[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-269":3,"related-269":70},{"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":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":64,"slug":67,"view_count":13,"doi":68,"paper":8,"created_at":69},269,"Digital agricultural technology innovation and agricultural green total factor productivity: county-level evidence from China","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026年8月3日Frontiers in Sustainable Food Systems上线的县级面板实证研究,基于2014-2021年中国县级平衡面板数据,结合县级社会经济统计与农业专利记录,开发县级数字农业技术创新(DATI)测量指标,系统考察DATI对农业绿色全要素生产率(AGTFP)的影响。研究发现:第一,DATI显著提升AGTFP,但其效应因区域、数字基础设施禀赋、农业发展条件而异;第二,DATI对AGTFP的影响呈现显著阈值特征,数字基础设施与财政支农力度是关键阈值变量,一旦跨越相关阈值,估计效应由负转正;第三,DATI呈现显著空间依赖性,但其空间溢出效应有限,表明数字农业技术创新所产生的绿色生产率增益主要呈现本地化特征。研究为通过加强农村数字基础设施、深化数字技术在农业生产中的应用、因地制宜推进数字化与绿色化协同转型提供县级证据支持。",null,"## Abstract\n\nDigital agricultural technology innovation (DATI) has become an important driver of agricultural modernization and green transformation. By integrating county-level socioeconomic statistics with agricultural patent records, this study develops a county-level measure of DATI and provides more fine-grained evidence on how digital agricultural technology innovation affects agricultural green total factor productivity in China. Using a balanced panel of Chinese counties data from 2014 to 2021, we examine the effect of DATI on agricultural green total factor productivity (AGTFP). The results reveal three main findings. First, DATI significantly improves AGTFP, although its effects differ across regions, digital infrastructure endowments, and agricultural development conditions. Second, the effect of DATI on AGTFP displays pronounced threshold characteristics, with digital infrastructure and fiscal support for agriculture acting as key threshold variables; once the relevant thresholds are crossed, the estimated effect turns from negative to positive. Third, DATI exhibits significant spatial dependence, but its spatial spillover effects are limited, indicating that the green productivity gains generated by digital agricultural technology innovation remain largely localized. These findings provide fine-grained county-level evidence on the role of DATI in promoting green agricultural development and underscore the need to strengthen rural digital infrastructure, deepen the application of digital technologies in agricultural production, and tailor the coordinated digital and green transformation of agriculture to local conditions.\n\n## 1 Introduction\n\nAgricultural green total factor productivity is widely recognized as a key indicator of agricultural green development and sustainability. Enhancing AGTFP is essential not only for improving resource-use efficiency and environmental performance but also for advancing agricultural modernization and rural revitalization. In recent years, the rapid integration of digital technologies into agricultural production has created new opportunities for agricultural green transformation. Existing studies suggest that digital technologies can improve information acquisition, expand market access, and enhance resource allocation efficiency, thereby promoting agricultural performance and rural development (; ; ). Meanwhile, the proliferation of digital platforms, e-commerce networks, and information and communication technology (ICT) services has significantly expanded market opportunities and improved agricultural productivity, particularly in rural areas where traditional information channels remain limited (; ; ). Digital agricultural technology innovation, represented by big data, artificial intelligence (AI), smart agricultural machinery, and digital management platforms, has emerged as a critical force driving agricultural green development.\n\nThe relationship between digitalization and AGTFP has attracted increasing attention in the agricultural economics. Existing evidence indicates that digital technologies can promote AGTFP through technological innovation, industrial upgrading, and improvements in production efficiency (; ; ). From green development perspective, technological innovation is widely regarded as a fundamental driver of green productivity growth, as it enhances resource-use efficiency, reduces environmental costs, and facilitates environmentally directed technological change (; ). These findings suggest that digital agricultural technology innovation is not only an important manifestation of agricultural digital transformation but also a potential pathway for simultaneously achieving productivity growth and environmental sustainability.