept such services. Moreover, the presence of local providers of socialized agricultural services can significantly increase farmers’ adoption behaviors.\n\nA growing number of studies have begun to focus on the role of the digital economy and digital technologies in farmers’ decision-making in agricultural production and management, as well as in agricultural sustainable transformation. examine how the digital economy affects Chinese farmers’ adoption of eco-agricultural technologies along dimensions such as digital sales, digital finance, and digital production. Other studies further explore the impacts of digital technologies on farmers’ credit access, labor mobility, and participation in supply-chain integration (; ; ).\n\nCompared with research on the adoption of specific digital technologies—which mainly emphasizes whether farmers use particular tools such as the Internet, smartphones, digital finance platforms, or e-commerce platforms—digital literacy (DL) places greater emphasis on farmers’ ability to understand, use, and transform digital information and digital tools. Therefore, digital literacy not only affects whether farmers come into contact with digital technologies, but also determines whether they can convert digital resources into actual capabilities for agricultural production and operational decision-making. Accordingly, in recent years, an increasing body of literature has directly investigated the importance of digital literacy in fields such as agricultural production, rural entrepreneurship, inclusive finance, and farmers’ market participation. construed survey information from 643 farm households in Shaanxi and Shandong, China, suggesting that enhancements in DL dramatically facilitated farmers’ adoption of green production technology, including water-saving irrigation, pest and disease control, use of contamination-free pesticides, and the return of straw. Analyzing 742 survey questionnaires from Shandong province, find that digital literacy dramatically increases farmers’ adoption probability for integrated pest management, conservation tillage, soil testing, and formula fertilization technologies. draw a similar conclusion from 923 valid household samples of the China Land Economic Survey. show that digital literacy helps rural entrepreneurs expand their businesses and connect with markets, thereby promoting rural entrepreneurship. According to micro-survey information from China’s five national e-commerce demonstration counties, Internet use significantly increases farmers’ probability of partaking in e-commerce entrepreneurship ([Song et al., 2024](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull#ref9002)). Using survey data from 360 farm households in Nigeria, [Nwangwu et al. (2024)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1907316\u002Ffull#ref9001) show that digital technology dramatically reinforces farmers’ market participation decisions and intensity of participation.\n\nHowever, existing studies have mainly examined the effects of the digital economy, digital technologies, or digital literacy on ecological agricultural technology adoption, rural entrepreneurship, and market participation, while relatively little attention has been paid to integrating digital literacy and the adoption of agricultural socialized services into a unified analytical framework. Agricultural socialized services provide an important pathway for smallholders to participate in the modern agricultural division of labor and connect with the modern agricultural system. Therefore, examining the effect of digital literacy on farmers’ adoption of agricultural socialized services can help extend the micro-level empirical evidence on digitally enabled agricultural modernization. Few studies have incorporated digital literacy and socialized agricultural services into a unified analysis framework, aimed at directly examining the influence of digital literacy upon farmers’ adoption of socialized agricultural services. Just a small amount of research provides preliminary evidence on this issue (; ; ). Nevertheless, such research relies on relatively simple measures of digital literacy and have not sufficiently uncovered the potential mechanisms through which DL affects farmers’ acceptation of socialized agricultural services. Our study explores the influence of DL on farmers’ socialized agricultural service adoption and interprets its potential mechanisms. The purpose is to deal with this problem. We used large-scale household information from China’s Thousand Villages Survey (CTVS), implemented by Shanghai University of Finance and Economics.