[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2127":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":49},2127,"Beyond digital access: how does digital information complement agricultural production services to promote green technology adoption? Micro-evidence from Jiangxi Province, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1916148","In the context of agricultural green transformation, whether digital information can move beyond information provision and promote smallholders’ adoption of green technologies through complementarity with agricultural production services is an important theoretical and practical question. Using survey data from 1,040 rice-farming households in Jiangxi Province, China, this study constructs indicators of digital information, agricultural production services, and the number of core green technologies adopted. A Poisson pseudo-maximum likelihood (PPML) model is used to estimate their individual and interactive effects, supplemented by marginal-effect analysis, a supermodularity test, robustness checks, endogeneity analyses, mechanism tests, and heterogeneity analyses. The results show that (1) both digital information and agricultural production services significantly promote the adoption of core green technologies. In the full model, their coefficients are 0.329 and 0.330, respectively, while the interaction coefficient is 0.702 and significant at the 5% level. (2) Further tests confirm that the marginal effect of agricultural production services increases with the level of digital information, the average cross-partial effect is significantly positive, and the predicted adoption count is highest under the high-digital-information and high-production-services scenario. The supermodularity difference is 0.132, providing further evidence of complementarity. (3) The main findings remain robust to alternative dependent variables, model specifications, sample adjustments, and the use of double machine learning, instrumental variables, and entropy balancing. (4) Mechanism analyses suggest that complementarity promotes green technology adoption by easing labor constraints and improving farmers’ awareness of green policies. (5) Within-group estimates further indicate that the effect is more pronounced among farmers who own agricultural machinery, live in villages with better transportation conditions, and operate larger rice-farming areas. Overall, digital information does not simply substitute for agricultural production services; rather, it strengthens their conversion into actual green technology adoption. These findings support closer integration between digital information platforms and agricultural service systems.","在农业绿色转型背景下，数字信息能否超越信息供给功能，通过与农业生产性服务的互补性促进小农户采纳绿色技术，是一个重要的理论与实践问题。本研究基于中国江西省1040户水稻种植农户的调查数据，构建了数字信息、农业生产性服务与核心绿色技术采纳数量的指标，采用泊松伪最大似然（PPML）模型估计其个体效应与交互效应，并辅以边际效应分析、超模性检验、稳健性检验、内生性分析、机制检验和异质性分析。结果表明：（1）数字信息与农业生产性服务均显著促进核心绿色技术采纳，在全模型中二者系数分别为0.329和0.330，交互项系数为0.702，且在5%水平上显著。（2）进一步检验证实，农业生产性服务的边际效应随数字信息水平提高而增大，平均交叉偏效应显著为正，高数字信息与高生产性服务情景下预测采纳数量最高，超模性差异为0.132，进一步佐证了互补性。（3）主要结论在替换因变量、模型设定、样本调整以及使用双重机器学习、工具变量和熵平衡等方法后依然稳健。（4）机制分析表明，互补性通过缓解劳动力约束和提高农户对绿色政策的认知来促进绿色技术采纳。（5）分组估计进一步显示，该效应在拥有农业机械、所在村庄交通条件较好以及水稻种植面积较大的农户中更为明显。总体而言，数字信息并非简单替代农业生产性服务，而是强化了其向实际绿色技术采纳的转化。上述发现支持数字信息平台与农业服务体系之间更紧密的融合。",null,"Frontiers in Sustainable Food Systems","2026-09-10T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于江西1040户稻农微观调查，实证揭示数字信息与农业生产服务的互补效应，方法规范、结论可靠，对数字乡村与绿色转型政策有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","农业信息化","绿色技术采纳","农业社会化服务","水稻种植",0,"10.3389\u002Ffsufs.2026.1916148",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":42,"direction":46,"ingested_from":48},"W7212107776",[36,38,40],{"name":37,"orcid":9},"Yanqiu Tong",{"name":39,"orcid":9},"Zaiwei Yang",{"name":41,"orcid":9},"Zhaojiu Chen",{"tldr":43,"method":44,"finding":45,"direction":46,"opportunity":47},"基于江西1040户稻农数据，检验数字信息与农业生产服务对绿色技术采用的互补效应。","PPML模型、边际效应、超模性检验、工具变量与双重机器学习。","数字信息与生产服务显著互补，通过缓解劳动约束和提升政策认知促进绿色技术采用。","农业绿色发展与碳","可探究数字平台与服务系统协同的机制设计，及不同作物和区域的异质性推广路径。","openalex","2026-09-11T23:30:07.699262Z"]