\n\nHowever, the effectiveness of digital agricultural technology innovation depends on the successful adoption and diffusion of new technologies. Existing studies emphasize that learning processes, social interactions, and absorptive capacities are crucial in determining whether technological advances can be translated into productivity gains (; ; ). As digital technologies become increasingly embedded in agricultural production systems, digital agricultural technology innovation can accelerate the diffusion of green technologies, improve production efficiency, reduce production risks, and facilitate agricultural modernization (; ). Nevertheless, its impacts are unlikely to be uniform across regions, as they are conditioned by differences in digital infrastructure, institutional environments, and local absorptive capacities. Existing evidence further shows that high-quality innovation can significantly enhance green productivity and strengthen the environmental benefits of technological progress (; ). Moreover, innovation activities often generate knowledge diffusion and interregional learning effects, enabling technological benefits to extend beyond local jurisdictions (; ). Given that knowledge spillovers and factor mobility frequently transcend administrative boundaries, innovation activities tend to exhibit significant spatial dependence and spillover effects (; ). Therefore, digital agricultural technology innovation may not only improve local AGTFP but also promote agricultural green development in neighboring regions through technology diffusion and knowledge spillovers.\n\nOverall, existing studies have highlighted the positive role of digitalization in agricultural green development. However, several important gaps remain. First, most existing studies rely on provincial- or prefecture-level data, while county-level evidence remains relatively scarce despite counties being the primary units for agricultural production and technology adoption in China. In China, counties constitute a key spatial unit for agricultural production, technology extension, infrastructure provision, and rural policy implementation, and a large share of agricultural activities is organized at the county level. Compared with analyses based on provincial or prefecture-level data, a county-level approach can more precisely capture local heterogeneity in agricultural resource endowments, digital infrastructure, technology diffusion capacity, and policy implementation. This finer spatial scale is therefore particularly suitable for examining the effect of digital agricultural technology innovation on agricultural green total factor productivity. By combining county-level panel data with an SBM-based measure of AGTFP that accounts for undesirable outputs, this study provides a more disaggregated empirical assessment of the green productivity effects of digital agricultural technology innovation. In doing so, it extends the literature on digital agriculture and agricultural green development by moving beyond broad measures of digitalization and by identifying the role of agricultural digital innovation at the spatial level where agricultural technologies are adopted and rural policies are implemented.\n\nDigitalization is typically measured using broad indicators such as digital economy indices, internet penet ration rates, or digital finance indicators, whereas relatively few studies directly capture digital agricultural technology innovation from the perspective of agricultural digital innovation and agricultural technology patents. Third, although increasing attention has been paid to threshold effects and spatial dependence, a unified analytical framework integrating digital agricultural technology innovation, threshold effects, spatial spillovers, and AGTFP remains underdeveloped. To address these gaps, this study employs Chinese county-level panel data and incorporates digital agricultural technology innovation, threshold models, and spatial Durbin models into a unified analytical framework to systematically examine the impact of digital agricultural technology innovation on AGTFP and its underlying mechanisms. This study provides new county-level evidence on how digital agriculture contributes to agricultural green transformation and sustainable development.\n\nThis study contributes to the existing literature in three principal respects. First, it shifts the analytical focus from general digitalization to digital agricultural technology innovation. Using county-level agricultural patent information, we capture the creation and accumulation of digital technological knowledge in agriculture and examine how agricultural digital technologies affect county-level AGTFP. Second, the study provides county-level evidence and thereby addresses the relative scarcity of fine-grained empirical evidence in the existing literature. Counties are key spatial units for agricultural production, technology adoption, infrastructure provision, and rural policy implementation in China; thus, a county-level perspective allows for a more precise identification of local heterogeneity in the effect of digital agricultural technology innovation on AGTFP. Third, within an integrated framework linking digital agricultural technology innovation and green productivity, this study examines whether DATI improves AGTFP, thereby deepening our understanding of the mechanisms through which digital agriculture contributes to green agricultural transformation.