\n\nOur research has four key findings: For starters, DL can dramatically raise the likelihood of farmers’ acceptation of socialized agricultural services. Second, digital literacy promotes farmers’ socialized agricultural service adoption mainly through three channels (broadening information acquisition, alleviating financing constraints, and driving the transition of agricultural production and management). Third, the extent of dependence on agricultural operating income and the marketization level exert apparent positive moderating effects on digital literacy’s role in driving socialized agricultural service adoption, whereas the digital governance level in villages exerts a remarkably negative moderating effect. Fourth, the driving force of DL is stronger among households with male heads, heads over the age of 50, and heads with junior high school education or below.\n\nThe following three aspects of our study reflect its potential marginal contributions: First, it combines DL with the analysis framework of farmers’ socialized agricultural service adoption, thereby extending associated studies according to digital literacy. Second, this research systematically examines the mechanisms via which DL affects the acceptation of socialized agricultural services from three perspectives: broadening information acquisition channels, easing financing constraints, and facilitating the transition of agricultural production and management modes, thereby deepening our understanding of the internal theoretical logic underlying the relationship between the two. Third, by combining a moderating effect analysis and heterogeneity analysis, our research identifies situational conditions and group differences in the impact of DL upon farmers’ socialized agricultural service adoption, compensates for existing studies’ insufficiency in examining contextual conditions and heterogeneity characteristics, and provides empirical proof for formulating differentiated digital capability enhancement policies and boosting the advancement of high-quality socialized agricultural services.\n\nThe rest of this article is structured as below: Section 2 describes the theoretical analysis and assumptions, along with the theoretical mechanisms via which DL impacts farmers’ adoption of socialized agricultural services. Section 3 elaborates on the information sources, variables, and econometric models. Section 4 exhibits the results of the benchmark regression, mechanism analysis, robustness tests, moderating effect analysis, and heterogeneity analysis. Section 5 discusses and interprets the empirical results. Section 6 concludes the paper and provides policy suggestions.\n\n## 2 Theoretical analysis and assumption development\n\n### 2.1 Digital literacy’s impact upon socialized agricultural service adoption\n\nFor farmers, deciding whether to adopt socialized agricultural services is essentially the result of a cost–benefit trade-off under multiple constraints, including labor, capital, technology, risk, and transaction costs (). However, some farmers have long relied on family labor and traditional production experience, and often have insufficient knowledge of specialized external services, limited trust in such services, and concerns about uncertain service outcomes and uncontrollable transaction processes. These factors inhibit their willingness to adopt socialized agricultural services.\n\nDigital literacy helps alleviate cognitive, trust, and decision-making constraints, thereby encouraging farmers to adopt socialized agricultural services. First, digital literacy reduces cognitive constraints. Farmers showing higher digital literacy levels usually have better learning capacity and stronger capacity to accept novel technologies (), making it easier to understand the effect of socialized agricultural services in saving labor input, ameliorating operational efficiency, reducing production costs, and enhancing business performance. This, in turn, improves their awareness and understanding of such services. Second, digital literacy alleviates trust constraints. Farmers with superior DL can better understand service contracts, operational standards, and service procedures, evaluate feedback, and are more likely to accept platform-based transactions and digital supervision methods, thereby reducing their distrust of external