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Digital economy and agricultural green total factor productivity\n\nDigital economy has become an important driver of agricultural green transformation. find that digital inclusive finance significantly improves AGTFP by promoting agricultural technological innovation and industrial structure optimization. emphasize that digital finance promotes AGTFP partly through digital village development. explored the impact of digital transformation on supply chain resilience among Chinese listed agricultural firms. The results demonstrate that digital transformation significantly enhances supply chain resilience. Overall, this strand of literature suggests that the digital economy improves AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, and rural digital transformation. Recent studies have extended this line of research from the perspectives of digitalization, digital technology adoption, and digital village development. shows that the use of digital technologies can effectively improve agricultural productivity across EU countries. further indicate that ICT adoption contributes to higher land and labor productivity in agriculture. provide recent evidence that agricultural and rural digitalization significantly increases agricultural green total factor productivity (AGTFP), while show that digital village development affects AGTFP through both supply-side and demand-side mechanisms. Taken together, the existing literature suggests that the digital economy can improve AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, ICT diffusion, and rural digital transformation. Based on the above analysis, we propose research hypothesis 1 as follows:\n\n> _H1_: Digital technology applications significantly promote AGTFP.\n\n### 2.2 Digital technology applications and agricultural green total factor productivity\n\nAnother strand of literature focuses on the application of digital technologies in agricultural production. show that big data in smart farming improves decision-making efficiency across agricultural production and the food supply chain. show that digital agriculture development significantly contributes to AGTFP improvement. demonstrate that deep learning can be applied to crop monitoring, disease detection, yield prediction, and agricultural automation. shows that meaningful participation in digital technologies varies across actors, and that differences in technological perceptions and interpretations influence the diffusion and adoption of digital agricultural technologies. emphasize that farmers’ technology adoption behavior determines whether precision agricultural technologies can effectively improve production efficiency. Therefore, digital technology applications promote AGTFP mainly by improving input-use efficiency, reducing agricultural pollution, strengthening monitoring capacity, and improving production decision-making.\n\nDigital agricultural technologies are not merely general ICT applications, but are embedded in agricultural production systems, farm management, and technology adoption decisions. discuss the opportunities and potential dilemmas associated with agricultural digitalization, suggesting that digital transformation in agriculture should be evaluated in relation to production efficiency, inclusiveness, and sustainability. emphasizes that digital transformation of the agricultural sector can reshape production practices through data collection, monitoring, decision support, and precision input application. further show that the digital revolution in agriculture is driven not only by farmers’ demand but also by supply-side actors, including input firms, software providers, digital service platforms, and technology start-ups. argues that digital innovations can contribute to sustainable and resilient agricultural systems by improving productivity, reducing environmental pressure, and strengthening adaptive capacity. also show that farmers’ preferences and perceived technology attributes are important determinants of precision farming adoption. In addition, agricultural technology innovation and agricultural patents provide an important foundation for understanding DATI. examine China’s agricultural patents and show that agricultural patent value has changed amid the rapid expansion of patent applications, indicating that agricultural patents can reflect important dimensions of agricultural technological innovation. Related studies highlight the roles of environmental regulation, digital technologies, and technological progress in agricultural green development. shows that environmental regulation promotes agricultural green innovation and AGTFP, emphasize the potential of agricultural IoT for monitoring, precision management, and data-driven decision-making. further demonstrate that green innovation, human capital, and infrastructure are important drivers of agricultural green productivity growth.