specialized services (). Finally, digital literacy eases decision-making constraints. Socialized agricultural service adoption involves multiple aspects including service selection, fee payment, quality assessment, and expected returns. Farmers showing superior DL can better compare different production options, accurately weigh cost–benefit relationships between self-production and service outsourcing, and reduce perceived uncertainty in the adoption process (). Therefore, digital literacy makes farmers content to accept socialized agricultural services, while raising the likelihood of adoption by improving their cognitive level, degree of trust, and decision-making ability. Our analysis puts forward the following assumption:\n\n> _A1_: DL significantly increases farmers’ socialized agricultural service adoption.\n\n### 2.2 Mechanism analysis of digital literacy’s impact upon socialized agricultural service adoption\n\n#### 2.2.1 Broadening information acquisition channels\n\nFarmers’ decisions to accept socialized agricultural services depend heavily on the extent to which they have access to relevant service information (). In accordance with transaction cost and information search theories, incomplete and asymmetric information increases farmers’ search, comparison, and monitoring costs in the service adoption process. When timely information about service providers, service prices, operational quality, service reputation, and policy subsidies is not available, farmers feel uncertain about service effectiveness and transaction security, which reduces their willingness to adopt such services (). In traditional rural societies, farmers mainly obtain information about agricultural services through acquaintances, neighborhood communications, or notifications from village cadres. Such information sources are relatively limited, which easily leads to mismatches between service supply and demand and insufficient adoption.\n\nDL can effectively broaden farmers’ information channels. Farmers showing superior DL levels can use digital tools and platforms excellently, such as smartphones, agricultural information platforms, WeChat groups, short-video platforms, agricultural service mini-programs, and government service apps, and obtain timely data relevant to socialized agricultural services (). Through multichannel information comparison, farmers can accurately assess service providers’ qualifications, reasonableness of service prices, reliability of service quality, and availability of subsidies, thereby reducing uncertainty in service selection. Digital literacy also allows farmers to evaluate information, helps them identify false advertising and low-quality information, improves the accuracy of their judgments, and reduces information-related risks in adoption decisions. The foregoing analysis leads to the following assumption:\n\n> _A2_: Digital literacy strongly motivates farmers to adopt socialized agricultural services by broadening their information acquisition channels.\n\n#### 2.2.2 Alleviating financing constraints\n\nSocialized agricultural service adoption usually requires farmers to pay service fees. While farmers facing tight financial conditions may recognize that agricultural socialized services can enhance production efficiency, decrease labor intensity, and enhance business performance, they may be unable to make timely purchases because of short-term liquidity problems. Therefore, financing constraints and insufficient payment capacity are important factors influencing farmers’ decisions to adopt socialized agricultural services ().\n\nIn rural financial markets, farmers generally face problems such as insufficient collateral, lack of credit information, long distances to financial service outl","Frontiers in Sustainable Food Systems Vol. 10 Article 1907316","2026-07-06T01:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,2,1,"基于千村调查数据，实证分析数字素养对农户采纳社会化服务的影响，方法严谨，结论有政策启示，但发表于2026年7月，时效性较低。",[24],{"name":10,"url":6},[26,27,28,29],"数字素养","社会化服务","农户采纳","千村调查",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"unclassified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":47,"reason":48},[37,42],{"code":38,"score":20,"supported":14,"title":39,"abstract":40,"tags":41},"G2",[],[],[28,27],{"code":43,"score":21,"supported":14,"title":44,"abstract":45,"tags":46},"I6",[],[],[26],true,"缺少足够的主题证据",[50,51],"社会化服务 农户采纳 千村调查 数字素养","社会化服务 农户采纳","社会化服务农户采纳千村调查数字素养-109",12,"10.3389\u002Ffsufs.2026.1907316\u002Ffull","2026-07-31T00:00:55.984490Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,166,210,279,343,402],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":72,"tags":74,"primary_topic":78,"secondary_topics":84,"classification":90,"search_phrases":131,"slug":134,"view_count":135,"doi":136,"paper":137,"created_at":165},3469,"ARTIFICIAL INTELLIGENCE FOR AGRICULTURE: A SYSTEMATIC REVIEW OF FARMERS' PERCEPTIONS, ACCEPTANCE, ADOPTION AND BARRIERS","https:\u002F\u002Fdoi.org\u002F10.64013\u002Fbbasrjlifess.v2026i1.70","Artificial intelligence (AI) is rapidly reshaping agriculture, enabling the application of data for efficient decision-making, precision farming, crop monitoring, pest management, disease detection, smart irrigation, yield prediction, and various aspects of farm automation. Yet, to successfully gain a foothold in farms, factors like farmers' perceptions, their acceptance, desire, and readiness to adopt, as well as the capability to surmount socioeconomic, technological, and institutional challenges, must also make a difference. This article systematically collates the available information on farmers' perspectives on, acceptance of, readiness to adopt, and the obstacles related to AI in the agricultural context. The PRISMA 2020 guideline has been followed to select studies that are related, evaluated for inclusion, and finally, per the criteria, combined into a systematic synthesis. The review mainly discusses the aspects that influence the use of AI, like perceived usefulness, ease of use, trust, digital literacy, affordability, farm size, socioeconomic characteristics, availability of digital infrastructure, and access to agricultural technical advisory services. Main challenges identified involve the high cost of implementing AI, poor connectivity, weak rural infrastructure, low levels of technological knowledge, unavailability of support services, concerns about data privacy, distrust of algorithmic bias, language barriers, and inequalities impacting smallholder farmers. Besides barriers, the review indicates the potential of leveraging AI through extension services, agricultural mobile apps, precision farming, climate-smart agriculture, early-warning systems, and tailored farm advisories. By drawing out technical, behavioral, socioeconomic, and institutional perspectives, the review pinpoints important research questions and presents a farmholder-oriented setup to explain the process of taking up AI in farming. The outcomes can be used by scientific experts, policymakers, extension personnel, and software developers to create low-cost, reliable, and accessible AI systems aimed at promoting sustainable agricultural development.","人工智能（AI）正迅速重塑农业，使数据得以应用于高效决策、精准农业、作物监测、病虫害管理、病害检测、智能灌溉、产量预测以及农场自动化的各个方面。然而，要在农场中成功立足，农民的认知、接受度、采用意愿和准备程度，以及克服社会经济、技术和制度挑战的能力，同样至关重要。本文系统梳理了现有关于农民对农业领域人工智能的看法、接受度、采用准备程度及相关障碍的信息。研究遵循PRISMA 2020指南，筛选相关研究，评估其纳入资格，并最终依据标准进行系统性综合。综述主要探讨了影响人工智能使用的因素，包括感知有用性、易用性、信任、数字素养、可负担性、农场规模、社会经济特征、数字基础设施的可用性以及农业技术咨询服务的获取。识别出的主要挑战包括人工智能实施成本高昂、网络连接不佳、农村基础设施薄弱、技术水平低下、支持服务缺乏、数据隐私担忧、对算法偏见的疑虑、语言障碍以及影响小农户的不平等问题。除障碍外，综述还指出了通过推广服务、农业移动应用、精准农业、气候智慧型农业、预警系统和定制化农场咨询来利用人工智能的潜力。通过梳理技术、行为、社会经济和制度层面的视角，本综述指出了重要的研究问题，并提出了一个以农场主为导向的框架，以解释在农业中采用人工智能的过程。研究结果可供科学专家、政策制定者、推广人员和软件开发者用于创建低成本、可靠且可及的人工智能系统，以促进可持续农业发展。","Journal of Life and Social Sciences","2026-09-24T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":69,"freshness":70,"relevant":21,"comment":71},13,9,"基于PRISMA的系统综述，系统梳理农户对AI的认知、接受度与采纳障碍，对智慧农业推广与政策设计有实质参考价值。",[73],{"name":65,"url":62},[75,76,26,28,77],"智慧农业","农业人工智能","小农户",{"code":79,"slug":80,"name":81,"path":82,"parent_code":83},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[85],{"code":38,"slug":86,"name":87,"path":88,"parent_code":89},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":91,"locked":14,"version":33,"method":34,"evidence":92},"classified",{"candidates":93,"agricultural_evidence":47},[94,99,105,113,120,127],{"code":79,"score":53,"supported":47,"title":95,"abstract":97,"tags":98},[96],"artificial intelligence",[96],[76],{"code":38,"score":100,"supported":47,"title":101,"abstract":102,"tags":104},8,[],[77,103],"smallholder",[28,77],{"code":106,"score":107,"supported":47,"title":108,"abstract":109,"tags":112},"I1",7,[],[110,111],"精准农业","smart agriculture",[75],{"code":114,"score":57,"supported":47,"title":115,"abstract":116,"tags":119},"H3",[],[117,118],"灌溉","irrigation",[],{"code":121,"score":122,"supported":47,"title":123,"abstract":124,"tags":126},"H8",3,[],[125],"可持续农业",[],{"code":43,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[26],[132,133],"农民 