\n\nIn this study, the role of agricultural technological innovation is conceptualized as a transmission mechanism rather than a moderating effect. A moderating effect would imply that agricultural technological innovation changes the strength or direction of the relationship between digital technology applications and AGTFP. However, our theoretical framework emphasizes that digital agricultural technology applications affect AGTFP through specific transmission channels. More specifically, digital agricultural technologies improve agricultural production efficiency, enhance agricultural value creation, facilitate technology adoption and diffusion, and improve rural development conditions. These changes subsequently contribute to the improvement of AGTFP. Therefore, the mechanism is examined through channel variables that capture productivity enhancement and rural development improvement. Based on the above analysis, we propose research hypothesis 2 as follows:\n\n> _H2_: Digital agricultural technology applications promote AGTFP by enhancing agricultural value creation and improving rural development conditions.\n\n### 2.3 Spatial effects of digital agricultural technology innovation on agricultural green total factor productivity\n\nAccording to the theories of new economic geography and spatial spillovers, knowledge, technology, and production factors can flow across regional boundaries, thereby generating significant spatial interactions. Digital agricultural technology innovation is characterized by strong openness, connectivity, and diffusibility. Consequently, it may not only improve local AGTFPbut also influence on neighboring regions through knowledge spillovers, technology diffusion, and factor mobility. Existing studies have documented significant spatial spillover effects of digitalization. found that the digital economy promotes agricultural green development and generates positive effects on neighboring regions through spatial interaction mechanisms. further demonstrated that the digital economy exerts significant spatial spillover effects on green total factor productivity, primarily through green technological innovation and industrial upgrading. showed that carbon trading policies not only improve AGTFP in pilot regions but also generate spillover effects on surrounding areas through technology diffusion and policy transmission. found that the digital economy significantly enhances green total factor productivity and exhibits strong spatial dependence. further confirmed that the digital economy improves green productivity through technological innovation diffusion and cross-regional factor mobility, thereby generating significant spatial spillover effects. Therefore, the knowledge spillover, technology diffusion, and resource allocation effects generated by digital agricultural technology innovation may extend beyond administrative boundaries and influence AGTFP in neighboring regions. Digital agricultural technology innovation may not only promote local agricultural green transformation but also facilitate agricultural green development in surrounding regions through interregional learning and information sharing. Based on the above analysis, the following hypothesis is proposed:\n\n> _H3_: Digital agricultural technology innovation exhibits spatial correlation and generate positive spatial spillover effects on agricultural green total factor productivity.\n\nTo clarify the theoretical basis of [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1), this study constructs the analytical framework by integrating technological innovation theory, innovation diffusion theory, green total factor productivity theory, and agricultural sustainable development theory. Digital agricultural technology innovation (DATI) refers to the innovation, diffusion, and application of digital technologies in agricultural production, agricultural resource allocation, rural governance, and agricultural industrial chains. It includes not only the generation of digital agricultural technologies, such as digital patents and intelligent agricultural equipment, but also their practical application in agricultural production and rural development.\n\nFigure 1\n\nBased on this conceptual framework, DATI affect AGTFP through three main pathways. First, the application of digital technologies in agriculture promotes agricultural digitalization and intelligent transformation, improves the allocation and utilization efficiency of agricultural resources, and thereby facilitates the green transformation of agricultural production. Second, DATI accelerates agricultural technology diffusion and innovation, promotes the modernization of agricultural industrial chains, and enhances agricultural technological progress and industrial upgrading. Third, DATI supports rural digitalization and green transformation, improves the rural ecological environment and factor mobility, and thus creates favorable conditions for the growth of AGTFP. [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1) illustrates these theoretical mechanisms.