AI 采纳 障碍","农业人工智能 系统综述","农民AI采纳障碍-3469",5,"10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":136,"openalex_id":138,"authors":139,"venue":65,"cited_by_count":156,"oa_url":62,"card":157,"direction":163,"ingested_from":164},"W7214189410",[140,142,144,146,148,150,152,154],{"name":141,"orcid":8},"MM JAMEEL",{"name":143,"orcid":8},"M SAEED",{"name":145,"orcid":8},"SA SHER",{"name":147,"orcid":8},"Z ALI",{"name":149,"orcid":8},"Q HAYYAT",{"name":151,"orcid":8},"S KIRBAG",{"name":153,"orcid":8},"S KHAN",{"name":155,"orcid":8},"HN AHMAD",0,{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"系统综述农民对农业AI的感知、接受度、采纳意愿及障碍。","遵循PRISMA 2020指南，系统筛选并综合相关文献。","成本、基础设施、数字素养与信任是主要障碍，小农户受影响最大。","数字乡村与农业信息化","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:09.670568Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":8,"source_name":171,"source_url":8,"published_at":172,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":183,"classification":184,"search_phrases":204,"slug":207,"view_count":53,"doi":208,"paper":8,"created_at":209},30,"数字素养与农民对社会化服务的采纳：基于CRRS数据验证的U型关系","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1722049\u002Ffull","基于2020年中国社会科学院乡村振兴调查（CRRS）数据，采用因子分析和回归方法考察农民数字素养与社会化服务规模经营的关系。研究发现数字素养对服务规模经营整体呈先抑制后促进的U型关系，转折点约为0.47，机制上通过自然、社会、金融和人力资本四重中介路径发挥作用；分维度看数字信息与社会素养呈U型，数字商业素养呈线性正向；异质性上偏远农村和东部地区U型显著、中部不显著、西部线性正向，城乡郊区因机会成本高而无显著效应。","Frontiers in Sustainable Food Systems · 2026-07-24","2026-07-24T00:00:00Z",76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":175,"relevant":21,"comment":176},4,"基于CRRS数据的实证研究，揭示数字素养与社会化服务采纳的U型关系，对数字乡村建设有参考价值。",[178],{"name":171,"url":169},[180,26,27,181],"农业信息化","U型关系",{"code":38,"slug":86,"name":87,"path":88,"parent_code":89},[],{"status":91,"locked":14,"version":33,"method":34,"evidence":185},{"candidates":186,"agricultural_evidence":47},[187,191,196,200],{"code":38,"score":53,"supported":47,"title":188,"abstract":189,"tags":190},[27],[27],[27],{"code":89,"score":122,"supported":47,"title":192,"abstract":193,"tags":195},[],[194],"乡村振兴",[],{"code":106,"score":21,"supported":14,"title":197,"abstract":198,"tags":199},[],[],[180],{"code":43,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[26],[205,206],"农业信息化 社会化服务 数字素养 U型关系","农业信息化 社会化服务","农业信息化社会化服务数字素养U型关系-30","10.3389\u002Ffsufs.2026.1722049\u002Ffull","2026-07-28T03:05:26.994652Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":67,"score_detail":218,"sources":220,"tags":222,"primary_topic":227,"secondary_topics":231,"classification":232,"search_phrases":257,"slug":260,"view_count":156,"doi":261,"paper":262,"created_at":278},5581,"How Does Digital Literacy Promote Inclusive Rural Growth? Micro-Level Evidence from Income Inequality Among Farm Households","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010689","Against the backdrop of the coordinated advancement of digital village construction and the common prosperity strategy, the widespread improvement in rural households’ digital literacy is emerging as a key force in reshaping rural income distribution and enabling sustainable rural development. Whether digital dividends can equitably benefit farm households with heterogeneous endowments has a direct bearing on the inclusive growth outcomes of digital village construction and the sustainable advancement of the common prosperity goal. Using three waves of panel data from the China Family Panel Studies (CFPS) covering 2018 to 2022, this paper empirically examines the impact of digital literacy on income inequality among rural households. The findings indicate that improvements in digital literacy significantly reduce rural household income inequality, and this conclusion remains robust across a series of robustness checks. Mechanism analysis reveals that digital literacy mainly curbs the widening of rural household income gaps through two core channels: expanding social capital and alleviating credit constraints. Heterogeneity analysis shows that the inequality-reducing effect of digital literacy is more pronounced among households that depend on non-agricultural income, have lower levels of human capital, or fall into relatively lower income brackets. Further analysis demonstrates that digital literacy significantly reduces inequalities in wage income, business income, and property income across rural households, whereas its mitigating effect on transfer income inequality is not statistically significant. Meanwhile, digital literacy significantly raises the probability of upward income mobility among rural households, thereby alleviating income inequality from a dynamic perspective. On the basis of these findings, this paper proposes policy implications in several areas: building a hierarchical and categorized rural digital literacy cultivation system, fostering a virtuous cycle of digital empowerment, equal opportunity, and income convergence, and optimizing income distribution structures and dynamic mobility mechanisms, with the aim of accelerating progress toward common prosperity.","在数字乡村建设与共同富裕战略协同推进的背景下，农户数字素养的普遍提升正成为重塑乡村收入分配格局、实现乡村可持续发展的重要力量。数字红利能否公平惠及禀赋异质性农户，直接关系到数字乡村建设的包容性增长成效与共同富裕目标的可持续推进。本文利用中国家庭追踪调查（CFPS）2018—2022年三期面板数据，实证检验数字素养对农户收入不平等的影响。研究发现，数字素养的提升显著降低了农户收入不平等，这一结论在一系列稳健性检验后依然成立。机制分析表明，数字素养主要通过拓展社会资本与缓解信贷约束两条核心渠道抑制农户收入差距的扩大。异质性分析显示，数字素养的减不平等效应在依赖非农收入、人力资本水平较低或处于相对较低收入区间的农户中更为明显。进一步分析发现，数字素养显著缩小了农户工资性收入、经营性收入和财产性收入的不平等，而对转移性收入不平等的缓解作用在统计上不显著。同时，数字素养显著提高了农户收入向上流动的概率，从而从动态视角缓解了收入不平等。基于上述发现，本文从构建分层分类的农户数字素养培育体系、形成数字赋能—机会均等—收入收敛的良性循环、优化收入分配结构与动态流动机制等方面提出政策启示，以期为加快共同富裕进程提供参考。","Sustainability","2026-10-21T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":69,"freshness":70,"relevant":21,"comment":219},"基于CFPS三期面板数据的微观实证，方法规范、结论有新意，对数字乡村与共同富裕政策有参考价值，值得进入每日精选。",[221],{"name":216,"url":213},[223,26,224,225,226],"数字乡村","共同富裕","农民收入","收入不平等",{"code":43,"slug":228,"name":229,"path":230,"parent_code":83},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":91,"locked":14,"version":33,"method":34,"evidence":233},{"candidates":234,"agricultural_evidence":47},[235,240,247,252],{"code":43,"score":100,"supported":47,"title":236,"abstract":237,"tags":239},[],[223,238],"digital village",[223,26],{"code":241,"score":57,"supported":47,"title":242,"abstract":243,"tags":246},"G6",[],[244,245],"农户收入","rural income",[],{"code":89,"score":122,"supported":47,"title":248,"abstract":249,"tags":251},[],[250],"rural development",[],{"code":253,"score":21,"supported":14,"title":254,"abstract":255,"tags":256},"G4",[],[],[224],[258,259],"CFPS 数字素养 农户收入","数字素养 农村收入差距","CFPS数字素养农户收入-5581","10.3390\u002Fsu182010689",{"doi":261,"openalex_id":263,"authors":264,"venue":216,"cited_by_count":156,"oa_url":213,"card":273,"direction":161,"ingested_from":164},"W7223105459",[265,268,271],{"name":266,"orcid":267},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":269,"orcid":270},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":272,"orcid":8},"Fangping Cao",{"tldr":274,"method":275,"finding":276,"direction":161,"opportunity":277},"基于CFPS三期面板数据，实证检验数字素养对农户收入不平等的影响。","使用CFPS 2018-2022三期面板数据，进行机制与异质性分析。","数字素养通过扩展社会资本和缓解信贷约束显著缩小农户收入差距。","可延伸研究数字素养对不同收入来源不平等的差异化机制及动态收入流动路径。","2026-10-22T11:20:47.675827Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":8,"content":8,"source_name":284,"source_url":8,"published_at":285,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":286,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":302,"classification":309,"search_phrases":330,"slug":333,"view_count":21,"doi":8,"paper":334,"created_at":342},5304,"Front. Sustain. Food Syst.:数字农业技术推广与贵州辣椒种植户极端气候适应技术采纳","https:\u002F\u002Fscienmag.com\u002Fdigital-agricultural-extension-linked-to-climate-adaptation-among-chili-farmers-in-mountainous-china","Gao H, Han H (2026) 在Frontiers in Sustainable Food Systems发表论文,以贵州辣椒种植户为研究对象,发现数字农业技术推广(DATP)对极端气候适应技术采纳呈显著正相关,通过更广泛信息渠道、信息信任和应用能力发挥作用,益处与风险预期确认起调节作用;数字信息素养较高种植户DATP效应更强(系数0.326、0.408 vs. 低素养0.084、0.133),规模化种植户效应同样更强。","Frontiers in Sustainable Food Systems(2026,Article 1914305)","2026-10-13T00:00:00Z",{"impact":287,"substance":288,"depth":17,"authority":69,"freshness":57,"relevant":21,"comment":289},16,21,"基于贵州辣椒种植户实证数据，揭示数字农业推广对气候适应技术采纳的调节机制，结论具体且具政策参考价值，值得入选。",[291],{"name":284,"url":282},[293,75,26,294,295],"数字农业","气候适应","辣椒种植",{"code":297,"slug":298,"name":299,"path":300,"parent_code":301},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[303],{"code":304,"slug":305,"name":306,"path":307,"parent_code":308},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":91,"locked":14,"version":33,"method":34,"evidence":310},{"candidates":311,"agricultural_evidence":47},[312,316,320,326],{"code":106,"score":69,"supported":47,"title":313,"abstract":314,"tags":315},[293],[293],[293,75],{"code":304,"score":53,"supported":47,"title":317,"abstract":318,"tags":319},[294],[294],[294],{"code":297,"score":321,"supported":47,"title":322,"abstract":324,"tags":325},11,[323],"辣椒",[323],[],{"code":43,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[26],[331,332],"贵州 