\n\n## 3 Data, variables and methodology\n\n### 3.1 Empirical model\n\nThe empirical models are organized according to the research hypotheses. [Equation 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E1) is used to estimate the baseline effect of DATI on AGTFP. [Equation 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E2) is used","Frontiers in Sustainable Food Systems 2026-08-03","2026-08-03T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,20,13,5,1,"基于县级数据的实证研究，揭示数字农业技术创新对绿色全要素生产率的影响及门槛效应，方法新颖，结论可靠，对政策制定有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"Frontiers","空间溢出","阈值效应","农业绿色全要素生产率","数字农业技术创新","县级面板",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,59],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},3,true,[],[51],"财政支农",[],{"code":54,"score":47,"supported":48,"title":55,"abstract":56,"tags":58},"I1",[],[57],"数字农业",[],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"I4",[],[],[28],[65,66],"农业绿色全要素生产率 数字农业技术创新 县级面板 空间溢出","农业绿色全要素生产率 数字农业技术创新","农业绿色全要素生产率数字农业技术创新县级面板空间溢出-269","10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026-08-07T00:18:31.266403Z",{"total":71,"page":22,"page_size":71,"items":72},6,[73,122,184,259,331,373],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":8,"content":8,"source_name":78,"source_url":8,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":85,"tags":87,"primary_topic":92,"secondary_topics":93,"classification":94,"search_phrases":106,"slug":109,"view_count":110,"doi":111,"paper":112,"created_at":121},5463,"中国粮食生产现代化时空演变特征及影响因素分析 (Spatiotemporal Evolution and Influencing Factors of Grain Production Modernization in China)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042","基于2011—2023年省级面板数据，六维构建粮食生产现代化指标，用核密度、空间自相关与GTWR分析。水平稳步提升，呈主产区＞主销区＞产销平衡区梯度；山东、江苏、安徽形成稳定高—高集聚区。数字普惠金融与财政支农正向。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",79,{"impact":17,"substance":82,"depth":17,"authority":20,"freshness":83,"relevant":22,"comment":84},21,9,"基于省级面板数据构建六维指标并采用GTWR方法，结论对粮食生产现代化区域差异与数字普惠金融作用有参考价值，但属学术论文，公共影响有限。",[86],{"name":78,"url":76},[88,51,89,90,91],"粮食安全","数字普惠金融","粮食生产现代化","空间集聚",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":95},{"candidates":96,"agricultural_evidence":48},[97,101],{"code":34,"score":21,"supported":48,"title":98,"abstract":99,"tags":100},[],[51],[89,51],{"code":102,"score":22,"supported":14,"title":103,"abstract":104,"tags":105},"A8",[],[],[88],[107,108],"粮食生产现代化 时空演变","数字普惠金融 粮食生产","粮食生产现代化时空演变-5463",0,"10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042",{"doi":111,"openalex_id":8,"authors":113,"venue":8,"cited_by_count":110,"oa_url":8,"card":114,"direction":118,"ingested_from":120},[],{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"构建六维指标测度2011—2023年中国粮食生产现代化水平并分析其时空演变与影响因素。","省级面板数据，六维指标体系，核密度、空间自相关与GTWR模型。","现代化水平稳步提升，呈主产区>主销区>产销平衡区梯度，山东、江苏、安徽为稳定高—高集聚区。","数字乡村与农业信息化","可探究数字普惠金融与财政支农的协同机制，并细化到县域尺度检验空间溢出效应。","agent","2026-10-21T00:10:08.522187Z",{"id":123,"title":124,"url":125,"summary":126,"summary_zh":8,"content":8,"source_name":127,"source_url":8,"published_at":128,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":129,"score_detail":130,"sources":134,"tags":136,"primary_topic":142,"secondary_topics":143,"classification":150,"search_phrases":169,"slug":172,"view_count":173,"doi":174,"paper":175,"created_at":183},4730,"政策型农业信贷担保能否激励农户采纳秸秆还田技术","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1948304\u002Ffull","基于微观数据与变量法,实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。研究发现该政策显著促进采纳,主要通过推动社会化服务购买与绿色信贷期限匹配生产周期实现,对数字素养较高、正式社会资本匮乏的农户效应更强","Frontiers in Sustainable Food Systems 2026-10-09","2026-10-09T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":131,"freshness":132,"relevant":22,"comment":133},14,8,"基于微观数据的实证研究，揭示政策型信贷担保通过社会化服务与绿色信贷期限匹配促进秸秆还田，结论具政策参考价值。",[135],{"name":127,"url":125},[137,138,139,140,141],"社会化服务","秸秆还田","农业信贷担保","绿色金融","农户技术采纳",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[144],{"code":145,"slug":146,"name":147,"path":148,"parent_code":149},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste","H",{"status":41,"locked":14,"version":42,"method":43,"evidence":151},{"candidates":152,"agricultural_evidence":48},[153,158,163],{"code":34,"score":20,"supported":48,"title":154,"abstract":156,"tags":157},[155],"农业信贷",[155],[139,140],{"code":145,"score":159,"supported":48,"title":160,"abstract":161,"tags":162},12,[138],[138],[138],{"code":164,"score":165,"supported":48,"title":166,"abstract":167,"tags":168},"G2",4,[],[137],[137],[170,171],"农业信贷担保 秸秆还田","绿色信贷 农户 技术采纳","农业信贷担保秸秆还田-4730",2,"10.3389\u002Ffsufs.2026.1948304\u002Ffull",{"doi":174,"openalex_id":8,"authors":176,"venue":8,"cited_by_count":110,"oa_url":8,"card":177,"direction":181,"ingested_from":120},[],{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。","基于微观数据与变量法进行实证分析。","政策显著促进采纳，通过社会化服务购买与绿色信贷期限匹配实现。","农业绿色发展与碳","可探究数字素养与信贷担保协同机制，及对异质性农户的精准政策设计。","2026-10-12T00:11:14.617567Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":8,"source_name":190,"source_url":187,"published_at":191,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":195,"tags":197,"primary_topic":203,"secondary_topics":204,"classification":205,"search_phrases":234,"slug":237,"view_count":173,"doi":238,"paper":239,"created_at":258},4481,"Climate policy uncertainty and agricultural digital-intelligent transformation in China: evidence from provincial panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1909691","Introduction Climate policy uncertainty can affect long-term investment decisions in agriculture, yet its influence on agricultural digital-intelligent transformation remains insufficiently understood. This study examines how lagged climate policy uncertainty affects agricultural digital-intelligent transformation and whether rural finance and agricultural service policies can moderate this relationship. Methods Using panel data from 31 Chinese provinces from 2013 to 2022, we construct an agricultural digital-intelligent transformation index. A two-way fixed effects model, instrumental variable estimation, heterogeneity analysis, and moderation tests are adopted for empirical analysis. Results The results show that climate policy uncertainty significantly inhibits agricultural digital-intelligent transformation, and this negative effect remains evident after addressing potential endogeneity. Heterogeneity analysis indicates that the inhibitory effect is significant only in regions with moderately developed digital finance and in major grain-consuming areas. Further analysis shows that loans to agriculture, forestry, animal husbandry and fishery industries and the pilot policy for full-process socialized agricultural services positively moderate this relationship and weaken the adverse effect of climate policy uncertainty. Discussion These findings contribute to the literature on climate policy uncertainty and sustainable agricultural transformation by linking policy expectations, agriculture-related loans, and digital-smart agriculture development. The results suggest that stabilizing climate policy expectations, improving rural credit support, and strengthening agricultural service systems are important for promoting climate-resilient and regionally differentiated digital transformation in agriculture.","引言 气候政策不确定性会影响农业的长期投资决策，但其对农业数字化智能化转型的影响尚缺乏充分认识。本研究考察滞后气候政策不确定性如何影响农业数字化智能化转型，以及农村金融和农业服务政策能否调节这一关系。方法 基于2013—2022年中国31个省份的面板数据，构建农业数字化智能化转型指数，采用双向固定效应模型、工具变量估计、异质性分析和调节效应检验进行实证分析。结果 结果表明，气候政策不确定性显著抑制农业数字化智能化转型，且在处理潜在内生性后这一负向效应依然明显。异质性分析表明，该抑制效应仅在数字金融中等发达地区及粮食主销区显著。进一步分析显示，农、林、牧、渔业贷款和农业生产全程社会化服务试点政策对这一关系具有正向调节作用，削弱了气候政策不确定性的不利影响。讨论 这些发现通过将政策预期、涉农贷款与数字智慧农业发展联系起来，为气候政策不确定性与农业可持续转型的文献作出了贡献。结果表明，稳定气候政策预期、完善农村信贷支持、健全农业服务体系，对于促进农业气候韧性型和区域差异化的数字化转型具有重要意义。","Frontiers in Sustainable Food Systems","2026-10-08T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":131,"freshness":83,"relevant":22,"comment":194},"基于31省十年面板数据的实证研究，揭示气候政策不确定性对农业数字化智能化转型的抑制效应及农村金融、农业社会化服务的调节作用，方法规范、结论有政策参考价值，值得进入每日精选。",[196],{"name":190,"url":187},[198,199,200,201,202],"数字乡村","智慧农业","农村金融","农业社会化服务","气候政策",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":206},{"candidates":207,"agricultural_evidence":48},[208,214,219,223,229],{"code":34,"score":209,"supported":48,"title":210,"abstract":211,"tags":213},7,[],[200,212],"rural finance",[200],{"code":54,"score":209,"supported":48,"title":215,"abstract":216,"tags":218},[],[199,217],"smart agriculture",[199],{"code":164,"score":165,"supported":48,"title":220,"abstract":221,"tags":222},[],[137],[201],{"code":224,"score":47,"supported":48,"title":225,"abstract":226,"tags":228},"D",[],[227],"渔业",[],{"code":230,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},"I6",[],[],[198],[235,236],"气候政策不确定性 农业数字化 省级面板","农业数字化智能化转型 农村金融","气候政策不确定性农业数字化省级面板-4481","10.3389\u002Ffsufs.2026.1909691",{"doi":238,"openalex_id":240,"authors":241,"venue":190,"cited_by_count":110,"oa_url":187,"card":251,"direction":256,"ingested_from":257},"W7221013071",[242,245,247,249],{"name":243,"orcid":244},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":246,"orcid":8},"Yuanyi Yang",{"name":248,"orcid":8},"Yanru Yu",{"name":250,"orcid":8},"Yanwei Zhang",{"tldr":252,"method":253,"finding":254,"direction":118,"opportunity":255},"研究气候政策不确定性如何抑制中国农业数字化智能化转型，并检验农村金融与服务政策的调节作用。","2013-2022年31省面板数据，构建农业数字化智能化转型指数，用双向固定效应","气候政策不确定性显著抑制农业数字化智能化转型，该效应仅在数字金融中等发达地区和粮食主销区显著。","可深入探究气候政策预期稳定机制与数字金融、农业社会化服务的协同路径，及区域差异化转型策略。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:06.185798Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":8,"source_name":190,"source_url":262,"published_at":265,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":272,"tags":274,"primary_topic":279,"secondary_topics":280,"classification":286,"search_phrases":306,"slug":309,"view_count":47,"doi":310,"paper":311,"created_at":330},4289,"When climate risk intensifies, does insurance pay off? A community case study of China's agricultural insurance reform","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1954205","In the context of mounting global climate threats, fostering resilience in food production systems has become a critical imperative for upholding national food security. This paper exploits the staggered implementation, beginning in 2018, of China's agricultural insurance reforms covering full production costs and planting income as a quasi-natural experiment. Drawing on provincial panel data from 2013 to 2024, we first develop a multidimensional composite index to capture food production resilience. A staggered difference-in-differences specification is then adopted to systematically