辣椒种植户 数字农业技术推广","极端气候适应 技术采纳 数字素养","贵州辣椒种植户数字农业技术推广-5304",{"doi":8,"openalex_id":8,"authors":335,"venue":8,"cited_by_count":156,"oa_url":8,"card":336,"direction":161,"ingested_from":341},[],{"tldr":337,"method":338,"finding":339,"direction":161,"opportunity":340},"研究数字农业技术推广对贵州辣椒种植户采纳极端气候适应技术的影响。","基于贵州辣椒种植户调查数据，采用回归分析检验中介与调节效应。","数字农业技术推广显著促进适应技术采纳，信息渠道、信任与应用能力起中介作用，数字素养和规模起调节作用。","可探究数字推广服务对不同作物、不同气候区农户适应行为的差异化机制与长期效果。","agent","2026-10-19T00:07:48.767992Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":8,"content":348,"source_name":349,"source_url":8,"published_at":350,"category":351,"cover_url":8,"hotness":13,"is_selected":14,"score":352,"score_detail":353,"sources":356,"tags":358,"primary_topic":8,"secondary_topics":361,"classification":362,"search_phrases":397,"slug":400,"view_count":156,"doi":8,"paper":8,"created_at":401},5252,"山东印发《关于大力发展智慧农业的实施意见》鲁政办字〔2026〕85号","http:\u002F\u002Fwww.daiyue.gov.cn\u002Fart\u002F2026\u002F10\u002F14\u002Fart_255813_10401578.html","山东省政府办公厅10月14日印发85号文,提出到2030年粮油作物水肥一体化应用面积达2000万亩、加力推进优势产业智慧转型、智慧赋能设施农业改造升级、智慧赋能畜牧业节本增效、智慧赋能渔业产业提档升级、培育农业人工智能产业生态等7项重点任务,打造全国智慧农业发展高地。","由农业农村部门牵头，加强与发展改革、财政、科技、工业和信息化、自然资源、畜牧兽医、大数据、气象等部门沟通配合，强化省、市、县三级协同联动，确保任务落到实处。统筹乡村振兴重大专项资金等各级相关领域财政资金推进智慧农业建设，重点支持关键技术攻关、数字农业发展县和应用场景建设。省级每年支持建设30个智慧农业应用场景。优化金融服务落实税收优惠政策，支持智慧农业科技企业和创新平台发展。推动高校、科研院所与智慧农业企业深化合作，培养精通信息技术与农业产业的复合型人才。持续推动高素质农民培育、基层农技推广队伍素质提升等项目，提升生产经营主体数字素养与技能。推进智慧农业领域从业人员职业技能培训与评价，吸引各类人才投身智慧农业发展。（省委金融办、省发展改革委、省教育厅、省科技厅、省工业和信息化厅、省财政厅、省人力资源社会保障厅、省自然资源厅、省农业农村厅、省大数据局、省畜牧局、山东金融监管局、省税务局、省气象局按职责分工负责）","岱岳区人民政府(2026-10-14)","2026-10-14T00:00:00Z","政策",75,{"impact":18,"substance":288,"depth":354,"authority":70,"freshness":57,"relevant":21,"comment":355},17,"省级智慧农业实施意见，含资金、场景数量与多部门分工等实质条款，具备政策参考价值。",[357],{"name":349,"url":346},[223,75,26,359,360],"应用场景","农业政策",[],{"status":363,"locked":14,"version":33,"method":34,"evidence":364},"needs_review",{"candidates":365,"agricultural_evidence":47,"reason":396},[366,370,376,382,388,392],{"code":106,"score":53,"supported":47,"title":367,"abstract":368,"tags":369},[75],[75],[75],{"code":371,"score":122,"supported":47,"title":372,"abstract":373,"tags":375},"C",[],[374],"畜牧",[],{"code":377,"score":122,"supported":47,"title":378,"abstract":379,"tags":381},"D",[],[380],"渔业",[],{"code":383,"score":122,"supported":47,"title":384,"abstract":385,"tags":387},"F3",[],[386],"水肥一体化",[],{"code":79,"score":122,"supported":47,"title":389,"abstract":390,"tags":391},[],[76],[],{"code":43,"score":20,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[223,26],"多个产业证据相当，需要确认主归属",[398,399],"山东 智慧农业 实施意见","智慧农业应用场景 山东","山东智慧农业实施意见-5252","2026-10-19T00:07:31.203786Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":8,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":419,"secondary_topics":423,"classification":424,"search_phrases":444,"slug":447,"view_count":156,"doi":448,"paper":449,"created_at":477},5160,"Evaluating learner satisfaction and confidence in a precision agriculture MOOC: a cross-sectional study in Palestinian higher education","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeduc.2026.1954745","Background Precision agriculture (PA) increasingly requires graduates who can work with sensor-based, geospatial and data-driven technologies, yet access to specialised training remains uneven in many under-resourced higher-education contexts. Although massive open online courses (MOOCs) offer a potentially scalable response to this gap, empirical evaluation of precision-agriculture MOOCs and of MOOC-based learning in Palestinian higher education more broadly remains limited, so evidence on learners' satisfaction and confidence in this specific educational context is needed. Methods This study addresses that gap through a cross-sectional, questionnaire-based evaluation of learners participating in a precision agriculture MOOC developed within the BENEFIT Erasmus+ project for Palestinian higher education; the design does not support causal inference. A questionnaire administered following learners' participation in the MOOC was completed by 62 of 69 invited learners (response rate 89.9%). The course-evaluation instrument comprised eight satisfaction items and nine confidence-related items rated on a 0-4 Likert scale; five negatively worded items were reverse-scored for composite analyses. We report descriptive statistics, one-sample tests against the scale midpoint, a paired comparison of the two composites, and correlation analysis, with Spearman's