assess the causal effect of these reforms on such resilience and to explore the underlying transmission channels. Moreover, from a novel climate change perspective, the analysis examines the heterogeneous treatment effects across varying degrees of climate risk exposure. The empirical findings reveal that the policy-led agricultural insurance reform significantly and robustly enhances China's food production resilience. Mechanism tests indicate that advances in agricultural technology and the expansion of large-scale farming operations serve as important pathways through which the insurance policy takes effect. Further investigation shows that climate change risk positively moderates the policy impact: as climate risk intensifies, the marginal contribution of insurance to resilience becomes stronger. Heterogeneity analysis also demonstrates that the policy effect is more pronounced in western China and in localities with higher rates of disaster-induced losses. Taken together, these results offer solid empirical backing for broadening the pilot programs of full cost and planting income insurance. They also establish a theoretical basis for positioning publicly supported agricultural insurance as an effective instrument for climate change adaptation, providing valuable guidance on strengthening the climate adaptability and resilience of food production to ensure national food security.","在全球气候威胁日益加剧的背景下，增强粮食生产系统的韧性已成为维护国家粮食安全的关键任务。本文以2018年起分阶段实施的中国农业保险覆盖完全生产成本和种植收入的改革为准自然实验，基于2013—2024年省级面板数据，首先构建多维复合指数以刻画粮食生产韧性，继而采用交错双重差分模型系统评估该改革对粮食生产韧性的因果效应并探究其传导机制。此外，本文从气候变化这一新视角出发，考察了不同气候风险暴露程度下的异质性处理效应。实证结果表明，政策主导的农业保险改革显著且稳健地提升了中国粮食生产韧性。机制检验表明，农业技术进步与规模化经营扩张是保险政策发挥作用的重要路径。进一步研究发现，气候变化风险对政策效应具有正向调节作用：随着气候风险加剧，保险对韧性的边际贡献增强。异质性分析还表明，政策效应在中国西部地区以及灾害损失率较高的地区更为显著。综上，上述结果为扩大完全成本保险和种植收入保险试点提供了坚实的经验依据，也为将公共支持的农业保险定位为气候变化适应的有效工具奠定了理论基础，为增强粮食生产的气候适应性与韧性、保障国家粮食安全提供了有价值的政策启示。","2026-10-05T00:00:00Z",88,{"impact":268,"substance":269,"depth":270,"authority":131,"freshness":132,"relevant":22,"comment":271},24,23,19,"基于2013-2024年省级面板数据的交错DID准自然实验，证实农业保险改革显著提升粮食生产韧性且气候风险具有正向调节作用，方法规范、结论可靠，对完善农业保险试点具有政策参考价值。",[273],{"name":190,"url":262},[88,275,276,277,278],"农业保险","农业政策","气候适应","生产韧性",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[281],{"code":282,"slug":283,"name":284,"path":285,"parent_code":149},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":41,"locked":14,"version":42,"method":43,"evidence":287},{"candidates":288,"agricultural_evidence":48},[289,295,301],{"code":34,"score":290,"supported":48,"title":291,"abstract":293,"tags":294},15,[292],"agricultural insurance",[275,292],[275],{"code":282,"score":13,"supported":48,"title":296,"abstract":297,"tags":300},[],[298,277,299],"气候变化","climate change",[277],{"code":102,"score":209,"supported":48,"title":302,"abstract":303,"tags":305},[],[88,304],"food security",[88],[307,308],"农业保险改革 粮食生产韧性","完全成本保险 种植收入保险","农业保险改革粮食生产韧性-4289","10.3389\u002Ffsufs.2026.1954205",{"doi":310,"openalex_id":312,"authors":313,"venue":190,"cited_by_count":110,"oa_url":262,"card":325,"direction":181,"ingested_from":257},"W7220367453",[314,317,319,321,323],{"name":315,"orcid":316},"Changzhe Deng","https:\u002F\u002Forcid.org\u002F0009-0006-0852-5997",{"name":318,"orcid":8},"Maimiao Sun",{"name":320,"orcid":8},"Xueyu Lin",{"name":322,"orcid":8},"Chencan Rao",{"name":324,"orcid":8},"Qiuping Guo",{"tldr":326,"method":327,"finding":328,"direction":181,"opportunity":329},"基于2013-2024省级面板数据，评估中国农业保险改革对粮食生产韧性的因果影响。","多维度韧性指数+交错双重差分，利用2018年起保险改革试点作准自然实验。","保险改革显著提升粮食生产韧性，气候风险越高其边际贡献越强，西部和灾损高地区效果更明显。","可探究保险改革与农业技术、规模化经营的协同机制，及气候风险下保险产品设计与精准赔付的优化路径。","2026-10-06T23:30:05.886883Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":8,"content":336,"source_name":337,"source_url":8,"published_at":338,"category":339,"cover_url":8,"hotness":13,"is_selected":48,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":350,"secondary_topics":351,"classification":352,"search_phrases":368,"slug":371,"view_count":22,"doi":8,"paper":8,"created_at":372},4249,"四部门联合印发加快农业保险高质量发展实施方案","https:\u002F\u002Fwww.fengxin.gov.cn\u002Ffxxrmzf\u002Fzfcgxgxx\u002Fpc\u002Fcontent\u002F2104516031187619840\u002Fcontent_2104516031187619840.html","财政部会同农业农村部、金融监管总局、国家林草局联合印发财金〔2026〕88号《关于加快农业保险高质量发展的实施方案》，是我国农业保险近二十年发展经验的提炼。我国农业保险保费规模从2007年52亿元增长到2025年超1550亿元，每年可为上亿户次农户提供风险保障超5万亿元。","为推动农业保险政策更加普惠、区域发展更加均衡、保障质效持续提升，本次《实施方案》明确了下一阶段发展目标：到2030年，基本建成与农户风险保障需求相契合、与现代农业发展相适应、中央与地方分工负责的多层次农业保险体系，总体达到国际先进水平，实现补贴有效率、产业有保障、农民得实惠、机构可持续的多赢格局。\n\n2019年出台的指导性文件提出了到2022年农业保险深度1%、密度500元\u002F人、三大粮食作物保险覆盖率70%以上等阶段性目标。在此基础上，《实施方案》进一步明确如下发展目标：一是“农业保险深度”，即农业保险保费与第一产业增加值的比值，反映农业保险对农村经济发展的贡献，2030年前达到1.9%。二是“农业保险密度”，即农业保险保费与第一产业就业人员数量的比值，反映农业保险人均惠及保障程度，2030年前达到1200元\u002F人。三是“农业保险科技投入强度”，即农业保险机构科技创新投入与农业保险保费的比值，反映农业保险行业科技创新应用水平，2030年前达到1%。其中，农业保险科技创新投入主要包括农业保险科技工作人员支出、科技服务费用、科技类无形资产和固定资产等项目。四是“三大粮食作物保险覆盖率”，对于关系国计民生和粮食安全的三大粮食作物，全国范围内农业保险覆盖率稳定在90%左右，各省份结合实际制定相关目标。同时，提升地方优势特色农业保险对特色农业产业的保障水平，更好发挥富农强农作用。