ρ as a non-parametric sensitivity check. Results Learners reported positive overall satisfaction (M = 2.74, SD = 0.57) and positive overall confidence (M = 2.54, SD = 0.40), both significantly above the scale midpoint (both p \u003C 0.001, d > 1.2). Agreement was strongest for perceived MOOC design quality (82.3%), course enjoyment (75.8%), confidence in mastering content (75.8%), and confidence in passing an examination (75.8%). Satisfaction and confidence were strongly and positively correlated [ r = 0.746, 95% CI (0.610, 0.839), p \u003C 0.001; ρ = 0.757]; because both constructs were measured concurrently by self-report within the same cross-sectional survey, this association cannot establish the direction of the relationship or a causal link between the two constructs. Satisfaction was significantly higher than confidence (mean difference = 0.20, p \u003C 0.001, dz = 0.53). A single item-perceived slide information density-did not differ significantly from the neutral midpoint ( p = 0.742), and given this non-significant result and a high proportion of neutral responses (51.6%), is presented as an area that may warrant further, exploratory review rather than a confirmed design weakness. Discussion The results suggest that a structured instructional-design approach was associated with positive learner perceptions in this agriculture MOOC, while cognitive-load management in slide design may merit further, cautious attention in future course iterations.","背景：精准农业（Precision Agriculture, PA）日益需要能够运用基于传感器、地理空间和数据驱动技术的毕业生，然而在许多资源不足的高等教育环境中，获得专业培训的机会仍然不均衡。尽管大规模开放在线课程（Massive Open Online Courses, MOOCs）为弥合这一差距提供了一种潜在的可扩展方案，但针对精准农业MOOC以及更广泛的巴勒斯坦高等教育中基于MOOC的学习的实证评估仍然有限，因此需要关于学习者在这一特定教育情境中满意度和信心的证据。方法：本研究通过对参与巴勒斯坦高等教育BENEFIT Erasmus+项目所开发的精准农业MOOC的学习者进行横断面问卷评估来填补这一空白；该设计不支持因果推断。在学习者参与MOOC后发放的问卷由69名受邀学习者中的62名完成（应答率89.9%）。课程评估工具包括八个满意度条目和九个信心相关条目，采用0-4 Likert量表评分；五个负向表述条目在综合分析时进行反向计分。我们报告描述性统计、针对量表中点的单样本检验、两个综合得分的配对比较以及相关分析，并以Spearman's ρ作为非参数敏感性检验。结果：学习者报告了积极的总体满意度（M = 2.74, SD = 0.57）和积极的总体信心（M = 2.54, SD = 0.40），两者均显著高于量表中点（均p \u003C 0.001, d > 1.2）。认同度最高的方面为感知的MOOC设计质量（82.3%）、课程愉悦感（75.8%）、掌握内容的信心（75.8%）以及通过考试的信心（75.8%）。满意度与信心呈强正相关[r = 0.746, 95% CI (0.610, 0.839), p \u003C 0.001; ρ = 0.757]；由于这两个构念是在同一横断面调查中通过自我报告同时测量的，因此该关联无法确定两者关系的方向或因果关系。满意度显著高于信心（平均差 = 0.20, p \u003C 0.001, dz = 0.53）。单个条目——感知的幻灯片信息密度——与中性中点无显著差异（p = 0.742），鉴于这一非显著结果以及较高比例的中性回答（51.6%），将其作为一个可能值得进一步","Frontiers in Education","2026-10-16T00:00:00Z",61,{"impact":100,"substance":17,"depth":19,"authority":53,"freshness":70,"relevant":21,"comment":412},"针对巴勒斯坦精准农业慕课的小样本横断面满意度研究，方法规范、数据透明，但样本仅62人且无因果推断，属区域性教育实证，公共影响有限，可作主题聚合素材而非每日精选头条。",[414],{"name":408,"url":405},[75,26,416,417,418],"农业慕课","巴勒斯坦","高等教育",{"code":106,"slug":420,"name":421,"path":422,"parent_code":83},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":91,"locked":14,"version":33,"method":34,"evidence":425},{"candidates":426,"agricultural_evidence":47},[427,433,440],{"code":106,"score":428,"supported":47,"title":429,"abstract":431,"tags":432},15,[430],"precision agriculture",[110,430],[75],{"code":434,"score":57,"supported":47,"title":435,"abstract":436,"tags":439},"I7",[],[437,438],"因果推断","causal inference",[],{"code":43,"score":21,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[26],[445,446],"BENEFIT Erasmus 精准农业 MOOC","巴勒斯坦 精准农业 慕课","BENEFITErasmus精准农业MOOC-5160","10.3389\u002Ffeduc.2026.1954745",{"doi":448,"openalex_id":450,"authors":451,"venue":408,"cited_by_count":156,"oa_url":471,"card":472,"direction":163,"ingested_from":164},"W7222418765",[452,454,456,459,462,465,468],{"name":453,"orcid":8},"Walid Khalilia",{"name":455,"orcid":8},"Hazem Horoub",{"name":457,"orcid":458},"Hani Iwidat","https:\u002F\u002Forcid.org\u002F0009-0007-4824-6711",{"name":460,"orcid":461},"Faiz Abdelhafid","https:\u002F\u002Forcid.org\u002F0000-0003-4758-9142",{"name":463,"orcid":464},"Radoslav Židek","https:\u002F\u002Forcid.org\u002F0000-0003-4751-1257",{"name":466,"orcid":467},"Ľubomí­r Belej","https:\u002F\u002Forcid.org\u002F0000-0001-8523-6650",{"name":469,"orcid":470},"Zuzana Palková","https:\u002F\u002Forcid.org\u002F0000-0002-5782-8656","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Feducation\u002Farticles\u002F10.3389\u002Ffeduc.2026.1954745\u002Fpdf",{"tldr":473,"method":474,"finding":475,"direction":161,"opportunity":476},"评估巴勒斯坦高校学习者对精准农业MOOC的满意度与信心。","横断面问卷，62名学习者，8项满意度+9项信心量表，描述统计与相关分析。","满意度与信心均显著高于中点且强正相关，满意度高于信心。","可研究资源匮乏地区农业MOOC的设计优化及满意度与信心的因果机制。","2026-10-17T23:30:16.666582Z"]