五是“农业保险综合费用率”，强调不得高于20%，引导农业保险承保机构切实降本增效，将更多资源用于风险保障和理赔服务上。","奉新县人民政府 转载财政部金融司 2026-10-01","2026-10-01T01:00:00Z","政策",92,{"impact":342,"substance":268,"depth":270,"authority":290,"freshness":71,"relevant":22,"comment":343},28,"四部门联合印发的全国性农业保险新政，明确2030年保险深度1.9%、密度1200元\u002F人等量化目标，政策条款与数据增量突出，权威性和专业深度俱佳，值得进入每日精选。",[345],{"name":337,"url":334},[88,347,275,348,349],"惠农政策","农业科技投入","风险保障",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":353},{"candidates":354,"agricultural_evidence":48},[355,359,363],{"code":34,"score":159,"supported":48,"title":356,"abstract":357,"tags":358},[275],[275],[275],{"code":102,"score":22,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[88],{"code":364,"score":22,"supported":14,"title":365,"abstract":366,"tags":367},"J2",[],[],[348],[369,370],"三大粮食作物保险覆盖率 90%","农业科技投入 农业保险 惠农政策 粮食安全","三大粮食作物保险覆盖率90%-4249","2026-10-06T00:04:59.342384Z",{"id":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":8,"source_name":190,"source_url":376,"published_at":379,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":129,"score_detail":380,"sources":382,"tags":384,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":409,"slug":412,"view_count":22,"doi":413,"paper":414,"created_at":428},4051,"Maturity-aware credit governance in digital agricultural finance: leakage-free selective fusion for agri-food SME risk assessment","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1946390","Introduction Digital agricultural lenders need risk estimates that remain trustworthy when loan outcomes mature slowly and contextual data become available at different times. This study introduces DAF-XCredit, a maturity-aware selective-fusion framework for agri-food SME credit-risk assessment. Methods DAF-XCredit combines approval-time loan records with outcome-observable historical priors and lagged agricultural context under a fixed 5-year charge-off target. Chronological out-of-fold base scores are calibrated, and a 2014–2015 validation cohort selects the source blocks used in the final fusion model. Experiments use 67,114 public U.S. Small Business Administration 7(a) agri-food loans approved during 2000–2019 and linked USDA data. The untouched 2016–2019 test cohort contains 12,339 loans. Results Across five seeds, the selected loan-plus-agricultural model achieved an AUC of 0.9497 ± 0.0004, recall of 0.6425 ± 0.0080, F1 of 0.4357 ± 0.0036, Brier score of 0.02356 ± 0.00005, and equal-width expected calibration error of 0.00272 ± 0.00022. DAF-XCredit achieved the highest AUC, recall, and F1 and the lowest Brier score and equal-width calibration error among the evaluated models, whereas M1 and M5 led PR-AUC and KS, respectively. A seed-2026 DeLong test found no statistically significant AUC difference from the loan-only model ( p = 0.254). State–year cluster resampling yielded a calibration slope of 0.990 (95% CI 0.897-1.090), and decision curves showed positive net benefit across thresholds of 0.05–0.30. Discussion Maturity-aware source selection, calibration, SHAP analysis, stability resampling, and subgroup auditing provide an auditable framework for agricultural credit governance. The method can support sustainability-oriented financing decisions but does not itself establish sustainability outcomes.","引言 数字农业贷款机构需要这样的风险估计：当贷款结果成熟缓慢、情境数据在不同时间可得时，这些估计仍然可信。本研究提出DAF-XCredit，一个面向农食中小企业信用风险评估的成熟度感知选择性融合框架。方法 DAF-XCredit在固定的5年核销目标下，将审批时的贷款记录与结果可观测的历史先验及滞后的农业情境相结合。对按时间顺序的折外基础分数进行校准，并由2014—2015年验证队列选择最终融合模型所使用的来源模块。实验使用2000—2019年批准的67,114笔美国小企业管理局7(a)农食贷款公开数据及关联的美国农业部数据。未触碰的2016—2019年测试队列包含12,339笔贷款。结果 在五个随机种子下，所选贷款加农业模型达到AUC为0.9497 ± 0.0004，召回率为0.6425 ± 0.0080，F1为0.4357 ± 0.0036，Brier分数为0.02356 ± 0.00005，等宽期望校准误差为0.00272 ± 0.00022。在受评估模型中，DAF-XCredit取得了最高的AUC、召回率和F1，以及最低的Brier分数和等宽校准误差，而M1和M5分别领先PR-AUC和KS。种子2026的DeLong检验发现，与仅贷款模型相比，AUC差异无统计学显著性（p = 0.254）。州—年聚类重抽样得到的校准斜率为0.990（95% CI 0.897-1.090），决策曲线显示在0.05—0.30阈值范围内具有正净收益。讨论 成熟度感知的来源选择、校准、SHAP分析、稳定性重抽样和子组审计为农业信用治理提供了一个可审计框架。该方法可支持面向可持续性的融资决策，但其本身并不确立可持续性结果。","2026-10-02T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":83,"relevant":22,"comment":381},"方法新颖、数据规模大且校准严谨，对农业数字信贷风控有直接参考价值，值得进入每日精选。",[383],{"name":190,"url":376},[385,386,387,388,389],"农业人工智能","农业中小企业","数字农业金融","信用风险评估","农业数据融合",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":393},{"candidates":394,"agricultural_evidence":48},[395,400,404],{"code":34,"score":47,"supported":48,"title":396,"abstract":397,"tags":399},[],[398],"agricultural credit",[],{"code":54,"score":47,"supported":48,"title":401,"abstract":402,"tags":403},[],[57],[],{"code":405,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},"I2",[],[],[385],[410,411],"DAF-XCredit 农业信贷 风险评估","SBA 7 a 农业贷款 信用模型","DAF-XCredit农业信贷风险评估-4051","10.3389\u002Ffsufs.2026.1946390",{"doi":413,"openalex_id":415,"authors":416,"venue":190,"cited_by_count":110,"oa_url":376,"card":422,"direction":426,"ingested_from":257},"W7216288378",[417,420],{"name":418,"orcid":419},"Ying Wei","https:\u002F\u002Forcid.org\u002F0000-0001-8217-4765",{"name":421,"orcid":8},"Yanjiao Liao",{"tldr":423,"method":424,"finding":425,"direction":426,"opportunity":427},"提出成熟度感知的选择性融合框架DAF-XCredit，用于农业食品中小企业信贷风险评估。","融合贷款记录、历史先验与滞后农业数据，用SBA 7(a)贷款和USDA数据做时序","所选模型AUC达0.9497，校准误差低，但相对仅贷款模型AUC提升不显著。","农业人工智能与决策模型","可探索成熟度感知融合在动态信贷治理与可持续农业融资中的可解释性与公平性。","2026-10-03T23:30:08.106441Z"]